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Soil fertility, nutient dynamics and socio-economic interaction in the middle mountains of Nepal Brown, Sandra J. 1997

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SOIL FERTILITY, NUTRIENT DYNAMICS AND SOCIO-ECONOMIC INTERACTIONS IN THE MIDDLE MOUNTAINS OF NEPAL  by SANDRA JEAN BROWN  B.Sc, The University of British Columbia, 1984 M.Sc, The University of British Columbia, 1990 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Interdisciplinary Studies in Resource Management Science)  We accept this thesis as coriforrning to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA  October 1997 © Sandra Jean Brown, 1997  In  presenting this  degree at the  thesis  in  University of  partial  fulfilment  of  of  department  this thesis for or  by  his  or  requirements  British Columbia, I agree that the  freely available for reference and study. I further copying  the  representatives.  an advanced  Library shall make it  agree that permission for extensive  scholarly purposes may be her  for  It  publication of this thesis for financial gain shall not  is  granted  by the  understood  that  head of copying  V  of (rtl&^iAbTtT  The University of British Columbia Vancouver, Canada  Date  DE-6 (2/88)  Per.  /O J 9 ?-  S^DJeJ  or  be allowed without my written  permission.  Department  my  11  ABSTRACT  Understanding soil fertility issues in the Middle Mountains of Nepal requires interdisciplinary research, integrating biophysical and socio-economic factors. Soil degradation is associated with a wide range of human activities, natural processes, and the wider economic, political and social aspects of their setting. This study focuses on a subwatershed in the Middle Mountains and addresses four research questions: What is the current soil fertility status? How is it changing? Why is it changing? and What are the implications for production, sustainability and management? Soil surveys, plot studies, nutrient balance modelling, household questionnaires and GIS mapping techniques are used to address these questions.  The overall soil fertility conditions of the study area are poor and appear to be declining under most land uses. Soil pH averages 4.8 + 0.4 and is below desirable levels for crop production. Soil carbon (0.99 + 0.5 %) and cation exchange capacity (10.8 + 4.1 cmol kg") are low, and available phosphorus (16.6 + 18.9 1  mg kg") is a concern given the low pH. Land use is the most important factor influencing soil fertility with 1  khet (irrigated agriculture) showing the best fertility status (pH 5.2, Ca 5.3 cmol kg" and available P 21.6 1  mg kg"), followed by bari, and grassland, with forest soil fertility being the poorest (pH 4.2, Ca 0.9 cmol 1  kg" and available P 0.7 mg kg"). Soil type is the second most important factor influencing soil fertility, 1  1  with red soils displaying significantly lower available P than non-red soils (9.8 versus 22.1 mg kg"). 1  Phosphorus sorption studies indicate the high P fixation capacity of red soils, 1.2 g kg" compared to 0.3 g 1  kg" calculated for non-red soils. Extrapolation from site specific data to a spatial coverage using statistical 1  analysis and GIS techniques indicates that only 14% of the classified areas have adequate pH, available P and exchangeable Ca, and 29% of the area has a high P fixation capacity (>1.5 g kg"). 1  Nutrient balance modelling provides estimates of nutrient depletion from the soil pool and raises concerns about the sustainability of upland farming, intensive vegetable crop production and forest nutrient cycling. Dryland maize production results in deficits of 188 kg N, 38 kg P 0 and 21 kg Ca per ha furrow slice. 2  5  Ul  Rice-wheat cultivation on irrigated land appears to have limited impact on the soil nutrient pool, but the addition of premonsoon maize to the rotation results in deficits of 106 kg N and 12 kg P2O5 per ha furrow slice. Rates of soil fertility depletion estimated from differences in soil fertility between land uses indicate substantial N and Ca lossesfromforest land (94 and 57 kg ha per furrow slice respectively).  Land use change, the impact on nutrient flows and relationships between nutrient inputs, crop uptake, nutrient balances and soil fertility provide an understanding of why soil fertility is changing. Historical forest cover data indicates substantial deforestation during the 1950-1960 period, a subsequent reversal in the 1972-1990 period associated with afforestation efforts, and renewed losses in the 1990s. Forest soils receive minimal nutrient inputs and large biomass removal results in a low soil fertility status. Expansion and marginalization of dryland agriculture were noted from 1972-1990, as former grazing, shrub and abandoned lands were terraced and cultivated. Nutrientfluxesindicate that inputs are insufficient to maintain the soil nutrient pool under dryland cultivation due to the high nutrient requirements of maize and nutrient losses through erosion. Nutrient balances for maize and wheat are positively correlated with nutrient inputs but relationships with soil fertility are weak. On irrigated khet lands, cropping has intensified and cash crop production has prompted the use of agrochemicals. Excess fertilization is leading to eutrophication and the high use of agrochemicals is a health concern. Nutrient fluxes on khet fields appear to be sustainable due to the addition of nutrients through irrigation and sediment trapping, but may be insufficient to maintain triple cropping. Grass and shrub land dynamics are characterized by minimal inputs and low productivity. The traditional farming system appears to have been sustainable, but triple cropping and increased vegetable production are threatening sustainability. The transfer of nutrients within the fanriing system is unbalanced. Under intensive production, nutrients on khet land are being depleted, poor farmers are shifting their limited compost inputsfrombari to khet fields, and biomass collectedfromforests, disrupts the natural nutrient cycle.  iv Population growth, land tenure, culture and poverty are the underlying socio-economic factors which influence farming system dynamics, directly impact nutrient inputs, and indirectly drive soil fertility degradation. Population growth rates of 2.6% have contributed to agricultural intensification and marginalization, and pressure on forest resources. The distribution of land is highly skewed with 15% of the surveyed households owning 46% of the land. Women play a central role in soil fertility management through their responsibilities for livestock care, litter collection and compost application, but increasing workloads related to commercial milk production, cash cropping and the off-farm employment of males are a major concern. Agricultural assets, farm gross margins, market oriented production, commercial milk production and off-farm employment provide indicators of economic well-being and are positively correlated with nutrient inputs. Total returns and gross margins are greatest for households growing vegetable crops as part of their rotation, and these households apply significantly more compost and fertilizer to both khet and bari land. Access to land is a key factor driving nutrient management and influencing economic well-being. Land is the main agricultural asset in the study area, khet land is the most productive and khet provides the greatest opportunity of cash crop production. However, given the increased labour demands for triple cropping, vegetable production and commercial milk production, the social sustainability is being threatened. Some 47% of the households were not able to fulfil their basic need requirementsfromthe land they farm. They will have no alternative but to exhaust the capital stock of soil nutrients rather than investing in soil fertility.  Maintenance of soil fertility is essential to meet the basic food and resource needs of the growing population. Organic matter management is critical, supplying macro- and micro nutrients, reducing acidification, maintaining soil structure and enhancing microbial activity. Water management and sediment trapping on lowlandfieldsprovide additional nutrients on khet land; soil acidity on uplandfieldsand forest land needs to be better managed given the increased fertilizer use on bari and high biomass removal from forests; and the incorporation of Nfixingspecies into agricultural production systems are an option which may provide additional animal fodder and help sustain soil fertility.  V  T A B L E OF CONTENTS  Abstract Table of Contents List of Figures List of Tables List of Plates List of Photos  ;  » v * *i xiii xiii x  1. INTRODUCTION 1.1 Soil Fertility Degradation 1.1.1 Inherent soil properties and natural processes 1.1.2 Human activities 1.1.3 Contextual framework 1.2 Problem Statement and Objectives 1.3 Study Area 1.4 Overview of the Dissertation  1 2 2 4 4 7 10  2. METHODOLOGY 2.1 Soil Fertility 2.1.1 Soil fertility dynamics 2.2 Socio-Economic Surveys 2.2.1 Farming system dynamics 2.3 Supplemental Data 2.4 GIS Mapping and Data Integration 2.4.1 GIS based mapping 2.4.2 Data integration  12 12 17 18 20 21 22 22 24  3. RESEARCH SETTING 3.1 Biophysical Setting 3.1.1 Topography 3.1.2 Erosion 3.1.3 Soil types 3.1.4 Land use 3.2 Cultural Setting 3.2.1 The caste system 3.2.2 Role of women 3.2.3 Cultural role of livestock 3.3 Farming Systems 3.3.1 Dominant cropping systems 3.3.2 Livestock operations 3.3.3 Forest products 3.4 Summary  25 25 25 26 28 28 29 29 30 32 32 34 37 40 41  8  vi 4. SOIL FERTILITY STATUS AND DYNAMICS 4.1 Soil Fertility Status 4.1.1 Factors influencing soil fertility 4.1.2 Fertility classification 4.1.3 Phosphorus fixation 4.2 The Management of Soil Nutrients 4.2.1 Initial soil nutrient pool 4.2.2 Compost and chemical fertiliser use 4.2.3 Erosion 4.2.4 Water management and sedimentation 4.2.5 Phosphate fixation 4.2.6 Nitrogen dynamics 4.2.7 Calcium availability 4.3 Crop Nutrient Uptake 4.3.1 Soil productivity relationships 4.4 Nutrient Budget Model 4.4.1 Nutrient budgets for the dominant cropping systems 4.4.2 Sensitivity analysis 4.4.3 Best management practices 4.4.4 Nutrient budgets for individual fields 4.4.5 Implications for soil fertility 4.5 Soil Fertility Dynamics 4.5.1 Rates of change 4.6 Summary  43 44 49 53 57 61 61 61 64 64 69 70 72  5. IMPACT OF LAND MANAGEMENT ON NUTRIENT DYNAMICS 5.1 Land Use Change 5.1.1 Forest cover 5.1.2 Cultivated lands 5.1.3 Shrub and grass lands 5.1.4 Recent trends in the Bela-Bhimsenthan region 5.2 Forest Dynamics: Quantity versus Quality 5.2.1 Nutrient Status and Biomass Removal 5.2.2 Implications of Nutrients Outflows 5.3 Bari Dynamics: Expansion and Marginalisation 5.3.1 Nutrient Gains and Losses 5.3.2 Implications of Marginal Inputs and Erosion Losses 5.4 Khet Dynamics 5.4.1 Nutrient Status and Water Management 5.4.2 Implications of Intensive Cultivation 5.5 Grass and Shrub Land Dynamics 5.5.1 Nutrient Losses  102 104 106 107 107 107 108 108 110 Ill Ill 111 114 121 124 124 125  7 4  76 78 81 86 86 89 92 92 96 97  vii 5.5.2 Implications of Degradation 5.6 Land Use Interactions and Soil Fertility 5.6.1 Implications for Production 5.6.2 Nutrient Dynamics and Future Soil Fertility 5.7 Summary  126 126 131 135 137  6. UNDERLYING SOCIO-ECONOMIC FACTORS 6.1 Population 6.1.1 Population growth, land use change and nutrient dynamics 6.2 Land Tenure 6.2.1 Land distribution 6.2.2 Share cropping 6.2.3 Sufficiency of farmed land 6.2.4 Open access resources 6.2.5 Unequal access to land, nutrient management and soil fertility 6.3 Culture 6.3.1 Ethnic distribution 6.3.2 Changing role of livestock 6.3.3 Women and soil fertility management 6.3.4 Cultural factors, nutrient management and soil fertility 6.4 Poverty 6.4.1 Agricultural assets 6.4.2 Farm gross margins 6.4.3 Cash income 6.4.4 Economic well-being, nutrient management and soil fertility 6.5 Summary and Implications of Socio-Economic Factors for Soil Fertility  141 143 144 145 145 147 147 148 149 154 154 155 156 159 161 162 163 170 173 177  7. SUMMARY AND IMPLICATIONS 7.1 Soil Fertility Status 7.2 Soil Fertility Dynamics 7.3 Management Factors and Options 7.3.1 Management factors 7.3.2 Management options 7.4 Socio-economic Factors and Options 7.4.1 Socio-economic factors 7.4.2 Socio-economic options 7.5 Sustainability of Farming Systems 7.6 Implications for Methodology 7.7 Implications for Future Research  181 182 183 184 184 185 187 188 189 191 192 193  PLATES  194  REFERENCES CITED  204  APPENDIX A. QUESTIONNAIRES A.l Bela-Bhimsenthan detailed household survey A.2 Bela-Bhimsenthan soil fertility site description A. 3 Balawa soci-economic questionnaire - women farmers A.4 Balawa soci-economic questionnaire - men farmers A. 5 Bela-Bhimsenthan key informant questionnaire  220 221 222 236 240  APPENDIX B. SUPPLEMENTAL DATA B. 1 Nutrient uptake by rice B.2 Nutrient uptake by maize B.3 Nutrient uptake by wheat B.4 Nutrient uptake by cash crops B.5 Nutrient uptake by tropical grasses B.6 Nutrient content of compost and animal manure B.7 Nutrient content of forest litter B.8 Nutrient inputsfromorganic and chemical fertilizer sources B.9 Variable costs - seed B.10 Variable costs - fertilizer B. 11 Variable costs - pesticides B.12 Variable costs - labour  246 247 248 249 250 251 252 252 253 253 254 254  ix LIST OF FIGURES  Figure 1.1 Figure 1.2 Figure 1.3 Figure 1.4 Figure 1.5  Factors influencing inherent soil properties in the Middle Mountains Human activities influencing soil fertility degradation in the Middle Mountains Contextual framework affecting soil fertility depletion Location of the Bela-Bhimsenthan study area Structure of the dissertation  Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4  Bela-Bhimsenthan soil fertility survey, 200 site locations Location of forest and nutrient cycling plots referenced in this study Bela-Bhimsenthan 85 household survey locations Hydrometric monitoring sites  14 14 19 19  Figure 3.1  Annual soil loss 1992-1994 a) erosion plots; b) pre-monsoon season; and c) two most damaging storms Fuelwood, fodder and litter collection by women Component interactions within farming systems Dominant cropping systems Livestock holdings Percent supply of household fuelwood by source  27 31 33 35 38 41  Figure 3.2 Figure 3.3 Figure 3.4 Figure 3.5 Figure 3.6 Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 Figure 4.5 Figure 4.6 Figure 4.7 Figure 4.8 Figure 4.9 Figure 4.10 Figure 4.11 Figure 4.12 Figure 4.13 Figure 4.14 Figure 4.15 Figure 4.16 Figure 4.17 Figure 4.18 Figure 4.19 Figure 4.20 Figure 4.21 Figure 4.22 Figure 4.23 Figure 4.24 Figure 4.25 Figure 4.26  Components and characteristics of soil fertility status and dynamics evaluated in chapter 4 Significantly correlated soil parameters Soil fertility variables stratified by soil type and land use Selected P sorption curves Relationships between P sorption, Fe and Al Comparison of measured P sorption capacity with calculated values from Borggaard's model Calculated P sorption under different land uses on red and non-red soils Nutrient enrichment in khet fields a) exchangeable Ca; b) exchangeable P; and c) % carbon Ca saturation % Approach for modelling soil N dynamics Approach for modelling soil P dynamics Approach for modelling soil Ca dynamics Nutrient budget for maize on bari Nutrient budget for wheat on bari Nutrient budget for the dominant cropping sequence on bari Nutrient budget for early maize on khet Nutrient budget for rice on khet Nutrient budget for wheat on khet Nutrient budget for a three crop sequences on khet Nutrient budget for a rice-wheat rotation on khet Sensitivity analysis of seasonal nutrient budgets for bari and khet Impact of best management practices on nutrient budgets Deficit elimination scenarios Field nutrient budgets for bari Field nutrient budgets for khet Variability in N budgets for dryland maize and irrigatedriceproduction  5 9 11  3 3  44 48 54 58 59 60  60  66 72 79 79 80 82 82 83 83 84 84 85 85 87 88 88 90 90 91  Figure 4.27 Estimated changes in the soil nutrient pool under dominant cropping systems for khet and bari Figure 4.28 Soil fertility characteristics under khet, bari and forest  93 95  Figure 5.1 Components of land use and soil fertility dynamics evaluated in chapter 5 103 Figure 5.2 Regional land use dynamics 1947-1994 a) forest dynamics; b) cultivation dynamics; and c) shrub and grass land dynamics 105 Figure 5.3 Land use trends in the Bela-Bhimsenthan region 1972-1994 108 Figure 5.4 Selected correlations between nutrient budgets, inputs, crop uptake and soil fertility on bari sites .: 113 Figure 5.5 Cropping intensity estimates 1980-1996 115 Figure 5.6 Pesticide application relative to manufacturers recommended guidelines 119 Figure 5.7 Selected correlations between nutrient budgets, inputs, crop uptake and soil fertility on khet land 122 Figure 5.8 Nutrientfluxesbetween land uses 127 Figure 5.9 Nutrient input dynamics for a) organic matter and b) chemical fertilizers 128 Figure 5.10 Types of fertilizers commonly used on a) khet and b) bari land in 1989 and 1996 130 Figure 5.11 Correlations between crop productivity, soil fertility and nutrient management on khet and bari land 133 Figure 5.12 Multiple regression analysis of yield, nutrient inputs and soil fertility conditions 134 Figure 6.1 Socio-economic factors driving nutrient management and the implications for soil fertility evaluated in chapter 6 Figure 6.2 Population dynamics 1972-1995 Figure 6.3 Agricultural land distribution among surveyed households Figure 6.4 Fertilizer and compost use versus land ownership for khet and bari land Figure 6.5 Management on owned versus share cropped land Figure 6.6 Livestock holding dynamics Figure 6.7 Stall feeding and grazing dynamics Figure 6.8 Returns, variable costs and gross margins by crop Figure 6.9 Total returns, variable costs and gross margins on khet and bari land Figure 6.10 Annual gross margins for agricultural production Figure 6.11 Correlations between economic indicators, nutrient inputs and soil fertility Figure 6.12 Contextual framework and quantified relationships linking socio-economic factors, nutrient dynamics and soil fertility degradation  142 144 146 150 152 155 156 165 166 170 175 178  LIST OF TABLES Table 2.1 Table 2.2  Summary of studies reported and principle investigators Bela-Bhimsenthan soil and household survey sampling design  12 15  Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5  Ethnic distribution within the Bela-Bhimsenthan sample Frequency of fuelwood, fodder and litter collection Reported nutrient inputsfromorganic and chemical fertilizer sources Tropical livestock unit equivalents Livestock concentration in the Bela-Bhimsenthan region  Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7 Table 4.8 Table 4.9 Table 4.10 Table 4.11 Table 4.12 Table 4.13 Table 4.14 Table 4.15 Table 4.16 Table 4.17  Current soil fertility status 45 Differences between factors affecting soil fertility 50 Significant factors related to soil fertility 52 Initial soil nutrient pool 61 Nutrient content of compost 62 Reported nutrient inputsfromorganic and chemical fertilizer sources 63 Erosion and associated nutrient lossesfrombari, forest, shrub and degraded lands 65 Estimated rates of annual nutrient enrichment on khet fieldsfromnewly accumulated sediments 67 Chemical composition of irrigation waters 67 Phosphate sorption potential 69 Acidification due to fertilizers 73 Chemical fertilizer application and equivalent acidity 73 Reported, locally measured and regional yields for dominant crops 75 Nutrient removal by the dominant staple crops 76 Soil parameters influencing maize and wheat yields 77 Significant differences in soil fertility variables between land uses 96 Soil fertility dynamics 97  Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11  Land use dynamics in relation to site conditions over the period 1972-1994 Forest production collection and associated nutrient losses Differences between factors affecting nutrient budgets on bari fields Cash crop dynamics on khet land for 1989 and 1996 Dominant pest problems of the major crops Main insecticides and fungicides used in the study area Differences between factors affecting nutrient budgets on khet fields Grass and fodder species in the region Differences in nutrient inputs with topography and land use Difficulties in obtaining chemical fertilizer Changes in production limitations, 1989-1996  109 110 114 116 117 118 123 125 128 130 135  Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9  Per capita availability of agricultural land Relationships between land ownership, crop nutrient budgets and soil fertility Land ownership by caste / ethnic group Dynamics of household allocation of labour by task Relationships between caste / ethnic affiliation, land management and soil fertility Land values in the study area Livestock values and assets Total production returns for the major khet and bari crops Variable costs for seed, chemical fertilizer, pesticide, oxen and labour  148 151 154 157 .... 160 162 163 168 168  30 31 37 39 39  xii Table 6.10 Table 6.11 Table 6.12  Crops sold by households Food products purchased by households Off-farm activities  171 172 174  xiii LIST O F PLATES  Plate 1. Plate 2. Plate 3. Plate 4. Plate 5. Plate 6. Plate 7. Plate 8. Plate 9. Plate 10. Plate 11. Plate 12. Plate 13. Plate 14. Plate 15.  Elevation zones Aspect categories Slope classes Red soils Land use a) 1994 and b) 1972 Exchangeable calcium 200 soil sites Soil acidity 200 soil sites Available phosphorus 200 soil sites Exchangeable calcium soil classification Soil pH classification Available phosphorus soil classification Composite soil fertility classification Classification of P fixation capacity Spatial distribution of interpreted P degradation Spatial distribution of interpreted Ca degradation  195 195 196 196 197 198 198 198 199 199 199 200 200 201 201  LIST O F PHOTOGRAPHS  Photo 1. Photo 2. Photo 3. Photo 4. Photo 5. Photo 6.  Women transplanting rice Women collecting litter Pine plantation on red soils Intensively used agricultural land Bari expansion onto shrub lands Farmers purchasing chemical fertilizer  202 202 202 203 203 203  1  1. INTRODUCTION  Maintenance of soil fertility is essential to meet the basic food and resource needs of Nepal's rising population. The population of Nepal reached 21.9 million in 1995. In 1961, it was 9.4 million. In response to declining mortality rates, the average annual population growth rate increased from 1.6% in 1961 to 2.7% in 1981 (FAO 1996). Population continues to grow at 2.7% per annum, implying Nepal's food requirements will double in 26 years. The implications of this growth are staggering given that only 16% of Nepal's 140,800 km are suitable for agriculture. Despite Government attempts to increase food 2  production and productivity, official statistics indicate that the growth in food production in Nepal has not kept up with population growth. Per capita food production decreased through the 1960's and 1970's, increased in the 1980's with the introduction of high yielding crop varieties, but has decreased in the 1990's. The total estimated cereal grain productionfrom1965 to 1990 has increased at an average of 2.2% per annum. This increase in total production is attributed to an expansion of the area under cultivation and increases in cropping intensity, while changes in crop yields have been small. Cropland has increased at an average of 2.2% per annum but expansion has largely occurred on low productivity and steeply sloping sites. To assist in meeting the increasing demand for food, double and triple annual crop rotations are applied where irrigation is available. Agricultural intensification, the use of short growing season crop varieties and the application of chemical fertilizers have helped increase total production, yet food shortages are still widespread (FAO 1996, Biot et al. 1995, Sharma 1993, Chitrakar 1990, Schreier et al. 1991a, Sharma and Banskota 1992, Carson 1992, Dahal 1987).  The Middle Mountains of Nepal make up 30% of the land area, are home to 41% of the country's population, and account for roughly 35% of the total agricultural production (Chitrakar 1990). Production problems are more acute in the Middle Mountains where the expansion of agriculture is limited by topographic conditions and yields are reported to be declining. Low and declining soil fertility has been recently recognized as a significant cause for the stagnation or decline in crop productivity, but how soil  2  chemical and physical properties are changing and why are poorly understood (Pandey et al. 1995, Sherchan and Baniya 1991). This study was conducted to examine the dynamics of soil fertility in the Middle Mountains of Nepal, and the human activities that impact soil fertility and the resultant production capacity.  1.1 Soil Fertility Degradation  Soil fertility degradation may be defined as a reduction in the quantitative and / or qualitative productive capacity of the soil. To sustain plant or crop production, the soil provides four basic functions: a rooting medium, gaseous exchange, water holding capacity, and exchangeable nutrients. Degradation results when at least one of these functions is impaired (Lal et al. 1988, FAO 1976, Barrow 1991, Lal and Stewart 1993). Soil degradation is associated with a wide range of human activities and natural processes, and the wider economic, political and social aspects of their setting.  1.1.1 Inherent Soil Properties and Natural Processes Inherent properties influencing soil fertility are a function of parent material, climate, biota, topography and time as illustrated in Figure 1.1. The dominant bedrock in the Middle Mountains consists of silica rich materials (sandstone, siltstone, quartzite and phyllite) which are inherently acidic and contain low phosphorus levels. Some of the soils are deeply weathered, and kaolinite is the dominant clay mineral in these red soils. Micro-organisms and termites play an active role in organic matter decomposition and the development of soil structure. Organic matter decomposition is enhanced with increasing soil temperature, but may be limited by waterlogging, while the physical and chemical weathering of parent material is promoted by high temperature and precipitation. The high monsoonal rainfall leads to high rates of leaching, and high intensity rainfall events increase soil erosion. Relief up to 2,000 metres is common in the Middle Mountains, slopes often exceed 30° and natural erosion rates are high (Foth 1990, Chitrakar 1990, Schreier et al. 1995, Schreier et al. 1990b, Sivakumar et al. 1992, Lavelle et al. 1992).  PARENT MATERIAL  TIME  silica rich mineralogy  old soils  BIOTA  extensively weathered  inherently acidic  organic matter decomposition structure  low phosphorous INHERENT SOIL PROPERTIES erosion  microclimate  leaching weathering organic matter decomposition TOPOGRAPHY  CLIMATE  Figure 1.1. Factors influencing inherent soil properties in the Middle Mountains.  fodder, fuelwood & litter collection chemical fertilizer  pine litter  crop uptake  agricultural intensification  NUTRIENT DEPLETION  ACIDIFICATION  SOIL FERTILITY DEGRADATION  ACCELERATED EROSION  STRUCTURAL ALTERATION  deforestation cultivation on steep slopes water management  free grazing  agricultural tillage paddy cultivation  Figure 1.2. Human activities influencing soil fertility degradation in the Middle Mountains.  4  1.1.2 Human Activities The human activities influencing soil fertility and productivity in the Middle Mountains are summarised in Figure 1.2. Soil acidification is a concern in relation to the use of chemical fertilizers and the addition of pine litter compost during the dry season. Soil pH is an important chemical characteristic as it influences the availability of plant nutrients and toxic elements. With intensification of the cropping system, the nutrient requirements have dramatically increased, and forest litter is heavily utilized as a soil amendment resulting in a one-way flow of nutrients from the forests to agriculture. Low nutrient inputs are negatively impacting the nutrient balance and contribute to poor productivity. Loams and sandy loams are common throughout the Middle Mountains, but the inherent soil structure is dramatically altered through paddy cultivation and free grazing, resulting in reduced infiltration and water holding capacity. Natural erosion rates are high in Nepal, but are accelerated by cultivation on steep slopes, deforestation and overgrazing. Erosion is a concern not only because of soil loss, but also the associated redistribution of nutrients (Lal 1993, Carson 1986, Schreier et al. 1995, Shah and Schreier 1991, Sherchan 1990, Miller and Donahue 1990, HMGN 1988, Carver 1995).  1.1.3 Contextual Framework The main social, economic and political factors influencing soil fertility depletion in Nepal are shown schematically in Figure 1.3. The four interrelated categories: population growth, poverty, land tenure, and culture, define the contextual framework under which soil fertility depletion is occurring in the Middle Mountains.  Population Growth Rapid population growth and the resultant increase in the demand for food are important factors in agricultural intensification and marginalisation, particularly with Nepal's subsistence oriented agricultural economy and poor transportation system. The availability of cultivated land per capita has decreased from 0.17 ha per capita in 1971 to 0.12 ha per capita in 1993. Agricultural intensification is placing increased  5 nutrient demands on soil resources, and expansion onto marginal lands has resulted in reduced productivity due to inherently poor soil fertility conditions and soil erosion (FAO 1996, Mather and Chapman 1995, Barrow 1991, Blaikie and Brookfield 1987, Seddon 1987, Sharma and Banskota 1992, Chitrakar 1990).  SOIL FERTILITY DEPLETION acidification nutrient depletion stuctural alteration erosion  Figure 1.3. Contextual framework affecting soil fertility depletion in the Middle Mountains.  6 Poverty Ninety percent of Nepal's population represent small scale farmers with average personal incomes of approximately $170 Cdn per annum. Nepal's rural poor lack long term property rights, are often forced to cultivate marginal lands, do not produce enough food to permit the fallowing of land, and lack the capital to maintain soil fertility through technical solutions (Blaikie and Brookfield 1987, World Bank 1992, NRC 1993, World Bank and UNDP 1991, Panday 1992, Dahal and Shrestha 1987, Blaikie et al. 1980a).  Land Tenure Land is a critically important production resource in Nepal. Access to land, its inherent fertility and tenure arrangements influence how soil is managed, and consequently how soil fertility may change. Land ownership varies dramatically, with 5% of owners controlling about 40% of the cultivated land, while 60% of owners control only 20% of the cultivated land and typically own less than 1 ha per family. Even under intensive agriculture, it is difficult to meet subsistence requirementsfromthese small holdings. Approximately 25% of households rent or sharecrop additional land, but lacking secure tenure, these poor farmers have little incentive to conserve, manage, improve or invest in soil fertility. Poor farmers are reliant on common property resources to meet their fodder and fiielwood needs, but extensive use and poor management have resulted in the degradation of open access grazing and forest lands (World Bank and UNDP 1991, Carson 1992, Paudel and Tiwari 1992, Seddon 1987, Regmi 1976, NRC 1993, Yadav 1984, Blaikie et al 1980a, Dahal 1987).  Culture Nepalese culture originatesfroma mix of Hindu and Buddhist philosophies and indigenous customs. Religious beliefs and traditions strongly influence social, economic, legal and political activities in Nepal. The caste hierarchy determines status (pure / impure) and influences social interaction, occupation and the division of labour. The dimensions of Nepalese culture which may influence soil fertility include ethnicity, the religious role of livestock and the role of women within the farming system. Ethnic divisions and caste  7  affiliation reflect socio-economic status and thus may influence access to land, capital availability, labour allocation, and consequently soil fertility. Livestock play an important cultural role in Hindu societies. Slaughtering of cows is prohibited by religion and law, resulting in overstocking with unproductive animals. Women are predominantly responsible for livestock husbandry, the collection of forest products, the provision of household water, manure collection and application, and the planting, weeding and harvesting of crops. Due to their traditional role as resource users and managers, women play an important role in soil fertility maintenance (Fox 1987, Gould 1987, Gurung 1995a, Hofer 1979, Mishra 1989, Bista 1991, Kennedy and Dunlop 1989, Thapa and Weber 1990, Panday 1992).  1.2 Problem Statement and Objectives Numerous papers and discussions have focused on soil degradation in Nepal, but there has been little long term research to verify if soil degradation is increasing, and if it is, to explain why. Low and declining soil fertility has been noted as a 'crucial' problem by the Nepal Agricultural Research Council, the Central Soil Science Division, FAO, USAID and the World Bank (Pandey et al. 1995, Maskey and Joshy 1991, World Bank 1996, Carson 1992). The Pakhribas Agricultural Centre has initiated soil related studies focusing on nutrient inputs and crop yield, and the Lumle Agricultural Research Centre has conducted Rapid Rural Appraisal (RRA) and Participatory Rural Appraisal (PRA) studies on farmers' perceptions of soil fertility trends. However issues of uncertainty, limited data, and data reliability raised by Thompson and Warburton (1985), Ives and Messerli (1989) and Kennedy (1989) have largely been ignored. Comprehensive and systematic research on soil fertility and relationships with nutrientflowsis lacking. Cultural / sociological and biophysical conditions need to be included, as they influence soil fertility in an integrated manner. Farms in Nepal need to be viewed as systems, incorporating the farming household, cropped fields, forests, and livestock. Soil fertility is a dynamic process influenced by both natural and social factors, and long term research is required to determine the underlying processes. Without knowledge of the underlying processes of soil degradation, it is difficult to suggest management practices which will improve the situation.  8  The main objectives of this research are: 1) to detennine the soil fertility status of a subwatershed in the Middle Mountains of Nepal; 2) to determine if the soil fertility status is changing; 3) to explain the underlying processes driving any changes; and  4) to draw implications of soil fertility status and change for production, sustainability and management.  1.3 Study Area The Bela-Bhimsenthan study area is located in the Kabhrepalanchok District of the Middle Mountains, approximately 40 kilometres east of Kathmandu (Figure 1.4). The study area covers 1,927 ha and is located within the Jhikhu Khola Watershed Project. It is part of a larger collaborative research program between the University of British Columbia and the International Centre for Integrated Mountain Development (ICIMOD) in Kathmandu. The Jhikhu Khola Watershed Project, sponsored by the International Development Research Centre (IDRC), is focusing on the long term monitoring of soil fertility and erosion processes, resource dynamics and rehabilitation options (Schreier et al. 1995). The study area is a site where land use intensities are some of the highest in Nepal and resource problems associated with population growth, agricultural intensification and deforestation are acute. Agriculture is the dominant economic activity, but off-farm employment is also an important source of supplemental income. Population pressure has led to double and triple annual crop rotations, and the cultivation of steeply sloping lands. Chemical fertilizers and forest litter are used in an attempt to maintain crop production, but total nutrient inputs are low. The region is subject to a monsoonal climate with an extensive dry seasonfromOctober to May, and erosion is a concern during the pre-monsoon period prior to biomass growth. Major afforestation efforts have occurred in the region, but heavy pressure on forest resources results in a transfer of nutrientsfromthe forest to agricultural lands. Overall soil fertility conditions are poor and the ability to sustain current production levels is uncertain (Shah and Schreier 1995a, 1991, Schreier et al. 1993).  9  Figure 1.4. Location of the Bela-Bhimsenthan study area.  10 The study area is typical of the Middle Mountains in many aspects, but is somewhat unique as the Arnica highway connecting Kathmandu with Tibet passes through the Jhikhu Khola Watershed. Traditionally, subsistence agriculture dominated the region but the highway has provided the opportunity to develop a market oriented economy. The increased nutrient and water requirements associated with agricultural intensification and cash crop production, however, are placing additional demands on soil and water resources. Understanding the dynamics of soil fertility in the region is critical to the success of future biomass production strategies. The potential to promote market oriented production makes the region somewhat futuristic and allows the documentation of soil fertility issues resulting from modernization. The implications of management strategies on soil fertility may then be applied to other watersheds in the Middle Mountains.  1.4 Overview of the Dissertation  The approach taken is summarised in Figure 1.5. The methodology used to determine soil fertility conditions and rates of change, assess socio-economic factors, and integrate data is presented in Chapter 2. Soil surveys, plot studies, nutrient balance modelling, household questionnaires and GIS mapping are techniques discussed. The research setting is described in Chapter 3, specifically the biophysical and cultural settings and interactions within the farming systems. Chapter 4 summarises current soil fertility conditions, evaluates inherent conditions versus the impact of land use management, and assesses how soil fertility is changing. The direction and rates of change are estimated through nutrient modelling and plot studies. Why soil fertility is changing is discussed in Chapter 5 relative to land management and nutrient dynamics. Nutrient management on forest, agriculture, grass and shrub lands are evaluated relative to their impact on soil fertility and production. The underlying socio-economic factors influencing nutrient management and soil fertility are discussed in Chapter 6, specifically population growth, land tenure, culture and poverty. Finally, Chapter 7 contains a summary of the major conclusions of this study, and options for soil fertility enhancement.  Ch.1 INTRODUCTION  -  soil fertility degradation defined natural processes & human influence problem statement & ojectives study area  Ch. 2 M E T H O D O L O G Y  - soil fertility: 200 site soil survey, plot studies & nutrient balance modelling - socio-economic: 85 household questionnaires, 27 repeated questionnaires - GIS mapping & data integration  I  '  C h . 3 R E S E A R C H SETTING  - biophysical setting: topography, soil types & land use - cultural setting: caste, role of women, role of livestock - farming systems: cultivation, livestock & forest interactions  C h . 4 SOIL FERTILITY STATUS AND DYNAMICS  -  what is the current status? inherent conditions vs. the impact of land use how is it changing? nutrient inputs, crop uptake, nutrient modelling rates of change  I C h . 5 IMPACT O F LAND MANAGEMENT ON NUTRIENT DYNAMICS  -  why is it changing? land use dynamics forest, khet, bari, grazing and shrub land dynamics impact on soil fertility and production  I C h . 6 UNDERLYING SOCIO-ECONOMIC F A C T O R S  - population, land tenure, culture & poverty: trends and implications - impact on nutrient management & soil fertility  Ch. 7 SUMMARY AND IMPLICATIONS  Figure 1.5. Structure of the dissertation.  12  2. METHODOLOGY Several approaches were utilized to assess soil fertility conditions and dynamics, and the socio-economic factors which influence soil fertility. Soil surveys and plot studies were used to determine the current soil fertility status and rates of change. Socio-economic data were compiled using household questionnaires. Hydrometric, soil fertility and land use data compiled as part of the Jhikhu Khola Watershed Project were used as supplement information. Geographic Information System (GIS) mapping techniques were used for data compilation and integration. Table 2.1 provides a summary of the studies reported and lists their principle investigators.  Year  Table 2.1 Summary of studies reported and principle investigators. Study Principle Investigators  1989  Baluwa household survey (n=27)  Kennedy and Dunlop  1989  Jhikhu Khola forest plot studies (n=12)  Feigl  1991  Dhulikhel agricultural nutrient status  Wymann  1992  Dhulikhel forest nutrient status  Schmidt  1993/94  Bela-Bhimsenthan soil survey (n=200)  Schreier, Shah and Brown  1994  Bela-Bhimsenthan household survey (n=85)  Brown  1994  Jhikhu Khola nutrient cycling plot studies (n=30)  Schreier, Shah, Lavkulich and Brown  1994  Jhikhu Khola forest plot studies repeated (n=12)  Schreier and Brown  1995  Jhikhu Khola land use dynamics  Shrestha and Brown  1996  Baluwa household survey repeated (n=27)  Brown  1996  Bela-Bhimsenthan key informant questionnaire (n=5)  Brown  1996  Bela-Bhimsenthan land use dynamics  Brown and Shrestha  1996/97  Bela-Bhimsenthan GIS integration  Brown  1997  Jhikhu Khola sediment dynamics  Carver  2.1 Soil Fertility Soil fertility evaluations previously conducted in the Jhikhu Khola and Dhulikhel watersheds identified topographic conditions and soil type as key factors influencing land use and productivity (Schmidt 1992, Schmidt et al. 1993, Wymann 1991). To determine the long term effects of land use management on soil fertility, detailed soil fertility surveys were conducted. Agricultural and forest soils were surveyed  13  separately, and soil chemical properties were measured. All surveys were georeferenced and their locations were transferred to the GIS for analysis.  A detailed soil fertility survey of cultivated and grass lands was conducted in 1993/94 in the BelaBhimsenthan region. The main factors believed to influence soil fertility were isolated, specifically topography, soil type and land use. The sampling design was a 2x2x2x3 factorial; elevation + 1200 m, north versus south aspects, red and non-red soils, and land use (irrigated agriculture, dryland agriculture and grassland). The two elevation zones represent a climatic break evident by a change in the natural vegetation. The aspect subdivision is important as south facing sites are significantly drier than north facing sites. The red and non-red soil types reflect different parent materials and age of soil development, and consequently inherent characteristics. Differences in soil fertility due to land use may then be ascertained, while keeping the main biophysical conditions constant. For each combination of factors, 10 fields were sampled for soil fertility (Table 2.2). At eachfield,10 samples were collectedfrom0-15 cm depth and one bulk sample was generated for analysis. A short questionnaire was conducted summarizing the crops grown, yields and nutrient inputs for each cultivatedfield(Appendix A, questionnaire 1). Note, of the 24 possible combinations only 20 occurred, as above 1200 m there is limited irrigated land and red soils. All samples were collected in the middle of the dry season (December - February). Soil analysis included pH, organic carbon, available phosphorus, exchangeable cations, base saturation and moisture content measurements. Sample locations were marked on enlarged aerial photographs and georeferenced to the GIS database for analysis. Figure 2.1 displays the spatial distribution of the 200 sampling locations; note these include only cultivated and grassland sites.  Forest soil fertility was examined through a series of plot studies. In 1989, 12 forest plots were selected within the Jhikhu Khola watershed for long term monitoring of soil fertility and biomass production. Seven of the plots are located within or near the Bela-Bhimsenthan study area (Figure 2.2). Of these seven, three  Figure 2.2 Location of forest and nutrient cycling plots referenced in this study.  15  Table 2.2. Bela-Bhimsenthan soil and household survey sampling design. Elevation i Aspect  j Soil Type j Land Use  (m) j north  < 1200  j  red  | non-red  >1200  j north  1 red 1 non-red  j south  Interviewed  j khet  red  1 non-red  j  No. Households  Descriptions 10  6  10  9  j grassland  10  0  j khet  10  5  I bari  10  5  | grassland  10  0  1 khet | bari  10  6  10  6  j grassland  10  0  ! khet  10  7  1 bari  10  5  i grassland  10  0  ! bari  10  7  I khet  10  7  j bari  10  8  ; grassland  10  0  1  I bari  \ south  No. Site  | red I non-red  2  | bari  10  7  j grassland  10  0  1 bari  10  7  1 grassland  10  0  200  85  total 1  khet = irrigated agriculture  plots are dominated by chir pine (Pinus understorey, and three are sal (Shorea  roxburghii) robusta)  2  bari = dryland agriculture  with no understorey, one is pine dominated with an  dominated. Each plot covers an area of 20 by 20 metres.  One soil pit was excavated and described, and 16 surface soil samples and 1-2 subsurface soil samples were collected in each plot (Feigl 1989). Samples were analyzed for pH, organic carbon, total nitrogen, available phosphorus and exchangeable cations. The plots were re-examined in 1994 for standing biomass and change in the number of trees relative to 1989 was determined.  All soil samples were analyzed in the Pedology Laboratory at the University of British Columbia. The soil samples were air dried and passed through a 2-mm sieve. Soil pH was measured in 0.01 M CaCl with a 2  1:2 soil-water ratio. CEC, Ca, Mg, K and base saturation were determined using the ammonium acetate method (pH 7.0). Available P was determined using the Bray 1, acid ammonium fluoride method and %C  16 was deteirnined using the Leco induction furnace (Soil Science UBC 1981, Peech 1965, Page et al. 1982, Olsen and Dean 1965). Soil characteristics were summarized and differences between soils evaluated using descriptive statistics (mean, standard deviation, minimum, maximum), Pearson correlation, analysis of variance, t-test and Mann Whitney U-tests (Siegel 1956, Norusis 1993a and 1993b, Easterby-Smith et al. 1991).  Phosphorus Fixation Phosphorus fixation was assessed by evaluating P sorption in relation to extractable Fe and Al for a subset of the 3x10 soil survey plot study. P sorption was measured for 16 red soil samples using a standard batch equilibrium technique (Yuan and Lavkulich 1994, Nagpal 1981). For each sample 15 g of soil were added to 300 ml of phosphate solution containing between 0 and 80 ppm P, and shaken for 24 hours. Differences in the phosphate concentration of the supernant before and after shaking were used to calculate the quantity of phosphate sorbed by the soil samples. Fe and Al were extracted using ammonium oxalate (AAO) and citrate-bicarcarbonate-dithionite (CBD) methods (McKeaque and Day 1966, Weaver et al. 1968). Sorption characteristics were related to Fe and Al concentrations using Borggaard et al.'s (1990) model: P + P, = 0.223 AUAO + 0.120 F m  e A A 0  +0.04(Fe D - F Ao) + 0.3 CB  eA  where: P = P sorption maximum Pi = initial P concentration AIAAO = oxalate extractable Al FeAAo = oxalate extractable Fe Fe BD = CBD extractable Fe m  C  Borggaard's model was then used to calculate P sorption characteristics under different land uses on red and non-red soils. The 30 samples in the soil fertility plot studies were extracted for Fe and Al using AAO and CBD methods and P sorption was calculated for red soils under khet, bari and forest land uses. P sorption on non-red soils was calculated for a subset of the 200 site soil survey (n=30) covering khet, bari  17 and grassland sites. For the non-red soils only AAO extractable Fe and Al were used to calculate P sorption as well crystallized Fe oxides (CBD extractable) were found to be less significant (section 4.1.3).  2.1.1 Soil Fertility Dynamics A nutrient budget model was developed to assess nutrient inputs, redistribution and losses relative to soil fertility. Compost, ferilizer, sediment, water and biota are the main sources of nutrient inputs; erosionsedimentation, mineralization-immobilisation and adsorption-desorption are redistribution processes incorporated in the model; and leaching, denitrification, volatilisation, chemicalfixation,erosion and plant uptake are loss mechanisms. Deficit / surplus values of N, P2O5 and Ca were calculated for the main land uses and changes in the soil nutrient pool were estimated. A sensitivity analysis was conducted to identify important model variables and sources of error. Best management practice and deficit elimination scenarios were run to assess alternative management options to reduce soil degradation. Nutrient budgets for individualfieldswere calculated to determine between site variability for the main crops and cropping rotations. Detailed descriptions of the management of soil nutrients and the nutrient budget model are provided in sections 4.2 to 4.4.  Rates of Change in Soil Fertility Nutrient cycling was evaluated as part of the Jhikhu Khola Watershed study (Schreier et al. 1994b) by comparing the fertility characteristics of soils originating from the same parent material, but subject to different land uses. The test area covered approximately 50 ha of red soils (Rhodustults) at 800-900 m elevation near the village of Shree Rampati (Figure 2.2). Three land uses were examined: irrigated agriculture, rainfed agriculture and plantation forest. All the agricultural land is terraced, and the irrigated fields have been used forriceproduction for 5-20 years. The rainfedfieldshave been under cultivation for more than 30 years and the plantation forest was established 17 years ago. Typical soil profiles under forest and rainfed agriculture consisting of AB, Bt and BC horizons were sampled to test the assumption that all soils in the test area are of similar origin and original composition, and that differences in surface  18  horizons resultfromrecent changes in land management. Rates of change in soil fertility induced by land use were then assessed by comparing composite surface samples (0-15 cm depth) takenfrom10fieldson rainfed terraces, 10ricepaddies and 10 sites within the pine forest. Soil fertility was characterised by measuring exchangeable cations (ammonium acetate), CEC, base saturation, available P (Bray method), total carbon (Leco combustion), total nitrogen (auto-analyzer) and pH in CaCl (Page et al. 1982). 2  2.2 Socio-Economic Surveys To understand why farmers choose different management and cultivation practices it is necessary to appreciate the intricacies of the system within which they are operating. Detailed surveys (Questionnaire 2 Appendix A) were conducted with 85 households (Figure 2.3) to compile informationfromthe farmers about their constraints (social, economic and physical) and their aspirations (individual, household and village-wide). A semi-structured interviewing approach based on the Rapid Rural Appraisal method (Lightfoot et al. 1988, Conway 1986, McCracken et al. 1988) was utilized. The main purpose of the interviews was to gather information about the household-farming system, thus, the selection of respondents was biased towards the decision-makers within the farm household and towards equal representation of men and women farmers. Interviews were conducted during April and May, 1994. Simultaneous and separate man / woman farmer interviews were conducted to incorporate a cross check system and to compare perceptions and problems of both the male and female farmers. In most cases, the interviewing was conducted at farmers' homes with the women (female Nepali interviewer and the woman farmer) holding the discussion indoors while simultaneously the men (male Nepali interviewer and the man farmer) talked out of earshot in the courtyard (Kennedy and Dunlop 1989).  The sample of 85 households was chosen as a cross-section of agricultural land types in the region and biophysical conditions in the study area. The study sample was not chosen to be statistically representative of the population and farm distribution in the region, but was a subset of the 200 site soil fertility survey. A total of 85 paired interviews (male and female farmers) were conductedfromthe possible 130 cultivated  19  Figure 2.3. Bela-Bhimsenthan 85 household survey locations.  Figure 2.4 Hydrometric monitoring sites (source: Carver 1997).  20 sites (Table 2.2). Information on crop production, livestock operations, forest products, sufficiency status, ethnic distribution, and off-farm employment were compiled and analyzed to evaluate household access to resources and indices of poverty. Female, male and combined responses were used depending on the activities most familiar to men and women farmers. Female farmers' responses were used to summarize activities such as livestock care and forest production collection. Male farmers' responses were used in the analysis of production information, land holdings and nutrient inputs. Combined responses were used to summarize sufficiency status and off-farm employment. Differences between households were evaluated using descriptive statistics (counts, minimum and maximum), Pearson correlation, Fisher's exact test, ttest and Mann Whitney U-tests. In the case of highly skewed distributions (e.g. compost use) median values were calculated (Siegel 1956, Norusis 1993a and 1993b, Easterby-Smith et al. 1991).  2.2.1 Farming System Dynamics  ^  Changes within the farming system, such as a shift toward market oriented production or an increase in chemical inputs, are often cited in the literature as key factors influencing soil fertility, but typically anecdotal evidence is provided. To quantitatively evaluate farm dynamics, 27 household surveys conducted in Baluwa by Kennedy and Dunlop in 1989 were repeated in 1996. A semi-structured interviewing approach based on Rapid Rural Appraisal methods was utilized (Khon Kaen University 1987, McCracken et al. 1998). The men and women farmers surveyed in 1989 were asked the same questions in 1996. Two surveys were conducted (Appendix A, questionnaires 3 and 4), focusing on the activities most familiar to men and women farmers. Topics discussed included labour allocation, land holdings, cropping systems, livestock operations and forest access. Simultaneous and separate man / woman farmer interviews were conducted to account for the typical division of labour, and to elicit open responses (Kennedy and Dunlop 1989, Schreier et al. 1991b).  Key informant questionnaires (Appendix A.5) were used as a cross check. Informantsfromthe five main villages in the study area (Bhewalthore, Acharyatol, Luietail, Bela and Bhimsenthan) were surveyed in  21 1996 on changes in cropping patterns, yield, market oriented production, water shortages and land ownership.  2.3 Supplemental Data Detailed hydrometric monitoring has been conducted as part of the Jhikhu Khola Watershed project since 1989, and is used to document nutrient redistribution through erosion and sedimentation. The monitoring network consists of four automated hydrometric stations, four manual hydrometric stations, five erosion plots, five tipping bucket rain gauges and 40 24-hour rain gauges. The majority of the hydrometric measurements are concentrated in the Andheri sub-watershed (Figure 2.4). Intensive flow and sediment monitoring programs were conducted through the entire 1992, 1993 and 1994 monsoon seasons (June through September). Sediment - discharge relationships were developed for each of the subcatchments and sediment budgets were calculated for individual storm events. Surface soil erosion was determined from runoff and soil loss measurements at thefiveerosion plots. The plots, located in upland bari fields, are delineated by metal sheets and encompass an area of 70-100 m over 2 sloping terraces. Runoff and 2  sediment are collected in a series of oil drums and a tipping bucket rain gauge located at each site records rainfall intensity and duration. Sediment accumulation in lowland irrigatedfieldswas measured using pins placed in 23 khetfieldsbefore the onset of the flood season. The depth of accumulated sediment was measured after harvest, and samples of the accumulated sediment and residual sub-soil were collected for nutrient analysis (Carver and Schreier 1995, Carver 1995, Carver 1997, Schreier et al. 1991b).  Land use mapping conducted under the Jhikhu Khola Watershed Project was used to document historic land use changes. Changes in land use between 1947 and 1981 were evaluated using historic 1:50,000 scale mapping. The original topographic basemap produced in 1947 was used. In addition to the topographic information, three land use classes were delineated: jungle, shrub with a few scattered trees and agriculture. The Land Resource Mapping Project (LRMP) utilized the same topographic base for displaying the land use survey conducted in the early 1980s. In this nation wide integrated survey, all  22  major land resources were mapped using aerial photo-interpretation and field verification. The study area was examined as part of the overall survey in 1981.  A second set of land use data was generated by photo-interpretation of 1972 and 1990 aerial photography at 1:20,000 scale. A 1:20,000 scale topographic basemap was produced using conventional photogrammetric techniques and the 1990 aerial photographs taken specifically for the Jhikhu Khola Watershed Project. After interpretation of the 1990 photos, a detailed field verification program was conducted. The same team then interpreted the 1972 photographs. All information was transferred into GIS format.  2.4 GIS Mapping and Data Integration Geographic Information Systems (GIS) provide tools for the collection, maintenance, analysis and display of geographically referenced data and facilitate data integration through a common format. GIS based analytical functions provide the capability to selectively search and reclassify data both spatially and temporally. Digital terrain modelling generates slope, aspect and elevation maps which may be analyzed in conjunction with other themes. Overlay operations combine multiple data layers in a vertical fashion to generate new attribute data tables. GIS provides the capability to build complex models by combining analytical functions and by integrating datafromexternal models (Aronoff 1989, Burrough 1986, Laurini and Thompson 1992, Star and Estes 1990).  2.4.1 GIS Based Mapping Topographic information was availablefrom1:20,000 scale aerial photographs flown in 1990. A 1:5,000 digital base map was produced using conventional photogrammetric techniques and this map provides information on planimetric position and topography, including: 5 m contours, rivers, streams, roads, trails, and houses. The TerraSoft™ GIS system (PCI Inc.) was utilized for compilation, analysis and integration  23  of the georeferenced data. Elevation, slope and aspect themes were generatedfromthe contour feature class using the terrain modelling capabilities.  The soils in the study area can be broadly divided into red and non-red soils (Schmidt 1992, Schreier et al. 1990). Soil type was mapped using enlarged (1:5,000 scale) aerial photographs and verified with soil pits and auger samples in the field. Soil colour was used to distinguish between red and non-red soils, with a hue of 2.5YR to 5YR and valuefrom3-6 required to meet the red soil criteria. The soil types delineated on the aerial photographs were then transferred to digital format for GIS analysis.  A historical comparison of land use was compiled for 1972 and 1994 using land use maps and aerial photographs. Enlarged aerial photographs (1:5,000 scale)from1990 and 1972 were interpreted by Nepali team members as part of the Jhikhu Khola watershed project. The 1990 photographs were interpreted and updated to 1994 through field verification. Six land use classes were designated: khet (irrigated agriculture), bari (dryland agriculture), forest, shrub (crown closure < 10%), grassland and 'other' land categories. For cultivated lands the dominant cropping pattern and the proportion of the area under cultivation were recorded. For forest land the cover type, species, crown density and maturity were recorded. Plantation forests were designated separately. Degraded lands, defined as regions with >50% soil exposure, were also identified and partitioned into degraded shrub and degraded grassland. The 'other' category includes sand and boulders, waterbodies and villages. All information was transferred into digital format and comparative evaluations were obtained using GIS reporting and overlay techniques.  Population dynamics in the study region were evaluated for 1972, 1990 and 1995 using house counts and family size data. The number of houses were counted on the 1972 and 1990 aerial photographs and compared to the number of houses observed in the field in 1995. Population numbers were calculated from the number of houses and the average family size, determinedfromcensus data and fields surveys. The recent growth in population is used as an indicator of pressure on the resource base.  24  2.4.1 Data Integration All information compiled as part of this study was collected in a georeferenced manner and entered into the GIS. Through the use of one standard format at 1:5,000 scale, data on soils, topography, land use and socio-economic factors are readily accessible from the same database in a compatible format. GIS is used to delineate, reference and measure spatial and temporal trends, and to display the results of queries from external databases. Biophysical data are typically collected and analyzed in a georeferenced framework, but sociological and economic data are often summarized by political subdivisions. Through collecting socio-economic data in a georeferenced manner, data integration is facilitated. GIS overlay and reporting functions can then be utilized to examine spatial relationships such as dryland agriculture by soil type, and temporal trends such as land use change. Area and object oriented themes can be integrated to analyze site characteristics such as soil pit data by land use. Statistical relationships and classification techniques can be used to model data both spatially and temporally. For example, soil chemical properties determined from soil pits can be extrapolated spatiallyfromrelationships with site characteristics. Rate of change data, such as soil fertility trends, can be used to develop scenarios documenting potential long term impacts. GIS combined with database, statistical and graphical software thus provides an interactive information system for data analysis, integration and modelling.  25  3. RESEARCH SETTING The biophysical and cultural characteristics of the study region, and a description of the dominant farming systems are presented to provide context for soil fertility issues within the study region.  3.1 Biophysical Setting The biophysical setting has a potentially important impact on soil fertility. Topography and the related microclimatic conditions influence soil formation through erosion, leaching, weathering, and decomposition processes. Soil type determines the inherent nutrient status and physical properties. Land use and its associated management influence soil fertility through anthropogenic processes such as accelerated erosion, impeded drainage, fertilizer induced acidification and cropping induced nutrient depletion.  3.1.1 Topography The topographic conditions are summarized in Plates 1-3. Plate 1 displays elevation ranges in 100 m increments. Elevation rangesfrom810 to 1729 metres, but 35% of the region is below 900 m and 55% is below 1000 m, while only 5% of the area is above 1500 m. Aspect (Plate 2) is dominantly north - south encompassing 32% and 34% of the area respectively. Elevation and aspect reflect micro-climate conditions in the region with high elevation north facing sites being cooler and moister than the hot dry, southerly sites. Plate 3 displays slope in 10° increments. Slope affects the terrace type, water management and erosion. The distribution of slope classes is relatively uniform with 12% of the area in the 'flat' category, 42% of the area has slopes less than 10° and 14% has slopes greater than 30°. The study region may be subdivided along the Jhikhu Khola into the Bela region (59%) to the south and the Bhimsenthan region (41%) to the north. Topographically, the Bhimsenthan region is steeper particularly above 900 m and is dominantly south facing, while the north facing Bela region has more incised river valleys and covers a greater elevation range (810-1720 m compared to 810-1451 m).  26 3.1.2 Erosion The loss of topsoil from cultivated and grazing lands is a serious problem, as soil fertility and productivity decline with topsoil loss (Carson 1986, Carson 1992, Carver 1995). Annual soil lossesfromeach of the erosion plots during 1992-1994 are shown in Figure 3.1a. Annual erosion rates rangefrom0.1 to 42 t ha", 1  and are within the range of values measured by other researchers in the Middle Mountains reported by Carver (1997). The large variation in soil erosion between plots is a reflection of the variation in soil properties. Regardless of soil properties, however, most of the annual erosion occurs in the pre-monsoon period (Figure 3.1b). If an intense rainfall occurs before the summer crop is developed, erosion losses can be large. Figure 3. lc shows the percentage of annual soil lossfromeach plot that occurred in the two most damaging storm events. In all years and on all plots, about 50-90% of the annual total soil loss occurred during only two events. In plots 2 and 3, during 1992 and 1993, intense pre-monsoon storms occurred very early in the pre-monsoon season when vegetation cover was at a minimum, and the resulting high erosion rates are reflected in Figure 3.1a.  In addition to vegetative cover, the condition of the soil surface is important in determining erosion levels. The erosion plots are situated on well managed cultivated fields, while large tracts of land in the Middle Mountains are degraded as a result of over utilization and extreme erosion. These degraded sites are characterised by exposed soil, extensive rills and gullies, and surface crusting. Carver (1995) used a paired catchment study to compare sediment outputfrombasins with different surface characteristics. The basins examined are of similar size but the Dhap Khola watershed has gentler slopes (dominantly <10°) than the Andheri Khola watershed (5-30°). Greater sediment outputfromthe steeper Andheri Khola watershed was anticipated, but the opposite effect was measured. The higher sediment outputfromthe Dhap Khola watershed is related to the extent of soil degradation. The Dhap basin has 14% gullied lands, while only 5% of the Andheri basin is degraded.  a) Annual soil loss (t ha" ) from erosion plots, 1992-1994. 1  b) Percent of total soil loss occurring in the pre-monsoon season.  c) Percent of annual soil loss occurring in the two most damaging storms.  Figure 3.1 Annual soil loss, 1992-1994 (data source: Carver and Narkarmi 1995).  28  3.1.3 Soil Types The red soils of the study region are Rhodustults and Haplustults formed on quartzitic phyllite, and the non-red soils are dominantly Ustochrepts and Dystrochrepts formed on phyllite, schist, quartzite, sandstone and siltstone (Schmidt 1992, Schreier et al. 1990a). Red soils are the oldest soils in Nepal and are widespread in the study region (Plate 4), covering 1190 ha (62%). In the Bela region, red soils are dominant below 1150 m except where they have been eroded along major river valleys, and red soils cover 68% of the Bhimsenthan region. Red soils play a unique role in Nepali society. They are used as surface sealant for walls and floors of farm houses and decoration for temples. Their high clay content and low infiltrability makes them hard when dry and slippery when wet, limiting their workability for agriculture and making them prone to erosion. Under good management, red soils may provide high crop yields, but some of the most degraded sites occur on red soils (Sherchan 1990, Schreier et al. 1989, Carver 1995).  3.1.4 Land Use The majority of land in the Middle Mountains is used for arable agriculture, livestock grazing or forestry. There are two major types of cultivated land: khet and bari. Khet land is comprised of bunded lowland terraces with sufficient water during the monsoon rains to grow a crop of rice. Bari land is comprised of flat or sloping terraces on rainfed uplands, and flood irrigation is not readily possible. Forest, shrub (degraded forest) and grasslands make up the remainder of the dominant land use types.  Plate 5a displays the current land use within the study region. Khet land is dominant along the Jhikhu Khola with 80% of the khet land occurring below 900 m elevation and 82% found on slopes <10°. Khet land is also important along the Andheri Khola where water is supplied through an intricate irrigation system. Bari cultivation dominates the land use covering 42% of the study region, and is an important source of biomass. Forest and shrub land cover 32% of the area, and a large degraded government forest is located NE of Bela. The density of the forests is generally low (75% of the forest has crown density  29  <50%), and pine trees comprise 40% of the forest area. Grasslands scattered throughout the region, are the result of lopping, cutting, and grazing in forests, and abandoned cultivation.  3.2 Cultural Setting  Nepalese culture is an intricate blend of Hindu and Buddhist values and traditions, and religion influences social, economic, legal and political activities. Nepali society is structurally complex with many different caste groups and ethnic minorities. Divisions of ethnicity and caste reflect class distinctions, influence the role of women within the household, and determine the cultural significance of livestock (Sayami 1980, Hofer 1979, Bista 1972).  3.2.1 The Caste System Caste distinctions within Nepali social structure are not rigid, but the caste hierarchy is correlated with ritual and economic allocation. The ethnic distribution within the region therefore has potentially important implications for soil fertility management as the class hierarchy influences access to land and capital resources. The ethnic distribution within the 85 households surveyed is presented in Table 3.1. The bulk are agriculturalists, but mercantile occupations are strong within some groups. Brahmins (priest caste) are the highest in the caste hierarchy and comprise 54% of the sample. Brahmins are traditionally the most influential and wealthy households. Kshatriya (rulers and warriors) include Chhetri and are ranked second in the hierarchy. Vaishya (agriculturalists and traders) include indigenous tribal hill groups such as Newar, Danuwar and Magar, and comprise 27% of the sample. Sudra (service groups) include blacksmiths (Kami), leather workers (Sarki) and musicians (Jogi). The Hindu occupational castes are considered as low caste or 'untouchables', and economically and socially inferior to other groups. Tamangs (8% of the sample) are a Tibeto-Burman community of relatively poor people who practice Bhuddism and are not part of the Hindu caste hierarchy, but may be ranked on the basis of their social status (Table 3.1). Although the caste system has not been anchored in legislation since 1963, the system of relations remains (Bista 1991, Fox 1987, Gould 1987, Hofer 1979, Kennedy and Dunlop 1989, Mishra 1989, Bista 1972, Muller-Boker 1988).  30 Table 3.1. Ethnic distribution within the Bela-Bhimsenthan sample.  1  Ethnic  Distribution  Group Brahmin  (%) 54  priests / fanning  Chhetri  6  rulers / military / farming  high  Newar  15  merchants / farming  medium  Jogi  1  fakirs / farming  medium  Magar  1  military / farming  medium  1  Traditional Occupation  Rank in Caste Hierarchy  2  highest  Tamang  8  military / farming  low  Danuwar  11  fishing / hunting / farming  low  Kami  3  blacksmiths / farming  low  Sarki  1  leather workers / farming  low  dataset: 1994 Bela-Bhimsenthan household survey, n=85; combined male and female farmer responses 2  Bista 1991, Shah 1996  3.2.2 Role of Women Women are allocated different roles and activities based on cultural ideas of capability and appropriateness. Nearly all women in the study area work in agriculture. They are largely responsible for the day-to-day tasks within the farming system such as fetching water, domestic activities (cooking, cleaning and child care), and gathering flielwood. Planting (Photo 1), weeding and harvesting are almost exclusively female tasks and constitute 85-95% of the labour requirements for the main crops grown (rice, maize, millet and potatoes). Women are solely responsible for 'polluting' work, which in this context refers to direct contact with human or animal excrement; therefore men do not carry manure. However, women should refrainfromworking with animals (ploughing) as it is considered sinful and causes pain to the animals. Men are responsible for the heavier work such as land preparation, terrace repair and irrigation system maintenance. In animal husbandry, the role of women depends on the availability of pasture. Children are often assigned the responsibility of animal grazing and watering, but women collect fodder, stall feed the animals, and milk any female goats, cattle or buffalo. Time use data for Nepal indicate that females work longer hours than males in all age groups. Adult women work an average of 10.8 hours per day compared to an average of 7.5 hours per day by men (Acharya 1982, Kennedy and Dunlop 1989, Pfanner 1987, Gurung 1995b, Scheper 1989. Bhatt et al. 1994, HMGN 1993).  31 The collection of fuelwood, fodder and litter (Photo 2) within the study region is representative of the high labour demands placed on women. Collection by the 85 households sampled in the Bela-Bhimsenthan region is summarized in Figure 3.2 and Table 3.2. Wives, daughters and daughters-in-law collect 86% of fuelwood, fodder and litter for a typical household. A median household makes one trip per week to collect fuelwood, seven trips per week to collect fodder, and three trips per week to collect litter. The median return trip takes two hours and up to 100 hours per household per week may be spent in the collection of fuelwood, fodder and litter.  Table 3.2. Frequency of fuelwood, fodder and litter collection (median values). Product  Frequency  (trips week" ) 1  Time / Trip  Time Spent  (hours)  (hours week")  Fuelwood  1.0  3.0  3.8  Fodder  7.0  2.0  14.0  Litter  3.0  2.0  7.0  total  13.5  7.0  2&.0  dataset: 1994 Bela-Bhimsenthan household survey, n=85; female farmer responses  Figure 3.2. Fuelwood, fodder and litter collection by women (dataset: 1994 Bela-Bhimsenthan household survey, n=85, female farmer responses).  32  3.2.3 Cultural Role of Livestock Livestock play a significant role in Nepali culture. Bullocks are used as draught power for land preparation. Cows are kept primarily for cultural / religious purposes and manure production (local cows are not good milk producers). Female buffalo are kept for milking purposes and for manure. Male buffalo are not extensively used for draught power due to the small terraced plots in the area but are raised and sold for meat. Many families require goats for religious sacrificial purposes, and goats are also sold for meat. Poultry is raised for meat and eggs; however, Brahmins traditionally do not eat eggs or poultry (Chitrakar 1990, Kennedy and Dunlop 1989, Fox 1987, Gurung 1995a).  Religious beliefs and the law prohibit the slaughter of cattle and female livestock in Nepal, resulting in the accumulation of low productivity livestock. The ratio of cows to calves provides an indication of the productivity of the cows kept (Fox 1987, Carson 1992, Chitrakar 1990). Farms sampled in the study region owning both cows and calves (n=37), had an average cow-calf ratio of 1.0, with a maximum cowcalf ratio of four (two Brahmin households with four cows and one calf each). In addition, 19 households maintained cows but have no calves, while only three households have calves but no cows. While cows provide status to Hindu households, maintaining cows beyond their reproductive period for religious reasons places additional stress on the already limited fodder resources.  3.3 Farming Systems Farming systems integrate rainfed and irrigated cultivation, animal husbandry, forest products and household labour. These interrelationships, illustrated in Figure 3.3, indicate the complexity of the farming system and soil fertility issues in the Middle Mountains. Pressure on one component will impact the entire system and alter the transfer of nutrients within the farming system. The farming system is extremely labour intensive: land is prepared primarily with a bullock drawn wooden plough; planting, weeding, harvesting and threshing are done by hand; and considerable time is spent on the collection of animal fodder, litter, fuelwood, and grazing supervision. In the traditional system, households tended to be self-  fodder  bedding  FARM LABOUR  ^jf  IRRIGATION SYSTEM  NON-FARM EMPLOYMENT  ^  MARKET Text  traditional system recent changes  Figure 3.3. Component interactions within farming systems.  GRASS / SHRUB LANDS  34  supporting for basic requirements, arable land was intensively utilized, and there was a heavy reliance on livestock and forest inputs for crop production. As market oriented production and off-farm employment opportunities developed in the 1980s, the potential for income generation and purchasing outside inputs increased. Cash crops include tomatoes, potatoes, garlic and onions; milk and meat are also marketed; and chemical fertilizers and pesticides are now utilized. However, these intensive agricultural systems are more extractive of both soil and human resources (Pound et al 1992; Panth and Gautam 1990; Yadav 1992, Carson 1992).  3.3.1 Dominant Cropping Systems The cropping systems determine nutrient requirements and where nutrient inputs are limited, soil nutrients may be depleted through crop uptake. The cropping systems adopted by farmers are influenced by soil tilth, variable rainfall, the temperature regime, available irrigation facilities, labour availability, household food requirements and preferences, and recently, market opportunities (Schreier et al. 1990c, Kennedy and Dunlop 1989, Chitrakar 1990). The prevalent cropping systems on khet and bari land in the BelaBhimsenthan region are shown in Figure 3.4. Based on the monsoon climate, the year is divided into three growing seasons: pre-monsoon (February-May), monsoon (June-September) and winter (OctoberJanuary). Irrigated lowland cultivation (khet) typically includes a premonsoon rice or maize crop followed by monsoon rice and a winter grain or cash crop. Rainfed upland cultivation (bari) commonly involves a fallow pre-monsoon period followed by maize or maize intercropped with beans during the monsoon, and a winter crop of wheat or mustard. With recent increases in market oriented production, a tomato crop may be grown during the winter season if sufficient water is available. Legumes, mainly associated with bari land are grown for home consumption, and are not incorporated into the soil as a green manure. Finger millet which is extensively grown in the Middle Mountains (Chitrakar 1990, Sakya 1986) is not common in the study area due to unfavourable climatic conditions.  35  KHET /  n=65 early mafee 28%  /  /  / /  I early rice 1 5 % / /  /  m o n  | ° 0  9  6  /  o  /  BARI  n=83  /  ,  M  A  M  pre-monsoon (Feb - May)  J \  mustard 18%  /  / '  /  P  Q T A T O  8  /  %  , wheat 41% / » t / / mustard 33% / / / tomato 11%  maize maize/beans 100%  F  / '  n r i c e  wheat48%  J  A  L  S  monsoon (Jun - Sep)  /  f  O  N h  D  J  F  M  A  M  winter (Oct - Jan)  Figure 3.4. Dominant cropping systems (dataset: 1994 Bela-Bhimsenthan household survey; 1996 key informant questionnaires; Shah 1996).  Of the surveyed households owning khet land (n=65), 63 grew monsoon rice on some or all of their khet land, while 14 grew premonsoon maize and 10 premonsoon rice. The dominant winter crops were wheat grown by 33 households, mustard (13 households) and potatoes (12 households). For the households owning bari land (n=83), during the monsoon period all households planted maize or maize intercropped with beans on some of their bari land. During the winter, 34 households grew wheat, 30 mustard, 21 tomatoes and 9 grew potatoes. Most farmers utilize the same cropping system each year (e.g. maize or rice dominated), but the winter crops are rotated within and between fields to minimise risk within a given year and to maintain crop yields between years.  36  Compost and Chemical Fertilizer Use Traditionally, Nepalese hill crop production systems have been sustained by recycling animal manure, and organic residuesfromcrops and forest land. Compost in the region is usually a mixture of livestock manure, wastefromthe maize crop (cobs and stalks) and livestock bedding materials (pine needles, straw and leaves) (Kennedy and Dunlop 1989, Subedi et al. 1995). While compost and farm yard manure are important sources of nutrient inputs within the traditional farming system, chemical fertilizers are becoming an important nutrient source (Pandey et al. 1995, Chitrakar 1990, Carson 1992). The dominant chemical fertilizers used in the study area are urea (46-0-0), complex® (20-20-0) and ammonium sulphate (21-0-0).  Table 3.3 summarizes reported organic matter and chemical fertilizer inputs and the corresponding N and P 0 inputs to the 130 cultivated fields of the 1993/94 Bela-Bhimsenthan soil survey. The nutrient content 2  5  of traditional compost measured at the Lumle Agricultural Research Centre (0.6% N and 0.06% P 0 on a 2  5  dry weight basis) and a 25% moisture content is utilized in the estimation of N and P 0 inputs from 2  5  organic sources (Subedi et al. 1995). The number of farmers growing crops varies between seasons and a few farmers did not provide input data. In the premonsoon period, 20 farmers grow crops, compared to 127 reporting input data in the monsoon and 91 in winter. Ninety-seven percent of the 85 households surveyed report using chemical fertilizers on some or all of their crops, while 87% apply compost. The median amount of organic matter and chemical fertilizer applications are greatest in the monsoon season and least in the winter. Monsoon inputs account for 86% of the organic matter applied and 58% of the chemical fertilizer application. Twenty-three percent of farmers report using no chemical fertilizer in the winter compared to 5% in the monsoon, and 77% of farmers report applying no organic matter to their winter crops. Nutrient inputs are greaterfromchemical fertilizer sources, with a median of 100 kg N and 47 kg P 0 applied per year, however, inputs are highly variable between farms. Complex® is the most 2  5  widely used chemical fertilizer and accounts for 50% of the inorganic N inputs and 100% of the inorganic P 0 inputs. 2  5  37  Table 3.3. Reported nutrient inputsfromorganic and chemical fertilizer sources. Source  n  1  Application  2  (kg ha ) 1  median  range  None Applied  3  NInput  P O Input  (kg ha' )  (kg ha )  2  (% Fanners)  median ! range  s  1  1  median  range  Organic Matter premonsoon monsoon winter P-M-W  20  0  0-19,658  60  0  0-9  127  8.477  0-98.288  20  38 ! 0-442  4  0-44  91  o ; 0-49,144  77  0 | 0-221  0  0-22  127  9,829 ; 0-98,288  13  j 0-442  4  0-44  37  0-98  0 |  44  Chemical Fertilizer  0-88  20  241  0-629  5  49 | 0-208  127  275  0-1,965  4  0-197  147  0-983  23  68 0-403 31 j 0-452  13  91  20  0-197  127  472  0-2,506  3  100 j 0-664  47  0-295  complex®  127  236  0-1,474  6  47 | 0-295  47  0-295  urea  127  125  0-747  39  57  j  0-344  0-982  82  0  j  0-206  premonsoon monsoon winter P-M-W  j  Fertilizer Type  ammonium sulphate 1  127  0;  -  -  dataset: 1993/94 Bela-Bhimsenthan soil survey, n=130; male farmer responses 2  3  includes farmers not applying nutrients  % of farmers growing crops who do not apply nutrients  3.3.2 Livestock Operations Livestock play an integral role in the farming systems of Nepal, and impact soil productivity through manure inputs, biomass removal to meet feed requirements, and compaction byfreegrazing. The typical livestock mix within the sampled households is shown in Figure 3.5. Fifty to sixty percent of households own a pair of bullock (oxen), a cow and calf, a female buffalo and calf, 4 goats, and 2 chickens. Two Newar households had no livestock as they are largely involved in small business, while the sample high was a Danuwar household with 79 animals (60 chickens).  Livestock is a major contributor to soil fertility maintenance through the production of organic matter from dung and dung / compost mixtures. Traditionally, the majority of farm yard manure / compost was applied to bari land, however compost is currently applied to khet land to meet increasing nutrient requirements due to crop intensification (Carson 1992, Vaidya et al, 1995). The concentration of livestock per unit area of cultivated land impacts the potential compost application to support crop production. To  38  E3 >1 animals E31 animal • 0 animals  Figure 3.5. Livestock holdings (dataset: 1994 Bela-Bhimsenthan household survey, n-85, female farmers responses). determine animal stocking densities, given different types of livestock, tropical livestock units (TLU) were calculated. Livestock units are based on the N content of manure produced by a standard cow, which has a liveweight between 200 and 350 kg in the tropics. The calculations relating cattle to other domesticated species are given in Table 3.4. For example, one bullock is equivalent to 5 swine or 125 chickens (FAO 1975, Williamson and Payne 1978, Ont. MoAF et al. 1976, Brisbin 1995, Fox 1987, Pagot 1992, LRMP 1986a). Livestock concentrations for the surveyed households are presented in Table 3.5. The median TLU per household is 3.9, while the maximum is 9.9 TLU (a Danuwar family with 4 bullock, 1 cow, 1 calf, 1 female buffalo, 5 young buffalo, 7 goats and 60 chickens). The median stocking density, TLU per ha of cultivated land, is 3.8 with a maximum stocking density of 42 TLU per ha (a Danuwar family with 0.15 ha of bari land, 2 bullocks, 1 cow, 1 calf, 1 female buffalo, 4 young buffalo, 2 goats and 3 chickens). These stocking densities are some of the highest in the world and place additional pressure on fodder resources (Joshi 1992, Panth and Gautam 1990, Chitrakar 1990).  Table 3.4. Tropical livestock unit equivalents. Animal Type  TLU  1  Cattle - Bullocks Cattle - Cow  0.8  Cattle - Calf  0.4  Buffalo - Bull  1.2  Buffalo - Cow  1  1  1  Buffalo - Calf  0.5  Goat  0.1  Pig Chicken  0.2 0.008  Duck  0.008  sources: FAO 1975, Williamson and Payne 1978, Fox 1987, Brisbin 1995, Ont. MoAF et al. 1987  Table 3.5. Livestock concentration in the Bela-Bhimsenthan region. Livestock Concentration  Household  T L U per household  average  1 maximum  3.9  j 9.9  Stocking Density (TLUha ) j 3.8 j 42.0 dataset: 1993/94 Bela-Bhimsenthan household survey, n=85; female farmer responses 1  Fodder deficiencies are a primary constraint to enhancing livestock production, and the quantity and quality of the manure produced. Buffalo are primarily stall fed, cattle are raised under a semi-extensive system, grazing a few hours per day on the limited grasslands or on cultivated land after crop harvest, while pigs and chickens are raised under a scavenging system. Animal feed levels available in Nepal are estimated to be less than 70% of requirements. In the Bela-Bhimsenthan region, 55% of the female farmers surveyed reported fodder shortages and 98% of those occurred in the winter dry season. Virtually all lands are grazed to some degree at some time during the year, and heavy grazing is responsible for much of the environmental degradation of public lands. Traditionally, forests were heavily relied on to meet fodder needs but farmers have begun to stall feed their livestock for much of the year. Most feed now originates from agriculture but fodder trees are often the only fresh matter available during the dry winter period (Fox 1987, Shepherd 1985, Shrestha 1992, Panday et al. 1991, Jain and Kumar 1995, Joshi 1992, Rasali et al 1995, Panday 1992, Pariyar et al. 1996, Mahat et al. 1987a). In the study region, the majority of  40  fodder comesfromcrop residues (51%) and terrace risers (26%), while purchasing fodder (11%), private trees (7%) and community or government forests (5%) provide additional sources.  3.3.3 Forest Products Forests are an integral part of the farming system in the Middle Mountains. Grass and green leaves collected from the forest are used as livestock fodder, and are particularly important for livestock maintenance during the dry season. Litter collectedfromthe forest floor is used as ariimal bedding, and bedding materials mixed with manure are subsequently applied to crop land (Schmidt 1992). Common species for fodder and litter reported by the female farmers in the study region (n=85) are kaingyo (Ligustrum confusum),  pithauli (Rhus parviflora) and sal (Shorea robusta), but 53-62% reported  difficulties in collecting these species compared to five years ago.  Fuelwood is a major energy source for cooking and heating in all parts of Nepal (Smith et al. 1993, Chitrakar 1990). Ninety-five percent of the households surveyed (n=85) depend on fuelwood as a major energy source. The main fuelwood sources and their contribution to household fuel supply (Figure 3.6) are private trees (35%), communal or government forests (24%) and purchased fuelwood (4%). Fuel deficits are reported by 62% of households. Scarcity of fuelwood, difficulty in fuelwood collection and labour shortages may promote the use of substitutes but only 8% of the households use any kerosene. The low reliance on forests for fuelwood supplies (24%) and the long trips required to collect fuelwood (3.2 hours per trip) reflect the scarcity of fuelwood sources.  Ninety-six percent of the households surveyed (n=85) report having trees on their private land. Fodder trees are grown by 85% of the households, and are typically grown between terraces on bari land. Ninetytwo percent of households report growingfruittrees which provide a supplemental food and/or income source. Fuelwood and timber are mostly grown on non-cultivated land, and are limited to fewer households (52% and 36% respectively). The majority of surveyed households report having the same number of  41  Kerosene Private Trees Crop Residues  Buy Fuelwood  Forest  Figure 3.6. Percent supply of household fuel by source (dataset: 1994 Bela-Bhimsenthan household survey, n=85, combined male and female responses). fuelwood, fruit and timber trees relative tofiveyears ago, but 60% report having more fodder trees on their own land, indicative of the shortage of fodder sources on common property. Households reporting fodder shortages during the dry season grow an average only 7 fodder trees on their own land, while households reporting no fodder shortages own an average of 17 fodder trees.  3.4 Summary The biophysical setting reflects inherent soil fertility and anthropogenic processes active within the study region. Topographic conditions indicate a diverse landscape, with elevation rangingfrom810 to 1927 metres, dominantly north-south aspect, and slopesfrom0 to >30°. Elevation and aspect reflect microclimatic conditions with high elevation north facing sites being cooler and moister than the hot dry south facing sites. Erosion is highest on cultivated uplands during the premonsoon season prior to the establishment of vegetative cover, and on degraded sites characterised by exposed soil, extensiverillsand gullies, and surface crusting. Red soils, which occupy 68% of the study area, are highly weathered, kaolinitic soils with low infiltrability. Under good management, red soils may be highly productive, but some of the most degraded sites occur on red soils. Irrigated (khet) land comprises only 10% of the area, compared to 42% dryland (bari), and 32% forest and shrub.  42  The cultural setting influences social and economic activities in the study region. Caste and ethnic distinctions are not rigid but reflect household ritual and economic allocation. Brahmins (high caste) are the largest ethnic group in the area and comprise 54% of the study sample. Women are responsible for the day-to-day tasks within the farming system, and constitute 85-95% of the labour requirements for the main crops grown. Due to their traditional role as resource users and managers, women are central in soil fertility maintenance. Religious beliefs and law prohibit the slaughter of cattle and female livestock in Nepal, resulting in the accumulation of low productivity livestock and additional pressure on the limited fodder resources.  Farming systems integrate rainfed and irrigated cultivation, livestock husbandry, forest products and household labour. Maize-wheat and rice-wheat rotations are the dominant cropping systems on bari and khet land respectively, with potatoes and tomatoes the main cash crops grown. Compost is largely applied to the monsoon maize crop, and chemical fertilizer (dominantly complex®) is mainly applied to rice and vegetable crops. Livestock are owned by 98% of the sampled households with a typical holding of four tropical livestock units per household. Crop residues are the main fodder source, but fodder deficiencies are reported during the dry season by 54% of the female farmers surveyed. Grass and green leaves collected from the forest and shrub lands provide an important source of livestock fodder during the dry season. Forest litter is used for animal bedding and subsequently applied to crop land, and fuelwood is used as an energy source by 95% of the surveyed households.  43  4. SOIL FERTILITY STATUS AND DYNAMICS The focus of this chapter is to evaluate the current status of soil fertility in the study region and to determine any changes. The components evaluated and their interactions are shown in Figure 4.1. Soil fertility data for agricultural sites in the Bela-Bhimsenthan region are compared to region values and desirable levels for crop production. The dominant factors influencing agricultural soil productivity are discussed, specifically topography, soil type and land use. From these relationships and supplemental data from forest plot studies, a fertility classification is developed to evaluate the spatial extent of fertility problems in the study area. Phosphorusfixationby Fe and Al oxides is a potential concern in relation to chemical fertilizer inputs and is evaluated through P sorption studies and modelling techniques. A classification of P sorption capacity is then developed to assess the spatial extent of Pfixationproblems in the study area.  Soil fertility dynamics are quantified using nutrient budget modelling and plot study data. Nutrient inputs, their redistribution, lossesfromthe soil-plant nutrient pool and crop nutrient uptake provide indicators of how soil fertility is changing. Insufficient inputs, poor quality compost, inefficient chemical fertilizer use, accelerated erosion and intensive cultivation may all negatively impact soil fertility. The components of nutrient management discussed include compost and chemical fertilizer use, the loss of nutrients via erosion, nutrient recapture through irrigation and sediment deposition, and nutrient losses associated with Pfixation,N leaching, denitrification, NH volatilization and Ca leaching. Nutrient removal through crop 3+  uptake and subsequent harvesting is discussed relative to yield and soil-productivity relationships. A nutrient budget model which incorporates nutrient inputs, crop uptake, erosion and other nutrient losses is developed to estimated surplus / deficit N, P and Ca flows for the dominant cropping systems and to assess how soil fertility may be changing. A sensitivity analysis, best management practices and deficit elimination scenarios are used to identify key model variables and potential options to minimize nutrient deficits. The nutrient budget model is applied to individualfieldsto evaluate the variability between sites and the direction of change in soil fertility under different land use and management regimes. Rates of  44  change in soil fertility are then determinedfromplot studies data conducted as part of the Jhikhu Khola Watershed Project (Schreier et al. 1994b).  -  compost chemical fertilizer erosion water management nutrient losses  - traditional versus cash crops - crop p-oductwity , - cropping intensity  Figure 4.1. Components and characteristics of soil fertility status and dynamics evaluated in chapter 4.  4.1 Soil Fertility Status The current soil fertility status of the Bela-Bhimsenthan region is summarized in Table 4.1, and compared to datafromthe Dhulikhel subwatershed (Figure 1.4) located in the headwaters of the Jhikhu Khola (Schmidt 1992, Wymann 1991), data for the entire Jhikhu Khola watershed (Schreier et al. 1991b), and desirable levels for tropical production derivedfromliterature sources. Overall soil nutrient reserves are low. In the Bela-Bhimsenthan region, soil acidity is one pH unit below desirable levels, and carbon and cation exchange capacity are low. Phosphorus, potassium and base saturation are adequate, but values are slightly higher relative to the Dhulikhel and Jhikhu Khola surveys. The Bela-Bhimsenthan survey focuses  on agriculture, while the Dhulikhel survey compares forest and agricultural soils, and the Jhikhu Khola survey covers the entire watershed. The Dhulikhel and Jhikhu Khola soil surveys are therefore more representative of the overall soil conditions.  Table 4.1. Current soil fertility status (0-15 cm depth). Variable (mean values)  Bela-Bhimsenthan (n=200)  Dhulikhel (n=256)  4.8 (0.4)  4.4  4.6  5.0-6.5  10.8 (4.1)  10.5  10.4  >15  exchangeable Ca (cmol kg" )  3.75 (2.04)  2.18  2.58  >3.0  exchangeable M g (cmol kg" )  1.40 (0.80)  0.61  0.99  >1.5  exchangeable K (cmol kg" )  0.28 (0.21)  0.27  0.29  >0.25  Base Saturation (%)  51.7 (16.7)  30.9  39.0  >50  available P (mg kg" )  16.6 (18.9)  11.6  2.1  >15  Carbon (%)  0.99 (0.47)  0.68  1.01  1.5-2.0  pH (CaCl ) 2  C E C (cmol kg') 1  1  1  1  1  Jhikhu Khola (n=225)  2  Desirable Levels  3  numbers in parenthesis are one standard deviation 1  Schmidt 1992, Wymann 1993 2  3  Schreier etal. 1991  Landon 1984, Miller and Donahue 1990  Soil pH is an indication of the acidity or alkalinity of the soil and influences the availability of plant nutrients, with a pH range of 5.0-6.5 considered optimum (Foth 1990, Miller and Donahue 1990, Landon 1984). The soils sampled in the Bela-Bhimsenthan data set are strongly acidic with an average pH of 4.8, and 71% of the samples have pH values less than 5.0. The soils in the study region are acidic for a number of reasons: 1) the dominant bedrock is sandstone, siltstone and quartzite which produce acidic soil material; 2) chemical fertilizer use is increasing, ammonium sulphate and urea being the most common, both of which acidify the soils; 3) pine litter used in compost has acidic decomposition products; and 4) no lime is currently applied in the farming system (Schreier et al. 1995). The effect of soil pH on the solubility of minerals is significant, with strongly acidic soils (pH <5) usually having high concentrations of soluble Fe and Al which fix P in a form not readily available to plants (Miller and Donahue 1990, Landon 1984).  46  The cation exchange capacity (CEC) refers to the sum total of exchangeable cations that a soil can adsorb. The type and amount of clay, and the organic matter content influence CEC. The higher the CEC, the greater the capacity of the soil to retain basic catonic nutrients in an available form against leaching. The average CEC in the sampled sites is 10.8 cmol kg" and 86% of the samples have CEC values <15 cmol 1  kg". The low cation exchange capacity in the study area is related to the inherited bedrock conditions 1  (sandstone, siltstone, and quartzite), extensive weathering leaving kaolinite as the dominant clay mineral in these soils, and low organic matter content. Kaolinite and other highly weathered clays are classified as variable charge clays, where CEC increases with soil pH. The low CEC values are indicative of a limited ability to retain nutrient cations and restricts the effectiveness of fertilizer applications (Willett 1994, Brady and Weil 1996, Miller and Donahue 1990, Sanchez 1976).  The basic cations held on the exchange sites of a soil are Ca, Mg, K and Na. Calcium, Mg and K are essential elements for plant growth (Miller and Donahue 1990). Normally, Ca deficiencies occur only in soils with low CEC at pH values <5.5 (Landon 1984). Calcium levels in the agricultural soils (BelaBhimsenthan survey) are adequate but overall (Dhulikhel and Jhikhu Khola surveys) values are low. The presence of a Mg deficiency in a crop is associated with low Mg values and with Ca:Mg ratios above 5:1 as Mg may become less available (Landon 1984). Magnesium values in the region are low, and Ca:Mg ratios are >5 for 86% of the samples. Potassium values appear to be adequate in all three surveys, but the variability of K values is high within the Bela-Bhimsenthan dataset, and sites with K below 0.2 cmol kg"  1  will likely respond to K fertilizer (Landon 1984).  Base saturation is the proportion of the CEC occupied by the bases Ca, Mg, K and Na. In general, as the base saturation of a soil increases, so do the pH and fertility level (Miller and Donahue 1990, Landon 1984). An increase in base saturation results in a quantitative increase in bases in the soil, and an increase in absorption by plants. Base saturation in the agricultural soils (Bela-Bhimsenthan survey) are adequate, but average valuesfromthe Dhulikhel and Jhikhu Khola surveys are low.  47  Phosphorus is one of the limiting nutrients in the soils of the Middle Mountains. The availability of phosphorus depends on soil acidity, organic matter content and microbial activity. Phosphorus is relatively available from pH 5.5 to 6.5, but below pH 5.5 phosphate is fixed by hydrous oxides of Fe and Al (Schreier et al. 1995, Willett 1994, Landon 1984). Phosphorus values for the agricultural soils in the study region are adequate and 32% of available P values are >15 mg kg". Overall P concentrations are low 1  (Dhulikhel and Jhikhu Khola surveys), and organic matter and soil acidity are major concerns with regard to P availability.  Organic matter content has many beneficial effects on soil physical, chemical and biological properties, and a low organic matter content is undesirable for plant growth (Tisdale et al. 1985). Soil carbon content is directly proportional to organic matter content. Carbon content in all three surveys is low and only 12% of the Bela-Bhimsenthan samples have C values >1.5%. Low carbon levels are related to historic losses of organic matter due to soil erosion, crop removal and litter collection. Organic matter management is critical to the maintenance of soil fertility within the study region as compost is a major soil additive used by Nepalese farmers (Carson 1992).  To identify relationships between soil fertility parameters, Pearson correlation coefficients were calculated for pairs of variables. Figure 4.2 displays variables with correlation coefficients significant at the 95% confidence level (two tailed). Strong positive correlation coefficients are shown between pH, exchangeable Ca, exchangeable Mg and base saturation. The influence of pH on the availability of plant nutrients is reflected in the relationship between the percentage of base (or H) saturation and pH. When the base saturation is less than 100%, an increase in pH is associated with an increase in the amount of Ca and Mg in the soil solution, since Ca and Mg are the dominant exchangeable bases replaced by H* (Miller and Donahue 1990, Foth 1990). No significant relation was found between pH and available P however, and may be related to other factors such as soil type or P inputs through compost.  48  The spatial distribution of selected soil fertility variables on cultivated sites are shown in Plates 6-8. Exchangeable Ca is indicative of overall soil fertility conditions, while pH and available P are potentially limiting factors. Calcium (Plate 6) is generally adequate with over one-half of the samples having exchangeable Ca >3 cmol kg", but low Ca values are prevalent on high elevation, south facing sites. Soil 1  pH (Plate 7) is generally very low with more than one half of the samples having pH <4.8. These acidic sites are distributed throughout the study area. Available P (Plate 8) displays a range of values, but 68% of the samples are below desirable levels (P>15 mg kg") and very low P values (available P <5 mg kg") 1  1  are concentrated in the Bhimsenthan region.  Variable  pH  CEC  (CaCl )  (cmol kg") 1  2  exch. Mg  exch. K  (cmol kg" )  (cmol kg" )  (cmol kg")  1  1  1  0.44  C E C (cmol kg" ) 1  exch. Ca (cmol kg" )  0.81  0.57  exch. M g (cmol kg" )  0.70  0.68  1  1  j  exch. K (cmol kg" ) 1  Base Saturation (%)  0.54  0.52  0.64  1  avail. P (mg kg" ) 0.42  0.29  j  0.65  1  Carbon (%)  exch. Ca  -0.26 | 0.51  0.25 -0.26  0.47  0.49  0.28  Figure 4.2. Significantly correlated soil parameters, r values significance of a<0.05 (dataset: 1994 Bela-Bhimsenthan soil survey, n=130).  49  4.1.1 Factors Influencing Soil Fertility The current soil fertility status is related to the main factors of soil formation (climate, parent material, soil organisms, topography and time) and modifications by human activity. Topographic conditions influence erosion rates and reflect local variations in temperature and moisture associated with changes in elevation (Plate 1) and aspect (Plate 2). High elevation, north facing sites tend to be cooler and moister, while low elevation, south facing sites are hotter and drier. Soil type, differentiating red and non-red soils (Plate 4), distinguish properties associated with parent material and incorporates the influence of time. Red soils are the oldest soils in Nepal, are highly weathered and kaolinite is the dominant clay mineral. Climatic conditions are generally favourable for soil organisms, but their activity is influenced by land use, particularly organic matter management. Land use (Plate 5a) has a major impact on soil fertility through management activities, particularly nutrient inputs, cropping intensity and water management. Irrigated agricultural land (khet) is intensively managed, dryland agriculturalfields(bari) receive the greatest manure inputs, and forest, shrub and grasslands are used to provide animal fodder, litter and fuelwood. Stratifying the 200 agricultural sites surveyed by elevation, aspect, soil type and land use accounts for differences in inherent properties, natural processes and human activities. The influence of these individual factors on soil fertility is summarized in Table 4.2. For each group, mean values are listed and differences between groups determined by Mann Whitney U test are displayed.  Topography Elevation and aspect classes are related to a number of important soil fertility variables. Statistically significant differences are found between elevation (<1200 m versus >1200 m) and pH, CEC, exchangeable Ca, exchangeable Mg, base saturation and available P. Higher elevations (>1200 m) have lower values of all variables with the exception of available P. Increased precipitation, stronger leaching conditions and greater erosional losses associated with higher elevations lead to the removal of bases, while deposition and the lateral movement of bases through surface and groundwaterflowmay account for the increased pH and bases at lower elevations. The elevated P levels at elevations >1200 m is probably  50  Table 4.2. Differences between factors affecting soil fertility (sample mean and Mann Whitney U test). Factor  pH  CEC  (CaCl )  cmol kg'  2  1  exch. Ca  exch. Mg  exch. K  Base  avail.P  Carbon  cmol kg'  cmol kg'  cmol kg'  Sat. (%)  mg kg"  %  1  1  1  1  Elevation (m) <1200 m (n=120) >1200 m (n=80) <1200 vs. >1200  4.9  •  4.7  11.2 10.1 O  4.18  •  3.10  Aspect north (n=100)  4.8  11.0  south (n=100)  4.9  10.6  north vs. south Soil Type red (n=90)  4.9  non-red (n=110)  4.8  bari (n=80) grazing (n=70) khet vs. bari bari vs. grassland khet vs. grassland  •  •  •  3.40  3.97  •  1.14  1.19  •  1.60  1.77  •  8.9  3.56  5.2  11.2  5.29  1.52  4.8  10.7  3.60  1.47  red vs. non-red Land Use khet (n=50)  13.0  4.09  1.56  •• •  4.7  10.6  •• •  2.81  1.09  1.22  •  0.28 0.28  0.33  •  0.23  0.37  •  0.21  0.23 0.35  ••  0.24  dataset: 1994 Bela-Bhimsenthan soil survey, n=200  55.1  14.5  0.98  46.7  19.6  1.02  20.9  0.88  •  53.2 50.2 O  O  •  12.2  •  1.11  9.8  0.99  22.1  1.00  63.9  21.6  0.89  52.6  20.6  0.98  8.3  1.09  46.8  •  55.8  •• •  •  42.0  ••  Significant differences between groups a<0.05 O Significant differences between groups ct<0.10  not caused by factors associated with topography, but rather to differences in land use with elevation. More organic residue is traditionally applied to upland bari systems than lowland khet systems, and likely accounts for the greater P values at higher elevations.  Aspect (north versus south facing slopes) shows significant differences between Ca, Mg, K, base saturation, available P and %C. Fertility conditions are generally better on the north facing slopes with the exception of Mg and %C. The north facing slopes are generally cooler, moister and better vegetated than the hotter and drier south facing slopes, and consequently more nutrient recycling occurs. South facing sites have greater %C than north facing sites but differences are small and likely related to land use. In the Bhimsenthan (south-facing) region there is a greater concentration of bari land which receives significant amounts of organic matter via compost.  51  Red Soils Soil type is related to a number of important soil fertility variables. Red soils display significantly higher CEC, Mg and K values, while non-red soils have higher base saturation and available P. Non-red soils, developed largely on quartzite and sandstone, have a high silica content and very low clay content accounting for the low CEC. Red soils have a higher exchange capacity due to their higher clay content, and are less sensitive to leaching losses. Available P is significantly greater in the non-red soils. Fifty-two percent of the non-red samples have available P >15 mg kg' while 87% of the red soil samples have P 1  levels <15 mg kg" (P >15 mg kg" is desirable for tropical production). No statistically significant 1  1  differences are noted between compost and chemical fertilizer inputs to red and non-red soils. The low available P in red soils is likely associated with high concentrations of hydrous oxides of Fe and Al which fix P and make it unavailable, particularly at pH < 4.5. Differences in CEC, Mg, K and P are clearly dominated by soil type. The soil fertility of red soils are key to mamtaining productivity, and organic matter inputs are vital to maintain soil structure and provide adequate levels of available nutrients. Factors affecting soil pH are also critical as pH must be maintained above 4.5 to limit Fe and Al solubility and P fixation, and as the optimal range for nutrient availability.  Land Use Land use and its associated management have a considerable impact on soil fertility. Significant differences in pH, Ca, Mg, K, base saturation and available P occur between land uses. The strong influence of land use, particularly khet, is indicated by differences in pH, Ca and base saturation between all three land uses. Overall, khet sites have the highest soil fertility status, followed by bari, while grassland sites have the lowest fertility. Khet land receives nutrient inputs via irrigation waters and sediment deposition while bari and grasslands suffer erosion and leaching losses. In addition to an input of cations, irrigation waters are slightly alkaline (pH 8.7) and impact soil pH (Schreier et al. 1994b). Seventy-four percent of khet soil samples have pH > 4.8, while 63% of bari and grass land soil samples have pH < 4.8. Potassium values for bari samples are significantly higher than khet or grassland sites, and  are likely associated with the greater manure and compost inputs to bari land. Available phosphorus levels for bari and khet sites are significantly higher than grasslands, and are related to manure and chemical fertilizer inputs to cultivated fields while biomass is removed from grasslands and nutrient inputs are minimal.  Interactions Individually, elevation, aspect, soil type and land use have a significant influence on soil fertility, however, these factors may not be independent and interactions between factors may account for some of the variation in soil fertility. Interactions between factors were evaluated using analysis of variance techniques and are displayed in Table 4.3. Overall, land use is most important followed by soil type. However, soil variables are influenced differently by individual factors. For example, C E C is differentiated by soil type, carbon by land use and exchangeable Mg appears to be influenced by all factors. For most variables, factors are independent, with the exception of aspect which shows significant interactions for C E C , exchangeable Mg, available P and % carbon. Only available P shows significant interactions between all four factors, making the interpretation complex.  Table 4.3. Significant factors related to soil fertility (based on analysis of variance).  Excta. Ca (cmol kg" )  • •  Exch. Mg (cmol kg" )  O  Exch. K (cmol kg" )  •  Elevation  Variables pH (CaCl ) 2  C E C (cmol kg" ) 1  1  1  1  Base Saturation (%) Available P (mg kg" ) 1  •  Aspect  • • •  O  Soil Type  • j  • • • •  •  Interactions  • ••  Aspect - Soil Type  Land Use  •  Aspect - Elevation  j Aspect - Land Use  Aspect - Elevation Aspect - Land Use Elevation - Land Use  Carbon (%)  o  •  Land Use - Soil Type Aspect - Elevation Aspect - Soil Type  dataset: 1994 Bela-Bhimsenthan soil survey, n=200 #  Significant differences between groups a<0.05  O Significant differences between groups a<0.10  53  As soil type and land use were the most significant factors related to soil fertility, the soil samples were stratified by soil type and land use. Using the Bela-Bhimsenthan agricultural soil survey and the forest plot studies (Feigl 1989), differences between khet, bari, grass and forest lands are displayed separately for red and non-red soils (Figure 4.3 a-h). The impact of parent material on soil fertility is illustrated by available P, exchangeable Mg and CEC. In these cases the relationship to land use is limited, but consistent differences occur between soil types. Red soils have greater clay content, higher CEC and greater exchangeable Mg values, with the higher Fe and Al content likely tying up available phosphorus in these soils (Schreier et al. 1994b).  The relationship between land use and soil fertility is exemplified by pH and exchangeable Ca. Khet land displays the best fertility conditions, followed by bari and grassland, while forest soil fertility is the poorest. The highest pH, exchangeable Ca and base saturation are found in khet lands, and may be attributed to the input of cations through sediments and irrigation water. Bari land appears to have slightly higher P values, likely related to greater manure applications in dryland agriculture. Grazing and forest lands, which receive minimal nutrient inputs are the poorest regardless of soil type. These trends are consistent over the entire study area and clearly indicate the influence of land use management on soil fertility.  Overall, nutrient deficiencies are widespread, and are the result of a combination of inherent soil properties and anthropogenic causes. Parent material and the age of soil development determines the inherent soil properties, but within a given soil type, land management is an important factor influencing soil fertility.  4.1.2 Fertility Classification Summarizing average values for soil fertility variables, comparing differences between individual factors which influence soil fertility, and evaluating the interactions between factors are useful in assessing the current fertility status and determining which factors account for these differences, but the extent of soil  a)  b) 6  0.8  S.6 0.6 5.2  o E  4.4 4  ii  0.4  8  0.2  Ul  3.6  m Red  Non-Red  C)  Red  Non-Red  Red  Non-Red  d)  g)  Red  Non-Red  Red  Non-Red  Red  Non-Red  n) 20  Figure 4.3. Soil fertility variables stratified by soil type and land use; mean, minimum and maximum (dataset: 1993/94 Bela-Bhimsenthan site soil survey, n=200; 1989 Jhikhu Khola forest plot studies, n=7)  55  fertility problems within the study area is not evaluated using these approaches. To determine the spatial extent of soil fertility problems a site factor approach is employed. From the analysis of variance (Table 4.3) and the stratification of variables by soil type and land use (Figure 4.3), key soil fertility parameters can be identified. Exchangeable Ca, pH and available P are used to summarize overall fertility conditions, and exemplify the affect of soil type and land use on soil fertility conditions.  Key fertility parameters are related to the factors which account for their variability, specifically soil type, land use, elevation and aspect. Relationships established using the 200 site agricultural soil fertility survey and the forest plot studies are then extrapolated to the entire study region utilizing the GIS. Overlay techniques are used to display the relevant combinations of land use (khet, bari, grassland, forest and shrub), soil type (red and non-red), elevation (+ 1200 m), and aspect (dominantly north versus south facing slopes). Of the 32 possible combinations, only 24 types occur within the study region. For a particular variable the soil fertility data are stratified by the significant factors determined by the analysis of variance and mean values are assigned to each combination of factors (e.g. khet land on red soil at an elevation <1200 m). The results are then grouped into three classes and displayed using the GIS system. Forest / shrub sites were not stratified by soil type or topographic conditions given the limited number of plot studies within the Bela-Bhimsenthan region. Although forest soils in the Dhulikhel subwatershed showed some differences with soil type and topographic conditions (Schmidt 1992), all base saturation, pH and available P values fell into the very low categories.  The main factors accounting for the variability in exchangeable Ca are land use, elevation and aspect (Table 4.3). Samples are stratified by these factors, average exchangeable Ca is determined for each group and the results are lumped into three categories: exchangeable Ca <3 cmol kg", 3-4, and >4 cmol kg". 1  1  Land use, elevation and aspect themes are overlain witiiin the GIS and assigned the appropriate exchangeable Ca value. The results displayed in Plate 9 indicate that 57% of the classified area has adequate exchangeable Ca values (>3 cmol kg"). All khet sites and low elevation, south facing bari fields 1  56  have moderate levels of exchangeable Ca. Conversely, forest, shrub and high elevation grasslands are classified as very low (exchangeable Ca <3 cmol kg"). 1  The main factors accounting for the variability in soil acidity are land use, elevation and aspect (Table 4.3). Soil samples are stratified by these factors, average pH is determined for each group and the results are placed into three categories: pH <4.8, 4.8-5.0, and >5.0. A break in the distribution occurs at pH 4.8 with roughly one-half of the values falling above and below pH 4.8, and pH >5.0 is considered optimum. Land use, elevation and aspect themes are overlain within the GIS and each polygon type is assigned an average pH value. The results displayed in Plate 10 indicate that only 28% of the classified area has adequate pH values (>5.0). The areas classified as adequate correspond to low elevation khet land on both north and south aspects, and low elevation bari land on south facing slopes. Conversely, areas classified as very low (pH <4.8) correspond to forest, shrub and grasslands irrespective of elevation or aspect, and low elevation bari land on north facing slopes.  The variability in available P is accounted for by soil type, land use and aspect (Table 4.3), however no significant differences are found between khet and bari land. Available P is summarized for each significant combination of soil type (red and non-red), land use (khet / bari, grassland and forest / shrub), and aspect (north and south). The results are classified into three categories: available P <5 mg kg", 5-15, 1  and >15 mg kg' . The relevant soil type, land use and aspect classes are overlain within the GIS and 1  assigned the appropriate available P values. The results displayed in Plate 11 indicate that 38% of the classified area has adequate P levels (available P >15 mg kg'). The areas classified as adequate 1  correspond to khet or bari land on non-red soils, and khet or bari land on north facing red soils. Areas classified as very low (available P <5 mg kg") correspond to all forest and shrub sites, and grasslands on 1  north facing red soils.  57 A composite soil fertility map was produced by combining the exchangeable Ca, pH and available P maps using GIS overlay techniques. Of the 27 possible combinations, only 11 occurred within the study region. The results displayed in Plate 12 are grouped into 4 categories: low in all three variables, low in at least one variable, moderate or adequate, and adequate in all three variables. Only 14% of the classified regions have adequate levels of exchangeable Ca, pH and available P, while 35% are very low in all three variables, and 61% have at least one limiting variable. The best fertility conditions are found on low elevation khet and bari lands on non-red soils. This is related to the higher nutrient inputs to cultivated lands, their proximity to water resources (low elevation) and the prevalence of non-red soils near the Jhikhu Khola river. The poorest composite fertility is found on forest and shrub lands, and is likely related to the human removal of biomass (nutrients)fromthese areas to support agriculture. Overall, soil fertility conditions are poor and the production potential may be limited in many regions.  4.1.3 Phosphorus Fixation Available phosphorus is generally low in the study area, and Pfixationby Fe and Al oxides is of concern particularly in relation to P additions on red soils. To evaluate the Pfixationcapacity of soils in the study region, P sorption studies were conducted, and relationships with Fe and Al are used to model the P fixation capacity under different land uses. Selected P sorption curves for red soil samples are shown in Figure 4.4. P sorption rangedfrom2-4 g P 0 per kg soil for the 16 red soil sites measured. These values 2  5  are below the 8-15 g POsfixationper kg soil noted for imogolite and allophane complexes in Andosols 2  which possess the highest Pfixationcapacity (Dabin 1980), but are comparable to the P sorption of 1-6 g P2O5  per kg soil determined for forest soils in British Columbia by Yuan and Lavkulich (1994).  Phosphate sorption in many acid soils is predominantly influenced by oxides and hydroxides of Fe and Al, and has been related to AAO and CBD extractable Al and Fe in many studies (Yuan and Lavkulich 1994, Borggaard et al. 1990, van der Zee and Riemsdijk 1988, Nagpal 1981). Relationships between P sorption,  58 Fe and Al are shown in Figures 4. 5a-c. AAO extractable Al shows the best relationship (r=0.80), but 2  many models relate P sorption to a linear combination of AAO extractable Fe and Al, and may include CBD extractable Fe. Phosphate sorption calculated using Borggaard's model, which includes CBD extractable Fe, shows good agreement (r=0.85) with measured P sorption (Figure 4.6). 2  -•— Pine Forest m— Sal Forest O— Khet a — Bari -A— Grassland  0  10  20  30  40  50  60  70  80  Solution Concentration ( u g ml" ) 1  Figure 4.4. Selected P sorption curves (dataset: 1994 Jhikhu Khola nutrient cycling plot studies; 1989 Jhikhu Khola forest plot studies). Given the good fit of Borggaard's model and measured P sorption, the model is used to calculate P sorption under different land uses. Figures 4.7a and 4.7b show mean, minimum and maximum calculated P sorption on red and non-red soils (n=60). Phosphate sorption capacity on red soils rangedfrom0.7-2.3 g per kg of soil, and is nearly one order of magnitude greater than P sorption calculated for non-red soils (0.06-0.56 g kg"). Within the red soils, forest sites sorbed an average of 1.7 g kg" which is significantly 1  1  greater (ct<0.05) than P sorption on the agricultural sites (1.2 g kg'). Using the site factor approach 1  outlined in section 4.1.2, a classified map of P sorption was developed. Samples were stratified by soil type and land use, the average P sorption capacity assigned to each group, and the results were classified into three categories: >1.5 g kg", 0.5-1.5, and <0.5 g kg". The results, shown in Plate 13, indicate that 1  1  29% of the classified area has high Pfixationcapacity (>1.5 g kg") and 61% has a Pfixationcapacity 1  59  >0.5 g kg". The high P fixation capacity of these soils has significant implications for phosphorus 1  management as P applied in water soluble forms will be quickly converted to insoluble or complex forms (Stevenson 1986). a)  200 '  150  CO  •  O  f |  100  f  50 20  •  30  40  60  50  P sorption (mmol kg ) 1  150  b) _  125 -  2  100  • « ! r =0.80 2  0  1 <  75]  ;j  •  25  20  30  40  50  60  70  60  70  Psorption (mmol kg ) 1  600  C)  V  z~ 500  •  O)  I  400  E Q 300  m  o  £ 200 100 20  30  40  50  P sorption (mmol kg ) 1  Figure 4.5. Relationships between P sorption, Fe and Al (dataset: 1994 Jhikhu Khola nutrient cycling plot studies; 1989 Jhikhu Khola forest plot studies).  60  80  0  20  40  60  80  Measured (mmol P kg" ) 1  Figure 4.6. Comparison of measured P sorption capacity with calculated valuesfromBorggaard's model (dataset: 1994 Jhikhu Khola nutrient cycling plot studies; 1989 Jhikhu Khola forest plot studies). a) red soils 2.5 -, T  1  •  2.0 -  in 1.0 a.  0.5  -I  1  1  forest  bari  khet  b) non-red soils 0.6 -| 0.5  f  S 0.4  0.0 -I  1  bari  1  grassland  khet  Figure 4.7. Calculated P sorption under different land uses on red and non-red soils; mean, minimum and maximum values (dataset: 1994 Jhikhu Khola nutrient cycling plot studies; 1989 Jhikhu Khola forest plot studies).  4.2 The Management of Soil Nutrients  The current soil fertility status and how it is changing will be strongly influenced by nutrient management. Within the study region, N, P and Ca are critical macronutrients which are potentially limiting to plant growth and may be significantly influenced by management. The dominant sources of nutrients in the study region are manure, compost and chemical fertilizers. Erosional losses of nutrients from upland sites, and nutrient inputs to irrigated lowland agriculture associated with water management and sedimentation are important redistributional processes. Available nutrients are lostfromthe rooting zone through crop uptake (discussed in section 4.3), Pfixation,N leaching, denitrification, NH volatilization and Ca 3  leaching.  4.2.1 Initial Soil Nutrient Pool The initial pool of soil nutrients may be calculatedfromthe current soil fertility status for each land use category. Given the measured soil nutrient concentration, and assuming a soil bulk density of 1400 kg m" 3  and a 15 cm rooting depth, the potentially available soil nutrient pool for N, P 0 and Ca are show in 2  5  Table 4.4. For each land use category, nutrient additions and/or losses will apply.  Table 4.4. Initial soil nutrient pool (mean values). Land Use  Soil Nutrient Concentration (mg/kg)  Soil Nutrient Pool (kg ha" furrow slice) N ! P O | Ca 1  1  N  PO 2  854 i 941 ! 1046 1  Ca  s  49 ! 47 i 19 1  2  s  1793 j 103 | 4452 1976 i 99 i 3030 2197 ! 40 ! 2365 1170 j 4| 759 557 ; 2 361 dataset: 1993/94 Bela-Bhimsenthan soil survey; 1989likhu J] Khola forest plot studies assuming p = 1400 kg m" and a 15 cm soil depth  khet bari grass/shrub forest  2120 1443 1126  j  1  3  B  4.2.2 Compost and Chemical Fertilizer Use Most Middle Mountain farmers' apply dry manure and compost to thefieldsto reduce pest problems and the labour required for transportation. Compost preparation and storage commonly utilizes a heaping  62  method, where dung, forest litter and crop residues are piled. However, the piles are not turned and undecomposed or semi-decomposed compost is often applied to thefields.Compost heaps are often left exposed to intense sunlight and monsoonal rains, resulting in nutrient loss through denitrification, volatilization, and leaching (Sherchan and Baniya 1991, Suwal et al. 1991, Sthapit et al. 1988). Research at the Lumle Agriculture Research Centre indicates that the resulting compost has a low nutrient content averaging 0.6% N, 0.06% P 0 , and 0.6% K 0 (Subedi et al. 1995). While these values are low in 2  5  2  comparison to literature sources (Table 6 Appendix B), they are representative of current composting practices in the Middle Mountains. Pit composting is representative of best management practices (BMP) and provides an estimate of potential nutrient inputsfromcompost (Table 4.5). The Ca content of compost was estimatedfromaverage values for forest litter in the Jhikhu Khola Watershed (Table 7 Appendix B) and the Ca content of cattle dung derivedfromliterature sources (Table 6 Appendix B). A typical compost comprised of 80% litter and 20% manure was used, and leaching losses during storage and handling of 2040% were assumed. Nutrient inputs were then calculated for traditional compost and best management practices assuming a 25% moisture content.  Nutrient Source  Table 4.5. Nutrient content of compost (dry weight basis). N j PO KO Ca Source 2  (%)  !  s  z  (%)  (%)  0 06  06  0.11  1.4  (%)  Middle Mountains Traditional Compost  06  Pit Compost (BMP)  1.1  j  Forest Litter & Manure  Suwal clal. 1991 Suwal et al. 1991 0.8-1.0  estimated  Nutrient inputs to rice, maize and wheat cropsfromorganic matter and chemical fertilizer sources are summarized in Table 4.6. The greatest amount of organic matter inputs are applied to maize grown on bari lands during the monsoon. Farmers apply 12 t ha" of organic fertilizer to bari lands during the monsoon 1  season on average, and up to 50 t ha" may be applied. While these levels are high by North American 1  standards, they are within rates of compost use reported by other researchers in Nepal (Carson 1992, Pandey et al. 1995, Sherchan and Gurung 1995, Suwal et al. 1991, Rasali et al. 1995). During the winter,  63  only 18% of farmers apply organic matter to barifields.Organic matter inputs to khetfieldsare lower, roughly 4 t ha" in total, but are distributed over the premonsoon and monsoon periods. Note that organic 1  inputs are highly variable betweenfields(Table 3.3). The resultant inputs show the relatively small N and P2O5  contributions made by organic matter with the exception of maize grown during the monsoon. In  contrast organic matter is the main source of Ca on bari fields.  The dominant chemical fertilizers used are urea (46-0-0), complex® (20-20-0) and ammonium sulphate (21-0-0), and mainly supply inorganic N. Chemical fertilizer inputs are applied throughout the year, with early and monsoon rice receiving the largest N and  P2O5  recommendations for Nepal (100 kg ha' N and 70 kg ha" 1  1  inputs. Inputs to rice typically meet fertilizer N P2O5).  However, fertilizer applied to maize and  wheat are well below the locally recommended application rates of 120 kg/ha N and 70 kg ha"  1  P2O5  for  maize, and 100 kg ha' N and 115 kg ha" P 0 for wheat (Chitrakar 1990, Thapa 1995). Nutrient inputs 1  1  2  5  from chemical fertilizer are significantly greater than organic inputs (Table 4.6), with the exception of maize grown during the monsoon which receives 52% of N inputs and 20% of P 0 inputsfromorganic 2  5  sources. At the other extreme, winter wheat typically receives all N and P2O5 inputsfrominorganic sources.  Table 4.6. Reported nutrient inputsfromorganic and chemical fertilizer sources. System  n  Median Organic Matter Inputs' N  P2O5  Amount j (kg ha") | (kg ha")  Median Chemical Fertilizer Inputs Ca  Amount  1  N  P2O5  1  1  1  (kg ha")  (kg ha")  (kg ha")  (kg ha") j  1  10  491  143 |  39  0  0  197  43 |  35  1  1  1  1  (kg ha") 1  premonsoon early rice (khet) early maize (khet)  5  1,622 |  j  0  2.457 =  11  1  15  334  90 |  39  53  5  7l"  236"  65 j  28  0  0  0  197  0i  0  0  139  12  0  monsoon ncc (khet) maize (bari)  lill 1111  7  11.795 j  j  winter wheat (khet)  30  wheatObari)  21  j  0 0i  dataset: 1994 Bela-Bhimsenthan soil survey, n=200, male farmer responses 1  Nutrient content based on fanners' practice  j  49 38 I  31 28  64  4.2.3 Erosion Nutrient losses through erosion are estimated from erosion rates determinedfromplot studies and sediment budgets (Carver 1997), and nutrient losses measuredfromthe erosion plots. Erosion rates, shown in Table 4.7, rangefrom26+5 t ha" on bari sites to zero on well managed grasslands, and 62+44 t ha" on 1  1  degraded lands. Degraded lands are defined as shrub, grass or barren lands with greater than 50% soil exposure and visible rills or gullies. Average erosion rates are used to estimate nutrient losses under farmers' practice and lower estimates of erosion rates are used to simulate best management practices. For bari lands, the nutrient content of sediments erodedfromthe plots are used to estimate annual losses. For other land uses, the current soil fertility conditions are used to estimate potential nutrient losses. Eroded sediments commonly contain a higher nutrient content than the topsoilfromwhich they are derived due to selective erosion of organic matter and surface soil high in nutrients. Enrichment factors commonly range from 2-4 (Young 1989, Sharpley et al. 1994), but given the low nutrient status of forest and shrub sites a conservative enrichment factor of 2 is used in the estimation of annual losses. Nutrient losses through erosionfrombari fields result in an average annual loss of 25 kg N ha" and 13 kg Ca ha". Available P 1  1  losses are small, but organic P losses may be high, particularly if a high intensity rainfall event occurs before compost and manure are incorporated into the soil. Best management practices reduce erosion 25% from upland fields and result in a corresponding reduction in nutrient losses. Lossesfromwell managed forests and grasslands are minimal, but shrub and degraded lands loose significant quantities of N and Ca annually (34 and 23 kg ha" respectively). 1  4.2.4 Water Management and Sedimentation The diversion of stream floodwaters which carry large amounts of suspended sediments (Carver and Nakarmi 1995) onto fields through the irrigation system may result in considerable sediment deposition. Carver (1997) measured sediment deposition on 23 khet fields using accumulation pins. Sediment accumulation was measured on 76% of the khetfieldsand 40% of thefieldshad more than 5 mm of accumulated sediment.  65 Table 4.7. Erosion and associated annual nutrient losses from bari, forest, shrub and degraded lands. Nutrient Content  (mg kg ) Eroded Residual 1  Bari N  avail. P205 Ca total bases Shrub . N  avail. P2O5 Ca total bases  941 47 1443 1937  1882 64 1980 2777  """2692""" 1046" 38 2252 3034  1  ™  1  1  10+2 r  21 0.4 23 30  2 ±0x7  11 0.2 11 1  0 1  2 361 566  avail. P205 Ca total bases  Depth Integrated Losses  Rate 1 Soil loss (kg ha" per soil loss depth) Losses Eroded t ha" mm j Residual i 26±5 | 2 49 1 24 25 0.5 1.7 1 1.2 51 28 13 72 I 50 22 1  19 1126 1517  Forest N  Grassland Degraded N  Erosion  15  10  i1 i  02 12  15  negligible 0 62V44T  0 4  negligible  69 35 34 557 0.2 ! 0.1 | 2 avail. P205 361 45 ! 22 Ca "23 70 ! 35 35 566 total bases Erosion ratesfromCarver 1997 Nutrients losses based on: erosion plot data and current soil conditions with an enrichment factor=2 N losses estimatedfrom%C based on correlation 1114 4 722 1132  01.  1  Nutrient analysis of the newly accumulated sediments and the underlying residual soils indicate significant nutrient enrichment. Figures 4.8 a-c illustrate the higher fertility levels of the newly accumulated sediments. Points below the 45° line represent enriched conditions between residual and accumulated soils in the 23 khetfields.In all but twofields,the newly accumulated material was higher in Ca, P and %C than the residual soils. The average rate of nutrient enrichment rangesfrom1.3 for exchangeable Ca to 2.7 for available P. Nutrient enrichment of low lying khet lands associated with irrigation and sediment deposition were also noted by Wymann (1991) in the Dhulikhel subwatershed.  Annual nutrient inputs to lowland khetfieldscan be calculated based on the amount of sediment accumulated and the nutrient content of those sediments. Given a median annual sediment accumulation of  a)  2  0  x 0  3  4  5 6 7 8 Exch. Ca (cmol/kg) in Accumulated Sediments  10  20  30 40 50 60 Exch. P (mg/kg) in Accumulated Sediments  \~p^ 0.5  9  70  80  •  1 1.5 % Carbon in Accumulated Sediments  2  Figure 4.8. Nutrient enrichment in khet fields a) exchangeable Ca; b) exchangeable P; and c) % carbon (data source: Shah and Schreier 1995b).  67  4 mm and assuming a soil bulk density of 1400 kg m", the annual nutrient enrichment is listed in Table 3  4.8. An additional 11 kg N ha" and 28 kg Ca ha" are potentially available for plant uptake. 1  1  Nutrients may also be input to khetfieldsthrough irrigation water. Spring and stream water samples taken during the dry season of 1990 (Schreier et al. 1994b) indicate that the water is alkaline and contains moderate quantities of Ca, Mg, N0 and P0 (Table 4.9). Assuming a 0.5 metre depth of water applied 3  4  three times perricecrop, irrigation water may contribute an additional 6 kg N ha" and 300 kg Ca ha". 1  1  Table 4.8. Estimated rates of annual nutrient enrichment on khetfieldsfromnewly accumulated sediments. Variable  Median Nutrient  Estimated Annual Inputs  Concentration  (kg h a for a 4 mm soil depth)  Accumulated j Residual  Accumulated j Residual : Enrichment  1  Units  % %  C N  2  mg kg' pH  available P2O5  1  pH  %  Base Saturation  cmol kg" mg kg" mg kg"  1  CEC Ca Total Bases 2  1  0.73  0.56  0.07  0.05  39  28  11  74.7  30.9  4.2  1.7  2.5  4.9  4.8  66.0  58.9 99  28  111  33  10.9  9.3  1  2273  1776  127  1  2564  1975  144  j  "data source: Shah and Schreier 1995b %N calculated from correlation with %C from nutrient dynamic plot study data Table 4.9. Chemical composition of irrigation waters. Variable Spring 1 Stream Annual Inputs Nutrient j kg ha" | 8.7 pH 8.2 1.7 i 1.9 NCMmgl") N 6.1 0.25 j 0.26 po ; 0.3 P0(mgl") 20.1 | 20.0 Ca j 300 CaOngr ) 3.3 j 1.4 MgOngr ) 1.9 j 1.8 KCmgl") 1  1  1  1  1  2  4  5  1  1  1  total bases |  509  data source: Schreier et.1994b a 1  based on standard methods (Inland Waters Directorate, 1979)  68 Flooding and Nutrient Availability Nutrient availability is impacted by flooding. The overall effect of flooding is to increase the pH in acid soils as OH" ions are released when Fe(OH) and similar compounds are reduced. Most paddy soils reach 3  pH values of 6.5 to 7 within one month after flooding and remain at that level until drained. The concentration of phosphorus in the soil solution increases upon flooding due to the reduction of ferric phosphates to more soluble ferrous phosphate, the hydrolysis of Fe and Al bonded phosphates and increased mineralization associated with therisein pH. As K , Ca and Mg are already in a reduced +  2+  2+  state they are not directly affected by flooding, but NH/, Fe and Mn released upon flooding may 2+  2+  displace K , Ca , Mg fromexchange sites into the soil solution (Sanchez 1976, Patrick 1982, Legg and +  2+  2+  Meisinger 1982).  Blue-green Algae Cyanobacteria (blue-green algae) provide supplemental N inriceproduction. Nitrogen fixation by bluegreen algae in the water column and on the surface of submerged soils is an important N source in rice production. Blue-green algaefixatmospheric N and produce soluble NFlV", generallyfixing20-65 kg 2  NFL/ ha". In addition tofree-livingblue-green algae, Anabaena azollaefixN in symbiotic association 1  with thefreshwaterfern Azolla. The algae are able to assimilate N dissolved in the water and in exchange receive photosynthatefromthe Azolla roots. ThisfixedN is used by thericewhen the Azolla die and the organic N is mineralized (Buresh and DeDatta 1991, Patrick 1982, Khan 1983, Eskew 1987, Grist 1986, App et al. 1980). Nutrient analysis of green manuring plants conducted at the Lumle Agricultural Research Centre (Suwal et al. 1991) indicates Azolla contains 4.5% N by dry weight, but the distribution of Azolla in the study region is limited. Blue-green algae are naturally occurring in the ponded water of roughly one-third ofricefieldsin the study area, and likely supply 10-20 kg N ha" to thericecrop. 1  69 4.2.5 Phosphate Fixation The high phosphatefixationcapacity of soils in the study region described in section 4.1.3 has important ramifications for P management. The P sorption potential for these soils is given in Table 4.10 and compared to valuesfromliterature sources. The P sorption capacity averages 6,700 kg P 0 ha" for red 1  2  5  soils and 1,500 kg P 0 ha' for non-red soils, and are comparable to tropical soils high in kaolinite. Given 1  2  5  the high potential forfixation,P released to the soil solution will be governed by the chemical equilibria between soluble and insoluble mineral forms of P, the slow release of inorganic P by mycorrhizal fungi and other micro-organisms, and by mineralization and immobilisation of organic P.  Table 4.10. Phosphate sorption potential. Soil  Description  P sorption capacity  Source  g P (kg soil)" | kg P 0 ha" 1 7 8200 1 2 [ 5900 0.3 1500 8-15 | 17,000-31,000 1  1  2  Red Soils  Forest Khet-Bari  Non-red Soils  Andosols Spodosols Oxisols, Ultisols Andepts  Khet-Bari-Grass Imogolite, Allophane B.C. Forest Kaolinite clay Allophane  1-6 0.5-1 >1  5  | 2,500-17,000 j 2,300-4,800 >5,000  Plot studies, n=10 Plot studies. n=20 Soil Survey, n=26 Dabin 1980 Yuan and Lavkulich 1994 Sanchez 1976 Sanchez 1976  The proportion of applied fertilizer P which remains available to plants, decreases with the degree of soil weathering. The higher Fe and Al oxide content typical of older soils results in a larger Pfixationcapacity. On moderately to highly weathered soils the portion of fertilizer P remaining as available P after six months is low, rangingfrom53-73% (Sharpley and Halvorson 1994). Mineralization of organic matter additions may provide an important source of available P. In the tropics net P mineralization commonly rangesfrom16-157 kg ha" yr" (15-20% of total soil organic P) and crops may recover 20-40% of P 1  1  applied in organic inputs annually (Sharpley and Halvorson 1994, Sommers and Sutton 1980).  70  4.2.6 Nitrogen Dynamics Nitrogen is the most mobile of the nutrients required for plant growth and subject to the greatest losses from the soil-plant system. Gaseous losses through NH volatilization are particularly high with the 3+  surface application of urea, other ammonia based fertilizers and manure. Denitrification, the reduction of N0 " to gaseous forms of N by chemoautotrophic bacteria, is regulated by the availability of NOV and C 3  compounds, and low 0 . As N0 " originates by nitrification, an aerobic process, alternating aerobic and 2  3  anaerobic conditions such as heavy rain or intermittent flooding are conducive to denitrification. Losses of N by leaching occurs mainly as N0 " because of the low capacity of most soils to retain anions (Pierzynski 3  et al. 1994, Miller and Donahue 1990, Stevenson 1986).  Fertilizer N Efficiency The efficiency for the recovery of applied Nfromchemical fertilizers in cropping systems rarely exceeds 60% and commonly rangesfrom30-50%. Up to 50% of N applied as urea and ammonium sulphate fertilizers may be lost by volatilization within 1-4 days of application. Leaching losses of N commonly average 25-50% and a large nitrate flush is associated with the beginning of the rainy season (Pierzynski et al. 1994, McNeal and Pratt 1978, Sanchez 1976, Rolston 1978, Broadbent 1978, Stevenson 1986).  Organic Matter Application and Nutrient Availability Nitrogen lossesfromorganic matter are strongly influenced by handling, storage and application procedures. Within the study region, compost heaps are often left exposed to rain and sun, manure is typically dried prior to transport, and organic matter may be placed on thefields2-3 weeks prior to ploughing, depending on the availability of labour (Joshi et al. 1995, Rasali et al. 1995). Nitrogen losses prior to incorporation into the soil may be as high as 40 or 50% (Kirchman 1994, Legg and Meisinger 1982, DeDatta and Buresh 1989, Muchovej and Rechcigl 1994). Once incorporated, compost and manure provide a slowly available N source. Organic matter mineralization in thefirstyear generally ranges from 35-40%, with 15-25%) mineralized in the second year (Broadbent 1978, Kirchman 1994). With application  71  rates of 20-30 t ha" leaching losses are generally small. Denitrification of applied organic matter is 1  maximum vvithin the first week and losses of 5 kg ha" day" are common during wet periods, but annual 1  1  losses are typically only 5-8% (Rolston 1978). The recovery of organic N by crops is commonly 15-30%, but 15-45% remains in the soil and is potentially available to subsequent crops (Legg and Meisinger 1982, Kirkmann 1994).  Rice Production Systems Rice production provides a unique set of chemical (oxidation - reduction), physical (puddled soil) and microbial (aerobic versus anaerobic) conditions which influence N transformations within the soil. When a soil is flooded, reducing conditions become prevalent in less than one day. Nitrates present in the soil are then denitrified and lost to the atmosphere. Under flooded soils, organic matter is decomposed to Nil/, which is stable in anaerobic conditions and therefore accumulates. The presence of a thin oxidized layer over the reduced topsoil leads to nitrification of surface applications of organic matter or ammonium fertilizers. The resulting nitrate ions may move downward by diffusion or leaching into the reduced layer where denitrification quickly occurs and the N gases produced escape to the atmosphere. Intermittent 2  flooding results in alternating oxidation / reduction conditions and large N losses. Right after flooding, nitrates quickly disappear and the Nrl/ content increases. When the soil dries, a portion of the NFL* is nitrified into N0". In the next flooding these N0 " ions are lost by denitrification or leaching. The recovery 3  3  of applied N may be lower under flooded rice and is typically 20-50% (Legg and Meisinger 1982, Patrick 1982, Stevenson 1986, Sanchez 1976, DeDatta and Buresh 1989, Buresh and DeDatta 1991).  Grasslands Within the study region, grasslands are low management, low productivity sites and have a minimum potential for N losses. Grasslands are N deficient and therefore limited N0 " is available for leaching. 3  Urine and dung patches result in high localized N concentrations and may be subject to high gaseous N  72  losses, but leaching lossesfromunmanaged grasslands are typically <1% (Muchovej and Rechcigl 1994, Owens 1994, Kirchmann 1994).  4.2.7 Calcium Availability High FT" activity impedes Ca uptake by plants and in acid mineral soils Ca is not readily available to plants at low base saturation. Soils with kaolinitic clays are able to satisfy the Ca requirements of most 2+  agricultural crops at 40-50% Ca saturation (Tisdale et al. 1985). The Ca saturation for soils in the study region is shown in Figure 4.9. Values are generally satisfactory for khet sites (average 48%), but below optimum for bari and grassland sites, and very low for forest sites.  75  0  .I  Khet  ,  Bari  ,  Grass  ,  Forest  Figure 4.9. Calcium saturation % (mean + 1 standard deviation) (dataset: 1993/94 Bela-Bhimsenthan soil survey, n=200; 1989 Jhikhu Khola forest plot studies, n=7). Calcium losses by leaching depend on the amount of rainfall, the Ca supply in the soil and soil texture. The Ca concentrations of stream and spring waters (20 mg l") in the study area (Table 4.9) are evidence 1  of Ca leaching. If 100 mm of drainage water passes through the soil, 20 mg l* Ca in the water represents 1  leaching losses of 20 kg ha". Leaching lossesfromlimed tropical soils may be as high as 40-75% but 1  lossesfromthe study area are likely near the lower range of these values. Under rice production considerable quantities of exchangeable Ca are added by flood waters and the increase in pH associated  73  with reducing conditions will likely result in Cafixation(Bohn et al. 1979, Cooke 1981, Bohan et al. 1997, Bolton 1972, Cahn et al. 1993, Wong et al. 1992, van der Pol and Traore 1993, Brady and Weil 1996).  Available Ca is also impacted by the use of N fertilizers. Ammonium based fertilizers are oxidized by bacteria to form NO" and tt, and unless sufficient liming material is present a reduction in pH will result 3  (Brady and Weil 1996, Foth 1990). Acidification due to fertilizers and the Ca required to neutralize their acidifying effects are given in Tables 4.11 and 4.12. Urea and complex® are widely utilized and moderately aci<Jifying, while ammonium sulphate is highly acidifying. Median fertilizer inputs require only moderate Ca to neutralise the acid formed, but high maximum values are indicative of isolated problems and the potential impact of future increases in fertilizer use.  Table 4.11. Acidification due to fertilizers. Fertilizer  Acidifying Effect (kg CaO per kg N applied)  Urea  -1  Complex®  -1  Ammonium Sulphate  -3  source: Landon 1984  Table 4.12. Chemical fertilizer application and equivalent acidity. Land Use  Fertilizer Applied (kg ha" ) 1  Equivalent Acidity (kg Ca)  Bari Khet  complex®  urea  ammonium sulphate  median  maize  0-786  0-491  0-491  7j  0-237  wheat  0-491  0-200  0-59  3;  0-28  early maize  0-315  0-265  0-118  rice  0-982  0-491  0-982  6 12 .  wheat  0-393  0-295  0  5  range  j  ;  dataset: 1993/94 Bela-Bhimsenthan soil survey, n=130, male farmer responses  4*62 0-474  b-iT  74 4.3 Crop Nutrient Uptake Soil nutrients are essential to the growth and development of plants, but the collection of forest products and the intensive cultivation of crops may deplete the soil nutrient pool if organic and chemical fertilizer inputs are not sufficient. Nutrients removedfromthe soil by plant growth vary with the variety of plant and its yield (Miller and Donahue 1990). Nutrient uptake by the main staple crops grown in the region is calculatedfromreported yield data and average values of N, P 0 and Ca uptake derivedfromliterature 2  5  sources. Reported yields forrice,maize and wheat are compared to regional values and locally measured yields to assess the validity and variability of yields reported by farmers.  Rice, maize and wheat yields reported by farmers for individual fields are summarized in Table 4.13 and compared to locally measured yields and regional averages. Locally measured yields were determined for rice, maize and wheat samples collectedfrom1 m quadrats immediately prior to harvesting, and dry 2  matter was determined for grain and total biomass. While measured yields are representative of site specific conditions, the spatial and temporal variability of yield data is high and the low sample size (n=62) may not be representative. Reportedriceyields average 5368 + 2207 kg ha" for premonsoon rice 1  and 3470 + 1706 kg ha" monsoonrice.Reported premonsoonriceyields are substantially higher than 1  monsoon yields but the small sample size (n=5) makes comparison difficult. Monsoonriceyields reported by farmers are considerably lower than values measured locally by MRM project staff, however, they are within the range of regional yields (800-7000 kg ha") reported by literature sources. Reported maize 1  yields are typically 3123 + 1800 for early maize and 4123 + 1772 kg ha" for monsoon maize, and 1  correspond well with locally measured values. Wheat yields reported by farmers are typically 1541 + 1085 kg ha" and are slightly lower than locally measured and regional values. Wheat yields reported for khet 1  land correspond well to measured values, while average yields on bari land are lower as anticipated. Overall, yields reported by farmers are highly variable but within reasonable limits.  75  Table 4.13. Reported, locally measured and regional yields for dominant crops. System  Reported Farm Yield  1  Local Measured Yield  Regional Yield  (kg ha )  (kg ha )  (kg ha ) 1  1  1  mean j stddev ! n  mean  std dev  n  range  source  premonsoon early rice (khet)  5368 j  2207 j  early maize (khet)  3123 |  1800  3470 !  1706 ! 49  j  5 12  monsoon rice (khet)  maize (bari)  4123 |  1786  j  65  6037 !  4561  j  2194 1 27  1436  j  19  800-7000  Carson 1992  2488  LRMP 1986  1560-4943  Suwal etal. 1991  1179-5050  Sherchan et al. 1991a  1112-1478  Shah et al. 1987  1599  Carson 1992  980  LRMP 1986  1323-2838  Suwal et al. 1991  837-3288  Sherchan et al. 1991a  803-1003  Shah et al. 1987  winter wheat (khet/bari)  1563 !  1099 |  51  2512 !  1159 ! 16  1415  Carson 1992  899  LRMP 1986  2310  Suwal etal. 1991  1675-5984 j  1293  j  30  2025 j  1048 !  8  521  Shah et al. 1987  1147 |  535  j  21  3000 j  1112 |  8  512  Shah etal. 1987  1854  wheat (khet) wheat(bari) 1  Sherchan et al. 1991a  dataset: 1993/94 Bela-Bhimsenthan soil survey, n=130, male farmer responses  Nutrient removal by rice, maize and wheat is summarized in Table 4.14 and supplemental data listing nutrient uptake from individual references is provided in Tables 1-3 of Appendix B. The percent nutrient composition by weight refers to the entire above ground portion of the crop. Nutrient uptake by the total biomass is utilized as crop residues are harvested and used for animal feed. Reported yield values are used to estimate total dry matter based on the ratio of grain to total biomass. For rice, maize and wheat, grain comprises roughly 45% of total dry matter (Grist 1986, Olson and Kurtz 1982, Cox et al. 1985, LRMP 1986a, Aldrich et al. 1975, Stoskopt 1985). The total estimated dry matter on a kg/ha basis is then multiplied by the percent nutrient composition to calculate N, POs and Ca uptake. 2  76  Table 4.14. Nutrient removal by the dominant staple crops (median values). System  Reported Yield  1  n  .(kg/ha)  Average Composition %N  premonsoon  Nutrient Uptake (kg/ha)  2  j  %P 0 | %Ca 2  P0  N  5  2  5  !  Ca  j  0.1  108 |  43 |  11  0.3  79 j  34 ]  17  0.4 |  0.1  74 |  30 |  7  55"T  27"  early rice (khet)  2876-7669  5  early maize (khet)  1054-6389  12  rice (khet)  959-7669  49  mai/e (ban)  688-8256  66  1.4 !  0.6 |  wheat (khet/bari)  334-5351  53  1.2 |  0.5 !  0.1  36 j  15 I  3  wheat (khet)  669-5351  31  1.2  0.5 ;  0.1  45 |  19 !  4  wheat(bari)  334-2675  22  1.2  0.5 j  0.1  36 j  15 !  3  monsoon  1.0 | 1.4  j  1.0 |  0.4 0.6 j  03  winter  1  1 1  dataset: 1994 Bela-Bhimsenthan household survey, n=85 male farmer responses 2  composition by weight, supporting data provided in Tables 1-3, Appendix B  Nutrient uptake is greatest for maize grown during the monsoon with a median of 128 kg N, 55 kg P 0 2  5  and 27 kg Ca removed per ha. Monsoonriceremoves roughly 74 kg N, 30 kg P 0 and 7 kg Ca per ha, 2  5  while premonsoon maize andriceremove intermediate levels of N, P 0 and Ca. Wheat, the main crop 2  5  grown during the winter, removes the least nutrients with a median uptake for khet and bari sites of 36 kg N and 15 kg POs per ha. These values cannot be taken as precise since the nutrient composition of crops 2  vary substantially with differences in soil nutrient availability, plant genotype and local environmental conditions. However, these estimates of nutrient uptake for specificfieldsprovide an indication of the level of nutrient inputs required to maintain the soil nutrient pool (Olson 1978, Western Canadian Fertilizer Association 1992).  4.3.1 Soil - Productivity Relationships Nutrients removed from the soil pool by crop uptake and harvesting are dependent on crop productivity which in itself is a function of available soil nutrients. To evaluate relationships between soil fertility and crop productivity, reported yields are correlated with soil chemical properties for the main staple crops grown in the study area, and differences in soil chemistry between high, medium and low productivity sites are examined.  77 Correlations between yield and soil chemistry are weak and likely reflect differences in management, but differences in soil chemistry are noted between productivity classes. Maize, wheat and rice productivity classes (low, moderate and high) are definedfromthe histograms of reported yields for the 130 agricultural sites. Tables 4.15 summarizes the significant soil factors influencing maize and wheat productivity within the study region. Low maize productivity sites (yield <3,000 kg ha") display 1  significantly lower pH, Ca, Mg, base saturation and %C than moderate or high productivity sites. Similarly low productivity wheat sites (yield <1,500 kg ha") have significantly lower pH, Ca and base 1  saturation than moderate or high productivity sites. No significant differences in yield were noted with soil P, as soil P is likely compensated by P additions through management. Low, moderate and high rice productivity sites did not show any significant differences in soil fertility. Given the intensive management onricefields,inherent soil fertility likely has a low impact on rice productivity.  Table 4.15 Soil parameters influencing maize and wheat yields (based on Mann Whitney U test) Soil Parameter Maize Yield (kg ha") n=65 Significant Factors <3000 3000-5000 | >5000 <3000vs. j <3000vs. (mean values) low 3000-5000 | >5000 moderate j high 4.6 pH 4.9 4.8 2.85 4.01 ; 3.74 Ca (cmol kg") 1.13 1.63 | 1.37 Mg (cmol kg") Base Saturation (%) 46.4 54.8 | 57.2 0.89 C(%) 1.01 ! 1.00 Soil Parameter Wheat Yield (kg ha") n=51 Significant Factors <1500 1500-2500 | >2500 <1500vs. | <1500vs. low 1500-2500 | >2500 moderate 1 high 4.9 5.1 | 5.1 pH 4.13 5.05 | 4.97 Ca (cmol kg") 63.99 | 61.85 Base Saturation (%) 5.83 1  • • • • •  1  1  + + + • •  1  1  + • + + + |  dataset: 1993/94 Bela-Bhimsenthan soil survey, male responses • Significance of a<0.05 + Significance of a<0.15  78 4.4 Nutrient Budget Model The impact of management practices on soil fertility for the dominant land uses in the study region is quantified by modelling nutrient inputs, redistribution and losses. Nutrient inputs are associated with compost, fertilizer, sediment, water and biota; redistribution processes include erosion-sedimentation, niineralization-immobilisation, and adsorption-desorption; and losses include leaching, denitrification, volatilization, chemicalfixation,erosion and plant uptake. As nutrient inputs to forest, shrub and grass lands are limited, a more detailed assessment is conducted for cultivated lands. Estimates of nutrient dynamics on forest and grass / shrub lands are presented in sections 5.2 and 5.5 respectively. Only nutrient budgets for cultivated lands will be discussed in this section.  The approach and assumptions used to model soil N, P 0 and Ca is shown diagramatically in Figures 2  5  4.10-4.12. Nutrientflowsare integrated over a 15 cm soil depth. Inputs from compost and chemical fertilizer sources to cultivated lands are based on responses from surveyed farmers (Table 4.6). Compost additions to maizefieldstypically supply 53 kg N, 5 kg P 0 and 71 kg Ca per ha, and additions to rice 2  5  fields supply a median of 11 kg N, 1 kg P 0 and 15 kg Ca per ha. Compost additions are subjected to 2  5  mineralization and retention, and provide nutrients to subsequent crops through organic residues. Organic matter mineralization is assumed to be 40% in thefirstyear, 25% in the second year and 15% in the third year (Broadbent 1978, Kirchman 1994). Inputsfromtraditional compost are subjected to storage and handling losses prior to incorporation and further N losses under crops will likely be small, however N losses under fallow may be substantial and are assumed to be 70% (McNeal and Pratt 1978, Kirchman 1994). Chemical fertilizer additions to ricefieldstypically supply 90 kg N and 39 kg P 0 per ha, and 2  5  additions to maizefieldssupply a median of 65 kg N and 28 kg P 0 per ha. Crop recovery of applied 2  5  fertilizer N is taken at 40%, that is 60% losses are assumed due to leaching, volatilization and denitrification (Pierzynski 1994, Cooke 1981, Stevenson 1986, McNeal and Pratt 1978). Phosphate fixation by Fe and Al oxides is assumed to be 73% of applied fertilizer P and 10% of mineralized organic P on red soils, and 53% of applied fertilizer P on non-red soils, with a subsequent slow release of 15% per  79  irrigation water  fertilizer  \  \  0.5 m flood 3 times per rice crop  40% fertilizer N efficiency  sediment accumulation  biological N fixation  15kgha' yr" to nee crop 1  /  Soil N P o o l  /  nd rd  plant uptake  crop & biomass harvest  / \  X  60% loss of applied fertilizer N 10% loss of mineralized organic N under crops 80% loss of premonsoon organic N under fallow  /  leaching  40% mineralization 1" year 25% in 2 year 15% in 3 year  4 mm to rice crop  1  compost & organic residues  261 ha' soil loss 25 kg N ha" on bari land 1  \  denitrification  1  X  volatilisation  erosion  Figure 4.10. Approach for modelling soil N dynamics.  sediment accumulation  active inorganic P  fertilizer  \  /  \  30% fertilizer P efficiency  4 mm to rice crop  15% slow release inorganic P  SoilP 0 2  /  5  70% fixation of applied fertilizer P 10% fixation of mineralized organic P  Pool  \  40% mineralization 1*year 25% in 2 year 15% in 3 year nd rd  crop & biomass harvest  261 ha' soil loss 0.5 kg P 0 , ha' on ban land  / P fixation  Figure 4.11. Approach for modelling soil P dyanmics.  compost & organic residues  1  1  ?  \ erosion  ->•  plant uptake  80  irrigation water  compost & organic r e s i d u e s  sediment accumulation  40% mineralization 1" year 25% in 2 year 15% in 3" year  4 mm to rice crop  0.5 m flood 3 times per rice crop  nd  Soil C a P o o l  20% leaching of Ca from soil 50% leaching of irrigation inputs  1 kg CaO per kg N applied  1  sediment accumulation  leaching  crop & biomass harvest  2  ^  l^i L °? 0,  l  plant uptake  8  on bari land  erosion  Figure 4.12. Approach for modelling soil Ca dynamics. annum by chemical and microbial processes (Sharpley and Halvorson 1994). Nutrient losses through erosion are based on erosion plot data and catchment studies (Table 4.7) Lossesfrombari fields by erosion are typically 25 kg N, 0.5 kg P 0 , and 13 kg Ca per ha. Nutrient additions through sediment 2  5  trapping on rice fields are estimated from annual enrichment rates (Table 4.8), and potential inputs from irrigation water are listed in Table 4.9. Sediment trapping on rice fields typically supplies 11 kg N, 2.5 kg P 0 , and 28 kg Ca per ha. Irrigation waters supply moderate levels of N (6.1 kg N ha") and significant 1  2  5  Ca (300 kg ha ). Ca losses by leaching are taken at 40% for all sources, and 30% Ca fixation is assumed 1  under rice production (Bohn et al. 1979, Cooke 1981). The Ca required to neutralize the acidifying effects of chemical fertilizers (dominantly urea) is taken at 1 kg CaO per kg N applied (Table 4.11). Nutrient removal by the dominant staple crops are based on reported yields (Table 4.14). Monsoon maize removes a median of 128 kg N, 55 P 0 , and 27 kg Ca per ha, and monsoon rice typically removes 74 kg N, 30 2  P 0 , and 7 kg Ca per ha. 2  5  5  81 4.4.1 Nutrient Budgets for the Dominant Cropping Systems Nutrient budgets for a dryland maize-wheat rotation and an irrigated early maize-rice-wheat rotation are shown in Figures 4.13-4.20. Calculated N, P 0 and Ca inputs and withdrawals are shown for individual 2  5  crops and a seasonal budget. Inputsfromcompost indicate the total nutrients contained in organic matter applied to a crop, while organic residues supply nutrientsfromprior compost applications, and organic retention refers to compost which is not decomposed during the growing season. Phosphorus fixation indicates the absorption of P by Al and Fe oxides, and active inorganic P refers to slow release of inorganic Pfromprior inputs. Median values of compost, fertilizer and crop uptake for households growing each crop are used, regardless of the actual cropping sequence.  Under dryland agriculture two crops are typically grown, the most common rotation involving a premonsoon fallow period, monsoon maize and winter wheat. The overall nutrient budget shows significant deficits in N and P (Figure 4.15), largely related to maize production (Figure 4.13). While the addition of compost to maize fields is typically 12 t ha", mineralization only supplies 13 kg N and 1 kg 1  P 0 per ha furrow slice, and the high nutrient requirements of the maize crop result in deficits. Dryland 2  5  wheat (Figure 4.14), which has relatively low yields, removes significantly less nutrientsfromthe soil, and receives nutrients through fertilizers and organic matter applied to the previous maize crop.  On khet lands, up to three crops may be grown, typically involving a premonsoon maize crop, monsoon rice and winter wheat. Under paddyricecultivation (Figure 4.17), chemical fertilizers provide the main source of N and P, but additional inputs are associated with the trapping of sediments, biological N fixation and irrigation waters. Small deficits are noted for N and P, and surplus Ca is associated with inputs through irrigation. Winter wheat (Figure 4.18) and early maize (Figure 4.16) are nutrient deficient, resulting in a negative overall budget for N and P when three crops are grown (Figure 4.19). If only two crops are produced (Figure 4.20), N and P deficits are significantly reduced.  82  a) Bari maize N budget  b) Bari maize P 0 2  5  budget  active inorganic P  organic residues 16  organic residues 2  Inputs Losses  crop uptake 128  erosion losses 25 N volatilization leaching denitrification 29  erosion losses  organic retention 4  P fixation 17 crop uptake 55  c) Bari maize Ca budget  Inputs Losses  deficit 38  organic residues 21  fertilizer acidity 5 organic retention 53  erosion losses 23 crop uptake 27  Ca leaching -j g  Figure 4.13. Nutrient budget for maize on bari (kg ha" furrow slice). 1  a) Bari wheat N budget  b) Bari wheat P 0 2  organic residues 13  5  deficit 24  Inputs Losses N volatilization leaching denitrification 1  crop uptake 36  crop uptake 15  c) Bari wheat Ca budget organic residues 8  Inputs Losses  Ca leaching 7  Figure 4.14. Nutrient budget for wheat on bari (kg ha" furrow slice). 1  budget  83  a) Bari fallow-maize-wheat N budget  b) Bari fallow-maize-wheat P 0 budget 2  5  active fertilizer inorganic P organic residues 21  organic residues 2  deficit 142  Inputs Losses  erosion losses 25  organic retention 32 crop uptake 164  deficit 49 erosion losses  organic retention 3  N volatilization leaching denitrification 30  P fixation 17 crop uptake 70  c) Bari fallow-maize-wheat Ca budget  Inputs Losses  Figure 4.15. Nutrient budget for the dominant cropping sequence on bari (kg ha" furrow slice). a) Khet early maize N budget  b) Khet early maize P 0 budget 2  5  active inorganic P 9 organic residues 2 Inputs Losses  N volatilization leaching denitrification 21  crop uptake 67  crop uptake 29  c) Khet early maize Ca budget organic residues 2  Inputs Losses  crop uptake 14  Figure 4.16. Nutrient budget' for early maize on khet (kg ha" furrow slice). 1  P fixation 22  84  c) Khet rice Ca budget sediment I 28  irrigation 300  Inputs Losses surplus 84 fertilizer acidity 8 g drop uptake retention g 11 o r  a n l c  Ca leaching 134  Figure 4.17. Nutrient budget for rice on khet (kg ha" furrow slice). a) Khet wheat N budget  b) Khet wheat P 0 2  Input  19 c) Khet wheat Ca budget  Inputs Losses  Figure 4.18. Nutrient budget for wheat on khet (kg ha" furrow slice). 1  5  budget  85  a) Khet early maize-rice-wheat N budget N fixation sediment 11 organic residues 4 compost Inputs 11 Losses  fertilizer 112  b) Khet early maize-rice-wheat P 0 budget 2  irrigation 6  sediment 3  deficit 106 N volatilization leaching denitrification 71  organic retention 7 crop uptake 187  5  P fixation 59  crop uptake 77  c) Khet early maize-rice-wheat Ca budget sediment 28  irrigation 300  organic residues 6 compost 15  Inputs Losses  fertilizer acidity 16 organic retention 9  crop uptake 26  leaching 136  Figure 4.19. Nutrient budget for a three crop sequence on khet (kg ha' furrow slice). a) Khet fallow-rice-wheat N budget sediment 11 organic residues 4  N  b) Khet fallow-rice-wheat P 0 budget 2  5  fixation  irrigation 6  fertilizer 59  sediment 3  deficit 54  compost Inputs 11 Losses  N volatilization leaching denitrification 51  organic retention 6 crop uptake 120  crop uptake 49  c) Khet fallow-rice-wheat Ca budget sediment irrigation organic residues 300 6  Inputs Losses  compost 15  fertilizer acidity 11 organic retention crop uptake 9 11  leaching 136  Figure 4.20. Nutrient budget for a rice-wheat rotation on khet (kg ha" furrow slice). 1  P fixation 37  86 4.4.2 Sensitivity Analysis A sensitivity analysis was conducted by varying each factor in the model by 10% and recalculating the nutrient budget. Results for the dominant cropping systems on bari and khet are shown in Figure 4.21a and 4.21b. The nutrient budgets are relatively insensitive to changes in model factors with the exception of crop uptake, leaching, volatilization and denitrification, and Ca inputs in irrigation water. Khet systems display greater sensitivity than bari systems due to the higher yields and a greater nutrientflux,but small changes in most variables will not significantly alter the nutrient budget. Estimates of nutrient inputs associated with irrigation water could be improved through water quality analysis and would enhance the nutrient budget calculations for paddy production. Leaching, denitrification and volatilization losses are difficult to quantify and estimates of fertilizer efficiency are well documented in the literature. Crop productivity is an important factor in the nutrient budgets but yield estimates are inaccurate and relationships between productivity and nutrient uptake are difficult to quantify (Dent and Young 1981, Davidson 1992, Schreier and Zulkifl 1986, Singer 1986). While the model is less sensitive to changes in nutrient inputs it provides a good index of the variability in management practices which may affect soil fertility.  4.4.3 Best Management Practices The potential impact of best management practices is assessed by comparing fanners' practice and best management practices (BMP) for selected components of the model. The best management practices evaluated are pit composting (Table 4.5), increased sediment accumulation on ricefields(median + 1 stdev, i.e. 7 mm), increased biological Nfixationby blue-green algae (45 kg ha"), reduced erosion 1  (median - 1 stdev, i.e. 21 t ha" on bari), and increased fertilizer efficiency (50%) through incorporation 1  and improved timing of application. The results shown in Figure 4.22 indicate that moderate reductions in N deficits may be obtained through best management practices. On barifields,improved composting reduced N deficits 17%, while on khetfieldsincreased biological Nfixationwas the most effective practice.  a) Bari maize-wheat rotation 20  n 15 •Q  01  1  • N • P 0 2 5 • Ca  10  •  0)  c  nL i compost  _  J  —  fertilizer  organic mineralization  _  L  •  active inorganic P  yield  b) Khet early maize-rice-wheat rotation  compost  sediment  organic mineralization  N fixation  fertilizer  irrigation  Figure 4.21. Sensitivity analysis of seasonal nutrient budgets for bari and khet.  L.  leaching volatilization dentrification fixation  active inorganic P  leaching volatilization dentrification fixation  88 b) Khet early maize-rice-wheat  a) Bari fallow-maize-wheat  ow slice]  N  t  a•  I default  40 -r I compost  0 E3 erosionsedimentation  .c Balan*  -40 -  • N fixation  -80 -  c  • fertilizer N efficiency  1 -120 -• z  Figure 4.22. Impact of best management practices on nutrient budgets. a) Bari fallow-maize-wheat N sufficiency  P O sufficiency  N sufficiency  2  s  2000 1750  _ 1500 •= 1250 a ^ 1000 3  i  7 5 0  °  500 250  default required  0 default required  default required  b) Khet early maize-rice-wheat N and P O sufficiency 2  s  P O sufficiency  N sufficiency  2  s  N sufficiency  2000  40000  1750 _ 1500 'w •= 1250 a  | °  10000  r  iooo  |  750  °  500 250  default required  default required  Figure 4.23. Deficit elimination scenarios.  0  default required  default required  89  Deficit elimination scenarios were run for compost, fertilizer and biological Nfixation.The results shown in Figures 4.23a and 4.23b indicate that 3-10 fold increases in inputs are required. On bari fields, increasing compost to levels near the current maximum would meet crop N demands. On khet fields, complex® fertilizer application would need to be quadrupled to eliminate N deficits. Alternatively, increasing Nfixationby blue-green algae and Azolla is a viable management option. Inoculation of Azolla may produce 15-20 t ha of green manure within 15-20 days, and 20-40% of Azolla N incorporated into 1  the soil is typically taken up by the firstricecrop (Khan 1983, Eskew 1987, Buresh and DeDatta 1991, Singh etal. 1991).  4.4.4 Nutrient Budgets for Individual Fields To assess between site variability, nutrient budgets for the main crops and cropping rotations were calculated for individualfields.Figures 4.24 and 4.25 display median, maximum and minimum values for N, P 0 and Ca budgets on khet and barifields.The variability is high with both negative and positive 2  5  budgets noted for most crops. The variability in N budgets for dryland maize and irrigatedriceare shown in Figures 4.26 a and 4.26b. The N budget is negative for 94 % of the sites growing maize while 57% of the sites growingricereceive sufficient inputs. A similar pattern is noted for maize-wheat and rice-wheat rotations with a negative N budget for most dryland sites and 71% of irrigated sites receiving sufficient N. Phosphorus budgets are slightly negative for both bari (Figure 4.24c) and khet (Figure 4.25d) cropping systems, while Ca is negative for 71% of bari sites under maize-wheat production and positive for most ricefields(Figure 4.25b). Large negative Ca budgets are associated with high ammonium sulphate fertilizer use, while high positive budgets are related to high organic matter inputs.  Generally, the median nutrient budgets determinedfromfieldbudget calculations are less deficient than budgets calculated for a 'typical' maize-wheat orrice-wheatsequence. The nutrient budgets for the dominant cropping systems (section 4.4.1) use median fluxes determined for each crop, while the field specific calculations are influenced by inputsfromother cropping sequences. For example, compost is  a) Bari maize 300  300  300  200  200  _ 200  I 1  <u  1 0 0  2  £-100  E  -i  £  0  •s 5-100 ga TO  TO  Z  8  1 3 100 1  ?! o •g  c) Bari maize-wheat  b) Bari wheat  2 2 o Ia Z  -200  -300  TO -200  -300 P  2°5  100  •§ 5 - 1 0 0  Z  -200  <u 8 | ^ 3 |  Ca  P  2 ° 5  C  -300  A  P  2°5  Ca  Figure 4.24. Field nutrient budgets for bari; median, minimum, maximum and outliers (n=85). a) Khet early maize  a, •  1  b) Khet rice  200  200  150  150  0  0  5 2  x..  150 100 u .a  8 £ 50 £ »  %  m S  200  . • 100  ^  £ » 50  —  c) Khet wheat  0  S -50  2  E  C  3  * | 50 £  2  o  g  I'.  i •=  JM00 -150 -200  2>-100  2°5  0  -5° w-100  4  -200 -  -200  Ca  §  s  -150  -150 P  ">  P  2°5  Ca  1  p 2  °s  d) Khet rice-wheat 200  *  150  85 £"  100 50 -  2 § o g 2  i i  i  i ii  i •= .g>-100 X  -150 -200  PoO, '2 5 U  Ca  Figure 4.25. Field nutrient budgets for khet; median, minimum, maximum and outliers (n=85).  Ca  91  generally applied to premonsoon rice but not maize, and the additional premonsoon inputs will have a residual effect on monsoon rice production. Field based calculations for maize-wheat and rice-wheat rotations are also based on a smaller sample size.  a) Bari maize (n=65) 100  "ST o  50  §  0  IT  -50  8 -ioo c «  TO m z  -150  -200 maize fields  b) Khet rice (n=49) 200 "cT 150 o  ~m 5  100  *=  50  p  illOllllllilll  o |  n ffi  "SO  -100 -150 rice fields  Figure 4.26. Variability in N budgets for dryland maize and irrigatedriceproduction.  92  4.4.5 Implications for Soil Fertility The direction of change in soil fertility is indicated by positive or negative nutrient budgets. Changes in the soil nutrient pool (initial pool + nutrient budget) under the dominant cropping systems are shown in Figure 4.27. Bari production displays the greatest annual decline in the soil nutrient pool with estimated deficits of 142 kg N and 49 kg POs per ha furrow slice. Khet production appears to be roughly sustainable under 2  a two crop rotation, but the introduction of a third crop results in substantial N deficits (-106 kg N per ha furrow slice). Nutrient budgets for individual fields are variable but support declining soil fertility on most bari fields and overall neutral conditions on khet fields (e.g. Figure 4.27b). While the model simplifies soil processes by assuming constant rates for mineralization-immobilisation and adsorption-desorption, the relative differences between cropping systems provides an indication of the long term impact on soil fertility  4.5 Soil Fertility Dynamics Interactions between components of the farming system depicted in Figure 3.2 result in a redistribution of nutrients between land uses, impact nutrient cycling and influence rates of change in soil fertility. As part of the Jhikhu Khola Watershed Project, Schreier et al. (1994b) evaluated nutrient cycling under khet, bari and forest land uses by comparing the fertility characteristics of soils originatingfromthe same parent material but subjected to different land uses. The differences in soil fertility induced by land use management over time are then used to estimate rates of change in soil fertility.  Typical profiles under forest and agriculture were compared to test the assumption that soils in the test area are of similar origin. The profiles display the same sequences of horizons (AB, Bt and BC) and the chemical composition of the Bt and BC horizons are similar, supporting the assumption that the soils in the test area originatefromthe same parent material and the climatic conditions between the sites are the same. The impact of land use management on soil fertility was evaluated for irrigated rice cultivation, rainfed maize cultivation, and a pine plantation. Some 30ricepaddies have existed on the site for 5-20  a) soil N pool 2000 bari: maize-wheat "ST 1900 o  s. I  - a O  khet: rice-wheat 1800  T  (0 s O)  i  1700 1600 premonsoon  b) available P 0 2  winter  monsoon  resultant  5  110  khet: rice-wheat  100 90 O  i»  Q.  i  80  5 | S f 70 w 60 50 40 premonsoon  resultant  winter  monsoon  c) exchangeable Ca 5000 i khet: early maize-rice-wheat _4500 _ O  o> =  khet: rice-wheat  3 S 4000 « o e c 5 ^„ 3500  J  o x — ~3000  Wt  UJ O)  2500  ™ bari: maize-wheat  J  premonsoon  winter  resultant  Figure 4.27. Estimated changes in the soil nutrient pool under dominant cropping systems for khet and bari.  94  years. The typical cropping sequence on khet is two crops of rice followed by a winter crop of wheat, tomato or potato. Irrigation water is supplied from a local spring but its availability is limited. The rainfed terraces have been under cultivation for more than 30 years. The typical cropping pattern is maize followed by winter wheat or millet and the bari land receives most of the organic matter inputs. The pine plantation was established 17 years ago under the Nepal-Australian Forestry Project. There has been no formal management or protection of the forest for the past 15 years, and the site is heavily used for grazing and litter collection. Lower tree branches are lopped for fuelwood arid the understorey has been removed for agricultural use.  The soil fertility of surface samples taken under irrigated agriculture, rainfed agriculture and forest are compared in Figure 4.28, and significant differences between land uses are summarized in Table 4.16. The forested sites have significantly less (a<0.05) exchangeable Ca, Mg, base saturation, available P, total N and organic C than khet or bari land, and CBD extractable Fe was significantly greater. Nutrient removal through biomass collection in the forest plantation over the past 17 years has resulted in a low nutrient status. The higher CBD extractable Fe is likely associated with tree root excudants and an acidic environment, and reflects the active component of total Fe which will occlude P, resulting in the lower available P in forest soils. Differences between khet and bari land are less significant. Available P, Ca, Mg and pH are greater in the irrigated site but only P and pH are significantly different at a<0.05. The differences between dryland and irrigatedfieldssuggest that nutrients and cations may be input via irrigation waters and the associated suspended sediments. Nutrient removal through harvesting will also impact soil fertility as rice is less nutrient demanding than either maize or wheat (Tables 1-3 Appendix B). Additionally, non-irrigatedfieldsreceive nearly double the organic matter input (Table 8 Appendix B) resulting in significantly greater organic C (a<0.10) and total nitrogen (a<0.05) than irrigated fields.  Khet  Bari  Forest  Khet  Bari  Forest  Figure 4.28. Soil fertility characteristics under khet, bari and forest; mean, minimum and maximum (data source: 1994 Jhikhu Khola nutrient cycling plot studies, n=30).  96  Table 4.16. Significant differences in soil fertility variables between land uses. Variable  Khet vs. Bari  Khet vs. Forest  Bari vs. Forest  PH C(%) N(gkg-') Ca (cmol kg ) Mg (cmol kg ) -  P(mgkg') BS (%) Fe - CBD (%) data source: Jhikhu Khola nutrient cycling plot studies Schreier et al. 1994, n=30 significant differences based on t-test and Mann Whitney U-test, a<0.05  4.5.1 Rates of Change Rates of soil fertility decline are difficult to address without data on the initial soil fertility conditions and requires monitoring nutrient inputs and outputs over several decades. However, in the study area differences in nutrient status induced by land use management are contributing to poor overall soil fertility conditions even infieldsreceiving the largest inputs. Nitrogen, phosphorus, cations and pH are all low to deficient for most crops grown in the region (Tables 1-3 Appendix B). Rates of change between forest and bari sites, and between bari and khet sites originatingfromthe same parent material (section 2.1.1) provide an indication of overall nutrient depletion. Table 4.17 lists current mean values of N, P, pH and base saturation for forest, bari and khet land uses. Forest soils, for example, have been depleted by 0.76 g N kg and 5.0 mg P kg of soil over the 17 years. Assuming a soil bulk density of 1400 kg m' and a soil 1  -1  3  depth of 15 cm, allows annual losses to be calculated.  Annual lossesfromthe forests (Table 4.17) are approximately 94 kg N ha' furrow slice and 0.6 kg P ha' 1  1  furrow slice. The nutrient content of stand leaf litter provides an estimate of the amount of nutrients removedfromthe forest through litter collection. The annual pine litter fall and nutrients measured at the test site by Feigl (1989) totalled 6.3 kg N ha', 0.44 kg P ha', and 9.1 kg total bases ha'. The potential N 1  1  1  removal through litter fall only accounts for 7% of the estimated total annual N decrease. Additional losses  97  of N by volatilization, leaching, erosion and timber uptake likely account for the difference. In contrast, the amount of P in the pine litter represents 73% of the estimated annual decline, and the remaining losses may be attributed to timber uptake and erosion.  Annual lossesfrombari lands are small, and reflect additional inputsfromorganic sources. Nutrient removal by maize is in the order of 130 kg ha", 55 kg P 0 ha" and 180 kg total bases ha" (Table 2 1  1  2  1  5  Appendix B), and suggests that annual lossesfromagriculture will be strongly influenced by management.  Table 4.17. Soil fertility dynamics Variable  Current Means (n^O)  1  Annual Rates of Change  Units  Forest j Bari j Khet Units Forest Bari 0.45 | 1.21 j 0.98 kg ha" f s. N -94 28 gkg mg kg" 8.6 kg ha" f.s. -0.6 -0.3 avail. P 1.4 | 6.4 mg kg" 3.2 | 14.7 | 19.7 -1.4 -0.7 avail. P2O5 kg ha" f s. pH 4.2 | 4.5 ! 4.9 -0.02 -0.02 pH PH mg kg" 1054 ; 1118 -57 -8 Ca 589 kg ha" f s. mg kg" 1574 | 1679 kg ha" f.s. 1 -13 Total Bases -92 826 0/ • Base Saturation % 37.6 | 40.3 -0.2 -1.3 16.1 data source: Jhikhu Khola nutrient cycling plot studies Schreier et al. 1994, n=30 f.s. = furrow slice (15 cm soil depth) 1  1  j  1  1  1  1  1  j j j  1  1  j  1  j  1  4.6 Summary  Soil Fertility Status The overall soil fertility conditions of the study site are generally poor. Soil carbon and pH are particularly problematic, and P availability is a concern given the low pH values. Land use is the most important factor influencing soil fertility, followed by soil type. However, soil variables are influenced differently by individual factors. Red soils have a greater clay content, higher CEC and greater exchangeable Mg values than non-red soils, with higher Fe and Al content likely tying up available P. Khet sites show the best overall soil fertility, followed by bari and grassland, while forest soil fertility is the poorest. Differences  98  between red and non-red soils reflect inherent soil properties but within a given soil type, land use management is an important factor influencing soil fertility.  The Site Factor Approach Traditionally, soil maps are based on surficial materials and may distinguish differences related to topography, but additional factors influencing soil fertility, such as land use management, are not incorporated. The site factor approach using soil type, topography and land use to extrapolatefrompoint data to a spatial coverage is a unique approach to the production of soil fertility maps. A rule set is developedfromrelationships between site specific soil fertility data and characteristics of the site. Mapped biophysical conditions (soil type, land use, elevation and aspect) are then related to soil fertility conditions for each combination of factors. GIS overlay techniques are used to display critical and adequate levels for key parameters. Exchangeable Ca is classified as moderate (>4 cmol kg") for 29% of the area 1  corresponding to khet land and low elevation south facing bari land. Soil pH is classified as critical (<4.8) for 55% of the area corresponding to forest, shrub and grasslands. Adequate levels of available P (>15 mg kg") are related to khet or bari land on non-red soils, and khet or bari land on north facing red soils (38% 1  of the classified area). Typically we assess only one limiting factor, but with the use of chemical fertilizer we may simply trade-off between factors. The GIS approach is useful in evaluating soil fertility in a cumulative manner. A composite soil fertility map was produced by combining the pH, available P and exchangeable Ca maps. Only 14% of the classified regions show adequate levels in all three parameters, while 61% have at least one limiting variable. This spatial analysis provides a more comprehensive picture of conditions throughout the study region, and highlights problem areas.  Phosphorus Fixation Phosphate sorption studies conducted on 16 highly weathered red soils indicate moderately high P sorption rangingfrom2-4 g P 0 per kg soil. Measured sorption displays good relationships with AAO and CBD 2  5  extractable Fe and Al, and P sorption calculated using Borggaard's model (r=0.85). Phosphate sorption 2  99  calculated for non-red soils using Borggaard's model was an order of magnitude less than red soils. Within the red soils, forest sites sorbed significantly greater P than agricultural sites. A classified map of P sorption developed using a site factor approach based on soil type and land use indicates that 29% of the study area has a high Pfixation>1.5 g kg" and 61% has a Pfixationcapacity >0.5 g kg' . The high P 1  1  fixation capacity of red soils in the study region is a concern for P management as fertilizer applications will be highly inefficient.  Nutrient Management Nutrient inputs, their redistribution and losses from the soil-plant nutrient pool will impact soil fertility conditions both spatially and temporally. Compost and chemical fertilizer are a major source of nutrients on cultivatedfields.Compost is largely applied to barifieldsand typically supplies 45% of the N and 100% of the Ca applied to maizefields.Chemical fertilizer is the main source of N and P 0 on khet fields 2  5  accounting for 81% of the N and 98% of the P 0 applied to irrigated rice fields. 2  5  Erosion and sedimentation are important redistribution processes. Erosionfromupland bari sites (26 t ha") results in an average annual loss of 25 kg N ha" and 13 kg Ca ha", and degraded lands loose an 1  1  1  estimated 34 kg N ha" and 23 kg Ca ha' annually. Alternatively, sediment and the associated nutrients are 1  1  recaptured on low lying khetfieldsthrough the irrigation system. Nutrient analysis of newly accumulated sediments on khetfieldsshow an enrichment in N, POs and Ca resulting in annual inputs of 11, 2.5 and 2  28 kg ha" respectively. Nutrients are also added to khetfieldsthrough irrigation water, particularly Ca. 1  Some 300 kg ha" Ca may be input annually duringriceproduction. 1  Potential lossesfromthe soil-plant nutrient pool are difficult to quantify but include Pfixation,N leaching, denitrification, NH volatilization and Ca leaching. The red soils in the study area are 3+  particularly problematic due to their high Pfixationcapacity (6,700 kg ha") limiting the efficiency of P 1  fertilizer inputs. Fertilizer N efficiency is commonly low and leaching, denitrification and volatilization  100  likely result in 30-50% losses. Organic matter inputs provide a slowly available nutrient source and while recovery by the first crop is commonly only 15-30 %, residual organic matter provides nutrients to subsequent crops. Calcium is less mobile than N in the soil but Ca concentrations in spring and irrigation waters provide evidence of Ca leaching. Available Ca is also impacted by the use of ammonium based fertilizers which may acidify soils unless sufficient liming material is present.  Crop Nutrient Uptake Estimates of crop nutrient removal based on reported production and average nutrient uptake values provide an estimate of the nutrient inputs required to maintain the soil nutrient pool. Maize is the most nutrient demanding of the main staple crops grown on a % dry matter basis, followed by wheat and then rice. However, due to the low productivity of wheat in the study area,riceis more nutrient demanding on a total biomass (kg ha") removal basis. Relationships between crop productivity and soil fertility are weak 1  suggesting that nutrient management may play a key role in productivity.  Nutrient Budget Modelling Nutrient budgets for khet and bari land indicate that the soil nutrient pool is being depleted under both intensive irrigated and extensive dryland production systems. Khet production appears to be sustainable under a two crop rotation, with 71% of the sampled fields displaying a positive N budget, but the introduction of premonsoon maize into the rotation results in a substantial N deficit (-106 kg ha"). On bari 1  land nutrient inputs are insufficient to meet crop requirements and the negative nutrient budgets are an indicator of soil degradation. The use of best management practices result in a slight reduction in nutrient deficits, but the trend of soil fertility depletion remains. Sensitivity analysis indicates that crop yield is an important factor in determining nutrient budgets but productivity is a poor indicator of soil dynamics. Relationships between productivity and nutrient uptake are difficult to quantify, there are inaccuracies in reported and measured yield estimates, relationships with soil fertility are weak, and external factors such  101  as disease or drought may impact productivity. Nutrient inputs provide a better index of the variability in management practices which affect nutrient budgets and soil fertility.  Rates of Change in Soil Fertility Soil fertility characteristics of soils originatingfromthe same parent material but subjected to different land uses were compared to determine how soil fertility is changing and to estimate rates of change. Due to the removal of litter and a lack of inputs, the forest sites showed the lowest N, P, exchangeable bases and pH values. The bari sites have the most C and N as they receive greater organic matter inputs. Irrigated sites have the greatest P, Ca and Mg due to enrichment by irrigation water and suspended sediments. The rate of soil fertility depletion may be estimatedfromdifferences in soil fertility related to land management. Annual nutrient lossesfromforest sites are substantial (94 kg N and 57 kg Ca per ha furrow slice), while lossesfrombari sites are small in comparison to crop uptake.  102  5. IMPACT OF LAND MANAGEMENT ON NUTRIENT DYNAMICS "Every year the amount of fertilizer  is being increased to get the same yield.  The soil has  become very tough... "  (Farmer S37) Overall soil fertility conditions in the study region are poor; forest, shrub and grassland soils are being depleted; and the status of cultivated soils appears to be marginal. The focus of this chapter is to evaluate why soil fertility is declining by assessing the impact of land management on nutrient dynamics. The components evaluated and their interactions with soil fertility data from chapter 4 are shown in Figure 5.1. Land use change in the Jhikhu Khola Watershedfrom1947 to 1990 is presented to provide the historical context for forest, cultivated land and shrub / grasslands. Recent land use trends in the Bela-Bliimsenthan study areafrom1972 and 1994 are then compared to the overall dynamics.  Detailed evaluations of forest, bari, khet and grass / shrub land dynamics are assessed relative to site conditions and their impact on nutrientflows.Forest dynamics include an assessment of afforestation, changes in the types of trees and nutrient removal through biomass collection. The implications of nutrient outflowsfromthe forest are discussed relative to nutrient cycling and soil fertility. Bari dynamics include an evaluation of agricultural expansion and marginalization. Relationships between nutrient inputs, crop uptake, nutrient budgets and soil fertility are presented, and nutrient budgets for maize and wheat are related to aspect, elevation and soil type. The implications of marginal inputs and nutrient losses through erosion are discussed relative to soil fertility conditions on bari land. Khet dynamics include an evaluation of agricultural intensification, market oriented production and agrochemical use. The impact of water management and sediment deposition on soil fertility is discussed relative to the nutrient status on khet lands. Relationships between nutrient inputs, crop uptake, nutrient budgets and soil fertility are presented, and nutrient budgets for rice and wheat are related to aspect, elevation and soil type. The implications of intensive cultivation are discussed relative to soil fertility and the sustainability of current management practices. Grass and shrub land dynamics include an assessment of crop nutrient uptake and nutrient  FOREST DYNAMICS section 5.7  \ J /"  Chapter 4  quantity versus qua 1> b om iss removal - impact on soil fertility 1  eypa t>un - marginalization e os on - impact on soil fertility n  r  forest cover • cultivated land - shrub / grassland  KHET DYNAMICS section 5.4  .  V  GRASSLAND/ SHRUB DYNAMICS  section 5.5  NUTRIENT INPUTS section A 2  \  /  (  LAND USE ^ \ INTERACTIONS & V SOIL FERTILITY J section 5.6 / - implications for production • future soil fertility  \ )  intensification water management impact on soil fertilitiy  / (  SOIL FERTILITY section 4.1  CROP UPTAKE section 4.3  )  erosion aegiadation • impact on soil fertility  NUTRIENT BUDGET section 4.4  Figure 5.1. Components of land use and soil fertility dynamics evaluated in chapter 5.  o  104  losses through erosion. The implications of over utilization are discussed relative to soil fertility and rehabilitation efforts.  Land use interactions, nutrient flows between land uses and their impact on soil fertility provide an understanding of why soil fertility is changing. Differences in nutrient inputs with elevation, aspect and land use are quantified. Changes in the use of compost and chemical fertilizer, and constraints to chemical fertilizer use faced by farmers are analyzed. The implications of nutrient deficits and unbalanced nutrient flows between land uses for forest, crop and grass / shrub productivity are assessed. The impact of nutrientfluxeson soil fertility dynamics are evaluated by comparing predicted nutrient deficits and current soil fertility conditions. From these relationships a GIS based classification was developed which displays the spatial distribution of Ca and P degradation.  5.1 Land Use Change Land use, as it reflects management, impacts soil fertility and an assessment of land use change is a precursor to understanding soil fertility dynamics. To document historic land use changes, land use evaluations conducted as part of the Jhikhu Khola Watershed Project (Schreier et al. 1994a) are presented in conjunction with data for the study region. Trends within land use categories are shown in Figure 5.2 ac. Over the 48 year period, there have been significant changes in forest, shrub, grass and cultivated land. Three scales of land use mapping are represented: 1) 1:50,000 scale regional overview based on a 1947 British survey and 1981 LRMP mapping; 2) 1:20,000 scale land use mapping for the Jhikhu Khola Watershed based on aerial photo-interpretation for 1972 and 1990; and 3) 1:5,000 scale detailed evaluation of the Bela-Bhimsenthan study area for 1972 and 1994. Note that the 1:50,000 and 1:20,000 scale mapping cover the entire Jhikhu Khola Watershed, while the 1:5,000 scale mapping only covers a subset of the area encompassing Bela-Bhimsenthan.  105  a) forest dynamics 800  50  plot studies  1:50,000 scale 40 -  600 * * ^^^^^^  (A O  1:20,000 scale  30 1  400 o  20  1 200  J!^-—"  1:5,000 scale  1 1  10  1940  1950  1960  1970  1957 nationalised forests  1980  1990  2000  1989  1994  1981 active afforestation  b) cultivation dynamics 60  •o c re _l  Scale 1:50,000 Jhikhu Khola 1:20,000 Jhikhu Khola 1:5,000 Bela-Bhimsenthan  re > 3  o  1940  1950  1960  1970  1980  1990  2000  Figure 5.2. Regional land use dynamics 1947-1994 (dataset: Jhikhu Khola 1:20,000 land use mapping; Bela-Bhimsenthan 1:5,000 land use mapping; Jhikhu Khola forest plot studies).  106 5.1.1 Forest Cover Historical forest cover data (Figure 5.2a) clearly indicates substantial deforestation during the 1950-1960 period and a subsequent reversal in the 1972-1990 period. The dotted line in Figure 5.2a shows the interpolated deforestation and recovery process. The lowest forest cover is likely to have occurred in the late 1960s and may partially be attributed to the nationalisation of forests in 1957, when all non-cultivated land was placed under the jurisdiction of the Forestry Department. This resulted in the clearing of forest land by villagers to maintain ownership. Post 1981, afforestation efforts initiated by the Nepal-Australian Forestry Project (NAFP) have resulted in significant increases in forest cover (Shrestha and Brown 1995, Schreier et al. 1993, Mahat 1987a, 1987b, Feigl 1989, Ingles and Gilmour 1989).  Recent forest dynamics (1989-1994) have been evaluated through a series of plot studies. Twelve forest plots (Figure 2.2) selected in 1989 for long term biomass monitoring (Feigl 1989) were re-examined in 1994. Individual trees were marked and these demarcations were maintained yearly. In 1994, changes in tree density and biomass over the six year period were determined. Between 1989 and 1994, the standing biomass diminished from 614 trees to 386 trees, a loss of 37% of the forest stand (Figure 5.2a). The losses varied greatly between plots and reflect different degrees of protection. The majority of trees lost to cutting were sal trees (shorea  robusta)  which are valuable as construction material and for brick making. Few  pine trees were removed.  These results suggest that the decline in forests occurs in cycles. The historic information indicates at least two cycles of deforestation followed by efforts of rehabilitation. Large losses of forest cover occurred in the 1950's. Afforestation resulted in significant increases in forest cover, but only 50% of the losses were recovered by 1990. Renewed losses have been observed in the 1990s due to the increased demand of firewood for brick making, and of timber for house construction. Recent community based afforestation programs may improve the situation in the short term, but the overall trend of cyclic change appears to be in a decreasing direction.  107  5.1.2 Cultivated Lands Historical trends on cultivated land (Figure 5.2b) show a slow but consistent increase in the area under cultivation. Agricultural land occupied about 45% of the Jhikhu Khola watershed in 1947 and has increased to around 55% in the 1990s. All three surveys (1:50,000, 1:20,000 and 1:5,000) support this trend. Recent increases in bari cultivation are largely associated with the conversion of grasslands, while changes in khet land were small, indicative of limited water availability.  5.1.3 Shrub and Grass Lands Historic changes in shrub and grass lands (Figure 5.2c) show the inverse trend to the forest situation. During the 1950-1960 period, shrub and grass lands increased significantly in parallel with deforestation. The dotted line in Figure 5.2c shows the interpolated degradation and recovery process. The greatest shrub and grassland cover likely occurred in the late 1960s, and decreased through the 1980s in association with afforestation efforts.  5.1.4 Recent Trends in the Bela-Bliimsenthan Region Land use in the Bela-Bhimsenthan region for 1972 and 1994 are shown in Plates 5a and 5b, and the dynamics between land uses are shown diagramatically in Figure 5.3. Over the 23 year period there were increases in forest cover (+7%), khet (+1%) and bari (+2%) land, and decreases in grass (-5%), shrub (-3%) and other land uses (-2%). Increases in irrigated agriculture were largely associated with the conversion of dryland agriculture, while at the same time, a net increase in bari land was also observed. The expansion of dryland agriculture was mainly associated with a loss of khet land, and the cultivation of grassland, abandoned areas and landslides. Forest cover increased significantly, however grass and shrub lands decreased considerably. The land use changes are dynamic in all categories, but the trends are indicate the main relationships.  108  Khet +1%  <  Forest +7% *  7 0  53  *  10  Bari +2%  Other -2%  ->• major trends minor change  Figure 5.3. Land use trends in the Bela-Bhimsenthan region 1972-1994, numbers indicate changes in ha (dataset: Bela-Bhimsenthan 1:5,000 land use mapping). 5.2 Forest Dynamics: Quantity versus Quality  In the Bela-Bhimsenthan region, the area under forest cover increased 7% between 1972 and 1994, largely at the expense of grass and shrub lands. A large proportion of the gains in forest area are associated with chir pine (46%) planted with assistance by the Nepal-Australian Forestry Project (NAFP), and most of this expansion occurred on moderate to steep slopes, below 1200 m elevation (Table 5.1). Afforestation efforts resulted in an increase in forest coverfrom1972-1994, but 75% of the current forest has crown density <50%, and 40% is pine forest. While plantations have increased the amount of chir pine in the study area, the plot studies indicate a reduction in sal and other hardwoods.  5.2.1 Nutrient Status and Biomass Removal Forest soils showed the poorest overall soil fertility (Figure 4.3 a-h) and the greatest annual nutrient losses (Table 4.17). Forest soils receive minimal nutrient inputs and nutrients are lost through biomass removal. Trees and branches are cut for fuelwood, lopping of tree branches for animal feed is common, and most forest floors are devoid of understorey and litter due to the intensive collection of forest floor material for animal bedding and fodder (Photo 3). The collection of forest products and associated nutrient losses are  109  Table 5.1. Land use dynamics in relation to site conditions over the period 1972-1994. Land Use Expansion  Site Conditions  Land Use Losses  Slope (%)  Area (ha)  Khet (ha)  i Bari (ha)  | Forest (ha)  Grass (ha)  | Shrub (ha)  <1 1-10  230  22  i8  j8  10  j7  588  42  | 53  | 31  33  1 25  11-20  377  7  | 29  i  37  1 31  21-30  463  4  i  !  35  j 37  | 269  4  19  | 27  i Forest (ha)  Grass (ha)  i Shrub (ha)  | 37  27  j 23  !  >30  30  33 3  I  6  25  Elevation (m) 1 Area (ha)  Khet (ha)  1 | Bari (ha)  800-899  675  68  !  900-999  380  2  1 40  | 44  61  1000-1199  393  5  | 38  36  1200-1399  j 301  3  j  ! 10  9 •  I  1  j 35 j 18 I  1 4  j  4  8  22 15  4 8  >1400  178  <1  !  Climate  Area (ha)  Khet (ha)  ; Bari (ha)  j Forest (ha)  Grass (ha)  | Shrub (ha)  hot dry  912  59  1 70  j 66  76  ! 55  warm dry  196  <1  ! 13  !6  8  warm moist  536  16  j 40  ! 6 | 52  cool moist  283  3  | 11  j 53 i  48 2  !  9  4  8  3  1  4  dataset: Bela-Bhimsenthan 1:5,000 land use mapping  summarized in Table 5.2. The surveyed households typically collect 715 kg fodder, 600 kg litter and 1365 kg of fuelwood per year from local government forests. Based on the number of households in the study area (1723) and the proportion of land classified as government forests within the region, an average of 0.3 ha of forest are available to each household. The amount of nutrients contained in foliage and litter for the dominant tree species found in local forests was determined by Schmidt (1992). The nutrient content of fodder is greater than litter due to a higher average nutrient content in fodder tree species and the translocation of nutrientsfromleaves to perennial organs before leaf fall. The amount of nutrients contained in branch and stem wood is estimatedfromaverages for multipurpose agroforestry trees summarized by Young (1989).  Estimated annual nutrient removal through forest product collection is listed in Table 5.2. Losses of N (56 kg ha"), P (7 kg ha") and bases (72 kg ha") are substantial, and within the range of lossesfromforests 1  1  1  soils determined through the plot studies (Table 4.17), and foliar and litter nutrient removal estimates  110  calculated by Feigl (1989) and Schmidt (1992). Nitrogen and phosphorus losses related to fuelwood collection account for 45% and 60% of the estimated total nutrient removal respectively. Animal fodder and leaf litter for compost production are in high demand and account for 55% of N and 74% of the total bases lostfromthe forest.  Table 5.2. Forest production collection (median values) and associated nutrient losses. Household Collection (kg yr")" 1  Fodder  |  Litter  Fuelwood  400  j  374  780  14  9.6  9.6  1,330  900  1,700  Nutrient Content  15  N(gkg ) 1  Pfmgkg' ) 1  total bases (mg kg") 1  18,650  ! 22,500  7,400  Annual Losses N (kg ha" ) 1  Pfkgha" ) 1  total bases (kg ha" ) 1  19  12  25  1.8  1.1  4.4  25  28  19  " dataset: 1994 Bela-Bhimsenthan household survey, n=85, female farmer responses b  Source: Schmidt 1992, Young 1989  5.2.2 Implications of Nutrient Outflows Despite increases in forest coverfrom1972 to 1990, recent decreases suggest a downward cycle indicative of renewed pressure on forest resources. Forty-five to 55% of the female farmers surveyed in the study region (n=85) report that the collection of fuelwood, fodder and litterfromforest sources is more difficult today thanfiveyears ago. As the majority of forest product collection is conducted by women, shortages will lead to an increase in their work load. In addition to changes in the area under forest, the composition of the forest has changed, with 46% of the recently afforested land under pine plantations. Although pine trees are useful in stabilising soils and improving timber production, they are not useful for animal feed and pine litter collected during the dry season as inputs to agriculture are likely acidifying the soils (Schreier et al. 1994a).  Ill  The collection of fodder and litter results in a significant transfer of biomass from the forest and interferes with the natural forest nutrient cycle. This biomass removal contributes to a low soil organic matter content and to soil acidification through the removal of bases. The removal of understorey vegetation not only results in poor soil fertility and low site productivity, but leaves the forestfloorunprotected to the monsoon rains and accelerates erosion. These adverse conditions may reduce the supply of forest products at a time of increasing demand (Carson 1986, Panday 1992, Gilmour, 1991, Schmidt 1992, Schreier et al. 1994a).  5.3 Bari Dynamics: Expansion and Marginalisation  From 1972 to 1994, a net increase in bari land (36 ha) was observed. This expansion of dryland agriculture is related to both marginalization and water shortages. Agricultural marginalization is reflected by the terracing and cultivation of former grass, shrub and abandoned lands (Photo 5), located on steeper slopes and at higher elevations than the gains in khet land (Table 5.1). Water shortages are associated with the conversion of irrigated to dryland agriculture in the Bhimsenthan region on hot dry slopes (Plates 5 a and 5b). Cultivation of marginal land is of particular concern as these sites have inherently poor soil fertility and are prone to erosion.  5.3.1 Nutrient Gains and Losses on Bari The nutrient status on bari lands is generally poor (Figure 4.3 a-h) and is depleted relative to khet lands (Table 4.17). Within the cultivatedfieldsof the study region, N and P are two critical macronutrients which are potentially limiting to plant growth and may be significantly influenced by management. Nutrientfluxesfor maize (Figure 4.13) and maize-wheat rotations (Figure 4.24) indicate that inputs to barifieldsare insufficient to maintain the soil nutrient pool. Ninety-four and 92 percent offieldsare deficit in N and P 0 respectively, and Ca inputs are insufficient to neutralise the acicUfying effect of chemical 2  fertilizers.  5  112  To identify relationships between nutrient budgets, inputs, crop uptake and soil fertility, Pearson correlation coefficients were calculated for pairs of variables. Figures 5.4a and 5.4b display variables with correlation coefficients significant at the 95% confidence level (two tailed) for maize and wheat. Under maize cultivation, nutrient budgets for N, P 0 and Ca are positively correlated with inputs and negatively 2  5  correlated with crop uptake due to their co-dependence. Under dryland winter wheat production, only the P 0 budget is correlated with fertilizer P inputs as organic residues supply the majority of inputs. Crop 2  5  yield and nutrient uptake are weakly related to inputs and soil fertility status, and relationships between nutrient budgets and soil fertility are poor, indicative of the low overall fertility conditions and high variability within bari fields.  Differences in bari nutrient budgets with aspect, elevation and soil type are summarized in Table 5.3. For each group, median values are listed and differences between groups determined by Mann Whitney U test are displayed. Statistically significant differences are found between POs budgets and aspect, elevation 2  and soil type. Phosphorus deficits are greater on south facing, high elevation and red soil types. Red soils which are dominant at lower elevations,fixmore soil P, while hot, dry, south facing barifieldstypically receive lower inputs than north facingfields.The nitrogen budget under maize production shows a greater nutrient deficit on southerly aspects, corresponding to significantly lower fertilizer inputs, while Ca deficits are smaller on south facing and high elevation sites due to lower fertilizer induced acidity.  5.3.2 Implications of Marginal Inputs and Erosion Losses Marginal nutrient inputs and erosion losses contribute to a negative nutrient budget on barifieldswhich can not be sustained in the long term. The expansion of dryland agriculture onto marginal lands will likely result in more rapid soil degradation. Marginal sites have inherently lower soil fertility, are less favourable for intensive nutrient management, have higher erosion rates and produce lower yields. Water shortages are particularly evident on south facing slopes during the dry season, while slope stability is a problem during the monsoon. The recent shift of organic matter inputs away from dryland agriculture into irrigated  113  a) bari - maize 0.40  organic matter N (monsoon)  0.39  %C N budget  0.40  , exch. Ca  0.59  fertilizer & organic matter N (monsoon)  0.52  CEC pH  0.36  -0.71  maize N uptake  fertilizer P (monsoon)  fertilizer & organic matter P (monsoon)  -0.75  Ca budget  0.65  -0.26  maize P uptake  organic matter Ca (monsoon)  -°-  avail. P  fertilizer acidity (monsoon)  6 2  0.59  ^CEC  maize Ca uptake  b) bari - wheat  exch. Ca 0.45 fertilizer N (winter)  N budget  0.52  wheat N uptake  %c  JV60 0.52  P A budget  A! 9  fertilizer P (winter)  0.53  wheat Ca uptake  pH  wheat P uptake  0.47 Ca budget  CEC  0.53  exch. Ca 0.45  base saturation  0.68 ^ - CEC  Z 1 Z  0.84^  %c Figure 5.4. Selected correlations between nutrient budgets, inputs, crop uptake and soil fertility on bari sites (signficance of a<0.05; — indicates co-dependence).  pH  114  Table 5.3. Differences between factors affecting nutrient budgets on bari fields (sample median and Mann Whitney U test). Factor Crop N budget Ca budget P O budget 2  (kg ha" f.s.)  north (n=35)  -72  -21  south (n=30)  -125  •  -43  1  Maize  north vs. south  >1200 (n=34) <1200 vs. >1200 Soil Type  (kg ha" f.s.) 1  •  -18  •  -39  red (n=30)  -34  non-red (n=35)  -15  red vs. non-red  +  Elevation (m) <1200 (n=ll) >1200(n=10) <1200 vs. >1200 Soil Type red (n=5)  Seasonal  1  Aspect  Elevation (m) <1200(n=31)  Wheat  s  (kg ha" f.s.)  9  2  •  9  -4  -10  non-red (n=16)  3  red vs. non-red  O  Soil Type red (n=5)  -49  non-red (n=16)  -10  red vs. non-red  O  •  O  Significant differences between groups oc<0.05  O  Significant differences between groups cc<0.10  +  Significant differences between groups a<0.15  cash crop production will increase the dependence on chemical fertilizers to supply nutrients to bari fields. Acidification is a concern particularly on red soils, and will be further aggravated by any increase in chemical fertilizer use.  5.4 Khet Dynamics From 1972 to 1994, there was a small increase in the area of khet land associated with the expansion of irrigation along the Jhikhu Khola, Andheri Khola and Namle Khola rivers (Plates 5a and 5b). The majority  115  of khet expansion occurred below 900 m elevation and on slopes <10° (Table 5.1). Eighty-nine percent of this increase was associated with the conversion of bari land (Figure 5.2). While the amount of irrigated land has remained relatively constant, there have been changes in the cropping intensity and the type of crops grown (Photo 4).  Cropping Intensity Cropping intensity is denned as the number of crops harvested each year from a parcel of land. Regional estimates of cropping intensity for 1980-1996 are summarized in Figure 5.5. In the 1980s Hagen (1980) and Panth and Gautam (1987) reported national averages of 1.3-1.6 crops per year. Riley (1991) reported averages of 2.0-2.45 crops per year in villages examined as part of the National Hill Crops Program. Estimates from the Jhikhu Khola Watershed Project rangefrom2.2-2.7 crops per year. Cropping intensity reported by khet farmers in the Baluwa 1989 and 1996 household surveys (n=27) increased from an average of 2.3 to 2.5 crops per year, although the change was not statistically significant (based on Wilcoxon signed ranks test). While these different estimates cannot be used to quantify changes, it does appear that the cropping intensity has increasedfromthe 1980's to the 1990's.  1980 Hagen  1987 1989 Panth & Baluwa Gautam  1990 Jhikhu Khola  1991 Riley  1994 Bela  Figure 5.5. Cropping intensity estimates 1980-1996.  1996 Baluwa  116 Market Oriented Production Agricultural production specifically targeted for local markets has been promoted within the Jhikhu Khola watershed since the establishment of the Panchkhal Horticulture Farm in the late 1970s. Potatoes were the first major cash crop in the region and were widely grown by 1989, while tomatoes, introduced about 15 years ago, have become a popular cash crop (Kennedy and Dunlop, 1989, Srivastava 1995). Changes in cash crop production on khet in Baluwa (n=27) are summarized in Table 5.4. The number of households growing cash crops on khet land has increased significantly, with 70% of the surveyed households reporting potatoes, tomatoes, mustard or garlic production on some of their khet land. The area under cultivation of cash crops is relatively small, but more than doubled on khetfrom1989 to 1996, indicating the importance of supplemental income derivedfromthe sale of agricultural produce.  Table 5.4. Cash crop dynamics on khet land for 1989 and 1996. Crop % Households Total Area Cultivated (ha) 1989 1996 1989 1996 1  Khet potatoes tomatoes mustard garlic total  33 11 22 -  48  52 19 26 7 70  2.1 0.9 1.0 0 4.0  4.5 1.6 2.9 0.1 9.1  dataset: 1989 and 1996 Baluwa household surveys, n=27, male farmer responses  Pesticide Use Intensive cultivation, market oriented production and the planting of high yielding varieties have promoted the use of pesticides in the study region. Information on the type and amount of pesticide applied, and application procedures are relevant to soil biology, human health and household economics (which will be further discussed in section 6.4.2). Grain and vegetable crops in the Middle Mountains are prone to infestationsfroma wide range of pests, and the resulting losses in yield may be as high as 15-20% (ADB 1987). The main pest problems reported by farmers are summarized in Table 5.5. Sixty-four percent of the households surveyed report using pesticides. Pesticide use is most intensive on high value cash crops.  117  Table 5.5. Dominant pest problems of the major crops. Crop  Pests (common names)  % of Producers Using Pesticides  hoppers, rice bug, beetles, armyworm, leaf folder, stem  Rice  62  borer, blight, whoral, smut, blast, leaf roller, white tip Maize  aphids, crickets, corn borer, armyworm, shoot fly, stem  8  pollen beetle, blast, whoral, white grub Wheat  termites, thrips, rust, wheat bug  17  Potatoes  blast, blight, white grub, cutworm,  90  Tomatoes  blight, beetles, green bug, tomato bug  83  Mustard  root rot, beetles, aphid, painted bug  63  dataset: 1994 Bela-Bhimsenthan household survey, n=85, male farmer responses  However, as rice is the largest crop grown, rice receives the most pesticide in absolute terms. Farmers report that 62% of rice fields are treated with pesticides, 90% of potato fields, 83% of tomato fields and 63% of mustard fields.  Insecticides dominate the market due to their use for malaria control, but both insecticides and fungicides are used in the study area (Table 5.6). The main insecticides used are parathion-methyl, malathion, fenvalerate, and dichlorvos, and the main fungicide used is dithane-M45 (mancozeb). Mancozeb is a broad spectrum dithiocarbamate fungicide that is suspected of being carcinogenic when applied in high doses. In British Columbia, the use of mancozeb against pests of fruits and vegetables was suspended in 1975 as studies indicated a breakdown product could be chronically detrimental to human health after the cooking of treated crops. Fenvalerate is a chlorinated pyrethroid insecticide with some residual activity and its use in the United States is restricted due to adverse affects to aquatic organisms. Parathion methyl, dichlorvos and malathion are organophosphorus insecticides. Parathion methyl has stomach, contact, and fiimigant action. It is highly toxic by inhalation and ingestion, is listed as extremely hazardous by the World Health Organisation, and its use in North America is restricted to certified applicators only. Dichlorvos is listed as a possible human carcinogen, it may affect the human immune system, and is highly toxic to bees. Its use is restricted in North America to certified applicators and protective clothing is required (BCMOE 1979, BCMOE 1990, EXTOXNET 1997, USDA 1980).  118  Table 5.6. Main insecticides and fungicides used in the study area. Action / Formulation  Type  Description  1  Farmer Application  Recommended 2  (kgai ha") n = 57 ave = 10.2 max = 157 (10 packs on 0.5 ropani potatoes) n= 30 ave = 0.34 max = 1.44 (11 bottle on 3 ropani tomatoes) n = 38 ave = 0.81 max = 4.61 (11 bottles on 3 ropani tomatoes) 1  Dithane  Fungicide 80 WP 500 g pack  -mancozeb - maneb - zineb  Fen Fen  Insecticide 20 E C 100 ml bottle  (fenvalerate)  Metacide  Insecticide 50 E C 100 ml bottle  (parathion methyl)  Nuvan  Insecticide 100 E C 100 ml bottle  (dichlorvos)  Malathion  Insecticide 50 E C 100 ml bottle  (cythion)  1  2  3  Group of broad spectrum carbamate foliage protectant fungicides; mancozeb suspended in Canada in 1975; cooking of treated crops chronically detrimental to human health Broadspectrum chlorinated pyrethroid insecticide; some residual activity; moderate mammalian toxicity; high toxicity to fish; USEPA restricted use. Broadspectrum organophosphorus insecticide; stomach, contact and fumigant action; short residual effect; extremely high oral and dermal mammalian toxicity, WHO extremely hazardous; USEPA restricted use, certified applicators only. Broadspectrum organophosphorus insecticide; nonpersistent, possible human carcinogen; high mammalian toxicity; highly toxic to bees and fish; USEPA restricted use; protective clothing required. Broadspectrum organophosphorus contact insecticide, nonpersistent, very low mammalian toxicity; toxic to fish and bees, banned in Indonesia due to adverse effects on predators.  Use (kgai ha") 0.9-2.6 3  1  0.01-0.03  0.3 -0.6  n = 34 ave = 1.76 max = 5.66 (1 bottle on 0.5 ropani tomatoes)  0.5 -1.12  n= 10 ave = 0.42 max = 0.98 (1 bottle on 1 ropani tomatoes)  0.5 - 1.4  WP = wettable powder; E C = emulsified concentrate; ai=active ingredient  dataset: 1994 Bela-Bhimsenthan household survey, n=85, male farmer responses  source: Meister 1991, Worthing 1987, USDA 1974, 1980 & 1997, Scopes andLedieu 1983, BCMOE 1979 & 1990, EXTOXNET 1997, BCMAFF 1995 & 1996, Gyawali 1996, ADB 1987.  The use of pesticides in the study area is highly variable. Application rates reported by farmers for the main pesticides are listed in Table 5.6 along with manufacturers' recommended use. Typically, farmers apply 1 bottle or pack of pesticide to an infested crop resulting in average application rates (kg active ingredient ha") above recommended use for many fields. The percentage of farmers applying pesticides 1  above or below guidelines are displayed in Figures 5.6 a-e. Excess application of pesticides is common with mancozeb, fenvalerate, parathion methyl and dichlorvos, particularly on potato and tomato crops. The number of different pesticides applied to a given crop are summarized in Figure 5.6f. While most  119  a) dithane  *  -100 J  a) fenvalerate  rice  mustard  potato  tomato  c) parathion methyl  rice  mustard  rice  mustard  potato  tomato  potato  tomato  d) dichlorvos  potato  tomato  e) malathion  rice  as -100 J  rice  mustard  f) number of different pesticides used per crop  mustard  potato  tomato  rice  mustard  potato  Figure 5.6. Pesticide application relative to manufacters recommended quidelines (dataset: 1994 Bela-Bhimsenthan household survey, n=85, male farmer responses).  tomato  120 farmers apply only one pesticide on a single crop, two or three pesticides are often used on tomatoes. The intensive use of insecticides and fungicides in vegetable production may result in unacceptable pesticide residues in the soil and crops, and in an expansion of pest resistance (ADB 1987, Jeyaratnam 1990, Lum et al. 1990).  The misuse of pesticides places farmers at risk to acute poisonings, and chronic health problems may result from prolonged exposure. Reports of pesticide misuse in the study area are listed in box 5.1. Farmers have been observed spraying pesticides upwind and the spraying tomatoes 1-2 days before harvest is not uncommon. When a farmer was asked why he used so much pesticide on his tomatoes he responded "I'm  not eating this, I'm not going to die. I'm selling it in Kathmandu. "  Baker and Gyawali  (1994), in a study of pesticide misuse in Nepal, report pesticide residues found in food, milk and water. Date expired pesticides are buried or dumped in open spaces, such as a river. Yield reductions due to pesticide misuse are estimated at 15-20%. The regular misuse of broad spectrum pesticides causes pests to adapt and become resistant so that more pesticides are required to achieve the same level of control. Increased pesticide use and the use of persistent organophosphate pesticides destroys natural enemies and secondary pest outbreaks (resurgence) may resultfromthe disruption of natural controls (Baker and Gyawali 1994, Graham-Bryce 1981).  Presently there are no regulations governing the pesticide sector in Nepal. A company can import or produce any chemical regardless of its efficacy in pest control or its effects on yield, health and the environment. Dangerous pesticides, such as DDT and BHC, banned in many countries are still imported and used in Nepal. Pesticide residues on food sold in markets are currently not regulated. Farmers do not receive appropriate information on proper pesticide use or on ways of reducing chemicals such as integrated pest management. The present extension service is prescriptive; farmers are not taught why, when or how to use a certain chemical. They are merely given a piece of paper with a prescribed chemical solution (Baker and Gyawali 1994, ADB 1987).  121 Box 5.1 Reports of pesticide misuse in the study region. spraying pesticides when there are no pests using pesticides after the damage is done using fungicides to control insects under or over dosing pesticides leading to increased pest resistance entering thefieldtoo soon after spraying ^^^^^^^^^^^^^^^^^^^^§arketed not targeting applications at specific pests mixing pesticides in containers which are later used for drinking water using pesticide containers for food or cooking oil storage storing pesticides and sprayers in living rooms, bedrooms and kitchens allowing children to play with empty pesticide containers not wearing a mask, gloves, shoes or any other form of protective clothing while spraying applying liquid pesticide with a broom instead of a sprayer spraying pesticides into the wind source: Shrestha 1996, Shah 1996 5.4.1 Nutrient Status and Water Management The nutrient status on khet lands is generally adequate (Figure 4.3 a-h) and is enriched relative to bari fields (Table 4.17). Nutrient dynamics within khet lands are largely driven by chemical fertilizer use, nutrient enrichment associated with irrigation and sediment deposition, and cropping intensity. Inputs to rice-wheat rotations are generally sufficient while a three crop rotation results in a significant N deficit (Figure 4.19). For arice-wheatrotation positive N, P 0 and Ca budgets are noted for 71, 40 and 93 % of 2  5  fields respectively (Figure 4.25d). Phosphorus deficits are generally small, and negative Ca budgets are associated with the high use of ammonium sulphate fertilizer.  Relationships between nutrient budgets, inputs, crop uptake and soil fertility, identified by Pearson correlation coefficients, are displayed in Figures 5.7a and 5.7b. Underricecultivation, nutrient budgets for N and P 0 are positively correlated with inputs and negatively correlated with crop uptake, which are co2  5  dependent variables. Under irrigated winter wheat cultivation, only the P 0 budget is related to inputs and 2  5  crop uptake, while crop N, POs and Ca uptake are weakly correlated with nutrient inputs. Soil C and pH 2  are weakly correlated with nutrient inputs toriceand wheatfields,but relationships between soil  122 a) khet - rice 0.52 0.56  N budget  -0.77  -0.29  fertilizer N (monsoon)  PH 0.35 0 32  n R? c —  fertilizer & organic matter N (monsoon)  CEC  rice N uptake fertilizer P (monsoon)  -0.48  avail. P  soil pH  fertilizer & organic matter P (monsoon)  -0.70  CEC  rice P uptake 0.62  -0.99  Ca budget  0.66  exch. Ca  fertilizer acidity (monsoon)  .0.48  0.60  base saturation  b) khet - wheat  N budget  fertilizer N (winter)  -°  3 9  organic matter N (winter) fertilizer & organic matter N, (winter)  0 A  ,  r U  0.44  0.59  C  E  pH  %c  C  . wheat N uptake  0.39  fertilizer P - (winter) fertilizer & organic matter P (winter)  0.56 0.55  0.39  P 0 budget 2  (  -0.38  avail. P . „ , , - wheat P uptake x  Ca budget  fertilizer acidity (winter)  -°-  organic matter P (winter) 3  4 6  C 0.43  organic matter Ca (winter) wheat Ca uptake fertilizer & organic matter Ca (winter)  0.44  PH  exch. Ca  0.66 0.48  Figure 5.7. Selected correlations between nutrient budgets, inputs, crop uptake and soil fertility on khet sites (significance of a<0.05; — indicates co-dependence).  base saturation  CEC pH  123 fertility and nutrient budgets are poor and likely reflects the sensitivity of the nutrient model to water management and crop yield.  Differences in khet nutrient budgets with aspect, elevation and soil type are summarized in Table 5.7. Statistically significant differences are found between N budgets and aspect, elevation and soil type under rice production. Nitrogen surpluses are noted for high elevation, north facing slopes and non-red soils. Khet fields above 1200 m elevation are significantly less productive than low elevation sites resulting in  Table 5.7. Differences between factors affecting nutrient budgets on khet fields (sample median and Mann Whitney U test). Crop Factor Ca budget N budget P O budget (kg ha" f.s.) (kg ha" f.s.) (kg ha' f.s.) Rice Aspect north (n=30) 30 -1 79 south (n=19) -15 -7 86 north vs. south O Elevation (m) <1200 (n=39) -1 -6 83 >1200(n=10) 79 43 2 <1200 vs. >1200 O O Soil Type red (n=20) -6 20 non-red (n=29) red vs. non-red O Wheat Soil Type -5 red(n=ll) 1 non-red (n=20) red vs. non-red O Rice-Wheat Aspect north (n=23) 35 73 south (n=7) -14 83 north vs. south + O Elevation <1200(n=22) 28 >1200(n=8) 52 <1200 vs. >1200 + 2  1  s  1  •  •  • O +  Significant differences between groups a<0.05 Significant differences between groups a<0.10 Significant differences between groups a<0.15  1  •  124 lower nutrient demands. North facing slopes receive significantly greater fertilizer N inputs, while non-red khetfieldsare dominantly located at higher elevations (Plate 4). Calcium deficits are predicted for low elevation wheatfields,and budgets are lower on north facing aspects as greater ammonium fertilizer inputs and equivalent acidity values are noted for these sites.  5.4.2 Implications of Intensive Cultivation Nutrient budgets on khetfieldsare roughly sustainable under rice-wheat production, but the introduction of premonsoon maize into the rotation results in negative seasonal budgets for N and P 0 (Figure 4.19). 2  5  Agricultural intensification and the introduction of cash crops has important implications for soil fertility as the nutrients removedfromthe soil by plant growth vary with the variety of plant and its yield. Tables 1-4 of Appendix B list nitrogen, phosphorus and potassium uptake by traditional staples and cash crops for typical yields in the Middle Mountains of Nepal. Tomatoes and potatoes require higher levels of N and P 0 than rice or wheat, and K 0 uptake is significantly greater than rice, wheat or maize requirements. 2  5  2  Cash crop production and the use of high yielding crop varieties are resulting in an increased dependence on chemical fertilizer, pesticide and micronutrient additions. Acidification on khetfieldsis less of a concern than on barifieldsas the basic irrigation waters compensate for the acidifying effect of chemical fertilizers. While insufficient water during the dry season has limited the expansion of khet land, potential water quality problems are associated with the heavy fertilizer and pesticide use (Sharpley and Halvorson 1994, Owens 1994, Kirchman 1994, Laws 1993, Nimmo 1985).  5.5 Grass and Shrub Land Dynamics The area under grass and shrub land decreased by 8% between 1972 and 1994, largely in association with the planting of chir pine, and the expansion of bari land (Figure 5.3). The majority of this expansion occurred on moderate to steep slopes, below 1200 m elevation (Table 5.1). This net loss of grass and shrub lands is significant as animal feed deficits have been identified as critical throughout the Middle Mountains (Schreier et al. 1991a, Fox 1987, Chitrakar 1990).  125  5.5.1 Nutrient Losses The nutrient status on grass and shrub lands is generally poor and is depleted relative to bari lands (Figure 4.3a-h). Nutrient inputs are limited to manure and urine from grazing animals, but nutrients are removed through grazing, fodder and litter collection, and erosion. Common grass and fodder species on grass and shrub lands in the region are listed in Table 5.8. Within the Middle Mountains, the productivity of grass and shrub lands is highly variable both spatially and temporally, with the highest productivity obtained during the monsoon season. Unfertilized and unmanaged native grasses have low nutritive values, low digestibility and generally low productivity. Rangeland studies from the Middle Mountains suggest average yields of approximately 1 t ha" yr" (Pariyar et al. 1996, Melkania and Tandon 1988, Ranjhan 1  1985, L R M P 1986a).  1  Tropical grasses may extract significant quantities of nutrients from the soil  ranging from 60-300 kg N ha' , 20-100 kg P 0 ha" , and 20-150 kg Ca ha" (Table 5 Appendix B), but 1  1  2  1  5  uptake from the low productivity sites in the study region are likely near the lower range of values. Open grazing and cut grass production on common grass and shrub lands disrupts the natural nutrient cycling and contributes to the low soil fertility status of these sites.  Table 5.8. Grass and fodder species in the region. Common Name Grass  ! Scientific Name  Common Name  j Scientific Name  j Arthunge  j Herterotogon contortus Fodder  j Angeri  i Lyonia ovalifolia  j Dubo  j Cynodon dactylon  j Babiyo  | Eulaliopsis binata  j Bans  1 Dendrocalamus spp.  \ Desmodium uncinatum  | Kalo  1 Ficus lacor  | Khans  ; Saccharum spontaneum  i Kanike  1 Ligustrum confusum  j Khar  \ Cymbopogon microtheca  1 Desmodium 1 Desmodium intortum  j Khari  \ Celtis australis  | Musekharki \ Pogonatherum paniceum  j Koiralo  | Bauhinia variegata  j Napier  j Pennisetum purpureum  j Pithauli  j Rhus parvifolia  | Siru  ! Imperata cylindrica  ; Sal  1 Shorea robusta  j Stylo  1 Stylosanthes humilis  1 Sirus  j Albizzia div. sp.  1 Stylosanthes guianensis  I Sissoo  i Dalbergia sissoo  | Utis  \ Alnus nepalensis  dataset: 1993/94 Bela-Bhemsenuian soil survey, n=70, male farmer responses; Gautam 1986; Pariyar et al 1996  126 Nutrient removal through erosion may also be significant on open access grass and shrub lands. Erosion on shrub lands of 10+2 t ha" yr" (Carver 1997) removes an estimated 10 kg N and 12 kg Ca per ha 1  1  furrow slice (Table 4.7). Well managed grasslands will have minimal erosion, but 20% are degraded (7590% soil exposure) and have high erosion rates (62+44 t ha" yr"). Additionally, 30% of shrub lands are 1  1  classified as degraded (50-75% soil exposure). Nutrient lossesfromthese degraded sites remove an estimated 34 kg N and 23 kg Ca per ha furrow slice.  5.5.2 Implications of Degradation Land classified as shrub and grasslands are largely degraded forests and agricultural wastelands. Nutrient inputs are minimal and biomass production is generally low. Heavy grazing and cut-and-carry fodder production have resulted in degradation of some 96 ha (5% of the study area). Carver (1997) identified these severely degraded sites as a significant source of sediments to the overall basin sediment budget. Grazing and surface erosion have resulted in soil compaction, surface crusting, reduced infiltrability and a reduction in water holding capacity. Rehabilitation of these degraded sites has proven difficult but promising results have been obtained by Shah et al. (1995) through the use of N-fixing fodder trees, lime and trickle irrigation. While long term rehabilitation efforts require resources which are unavailable to most farmers, research projects may aid in the establishment of productive biomass.  5.6 Land Use Interactions and Soil Fertility  Nutrient flows between land uses are a critical component of the soil nutrient budget modelling. Fluxes between land uses are illustrated in Figure 5.8, and indicate that the transfer of nutrients within the farming system is unbalanced. Organic matter production and cycling on grass, shrub and forest lands is disrupted. Inputs are small, while nutrients are redistributed to khet and barifieldsthrough litter and manure. Barifieldsreceive inputs via compost and fertilizer, but nutrients are transferred to khet fields through erosion. Khetfieldsact as a nutrient sink receiving inputsfromcompost, fertilizer, sediment and water.  127 no Inputs Forest litter manure  G rass/ Shrub  compost fertilizer sediment water  Khet  t  erosion  Bari  organic matter  •  5>  m i n o r flow m o d e r a t e flow m a j o r flow  Figure 5.8. Nutrient flows within the farming system.  Nutrient inputsfromchemical fertilizer and organic matter sources to topographic and land use classes are shown in Table 5.9. Significant differences are noted for elevation, aspect and land use categories, while no differences were noted with soil type. The greatest difference in inputs is noted between khet and bari sites, with khet fields receiving significantly greater chemical fertilizer and total inputs of N and P2O5, and bari fields receiving significantly greater organic matter inputs (based on Mann Whitney U test). Differences with elevation are largely a manifestation of changes in land use above and below 1200 m, while differences with aspect reflect management. Both khet and bari fields on north facing slopes receive significantly greater fertilizer inputs than the hot, dry south facing slopes.  Dynamics in Nutrient Inputs Changes in the agricultural production system to include more cash crops has important implications for nutrient fluxes. Organic matter traditionally applied to bari land is used to supplement production on khet lands, and chemical fertilizers are acquiring greater importance within the farming system (Photo 6). Relative changes in the amount of organic matter and chemical fertilizer inputs are presented in Figure 5.9a and 5.9b. In 1989 and 1996, farmers in Baluwa were asked to compare their current nutrient inputs to levels 5 years ago. The number of female farmers reporting organic matter use increased significantly  128  Table 5.9. Differences in nutrient inputs with topography and land use (sample median and Mann Whitney U test). Factor  Fertilizer N  PiO  s  (kg ha ) | (kg ha ) 1  Total  Organic Matter N 1  P*O  (kg ha ) 1  ]  s  N  Ca  (kg ha ) | (kg ha ) 1  1  P20  S  (kg ha ) | (kg ha ) 1  1  Elevation (m) 114 |  <1200 m (n=80) >1200 m (n=50) <1200 vs. >1200 Aspect  •  north (n=70) south (n=60) north vs. South Land Use  81 |  •  144 |  •  65 !  69  44  25  54  63  44  39  45  79  33  bari (n=80)  66 |  34  53  •  •  4 5  •  59 72 O  O  162 !  •  j j  o  O  khet (n=50) khet vs. Bari  4 5  3 5  j  59  1  60  1  44  j  •  71  dataset: 1993/94 Bela-Bhimsenthan soil survey, n=130 •  Significant differences between groups a<0.05  a) organic matter input dynamics  j 147 j 178  79  •  68  195  •  118 |  44 O 80  201 !  •  122 |  31  •  41  O Significant differences between groups oc<0.10  b) chemical fertilizer input dynamics 100  100  more applied  Figure 5.9. Nutrient input dynamics for a) organic matter and b) chemical fertilizers (dataset: 1989 and 1996 Baluwa household surveys, n=27; a) female farmer responses b) male farmer responses). from the 1984-89 period to the 1991-96 period. In 1989, 86% of female farmers reported using the same amount of organic matter as in 1984, while in 1996, 58% reported applying more organic matter. Chemical fertilizer application showed a somewhat different trend, with 88% of the male farmers interviewed in 1989 reporting more fertilizer being applied compared to 1984, while 55% report using  129  more chemical fertilizer in 1996 and 41% use less fertilizer. The main reasons given for applying less chemical fertilizer in 1996 were high cost and the negative impact on soil structure. Alternatively, the main reasons listed for using more fertilizer were: 1) to increase yield; 2) more fertilizer was required to maintain yields; and 3) declining yields. The increased use of fertilizer to maintain current levels of production or because of declining yields was also reported by 41% of farmers in the Bela-Bhimsenthan 200 site soil survey. The increased reliance on chemical fertilizers to maintain productivity levels is indicative of declining soil fertility and the higher level of inputs may negatively impact soil pH and structure.  Mineral fertilizer use in the Middle Mountains is constrained by availability (fertilizer type and timing) and cost. The availability of chemical fertilizer is strongly tied to foreign aid which varies from year to year, contributing between 20 and 90% of the supply. Since 1972, fertilizer pricing has beenfixedby the Agricultural Inputs Corporation and transportation costs to the hills have been subsidised. Subsidies vary between years and between fertilizers. The relative fertilizer prices in Nepal and India determine the profitability of smuggling fertilizer across thefreeinternational border. Informal estimates indicate up to 85% of some fertilizer shipments intended for Nepal have gone to India. In 1993, the government eliminated a major portion of the subsidy on most fertilizers (except urea) and prices doubled limiting the affordability to only the more prosperous farmers. Fertilizer availability is particularly problematic for farmers who may only have experience with a particular type of chemical fertilizer but may only be able to purchase a different kind. For example, the Lumle Agricultural Research Centre reports farmers complaining of a lack of effectiveness of double superphosphate (0-46-0) compared with complex® (20-20-0) suggesting a lack of understanding of effective fertilizer use and a need for extension. (Chitrakar 1990, Kennedy and Dunlop 1989, Pandey et al. 1995, Srivastava 1995, Sthapit et al. 1988, Wallace 1986, Thapa 1995).  130  Problems reported by farmers in the Baluwa region related to obtaining fertilizer are summarized in Table 5.10. In 1989, farmers reported significant problems related to fertilizer availability, but in 1996 availability was not an issue. Cost, however, has remained an issue with farmers. The type of fertilizer most commonly applied on khet and bari land in 1989 and 1996 are summarized in Figure 5.10. Complex® (20-20-0) was the most commonly used fertilizer on both khet and bari land in 1989, but urea (46-0-0) was more heavily used in 1996. The reduction in the use of complex® is linked to limited availability from 1993 and DAP (diammonium phosphate) was recommended as an alternative (Srivastava 1995). The increased use of urea is likely price related, as it became relatively less expensive with the removal of subsidies on alternative fertilizers. Both urea and complex® are ammonium based fertilizers, and the increased N concentration of urea will negatively impact soil pH with sustained use. While chemical fertilizers have the potential to enhance soil fertility, the traditional recycling of organic residues is vital to mamtaining soil tilth (Carson 1992, Foth 1990, Sherchan and Gurung 1995). Table 5.10. Difficulties in obtaining chemical fertilizer. Problem  % Responses  1  j  1989 74 !  Wrong type  j  Wrong time  74 41 |  Unable to pay 1  1996  7 7 30  dataset: 1989 and 1996 Baluwa household surveys, n=27, male farmer responses  a) khet  b) bari  0  10  20  30  % Responses  40  50  0  10  20  30  40  50  % Responses  Figure 5.10. Types of fertilizer commonly used on a) khet and b) bari land in 1989 and 1996 (dataset: 1989 and 1996 Baluwa household surveys, n=27, male farmer responses).  131  5.6.1 Implications for Production Nutrient deficits and unbalanced nutrient flows between land uses will negatively impact future biomass production. The farming system integrates bari, khet, forest, shrub and grass lands such that pressure on one component will impact the entire system and alter the transfer of nutrients. Current and future productivity are linked to nutrientfluxesboth within and between land uses.  Forest Productivity Forest biomass estimates in relation to site quality provide an estimate of the impact of soil fertility on site productivity. In the Sindhu Palchok District, Applegate et al. (1988) found total above ground biomass on high and low quality sites of 35+5 t ha" and 8+2 t ha" respectively. The mean annual increment at age 1  1  nine years was 7 t ha" on high quality sites and 2 t ha' on low quality sites, indicating a substantial 1  1  reduction in productivity rates. In the Dhulikhel subwatershed, Schmidt (1992) found that most foliar samples for sal and pine fall below critical levels for N and P indicating the low soil nutrient status of these sites.  In a typical forest ecosystem the annual cycling of most nutrients via litterfall and foliar leaching is substantially greater thanfluxesinto and out of the system. Litterfall is the predominant mechanism in the aboveground return of N, P, Ca and Mg to the soil. The collection of fodder, litter and fuelwood, however, disrupts nutrient recycling, and may lead to a depletion in key soil nutrients (Johnson and Todd 1990, Richter et al. 1994, Sanchez et al. 1985, Young 1989). Feigl (1989) notes a relationship between total biomass and guarding status (protection afforded by human action) in the Jhikhu Khola watershed. Total biomass in chir pine and hardwood stands with no protection rangedfrom7 to 22 t ha" while protected 1  stands rangedfrom18 to 53 t ha". Differences between total biomass produced on stands of similar age 1  suggest a 15% increase in biomass production when forests are guarded and no litter, fodder or timber is removed. Both Feigl (1989) and Schmidt (1992) found that protected stands in the Jhikhu Khola Watershed are characterised by significantly higher soil pH, CEC, base saturation, Ca, Mg, K and P.  132  During the past three decades, afforestation projects in the study region have been active planting chir pine which now comprises 40% of the forest area. The influence of forests on soil fertility is often assumed to be beneficial, however long term data sets (10-30 years) suggest that fast growing pine trees often deplete exchangeable bases and decrease soil pH. Some species, such as Gmelina capture and recycle soil nutrients more efficiently, but Pinus  sp.  arborea  (khamari) are able to  produce litter with significantly lower Ca  and Mg contents (Johnson and Todd 1990, Richter et al. 1994, Sanchez et al. 1985).  Crop Productivity Farmers are not interested in soil fertility per se, but in the resulting productivity. To identify relationships between crop productivity, soil fertility and nutrient management, Pearson correlation coefficients and multiple regression models were calculated. Figures 5.11a and 5.11b show correlation coefficients for the dominant crops on bari and khetfields.Maize and wheat yields are weakly correlated with nutrient inputs and soil fertility, while no significant relationships were noted for rice yields suggesting the importance of irrigation water and sediment deposition in supplying nutrients to paddyfields.Multiple regression analysis using both nutrient inputs and soil fertility parameters improved relationships with yield over relationships with inputs or fertility in isolation. Scatter plots of reported and modelled yield for maize, rice and wheat are shown in Figures 5.12 a-c. Maize yield is estimated using fertilizer N, base saturation, exchangeable K and CEC. The model has an r value equal to 0.52 and predicts a median maize yield of 3988+1840 kg ha" (two standard deviations). Rice yield is estimated using fertilizer acidity, fertilizer N 1  and P, soil C, exchangeable Ca and pH. The relationship is somewhat weaker with an r value equal to 0.43 and predicts a median rice yield of 2485+1464 kg ha". Wheat yield on both khet and bari land is 1  estimated using organic matter, fertilizer N and P, base saturation, exchangeable K and CEC The model predicts a median wheat yield of 1275+1680 kg ha", and has an r value of 0.68. For all three crops, under 1  dryland and irrigated conditions, farmers applying more nutrient inputs report higher yields.  133  a) bari yield exch. Ca  0.34 0.28  maize yield  fertilizer N (monsoon)  0.36  fertilizer & organic matter N (monsoon) base saturation dryland wheat yield  0.52  0.53  b) khet yield  irrigated wheat yield  0.44  fertilizer N (winter)  fertilizer P (winter)  organic matter N (winter)  Figure 5.11. Correlations (r values) between crop productivity, soil fertility and nutrient management (significance of a<0.05). The weak relationships between crop yield and soil fertility, and improved relationships with nutrient inputs and multiple regression analysis suggest that the soil is providing short term nutrient storage and that nutrient inputs are more important in determining yield. Soil conditions may limit nutrient availability (e.g. low available P on red soils) or limit plant growth through toxicity (e.g. Al toxicity at low pH), but nutrient inputs are the dominant factor driving yield.  Grass and Shrub Productivity Current biomass production on grass and shrub lands is low, and cut-and-carry fodder production and extensiVe grazing will likely perpetuate low productivity. The majority of these sites are agricultural wastelands and degraded forests, and continued biomass removal with minimal nutrient inputs contributes to their low soil fertility status. The potential impact of heavy exploitation and the resulting negative nutrient budget on grass and shrub lands is represented by degraded sites within the study region. One quarter of the current grass and shrub lands are classed as degraded, having >50% soil exposure and subject to high erosion rates.  a) bari - maize 10000 ^ a  8000  ~  6000  Y=596+(8*fertilizer N)+(40*base saturation)-(2405*exch. K)+(158*CEC) r=0.52  £  •  0)  "8 u  1 £  •• ^ ; t*>- !• » i * !*  4000  2000  I h *  0^ 2000  4000  6000  8000  10000  Reported Yield (kg ha" ) 1  b) khet - rice 10000  Y=-2073-(8*fertilizer acidity)+(4*fertiliser P)+(1359*%C)(363*exch. Ca)+(5*fertilizer N)+(1078*pH) r=0.43  8000 • n  CT  ~  6000  U  4000  «. •  1 £  *  • t  •  •  k.....#...*i...#L....*  •  * T  i  2000 2000  4000  6000  8000  10000  Reported Yield (kg ha") 1  c) winter wheat 6000 _ 5000 'co  Y=-2352+(organic matter)+(7*fertilizer N)-(26*base saturation)(1159*exch. K)+(7*fertilizer P)+(51 *CEC) r=0.68  w 4000 £  3000  2 2000  I  °~ 1000 0  b  V*  1000  * 1*  i  •.  \  t  2000  ....... •  3000  4000  5000  6000  Reported Yield (kg ha") 1  Figure 5.12. Multiple regression analysis of yield, nutrient inputs and soil fertility conditions.  Protection from grazing is a prerequisite for grassland improvement. In Mid-Himalaya grasslands, Melkania and Tandon (1988) found yields increased from 0.9 t ha" underfreerange grazing to 2.4 t ha" 1  1  after two years of protection, and a further increase to 3.0 t ha" after three years of protection. Rotational 1  grazing supported by stall-feeding is suggested as an alternative to extensive grazing.  Production Constraints To determine the main production constraints on cropland, the male household heads were asked "What is the biggest problem preventing youfromincreasing your yields per ropani?" The results for Baluwa in 1989 and 1996 are presented in Table 5.11. A lack of irrigation facilities, and the availability and prohibitive cost of chemical fertilizer remain the dominant production constraints. The increasing demands placed on water resources are indicated by the large number of responses (60%) reporting a lack of irrigation as a major limitation in 1996 compared to the 43% noted in 1989. Disease problems were not reported by surveyed farmers in 1996 compared to 14% in 1989, and are likely related to increased pesticide use in the study region. Agricultural equipment and improved seeds were less of an issue in 1996, but 5% of farmers cite labour as a major constraint.  Table 5.11. Changes in production limitations, 1989 - 1996. Limitation  % Responses 1989  1996  i meal ion  43  60  chemical Icrlili/cr  29  33  disease  14  (I  7  0  improved seeds  5  2  labour  2  5  agricultural equipment  dataset: 1989 and 1996 Baluwa household survey, n=27, male farmer responses  5.6.2 Nutrient Dynamics and Future Soil Fertility The impact of nutrientfluxeson future soil fertility can be assessed by comparing predicted nutrient deficits and current soil fertility conditions. Nutrient budgets for individual fields are related to the factors  136  which account for their variability, specifically land use, soil type, aspect and elevation. Using the soil fertility classification developed in section 4.12 and predicted nutrient budgets for each class, soil degradation can be assessed and extrapolated to the entire study region using GIS overlay techniques.  Degradation maps are developed for soil P and Ca using nutrient budgets based on rice for khet land, maize for bari land, plot study data for forest and shrub lands, and plant uptake estimates for grasslands. Soil carbon is not evaluated as levels are currently very low under all land uses. The main factors accounting for the variability in soil P budgets are land use, soil type, aspect and elevation. Samples are stratified by these factors and changes in the soil nutrient pool are estimated for each group. The results are lumped into two categories delineating areas of small (<20 kg per ha furrow slice) and large (<20 kg per ha furrow slice) nutrient deficits. Land use, soil type, aspect and elevation themes are combined within the GIS, assigned the appropriate P change category and overlain with the current soil P classification (Plate 11). The results shown in Plate 14 indicate that 25% of the area has adequate available P and is subject to low change (shown in green). Management concerns exist on 37% of the area (shown in red, orange and yellow) corresponding to regions of adequate available P subject to large deficits, and low available P subject to large or small deficits. These areas correspond to bari land on red and non-red soils, south facing khet land on red soils, grasslands on non-red soils, and south facing grasslands on red soils. Areas shown in blue are already very low in available P and are being degraded further.  The main factors accounting for the variability in soil Ca budgets are land use and aspect. Samples are stratified by these factors and changes in the soil Ca pool are estimated for each group. The results are lumped into three categories: surplus Ca, small deficits (0-40 kg per ha furrow slice) and large deficits(>40 kg per ha furrow slice). GIS overlay techniques are used to extrapolate Ca change spatially and identify areas of concern relative to current soil Ca conditions (Plate 15). Minimal change is anticipated on khet lands which have moderate soil Ca and surplus Ca budgets (represented in green). Management concerns exist on regions of moderate and adequate soil Ca subject to high or low deficits  137  (represented in red and yellow). These areas cover 46% of the study region and correspond to south facing, low elevation bari land; north facing bari land; and low elevation grasslands. Regions shown in blue are already below desirable Ca levels.  The soil Ca and P degradation maps illustrate regions where current land use practices are having a limited impact on soil fertility and regions of management concern. Khet production appears to be sustainable under a rice dominated cropping system but intensive cash crop production will likely result in negative nutrient budgets and soil degradation. Regions of management concern are dominated by bari lands. Current soil fertility conditions on non-red bari sites are generally adequate and subject to small nutrient deficits with the exception of high elevation, south facing sites. Barifieldson red soils have low to adequate soil fertility but are subject to high P deficits. These sites are of particular concern due to their high Pfixationcapacity. The timing of organic matter inputs is critical to soil P availability for crop uptake as fertilizer P inputs on red soils will be largelyfixed.Calcium and N deficits are prevalent on most bari sites sampled and significantly greater deficits occur on south facing sites. While organic matter inputs supply roughly one-half the N and all the Ca applied to barifields,N and Ca deficits imply insufficient organic matter inputs. Regions with poor current soil fertility conditions are largely forest, shrub and high elevation grasslands. Rehabilitation of these sites will require substantial effort and resources, and is complicated by government ownership.  5.7 Summary Land use change, its impact on nutrient flows and relationships between nutrient inputs, crop uptake, nutrient budgets and soil fertility are used to assess why soil fertility is changing. General land use trends show a cyclical decline in forest cover, limited expansion of cropped land and recent decreases in grass and shrub land.  138  Forest dynamics may be described as a function of quantity versus quality. Afforestation efforts during the 1980's resulted in a 50% recovery of previous losses but the focus on chir pine resulted in large areas of monoculture forest, and renewed losses of hardwoods have been observed in the 1990's. Forest soils show the lowest overall soil fertility and the greatest annual nutrient losses. The collection of fodder, litter and fuelwood results in a significant transfer of biomassfromthe forest and interferes with the natural forest nutrient cycle. Biomass removal and the associated outward flow of nutrients is the dominant management factor causing soil fertility degradation on forest land.  Bari dynamics may be described as a function of expansion and marginalisation. Expansion of bari land from 1972 to 1994 is related to the terracing and cultivation of former grazing, shrub and abandoned lands located on steep, high elevation slopes, and the conversion of previously irrigated fields due to water shortages. Cultivation of marginal lands is of concern as they have inherently poor soil fertility and are prone to erosion. The nutrient status on bari lands is generally poor and nutrient fluxes indicate that inputs are insufficient to maintain the soil nutrient pool. Nutrient budgets are related to fertilizer and organic matter inputs but relationships with soil fertility are poor, indicative of the low overall fertility conditions and high variability within bari fields. Phosphorus deficits are significantly greater on red soils which have a high Pfixationcapacity, and on south facing sites which receive less organic matter inputs. Acidification is a concern for nutrient availability particularly on red soils, and will be aggravated by increases in chemical fertilizer use. Changes in soil fertility on bari land are complicated by topographic conditions and inherent soil characteristics, but insufficient nutrient inputs and the cultivation of steep slopes which results in nutrient losses through erosion are the main management factors driving soil fertility degradation.  Khet dynamics is best described by intensification. The amount of irrigated land has remained relatively constant but cropping has intensified and shifted towards more market oriented production. Intensive cultivation, cash crop production and the planting of high yielding varieties have prompted the use of  139  agrochemicals, and pesticides use is widespread. Fungicides and insecticides are often applied above manufacturers recommended rates, and inappropriate use raises concerns about human health and environmental pollution. The soil nutrient status on khet lands is generally adequate. Khetfieldsact as a nutrient sink receiving inputsfromcompost, fertilizer, sediment and water. Inputs to rice-wheat rotations are usually sufficient but triple cropping results in significant N deficits. Nutrient budgets are related to fertilizer and organic matter inputs, but relationships with soil fertility are poor reflecting the sensitivity of the nutrient model to water management and crop yield. Nitrogen budgets are significantly lower at elevations below 1200 m and on south facing slopes as high elevation sites are less productive and therefore less nutrient demanding, and north facing slopes receive greater fertilizer inputs. Water management and the associated sediment accumulation on low lying khetfieldsis the key management practice maintaining soil fertility on the intensively managed khet lands. However, nutrient inputs may not be sufficient to sustain triple cropping and increased agrochemical use threatens the environmental quality.  Grass and shrub land dynamics are characterized by minimal inputs and low productivity. The nutrient status is generally poor, and tropical grasses extract significant quantities of nutrientsfromthe soil. Open grazing and cut-and-carry biomass removal disrupt the natural nutrient cycling and contribute to the low soil fertility status. Nutrient removal through erosion is also important, particularly on degraded sites (>50% soil exposure). The lack of nutrient inputs and over utilization resulting in biomass removal and accelerated erosion are the dominant management factors contributing to soil fertility degradation on grass and shrub lands.  Overall, the transfer of nutrients vvithin the farming system is unbalanced, organic matter cycling is disrupted and nutrient inputs vary both spatially and temporally. Chemical fertilizer use has increased, to counteract declining yields, but varies year to year with pricefluctuations.Nutrient deficits and unbalanced nutrientflowsbetween land uses will negatively impact future biomass production. Related studies found that forest productivity is lower on sites with extensive biomass removal compared to  140 protected sites. Crop productivity is related to both soil fertility and nutrient inputs on irrigated and dryland sites, but nutrient inputs are more important than soil fertility in determining yield. Grassland and shrub productivity is low, but protection has been found to increase yields in other parts of the MidHimalaya. Soil degradation maps suggest that khet production is currently sustainable but management concerns exist on bari lands. Red soils are particularly problematic due to their high P fixation capacity and organic matter management is critical on these soils.  141 6. UNDERLYING SOCIO-ECONOMIC FACTORS The pattern of life in most rural communities of the Middle Mountains has been largely determined by processes acting at the village level and changes within their biophysical environment. Within these traditional communities, local knowledge and experiences play an integral role in decision making. In recent years, however, rapid population growth, increasing pressure on the resource base, and the increased availability of Western technology have accelerated change. The introduction of improved varieties of wheat and rice in the 1970s and 1980s made agricultural intensification possible, but this intensified agriculture is more demanding on soil, water and human resources. Chemical fertilizers were also adopted in the 1970s, and their usage in the Middle Mountains has increased an average of 9% per annum, furthering the need for capital. Communities near major markets such as Kathmandu are no longer isolated, but are changing as they become more integrated into the cash economy. Local farmers are innovators and experimenters, adapting technology to local conditions and incorporating elements from the industrial world into their traditional system. Chemical fertilizers for example, are used in conjunction with traditional compost, and plastic pipe is replacing hollowed log conduits within irrigation systems. However, traditional farming systems evolved over many generations in a relatively stable environment, and their ability to adapt to the rapidly emerging social, economic and biophysical pressures is limited (Chitrakar 1990, Kennedy and Dunlop 1989, Conway 1986, Ehrlich et al. 1971, Gill 1991b, Chambers et al. 1989, Rhoades 1989, Mwadime 1996).  Population growth, land tenure, culture and poverty are the underlying socio-economic factors influencing farming system dynamics in the Middle Mountains, and define the contextual framework under which soil fertility depletion is occurring. Their current status and recent changes impact land use, nutrient management and consequently soil fertility. The implications for soil fertility are both direct and indirect, and provide further insight into why soil fertility degradation is occurring. The components evaluated, and their interaction with soil fertility datafromchapter four are shown in Figure 6.1. For each socio-  - growth - implications for soil fertility  - land distribution - sharecropping - sufficiency - open access - imp'ications tor soil fertility  6.5 IMPLICATIONS OF SOCIO-ECONOMIC FACTORS FOR SOIL FERTILITY - population growth - access to land • culture • poverty  - ethnic distribution role of livestock • women and soil fertility - implications for soil fertility  - agricultural assets - farm gross margins • cash income - implications for soil fertility  Figure 6.1. Socio-economic factors driving nutrient management and soil fertility dynamics evaluated in chapter 6.  143  economic factor key indices are presented and the implications for soil fertility are examined. Population growth is discussed relative to land use change and nutrient fluxes both within and between land uses. The influence of land tenure is evaluated through the distribution of land ownership, share cropping, household sufficiency from farmed land and open access resources (forest, shrub and grasslands). The cultural factors considered are ethnic distribution, the changing role of livestock and the involvement of women in soil fertility management. The poverty indices selected are agricultural assets (land and livestock), farm gross marginsfromcrop production (total returns and variable costs), cash income derivedfromthe sale of agricultural products (vegetable crops and milk production) and off-farm employment. Relationships between each index, management factors and soil fertility are evaluated using correlation, regression analysis and Mann Whitney U tests. The management factors evaluated include chemical fertilizer, organic matter and pesticide inputs to khet and bari land; N, Ca and P 0 budgets for individual crops, 2  5  and nutrient budgets for the dominant crop rotations on khet and bari land. The soil fertility variables evaluated include base saturation, CEC, Ca, Mg, K, available P, pH and %C. The overall implications for nutrient management and soil fertility are then summarized.  6.1 Population To evaluate the current population and recent trends within the study area, a survey of constructed houses was undertaken for 1972, 1990 and 1995. The number of houses identified on 1972 and 1990 aerial photographs were counted and compared to the number of houses observed in the field in 1995. Population numbers were calculatedfromthe number of houses and the average family size (6.7 people per household) determinedfromhousehold surveys. Figure 6.2 displays the recent changes in the number of houses and population. The number of houses in the study area increasedfrom1104 in 1972 to 1723 in 1995. The average population growth for the 1972-1990 period was 1.8% per annum and increased to 2.6% per annum for the 1990-1995 period. The recent increase is due to both population growth and immigration. Recent household surveys show that the average family size has not decreased, that is the increase in the number of houses is not due to the extended family being broken up, but a number of young  144  Figure 6.2. Bela-Bhimsenthan population dynamics 1972-1995.  families from nearby communities have immigrated to the area and are currently building houses in the region.  6.1.1 Population Growth. Land Use Change and Nutrient Dynamics Population growth and the resultant increased demand for food places additional pressure on the resource base and specifically on soil resources. The per capita availability of land in the study area decreased from 0.26 ha in 1972 to 0.17 ha per capita in 1995, which is a greater decrease than for Nepal as a whole. Double and triple crop rotations are applied where water is available but nutrient inputs may not be sufficient to sustain these intensive levels of production (Figure 4.19). Agricultural marginalisation (Table 5.1) in response to population pressure has brought steeply sloping and low soil fertility lands under cultivation to provide additional food supplies. Recent declines in forest cover (Figure 5.2a) and reported shortages of forest products are indicative of the continuing pressure on forest resources, and the increased demand for wood in house construction and brick making. Population growth, both locally and regionally, is a dominant factor driving land use dynamics within the study region, and the associated increase in demand for food, animal feed and fuelwood results in increased nutrient removal.  145  6.2 Land Tenure Historical land tenure policies have shaped agricultural development in Nepal. The feudal land tenure system (abolished by the 1957 Birta Abolition Act) resulted in a small minority of larger landowners possessing a substantial portion of the arable land. Land registration implemented in 1963 encouraged the private registration of previously public lands through low tax rates, and resulted in increased pressure on the remaining public forest and grass lands. Land surveys were conducted demarcating areas according to ownership, but confusion between the Land Administration Office and Land Revenue Office resulted in many occupied public lands being registered as private land. The Forest Nationalisation Act of 1957 likely accelerated deforestation as land with trees on it could not be registered as private land. The 1964 Nepali Lands Act strove to improve the status of land tenure for small scale farmers by establishing a ceiling on land holdings and providing rights to tenants, but the program has been largely ineffective (Seddon 1987, Regmi 1976, Jha 1987, Yadav 1984, Dhungel 1987, Dahal 1987).  6.2.1 Land Distribution Land ownership within the agrarian economy of the study area provides a major source of income, and inequity in land distribution translates to economic disparity. Farmers with limited access to land, or poor quality land will have little economic incentive or ability to invest in soil fertility management (Blaikie et al. 1980b, World Bank and UNDP 1991, Seddon 1987). In the Bela-Bhimsenthan household survey (n=85) the median land holding per household is 0.92 ha (1 ha = 19.7 ropani), but the amount of land varies significantly across different size categories (Figure 6.3). Land ownership is unevenly distributed with 53% of the households owning only 25% of the total agricultural land area, with total holdings per household <1 ha. Large landowners (holdings >2 ha) make up 15% of households, but own 36% of the agricultural land. Two families own no land. The average amount of khet land is 0.24 ha per household, but 24% of households own no khet land. Households with larger khet holdings (>0.5 ha) comprise only 15% of the households and own 46% of the khet land. The average amount of bari land per household is  a) agricultural land distribution  <1.0  1.0-1.9 2.0-3.4 agricultural land holdings (ha)  b) khet land distribution 60 50  • %households • %area  0  0.05-0.24 0.25-0.49 khet land holdings (ha)  0.50-1.12  c) bari land distribution • %households • %area  <0.5  0.5-0.9  1.0-1.9  2.0-2.6  bari land holdings (ha)  Figure 6.3. Agricultural land distribution among surveyed households (dataset: 1994 Bela-Bliimsenthan survey, n=85, male farmer responses).  147 0.81 ha but ownership of bari land is also unequally distributed. Large landowners (bari holdings >2 ha) comprise 12% of the households and own 32% of the bari land.  6.2.2 Share Cropping Secure land tenure is necessary before farmers are willing to invest in soil fertility. Twenty-eight percent of the households surveyed in Bela-Bhimsenthan (n=85) report an involvement in sharecropping. A total of 7.2 ha of bari and 6.6 ha of khet land are reported as shared cropped by the surveyed farmers. These estimates should be considered lower bounds as farmers were hesitant to answer questions relating to share cropping. The 1964 Nepali Lands Act requires landlords to provide compensation to tenants amounting to 25% of the value of the land in the event that part or all of the rented land is resumed by the landlord. As a result, informal sharecropping is common and information on land tenure is tenuous. Typical share cropping arrangements involve the landlord receiving 50% of the crop, coincidentally the maximum permitted under 1957 legislation. Land used to produce potatoes or tomatoes may be share cropped for only part of the growing year, avoiding the 25% compensation regulation (Kennedy and Dunlop 1989, Regmi 1976, Hitchcock 1963).  6.2.3 SufficiencyfromFarmed Land Fulfilment of subsistence requirements is the primary objective of the majority of farmers in the Middle Mountains. If the land farmed cannot provide a household's basic needs, soil fertility maintenance will not be a priority and labour will be diverted to off-farm employment (Seddon 1987, Panday 1992, Carson 1992). Basic needs are defined as access to a minimum of food, water, shelter, primary health care and basic education (McHale and McHale 1977, Acharya 1982). The unequal distribution of land suggests that some households may not be able to produce sufficient food to feed their families. As an indication of the amount of land required to support a household living in the Bela-Bhimsenthan region, farmers were asked: "Does the land that you farm generate enough food and income to meet your family's basic needs?" Fifty-three percent of the households surveyed report that the land they farmed was enough, while 13%  148  responded that the land they farmed was insufBcient. Thirty-four percent of the responses from male and female farmers within one household were contradictory suggesting a marginal sufficiency status. The ratio of the land farmed to the number of people in a household, for sufficient and non-sufficient households, provides a rough estimate of the amount of land required to support each individual (Table 6.1). Among the households reporting that the land they farmed was not enough, the average ratio of land owned to the number of people is 0.13 ha per person, of which 0.11 ha per person is bari land. Among the households reporting that their land provided enough food and income, the average ratio is 0.20 ha per person, and the amount of khet land per person is more than double. In the Bela-Bhimsenthan region, roughly 0.15 ha of land (0.03 ha of khet and 0.12 ha of bari) is required for every person a household hopes to feed.  Table 6.1. Per capita availability of agricultural land. n Sufficient Per Capita Land Ownership (ha per person) Cultivated Land j Khet Land 1 Bari Land 11 0.13 0.02 0.11 No 1  ifllMIL'IIMl  1  v  " 6 . " J 5 1 0 . 0 3 1 0 . 1 2  45 Yes 0.20 j 0.05 0.15 dataset: 1994 Bela-Bhimsenthan household survey, n=85, combined male and female farmer responses  6.2.4 Open Access Resources Historically, hill tribes managed land communally under the traditional kipat system. Land was distributed amongst tribal members according to need. Individuals held exclusive use to agricultural land, but ownership was tribal. Fodder and fuelwood collecting rights were controlled and specified areas were designated for grazing. The kipat system did not provide secure land tenure and thus discouraged investment in the land base, but it was effective in controlling resource over-utilisation on forest and 'waste' lands. With the expansion of Hindu groups into the Middle Mountains and the development of a centralised government, the kipat tenure system deteriorated. Legislation introduced in 1963 and 1968 resulted in the transition of kipat lands to government aajninistered raikar lands. Less controlled resource  149  exploitation was introduced at a time when population growth was increasing resource usage (Poffenberger 1980, Yadav 1992, Regmi 1976).  Community forestry was initiated in 1978 and strove to devolve authority and responsibility for forests and their management to the forest users. The transfer of government forest land to community management requires the formation of a forest users group, development of a management plan by the users, and plan approval by the District Forest Officer. Operational plans typically involve restocking and restricted access, with technical assistance provided by the Department of Forestry. Progress in implementing community forestry was initially slow, and the transfer of forest lands to local control was limited. By 1989, the total area of government forest transferred to forest user groups in the Kabhre District was only 2.6%, but since democracy the formation of forest user groups has accelerated (Gilmour 1991, Gilmour and Fisher 1991, Schmidt 1992).  6.2.5 Unequal Access to Land. Nutrient Inputs and Soil Fertility Soil fertility management is influenced by access to land and it's inherent soil fertility. Relationships between land holdings, sufficiency, land tenure, nutrient fluxes and soil fertility are examined using Pearson correlation, regression analysis and Mann Whitney U tests.  Farm Size Nutrient inputs may vary with farm size due to limited availability or economic constraints. Regression functions between land ownership, and fertilizer and compost use (Figures 6.4 a-d) show increasing total inputs with land ownership on both khet and bari land for small and medium sized farms, but decreasing inputs on larger farms. On a kg ha' basis, relationships are not statistically significant, but inputs to bari 1  fields display a decreasing trend with bari land ownership, while inputs to khet fields are roughly constant. Relationships between land ownership, crop nutrient budgets and soil fertility are summarised in Table 6.2. Significant differences in nutrient budgets are noted between small, medium and large farms and  a) khet fertiliser use  1200 1000 800 600 400 200  !-•.• J  •  0 0.00  0.20  0.40  0.60  0.80  I 1.00  • 1.20  Khet Ownership (ha)  b) khet compost use 20000 17500  |  15000 •  a. a. 12500 <  to 10000  §. E oo  7500 5000 2500 0 0.00  un.. : y 0.20  0.40  0.60  0.80  1.00  1.20  Khet Ownership (ha)  c) bari fertiliser use  1500 oi 1250 1000 ,<2 750 01  500  4»4j^  3  0.0  d) bari compost use  0.5  1.0  1.5  2.0  2.5  3.0  2.5  3.0  Bari Ownership (ha) 25000  1.0  1.5  2.0  Bari Ownership (ha)  Figure 6.4. Fertiliser and compost use versus land ownership for khet and bari land (dataset: 1994 Bela-Bhimsenthan household survey, n-85. male farmer responses).  151  reflect decreasing fertilizer inputs on large farms and the distribution of land types with farm size. Both small and large farms are dominated by bari land and display lower nutrient budgets. Households owning moderate amounts of land (1.0-1.9 ha) typically own a mix of bari and khet land, apply the most fertilizer and compost to their fields, and sustain the best nutrient budgets. Differences in soil variables are also noted with farm size. Larger farms display lower exchangeable Ca, CEC and %C, suggesting a limited availability of organic matter inputs on larger farms.  Table 6.2. Relationships between farm size, crop nutrient budgets and soil fertility (median values and Mann Whitney U test). P-M-W nutrient budget (kg ha" furrow slice) Farm Size N-budget P O -budget | Ca-budget -16 -24 j -29 | small <1.0ha (n=20) j medium 1.0-1.9 ha (n=8) j -18 i 35 j 9 -15 -32 ! -43 j large 2.0-3.4 ha (n=6) j small vs. medium farms o 1 medium vs. large farms o O 1  2  s  •  •  Exch. Ca j  •  CEC  C  (cmol kg") i (cmol kg") 11 | small <1.0ha (n=20) j 3.4 | 4.4 j 12 i medium 1.0-1.9 ha (n=8) | 3.3 | 9| large 2.0-3.4 ha (n=6) 1 medium vs. large farms + | o j Farm Size  1  1  (%)  •  0.9 1.0 0.8  # Significant differences between groups a<0.05 O Significant differences between groups ct<0.10 + Significant differences between groups a<0.15  Share Cropping It is anticipated that share cropping may have a negative impact on land management due to the uncertainty of land tenure arrangements, and an unwillingness on the part of tenant farmers to invest in share cropped land. Differences in inputs and production on owned and share cropped land for rice and maize, the dominant crops grown on share cropped khet and bari fields, are shown in Figure 6.5. Significant differences between owned and share cropped land are noted for total pesticide expenditures on  a) rice pesticide use  b) rice fertilizer use  40  200  600  1800  -i  500  1500  J> 400 • ilise  hm  r  u.  1200  300  900  200  f  01  100 -  300  0  0  U  01  >  <2 Q.  6  c) maize compost use  600 -  ^  -gg u  d) maize fertilizer use  25000  50000  1000  1800  20000  „ 40000 n '  800  1500  s.  1200  o> 30000  ot v>  if  §. 20000 °  600  01  300  10000 0  900  0 c 5 o  •o  0) O)  is °2 p.  e) khet rice production  Ol  c o  f) bari maize production 12000  4000  10000  7000 -|  _ 10000  _ 6000  *  J= 5000  8000  —  a>  — 4000 •a  6000 4  "D  « o  4000  >  s  3000  .H 2000 n 5 1000  2000  "S  oi "S  is 2 °-a •5 £o  13 Ol  c  i  Figure 6.5. Management on owned versus share cropped land; median, minimum and maximum values, significant differences (a<0.05) highlighted (dataset: 1994 Bela-Bhimsenthan household survey, n=85, male farmer responses).  153 ricefields,total compost use on maizefields,total fertilizer use on both rice and maizefields,and maize production. On a per ha basis, differences in inputs are not significant, but median pesticide, fertilizer, and compost values are all lower on share cropped land. No difference is noted in rice yield between owned and share croppedfields,but maize production on share cropped barifieldsis significantly lower. The variability in agrochemical use and crop production is substantially greater on owned versus share cropped land, with the greatest inputs applied to owned land. While this limited data set is not conclusive, share croppedfieldson both khet and bari lands appear to be less intensively managed thanfieldswith secure land tenure. Rice yields are comparable on both owned and share croppedfields,but maize productivity appears to be negatively impacted under share cropping.  Open Access Resources The poorest soil fertility conditions within the study region are found on grass and forest lands (Figure 4.3 and Plate 12). Grasslands are largely unmanaged and community forestry has historically focused on short term biomass production through pine plantations. Erosion on well managed grasslands and forests with understorey vegetation may be minimal, but degraded shrub and grasslands have elevated erosion rates (Table 4.7). Community forestry initiatives have been successful in establishing forest cover and in transferring decision making to user groups. Within the study region all national forests are currently managed by local user groups. Initially, afforestation efforts focused on pine plantations, but user group priorities have prompted the planting of fodder species such as Dalbergia sissoo.  Options to improve dry season production on grass lands, such as the incorporation of legumes and deferred grazing are constrained by the current land tenure system. The successful implementation of pasture management techniques will require the establishment of property rights either privately or communally, similar to the establishment of forest user groups. To date, the Department of Forestry has concentrated on planting trees and limited resources have been focused on grasses.  154  Culture Cultural practices influence land management in the Middle Mountains. Ethnic distribution, the role of livestock, and women as resource users and managers are three components of Nepali culture which may potentially impact soil fertility in the study region.  6.3.1 Ethnic Distribution Caste affiliation and the ethnic distribution generally reflect the class structure and influence access to resources within the study area. High caste groups tend to be larger landowners, while the low caste households have the poorest access to arable land. The relationship between caste and landownership is related to historic land tenure policy, with the state traditionally granting land to members of the ruling class and local notables. However, class relations are dynamic and in many villages, high caste groups may be landless or poor peasants (Bista 1972, Seddon 1987). In the Bela-Bhimsenthan sample (n=85), land ownership is unequally distributed both across caste/ethnic groups and within each group (Table 6.3). Median land holdings vary by caste from 0 to 0.31 ha of khet land, and 0.2 to 1.42 ha of bari. Brahmin, Newar and Tamang families have the largest median holdings. The sample high (a Brahmin household) owns 1.12 ha (22 ropani) of khet land and 2.24 ha (44 ropani) of bari land, while two households (Danuwar and Chhetri) own no land. Note that the 1964 Lands Act imposed a ceiling on agricultural land holdings of 4.07 ha (80 ropani) in the Middle Mountains (Regmi 1976).  Table 6.3. Land ownership by caste / ethnic group. Caste  : Sample  %  Khet / Household (ha)  Bari / Household (ha)  Owning Brahmin  j  Newar Tamang Danuwar Chhetri  i I  min. :  % Owning  max.  Khet  median  min.  max.  Bari  I  1.12  85  0.81  0.10  2.54  100  o 1  0.76  77  0.61  0.25  1.07  100  0.81  71  0.41  0.15  2.14  100  0  i j  0.20  67  0.20  0  1.02  89  0  !  0  0  0.46  0  0.71  90  0.66  2.14  100 >>4  No.  median  46  0.25  13  0.25  7  0.31  0  9  0.05  5  0  0  Others  10  0.13  0 05  0.20  Median  85  0.20  0  L. 12  *~  100  1.42  76  0 71  dataset: 1994 Bela-Bhimsenthan household survey, n=85, male farmer responses  155  6.3.2 Changing Role of Livestock Livestock, via the production of manure, are a major contributor to traditional soil management practices in the Middle Mountains. The quantity of compost is a critical issue given the increased nutrient demand of higher yielding varieties ofriceand wheat, and cash crop production (Carson 1992). Changes in the livestock holdings of surveyed farmers in the Baluwa region (n=27) from 1989 to 1996 are provided in Figure 6.6. The number of calves for both cattle and buffalo, and the number of chickens decreased from 1989 to 1996, while the number of female cattle and buffalo increased slightly. The increase in female buffalo per household is related to the establishment of a local dairy collection centre, promoting the sale of milk. The TLU (tropical livestock units) remained similar, however, indicating limited change in the potential nutrient supplyfromorganic sources.  100  01989 • 1996 E 10  -  01  Figure 6.6 Livestock holding dynamics (dataset: 1989 and 1996 Baluwa household surveys, n=27, female farmer responses).  156  The free grazing of animals, and the resultant removal of vegetation and trampling of the soil surface impact soil infiltrability and erosion, and heavy grazing is responsible for much of the environmental degradation on government lands (Carson 1992, Riley 1991). The proportion of stall-fed versus grazed animals during the wet and dry seasons reported by the female farmers surveyed in 1989 and 1996 are reported in Figure 6.7. No change is noted between dry season feeding in 1989 and 1996, but stall-feeding during the wet season increased significantlyfrom63% in 1989 to 85% in 1996. Female farmers were asked: "Compared to five years ago, has the availability of grazing areas for your animals changed?" For the 1991-1996 period, 79% responded that there were significantly fewer grazing areas available in 1996. Traditionally the forests supplied 40-60% of the total fodder (Uprety 1986, Gurung 1987, Dhungel 1987), but pressure on the forest ecosystem from increasing human and livestock populations, and agricultural encroachment has lead to reduced fodder availability, and a recent decrease in livestock holdings.  8 5  20% - ' 0%  11  U  1  1989 1996 Dry Season  L—I 1989 1996 Wet Season  Figure 6.7. Stall-feeding and grazing dynamics (dataset: 1989 and 1996 Baluwa household surveys, n=27, female farmer responses). 6.3.3 Women and Soil Fertility Management Women are central in soil fertility management due to their traditional role within the farming system. Their responsibility for livestock husbandry, manure and litter collection, and compost application directly impact soil fertility. Changes within the farming system, however, impact the responsibility and labour requirements imposed on women. The allocation of labour by task is summarised in Table 6.4 for  157  Table 6.4. Dynamics of household allocation of labour by task (n=26, shaded tasks show significant differences based on Fisher exact probability cc>0.05). Task  Change in Labour  Labour Allocation (# of households) 1996  1989 M  F  M  S  F  1989 to 1996 f M  s  |M  IF  tF  Land Preparation ploughing  11  0  5  14  1  2  5  2  1  0  terrace repair  16  0  1  14  1  2  3  5  1  0  irrigation  18  1  6  20  1  2  6  4  1  1  gather litter / manure  0  18  1  0  26  0  0  0  8  0  composting  0  21  5  17  3  1  17  0  1  19  apply compost  0  12  13  2  15  2  2  0  8  5  apply fertilizer  5  6  14  15  6  3  12  2  5  5  what to plant  21  3  2  23  1  2  4  2  1  3  nursery  18  1  5  22  0  2  7  3  0  1  transplanting  2  4  17  2  0  22  20  0  0  4  seeding  2  0  20  1  2  23  0  1  2  0  cutting  0  1  22  2  9  7  2  0  9  1  threshing  1  0  21  11  4  2  0  0  4  0  gather fodder  2  9  3  0  25  1  0  2  16  0  grazing  0  4  6  3  12  3  3  0  8  0  0  20  5  1  22  3  1  0  4  2  0  20  3  1  23  1  1  0  5  2  Fertilising  Planting  Harvesting  Livestock  stall feeding make dhana  1  watering  0  22  2  2  24  0  2  0  2  0  milking  3  7  6  6  18  2  3  1  11  0  Household Care collect fuelwood  0  17  6  0  22  4  0  0  8  3  fetch water  0  22  3  1  23  2  1  0  4  3  household money  12  5  8  14  12  0  5  3  9  2  what to buy  20  4  2  15  10  1  5  16  10  4  what to sell  20  4  2  20  3  3  4  4  3  4  farm labourers  17  4  4  16  6  3  7  8  5  3  purchase seed  22  1  3  22  0  1  4  4  0  1  purchase fertilizer  20  1  4  22  0  1  6  4  0  1  purchase livestock  20  1  0  24  0  0  6  2  0  1  Farm Management  dataset: 1989 and 1996 Baluwa household surveys, n=27, female responses 'dhana = cooked feed made from maize (wheat)flourand grains, water and salt for buffalo (cattle) M = male; F = female; S = shared responsibility  158  dominantly adult male, adult female or shared responsibilities based on the Baluwa household surveys (n=27)from1989 and 1996. Change in the responsibility of male and female adults is also listed. Adult male and female family members (over 16 years of age) are presented as they hold the main responsibility for most tasks either independently or shared, while children and hired labour are typically minor contributors. Hired labour is largely utilized for 'heavier' work such as ploughing and terrace repair, and children are active in supervising livestock grazing. Tasks where there has been a significant change in labour allocation over time are highlighted (Table 6.4 based on Fisher exact probability test).  Land preparation, planting, household care and farm management have remained relatively stable with respect to labour allocation over time with the exception of keeping household money and purchasing livestock. Fertilising related activities have been the most dynamic. Female responsibilities for gathering forest litter and manure, and applying compost and organic fertilizer have increased significantly from previously shared responsibilities. Adult males are more active in composting and the application of fertilizer. Tasks related to livestock have also been dynamic with significant increases in female work loads due to an increase in the number of households raising female buffalo. Harvesting activities have also shiftedfromdominantly shared tasks to greater individual responsibility.  Though the same questions were asked to the same individuals, the shiftfromshared to greater individual responsibility may reflect a difference in the interpretation of shared versus dominantly male or female activities. Even considering possible differences in interpretation between 1989 and 1996, the allocation of labour appears to have shifted toward greater responsibility for adult females. In addition to their usual household duties, females are taking greater responsibility for gathering organic fertilizing material and livestock care. The daily feed requirement of improved breed buffalo is 84 kg or 2 head loads of fodder per day, and an extra 2-3 head loads (110 kg) of fuelwood per week are required for preparing the dhana (cooked feed). Scarcity of fodder and fuelwood has meant that women and girls travel great distances, and spend more time foraging for household and livestock needs. More girls are dropping out of school to  159  assume livestock responsibilities, and households with few daughters seek early marriage for their sons to gain an additional labour source. While women are the primary caretakers of livestock, few have access to or control over earnings from milk production or livestock sale. Women in households which sell milk typically are not aware of the rupees earned per litre of milk, monthly income or annual incomefrommilk sales. Nor do they receive any direct rewards for added milking chores. Womenfrequentlyresponded that "I received two sarees a year prior to raising buffalo. I receive two now" (Acharya 1982, Bhatt et al. 1994).  6.3.4 Cultural Factors. Nutrient Management and Soil Fertility Caste and Ethnic Affiliation A household's access to capital and other resources is influenced by caste and ethnic affiliation, and thus compost, fertilizer and pesticide use may vary between groups. Differences in inputs used by high, medium and low caste groups are presented in Table 6.5. On khet lands, high caste households apply more fertilizer while low caste households apply more compost suggesting affordability may limit chemical fertilizer use by low caste households. On barifields,high caste households apply more total fertilizer and compost, but no significant differences are found on a kg ha" basis. Lower caste households own 1  significantly more livestock on a TLU ha" basis and distribute compost differently than high caste 1  households. Low caste households concentrate their manure inputs on khetfieldswhile high caste households apply more compost to barifields.Differences in soil fertility are also noted between fields owned by high and low caste groups, with high caste households owningfieldswith better soil fertility conditions, but differences may reflect the sampling design. Sampledfieldswere selected based on soil fertility conditions and more khetfieldsowned by high caste households were sampled. Recognising the complexity of the Nepali class structure, caste and ethnic affiliation appear to influence nutrient management and potentially soil fertility conditions.  160  Table 6.5. Relationships between caste/ethnic affiliation, land management and soil fertility (median values and significant differences based on Mann Whitney U test). Caste high  j  Significant Differences  medium  high vs. medium  low  high vs. low  khet compost  1313  (kg) (kg ha )  fertilizer  (kg) (kg ha )  j 2j  522  1  pesticide  i  2725 j  1  ($Cdn)  o i  1124  o  0!  4786  15 i  50  128 j  284  •  o ;  O  0  bari compost  7500 i  5160  2500  8346 |  7097 =  7259  (kg) (kg ha )  274 |  220  155  320  j  236 |  359  TLU  3.7  j  3.8 |  4.1  (kg) (kg ha ) 1  fertilizer  1  o  i  • • • • O  livestock 5.7  TLU ha"  3.6 |  (%)  60  57  j j  3.2  4.9  4.8 :  4.6  1  soil fertility base saturation exch. Ca pH  3.0  1  45  • •• •  high caste = Brahmins (n=46); medium caste = Chhetri, Newar, Jogi & Magar (n=20); low caste = Tamang, Danuwar, Kami & Sarki (n=19) #  Significant differences between groups a<0.05  O  Significant differences between groups a O . 10  Livestock and Traditional Soil Management Livestock type and holdings influence compost application to a household's farm land. Manure lost through grazing is decreasing as more households are stall feeding their livestock (Table 6.7), and the selling of manure is rare. The relative manure production potential of different types of livestock is represented by tropical livestock units (TLU). Compost applied to bari land as a function of TLUs displays a weak positive correlation, but only 10% of households own more than 10 TLU ha". 1  Women and Soil Fertility Management Females are taking greater responsibility for the gathering of organic fertilizing material and livestock care. Time use studies indicate that deforestation may increase the workload of women in forest product  161 collection by 1-1.5 hours per day, and dairy development adds an average of three extra hours of work per day. The farming systems are extremely labour intensive with women accounting for 60% of the labour requirements of fanning in addition to their household responsibilities. The already heavy workload of women farmers is increased with the addition of market oriented vegetable and milk production, and diminishes their capacity to be effective resource managers (Gurung 1995a, Acharya 1982, Pfanner 1987, HMGN 1993).  6.4 Poverty Rural poverty in Nepal is associated with a number of factors mcluding: population growth, minimal land holdings, poor land productivity, limited marketing infrastructure, limited alternative employment opportunities, poor educational attainment and a socio-economic structure (caste system) which favours class division. Changes in factors such as land holdings, the type of crops grown, yields, input costs, livestock holdings and soil fertility will impact a household's ability to fulfil their subsistence requirements over time. To assess the temporal dimension of household sufficiency from farming, responses from male farmers in Baluwa surveyed in 1989 and 1996 (n=27) are compared (note this is a different dataset than presented in section 6.2.3). In response to the question: "Does the land that you farm generate enough food and income to meet your family's basic needs?" 74% of the male farmers interviewed in Baluwa in 1989 responded "yes" and in 1996 there was little change (78%). Still, one quarter of the households reported that they were not able to fulfil their basic needsfromfarming, and most were not able to improve their situation over the six year time span (1989-1996).  The impact of poverty on nutrient inputs, nutrient budgets and soil fertility are evaluated using relationships with indicators of economic well-being. Agricultural assets (land and livestock), farm gross marginsfromcrop production (total returns less variable costs), cash income derivedfromthe sale of crops and milk, and off-farm employment are utilized. Relationships between each indicator, nutrient  162 inputs, nutrient budgets and soil fertility are evaluated using correlation analysis, and the implications for soil fertility are discussed.  6.4.1 Agricultural Assets The main agricultural assets of farmers in the region are land and livestock. Farmers own virtually no machinery and only basic implements such as a hoe, wooden plough or sprayer (Kennedy and Dunlop 1989). The dollar value of land and livestock owned by a household provides one indicator of poverty. To compute agricultural asset values for each household, average local prices for land and livestock were used.  Land Local land values differentiate land on the basis of quality, with poor bari land valued as low as $4,700 Cdn ha" and good quality khet land valued at up to $56,775 ha" (Shah 1996). Average prices for bari 1  1  ($9,462 ha") and khet ($28,386 ha") thus provide a weighting mechanism to sum total land holdings in a 1  1  manner which reflects not only value but production potential. Land values are summarised in Table 6.6. In general, the land values should be considered as rough estimates. The median land holding is valued at $5,776 for khet and $6,734 for bari per household. While khet holdings account for less than 25% of the land owned on an area basis, their value makes up nearly 50% of total land values.  Table 6.6 Total land values per household in the study area. Bari  Khet ha min.  1 $ Cdn 0  j  0  median  0.20 ;  5.776  max.  1.12 !  31,768  ha  | 0  Total  $ Cdn  ha  j  0  2.54 !  n.-'U 24,050  o.-i  $ Cdn 0 ;  0  0.92  12.5H8  3.36 j  52,932  dataset: 1994 Bela Bhimsenthan household survey, n=85, male farmer responses  163  Livestock Values for the various types of livestock are based on key informant interviews conducted under the Jhikhu Khola Watershed project in 1996. Livestock will vary between households in age, size, quality etc., so these values should be considered rough estimates. The values assumed for each category of livestock are given in Table 6.7 along with the median, minimum and maximum values for livestock owned per sampled household. The median value of livestock is $870 per household, 50% of which is accounted for by buffalo cows.  Table 6.7. Livestock values and assets. Type of Livestock  Local Value Rupees  Household Assets ($ Cdn)  $Cdn.  min.  | median j 0j  max.  Cattle - Bull  2,000 - 4,000 1  49-96  Cattle -Cow  4,000 - 5,000  |  96 - 120  0!  108 j  432  Cattle - Calf  1,500  ;  36  0j  0  216  Buffalo - Bull  3500  84  0  j 0j  Buffalo - Con  16,000-2H.(ii)0  433  o;  Buffalo - Calf  12.000  289  o i  0i  2312  29- 156  0j  242 j  1029  1.200-6.500  Goat - female  600 - 1,800  14-43  0 ]  j  Pig  500 - 3,000  12-72  oj o 1 o| oj  0 |  Duck Total  125 - 300 200  j i  3-7 5  168 12'W  Goat - male  Chicken  |  j  290  j i 870 j  42  10  300  0  20 3036  dataset: 1994 Bela-Bhimsenthan household survey, n=85, female farmer responses  6.4.2 Farm Gross Margins The relative profitability of agricultural production between farms provides a mechanism to compare the economic status of farming households with diversified cropping systems. An indication of the profitability of each farm can be obtained by computing gross margins, defined as total returns less total variable costs. Total returns are equal to the value of all crops produced (including crop residues), irrespective of whether the crop is sold. Total variable costs include the purchase of seed, fertilizer and pesticides, hiring oxen and all labour involved in cultivation activities. Labour includes the time spent in planting, irrigation,  164  fertilizing, spraying, weeding, harvesting, transportation and selling, and includes the opportunity cost of family labour. The gross margin can thus be viewed as the return to fixed costs (land and livestock) and management.  Gross margins for the 85 surveyed households in Bela-Bhimsenthan were calculated from production information provided by the male farmers. For each crop grown on khet and bari land, farmers indicated the amount of land farmed (ha), crop production (kg), seed use (kg), fertilizer (kg), pesticide (g or ml), bull oxen use (days) and total labour (days). Occasionally farmers responded 'don't know' to a particular question (e.g. seed rate for wheat). Any missing data were estimated from site specific information provided by the same farmer in the 200fieldsoil survey when the same crop was reported, or an average value was used if necessary. Selling price and costs of inputs for 1996 were then used to calculate returns and variable costs for each crop. For crops grown under a share-cropping arrangement, 50% of the total returns and variable costs were accrued to both the tenant and landlord. Total gross margins from cultivation activities for each household were obtained by summing returns minus variable costs for all crops. Production returns, variable costs and gross margins for individual crops and totals for each household are summarised in Figures 6.8 and 6.9.  Total Returns Calculation of the total returnsfromcrop production are summarised in Table 6.8. For each crop the median area cultivated is listed. Crop production (kg) represents grain or vegetable components and does not include crop residues. Estimates of crop residues were obtained by multiplying the grain or vegetable production by the ratio of residues to crop. A ratio of 1.25 was used for rice, and 1.22 for maize and wheat (Grist 1986, Olson and Kurtz 1982, Cox et al. 1985, LRMP 1986a, Aldrich et al. 1975, Stoskopt 1985). Selling price is the market value of the crops and does not include the value of crop residues. The value of crop residues is included separately as it represents the opportunity cost of residues for animal fodder or soil amendment. Residues are valued at roughly 5-10 rupees per doko (basket) or $0,007 kg". 1  165  a) Returns by crop  8,590  4000  0  1  J  early rice  1  rice (khet)  —  1  early maize  1  maize (bari)  —  1  wheat (khet)  ~  1  wheat (bari)  b) Variable Costs by crop.  11,559  1  potato (khet)  1  potato (bari)  , 7,332  h tomato tomato (khet) (bari)  4,167  3000 — 2500  • - median  •s o u  1500  -  + -r  min. max.  - 1 000 .2 *c ra > 500 0  J  1  early rice  1  rice (khet)  1  1  early maize  maize (bari)  1  wheat (khet)  c) Gross margins by crop  -  1  1  wheat (bari)  potato (khet)  7,549  4000  h  1  potato (bari)  tomato (khet)  tomato (bari)  10,477 , 5,956  • - median  -  early rice  rice (khet)  early maize  maize (bari)  wheat (khet)  wheat (bari)  potato (khet)  Figure 6.8. Returns, variable costs and gross margins by crop.  potato (bari)  tomato (khet)  tomato (bari)  min. max.  166  a) Total returns from khet land.  2500  2500  5  • staple crops only  c) Variable costs on khet land.  1000  I  ;  1  10  10 15 20 Land Fanned (ropani)  • with cash crops  1200  b) Total returns from bari land.  20 30 40 Land Farmed (ropani)  • with cash crops  50  • staple crops only  d) Variable costs on bari land. 1200  ;  \  •o  0 Land Fanned (ropani) • with cash crops  • staple crops only  e) Farm gross margins for khet land.  5  10 15 20 Land Farmed (ropani)  4> with cash crops  • staple crops only  10  20 30 40 Land Farmed (ropani)  • with cash crops  • staple crops only  f) Farm gross margins for bari land.  20 30 40 Land Farmed (ropani) # with cash crops  • staple crops only  Figure 6.9. Total returns, variable costs and household gross margins on khet and bari land.  50  167 Total returns on a per ha basis are greatest for tomatoes and potatoes grown on both khet and bari fields (Figure 6.8a). Median returns for tomatoes, potatoes and wheat on khet are higher than returnsfromthe same crops grown on bari, indicative of the greater production potential of khet lands. The variability in total returns per ha is high between households, and may be related to farmer knowledge, marketing skills, soil quality or data irregularities. Returns to the farming householdfroma particular crop are dependent on the returns per ha and the area under cultivation (Table 6.8). For khet land, tomatoes and potatoes have the highest total returns, butricegrown during the monsoon is also an important crop as a relatively large amount of land is underricecultivation. For bari land, tomatoes and maize make up the greatest proportion of total returns reflecting the high returns per ha of tomatoes and the large area under maize cultivation. Total returnsfromall crops separated by households growing only staple crops and those which incorporate some vegetable production are shown in Figures 6.9a and 6.9b. For both khet and bari land, bestfitregression lines illustrate the significance of cash crops to total returns ($ per household from all crops) with households producing some vegetables displaying greater total returns.  Variable Costs The break down of variable costs for seed, chemical fertilizer, pesticide, oxen and labour expenditures are listed in Table 6.9. Typical rates and prices are given in Tables 9-12 of Appendix B. The total variable costs are dominated by labour and oxen costs, and represent the opportunity costs of alternative activities. Labour costs are greatest for tomatoes and potatoes on a per ha basis, but labour inputs toriceand maize are significant on a total cost basis ($ per household). The purchase of chemical fertilizers contribute significantly to the variable costs ofriceand potatoes on khet, and maize on bari sites. Pesticides are generally a small expenditure with the exception of households growing tomatoes on khet, but application rates are highly variable between sampled households. In addition to the costs listed above, 20% of the farmers apply micronutrients to their tomato and potato crops.  168 Table 6.8 Total production returns (median values) for the major khet and bari crops. Crop  n  Area  Production  Price  (ha)  (kg)  (Skg )  1  Returns  2  total $  1  j  $ha"'  Khet early rice  12  0.11  360  0.20  23  0.10  325  0.16  76 58 j  j  639  early maize  69  0 25  964 f  0.19  175 |  708  w heal  47  oil's  280  0.12  37 |  197  tomato  10  0  0.52  322  3165  [lOldtll  iff  0 ov  4"  041  lvt>  2251  mustard  17  0.05  37  0.36  72  0.25  rice  mon.soon  Total  10  mai/c  84  '  M heal  40  0.31  tomato  15  DDS  : :  0.71  mustard Total  197  j  1148  16  289 |  111  0 12  52 !  118  0  385  2"^2  10  239  40  0.31  124  85  0.71  0  potato  13 j 287  Bari  531  1533 0.36  187 429 1  604  1  production of grain/vegetables only, does not include kg of crop residues  2  selling price of grain/vegetables, does not include value of crop residues  Table 6.9. Variable costs (median values) for seed, chemical fertilizer, pesticide, oxen and labour. Crop  n  Seed  Fertilizer  Pesticides  Oxen  Labour  ($)  ($)  ($)  ($)  ($)  Variable Costs total$ !  $ha"'  Khet  j  early rice  12  3  19  0  15  24  early maize  23  2  8  0  14  20  67 58 |  228  monsoon rice  69  5  30  2  36  72  155 j  609  wheat  47  6  15  0  29  24  tomato  10  5  8  322  22  66  potato  16  20  27  5  18  mustard  17  <1  5  0  7  Total  j  585  413  80 111 •  low  31  109 .  1140  8  27 i  472  263 j  993  72  Bari maize  84  8  wheat  40  tomato  15  potato mustard Total  j  60  0  43  96  6  5  0  36  23  5  8  9  7  9  19  15  4  14  40  1  10  0  29  85  246  I  j  373 215  36  74 61 |  IOXI  30  84 j  826  20  61 !  177  320 j  449  169  Total variable costs for the dominant crops grown on khet and bari land are shown in Figure 6.8b. Costs are greatest for tomatoes and potatoes, and somewhat higher on khet fields. The distribution of variable costs per ha is skewed, and households reporting the highest costs do not always report the greatest returns. Variable costs separated by households growing only staple crops and those which incorporate some vegetable production are shown in Figures 6.8c and 6.8d. Variable costs diminish with the amount of land farmed, suggesting that economies of size exist. Cubic functions which reflect these dimishing costs are fit to data for households growing only staple crops, and illustrate the decreasing costs with larger farm size, and the higher variable costs on khet fields.  Household Gross Margins Gross margins for the main crops grown in the study region are shown in Figure 6.8c. Tomatoes and potatoes are the most profitable, on both khet and bari land, although differences between households are highly variable. Median gross margins forriceand maize are low, and gross margins for wheat are slightly negative. Gross margins for maize, potatoes and tomatoes, and the relative profitability between crops are similar to estimates by Kennedy and Dunlop (1989) and Srivastava (1995). One exception is potatoes where Srivastava found a negative gross margin related to a dramatic increase in the price of chemical fertilizer. Since 1993, the selling price of potatoes has quadrupled, resulting in an increase in their profitability.  Total gross margins for a household are determined by summing total returns less variables costs for all crops grown on all the land farmed by a household. Farm gross margins, based on all crops grown by a household, rangefrom-$566 to $1736 dollars per annum (Figure 6.10). Twenty-eight percent of the households surveyed are very poor having gross margins <0 and 47% have gross margins below $100 per year. Negative gross margins imply households are not earning their opportunity costs of labour on their own farms and could earn more by working off-farm. Thirty-seven percent of households fall within the $100-$400 per annum range, and 16% have gross margins above $500 per year. Farm gross margins  170  -1000  >  0  i  i  10  i  i  20  i  i  30  -i  i  40 50 Sample number  •  60  •  •  70  i  ;  1  80  Figure 6.10. Annual gross marginsfromagricultural production (dataset: 1994 Bela-Bhimsenthan household survey, n=85, male farmer responses). separated by households growing only staple crops and those which incorporate some vegetable production are show in Figures 6.9e and 6.9f. The highest gross margins are noted for households growing cash crops as part of their rotation and households with greater land holdings. However, households with negative gross margins include both vegetable growers and large landowners reflecting crop failures and poor management.  6.4.3 Cash Income A lack of capital is a important constraint to agricultural production in Nepal. Two sources of cash income available to farmers in the study region are the sale of agricultural products and off-farm employment.  Agricultural Products Involvement in market oriented production is one way a household can generate income for the purchase of chemical fertilizer, and reduce the reliance on adjacent grassland and forest resources for the maintenance of soil fertility. The amount and type of crops sold and purchased by fanning households is indicative of  171  their level of involvement in the market (Carson 1992, Seddon 1987, Panday 1992). Farmer's were asked: "Do you sell any of your crops?" and "Do you buy additional food for your family members?"  Farmers sell a variety of crops (Table 6.10) including traditional staples (cereal crops) and non-traditional cash crops (vegetables). Only a small minority of farmers systematically produce for the market, but the majority of farmers derive some incomefromthe sale of agricultural produce. Maize is sold by 38% of the households, followed by rice (26%) and wheat (14%). The majority of producers sell <50% of the crop, suggesting that sales are surplus production. Only 6% of the surveyed households sell >50% of their total crop production (on a weight basis) and just 14% sell >25% of their total production. Tomatoes and potatoes are the main cash crops, with the largest amount sold on both a weight and revenue basis.  Table 6.10. Crops sold by households. Crop Sold  % Households j % Producers  Gross  Amount Sold (kg yr' ) 1  Revenue Selling Crops  Selling >50%  j  Maize Rice  38 26 j  Wheat  14 j  Tomato*  ^ ^ ^ ^ ^ ^ ^  i  0 |  13  0 0 j  0  0 |  0!  28  50  I  Potato  8  Mustard  4j  Onion  1  j  100  1 45 |  j  100  Garlic Total  median j  min.  43 67  j  i  iBBl o  0  480 190 | j  - 1  -i  0i  i 96 j 52  max.  (1996 $CDN)  2,100  0-602  1,920  0-337  490 42.(11)0  4.275 93  0-43 II-C. IS 0-425 0-32 24  -  48 0-6,941  dataset: 1994 Bela-Bhimsenthan household survey, n=85 , male farmer responses * 1 large tomato producer  Farmers in the study area have been hesitant to produce primarily for the market. Current transportation and marketing systems are rudimentary. The high costs of inputs such as fertilizer, pesticides and seed restrict opportunities for farmers with limited access to capital. Retail pricesfluctuateboth seasonally and from year to year. Vegetable yields are often erratic and small farmers are hesitant to plant a large proportion of their land in cash crops. Labour requirements may be increased dramatically; for example  172  tomato production requires 2-3 times more labour than rice (Srivastava 1995, Kennedy and Dunlop 1989, Villareal 1980). From 1989 to 1996, farmers surveyed in Baluwa (n=27) report selling more crops, but when corrected for inflation, gross revenues decreased 6%. In 1989, farmers report a median gross revenue of $119 (corrected for inflation) compared to $112 in 1996. Over the past 10 years inflation has averaged 11% per annum (World Bank 1996) resulting in a reduction in the purchasing power of income derived from the sale of crops by local farmers.  Households purchase a range of food products to supplement or complement the crops they grow. Table 6.11 lists the main crop purchases by households within the sample. Fifty-three percent of households report buying additional food. Rice is the crop purchased most often (33% of households) followed by potatoes and maize. The largest amounts purchased are rice and maize on a weight basis, and rice and tomatoes on an expenditure basis. Thirty-two percent of the farmers surveyed purchase but do not sell any crops, indicating their need to supplement subsistence food production.  Table 6.11. Food products purchased by households. Food Product Bought  % Households  Amount Bought j  Expenditure  (kg year" )  (1995 $CDN)  1  Rice  33  0-700  0-282  Potato  26  0-380  0-183  Maize  15  0-560  0-87  Wheat  8  0-375  0-36  Mustard  6  0-124  0-39  Total  53  0-460  dataset: 1994 Bela-Bhimsenthan household survey, n=85 , male farmer responses Milk Commercial milk production provides an alternative source of income for local farmers. Forty-five percent of the households surveyed in the Bela-Bhimsenthan study (n=85) sell buffalo milk. Median milk production per cow buffalo is 5.0 litres per day in the monsoon and 4.0 litres in the dry season, of which 60% is sold. The median gross revenuefrommilk sales is $43 per year, but rangesfrom0-182 $ per year.  173  Forty percent of these households have been selling milk for less than 5 years, and 20% are currently selling more milk than 5 years ago. The first commercial dairy operation in the region was established in the Jhikhu Khola watershed in 1994, and milk production will likely continue to expand.  Off-Farm Employment Off-farm activities are an important source of family income. Many households have at least one male member who is employed outside the community at least on a seasonal basis. Off-farm employment in the Bela-Bhimsenthan sample for 1994 is summarized in Table 6.12. Income denotes both cash and other forms of payment (e.g. meals) which have been converted to $Cdn. Sixty percent of households were involved in off-farm activities and grossed a median of $439 Cdn. per year. The highest reported off-farm income was a priest earning $1,160 per year. Small businesses and brick making provide a median income of approximately $549 per person per year, and farm labour $220 per year. Forty-one percent of husbands spend time off-farm and the dominant activities are brick making / masonry (17%) and carpentry (14%). Wives are less involved in off-farm activities (12%), and the main activities are shop / business and household labour. Sons and daughters spend a large portion of their time studying, but brick making / masonry activities are also noted. The daughter-in-laws and brothers of the household head which are involved in off-farm activities all work in brick making. Increased brick making activities in the last five to ten years reflect the increased demand for construction materials in Kathmandu. There were only 35 brick making units in 1981/82 in the Kathmandu Valley, but in 1991/92 there were 142 (Mishra 1995). In addition to money earned by the immediate household, family members living away often send money home. Forty-two percent of the surveyed households receive moneyfromtheir extended family, and typically receive $36 per year.  6.4.4 Economic Weil-Being. Nutrient Inputs and Soil Fertility As soil fertility impacts crop productivity, households farming more productive land will be more likely to meet their families' basic needs through farming. Alternatively, soil fertility is impacted by management,  174  Table 6.12. Off-farm activities. Household  n  Position Husband  % Involved  Dominant  Time per  Median Income  Off-farm  Activity  Activity (%)  (SCdnyr" )  85  17  3X7  14  127  Shop / Business  9  435  Farm Labour  6  179  Shop / Business  36  303  Farm Labour  18  220  41 Carpenter  Wife(s)  91  12  1  176  Brick ' M.mon Sons  98  112  Studying  78  Brick / M.isnn Daughters  83  78  Daughter-in-law  23  100  Bikk Making  9  78  Biu.k \1:ikini!  Brothers  Studying  n/a . . .  100  "356 n  .i  330 ion  351  Others  26  38  Whole Family  51  60  439  Money Sent Home  36  42  36  dataset: 1994 Bela-Bhimsenthan household survey, n=85, combined male and female farmer responses  which may be constrained in poor households. Relationships between economic indicators and management are assessed using Pearson correlation. Agricultural assets (land and livestock), household gross margins (total returns and variable costs) and cash income (vegetable crops, milk production and off-farm employment) are evaluated relative to nutrient inputs and soil fertility. The results shown in Figure 6.11 indicate that households with higher agricultural asset values and gross returns apply greater amounts of compost and fertilizer to both khet and bari land. Combined agricultural assets (land and livestock) are positively correlated with total compost and fertilizer applied (kg) to both khet and bari fields, but relationships on a kg ha" basis are not significant. Compost applications per ha on bari land are 1  typically greatest for farms with low and moderate agricultural assets and least for high asset farms. Fertilizer use per ha on khet land increases with agricultural assets up to roughly $25,000 but inputs are low on farms with high asset values. Both trends suggest a limited availability of inputs on large farms (high agricultural assets) and diminishing economies of size.  NUTRIENT INPUTS  ECONOMIC INDICATORS  SOIL FERTILITY  compost applied to khet (kg) compost applied to bari (kg)  fertilizer applied to khet (kg) fertilizer applied to bari (kg)  compost applied to khet (kg)  0.49  fertilizer applied to khet (kg)  compost applied to bari (kg)  0.32  CEC  0.24  %C  0.55  fertilizer applied to bari (kg)  fertilizer applied to khet (kg)  0.30  0.25 returns to milk production ($)  pesticide expenditure (kg)  0.33  0.35  base saturation exch. Ca PH  Figure 6.11. Correlations between economic indicators, nutrient inputs and soil fertility (a<0.05; — indicates indirect relationships).  176 Total returns ($) to khet and bari land for all crops are also positively correlated with total compost and fertilizer use (kg). Total returns increase with the amount of land farmed (Figure 6.9a) and nutrient inputs tend to be lowest on small farms (Figure 6.4). Relationships on a kg ha' basis are highly variable with the 1  highest inputs applied to a few farms which have both high and low total returns. Relationships with gross margins are weak and reflect the relationships with total returns; gross margins are weakly correlated with total compost applied to bari and total chemical fertilizer use on khet. Variable costs incorporate agrochemical use, but labour is a large component of the costs for the main crops grown. Consequently relationships between gross margins (total returns - variable costs) and inputs are weaker than correlations with total returns.  Cash income derived from the sale of milk is weakly correlated with total pesticide and chemical fertilizer use. While the average gross revenue from milk sales is low ($43 per year) these relationships suggest that the purchase of agrochemicals may be limited by cash availability.  Relationships with soil fertility are weak but households with higher returns to khet land and milk production appear to farm fields with better soil fertility conditions. Sites with better soil fertility should produce higher yields and consequently greater total returns, while households with greater cash income are more likely to be able to afford better quality land or to maintain soil fertility. Relationships between nutrient budgets (N, P 0 and Ca) and indices of economic well-being show no discernible trends. While 2  5  agricultural asset values, total returns and milk production are related to nutrient inputs, the nutrient budgets are strongly influenced by yield (crop nutrient uptake).  No relationships are noted with off-farm activities, but male out-migration in pursuit of wage employment leaves women with greater responsibility for decision making and carrying out major farm activities. Increasing access to education mainly to males also affects the workload of women as tasks previously carried out by older children now have to be absorbed by women. While off-farm employment provides  177 capital resources, there is less labour available for livestock tending, compost and manure carrying, the collection of fodder and litter, and other on-farm activities which influence soil fertility (Gurung 1995a, Carson 1992, Vaidya et al. 1995, Biot et al. 1995).  6.5 Summary and Implications of Socio-Economic Factors for Soil Fertility  Socio-economic factors, farm management and soil fertility are interrelated within the study region. Relationships are both direct and indirect as displayed in Figure 6.12. Open circles represent the contextual framework, quantified relationships are shaded, direct linkages are shown by solid arrows and the dashed arrow signifies indirect relationships. Socio-economic factors directly impact farm management, which in turn impacts soil fertility. Population growth, land tenure, poverty and culture are the underlying socio-economic factors influencing farming system dynamics. Population growth rates of 2.6% have lowered available land per capita, contributed to agricultural intensification and marginalisation, and placed additional pressure on forest resources. Land distribution is highly skewed, with 15% of the surveyed households owning 46% of the khet land. Share cropping is practised by roughly one-third of households. Greater than one-half of households (55%) are not able to meet their basic need requirementsfromthe land they farm. Caste affiliation and ethnic distribution reflect the class structure and access to resources, the role of livestock is shifting towards commercial production, and the workload of women is increasing. Agricultural assets (land and livestock), farm gross margins (total returns less variable costs), market oriented production, commercial milk production and off-farm employment provide indices of household well-being, and reflect differences in production constraints faced by poor households. Strong relationships are noted between economic indicators and nutrient inputs; land holdings, gross returns and milk production are positively correlated to compost and fertilizer use. Significantly greater inputs are applied to owned versus shared cropped land, and high caste groups (Brahmins) apply more fertilizer and pesticides, and typically own better quality land.  - land holdings - land tenure - gross returns - milk production - ethnic distribution  -  land use compost applied fertilizer applied crop production nutrient budgets  -CEC -pH - available P - exchangeable Ca - base saturation  Figure 6.12. Contextual framework and quantified relationships (shaded) linking social factors, nutrient dynamics and soil degradation.  oo  179  Farm management, particularly nutrient management, impacts crop productivity, nutrient budgets and soil fertility conditions. Soil acidification, organic matter depletion, nutrient depletion and erosion are degradation processes supported by plot studies and nutrient budget modelling. Crop yield under dryland and irrigated production is correlated with both nutrient inputs and soil fertility (Figure 5.11). Nutrient fluxes under irrigated cultivation appear to be sustainable, but are dominantly negative under dryland cultivation. Nutrient budgets for cultivatedfieldsare dependent on compost and fertilizer inputs, and crop yield, while budgets on forest, shrub and grasslands are related to biomass collection. Relationships between nutrient budgets and soil fertility are poor reflecting the low overall fertility conditions and high site specific variability, but correlations are noted between compost and fertilizer use, and soil fertility (Figures 5.4 and 5.7).  Indirect relationships between socio-economic factors and soil fertility provide verification of the more direct linkages. Households owning moderate amounts of land (1-2 ha) typically own a mix of bari and khet land, apply the most fertilizer and compost to theirfields,and display the best soil fertility conditions (Table 6.2). High caste groups own land with significantly better soil fertility conditions (Table 6.5), and households with higher returns to khet land and milk production appear to farmfieldswith higher soil fertility (Figure 6.11).  The socio-economic factors driving nutrient dynamics and consequently impacting soil fertility are not isolated, but interrelated and these factors may be influenced by soil fertility degradation. Population growth, access to land and cultural practices are closely tied to poverty. Poor families own smaller land holdings, typically own poorer quality land, and will be impacted most by soil degradation. Population growth is driving land use change and thus altering nutrient flows within and between land uses. To meet the demands of a growing population agriculture has been intensified and marginalized, and evidence of renewed deforestation is beginning to appear.  180 Access to land is a key factor driving nutrient management and influencing economic well-being. Land is the main agricultural asset of households in the study area, and khet land is more productive and provides greater opportunities for cash crop production than bari land. The poorest soil fertility conditions are found on common property lands (grassland, shrub and forest). Share cropped land is less intensively managed, receiving lower inputs of chemical fertilizer, organic matter and pesticides. Khet land receives the most nutrient inputs per ha, shows the best nutrient budgets and has the best overall soil fertility conditions.  Culture plays a subtle but important role by influencing the division of labour and access to resources. Women are central in soil fertility maintenance through their role in fertility management and livestock care, but time constraints may hamper rehabilitation efforts if the impact on women is not considered. Under the feudal land tenure system, land was granted to the local aristocracy and the resulting unequal distribution of land is evident today. While caste / ethnic distribution is not equivalent to class structure, differences are noted between Brahmins and other groups in the study area. Brahmins tend to use more agrochemicals, own more land and own land with better soil fertility.  The economic well-being of households in the study area is strongly tied to the quantity and quality of land owned, and reflects traditional versus market oriented agriculture (vegetable and milk production). Households with lower production returns, lower agricultural assets and lower cash income tend to apply less nutrients to their fields. Alternatively, households growing vegetable crops (which have higher gross margins) may still negatively impact soil fertility due to the high nutrient demands of these crops. Farm management and nutrient dynamics are influenced by a combination of socio-economic factors, but within this study area population growth and access to land are two key components indirectly impacting soil fertility conditions.  181  7. SUMMARY AND IMPLICATIONS Soil fertility, nutrient dynamics and socio-economic interactions in a Middle Mountain watershed have been studied using soil surveys, plot studies, nutrient budget modelling, household questionnaires and GIS mapping techniques. The study area located approximately 40 km east of Kathmandu is intensively utilized and experiencing population growth, agricultural intensification and shortages in forest products. A 200 site soil fertility survey of cultivated and grass lands was conducted in 1993/94 which isolated topography, soil type and land use. Forest soil fertility was examined through a series of plot studies initiated by Fiegl in 1989 and re-sampled in 1994. Phosphorus dynamics were assessed by evaluating P sorption in relation to extractable Fe and Al, and a nutrient budget model was developed for the dominant cropping systems to assess the impact of management practices on soil fertility. Nutrient cycling examined as part of the Jhikhu Khola Watershed study was used to assess rates of change in soil fertility induced by land use.  A short questionnaire summarizing the crops grown, yields and nutrient inputs was conducted for each of the 200fieldssampled. Detailed questionnaires were collectedfroma subset of 85 households to gather information about the household farming system. Paired male-female interviews were conducted simultaneously and separately to illicit open responses. Changes within the farming system were quantified by repeating 27 household surveys originally conducted by Kennedy and Dunlop in Baluwa in 1989, and key informant questionnaires were used as a cross check.  Geographic Information System (GIS) techniques were used for data compilation and integration, using a 1:5,000 scale topographic basemap. Elevation, slope and aspect themes were generated using terrain modelling. Soil types delineated on aerial photographs were transferred to digital format. A historical comparison of land use was compiled for 1972 and 1994 using land use maps and aerial photographs. Population dynamics were evaluated for 1972, 1990 and 1995 using house counts and family size data.  182 All soil sample, plot study and household survey locations were georeferenced and transferred to the GIS database for analysis.  7.1 Soil Fertility Status The overall soil fertility conditions of the study site are poor. Soil carbon to one standard deviation (0.99 + 0.47%) and pH (4.8 + 0.4) are particularly problematic, and available P (16.6 + 18.9 mg kg") is a 1  concern given the low pH values. Land use is the most important factor influencing soil fertility, followed by soil type. Khet sites show the best overall soil fertility (pH 5.2, Ca 5.3 cmol kg", available P 21.6 mg 1  kg"), followed by bari and grassland, while forest soil fertility is the poorest (pH 4.2, Ca 0.9 cmol kg", 1  1  available P 0.7 mg kg"). Red soils have a greater clay content and higher CEC, but lower available P than 1  non-red soils (9.8 versus 22.1 mg kg'). A site factor approach based on relationships between soil fertility 1  and site characteristics facilitated the extrapolation from point data to a spatial coverage and was useful in assessing the extent of soil fertility problems. The composite soil fertility map indicates that only 14% of the classified regions have adequate pH, available P and exchangeable Ca.  Phosphorus sorption studies indicate the high P fixation capacity of red soils. Sorption ranged from 2-4 g P2O5  per kg soil for the 16 red soil sites evaluated. Phosphate sorption calculated using Borggaard's model  which includes AAO extractable Fe and Al, and CBD extractable Fe showed good agreement (r=0.85) 2  with measured P sorption and was used to calculate P sorption under different land uses. The P sorption capacity on red soils is nearly one order of magnitude greater than calculated values for non-red soils, and forest sites sorbed significantly greater P than agricultural sites. A classified map of P sorption developed using a site factor approach indicates that 29% of the area has very high P fixation capacity (>1.5 g kg") 1  and 61% has a P fixation capacity >0.5 g kg". The high P fixation capacity of these soils has important 1  implications for phosphorus management as fertilizer P will quickly be converted to insoluble or complex forms.  183  7.2 Soil Fertility Dynamics The direction and rate of change of soil fertility impact both current and future biomass production. Plot studies and nutrient budget modelling indicate declining soil fertility on bari, forest, shrub and grasslands, and marginal conditions on khet land subject to intensive cultivation. Fertility characteristics of soils originating from the same parent material but subject to different land uses show that forest sites have the lowest N, available P, exchangeable bases and pH values resultingfromnutrient removal through biomass collection. Bari sites which receive the highest organic matter inputs have the highest C and N levels. Khet lands enriched by irrigation water and suspended sediments have the largest available P, Ca and Mg. Rates of soil fertility depletion estimatedfromdifferences in soil fertility between land uses indicate substantial N and Ca lossesfromforest land (94 and 57 kg ha" furrow slice respectively). Annual lossesfrombari 1  lands are small due to additional inputsfromorganic sources, suggesting that lossesfromagriculture are strongly influenced by management.  Nutrient budget modelling of N, P and Ca levels for the dominant crops and cropping patterns is used to estimate nutrient depletionfromthe soil pool and identify management practices contributing to soil fertility degradation. Practices related to maize production result in large deficits in N, P 0 and Ca (118, 2  5  38 and 32 kg ha" furrow slice respectively). Rice andrice-wheatcultivation on irrigated land appear to 1  have limited impact on the soil nutrient pool, but the addition of premonsoon maize in the rotation results in deficits of 106 kg N and 12 kg P 0 per ha furrow slice. The collection of forest biomass results in 2  5  annual nutrient losses of 56 kg N ha", 16 kg P 0 ha" and 34 kg Ca ha', and is comparable to nutrient 1  1  2  1  5  depletion determinedfromplot studies. Biomass removalfromgrasslands results in nutrient losses roughly estimated at 60 kg N ha", 20 kg P 0 ha" and 20 kg Ca ha", while soil erosion on degraded grass and 1  1  2  1  5  shrub lands results in comparable losses (34 kg N and 23 kg Ca per ha furrow slice).  184  7.3 Management Factors and Options Land use change and soil nutrient budget modelling are useful in assessing the impact of management factors on soil fertility and identifying management options which may reduce nutrient deficits. Key factors influencing soil fertility degradation include forest litter removal, agricultural marginalisation, erosion and agricultural intensification. Potential options to reduce nutrient deficits include improved organic matter management, water management, lime, integrated nutrient management, Azolla and onfarm fodder production.  7.3.1 Management Factors Litter Removal Forest soils show the lowest overall soil fertility and largest annual nutrient losses. The collection of fodder, litter and fuelwood results in significant nutrient removal (56 kg N, 7 kg P and 72 kg total bases ha") and disrupts the natural nutrient cycling. Historical forest cover data suggest a cyclical decline in 1  forest cover with at least two cycles of deforestation followed by efforts of rehabilitation. Biomass removal contributes to a low soil organic matter content and soil acidification through the removal of bases from forest land, and pine litter inputs to agricultural fields are likely acidifying cultivated land.  Marginalization The nutrient status on bari lands is generally poor and nutrient modelling indicates that N, P and Ca inputs are insufficient to maintain the soil nutrient pool under maize-wheat production. Bari soils are acidic with pH values rangingfrom4.1 to 4.9, acidificationfromchemical fertilizer use is a concern and P availability is limited on red soils due to their high P fixation capacity. Rainfed agriculture has expanded onto former grazing, shrub and abandoned lands located on steep slopes and at high elevations. These marginal lands have inherently lower soil fertility and are less favourable for intensive nutrient management.  185 Erosion Erosionfromupland bari fields and degraded lands result in substantial nutrient transfer to lowland agriculturalfields.Erosion on bari land removes an average of 25 kg N and 13 kg Ca per ha furrow slice, and marginal upland agricultural sites are prone to higher erosion rates. Nutrient losses through erosion on forest land are small when understorey vegetation is maintained, but litter removal leaves the forest floor unprotected to the monsoon rains. Degraded shrub and grasslands (>50% soil exposure) cover 5% of the study area and are a significant source of sediments, removing an estimated 34 kg N and 23 kg Ca per ha furrow slice.  Intensification Khetfieldsact as a nutrient sink receiving inputsfromcompost, fertilizer, sediment, water and biological fixation. Soil fertility conditions on khet are adequate and nutrient modelling suggests that inputs are sufficient to maintain a rice-wheat cropping system. The amount of irrigated land has remained relatively constant over the last 25 years but cropping has intensified and shifted toward cash crop production. Nutrient budgets under triple cropping are N and P 0 depleting (-106 kg N and -12 kg P 0 per ha 2  5  2  5  furrow slice), and cash crops such as tomatoes and potatoes require higher N, and P 0 levels than staple 2  5  grain crops. Vegetable production and the use of high yielding varieties has resulted in an increased dependence on agrochemicals, and water quality problems are associated with heavy fertilizer and pesticide use.  7.3.2 Management Options Organic Matter Management Organic matter inputs have many beneficial effects on soil chemical, physical and biological properties; providing macro- and micro-nutrients, reducing acidification, mamtaining soil structure and enhancing microbial activity. Best management practice and deficit elirnination scenarios identified improved composting as a practical option for improving nutrient budgets on bari land. Pit composting nearly  186 doubles the N and P2O5 content of compost, and improved composting reduced estimated N deficits on bari fields by 17%.  Water Management The diversion of stream floodwaters through the irrigation system carries suspended sediments and nutrients onto khet fields. Sediments are enriched in Ca, P and C, and an average accumulation of 4 mm per year supplies an additional 11 kg N and 28 kg Ca per ha furrow slice. In addition to nutrient enrichment from sediment accumulation, irrigation water is alkaline, contains moderate quantities of N0  3  and contains substantial Ca. While additional work is required to quantify nutrients supplied through irrigation waters, enrichment associated with irrigation and sediment deposition appears to be key to maintaining soil fertility conditions on khet land.  Lime Bari, forest, shrub and grasslands all have median pH values <5.0, and further acidification is a concern with the increasing use of chemical fertilizers on bari land and biomass removal from forest, shrub and grasslands. Calcium and magnesium based rocks provide a source of liming materials that are locally available in limestone and marble deposits distributed in the lower Jhikhu Khola Watershed. Rehabilitation studies with lime and manure have shown increased fodder production and a slight increase in soil pH. However, detailed soil testing and analysis is required to determine the soil buffering capacity, lime requirements and yield response.  Integrated Nutrient Management Nutrient deficit elimination scenarios suggest chemical fertilizer use would need to be quadrupled to meet crop N requirements under a triple crop rotation. The high cost of fertilizer constrains application rates and the associated soil acidification would be detrimental to crop productivity. Many farmers already report that soils are becoming 'hard' due to continued chemical fertilizer use. Integrated nutrient  187 management, combining chemical fertilizers and compost is critical to maintaining soil fertility. Compost alone will be insufficient to meet crop nutrient demands with increasing cropping intensities and vegetable production, but organic matter additions improve soil structure, provide slowly available nutrients, and are less prone to nutrient losses through leaching.  Azolla  Nfixationby blue-green algae and Azolla may provide sufficient N to meet the requirements of rice grown on khet fields. Azolla production of 15-201 ha", with a N concentration of 4.5%, incorporated into the soil 1  would supply some 135 kg N if 20-30% is taken up by thefirstrice crop, and residual organic matter would supply nutrients to subsequent crops. Given the success of Azolla cultivation in countries such as the Philippines, it provides a viable management option.  On-Farm Fodder Production Eighty-five percent of the households surveyed report fodder trees on their private land, but fodder shortages are common during the dry season. Additional fodder may be produced by planting species such as napier grass on terrace risers and 'waste' lands. Regular cutting would minimise rodent problems, provide a source of fodder close to the house, and reduce pressure on forest resources. Nitrogen fixing fodder trees such as sissoo and sirus planted as hedgerows are able to grow on N deficit sites, and litter adds organic matter to the soil. Regular cutting would provide fodder andfirewood,and minimise the shading effect on agriculture.  7.4 Socio-Economic Factors and Options  Population growth, land tenure, culture and poverty are key socio-economic factors influencing nutrient management and driving soil fertility degradation. Potential options which may counter the negative impact of socio-economic influences on soil fertility include off-farm employment, community forestry, cash cropping and population stabilisation.  188 7.4.1 Socio-economic Factors Population Growth Population growth rates estimated at 2.6% are placing additional pressure on soil resources. The per capita availability of land has decreased to 0.17 ha, and double and triple crop rotations are required to meet the increased demand for food. Agricultural marginalisation in response to population pressure has brought steeply sloping and low soil fertility lands under cultivation, and recent declines in forest cover are indicative of continuing pressure on forest resources. Population growth is a dominant factor driving land use dynamics within the study region, and the increased demand for food, animal feed and fuelwood results in increased nutrient removal.  Land Tenure Land ownership varies dramatically with 15% of the surveyed households owning 36% of the agricultural land, and 53% of households owning only 25% with total holdings <1 ha per household. Share cropping is practised by approximately one-third of the households, and 47% of households report that the land they farm does not generate enough food and income to meet their family's basic needs. The poorest soil fertility conditions within the study region are found on forest and grasslands, which are primarily under government ownership. Agricultural land holdings are positively correlated with total fertilizer and compost applications, and significant differences in nutrient budgets and soil fertility are noted with farm size. Share cropped land receives significantly lower compost, fertilizer and pesticide inputs, and grasslands are largely unmanaged.  Culture Ethnic distribution, the role of livestock, and women as resource users and managers are three components of Nepali culture related to soil fertility in the study region. High caste farmers (Brahmins) typically own the most khet land, and apply more fertilizer and pesticides to their land. Livestock are important in Nepali culture, particularly cows and goats, and impact soil fertility through manure inputs. Female buffalo are  189  obtairiing economic importance with increased commercial milk production. Women are central in soil fertility management due to their responsibilities for livestock care, litter collection and compost application, and the commercialization of milk production is dramatically increasing the workload of women farmers.  Poverty Agricultural assets (land and livestock), farm gross margins (total returns less variable costs), and sources of cash income (crop sales, milk production and off-farm employment) are used as indicators of household economic well-being. Access to land and land quality assessed by local land values is highly skewed and ranges from $0-$53,000 Cdn. Livestock values are highly variable between households, rangingfrom$0$3,000 Cdn and 50% of household livestock assets are accounted for by female buffalo. Vegetable crops involve higher levels of resources (labour, pesticides, fertilizer, compost and water), but farmers have an economic incentive to adopt vegetable crops. Total returns and household gross margins are greatest for households growing vegetable crops as part of their rotation, but 47% of households have gross margins <$100 Cdn per year. Farmers sell a variety of crops, but only a small minority systematically produce for the market. Forty-five percent of the households surveyed sell milk, and 40% of these households have been selling milk for less thanfiveyears. Off-farm activities are an important source of family income for 60% of the households with a median gross income of $439 Cdn. per year. Households with higher agricultural assets and gross returns apply more compost and fertilizer to both khet and bari land, and households with higher returns and milk sales appear to farm sites with better soil fertility conditions.  7.4.2 Socio-economic Options Off-farm Income Off-farm activities provide cash income to households which may be used to purchase chemical fertilizer, thereby reducing nutrient deficiencies associated with intensive cultivation. If one-half of the current median off-farm income ($220 Cdn) was used to purchase chemical fertilizer, some 500 kg of complex®  190 could be purchased, supplying 100 kg N and P2O5. The nutrient deficit estimated for an early maize-ricewheat rotation could be eliminated by such input levels. Male out-migration in pursuit of wage employment however, negatively impacts the household farm by increasing the workload of women farmers and increasing their responsibility for farm decision making. The workload placed on women farmers is particularly problematic given parallel increases in demand for their labour associated with agricultural intensification, vegetable crop production and commercial milk production.  Community Forestry Community forestry initiatives have been successful in increasing biomass through restocking and restricted access, but long term investments are required. Local farmers now have incentives to manage common forest land, but the necessary capital resources to initiate community forestry projects require outside subsidies. Community grassland initiatives which establish access rights and aid in pasture management, similar to the forest user groups, are an option for increasing fodder production but long term capital investments are required to establish productive grasslands on currently degraded sites.  Cash Cropping Market oriented production provides a source of income which may be used to purchase commercial inputs. If farmers were to invest one-half of the gross margin of tomatoes grown on 0.25 ha of khet land ($264 Cdn) in chemical fertilizer, some 625 kg of complex® could be purchased providing 125 kg N and P 0 , far in excess of the nutrients required by the tomato crop. Vegetable production, however, increases 2  5  water and pesticide requirements, and increased chemical fertilizer use is associated with soil acidification and water quality problems. Integrated nutrient management is required to rninimise the negative effects of agrochemical use and water use efficiency issues need to be addressed.  191  Population Stabilisation Reducing the demand on land resources may be obtained through population stabilisation. Family planning is a long term solution, while outmigration provides a short term option. Male outmigration in search of off-farm income reduces the supply of farm labour and places additional responsibilities on women farmers. Problems of population size and growth, resource utilization and depletion, and soil degradation must be considered jointly.  7.5 Sustainability of Farming Systems Farms in Nepal need to be viewed as systems, integrating forest, livestock and cultivation activities. Biomass collectedfromforest, shrub and grasslands provide nutrients to the agricultural system with livestock, through manure, playing a central role in nutrient redistribution. Erosion is an important natural process but water and sediment regimes are modified through a complex irrigation system. Off-farm employment, cash crop production and milk sales provide income to the farm household which is partially used to purchase agrochemicals impacting agriculture. Nutrient flows must be evaluated within and between components as nutrient fluxes are interlinked. Pressure on one component will impact the entire system and alter the transfer of nutrients.  The traditional farming system appears to have been sustainable. Despite high rates of erosion, nutrients were recaptured on khet land, and compost was used to replace nutrients lostfrombari lands. But triple cropping and increased vegetable production are now threatening sustainability. Both require more fertilizer, pesticides, water and labour. As a result nutrients on khet land are being depleted and bari land receives less compost. Cultivation of low soil fertility sites leads to low productivity, low returns to human and capital inputs, and the inefficient use of scarce nutrient resources. Forests are cleared of understorey vegetation, short circuiting the natural nutrient cycle and more erosion results. Social sustainability is also being threatened, given the increased demands particularly on female labour required for triple cropping and vegetable production. Although milk production provides more manure, it increases labour demands  192  for fodder collection, feeding and milking, which are tasks mostly fulfilled by women. Increasing off-farm employment and schooling remove male and child labourfromthe system. Some 25% of the farmers cannot provide for their families. They will have no choice but to take a short run view and use up the capital stock of soil nutrients rather than investing in soil fertility.  7.6 Implications for Methodology The biophysical and socio-economic data collected as part of this study, their analysis and its interpretation are not without limitations. Potential sources of error are associated with survey variability, sampling density, positional accuracy, laboratory analysis, historical records and the subjectivity of interview interpretation. A number of techniques were used to minimize errors during data collection and to validate the results. Soil sampling used a composite approach with ten samples bulked for each field, tenfieldswere sampled for each of the factors analyzed, and duplicate laboratory analysis was conducted for 10% of the samples. The nutrient budget model indicates the relative changes in soil nutrient fluxes under different management regimes and sensitivity analysis was used to identify key model variables. Biophysical processes need to be monitored over a long time period to determine rates of changes in soil fertility, but changes induced by land use are indicative of the general trends.  The accuracy of spatial data collection is implied in the map scale, but aerial photo interpretation, data transfer and digitizing are potential sources of error. Ground truthing was used to validate and update the interpretation of recent (1990) air photographs, but land use data derivedfromhistorical photographs could not be validated.  Obtaining accurate and consistent socio-economic data can be challenging. Bias may be introduced by poor question design, interviewer interpretation or respondent interpretation. Data were collected at different times, by different interview teams. Farmers are often illiterate, do not keep records and farm a  193  series of small plots. Interview results were cross checked by comparing male and female farmer responses and key informant questionnaires.  Recognizing these limitations, this study contributes to the understanding of soil fertility issues in the Middle Mountains by showing the dominant trends in nutrient and soil fertility dynamics in onesubwatershed. While the Bela-Bhimsenthan study area is somewhat atypical of the Middle Mountains due to the Aranico Highway and its proximity to Kathmandu, the results are indicative of future conditions in many regions of the Middle Mountains. As market oriented economies develop in other areas of the Middle Mountains similar soil degradation issues may arise. There is a need to consider both biophysical and socio-economic factors, and while these factors may be similar in other areas of the Middle Mountains, their interactions will likely be different.  7.7 Implications for Future Research Understanding how soil fertility is changing, and why, requires the integration of biophysical and socioeconomic factors. Farmers' perceptions of soil fertility are useful in identifying past trends. However, given the nonlinearly of relationships between soil parameters (e.g. pH and Pfixation),farmer perceptions do not indicate the proximity to threshold values. Long term research on soil fertility and nutrient dynamics are necessary to understand the physical processes. While soil fertility research provides an index of soil resilience and an understanding of the agronomic processes, it does not indicate the underlying socio-economic factors driving nutrient management. Household, farm and off-farm activities need to be viewed in terms of their interactions with natural and socio-economic environments. Interdisciplinary efforts that seek to integrate our understanding of these subsystems are needed to more fully comprehend soil fertility issues in the Middle Mountains of Nepal.  PLATES Plate 1. Plate 2. Plate 3. Plate 4. Plate 5. Plate 6. Plate 7. Plate 8. Plate 9. Plate 10. Plate 11. Plate 12. Plate 13. Plate 14. Plate 15.  Elevation zones Aspect categories Slope classes Red soils Land use a) 1994 and b) 1972 Exchangeable calcium 200 soil sites Soil acidity 200 soil sites Available phosphorus 200 soil sites Exchangeable calcium soil classification Soil pH classification Available phosphorus soil classification Composite soil fertility classification Classification of Pfixationcapacity Spatial distribution of interpreted P degradation Spatial distribution of interpreted Ca degradation  PHOTOGRAPHS Photo 1. Photo 2. Photo 3. Photo 4. Photo 5. Photo 6.  Women transplanting rice Women collecting litter Pine plantation on red soils Intensively used agricultural land Bari expansion onto shrub lands Farmers purchasing chemical fertilizer  Plate 1. Elevation ranges.  Plate 2. Aspect.  Plate 5a. Land use 1994.  Plate 5b. Land use 1972.  Exchangeable Ca (meq 100g ) 1  •  •  No. Sites  <3  78  3-4  51  >4  71  Plate 6. Exchangeable Ca 200 soil sites.  PH (CaCI ) 2  •  m  <4.8  108  4.8 - 5.0  34  >5.o  Available P (mg kg )  No. Sites  58  Plate 7. Soil pH 200 soil sites.  1  •  m  <5  No. Sites 48  5-15  88  >15  64  Plate 8. Available P 200 soil sites  Exchangeable Ca (meq 100g )  Area (ha)  •  <3  772  3-4  501  >4  510  1  •  unclassified  144  Plate 9. Exchangeable Ca soil classification.  •  PH (CaCIJ  Area (ha)  <4.8  981  4.8-5.0 •  >5.0 unclassified  Available P (mg kg ) 1  •  303 499 144  Plate 10. Soil pH classification.  i i  Area (ha)  <5  676  5-15  427  >15  680  unclassified  144  Plate 11. 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Soil Science Society of America Journal 58(2) 343-346.  220  APPENDIX A. QUESTIONNAIRES A . l Bela-Bhimsenthan Soil Fertility Site Description  Sample # Location Elevation  Aerial Photo # VDC Aspect  Date Ward# Slope  Local Land (khet/bari) Classification Types by:  Land Type Soil Texture, colour, depth Field Size Ownership Years Owned Farmer's Name Crop Rotation/Variety  Climate Yield Slope Total Khet Ethnic Group Yield (kg/ha)  Bari  Fertiliser Application (kg/ha)  Chemical  Organic  A B C D  Workability Irrigation Yield Trends with Mineral Fertiliser  Penetrometer Reading Years of Mineral Fertiliser Use  Occurrences of Disease and Pest Control Measures Grazing Land Ownership Government Communal Private Farmer's Comments:  Species Composition & %  Ground Cover %  221 A.2 Bela-Bhimsenthan Detailed Household Survey  Site Description (to match soil survey)  Date:  1994  Sample # Farmer's Name Mark the house location on the air photos (sample # + H) What could be done to improve your situation (prioritise) 1. 2.  3.  General Comments  Goals  If you had some extra money, what would you buy? (prioritise) 1. 2.  3.  222  Family How many people live in this household?  people  What is their position in the household and their ages? What percent of their time do they spend working on the farm? What percent of their time do they spend working on off farm activities, what are the activities by season (Premonsoon, Monsoon, Winter) and what are the wage rates ($ and food)? Position  Age  On Farm Work  (yrs)  (%Time)  Off Farm Work % Time  Activity  Season  Wage per day Rupees  Father  ! PMW  Mother  ! PMW  Husband  | PMW  Wife  \ PMW  Son I  | PMW  SonH  j  SonTJI  1PMW | PMW  I  j PMW  Daughter I  j PMW  Daughter H  | PMW  Daughter Dl  | PMW  Daughter In Law  j PMW  Daughter In Law  |  | PMW  j PMW  Are there any family members who live outside of the house and send money home? Yes  No  If yes, how much money do they send/ bring home per year ?  rupees  j Food  223  Production How much land do you own? Khet  (ropani)  Bari  (ropani)  Forest  (ropani)  Grazing  (ropani)  Other  For the land you own, what crops are grown and how much land is cultivated for each crop? What amount of seed, fertiliser and compost do you apply to each crop? What are you oxen and labour requirements (days per crop)? What is your current production and compared to 5 years has it increased, stayed the same or decreased? Area  Crop  (ropani)  Inputs  Production  Seed j Fertiliser 1 Compost j Oxen (kg)  Khet Early Rice Monsoon Rice Maize Wheat Tomato Potato Mustard  Bari Maize/Beans Potato Wheat Mustard Tomato  |  (kg)  j  (kg)  |  (days)  Labour (days)  Current ! (kg)  Yield  ! 5yrsago  224  Share Cropping Is any land rented or share-cropped? If yes, how many ropani? If yes, are you tenant  Yes  No  Khet  Bari  or landlord  ?  What are the rental arrangements (share percentage or amount paid in rupees)? For the land you share-crop, what crops are grown and how much land is cultivated for each crop? What amount of seed, fertiliser and compost do you apply to each crop? What are you oxen and labour requirements (days per crop)? What is your current production and compared to 5 years has it increased, stayed the same or decreased? Area  Crop  (ropani)  Production  Inputs Seed j Fertiliser j Compost j Oxen (kg)  Khet Early Rice Monsoon Rice Maize Wheat Tomato Potato Mustard  Bari Maize/Beans Potato Wheat Mustard Tomato  j  (kg)  (kg)  |  (days)  Labour (days)  Current j  (kg)  Yield  | 5yrsago  225  Cropping System Have you recently changed the type of crops you are growing? Yes  No.  If yes, which crops, when and why? Current Crop  Previous Crop  Year of Change  Reason  Is your cropping system the same each year or do you rotate crops? In which season and why?  Premonsoon  Monsoon  Winter  Khet Bari  What is the biggest problem preventing youfromincreasing your yield per ropani? (one only)  226  Food and Income Does the land that you farm generate enough food and income to meet your family's basic needs? Yes  No  Do you sell any of your crops? Yes  No  If yes, which crops, what amount is sold, at what price, in which season (Premonsoon, Monsoon, Winter) and how long have you been selling each crop? Crop  Sold (per year) kg  Price  rupees  per unit  Season  # Years  Sold  Sold  Rice  PMW  Wheat  PMW  Maize  PMW  Potato  PMW  Tomato  PMW PMW  Do you buy additional food for your family members? Yes  No  If yes, what food do you buy, how much (per year) and at what cost? Food Type  Bought (per year) Amount  Unit  Cost  Rupees  per unit  Rice Wheat Maize Potato Salt  Are there any other crop that you have considered growing? Why are you not growing these crops?  Pesticides Do you apply any pesticides, herbicides or insecticides? Yes  No  What type do you apply, to which crops, how much and at what cost? Crop  Pesticide  Rice  Malathion Dithane M45 Metacide Fen Fen Nuvan  Wheat  Maize  Furadol Fen Fen  Potato  Dithane M45 Fen Fen Nuvan  Mustard  Dithane M45 Fen Fen  Tomato  Dithane M45 Fen Fen Nuvan  Diseases  Quantity  228  Trees  What type of trees do you currently have on your farm? How many of each type? Compared to 5 years ago has the number of trees increased or decreased? By how many? Number Now  Type of Tree  Fodder  a Kutimiro  b Gayo  e Koeralo  f Tanki.... g Timilo  # 5 years ago  b Bakaino.... d Kabro h Gogan  a  b  c  d  e  f.  g  h  Fuelwood  Fruit  a Mango  b Lichhi  c Anar  d Guava  a  b  c  d  e Banana  f Aaru  g Lemon  h Nibuwa  e  f  g  h  Timber  Forest Products  Who goes to the forest to collect fuelwood, fodder and litter (e.g. daughter in law and mother)? How often? How long does the entire trip take? Product  Person  Frequency  Amount  (per week)  (kg)  Easy or Difficult  Season  5 years ago  Time/ Trip (hours)  Fuelwood  PM W  Fodder  PMW  Litter  PMW  Location  229  Compost What type and amount of forest litter do you incorporate in compost? Is litter collection easier, the same or more difficult than 5 years ago? What type of litter do you prefer? Litter Type  Amount (%)  Easy or Difficult 5 years ago  Comments (Preferred litter type)  Sal Katus Kangiyo Pine Sallo Pithauli  Livestock What animals do you own now? How many? Have your livestock numbers increased or decreased compared to 5 years ago? By how many? Animal Cattle - Bull Cattle - Cow Cattle - Calf Buffalo - Bull Buffalo - Female Buffalo - Calf Goat Pig Chicken Duck Total  Number Now  # 5 years ago  230  Fodder Are there fodder shortages? When?  Yes  Premonsoon  Monsoon  No Winter  What are the sources of fodder that you feed your livestock through out the year? Compared to 5 years ago is collection easier now, the same or more difficult? Source  Fodder  Easy or Difficult  (%)  5 years ago  Crop Residues Terrace Risers Your own Trees Buying Fodder Forest Concentrated Feed Total  100 %  Milk  How many animals do you milk? How much milk is produced in the monsoon (6 months) and in the dry season (6 months)? What is the fat content of the milk produced? How much milk do you consume? How much milk do you sell and at what price? Animal  #  Milk Produced Monsoon (litres/d)  Cow Buffalo Goat  Winter (litres/d)  Fat Content Monsoon (/litre)  Winter (/litre)  Price  Production  Consumed  Sold  (/litre)  5 yrs ago  (%)  (%)  Energy What are the sources of fuel that you use through out the year? Compared to 5 years ago is collection easier now, the same or more difficult? Fuel Source  Fuel Sufficiency  By Months  Easy or Difficult 5 Years ago  Crop Residues Your Own Trees Forest Kerosene Buying Fuelwood  Is there enough fuel for your household? Yes If yes, It is Excess  Sufficient  No  Barely Enough  Have you considered any alternative source of fuel? Why are you not using them?  232  A.3 Balawa Socio-Economic Questionnaire - Women Farmers Area Ward No.  Village Household No. (same as George's)  Farmer's Name Position in Household Caste / Ethnic Group  Years Lived Here Age  1. How many people live in this household? How many years of schooling has each person completed? Remarks  Years of Schooling  How Many  1 2 |3  4  5 |6  7  8 j 9 10 j SLC IA BA  MA  Girls Boys Women Men  TOTAL: (girls / boys: under 16 years old) (women / men: 16 years and older) 2. No Does your household sell any-» cottage industry products? Yes What Products? Price?  Quantity?  Where are the products sold?  Market for the raw materials?  3. Do any household members work for wages or outside income? No Yes -» Who? Proportion of time?  What type of work? Wages / income? (Rs./day)  Gender?  If no schooling, why?  4. Which household members carry out the following activities? (M = man W = woman B = both M & W C = child Land Preparation  * = hired labourer)  Livestock Care  ploughing terrace repair irrigation Fertilising gathering forest litter / manure composting applying compost / organic fertiliser applying chemical fertiliser Planting deciding what to plant nursery transplanting  gathering fodder grazing stall feeding Dhana making watering milking Household Care gathering fuelwood fetching water keeping household money deciding what to buy deciding what to sell  throwing seed  Farm Management Activities  Harvesting cutting threshing  management of farm labourers purchasing seeds purchasing chemical fertiliser purchasing livestock storage  5. What animals are kept by this household? How many? Cows M Goats M Cows F Goats F Cows Y Goats Y Bullocks A Sheep Bullocks Y Pigs Buffalo M Chickens Buffalo F Other Buffalo Y (M = male F = female Y = young A = adult) Are any of these animals not owned by you? (indicate with a * next to the number NOT owned e.g.) 4 Cows F *2) 6. Compared to five years ago, have your livestock numbers changed? No Yes More or less animals now? By how many?  Why?  7. Are your animals mainly stall-fed (S) or grazed (G)? during the dry season during the wet season 8. Compared tofiveyears ago, has the availability of grazing areas for your animals changed? No Yes More or less areas now? Why?  234  9. When your animals are stall-fed, what do you feed them during the different seasons? % of Total  Type  Season (circle)  Crop Residue  PMW  Grass  PMW  Dana  PMW  Tree Fodder  PMW  Buying Fodder  PMW  Other  PMW  TOTAL  100%  10. Compared to five years ago, has the availability of grass and fodder changed? No Yes More or less now? Why? 11. What kinds of tree/shrubs do you use for fodder? Distancefromyour house? Which ones do you prefer to use for fodder (indicate with ranking of 1,2,3...) Why do you prefer these fodder trees/shrubs? Name of Tree / Shrub  Distance (hours walking)  Rank  Why preferred  12. Do you tliink your animals are currently getting enough to eat? Yes No -> What solution do you suggest? 13. Compared to five years ago, has the amount of organic fertiliser you put on yourfieldschanged? No Yes More or less organic fertiliser now? How much? Why?  235  14. What type of fuel does your household use? Where is the fuel from? If the fuel is purchased, what is the price? Fuel Type  % of Total  (100%)  Source (%) Own Land j Forest  Purchased  Price  cut wood small branches crop residues manure banmara kerosene other  15. Compared tofiveyears ago, has the availability of fuel in this are changed? No Yes More or less fuel now? Why? 16. If you had some extra money, what would you do?  17. What are the biggest problems in your personal life, on the farm and in your village? personal life farm village Farmer quotes and remarks:  236  A.4 Balawa Socio-Economic Questionnaire - Men Farmers  Area Ward No.  Village Household No. (same as George's)  Farmer's Name Position in Household Caste / Ethnic Group  Years Lived Here Age  1. What type of khet land do you farm? What crops do you grow on each type? How many ropani of each? Khet Type  Pre-monsoon Crop Monsoon Crop  Winter Crop  # of ropani  Total ropani 2. What type of bari land do you farm? What crops do you grow on each type? How many ropani of each? Bari Type  Pre-monsoon Crop Monsoon Crop  Winter Crop  # of ropani  Total ropani 3. Which of the above crops do you grow on share cropped land? (circle crops) 4. Do you share crop out any land? No Yes  How many ropani? Khet  5. If you use share cropping (either in or out), what are the arrangements? What % of the output do you keep? % Do you buy the seeds and fertiliser? Yes No  Bari  237  6. If you do not use sharecropping, what are the reasons? 7. Why did you choose this mix of crops? 8. Is your cropping system the same each year? Yes  No  Why do you rotate crops?  9. Are there any other crops you have considered growing? What are they and what are their advantages? Why aren't you growing these crops? 10. What type of commercial fertiliser do you use on your khet and bari land? Type  Khet  Bari  Complex Urea Ammonium Sulphate Other None  11. When buying chemical fertiliser do you have difficulty in: - Getting the right type Yes No - Getting it at the right time Yes No - Being able to pay for it Yes No 12. Compared to five years ago, has the amount of commercial fertiliser you put on your fields changed? No Yes —> More or less commercial fertiliser now?  How much? Why? 13. What are the biggest limitations (constraints) to increasing your yields?  238  14. What do you sell from your farm and at what price? (e.g. crops, livestock, milk, fruit, vegetables etc.) Products  Quantities  Prices  15. Does your farm generate enough income to support your family? Yes No -> Explain: 16. Do you work for wages or outside income? No Yes -> What type of work? Proportion of time? Wages/income? Where? 17. What type of trees do you have on your own land? Type  Fodder  Fuelwood  Timber  Nepali Name  Number of Trees  239  18. Compared tofiveyears ago, has the number of trees on your land changed? No Yes -> More or less now? Number of trees different? Source of seedlings? 19. If you had some extra money, what would you do?  20. What are the biggest problems in your personal life, on the farm and in your village? personal life farm village Farmer quotes and remarks:  A.5 Bela-Bhimsenthan Key Informant Questionnaire  Area  Village  Farmer's Name Caste/Ethnic Group  Ward Age  Years Lived Here  1. Critical Problems What could be done to improve the situation in your region? (prioritise) 1. 2.  3.  General Comments:  2. Cropping Pattern What are the common cropping patterns in this region? Khet land Bari land What were the common cropping patterns 5 years ago? Khet land Bari land  241  2. Yields Overall are yields increasing, decreasing or staying about the same within this region?  What are average yields, fertiliser use, and labour requirements for commonly grown crops in the region? 5 years ago were yields and fertiliser use less, the same, or more than now? Khet Land Crops  Average Yield  Typical Fertiliser Use  per ropani  type  I 5 yrs ago  amount  Ave. Labour j 5 yrs ago  days per crop  pre-monsoon rice pre-monsoon maize monsoon rice potatoes wheat tomatoes mustard / tori others  Bari Land Crops  maize wheat mustard / tori millet beans tomatoes potatoes others  Average Yield  Typical Fertiliser Use  per ropani  type  j 5 yrs ago  ! amount  Ave. Labour 1 5 yrs ago  days per crop  3. Market Oriented Production Within this region, what is the relative importance of market oriented production compared to the production of crops for consumption?  Typically, what % of the crops grown is consumed and what % is sold? 5 years ago was the % sold more, the same or less than now? Crops Grown  % Consumed % Sold  5 yrs ago  rice wheat maize potatoes tomatoes mustard others  Typically, what % of the crops consumed isfromthe farm and what % is purchased? 5 years ago was the % bought more, the same or less than now? Crops Grown rice wheat maize potatoes tomatoes mustard others  % From Farm  % Bought  5 yrs ago  243  4. W a t e r A v a i l a b i l i t y  Overall is water availability a major production constraint in the region?  Within this region describe water shortages for the common crops. 5 years ago were there less water shortages, the same or more water shortages? Crop  5 yrs ago  Water Shortages yes/no  ; when PMW  j how long (days)  less, same, more  rice maize potatoes tomatoes others...  5. Population  Of the new houses built in the region in the last 5 years, what % are local residents versus families from outside the region?  % local  %fromoutside  Where did the families moving into the region comefrom?(e.g. Kathmandu, Dhulikhel, Panchkhal etc.)  6. Land Ownership For the families immigrating to the region (last 5 years) what type of land did they purchase? Land Type  % of new families  mostly khet mostly bari mixed khet and bari degraded other... total  What % of the farms are absentee land owners?  100%  %  What % of the absentee land owners have purchased land in the last 5 years?  %  APPENDIX B. SUPPLEMENTAL DATA  Table B. 1 Nutrient uptake by rice. Yield kg ha 2500  Component  1323 1500 5040 6720 3360 1500 1500 3000 7900 10000 9800 8300 2430 4930 4030 5600 ave.  Nitrogen N kg ha" j (%) 1  1  grain whole plant grain whole plant grain whole plant rough rice straw whole plant grain whole plant grain straw whole plant grain straw whole plant rough rice straw whole plant rough rice straw whole plant rough rice straw wholeolant whole plant  30  1  Phosphorus P 0 kg ha" (%) 2  5  1  K 0 kg ha" j %  Ca kg ha'" j %  2  1  Reference  ..Mg.  kg ha"  1  1  %  Carson 1992 0.5  15  0.3  45 j Suwal et al. 1991  54 !  1.8  7  0.2  74 1  1.2  18 27 13 40  0.5  29 I 13 1 90 | 103 1  60 16 2 18  0.8  LRMP 1986b 42 67 40 107  I i  ! 1  0.9  54 ! 35 | 7! 42 1 85 j 40 ! 125 !  0.7  0.3  0.9  4j 12 ! 16 !  0.1  6 7 13  U.S. Borax, 1979 0.1 Landon 1984  23 I 22 ! 45 ! 56 | 34 j 90 !  1.4  0.6  46 ! 12 1 22 I 34 1  0.6  1.1  1.4 I 2.6 | 4.0 1  0.1  0.3 2.2 2.5  Sanchez 1976 0.1 Olson and Kurtz 1982  0.7 1.5 0.9 1.2  0.6  0.9 1.0  0.6 0.1 0.4 12 11 23 23 13 36  0.3  0.4 0.4  1 12 j 52 | 64 i ii i 78 | 89 i  <0.1 0.3 0.2  0.3 2.8 1.4  0.8  0.9 1.0  1i 10 | 11 1 3 1 10 \ 13 |  0.1  0.1 0.1  0.1 0.2 0.1 3 3 6 4 6 10  DeDatta and Mikkelsen 1985 Grist 1986  0.1 Foth 1990 0.1 0.1  246  Table B.2 Nutrient uptake by maize. Yield kg ha" 1600  Component  1560 6270 11760 10080 1000 1500 2500 6272  5000  10000  9450 9400 10080 9000 8400 ave.  Nitrogen N kg ha- | (%) 1  1  grain whole plant grain whole plant cob whole plant cob stover whole plant grain stover whole plant grain stover whole plant cob stover whole plant grain dry matter grain stover dry matter grain stover whole plant dry matter grain wholejjlant whole plant  Phosphorus P 0 kg ha (%) 2  5  1  K 0 kg ha"  Ca kg ha"  2  1  i %  1  Reference  .MB.  %  kg ha  1  j % Carson 1992  1  2.1  25  0.7  50  53 j  1.5  23  0.7  12 1  165 ! 151 j 113 | 264 ! 25 ! 15 ! 40 ! 109 j 63 j 171 1  1.2  55 60 40 100 14 7 21 49 21 71 34 13 47  0.4  135 j 44 1 161 ! 205 ! 18 j 22 j 40 1  75 71 18 89 59 41 100  0.3  75  1 Suwal et al. 1991 Landon 1984  1.2  1.6  1.2  0.5  0.8  0.5  0.9  1.6  17 30 47 3.0 4.5 7.5  0.2  0.3  25 j 20 1 45 j 2.0 j 3.0 I 5.0 !  U.S. Borax 1979 0.2 Sanchez 1976 0.2 Western Canadian Fertilizer Association 1992 Hanway and Olson 1980  31 i  0.4 Olson 1978  170 ! 129 j 62 ! 191 1 151 j 112 ! 263 ! 115 j 246 j  0.8  2.0  1.4 1.3 1.3 1.4  0.9  210 1 47 j 188 ! 205 I  0.9  2.5  40 1 39 40  0.2  0.4  45 ! 11 ! 33 ! 44 !  0.2 Olson and Sander 1988 0.5 Foth 1978  0.5 Russelle et al. 1983 Miller and Donahue 1900  90  0.5 0.6  218 I  1.1 1.4  65  0.3 0.3  56  j  0.3 0.3  247  Table B.3 Nutrient uptake by wheat. Yield kg ha" 1415  Component  1  2310 4804 1675 2425 1675 600 1000 1600 3360  5000 7300 4030 3600 4500 2690 3360 ave.  Nitrogen N kg ha" i (%) 1  grain whole plant grain whole plant whole plant grain whole plant grain grain + straw grain whole plant grain straw grain straw whole plant grain whole plant grain whole plant grain whole plant grain straw whole plant grain straw wholejilant whole plant  Phosphorus P Os 2  5kg ha j '('%)' 1  K20 kg ha  1  Ca kg ha  i %  1.0  15 !  0.5  30  1  1.0  54 ! 64 j  1.1 1.3  23 j 11 j  0.4 0.2  32 1 28 |  0.6 0.6  20 !  0.5  10  i  ..Mg kg ha  j %  Reference !%  1  Carson 1992  30 !  45 !  1  Suwal etal. 1991 Sherchan et al. 1991 Sherchan et al. 1995 LRMP 1986b  42 !  0.8  8 |  0.1  54 ! 12 j  1.5  0.7  1.0  26 ! 5I 1 ! 6 i 29 ! 6 i 35 I  0.5  19 !  1.0  50 !  0.5  70 !  0.6  176 1  1.1  47 !  0.3  133 !  1.8  140 ! 84 j 34 | 118! 56 j 22 1 78 !  1.6  56 ! 43 j 9| 52 I 28 j  0.6  123 1 26 i 59 ! 85 ! 17 j 39 | 56 !  1.4  Landon 1984  I  3 15 I 58 j 17 I 75 | 110  1  0.9  0.4  13 ! 3! 17 | 20 !  0.3  1.2  0.3 | 2.0 | 2.3 !  15  1  0.1  1.0 1 2.0 ! 3.0 i  Sanchez 1976 0.2 Western Canadian Fertilizer Association 1992 Olson 1978  0.1  20 !  0.2 Halvorson et al. 1987  1.5  0.6  I  1.3 1.2  8 36 !  0.6 0.5  1.0  0.9 0.9  18 ! 2j io! 12 1 1 j 7 | 8 !  0.2  20 1 10 j 6  0.1  0.1 0.1  Miller and Donahue 1990 0.2 U.S. Borax 1979  I  16 1 7j  0.2  1  3 10 !  Foth 1990  0.2 0.2  248  Table B.4 Nutrient uptake by cash crops. Crop Yield kg ha" ! component N 1  tomato  5000 | fruit  Source  1  | 15 j  P2O5  |  5j  K 0 2  [ Ca  1  24 j  12 |  Mg 2  10000 I fruit  29 |  9 |  48 |  24 !  5  25000 j fruit  73 !  23 !  120 |  59 |  11  27800 ! fruit  54  17920 1 fruit 30000 | fruit  j  134 1  i  15  45 !  179 !  8j  12  U.S. Borax 1979 Landon 1984  65-110 j  160-240 |  79 j  39 j  122 !  26 j  8  40000 j  200 ]  75 ]  270 j  20 \  25  44800 | tubers  143  41 j  242 j  j  vonUexkull 1978  Splittstoesser 1990  83 |  100-150 i  I average  potato  Nutrient Uptake (kg ha") for whole plant  Olson 1978 Western Canadian Fertiliser Assoc. 1992  44,000 | tubers  77 j  32 |  269 j  20000 | tuber  68 |  29 j  98 j  26800 | tuber  224 j  62 j  348 j  56 j  17  Miller and Donahue 1990  50,000 j tuber  180  j  240 j  10 !  15  Simpson 1986  50  52 1  10 i  71 j  24000 i tuber  90 i  34 i  168 j  3 i  7  40300 1 tuber  134 I  50 !  252 !  6j  10  218 !  17 j  14  130 i  43 |  21955 | bulb  49 j  5j  24 I  16800 | bulb  50 j  23 j  44 I  36000 j bulb  86 j  j  15000 | bulb  60-100 j  25-45 j  45-80 |  69 j  24 |  48 |  j average  Sanchez 1976 Splittstoesser 1990  15000 j tuber  j average  onion  j  4!  9  Russell 1973 Foth 1990 U.S. Borax 1979 Splittstoesser 1990 12 !  2  Foth 1990 Broadbent 1978 Landon 1984  12 |  21  249  Table B.5. Nutrient uptake by tropical grasses. Type Common Name  Dry Scientific Name  Nutrient Uptake (kg ha") for whole plant  Source  1  K 0  matter  N  P O  (tha )  kg ha" | %  kg ha" | %  kg ha"  %  kg ha" | %  1  2  1  1  Mg  Ca  2  5  1  1  kg ha  1  j %  Elephant, Napier  Pennisetum purpureum  10  107 ! 1.1  62 j 0.6  434  4.3  78 | 0.8  49 j  0.5  Sanchez 1976  Elephant, Napier  Pennisetum purpureum  25  288 j 1.1  101 j 0.4  875  3.5  148 j 0.6  Elephant, Napier  Pennisetum purpureum  28  338 I 1.2  165 i 0.6  678  2.4  107 j 0.4  99 ]  0.4  Sanchez 1976  71 |  0.3  Sanchez 1976  Pangola grass  Digitara decumben  10  120 I 1.2  50 ! 0.5  432  4.3  36 j 0.4  28 j  0.3  Sanchez 1976  Pangola grass  Digitara decumben  23  299 1 1.3  108 j 0.5  859  3.7  106 | 0.5  67 1  0.3  Sanchez 1976  Pangola grass  Digitara decumben  26  335 | 1.3  121 | 0.5  481  1.9  122 I 0.5  75 1  0.3  Sanchez 1976  Guinea grass  Panicum maximum  10  107 j 1.1  Guinea grass  Panicum maximum  23  288 j 1.3  62 ! 0.6  434  4.3  78 j 0.8  49 j  0.5  Sanchez 1976  101 j 0.4  875  3.8  149 j 0.6  99 j  0.4  Sanchez 1976  Guinea grass  Panicum maximum  25  322 j 1.3  115 j 0.5  487  1.9  167 j 0.7  110 j  0.3  Sanchez 1976  Para grass  Panicum purpurascens  8  80 j 1.0  39 j 0.5  386  4.8  28 j 0.4  16 j  0.2  Sanchez 1976  Para grass  Panicum purpurascens  Stylo  Stylosanthes humilis  24  307 j 1.3  98 | 0.4  923  3.8  115 ] 0.5  79 I  0.3  3  ! 2.4  i  0.1  Humphreys 1987  Stylo  Stylosanthes humilis  5  1  j 0.1  Humphreys 1987  Stylo  Stylosanthes humilis  | 0.6  Humphreys 1987  2.7  | 0.2  Sanchez 1976  Desmodium  Desmodium intotum  i 0.2  0.7  Humphreys 1987  Desmodium  Desmodium uncinatum  j 0.2  0.7  Humphreys 1987  Blue grass  Poa pratensis  2  60 j 3.0  21 | 1.0  60  3.0  16 j 0.8  7 |  0.4  Foth 1990  Bermuda grass  Cynodon dactylon  8  185 j 2.3  71 j 0.9  269  3.4  59 j 0.7  24 |  0.3  Foth 1990  Bermuda grass  Cynodon dactylon  6  150 | 2.5  60 | 1.0  180  3.0  33 j 0.6  22 |  0.4  Miller and Donahue 1990  3.1  j 0.6  j  0.4  average  !  1  5  | 0.5  250  251  Table B.6. Nutrient content of compost and animal manure (% dry weight basis). Nutrient Source  N  K0  Ca  Mg  (%)  (%)  2  Source  (%)  (%)  (%)  Traditional Compost  06  0 06  06  Suwal etal. 1991  Pit Compost  1.1  j  0.11  1.4  Suwal etal. 1991  Farm Yard Manure  0.5  |  0.2  1.2  Sherchan and Gurung 1995  Maize stubble +  0.7  j  0.01  1.1  Suwal etal. 1991  0.9  j  0.11  1.5  Suwal etal. 1991  Dairy Manure  0.7  !  0.2  0.5  Dairy Manure  3  0.4  2  Middle Mountains  10% dung 60%FYM + soyabean, maize stubble  General Landon 1984 1.3  0.3  Sommers and Sutton 1980 Suwal etal. 1991  Cattle Dung (India)  0.4  |  0.2  0.3  Cattle Dung (India)  1.7  |  1.7  0.6  0.4  0.5  Jain and Kumar 1995  Cattle Manure  2.3  j  0.9  0.7  2.0  0.6  Kirchmann 1994  Cattle Manure  0.6  |  0.3  0.5  0.8  0.2  Simpson 1986  Beef / Dairy Manure  2  0.5  1.2  Beef Manure  2  6.5  2  1  1  Miller and Donahue 1990  6.6  0.4  ' Sommers and Sutton 1980  Beef Manure + bedding  1.0  j  0.9  1.3  0.8  0.4  Follettetal. 1981  Poultry Manure (India)  2.2  !  2.0  4.2  2.3  1.4  Jain and Kumar 1995  Poultry Manure  1.6  1  1.1  0.8  Poultry Manure  5.1  j  1.9  1.8  Landon 1984 6.7  0.6  Kirchmann 1994  Poultry Manure  5  j  2  1.2  2.4  Miller and Donahue 1990  Poultry Manure  1.6  j  0.9  0.5  3.7  0.3  Follettetal. 1981  Pig Manure (India)  2.2  1  2.0  4.2  2.3  1.4  Jain and Kumar 1995  Goat Manure (India)  0.6  1  0.5  0.03  Goat Manure  2.8  I  1.4  2.4  average for Cattle  1.5  |  0.6  1.3  Jain and Kumar 1995 Landon 1984  1.0  0.5  252  Table B.7. Nutrient content of litter. Nutrient i Litter Composition i g/kg ! % N 9.6 1.0 2.06 i 0.2 P2O5 6.6 0.7 K0 13.9 1.4 Ca Mg 1 3.1 0.3 source: Schmidt 1992 2  Table B.8. Nutrient inputsfromorganic and chemical fertiliser sources. System  n  Mean Chemical Fertiliser Inputs  Mean Organic Matter Inputs'  1  Application i N Input P 0 Input Application 1 N Input (kg/ha) | (kg/ha) (kg/ha) | (kg/ha) (kg/ha) 2  5  P0Input (kg/ha) 2  5  premonsoon  early rice (khet) early maize (khet)  5  2290 |  14  1  480 |  145  45  12  3686 j  22 j  2  202 j  60  35  49  5555 |  33  3  301 1  109  45  65  16389 j  10  214 j  80  38  51  977 !  6  1  137 |  42  25  30  1662 j  10 j  1  153 j  48  30  112; source: 200 site soil survey, male farmers  33  20  monsoon  rice (khet) maize (bari)  98 j  winter  wheat (khet/bari) wheat (khet) wheat(bari)  0j  21 1  0 |  0  253  Table B.9. Variable Costs - Seed Price Seed Rate per ropani 1 kg/rop. Rs/kg j $/kg  Khet rice wheat tomato potato maize mustard Bari maize wheat tomato potato mustard barley niger  1 pathi j  2.4  12 1  0.28  3 pathi j  10.5  9 I  0.21  0.25 mana \  0.25  450 i  10.83  6 dharni j  14.0  28 j  0.67  5 mana I  2.5  16 i  0.39  2 mana 1  1.0  16 1  0.39  3 mana j  1.5  16 j  0.39  1.5 pathi j  5.2  9 |  0.21  0.25 mana j  0.25  450 j  10.83  8 dharni j  18.0  28 j  0.67  2 mana 1  1.0  16 j  0.39  1.5 pathi 1  5.2  9i  0.21  2 mana j  5.2  12 j  0.29  1  Fertiliser Type  1  1  Crop  Source: MRM 1996  Table B.10. Variable Costs - Fertiliser Unit j Price (kg) ! (Rs) 1  Urea Complex (Gede) (Chimi) Ammonium Sulphate Di-Ammonium Phosphate (DAP) Triple Super Phosphate (TSP) 1  1  Price ($/kg)  46 - 0 -0  50 i  400  9.6  20 -20 -0  50 1  850  20.5  21 - 0 -0  50 i  400  9.6  20 -46 -0  50 1  850  20.5  0-•46 -0  50 |  425  10.2  Source: MRM 1996  Table B.ll. Variable Costs - Pesticides Pesticide Type Unit Price I Price (Rs) j (kg) ($) 1  Dithane M45  j (mancozeb)  Fenvalerate  ; (fen fen)  1  500 g pack  175 j  8.42  100 ml bottle  80 1  1.92  Parathion methyl j (metacide)  100 ml bottle  75 j  1.81  Dichlorvos  1 (nuvan)  100 ml bottle  70 j  1.68  Malathion  j (cythion)  100 ml bottle  38 j  0.91  Edifenphos  j (hinosan)  100 ml bottle  150 |  3.61  Deltamethrin  j (decis)  100 ml bottle  115 j  2.77  Phorate  j (thimet)  kg  85 j  2.05  kg 50 kg  85 !  2.05  350 ;  8.42  Carbofuran  1 (furadane)  BHC dust  1 (kanpure) 1  Source: MRM 1996  Table B.12. Variable Costs - Labour Crop Labour Rate Price 1  (days/ ropani)  rice wheat tomato potato maize 1  ($/ropani) 15  18  8  10  30  36  15  18  10  12  Source: Kennedy and Dunlop 1989, Srivastava 1996  

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