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Vulnerability to phosphorus loss : subtitle identifying sites and their characteristics in the Elk Creek… Schendel, Emily Kate 2001

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V U L N E R A B I L I T Y T O PHOSPHORUS LOSS: I D E N T I F Y I N G SITES A N D THEIR CHARACTERISTICS IN T H E E L K C R E E K W A T E R S H E D , C H I L L I W A C K , B.C. By Emily Kate Schendel B.Sc. Geography and Environmental Resource Science Trent University, Peterborough, Ontario, 1998 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in The Faculty of Graduate Studies (Department of Soil Science) We accept this thesis as conforming to the required standard T H E UNIVERSITY OF BRITISH COLUMBIA September, 2001 ©Emily Kate Schendel, 2001 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department The University of British Columbia Vancouver, Canada Date DE-6 (2/88) Abstract A growing concern over the quality of water in agricultural areas has increased the interest in interactions between land use and aquatic ecosystems. Of particular interest is the role phosphorus (P) has in increasing eutrophication and degrading water quality. In freshwater systems, P is usually the limiting nutrient in the cycle of events leading to the increase in plant productivity, the depletion of dissolved oxygen, and the destruction of fish habitat. This thesis attempts to address the source of these problems by documenting the P status and the P dynamics in the Elk Creek watershed. Linkages are made between land use, fertilizer and manure applications, and inherent soil characteristics with sediment and water quality analyses. Several different P measurements, including soil test P (STP) using both Bray PI and P2 extractions, organic P, total P, and P adsorption, are used to determine the current status of P in the watershed. Base saturation, manganese, carbon content, pH, and P inputs have the greatest influence on the amount of available P in soils, while nitrogen, carbon, and base saturation have the strongest relationships with total P. The adsorption of P in the Elk Creek soils is influenced by the carbon content, texture, pH, and the cation exchange capacity. The land cover also influences adsorption capacity. Organic soils in this study appeared to have unlimited adsorption capacity, while the naturally occurring sesquioxides in the forest soils allowed for very high sorption capacities. In contrast, the sandy soils of the golf course located in the eastern hillside area of the watershed had extremely low adsorption capacity. The STP measurements as well as two indexes, the P Index and the Degree of P Sorption (DPS) index, were used to identify areas within the watershed that are vulnerable to P loss. Spatially, assessments of available P show an increase in concentration with distance downstream. The sites located near the mouth of ii the watershed are described as having excessive available P and are vulnerable to P loss. This assessment agrees well with the findings of the P Index, which also shows an increase in vulnerability for P loss, particulady in the mouth area. The P Index, which incorporates both input and transport variables, is strongly influenced by the degree of land cultivation. The DPS index did not exhibit spatial trends. However, the estimation of the Langmuir adsorption maxima (which is the basis of the DPS index) did show a general decline in the sorption capacity with distance downstream. Again, this would agree with an increase in vulnerability. The phosphorus content in sediments and streamwater correlated well with the soil vulnerability rating. The sediments are depleted of nutrients with respect to the corresponding soils, but do show and increase in concentration with distance downstream. Of the water quality indicators, ammonia, total P, fecal coliform, and dissolved oxygen concentrations show strong spatial relationships and agree with the vulnerability assessments. The DPS index did not relate well to the water quality indicators. This model is effective at indicating the degree of saturation, but does not take into account the amount of organic P in soil or the transport factors that make the P Index the more successful model. Accounting for organic P in soils is particularly important, as it has been shown within the literature to be less readily adsorbed, highly mobile, and a major component of the P contributions to runoff and leachate. The possible impacts of future land use changes, such as further agricultural intensification or urban expansion into the forest soils, are now better understood to be actions that would increase the vulnerability for phosphorus loss. Mitigation options to reduce P loss are currently needed, and will be necessary in the future to protect the balance maintained in the Elk Creek watershed. iii T A B L E OF C O N T E N T S Abstract Table of Contents 1V. list of Figures >i1 V 1 List of Tables l x . Acknowledgements x Chapter 1 INTRODUCTION 1 1.1 Thesis framework 4 1.2 Thesis objectives 6 Chapter 2 STUDY A R E A 7 2.1 Physical setting 7 2.2 Surface water quality 12 2.3 Land use / Land cover 19 Chapter 3 LITERATURE REVIEW 23 3.1 Phosphorus in soil, sediment, and plants 23 3.2 Soil P adsorption 26 3.21 Adsorption parameters 29 3.22 Phosphorus adsorption indexes 32 3.3 The P Index system 34 3.4 Movement of P in the environment 36 3.41 Transport of phosphorus by surface flow 37 3.42 Transport of P by subsurface flow 38 3.43 Influence of agriculture in P movement 39 Chapter 4 M E T H O D O L O G Y 41 4.1 Sampling and fieldwork 41 4.2 Land use surveys 42 4.3 Laboratory analysis 43 iv 4.4 Degree of phosphorus saturation 44 4.5 Phosphorus index 44 4.6 Spatial display and GIS techniques 46 4.7 Statistical techniques 47 Chapter 5 RESULTS A N D DISCUSSION 49 5.1 Soil fertility 50 5.2 Land use survey results 56 5.3 Phosphorus 59 5.4 Soil P adsorption 66 5.5 Phosphorus vulnerability indices 73 5.6 Environmental relationships 77 Chapter 6 SUMMARY A N D CONCLUSIONS 91 6.1 Soil fertility summary 91 6.2 Land use summary 92 6.3 Phosphorus status summary 93 6.4 Vulnerability indices summary 93 6.5 Environmental indicators 94 6.6 Conclusions 95 REFERENCES 97 Appendix 1. Land use survey and field work worksheet 103 Appendix 2. Phosphorus adsorption procedure 106 Appendix 3. Soil Fertility data. 107 Appendix 4. Phosphorus data. 108 Appendix 5. Adsorption isotherms. 109 Appendix 6. P Index Data 110 Appendix 7. Western Washington Worksheet 111 v LIST OF F I G U R E S Number Page 1.1 Thesis framework for studying phosphorus vulnerability 4 2.11 Location of the Elk Creek watershed in the Lower Fraser Valley of British Columbia 7 2.12 Elk Creek Watershed. 8 2.13 Profile of a Banford Muck (Terric Humisol) soil, located east of Annis Rd. at Hwy. 1 9 2.14 Soils of the Elk Creek watershed. 10 2.21 Seasonal variation in nitrate-N for three sites on Elk Creek. 12 2.22 Spatial and temporal variation in stream water nitrate-N. 13 2.23 Spatial and seasonal variation in dissolved oxygen (DO) in the Elk Creek watershed. 14 2.24 Trends in pH for the Elk Creek watershed. 15 2.25 Spatial and seasonal variation in total phosphorus and total Reactive phosphorus. 16 2.26 Ammonia and ortho-P for the Elk Creek watershed. 16 2.27 Fecal coliform counts in the Elk Creek watershed. 17 2.31 Changes in land use activities for 1995,2000, and 2001 in the Elk Creek watershed. 21 3.11 The soil phosphorus cycle. 22 3.21 Potential pathways for solutes in soil 26 5.11 pH and plant nutrient availability 51 vt 5.12 Relationship between carbon content and CEC. 54 5.21 Manure and fertilizer use in the contributing areas of the Elk Creek watershed. 58 5.31 Spatial display of available P concentrations in the watershed. 60 5.32 Relationships between available phosphorus and soil components in Elk Creek soils. 61 5.33 Spatial distribution of available P by contributing area. 62 5.34 Relationship between available organic P and pH. 63 5.35 Organic phosphorus concentrations in the available phosphorus fraction of Elk Creek soils. 64 5.36 Relationship between total P (% in soil) and total C (% in soil). 66 5.41 Selected adsorption isotherms for soils in the Elk Creek watershed. 67 5.42 Isotherms showing secondary adsorption. 68 5.43 Illustration of ion sorption reactions. 68 5.44 Sketch of adsorption reactions. 68 5.45 Relationships between P adsorption at 45 mg/1 solution concentrations and soil components for all soils and sediments in the Elk Creek watershed. 70 5.46 Spatial distribution of adsorption levels. 71 5.51 Plot of %AOP with the adsorption maxima. 74 5.52 Phosphorus vulnerability in the Elk Creek watershed. 76 5.61 Map of soil, sediment, and water quality sampling locations. 77 5.62 Relationships between available P (Bray PI) in sediment and pH in adjacent soils. 78 vii 5.63 Relationships between available P and aluminum concentrations in sediment. 80 5.64 Concentration of available P (Bray P2) by contributing area. 81 5.65 Carbon enrichment. 82 5.66 Nitrogen enrichment. 82 5.67 Enrichment of the adsorption maxima. 82 5.68 Enrichment ratios of available phosphorus. 83 5.69 Enrichment ratios of available organic phosphorus. 83 viii LIST OF T A B L E S Number Page 2.31 General categories of land uses. 20 3.31 The Phosphorus Index : Weighed raring calculations. 35 3.41 Factors responsible for controlling P loss at various geographical/ organizational scales. 37 4.41 Methods used to complete the phosphorus index worksheet. 45 4.51 Sources of data for GIS applications 46 5.11 Current soil fertility status (0-15 cm depth). 50 5.31 Spearman's rank correlations for total phosphorus. 65 5.41 Spearmans correlations coefficients for selected soil properties with adsorption at 45 and 5 mg/L. 69 5.61 Spearman correlation coefficients between sediment P fractions and sediment properties and soil P fractions with soil properties.. 79 5.62 Enrichment ratios of selected variables in the sediments from the Elk Creek watershed. 81 5.64 Spearman correlation coefficients for selected soil components and water quality indicators. 85 5.65 Spearman correlation coefficients for selected sediment components and water quality indicators. 86 5.66 Correlation matrix for P fractions in soil and sediment with water quality indicators. 87 5.67 Correlations between P Index rankings and water quality indicators. 89 ix A C K N O W L E D G M E N T S I would like to thank my supervisor Hans Schreier for his constant support, encouragement, and direction. I would also like to thank my committee members Les Lavkulich and Sandra Brown for their help and motivation. Al l three members of my committee were always wonderfully gracious and giving under the constant barrage of questions I pestered them with. For this I am very grateful. I would also like to thank several people for their unconditional help. Keren Ferguson and Carol Dyck were my lifelines in the soil lab. Elyn Humphreys and Laurens Van Vliet for their help with data sources and analyses. All of the members of the Elk Creek community who gave their time and access to their property. I would also like to thank all of my friends here in British Columbia and in Ontario for their support, friendship, and distraction. A very special thanks to my parents for their love and for keeping me going. Thank you to Environment Canada for their financial support, under the direction of George Dirkson. Chapter 1 INTRODUCTION It is well known that phosphorus (P) is the limiting nutrient in freshwater systems. Excess P inputs into aquatic systems lead to eutrophication, which is an environmental problem faced in many urban and rural watersheds. As the degradation of local waters occurs at an accelerating rate, emphasis is placed on understanding how, where and why P escapes from the terrestrial environment into the aquatic environment. Due to the former belief that P was relatively immobile in soil, environmental phosphorus has historically not been studied to the extent of other nutrients such as nitrogen. It is now known that considerably more bio-available P moves over and through soils to reach surface and groundwater by runoff, erosion, macropore, or by-pass flow than originally suspected. And although it has no known toxic effects, the environmental consequences of eutrophication by excess P can be severe, including depletion of dissolved oxygen, destruction of fish habitat, and shifts in aquatic plant populations. (Alberts and Moldenhauer, 1981; Carpenter et al., 1998; Edwards and Withers, 1998; Guertal et al., 1991; Pierzynski et al., 1994). The loss of P becomes significant when the concentration of soil P exceeds a certain saturation value, creating a surplus. The size of the P surplus in soil is determined by the type and rate of inputs, geographical and hydrologic factors, and most importandy, the soil itself (Edwards and Withers, 1998). This being known, the question then becomes whether soil P loss to water is due to characteristics inherent to the soil, climatic conditions, conditions influenced by human usage, or a combination thereof. When the source or origin of P is 1 uncertain, there is a resulting weakness in determining specific zones or source and sink areas within a watershed (Gburek, 1990). The body of work presented in this thesis is a contribution towards answering the questions of P dynamics in a watershed setting. These questions include: • What are the characteristics of soil that make it more or less vulnerable to P loss? • How can areas within a watershed be identified or indexed based on their potential of P contributions? • How does the retention of P by soil influence the amount moving through the environment? • What relationships do these indices have with sediments and water quality? Forty-five sample areas in a small watershed east of Chilliwack, British Columbia were used in an attempt to address these questions. What are the characteristics of soil that make it more or less vulnerable to P loss? Many landowners at some point will submit a soil sample for testing. The results of these tests often reveal a wealth of information about the fertility of the soil. However, it is possible that the information gathered from these tests is not being put to optimal use in areas outside of plant nutrition; most fertilizer recommendations are based on nitrogen levels and not phosphorus. There is a growing body of knowledge in the area of environmental soil management that is regulady finding strong correlations between traditional soil test P criteria and environmental indices. How can areas within a watershed be indexed based on their potential P contributions? A common and appropriate tool used for assessing the potential for off-site movement of nutrients is the nutrient budget. For a relatively immobile element such as P, the budget measures inputs from organic and commercial sources and compares these with estimates of output by plants and water. The nutrient 2 budget can then determine if a potential exists for water quality problems by identifying areas of surplus (Brown et al., 1999; Pierzynski et al., 1998). Recently, two new schools of thought on creating indexes to assess vulnerability for water contamination by P have been introduced. The first is a system developed around phosphorus adsorption capacities. The concept is based on the theory that adsorption mechanics are largely responsible for the storage and release of P to solution. By characterizing the relative capacities to store P, a means of comparison between sites has been gained. Taking this concept one step further is to determine the degree of P saturation (DPS). The DPS index measures the current soil test P against the phosphorus adsorption maxima (PAM) or other similar estimates such as the phosphorus sorption index (PSI) to achieve a percentage of saturation. The Netherlands is currently employing this technique and have set critical limits (e.g. 25% saturation) as the benchmark for management practices (Breeuwsma et al., 1995). The second approach is the Phosphorus Index, which is much less laboratory-intensive. It uses readily available soil test and field observation data to estimate an index of vulnerability for phosphorus loss. The P index considers the nutrient budget as well as the reasons for movement of P with soil particles. The index rates source and transport factors of sites on a weighted scale to produce numerical rankings (Lemunyon and Gilbert, 1993). The rankings are then categorized in terms of their vulnerability, from low to high. This approach is widely used in the USA (Gburek et al., 1996; Gburek et al., 2000; Sharpley, 1995; Sims, 1998; Stevens et al., 1993). How does the retention ofPby soil influence the amount moving through the environment? As described above, efforts have been made to limit the need for additional time consuming and expensive laboratory testing. However, until relationships between the traditional soil tests and the environmental indices can be confirmed, 3 especially in different climatic regions, additional environmental tests are needed. Tests such as P adsorption isotherms, equilibrium phosphorus concentrations (EPC0), and P saturation indexes (PSI) as well as additional field surveys can indicate the soil P concentrations and the potential for transport or retention. The adsorption isotherms and EPC Q values are ideal as they indicate over a range of concentrations the amount of P that is adsorbed at equilibrium. While this type of laboratory setting may not apply to the actual field conditions, they are useful for two reasons; comparisons can be made between soil types, land use, and management, and the adsorption maxima may be used for management purposes. The PSI values are gaining popularity due to their ease and inexpense in the laboratory as well as their consistent strong correlations to the adsorption maxima. 1.1 Thesis framework The approach taken in this thesis is depicted schematically in figure 1.1. The geographic area used in the study, the Elk Creek Watershed, is described in Chapter 2. The methodologies used in field sampling, surveying, laboratory analysis, statistical investigations, GIS techniques, and the P index system is presented in Chapter 3. Chapter 4 gives an overview of the past and current research in areas including phosphorus in soil and plants, soil P adsorption, P movement in the environment, and the P index system. The results and a corresponding discussion about the soil fertility, phosphorus status, P adsorption, the two models, and the environmental relationships within the watershed are found in Chapter 5. A concluding summary of the project is found in Chapter 6. 4 IDENTIFICATION OF GOALS AND RESEARCH OBJECTIVES FIELD RESEARCH Farm Interviews Summary of Land Use and land Management Soil and Sediment Sampling i : Summary of Soil Characteristics, P Status, and P Adsorotion CREATION OF INDEXES P Index Degree of P Sorption ENVIRONMENTAL RELATIONSHIPS / \ Sediment Enrichment Wet/Dry Season Water Quality Figure 1.1 Thesis framework for studying phosphorus vulnerability. 5 1.2 Thesis objectives This study is designed to characterize the P status of the Elk Creek watershed and to discover the factors and conditions that contribute to that status. It is also intended to compare two phosphorus-indexing systems to determine the best technique for identifying areas vulnerable to P loss. A comparison is then made between phosphorus indices, sediments, and local water quality data. Making linkages between soils, sediments and water quality provides a method of evaluation of the index itself and the influence of land and soil parameters on the environment The specific objectives are as follows: • To describe the soils and sediments of the Elk Creek watershed for general soil and sediment characteristics, P status, and P adsorption capacity. • To estimate the degree of phosphorus saturation for the soils and sediments. • To rank the sites sampled according to the Phosphorus Index criteria • To determine relationships between phosphorus indicators, sediments, and water quality. • To evaluate all of the objectives above in a watershed context to address spatial variability. 6 Chapter 2 STUDY A R E A The Elk Creek watershed is located to the southeast of Chilliwack, British Columbia. This small basin (33.5 km2), with its headwaters originating from Elk and Thurston mountains, flows to the northwest where it empties into the Hope Slough which flows into the Fraser River. Figure 2.1 depicts the watershed's location in the Lower Fraser Valley, and figure 2.2 is a snap shot of the watershed itself. 2.1 Physical Setting Climate The watershed experiences the temperate climate of the Lower Fraser Valley, with mild wet winters and cool dry summers. Chilliwack regional airport houses the nearest weather station and reports an annual rainfall of 1880 mm yr 1 (Wemick,1996). Geology, Geomorphology, and Soils The headwaters area of the watershed is steep, mountainous terrain. The two mountain peaks reach elevations of approximately 1400 to 1700 m, with a lookout ridge at 700 m. The valley bottom sits at approximately 10 m above sea level. The hillside consists of Mesozoic and upper Paleozoic bedrock from the Pre-Tertiary era. The bedrock is covered by shallow, medium-textured materials 7 Figure 2.12 Elk Creek Watershed Key to Features 9 comprised of glacial, colluvial, and eolian sediments. These parent materials along with the vegetative inputs of the Douglas fir forest form the Ryder (Dystric Brunisol) soil series that covers most of the hillside area. Eolian deposits dominate the toe of the slope: sand, silt, and silt loams 1 to 3 m thick Adjacent to the toe of the slope lies an area that has been flooded for extended periods of time. Bog, swamp, and shallow lake deposits are overlain by up to l m of laterally accreted silt and silt loam sediments. The bog remnants form a Terric Humisol referred to as the Banford muck. Figure 2.13 is a photo of this type of soil. The influence of the flooding lessens with proximity to the mouth of the creek, and Fraser River sediments begin to dominate. Laterally and vertically accreted silt loam and silty clay loams form the parent materials in the lowland areas, leading to the development of gleysols or gleyed regosols. Figure 2.14 is a map depicting the soils of the watershed. Figure 2.13 Profile of a Banford Muck (Terric Humisol) soil, located east of Annis F at Hwy. 1. 10 Figure 2.14 Soils of the Elk Creek Watershed (B.C. Soil Survey, 1963). Soil Name Soil Classification Texture Annis Peaty Gleysol Organic Banford Terric Humisol Humic Organic Material Calkins Orthic Dark Gray Gleysol Silt Loam Cheam Dystric Brunisol Gravelly Sandy Loam Elk Orthic Dark Gray Gleysol Silt Loam to Gravel Fairfield Gleyed Mull Regosol Silt Loam Grevell Orthic Regosol Loamy Sand Grigg Orthic Dark Gray Gleysol Silty Clay Loam Isar Orthic Regosol Loamy Sand Marble-Hill Dystric Brunisol Silt Loam Monroe Mull Regosol Silty to Sandy Loam Niven Miscellaneous Silty Clay Loam Pelley Orthic Dark Gray Gleysol Silty Clay Loam Prest Orthic Gleysol Silt Loam Ryder Dystric Brunisol Silt Loam 11 Water Features The major waterway in the watershed is Elk Creek (12 km), with major tributaries MarbleriilL Calkins, and Ford creeks. In the steep upper reaches of the hillsides area Elk Falls provide a significant water feature. Peak runoff occurs in late spring during the snowmelt period, with very low flows ensuing in late summer and early fall. The estimated mean annual flow for the watershed is 0.61 mV 1 (Rood and Hamilton, 1995). Subsurface water resources include several deep aquifers in the Eastern Hillsides area. The water table is relatively shallow, with local wells pumping from depths of approximately 20 m, although some of the smaller wells on the hillside reach depths of 100 m. Al l the groundwater aquifers are considered vulnerable to contamination due to coarse surface materials and the shallow depth to the water table (Litke, 1997). 2.2 Surface Water Quality The Elk Creek watershed provides spawning ground for several species of salmon including Coho and Chum. Mamtaining a high quality habitat for these species is a provincially mandated objective. Consequendy, a water quality assessment was performed by the B.C. Ministry of Water, Lands and Air Protection and Environment Canada between 1998-2001 at several sites in the watershed. The field and laboratory testing includes stream water include analysis of dissolved oxygen (DO), pH, nitrate and nitrite (NOj/NOj), ammonia-N (NH3), total inorganic nitrogen (TIN), total phosphorus (TP), total reactive phosphorus (TRP), orthophosphate, and fecal coliforms. The data shows spatial and temporal variations for most of the measured values. The temporal variability is shown using wet and dry season averages. High flow data are represented by an average of the October through March values, while dry season or low flow data are an average of the April to September measurements. 12 Nitrate — Nitrogen Nitrate nitrogen is an essential, naturally occurring nutrient in aquatic systems. At high concentrations it is toxic to aquatic and human life forms. Increased levels often occur due to contamination by sewage, fertilizers and manure inputs to stream water (Waite, 1984). During the three-year sampling period, the average NOj-N values ranged from 330 to 987 *gL~\ with a median of 525 *gL/\ The highest value (1640 agL'1) was measured at Lower Elk in mid-November 1999. The lowest values were measured consistendy at Upper Elk creek. All values measured meet the Canadian Water Quality Guidelines for drinking water and aquatic habitat (Ministers, 1987). Spatial patterns do exist for nitrate in the watershed. The headwaters of the Elk tend to have the lowest levels, although the three other contributing creeks appear to have elevated readings. Seasonally, the values are higher in the winter months and lower in the summer months, corresponding to the level of biological uptake of nitrogen in the water. However, the differences in peaks and low points in concentration are greater at the mouth (Lower Elk). This is reflective of both natural downstream accumulation and the increasing land use intensity. Figure 2.21 shows these trends. Elk Creek Nitrate Sample Date Figure 221 Seasonal variation in Nitrate-N for three sites on Elk Creek. 13 Ammonia-N The overall ammonia-N levels met the Canadian Water Quality Guidelines for freshwater aquatic life as reported by the Canadian Task Force on Water Quality Guidelnes (1987). However, the levels measured at the lower stations (e.g. Lower Elk #6) exceeded the B.C. Objectives for freshwater aquatic life. The overall sampling averages range from 6 «gL 1 to 120 agL"1. The spatial patterns are similar to those found with the nitrate data, as the levels of ammonia decrease with distance away from the mouth. Seasonally, peaks occurred in December 2000 and January 2001 at both the mid stations (286 #gL" at #5) and the lower stations (>500 agL"1 at #6). These peaks were reflective of the seasonal patterns ammonia-N tends to follow. During the dry season months, ammonia levels are low due to consumption by aquatic plants and organisms as well as increased nitrification encouraged by warmer water temperatures. The concentrations are higher during the winter months due to decreased uptake and increased inputs via surface runoff and erosion. Figure 2.22 depicts the seasonal and spatial trends for ammonia in the Elk Creek watershed, summarized by contributing area. Ul to c o E I 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 • Ammonia (wet season avg.) • Ammonina (dry season avg.) J Tributaries • Main Creek / "I • i Marble Calkin Ford Upper Elk Mid Elk Mouth Sampling Stations Figure 222 Spatial and temporal variation in stream water nitrate-N. 1 4 Dissolved Oxygen Oxygen is an essential element in water for plants and other aquatic life. It is a common and useful indicator of stream health, particularly in areas subject to eutrophication. Oxygen moves from the atmosphere to the water through dissolution, prompted by the movement of water by wind or rapids. Photosynthesis by aquatic plants is also an important contributing factor (Waite, 1984). In the Elk Creek watershed, the average D O values ranged from 3.6 to 17.5 mg/L, with a mean of 11.1 mg/L. These levels are adequate to good. When summarized for each of the contributing areas, dissolved oxygen shows both spatial and temporal variation. As seen in figure 2.23, there is a steady decline in dissolved oxygen along the main creek in the wet months, while the decline levels off during the summer months. This is an indication of the increased plant productivity and photosynthesis during the warmer temperatures. The tributaries show interesting trends. While Marble and Calkin remain relatively constant through the waterhsed, the amount of D O increases from Ford Creek to the Mouth sites during both the wet and dry seasons. This may be an indication of the effects of land use, as this trend is also shown in the pH results and correlates well with the fertilizer inputs as will be shown later in figure 5.21. 13.000 12.000 4 S, 11.000 5 X O s 10.000 9.000 8.000 £ A DO (w) • DO (d) A • A ^ « — — • • Tributaries Main Creek 1 , ' 1 1 Marble Calkin Ford Upper Mid Elk Mouth Elk Figure 223 Spatial and seasonal variation in dissolved oxygen (DO) in the Elk Creek watershed. Sampling Sites 15 pH The pH range for most natural waters lies between 6.5 to 8.5. The level of pH is important as it determines the availability of most nutrients for plants. The range of pH values found in the watershed are 7.14 to 8.36 with a mean of 7.9. As noted above, the pH values show nearly identical trends to those depicted by dissolved oxygen. A steep decline in pH exists in the main stream during the dry months, while the dry period tends to level off the acidification spatially. 8.400 8.200 8.000 £ 7.800 7.600 7.400 7.200 A -pH(w) *pH(d) A • A - . . A A • • • Tributaries Main Creek Marble Calkin Ford Sampling Sites Upper Mid Elk Mouth Elk Figure 224 Trends in pH for the Elk Creek watershed. Phosphorus The stream water was tested for three different forms of phosphorus: total (TP), total reactive (TRP) and ortho-phosphorus. The overall averages for TP ranged from 6 to 46 agL"1. Spatially, total phosphorus increases with distance downstream, although the three contributing creeks to the Elk (Marble, Calkin, and Ford) all have relatively high but consistent values. Seasonally, TP values peak in the fall months, remain high during the winter, and are lower in the summer. Total reactive phosphorus follows very similar trends but at a much lower level than TP, as evidenced in figure 2.25. 16 Figure 2.25 Seasonal and spatial variation in total phosphorus and total reactive phosphorus. 60.00 50.00 40.00 • Total Phosphorus (wet) • Total Phosphorus (dry) • Total Reactive Phosphorus (wet) • Total Reactive Phosphorus (dry) f 30.00 | 20.00 10.00 0.00 Marble Calkin Ford Upper Elk Mid Elk Mouth Sampling Sites Ortho-phosphate also followed neady identical trends. Ranging from 1 to 12 (ug/L), this readily available form of P is influenced by soluble and particulate P inputs. As seen below, the plot of ammonia and ortho-P shows the lag-time between soluble inputs and those from particulate inputs. Lower Elk Creek Ammonia and Ortho-P 1 Year 6 q •s s Sam pie Date Figure 2.26 Ammonia and ortho-P for the Elk Creek watershed. The phosphorus concentrations found in Elk creek are relatively low. As indicated by Boyd (2000), TP rarely exceeds 0.5 mg/L except in eutrophic waters. The levels in the creek are far below this level. 17 Fecal Coliform Fecal conforms are found in surface waters due to contamination by human or animal fecal mater. The trends in fecal coliform counts varied from the other criteria measured. The spatial trends between the upper and middle sampling stations were not as evident as for nitrate or ammonia, although large increases at the lower portion of the watershed still occurred. The increase at the lower portion of the watershed indicates that the source is most likely animals, as the land use is primarily agricultural in that portion of the watershed. The temporal trends also varied. The usual winter peaks did not occur. Instead, high counts were noted in late October to early November and during the summer months. These increases coincide with the high-rate application periods for manure onto agricultural land. In particular, the post-harvest application of manure in October accompanied by seasonal rainfall would increase microbial counts via elevated surface runoff and increased stream discharge. Figure 2.27 depicts these temporal trends. Bk Creek Fecal Coliform 3000 S a m p l e Date Figure 2.27 Fecal Coliform counts in the Elk Creek Watershed. 18 Relationships between water quality values Most of the variables tested in the stream water correlated well with each other and with the different forms of the same variable (e.g. N 0 3 vs. N H 3 , R 2 = 0.65). As a dominant influence in most chemical systems, pH related strongly to most of the water quality data. It is well known that pH affects the availability of nutrients in most systems. This is reflected in negative Spearman's correlation coefficients: R 2 = -0.60 (NH3), and R 2= -0.46 (TP). These correlations occur between variables during the wet season. The correlations are similar for the dry-season data but the relationships are slighdy weaker. Fecal coliform levels also show strong relationships with the other indicators. Increases in coliforms correlate positively with N 0 3 , N H 3 , and TP (R2 = 0.73, 0.60,0.84), and negatively with dissolved oxygen (R2 = -0.49). 2.3 Land Use / Land Cover The Chilliwack area is a growing community, influencing the nature and character of the soils and water as it expands. The population of the watershed in 1990 was 300. The future plans for the area indicate a large growth in population, as the eastern hillsides area is slated for urban development. Currendy, the headwaters and upper portions of the watershed are forested with recent, small sections of logging activity. The lower portion of the watershed is primarily agricultural, with less than 2% of the land area being urban or industrial. Farms, primarily dairy, surround the stream channel and most of the lower 4 km of the basin are ditched with most of the riparian vegetation removed. At the mouth of the watershed are intensive horticulture operations, most with drainage and irrigation systems in operation. Table 2.31 describes the land use categories that were used to characterize the watershed. 19 Table 2.31 General categories of land uses. Category Description Forest Includes all forested land and any rural, non-farm home in the hillsides area Grass Land cultivated under grass for harvest or for use as pasture Com Includes all areas under com and any other grain. Horticulture Land used to produce nursery stock, vegetables, flowers, and small trees. Also included areas under greenhouse production. Small fruit / Berry Areas used for growing small fruits and berries. Wild Land Land for which no perceived use was designated. Uncultivated Used for agricultural areas left idle for a season or those under construction towards a new land use. Recreational Used for outdoor recreation and open space including golf courses, sky dive landing sites, and school playgrounds. Residential Used for the accommodation of persons in single and multiple family dwellings. Through the use of orthophotographs and windshield surveys, the land use in the watershed has been characterized for 1995,2000, and 2001 summer seasons. The data was digitized and incorporated into a GIS database using ARCview for quantitative comparisons of the land use changes. Figure 2.31 depicts the changes occurring over the past six years. Litde change has occurred in the headwaters area. There has been a slight increase in the number of rural non-farm residences in the hillsides area, but at this point it is not a significant change in land cover. There has also been some [insignificant] growth in the residential area at the toe of the slope. As stated earlier, this is expected to change dramatically in the near future. Between 1995 and 2001 a marked decrease in the amount of area under grass occurred in the agricultural area. The grass was replaced by com, horticultural, and other (small fruit and uncultivated) land uses. The summer 2000 season 20 appeared to be a time of transition, as large areas of the watershed were not cultivated and others were undergoing agricultural construction. This is noted in the shift from "other" land uses to even greater horticulture and com coverage. The grass-com conversion is likely a traditional rotation where as horticulture is a new permanent change. This shift in land use should be noted for its related change in intensity. Enlarged horticulture implies greater inputs, increased reliance on the soil resource, and greater potential for ground and surface water contamination. These changes are particularly evident at the mouth of the watershed, in the Lower Elk contributing area, and the peaks in stream water nutrients reflect this. 21 Chapter 3 LITERATURE REVIEW There is a large volume of current and historical work pertaining to phosphorus in soils and the environment. The following sections give an overview of the research pertaining to phosphorus in soil, soil characteristics influencing P adsorption, P movement in the environment, and the P indexing system. 3.1 Phosphorus in soil, sediment, and plants The fundamentals of soil phosphorus are relatively well understood. A detailed descriptions of the phosphorus cycle, the forms of P, and other relevant topics can be found in Pierzynski et al. (1994), Sanyal and De Datta (1991), Singh and Jones (1976), and Stevenson (1986). Figure 3.11 is an example of the soil phosphorus cycle. As evidenced in the figure, the P cycle in soil is a dynamic system in spite of the immobility of many inorganic compounds. INPUTS Fertilizers Plant Residues Agricultural Wastes Municipal and Industrial By-Products SORBEDP Clays, Al & Fe Oxides Des°*Ption (SOIL SOLUTION Apatites LOSSES Plant Uptake Sediment and Soluble P by erosion, runoff, and leaching ORGANIC P Soil Biomass Soil Organic Matter Soluble Organic P Figure 3.11 The soil phosphorus cycle. (Adapted from Pierzynski et al. 1994). 23 Inorganic Phosphorus The forms of P in soil fall into three main categories: inorganic, organic, and solution. The inorganic P fractions in soil can be generalized into two groups: (1) calcium bound and (2) iron and aluminum-bound. Those containing calcium are generally more soluble in soils than the A l and Fe groups, although this is highly dependent on the soil pH. The least soluble of the calcium compounds are the apatite minerals ([3Ca3(P04)JCaO), which also tend to be the most common natural sources of P in the soil system (Sanyal and De Datta, 1991; Stevenson, 1986). The chemistry of inorganic phosphorus in soils is well understood and is described in detail in many of the references described above. Organic Phosphorus Due to the difficulty in isolating and describing organic P, less is known about its role in soiL Varying gready with region, climate, and vegetation, much of the organic P is derived from plant residues, manure and soil (micro)organisms. These regional differences cause a broad range of representation in soil, with recorded ranges of 20 to 60% of the total P being organic (Tiessen and Stewart, 1994). Some known relationships do exist, such as the correlations between higher levels of organic P in clay soils than in coarse-textured. In addition, organic P is found primarily in the fulvic acid fractions (more than 40%) (Sanyal and De Datta, 1991). Decreasing with depth into the profile, the organic P compounds found may include inositol phosphates, phospholipids, nucleic acids, phosphoproteins, and metabolic phosphates (Sanyal and De Datta, 1991; Stevenson, 1986). Of the P associated with soil organisms, the predominance is towards bacteria and fungi, although higher organisms may play important roles in net mineralization. 24 Phosphorus in Solution The group responsible for mineralization, the soil microbial biomass, is often referred to as the "driving force" of the P cycle, converting materials into new products (Tiessen and Stewart, 1994). However, mineralization is just one of the methods phosphorus is transitioned from the soil matrix to the soil solution. The forms of P that can be used by plants are found only in soil solution, most commonly in the forms of (H 2 P0 4 )" and (HPO^)2". Aside from direct contributions from chemical fertilizers, the primary processes that solubilize P are desorption, dissolution, and mineralization. The rates of reaction of these processes are influenced by; p H , saturation level, vegetation type, A l and Fe content, anion exchange capacity ( A E Q , organic matter content, clay content, and the adsorptive capacity of the soil (Pierzynski et al., 1994). It is important to note that phosphorus in soil solution is vulnerable to movement i f not used by plant material. This contribution to the ground or surface waters is recognized as the dissolved fraction, generally in runoff and leachates (Pierzynski et aL, 1998). Sediment Phosphorus While solubilized P may contribute to surface and groundwater in the form of dissolved P, environmental inputs of P may also originate through particulate P. Once deposited, the particulate P contributes to the sediment load of the system. However, these eroded sediments may exhibit significant differences from the original soil. House et al., (1998) summarize three main reasons for the differences: the size selectivity of erosion, the degree of mixing of particles from different origins, and the prevailing physiochemical conditions. Generally, the smaller and lighter soil aggregates will be carried the furthest extent and are the most likely to enter the drainage system. Consequently, the percentage of coarse materials in the sediments is generally less, resulting in a reduced level of acid 25 extractable P normally associated with primary minerals. Accordingly, there is an increase in the contribution of iron and aluminum associated P that is common in smaller particle sizes (House et al., 1998). In addition to the change in form, the variety of materials that mesh in an erosive environment can alter the nature of P in sediment from that in adjacent soils. Finally, one of the most predominant factors in altering sediments is the physicochemical depositional environment According to Sallade and Sims (1997), the reducing conditions created by standing water have been noted to augment solubilization of sediment-bound P as iron phosphates become more soluble. These conditions, frequendy found in wet seasons, may coincide with major storm events. As a result, the inputs from sediments into the water column are three-fold; ® the solubilization during reducing conditions as described above, (ii) the inputs from storm related runoff and erosion, and (iii) the resuspension of deposited sediment (Haggard et al., 1999; House et al., 1998; Sallade and Sims, 1997). Summer conditions tend to do a reversal, dropping the water column P concentrations. The shift in chemical conditions often increases the level of adsorption by base sediments, creating a buffering mechanism to regulate the P concentrations in the water column. In addition, the presence of phytoplankton and other epiphytic communities act as P consumers during warm periods (Haggard et al., 1999, House, 1998). These seasonal conditions create a predictable cyclic pattern with P concentrations peaking for most water bodies in late fall. 3.2 Soil P Adsorption It is the ability of a soil to store, filter, and transmit water that regulates water availability to plants and transport of environmental pollutants to surface and 26 groundwater (Doran et al., 1999). Depending on the characteristics of the soil and of the solutes, a substance or element undergoes various biological, chemical, and biochemical processes in the soil. These may include uptake by plants, adsorption and desorption by soil colloids, precipitation and dissolution of secondary solids, degradation by soil organisms, mineralization, immobilization, and volatilization (Sparks, 1995). Figure 3.21 shows some of these potential pathways of solutes in solution within the soil. Organic matter formation and decomposition Exchange and adsorption by organic matter Uptake and release by plants Fixation and release by microbiota Precipitation and mineral formation Exchange and adsorption by mineral constituents Exchange with gaseous phase Figure 3.21 Potential pathways for solutes in soil. Soil adsorption is a component of figure 3.21. Although it is only one of many potential processes its role in soil is of great importance. Adsorption is defined as the accumulation of a substance or material at an interface between the solid surface and the solution. It occurs due to differential forces of attraction or repulsion among molecules or ions of different phases at their exposed contact surfaces (Frissel et al., 1970). Soil adsorption determines the quantity of plant 27 nutrients, metals, pesticides, and other organic chemicals that are retained on soil surfaces and is therefore one of the primary processes that affects transport of nutrients and contaminants in soils (Sparks, 1995). When referenced specifically to phosphorus, adsorption refers to the removal of ionic P (H2P04", HP0 4 2") from the soil solution to the solid phase via chemical reactions (Pierzynski et al., 1994). In addition to its significance in the soil, the soil adsorption parameter is useful in research and experimentation. Using samples from the field, a test of relative ease and low cost can be performed to achieve reasonable results. The laboratory results, termed adsorption isotherms, yield a substantial amount of information. The isotherm, a partition diagram of the concentration of a substance or adsorbate on a solid or adsorbent versus the concentration of that remaining in the external phase at standard temperature and pressure (STP), gives very detailed information about the soil being sampled. The information gleaned may include the nature of the reaction between the solid surface and the adsorbate, the heat, free energy, and entropy of the reaction, the specific surface of the solid, and the amount of the adsorbate already being held on the particle surface (Giles, 1970). Much of the information described above is gathered by applying the data to an adsorption model Porter and Sanchez (1992) applied four models to their data from soils in the Florida Everglades. These models included: Freundlich x/m = a * c 1 / n Langmuir x/m = (a * X m * c) / (1 + a * c) Tempkin x/m = [(Xm * R * T) / b] * ln(A * c) Dubinin - Radushkevich x/m = X m * exp[-B * R * T * ln(l + 1/c)] Where: x/m = mass sorbed per mass of soil. A , a, B = constants, T = temperature, and R = gas constant 28 They found that while all four models had individual benefits, the Langmuir equation provided the strongest correlations and indicated an adsorption maximum. 3.21 Adsorption Parameters The adsorption isotherms give specific, quantitative chemical data about the ability of a soil to adsorb a particular chemical or element. However, in the field, the dynamic nature of soils indicates that several parameters influence the adsorptive capacity of a soil. The primary components of soil responsible for adsorption reactions are oxides or hydroxides of iron and aluminum, clays calcium carbonates, and organic matter (Pierzynski et al., 1994; Sanyal and De Datta, 1991). Indirectly, several other forces may influence adsorption including; pH, CEC, A E C , ionic concentration (activity of the ion), charge of the replacing ion, size of the ion, % base saturation, biological activity, presence of metals, and saturation percentage of the element in question (Mott, 1970). Aluminum andiron Oxides and Hydroxides In a review of the research, Sanyal and De Datta (1991), provided an excellent overview of the influence each property may have on phosphate adsorption. They begin with the contribution of two different extractions of iron and aluminum [ammonium oxalate (AAO) extractable and ethylenediarninetetraacetate (EDTA)}, and found that both tests show close correlations between P adsorption capacity and Al/Fe content. Shortly thereafter, Yuan and Lavkulich (1994), confirmed these findings in Spodosols in British Columbia, Martinez et aL (1996), in mine soils in Spain, Lookman et al. (1995), in slightly acid sand loam soils in northern Belgium, and Lyons et al. (1998), in a riparian forest soil on Rhode Island. In each case, significant positive correlations existed between the oxalate-extractable A l and Fe contents and P 29 adsorption capacities, or in the case of Lyons et al. (1998), A l and Fe negatively correlated with the EPC 0 . Similarly, other forms of A l extracts, such as citrate-diothionite-bicarbonate (CBD) and copper chloride (CuCLJ, were reflective of the P sorption capacities (Villapando and Graetz, 2001). The CuC^ — extractable A l was determined to be the single most important chemical property contributing to P retention, accounting for greater than 60% of the variation in selected Florida Spodsols. In the same study, the influence of A l on desorption was also noted. It was shown that even after nine successive extractions, more than 70% of the added P remained in high-Al soils, indicating a large hysterisis effect. Texture Sanyal and De Datta (1991) noted the importance of texture, specifically clays, in sorption studies. The umbrella of texture not only includes the role of differing particle sizes, but also all the properties influenced by texture; specific surface, surface charge, and other physical properties. However, many of the more recent studies did not find significant relationships between texture and P sorption (Haggard et al., 1999; Lookman et al., 1996; Lyons et al., 1998; Martinez et al., 1996; Porter and Sanchez, 1992). This might suggest that it is not the particle size that influences, but the coatings of A l and Fe or organic colloids on the clay surfaces that have the greatest influence. Organic Matter Organic matter content has been reported to play a role in the sorption activities. This contribution may be due in part to the stabilization of the soil by "free" sesquioxides, through additional anion exchange sites, or again due to the metal chelates associated with organic matter (Sanyal and De Datta, 1991). Most often, one would predict a positive relationship between the two variables. However, as Singh and Jones (1976) determined, the P content of the organic matter 30 influences the adsorption capacity in both directions. Residues having P contents <0.3% increase P sorption, whereas residues or manures having P contents >0.3% decrease sorption. These finding were confirmed by Eghball et al. (1996) through long-term applications of poultry manures and fertilizer in western Nebraska (increase in P sorption capacity), and by Nziguheba et al. (1998) through additions of high quality tithonia (decrease in P sorption capacity). Generally, it is accepted that organic matter and organic carbon do influence adsorption in some form, positively or negatively (De Cristofaro et al., 200; Lyons et al., 1998; Martinez et al., 1996; Sanyal and De Datta, 1991). However, Haggard et al. (1999), Lookman et al. (1996), and Villapando and Graetz (2001), observed no significant relationship between the P saturation parameters and organic C% or organic matter. Other Parameters While most of the literature concentrates on the parameters discussed above, other variables that may influence adsorption are occasionally mentioned. Sanyal and De Datta (1991) note the role of pH and supporting electrolytes, explaining the increase in pH decreases the charge and electrostatic potential of positive sites, causing a fall in P retention. This would imply a negative correlation between the two variables. This was confirmed by Martinez et al. (1996) who showed that pH along with Fe, A l , and organic carbon explained 79% of the variance in the log of the adsorption index. Porter and Sanchez (1992) had similar findings in their study of Florida Histosols. They found significant correlations with ash content, free C 0 3 , extractable and total Ca, and extractable and total Fe. Finally, Indiati (2000), managed to look to the heart of the situation, and correlate the current P concentration in solution to the sorption capacity. They found that there was a significant relationship between the two, with the slope of the regression equation decreasing with increasing soil P adsorption. 31 3.22 Phosphorus Adsorption Indexes The data gathered from the adsorption procedure can be very useful for comparisons between soils or for prescribing optimal fertilizer requirements. Perhaps the most common use of the model output is the estimate of the capacity of P that can be adsorbed (Pierzynski et al., 1994). However, there are several problems associated with using the P adsorption maxima (PAM). Not all soils will fit to the Langmuir model (which gives the maxima) and not all soils will have an identifiable maximum in the laboratory. Instead, the phosphorus sorption index (PSI) has been developed to approximate the sorption capacity maxima (Bache and Williams, 1971). This index is defined as the ratio of P adsorbed from solution to the logarithm of the equilibrium solution P concentration. It is calculated as PSI = (X + E) / log (C), where X is the amount of P lost from the solution, and E is the resin extractable P (Martinez et al., 1996; Porter and Sanchez, 1992). Several recent studies have evaluated this technique with a comparison of the determined PSI value to the actual P A M . Most found strong significant correlations between the two (average r2 = 0.80) (Leclerc et al., 2001; Martinez et al., 1996; Paulter and Sims, 2000; Porter and Sanchez, 1992). The benefit to this technique is in achieving a reasonable estimate of the maxima while simplifying the lengthy laboratory technique. Another option that can be used when the P A M is not possible is the equilibrium concentration of P at the point of zero sorption (EPQ). It is described as the point where the isotherm crosses the x-axis (Yuan and Lavkulich, 1995). Unlike the PSI, evaluating EPC 0 does require the completion of the full adsorption procedure. One final use of the adsorption data is to evaluate saturation percentages. Heathwaite (1997) makes an important point when describing pathways for P loss from agriculture; "It is important to establish the point at which the capacity of soil to adsorb P becomes saturated, because this will influence the potential for P export in drainage waters. The degree of saturation is dependent on the 32 concentration of P in soil, because P becomes weakly held as the adsorption capacity of the soil approaches saturation." (p. 212). Logic such as this prompts the development of the P saturation index. Established in the Netherlands in the early 1990's, the degree of P saturation (DPS) or degree of soil saturation with P (DSSP), were developed to evaluate soils proximity at a critical limit of 23%. The concept is meaningful as it integrates a measure of the intensity of P accumulation with respect to the finite adsorption capacity with a measure of the potential for P to desorb from the soil matrix (Beauchemin and Simard, 1999; Sharpley, 1995; Sims et al., 1998). Beauchemin and Simard (1999) provide a tabular review of some P saturation indices retained in different studies. The information given in the table includes general characteristics, observations, and the calculation used for the index. Generally, the studies identified by the author used one or two different approaches to calculate the index. More recently, the related research has employed several of the different techniques and performed comparisons within and between those techniques. Paulter and Sims (2000) evaluated the degree of saturation using three equations: DPS L a n f f n u i r (%) = P o x / ( P A M ^ J x 100 (1) D P S O T (%) = {STP (mgkg1) / [PSI + STP (mg kg1)]} x 100 (2) DPS o x (%) = \P„ (mmolkg1) / P A M ^ + F e o x ] ) (mmolkg1)] x 100 (3) [STP = Soil Test Phosphorus] They found that with equation (1), significant linear relationships did exist with other forms of soil test P and soil characteristics, in particular, CaC^ —P (r2 = 0.53), P w (r2 = 0.43), STP (r2 = 0.48), and strip P (r2 = 0.60). To further this analysis, curvilinear regression equations were determined to better fit the data (r2=0.65 to 0.82). Equations 2 and 3 were then used to determine if less time-consuming methods could be used to assess the degree of saturation. The DPS o x 33 (3) did correlate lineady with soluble and desorbable-extractable P, as did the DPS S T P . The DPS S X P (2) also highly correlated with % clay, thus making it possible to classify a watershed based on textural analysis. It was also highly recommended, as this second equation would take advantage of existing agronomic STP databases, encouraging efficient management efforts to reduce nonpoint-source pollution. Leclerc et al. (2001) also used equations 2 and 3 in their study of grouping soils according to fertility and P sorption and desorption characteristics using multivariate analysis. The study indicated that the physical and chemical properties of the surface soils with the DPS values contributed to fertility classifications that may also be helpful in assessing vulnerability to water pollution by P. 3.3 The P Index System The phosphorus saturation index, as described earlier, uses laboratory techniques to describe the amount of P held in soil versus the potential maximum that can be held. The main drawback to this vulnerability index is the necessity to use additional lab testing. Strategies that can identify critical source areas within watershed more efficiendy may improve P management techniques (Sharpley et al., 1994). In response to this need, the National Resources Conservation Service and the United States Department of Agriculture developed a concept for a field-scale assessment tool to be used by staff, planners and farmers (Lemunyon and Gilbert, 1993). The concept, simply named the P Index, rates source and transport factors of a site to provide a numerical ranking. Each of the source (soil test P, fertilizer, and manure inputs) and transport factors (runoff and erosion potentials) are weighed based on their relative influence from none to very high. Table 3.31 is the original calculation table from Lemunyon and Gilbert (1993). 34 Table 3.31. The Phosphorus Index: weighed rating calculations and site vulnerability evaluation (Lemunyon and Gilbert, 1993). Phosphorus loss rating (value) Site Weight None (0) Low(l) Medium (2) High (4) Very Characteristic high (8) Soil erosion 1.5 NA <10 10-20 20-30 >30 Mg/ha Mg/ha Mg/ha Mg/ha Runoff class 0.5 Negligible Very low Medium High Very high or low Soil P test 1 NA Low Medium High Very high P fertilizer 0.75 None 1-15 16-45 46-75 >76 application rate applied kgP/ha kgP/ha kgP/ha kgP/ha P fertilizer 0.5 None Placed Incorporated Incorporated Surface application applied with immediately >3 months applied >3 method planter before crop before crop months deeper or surface before than 5cm applied <3 crop months before crop Organic P 1 None 1-15 16-45 46-75 >75 application applied kgP/ha kgP/ha kgP/ha kgP/ha Organic P 1 None Injected Incorporated Incorporated Surface application applied deeper immediately >3 months applied >3 method than 5cm before crop before crop months or surface before applied <3 crop. months before crop Total of weighted Site Vulnerability rating values <8 Low 8-14 Medium 15-32 High >32 Very High Following its introduction, several researchers used the index in different areas of the United States. As stated by Sharpley (1995), the P index is a valuable tool to identify P sources within a watershed. Gburek et al. (1996) noted its capacity as a comparative tool for between-site comparisons. Less positively, the Index is only set up to determine vulnerability on an annual basis, while this may not be an 35 accurate representation of sites in some cases (Sharpley, 1995). In addition, the weightings used in the index are not suitable universally. In response, many individuals and state agriculture departments are modifying the P Index to better suit their geographic region (Gburek et al. 2000, Stevens et al. 1993, Maryland Cooperative Extension 2000, Oregon D.A. 2000, Georgia D.A., 2000). In addition to continuing the geographic modifications, the next phase of research for this potential tool are validation studies to determine if high vulnerability sites are affecting surface waters. 3.4 Movement of P in the Environment In order to effectively manage phosphorus export from soil to water a detailed understanding of the sources, characteristics of those sources, and the pathways through which P travels is essential. Movement of P must meet both internal and external criteria before movement can occur. Outside forces or controls, both natural and anthropogenic, include climate, drainage, runoff incidence and hydrological pathways, erosional forces, land use, timing and form of fertilizer application, and the presence or absence of grazing animals (Heathwaite, 1997). Within the soil system, the amount of P lost from the soil is a direct result of the concentration in soil solution and the quantity and chemical form of P tied to the soil surfaces (McDowell and Condron, 2000). However, as Pierzynski et al. (1994) point out, all forms of P (soluble, adsorbed, precipitated, and organic) have been shown to be vulnerable to transport if the conditions are ideal. What is important to note in reference to the factors responsible for controlling P loss is the shift between internal and external control as the scale changes. From the table (3.41) provided by Edwards and Withers (1998), it is evident that internal soil characteristics predominate at the site scale but external characteristics dominate as one moves to the watershed scale. 36 Table 3.41. Factors responsible for controlling P loss at various geographical/organizational scales (Edwards and Withers, 1998). Scale Leaching Erosion Leaching and Erosion Profile Sorption properties, pH, electrolyte concentration, soil: solution ratio. Aggregate stability, soil texture Field Soil mineralogy, %saturation/P index, artificial drainage Extent and nature of crop cover, cultivation practices Rainfall intensity, antecedent moisture content Farm Farm category, P surplus Proximity to river, slope, field boundary conditions Soil type, farm type Catchment Climate, land use, soil type, relief, farm types and numbers. 3.41 Transport of Phosphorus by Surface Flow The transport of P across the soil surface occurs in two forms, soluble or particulate. Pierzynski et al. (1994) describe in detail both of the forms as well as the transport mechanisms that move them. The soluble or dissolved form is comprised mostly of orthophosphate, which is bioavailable. At the point it reaches the surface water body, it has desorbed or dissolved from the soil matrix (or in the case of fertilizer P, remained in soil solution), interacted with the thin layer of surface soil (0-2.5cm) and moved via runoff processes (Sharpley et al., 1994). Depending on the nature of the land-water interface, soluble P may be intercepted by buffer zones, residue strips, forests, and other nutrient uptake systems. These methods of interception are also effective in limiting particulate P movement. Originating from the soil, beds, and banks of streams or drainage 37 areas, the particles are detached during erosion by water or wind. During this process, the finer particles are preferentially eroded. This fine material is normally higher in P, C, and other nutrients due to the increased sorption capacity of the clay and organic particles it is comprised of (Sharpley et al., 1993). This change from the original material has been termed the "enrichment ratio", and is the value of soil property in runoff sediment to the value of soil property in source soil before runoff (Pierzynski et al., 1994). Alberts and Moldenhauer (1981) studied the size of aggregates transported from different agricultural fields to determine if enrichment ratios differ between sizes. They found that the enrichment ratios generally increased as aggregate size decreased. However, they did show that in every case, some form of enrichment occurred regardless of size. It was indicated that this is a result of the transport capacity of runoff. 3.42 Transport of P by Subsurface Row Downward movement or leaching of P is often described as insignificant. An increased sorption capacity of P-deficient subsoils halts the flows towards subsurface pathways or groundwater storage areas (Daniel et al., 1998). This was confirmed by Haygarth et al. (1998), who observed a 10-fold decrease in Olsen-P extracts below 45cm, and by Guertal et al. (1991) whose adsorption tests showed increased P buffering capacities with depth. However, there are exceptions to this rule including; areas high in excess of soil test P (STP), deep sandy soils, high organic soils, and acid soils. It is accepted that reasonable rates of fertilizer P application do not incur significant leaching. However, there is a "change point" that below which STP is strongly retained in the plow layer, but above it P losses in the drainage water may become significant. Heckrath et al. (1995) estimated this point to be at 60 mg Olsen-P kg"1. Aside from over-fertilization, susceptibility to movement may occur in sandy soils due to low sorption capacities. It may also occur in organic 38 or acid soils because the adsorption affinity tends to be low due to the dominance of negatively charged surfaces and complexing by A l and Fe (Daniel et al., 1998; Sims, 1998) One final common cause for the downward movement of P in soils is the type of P applied to the surface. It is increasingly common to find that P movement in soil is completely unaffected by P adsorption (Eghball et al., 1996). This occurs in areas of high manure or organic material loadings (Chardon et al., 1997; Eghball et al., 1996; Ulen, 1999). Chardon et al. (1997) found that in a manured sandy soil column, over 90% of the leached P was present in the organic form. The explanation for this movement goes back to the findings by Singh and Jones (1976), where organic residues have P contents >0.3% increase P sorption, where residues having <0.3% decrease P sorption. Obviously special attention to leaching of P from topsoil is required in areas supplied with slurries, manures, or other amendments high in organic P. 3.43 Influence of Agriculture on P Movement Land use that changes the natural conditions of a soil is destined to have an impact on that soil. Increased runoff and erosion are common occurrences in poorly managed forestry practices, inadequately planned urban developments, and agriculture (Abrams and Jarrell, 1995). Heathwaite (1997) produced an overview of how agricultural land use affects the sources and pathways of P loss. The three main areas of agriculture found to influence P movement included farm type, tillage method, and fertilizer type and application. Edwards and Withers (1998) reported the calculated P surplus from different farm categories for the EU. Large surpluses were associated with intensive-livestock production (>20 kg ha"1), with lower values in arable systems (<10 kg ha"1). Intensive livestock production systems produce large volumes of waste. It is economically difficult to eliminate other than through land application. Overgrazing may 39 enhance this over application. Heathwaite (1997) found that export from overgrazed land was at least 16x greater than lighdy and ungrazed grass. They accounted for this increase citing removal of vegetation cover, soil compactions, and direct additions of P in excreta. Tillage techniques have also been found to influence P movement. Mueller et al. (1984) found greater total phosphorus losses from conventional tillage sites than from chisel and no-till sites. However, losses of algal-available P were greatest from no-till sites. This was explained by an increase in dissolved P from the accumulation of P concentration in the top 3cm of soil in the no-till soils. Evidendy some form of incorporation of manure into the soil is beneficial (Sharpley and Menzel, 1986). Incorporation has also been recommended for broadcast fertilizer P. Decreases of lOOx were found from injections 5cm below the surface versus surface broadcast (Sharpley et al., 1994). Combinations of injection fertilizers and low-tillage field preparation during the high-risk spring planting season may reduce the risk of P loss. 40 Chapter 4 MATERIALS A N D M E T H O D S The following chapter describes the methods and materials used to carry out this thesis. Areas covered include a discussion of the field sampling, land use surveys, laboratory analysis, statistical investigations, GIS techniques, and the phosphorus index system. 4.1 Sampling and Fieldwork The watershed was delineated via orthophotographs, with soil road, and topographic maps to complement the initial analysis. Within the watershed, 21 landowners agreed to be interviewed and have a portion of their property sampled. A fairly even spatial distribution as well a reasonable balance of differing land use types was achieved throughout the watershed. The field sampling took place over a period of approximately two months (09/21/00 - 11/02/00), with the agricultural land use sites pre-arranged to be sampled after the final fall harvest This was done to minimize the effects of the present year's growing crop and to study the soil at a point where only residual nutrients are left in the soil. The pre-arranged interviews and sampling areas were predominandy agricultural sites, although the sites visited also included horticulture, recreational, institutional, and residential land uses. In addition to these areas, the forested headwaters area was accessed by the Elk Mountain hiking trail and small un-owned areas such as highway greenbelts were accessed on foot for sampling. 41 Any open area was sampled according to Bolen (1991). Sample areas ranging in size from 0.5 to 10 ha were sampled in a random fashion, although any extremely high or low points were avoided. From each area approximately 15 sub-samples were taken and mixed to make one bulk sample per field. When appropriate, the sub-samples were small 20x20 cm squares dug to a depth of 15 cm to minimize abnormalities. Occasionally when time or the landowner did not permit this, the standard soil core was used to a depth of 15 cm. On the golf course, the staff took the samples to the same depth using a hole-changer. This was done to minimize damage to the greens, tees and fairways. The forest soil samples were taken from smaller areas of approximately 20 m2. Several small pits were dug to the 15cm depth, but as the samples were taken, they were divided into their component horizons of A(h)e and Bfj. This was done to retain consistency with the lowland and agricultural samples where the horizon did not change within the 15cm depth, but to also recognize differences that may arise in forest soil samples through additions, loses, transfers and transformations. In addition to the soil samples, bed sediments were also taken when a stream or ditch was adjacent to a soil-sampling site. Accessing the watercourses was often difficult as blackberry bushes often dominated the buffer strips. The bed sediments were taken at the furthermost downstream point when possible, or at any entry point when access was limited. In total, 45 soil samples were collected and 10 sediment samples. Figure 4.1 shows the sample locations and the land use type. 4.2 Land Use Surveys Prior to sampling the properties, the land owner/operators were interviewed with regards to their land use practices. Separate questions were complied for agricultural and non-agricultural interviews. Appendix 1 provides the list of questions asked. The agricultural survey was completed for whole-farm 42 information as well as for the particular fields being sampled. The interviews took approximately 20 to 30 minutes and were responded to easily, although exact application rates of manures and fertilizers were not always known. 2000 0 2000 Ua(«n> Figure 4.11 Sample locations and land use type. 4.3 Laboratory Analysis For general laboratory analysis the methods provided by Lavkulich (1978) were followed. This included testing of soil and sediments for pH, cation exchange capacity, and soil texture. Total carbon (Q and nitrogen (N) were determined on ignition using the Leco analyzer. 43 The different partitions of P in soil were assessed using several different methods. Available P was measured using both the strong and weak Bray extracting solutions, which were idea for the acidic soils (Bray and Kurtz, 1945). Total P, A l and Fe were assessed using the Parkinson and Allen digestion and assessed using the ICP spectrometer (Cade-Menun and Lavkulich, 1997). Organic P was extracted using a 1:1 mix of 0.5M NaOH and 0.1 M EDTA (Bowman, 1989; Bowman and Moir, 1993; Cade-Menun and Lavkulich, 1997). This method has shown to be suitable for the mineral soils that comprise most of the samples taken (Cade-Menun and Lavkulich, 1997). Finally, the phosphorus adsorption procedure was derived from several sources (Bache and Williams, 1971; Nair et al., 1984; Yuan and Lavkulich, 1995). The final protocol for this procedure is given in Appendix 2. 4.4 Degree of Phosphorus Saturation (DPS) There are several different methods available to determine DPS. The method used in this study is based on Sharpley (1995), Beauchemin and Simard (1999) and Paulter and Sims (2000). The equation used is a ratio of the amount of soil test P (STP - Bray PI) in the sample to the adsorption maxima (b). It is written as: DPS = STP / b (1) 4.5 Phosphorus Index The phosphorus index worksheet for western Washington was used for the Elk Creek watershed. This worksheet was geographically the closest system, adjusted for climate and topographic factors more appropriately than Ontario's worksheet, which is currently the only published Canadian system. The techniques used to evaluate each of the parameters are described in table 4.41, and the Western Washington worksheet is found in appendix 7. 44 Table 4.41 Methods used to complete the phosphorus index worksheet. Factor Method Source Soil erosion — R (climatic erosivity) = 86 (from (Van VUet, 2001; RUSLE Chilliwack Airport, closest station) Wall etal., 1997; (ton/ac/yr) K (soil erosivity) = estimated from sources L S (slope length) = slope lengths of 40-500m, steepness <1% for valley, 3% for toe of slope, and >20% for steep slopes. C (land cover) = from Wall et. al. P (conservation practices) = 1 (no practices currently in use) Wischmeier and Smith, 1978) Soil Erosion Estimated from field surveys and from sprinkler observations irrigation Runoff class Estimated from texture, soil permeability, and slope (Wallet aL, 1997) Flooding hazard Estimated based on distance from water source at never or very rare, rare, and occasional Distance to Gathered this information during perennial surface field work and from maps (GIS and waters/buffer topographic) widths Subsurface Used presence of tile drain when drainage known, otherwise assumed no tile drains for agricultural fields Soil test P (Bray From laboratory analysis (Bray and Kurtz, PI, ppm) 1945) Commercial P From field interviews fertilizer application rate Commercial P From field interviews, based on application timing of application method 45 Organic P source application rate Organic P source application method Known stocking densities based on survey, converted all animals into dairy cow equivalents. Assumed 0.061 kgP/cow/day Assumed average rating of 3.0 for those with manure applications, based on personal communications with farmers (not included in formal survey). (Ritter,2001; Sharpley et aL, 1998) The P index values were calculated following the instructions on the worksheet and ranked using the same criteria. The data were then plotted into ArcView to be displayed spatially. 4.5 Spatial display and GIS techniques ArcView© and Maplnfo© software packages were used to produce spatial analyses and spatial output The spatial data inputs were from several sources. Table 4.51 details the input variables and their source. Table 4.51 Sources of input data for GIS applications-Input Variables Source Contours Digital, Municipality of Chilliwack Drainage network Digital, Municipality of Chilliwack Watershed areas Digital, Municipality of Chilliwack Streams Digital, Municipality of Chilliwack 1995 Orthophotos U T M Projection, N A D '83 Triathalon Mapping Corporation, Burnaby B.C. 1999 Orthophotos Environment Canada Soils Manually digitized (Branch, 1962) Land Use Manually digitized from orthophotos, farm surveys, and windshield surveys. Roads Manually digitized from orthophotos Sampled Areas Manually digitized from field surveys and orthophotos. 46 The input variables were then processed using Arc View's geoprocessing wizard to intersect, union, and clip features. This allowed input data to be formatted to the size of the watershed, assessed for size, and divided by contributing area. The query tool was essential for quantitative data analyses, as was the layout function to produce map output. 4.6 Statistical techniques The SPSS for Windows© statistical software was used to perform all statistical analyses. As several different tests were to be performed, all the data were evaluated for compliance with the six basic assumptions of parametric statistics. Due to the complex nature of soil, the data failed to meet the first assumption that all variables are independent of one another. In addition, the majority of the variables tested were not normally distributed. Therefore, non-paramteric tests were used for all investigations. In order to determine significant relationships a Spearman rank correlation coefficient matrix was generated. Strong positive or negative correlations may imply the influence of one variable on another, but cause-and-effect relationships need to be established separately. Also of interest were differences within and between groups. The Kruskal-Wallis non-parametric test for analysis of variance was used to determine if significant differences existed between the independent variables and nominal groupings such as soil texture class, land use type, and contributing area. Bonferoni's post hoc test was used to show which groups were significandy different. These values were used for qualitative purposes only, as most of the data in this situation were not normally distributed. 47 The Mann-Whitney U test was used as a non-parametric replacement for the t-test This assessment tool was used to determine if there were significant differences between the characteristics of soils and sediments. 48 ChapterS RESULTS A N D DISCUSSION The focus of this chapter is to evaluate and present the findings for all aspects of this thesis. The soils are characterized by their soil test results of samples taken between late August and early November 2000. The component data, including pH, CEC, %B.S., %C, % N , C:N ratio, Fe, A l , and texture, are described using descriptive statistics with some interpretation of the soil test values. Also indicated are the relationships with other components as well as any spatial trends. Since land use and land cover are dominant factors influencing environmental soil quality, a summary of the land use and management survey results follows the soil fertility discussion. Topics covered include land parcel size, inputs, and stocking densities. As per the goals outlined in the first chapter of this project, the P status of the watershed is characterized. The third portion of this chapter reviews the phosphorus results for all the forms tested; Bray PI and P2, total P, and organic P. As a follow-up to the P characterization, the results of the soil P adsorption are provided. The adsorption isotherms are presented, along with the data fit to the Langmuir model. The sampled areas that did conform to the Langmuir equation are then described by their P adsorption maximas. Using the "b" maxima values, the relationships between adsorption and general soil characteristics is then assessed. The completed data sets are then applied to the two vulnerability models assessed in this project: the Degree of Phosphorus Sorption (DPS), and the P Index. The 49 results of the two models are shown and are discussed in terms of their spatial variation and relationships with each other. A final step in this chapter is to make the critical link between the findings and how they apply to the local environment. Relationships between the models and the water quality data are described, as are those between the models and land use. In addition, sediment enrichment is assessed and used as a tool to evaluate the effectiveness of the models. 5.1 Soil Fertility The current soil fertility status of the Elk Creek watershed is summarized in Table 5.11, and the full chemical and physical analysis is found in Appendix 3. Overall soil nutrient reserves are rated as adequate or medium. Soil acidity and percent base saturation are at the low end of their desirable levels, while carbon, exchangeable cations, and CEC are all rated as adequate to good. Table 5.11 Current soil fertility status (0 -15 cm depth). Variable Mean Value Desirable Levels1 pH (CaCl2) 4.85 5.0 - 6.5 CEC (meq/lOOg) 29.87 >15 Exchangeable Ca (meq/lOOg) 6.6 5-10 Exchangeable Mg (meq/ lOOg) 1.2 0.5-1.5 Exchangeable K (meq/lOOg) 0.46 0.4-2.0 Exchangeable Na (meq/lOOg) 6.7 x IO"2 n/a %BS 39% >60 Carbon (%) 4.87% 2 - 6 % Total Nitrogen (%) 0.36% n/a C:N ratio 15:1 8 -15% Total Al 0.75 n/a Total Fe 1.11 n/a Clay (%) 15.3% n/a Silt (%) 48.0% n/a Sand (%) 36.6% n/a 1 (Loomis and Connor, 1992; Marx et al., 1999) 50 pH Soil p H is one of the most influential characteristics in soil, and has a profound effect on the availability of nutrients to plants and on the activity of soil microorganisms (Loomis and Connor, 1992; Sparks, 1995; Stevenson, 1986). As seen in figure 5.11, very little of the essential nutrients are available below a p H of 5, while potentially toxic metals are more available. The p H is a measure of the concentration of hydrogen ions in a system. The soil p H results listed here were determined in a dilute salt solution of 0.01 M CaCl 2 . The soils sampled in the watershed are strongly acidic with an average p H of 4.9, ranging from 3.23 to 6.65. There are a number of reasons for the acidity in the soils of the region: 1) the dominance of acid producing parent materials, 2) acidification of agricultural soils by chemical fertilizers and manure inputs, 3) acidic organic inputs under the coniferous forest cover of the hillside PH 4 5 6 7 8 9 Fungi e — • — " Bacteria and sctlnomycetes »—- Ca, Mg K . — - s — Mo —""" B area, and 4) atmospheric inputs. F i g u r e 5 A 1 p H m d p l a n t n u t r i e n t availability (Sparks, 1995) Spatially, significant differences did exist between the contributing areas (CA). The Kruskal-Wallis non-parametric test for differences between means (df = 5, 48) showed that the soils of the forested headwaters of Upper Elk Creek (pH = 4.33) had significandy lower p H than the soils sampled in the Ford Creek C A (pH = 5.67) and Lower Elk #2 (pH = 5.38). These differences are a reflection of the inputs to the soil system, the natural decomposition of coniferous needles in Upper Elk versus lime inputs to the primarily recreation/horticulture samples of Ford and Lower Elk. The mid-watershed contributing areas did not vary significantly from each other. 51 Significant differences also existed when the soils were divided into three distinct groups of sands, mineral soils and organic soils. The sandy soils had significandy higher pH values than the organic and mineral soils. This is not a reflection of the natural processes occurring in sandy soils, but is a result of the recent lime applications to the golf course sands, which would increase the pH. Exchangeable cations, CEC, and % Base Saturation Cation exchange capacity (CEC) is a measure of a soil's capacity to retain and release exchangeable cations such as potassium, calcium, magnesium, and sodium. Soils with high clay or organic matter content tend to have a high CEC. Sandy soils have a low CEC. Soil CEC is relatively constant over time, so there is no need for repeated analyses. A desirable level is greater than 15 meq/lOOg. The soils of Elk Creek had a mean CEC of 29.71, with a broad range of 2.2 to 140.3 meq/lOOg. The influence of texture and organic matter content is evident in these soils. Spearman's rank correlations showed strong R 2 values between CEC and carbon (R2 = 0.80), and CEC and nitrogen (R2 = 0.78), as both are indicators of organic matter content. Organic matter is well known for its high specific surface, accounting for 20 - 70% of the CEC in soils (Sparks, 1995). The CEC values also showed moderate negative relationships with sand content (R2 = -0.40), and weakly positive relationships with silt and clay (R2 = 0.35 for both). The CEC values did not vary spatially with the exception of the golf course soils in Ford Creek CA, those being extremely low due to their near 100% sand content. This spatial variation is also reflected in the significant differences between soils when grouped as sand, mineral, or organic. Al l three groups had significandy different mean CEC values, with the organic group being highest and the sand group being lowest. These findings follow general soil properties, and do not alter but do influence the strength of the relationships between variables. 52 Percent base saturation is the percentage of the CEC that is occupied by cations other than hydrogen (Fl) and aluminum (Al). Base saturation and pH increase together. For agricultural soils, ideal levels are approximately 50% for coarse texture soils, 60% for fine texture soils. Results reported as greater than 100% occurred due to recent additions of lime or gypsum that had not been fully incorporated into the soil matrix. The studied soils ranged from less than 1% to greater than 100%, with a mean of 38.8%. Base saturation appeared to be influenced by many of the same variables as CEC (with the opposite sign). Again, there were no trends between the contributing areas through the watershed, although the organic soils did vary significantly from the sandy soils. The individual cations that make up the base saturation value covered the full range of desirability levels, from low to excessive. Calcium (0 - 21.2 meq/lOOg), magnesium (0.09 - 4.4 meq/lOOg), and potassium (0.05 - 1.5 meq/lOOg), all exhibit spatial trends, while sodium (0.01 — 0.24 meq/lOOg) does not. The Upper Elk area was significandy lower than Mid, Lower, and Mouth for calcium and magnesium concentrations, while all the upland contributing areas (Marble Hill, Upper Elk, and Ford) were significantly lower than Mouth of the Elk for potassium. The cations appear to be strongly influenced by land cover, as the differences exist primarily between forest and agricultural soils. % Carbon, % Nitrogen, and C:N ratios Soil carbon is a measure of organic matter content As indicated in section 2.1, there are intermittent sections of organic soils in the watershed. Therefore, the range of carbon contents in the watershed is quite large (0.22 - 33%). The majority of the soils have carbon contents described as desirable. While the carbon contents did not show statistically significant spatial trends by contributing area, the Mid-Elk area (where the organic soils are primarily located) does have an elevated carbon content. This is reflected in the Kruskal-Wallis test 53 for differences between means. The carbon content was significantly higher in organic soils than mineral or sandy soils. As described earlier, %C influences CEC, and this is reflected in a strong positive correlation of R 2 = 0.80. This relationship is shown in figure 5.12. Percent carbon also has moderate correlations with texture (R2 = -0.38 sand, R 2 = 0.31 clay), and pH (R2 = -0.62). There is also a strong positive correlation with total nitrogen (%N) (R2 = 0.76), as both are indications of organic matter. 160 140. 5 120, S 100 I 80. o » g 40 20. 0. 000 000 000 000 000 000 000 000 000 ***** 100 5.00 10.00 15.00 20.00 25.00 30.00 35.00 % Carbon Figure 5.12 Relationship between carbon content and CEC. Nitrogen contents ranged from 0.02 to 2.54%. In addition to correlating strongly with carbon, N showed a strong negative relationship with pH (R2 = -0.68), and a strong positive relationship with CEC (R2 - 0.78). This latter relationship is again a good indication of how organic matter increases the cation exchange capacity of a soil. The C:N ratio is an important variable in soil testing. Typically, the ratio is relatively constant in soils, and therefore the maintenance of organic matter is constrained by the nitrogen levels. In addition, as the carbon content increases, the competition for available soil nitrogen increases. The C:N ratio in soil depends on the maturity of the plants contributing to the organic matter content. 54 The older the plant the larger the C:N ratio. The type of plant material (nonlegume vs. legume) also affects the ratio, where nonlegume plant material is generally higher in carbon (Brady, 1990). The range of ratios is quite large, ranging from 6.6 to 54.5. The higher ratios seem to occur in the forest and recreational soils, although there are no statistically significant spatial trends. Iron, Aluminum, and Manganese The metals were determined through the Parkinson and Allen digestion method for total A l , Fe and Mn in soil (Cade-Menun and Lavkulich, 1997). Aluminum ranged from 0.32 to 1.84% in soil, while iron was slighuy higher at 0.47 to 2.62%. Manganese was quite low in comparison, ranging from 0 to 0.74% with a mean of 0.08% in soil. The metals correlated weakly with texture and showed no spatial trends, nor any differences between organic and mineral soils. Texture Texture is the most influential physical property in soil. Affecting physical and chemical processes such as water infiltration, retention, and drainage, cation exchange capacity, pH, and organic matter content, understanding a soil's texture can improve the quality of soil analyses. The soils of Elk Creek showed a broad range in textures, although silts and silt loams predominated. Qays ranged from 0 to 33.7%, silts from 1.25 to 82.78%, and sands from 7.95 to 98.75%. The high percent sand (>90%) in soils were found only on the golf course and not of natural origin. The sand, silt, and clay contents did not show significant differences between contributing areas with the exception of the Ford Creek CA, which houses the golf course. A Kruskal-Wallis non-parametric test for differences between means was performed on the soils to assess variation between soil classes. The soils were classed into nine groups, ranging from coarse to fine textures: 55 1 2 3 4 5 6 7 8 9 sand loamy sand sandy loam loam clay loam silty clay loam silt loam silt organic. While many differences between texture groups were expected, only the exchangeable cations, iron, and aluminum were found to vary significantly between texture classes. Grouping the soils into the three groups of sand, mineral, and organic classes appears to have more meaningful differences. This may also be due in part to the lack of variability in clay contents through the watershed. 5.2 Land Use Survey Results The oral interviews with local landowners provided valuable input and management information. The sites were visually assessed for erosion, slope, land use, horizon designations, and vegetation. The interviews complimented these visual observations with more detailed land management information. Of the agricultural farms surveyed, the average farm size was 19.2 ha. The field sizes ranged from less than 1 ha to 10 ha, with a mean of 2.7 ha. The stocking density was determined to be 2.8 AU/ha, with one animal unit calculated as one diary cow or the equivalent This density was assessed only on agricultural farms with animals. When determined for all the sampled areas, including forested areas and farms with no animals, the sampled animal density was lowered to 1.3 AU/ha. 56 The inputs of P to soils in the watershed were concentrated in the agricultural areas, with some application on recreational soils. Of the farms surveyed, the organic inputs were composed entirely of dairy manure. There are a small number of poultry and hog operations in the watershed. However, the owner/operators of those farms chose not to participate in the survey. Due to the small size of the land area covered by these types of farms, it is suspected that the pork and poultry manure is being exported off farm, and most likely out of the watershed. Of the farms that do apply manure, the rate of input ranged from approximately 14 to 44 kgP/ha/yr, with a mean rate of 28 kgP/ha/yr. These values were estimated based on the stocking density of the farms multiplied by the fresh manure production per typical live animal mass (Ritter, 2001). The manure input results corresponded well with the loadings reported accurately by certain farmers, and was more consistent for those who did not know or were vague in reporting their application rates. Inorganic P applications in the form of chemical fertilizers were applied to grass and com crops, all horticultural crops, recreational areas, and some residential lawns. Figure 5.21 shows the spatial distribution of manure and fertilizer use through the watershed. Common fertilizer ratios used included 18:18:18, 25:10:10, 40:0:0 and 5:20:15. The landowners were more detailed in their knowledge of fertilizer application rates [than manure], and these ranged from 0.17 to 4.61 kgP/ha/yr with an average of 1.24 kg/ha/yr. While these input rates are five-fold less than those of the organic P, a greater percentage of sites sampled applied chemical fertilizers (62% vs. 36%). Twenty-six percent of sites surveyed applied both manure and chemical fertilizers. 57 30.0 _ 25.0 f" 20.0 5* — 15.0 | g. 10.0 Z 5.0 0.0 I Fertilizer P I Manure P Marble Ford Upper Elk Mid Elk Lower Elk Mouth Contributing Area Figure 5.21 Manure and fertilizer use in the contributing areas of the Elk Creek watershed. Both total manure and fertilizer P applications correlated with soil fertility characteristics. As a source of organic material in soil, the rate of manure input correlated with total nitrogen (Spearman's R 2 = 0.52), but not with total carbon. An increase in manure application did correspond with a decrease in the C:N ratio (R2 = -0.46). Manure inputs also correlated with the basic cation (Ca, Mg, Na) concentrations (R2 = 0.47, 0.56, and 0.38). The manure inputs did not correlate to the forms of available P or organic P, but did show a positive relationship with total P, at R 2 = 0.56. The fertilizer P applications appear to influence the soil characteristics differendy than the manure inputs. While fertilizer P correlated well with the exchangeable cations, it did not relate to any of the measures of organic matter such as carbon or nitrogen. However, fertilizer P did correlate with the different forms of P. While the correlation was equal with both types of Bray phosphorus (R2 = 0.45 for Bray PI and Bray P2), fertilizer P also weakly correlated with the available and total organic P, and TP (R2 — 0.30 for all). Other significant relationships included a moderate positive relationship with pH (R2 = 0.42), and a slighdy stronger correlation with base saturation (R2 = 0.54). The relationship between fertilizer inputs and pH is not a cause and effect relationship. It is generally noted that ammonia base fertilizer and urea lower pH, implying a negative relationship. 58 However, the positive relationship found here may be explained by the possibility that land managers generally apply gypsum or lime in addition to the fertilizers. 5.3 Phosphorus Laboratory analyses of the soils were performed for several different forms of soil P. These forms included available, total, inorganic, and organic P. The analytical results are listed in Appendix 4. Available P Often described as plant-available P, the Bray extractions were chosen for their suitability for acid soils. The fluoride extraction allows for complexes to form between A l 3 + and Fe 3 + ions with the F ions, releasing P. Bray PI, or "weak' Bray and Bray P2 - the "strong" extraction, were both carried out on the Elk Creek soils. Bray PI, which is a reflection of the labile P in soil, ranged from 3.2 to 367.7 ppm in soil. Bray P2, which includes labile P and residual P, ranged from 6.4 to 488.0 ppm in soil. The mean values were 75.2 and 133.7 ppm in soil, respectively. Marx et. al (1999) indicate that concentration greater than 100 ppm PI in soil is excessive, while Woods End (1996) rates 45 ppm P2 ideal for crops such as grass and com. There are evidently several sites within the watershed with soil test P levels in excess of plant needs. These sites are shown in figure 5.31. The two forms of available P correlated well with each other, R 2 = 0.91, and both responded similady to several other soil characteristics such as C:N ratio (R2 = -0.4), Mn (R2 = 0.42), and fertilizer P (R2 = 0.45). Other variables were different between the two forms. Exchangeable cations responded better to shifts in weak Bray, with R 2 values being 0.4 (Mg) and 0.61 (K) versus 0.37 and 0.5 in the strong Bray. However, the stronger Bray showed a stronger relationship with base saturation at R 2 = 0.62 vs. 0.54. Other differences include a relationship between PI and TP (R2 = 0.48) and no significant correlation with P2. Conversely, P2 59 showed a moderate correlation with pH (R2 = 0.54), while PI was much less at R 2 = 0.30. As noted above, many of the soil components do correlate with available P, and there are many correlations within those components. A simple schematic of the strongest relationships best describes what influences the concentrations of available P. This is shown below in figure 5.32. Fertilizer And Manure Inputs Mn ** Relationships significant at 0.05 level. %c Figure 5.32 Relationships between available phosphorus and soil components in Elk Creek watershed soils. Spatially both forms of available P showed the same trends. As shown in figure 5.33, the only significantly different areas in the watershed were the highest contributing area, Upper Elk, and the lowest contributing area, Mouth of the Elk. The Upper Elk area was significantly lower, while the Mouth area was significantly higher in available P than all other areas. This variation may be partially explained by land use type. The Mouth contributing area is dominated by horticultural land use. Using the Kruskal-Wallis test, it was found that PI and P2 are significantly different in areas under horticulture than those under forest cover. The fertilizer input rates may also be a factor in the variation in available P, as the Mouth area receives nearly double the P inputs than other contributing areas. 61 Marble Ford Upper Elk Mid Elk Lower Elk Mouth Contributing Area Figure 5.33 Spatial distribution of available P by contributing area. Organic Phosphorus The organic P fraction in soil was assessed using the Bowman and Moir extraction method (Bowman, 1989; Bowman and Moir, 1993). This technique uses N a O H to solubilize the P associated with organic matter with E D T A chelating the metal cations to improve efficiency and to prevent re-adsorption. Using both acids and bases, P is determined in the extract using the ICP spectrometer (Cade-Menun and Lavkulich, 1997). Two pools of organic P were then derived from the data: the percent organic P in the available fraction (%AOP), and the percent total organic P (%TOP). The % A O P is determined as the ratio of the N a O H - E D T A extracted P to the sum of the P2 and N a O H - E D T A extracted P. This ratio is significant as it is a measure of the amount that is highly soluble, more plant available, and more susceptible to removal by leaching and runoff. Within the soils, the % A O P ranged from 1.9 to 82.7%, with a mean of 31.7%. One of the most predominant differences found in this data set was the variation between organic, mineral, and sandy soils. Each group was significantly different from each other, and these differences influenced the strength of the relationships between % A O P and other soil variables. A n example of this is shown in figure 5.34, the relationship 62 between available organic P and pH. While there still is an obvious negative trend, the organic and sand extremes influence the strength of the relationship. 900 70.0 -S3 OU.U c S« 50.0 I a £ 30.0 § ° 2 0 . 0 3 re ' 5 3 10.0 • • • « • • • • • • • • • " " " '"• • • • • _ • • 41 A * * A A A • Organic • Mineral A Sand 45 5 5.5 PH 65 Figure 5.34 Relationship between available organic P and pH. The organic soils were able to maintain high levels of available organic P at low pH due to the naturally high levels found in the soil and also from the increased anion adsorption capacity of the organic matter. The Bfj horizons in the forest soils of the upper left comer also show high concentrations at low pH. This is most likely due to additions from the ovedying Ae horizons, leached down during the heavy rainfall of the Pacific winter, and then retained by the concentrated sesqui-oxides. Conversely, the sands of the lower right comer of figure 5.34, are low due to low organic matter contents and also from a lack of adsorption capacity to retain any type of P. 63 In addition to the variation between the three soil classes, the % A O P was found to vary significantly between land use types and contributing areas. The concentration in forest soils was found to be significantly higher than that in recreational, grass, and horticultural land covers. These three types of land use typically receive more fertilizer P and less manure, thus reducing the amount of anthropogenic organic P inputs. The forest soils did not vary significantly from the control sites, com, and pasture areas. This is most likely due to the manure inputs, increasing the levels of A O P to match those naturally occurring in forest soils. The spatial differences varied similarly to the land use differences, where the forested Upper Elk area was significantly higher from the Ford and Mouth of the Elk Contributing areas. As indicated earlier, these two contributing areas are dominated by recreation and horticulture, respectively. The spatial distribution of % A O P is depicted in figure 5.35. » 6 0 3 Marble Ford Upper Elk Mid Elk Lower Elk Mouth Contributing Area Figure 5.35 Organic phosphorus concentrations in the available phosphorus fraction of Elk Creek soils. 64 The total organic P data was calculated as the ratio of NaOH-EDTA extracted P to the total P determined using the Parking and Allen digestion. The values ranged from 0.7 to 6.4 (% in soil), with a mean of 3.1. The spatial trends, relationships with other soil variables, and relationships with land use types are very similar, although slightly weakened, to those found with the percent available organic phosphorus. Total Phosphorus Total P was measured in the soils using a Parkinson and Allen digestion. The digestion uses H 2 S 0 4 and H 2 O z as oxidants with LijSO,^ to elevate the digestion temperature and Se as a catalyst (Cade-Menun and Lavkulich, 1997). This test is suitable for mineral soils low in organic matter. The TP as determined by Parkinson and Allen ranged from 0.02 to 0.36 percent in soil, with an average value of 0.11%. Many of the measured soil characteristics correlated well with total phosphorus, table 5.31 presents the findings. Table 5.31 Spearman's rank correlations for total phosphorus (correlations are significant at the 0.01 level). Variable Spearman'sR2 Bray PI (% in soil) 0.45 Ca (meq/100g) 0.57 Mg (meq/lOOg) 0.59 Na (meq/lOOg) 0.35 K (meq/lOOg) 0.49 CEC (meq/lOOg) 0.54 % Carbon 0.58 % N 0.63 % Clay 0.35 Total Manure (kg/ha/yr) 0.59 The correlations suggest that the phosphorus in these soils is predominantly calcium-bound as indicated by the strong correlations with the exchangeable cations and cation exchange capacity. However, this may be influenced by the land use practice of liming rather than natural (unmanaged) conditions. 65 Phosphorus in these soils also shows an affinity for organic matter, as suggested by the strong correlations with carbon and nitrogen. Figure 5.36 shows the relationship between total P and total C. Total P did not show any significant trends spatially, by land use, or by texture class. • 0.25OO • Total Pin soil 4> Total P in sediments 600 Total C (%) Figure 5.36 Relationship between total P (% in soil) and total C (% in soil). 5.4 Soil P Adsorption The laboratory procedure for soil P adsorption was altered slighuy from that proposed by Nair et. al. (1984), in that the solution concentrations were made up to approximately 4 times the original strength. This alteration was appropriate, as the majority of the soils had not approached their maximum by 10 mgP/L. The data were fit to the Langmuir model in order to determine the adsorption maximum. The maximum (b) is estimated as the slope of the line c / q = l / k b + c/b (1) where c is the concentration in solution, q is the amount adsorbed, and k is a constant related to the binding strength (Sparks, 1995). 66 Eighty percent of the soils sampled had a definable maximum from the Langmuir model, while 16 percent do not achieve a finite level of adsorption, and four percent did not fit the model. Those that did fit the model have been divided into five groups for analysis and display. These five groups are based on the estimated Langmuire adsorption maxim as and are: <25 mg/kg 25-50mg/kg 50-75 mg/kg 75-100 mg/kg >100 mg/kg. Appendix 5 shows all the adsorption isotherms, divided into the five groups plus those that did not achieve a maximum as well as those that did not fit the model. Figure 5.41 is a representative isotherm from each of the five groups plus the two outliers. 45.0 Solution Concentration (mg/L) Figure 5.41 Selected adsorption isotherms for soils in the Elk Creek watershed. 67 The first five isotherms depicted above portray the classic Langmuir curve, where a maximum amount of adsorption is expected. However, for several of the other soils tested, the curves did not exhibit the same smoothness. The initial inflection point does occur as predicted by Langmuir, indicating the initial physi-sorption onto the soil's surface. After this point, instead of leveling off, the adsorption process then continues at a new rate. This implies the creation of a second layer of phosphate adsorbing not to the original soil surface, but to the adsorbed ions themselves. Figure 5.42 gives several examples of this type of isotherm, figure 5.43 is an adapted illustration of ion sorption reactions, and figure 5.44 shows a schematic of the possible chemical reactions. Solution Concentration (mg/L) Adsorption Nucleation Precipitation Clustering Figure 5.42 Isotherms showing secondary adsorption. o i 2nd 1st C JP R OH 0 OP 0 O R C ! p OH OP O c R OH Figure 5.43 Illustration of ion sorption reactions rSoarks. 1995 #1131. 68 Figure 5.44 Sketch of adsorption reactions. The types of reactions depicted in figures 5.43 and 5.44 are provided as an explanation for the soils that do not follow the traditional adsorption curve and also for those with very high adsorption maximums. The soils that did not have a defined maximum consisted of all of the forest soils as well as soils high in carbon (>10%). The sesquioxides of the forest soils are assumed to play a role in the amount of P being adsorbed. The three soils that did not fit the Langmuir model are all organic soils. These three soils showed near perfect 1:1 adsorption of phosphorus from solution. This may be explained by the increased stabilization of the soil by "free" sesquioxides, through additional anion exchange sites, or due to the metal chelates associated with organic matter (Sanyal and De Datta, 1991). While the organic soils adsorbed phosphorus differendy due to their unique properties, the entire data set shares combined properties that explain the amount of adsorption. As the estimated maximum values had a very broad range, the amount of P adsorbed at 4 and 45 mg/L solution concentrations have been used to determine relationships with selected soil properties. Table 5.21 presents the Spearman's correlation coefficients for the two concentrations. Table 5.41 Spearman's R 2 correlation coefficients for selected soil properties with adsorption at 45 and 4 mg/L. All values are significant at the 0.05 level. Soil Property Adsorption at 45 mg/L Adsorption at 4 mg/L pH -0.74 -0.4 Bray P2 (ppm in soil) -0.66 -0.46 Bray P2 (ppm in soil) -0.44 -0.50 Langmuir maximum 0.66 0.54 Potassium (meq/lOOg) -0.34 No corr. CEC (meq/lOOg) 0.76 0.33 %BS -0.69 -0.50 % Carbon 0.83 0.44 % Nitrogen 0.67 0.38 C:N Ratio 0.36 No corr. Total P (% in soil) 0.35 No corr. Al (% in soil) No corr. No corr. 69 Fe (% in soil) No corr. No corr. Mn (% in soil) 0.32 0.31 % Sand -0.39 -0.31 % Silt 0.36 No corr. % Clay 0.34 No corr. Fertilizer P (kg P^s/ha/yr) -0.39 No corr. P Index Rating -0.40 -0.38 Adsorption at 4 mg/L 0.713 1.00 The two levels of adsorption correlated well with each other, while the stronger solution (45 mg/L) had stronger relationships with other variables. From the table, the relationships existing between the different variables become evident. Figure 5.45 is a schematic diagram of the major relationships between soil P adsorption and soil components. Figure 5.45 Relationships between P adsorption at 45 mg/L solution concentrations and soil components for all soils and sediments in the Elk Creek watershed. Relationships are significant at the 0.05 level. The spatial distribution of adsorption capacities through the watershed is quite interesting. As depicted in figure 5.46, there is a general decline in the amount adsorbed from the headwaters to the mouth. Ford Creek contributing area is an 70 Figure 5.46 Spatial distribution of adsorption levels. Marble Ford Upper Mid Elk Lower Mouth Elk Elk Contributing Area 71 exception, as its man-made sandy soils drastically reduce the amount of sorption. The Kruskal-Wallis non-parametric test for differences between groups shows that Ford creek is significantly different from all other areas except Mouth of the Elk. The Mouth contributing area is also significantly different from Upper Elk and Mid Elk. This difference is not easily explained by one variable. Possible influences in the Mouth area may include the slightly higher mean pH, the increased fertilizer P inputs, and the higher available P levels; all of which have a negative influence on the amount adsorbed. In order to analyze the Langmuir maximum values the data set was reduced to only those that fit the equation and had a defined maximum. This eliminated all organic soils, all forest soils, and several high carbon-content soils from the data set. Understandably, this lessened the variability of the data and weakened the strength of the correlations. It also reduced the spatial variability, with Ford Creek being the only contributing area to be significantly different. The Langmuir sorption maxima ranged from 8.0 to 327.8 mg/kg, with a mean of 101.0. These values agree with those obtained by Indiati (2000) and Sumner (2000), but are lower than values obtained by Villapando and Graetz (2001) in Florida Spodsols and Yuan and Lavkulich (1994) British Columbia Spodsols. It is most likely that had maximum adsorption been achieved in the forest soils they would strongly agree with the findings of Yuan and Lavkulich (1994). Similar to the relationships found with the soil components and adsorption at 45 mg/L, carbon and nitrogen show the strongest relationships. There is also a noticeable influence from the soil pH, CEC, and texture. 72 5.5 Phosphorus Vulnerability Indices This next section culminates all of the information gathered from the field and laboratory analyses. Presented in two different assessment tools, the vulnerability for phosphorus loss is described for the soils and the watershed. The assessment tools are the Degree of Phosphorus Saturation (DPS) index and the Phosphorus Index. Degree of Phosphorus Saturation The DPS, as presented by Breeuwsma (1995), Beauchemin and Simard (1999), Paulter and Sims (2000), and Leclerc et. al (2001), is a ratio of the amount of available P to the amount capable of being adsorbed. Calculated as STP/b, the DPS was determined only for the soils that had a defined maximum as described in the previous section, and thus eliminated the organic soils and the forest soils. The degree of saturation ranged from 0.1 to 27.9%, with a mean of 4.6%. Within the related literature, critical limits have been established between 20 and 40%, 23% being the most common. Only one of the soils examined in the watershed exceeds the critical limits recommended by Breeuwsma et. al (1995) and Yuan and Lavkulich (1995). This would imply that the soils are generally not susceptible to P loss by leaching, and do not exceed crop needs. However, as approximately 20% of soils are described as "excessive" in terms of the Bray PI concentrations by Marx et al (1999), there appears to be a discrepancy between the two evaluations. The discrepancy between the two evaluations can be partially accounted for by assessing the organic P status. As described in chapter 3, the movement of P through the environment is dominated by the organic P fraction. While the adsorption isotherms reflect sorption of primarily inorganic P, a high adsorption capacity of inorganic P may also represent a strong ability to retain organic P. 73 The relationship between the adsorption maxima and the %AOP is shown in figure 5.51. 150.0 130.0 110.0 | 90.0 s I 70.0 50.0 30.0 -10.0 Mineral A Sand 00 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 Available Organic Phosphorus (% in soil) Figure 5.51 Plot of %AOP with the adsorption maxima. From the figure above, it is evident that soils with high adsorption capacities also tend to also have greater proportions of organic P in the available fraction. Spatially, this relationship is also apparent, as the adsorption maxima and the %AOP both decrease with distance downstream. Figures 5.46 and 5.35 show those trends. While the relationship may not be cause and effect, it can be concluded with a degree of confidence, based on figure 5.51 and earlier assessments, that the variables that influence adsorption also influence the proportion of organic P in the soil. Looking back at why the DPS index did not agree with the Bray P assessment, it is important to note the rates of fertilizer and manure applications in highly 74 vulnerable areas such as the Mouth of the Elk contributing area. From the interviews and assessments made in section 5.2, it is obvious that higher rates of organic P inputs are being made. Therefore, it can be assumed that because we know organic P is being put into the soil system, and due to the low adsorption capacity (which has been shown to be reflective of organic P retention), that this highly soluble P is moving into the aquatic ecosystem. Therefore, for the DPS index to be effective in this region, it needs to take into account the total available P, and not just the STP. Phosphorus Index The Western Washington P Index worksheet was used to evaluate the vulnerability to P loss in the watershed. The worksheet provided interpretation of the results via four vulnerability classes: The evaluations for the Elk Creek watershed are found in Appendix 6. The rankings ranged from 1.12 to 43.96, with a mean of 13.98. Of the sites evaluated, 52% were rated low, 27% medium, and 21% high. No soils were evaluated as 'Very high". Spatially, the P Index showed strong variation through the watershed. Figure 5.52 shows the P Index rankings for the watershed. There is a noticeable gradient increasing from the low-vulnerability sites in the forested headwaters to high-vulnerability at the mouth. With the figure is a description of the land use activities. Similar to the findings of Sharpley (1995), there is a general increase in rating with the degree of land cultivation. < 13.0 13.0-25.0 25.1 - 50.0 > 50.0 High Very High Low Medium 75 5.6 Environmental Relationships Identifying areas that have the potential to lose phosphorus to the environment is an important start. However, it is necessary to link the predictions to environmental indicators in order to assess their accuracy. Within this project are two sources of data that provide such a link: sediment testing and water quality testing. Figure 5.61 shows the locations where the sediment and water samples were taken. Sediment Results Several of the tests that were performed on the soils were also performed on adjacent sediments. The sediments are composites of the natural underlying riverbed as well as particles eroded from the soil surface. Consequently, relationships between the sediments and the adjacent soils are expected. While 77 not all of the relationships are particularly useful, several of the correlations are good indications of how the soil's characteristics influence the amount of phosphorus in sediment. It is well understood that an increase in pH increases the availability of P in solution. Correspondingly, the amount of available P in sediment correlates positively with soil pH (R2= 0.61 for Bray P2, and R 2 = 0.68 for Bray PI). These relationships are shown in figure 5.62. Other notable relationships include the increasing available P concentrations with decreasing organic matter and increasing sand. The soil's ability to "hold" phosphorus influences the amount eroding in sediment 250 200 £ 150 '6 m c 100 4 50 0 4.5 5.5 6 pH In Soil 6.5 7.5 Figure 5.62 Relationship between pH in soil and the available P (Bray PI) in sediment As sediment is also influenced by its inherent properties, the relationships between sediment properties are also revealing. Summarized for the various forms of P in sediment and soil, table 5.61 presents the correlations between sediment and soil properties. 78 Table 5.61 Spearman correlation coefficients between sediment P fractions with sediment properties and soil P fractions with soil properties. Sediment Bray PI BrayP2 Available Total P Adsorb. Property Organic P Maximum PH -0.35 -0.41 PI 1.00 0.42 -0.44 -0.49 P2 0.42 1.00 -0.34 %C 0.61 % N 0.58 0.54 0.60 Organic P 1.00 TP -0.44 -0.34 1.00 0.48 Al -0.47 -0.77 -0.71 0.77 Fe -0.58 -0.40 0.61 Mn % Sand 0.32 -0.78 % Silt 0.78 % Clay 0.70 Soil Bray PI BrayP2 Organic P Total P Adsorb. Property Maximum PH 0.39 0.54 -0.73 -0.57 PI 1.00 0.92 0.45 -0.47 P2 0.92 1.00 0.31 -0.34 %C -0.51 -0.34 0.72 0.38 0.68 % N 0.62 0.59 Organic P 1.00 TP 0.31 0.45 1.00 Al Fe Mn % Sand -0.39 % Silt 0.39 % Clay 0.34 Many of the relationships in sediments are similar to those in soil. The noticeable difference between the sediment and soil relationships is the correlations between the forms of P with the metal contents. While iron and aluminum showed no significant relationships to any form of P in soil, they correlate quite strongly in the sediment. The negative relationship with the forms of available P and the positive relationship with total P indicate that the dominant form of P in 79 sediment is Al/Fe-bound P. This is a shift from the organic or calcium-bound P in soils. The relationship between metal content and available P in sediment is shown in figure 5.63. 200 180 160 -I 140 120 100 80 60 40 20 -I 0 0.0 70 \ 60 + 50 | 40 5 30 E t o 20 « 0-10 >. P2 P1 Linear (P1) - Linear (P2) -10 0.5 1.0 1.5 2.0 Aluminum concentration (%in Sediment) Figure 5.63 Relationships between available phosphorus and aluminum concentration. Spatially, the sediment characteristics varied similady to the soils. The sediments from the Upper Elk contributing area were significandy different in pH, Bray PI and P2, % C, TP, all metals, and all textures from Mid, Lower, and the Mouth of the Elk. The pH decreases with distance from the headwaters, changing the availability of nutrients. The concentration of metals and percent sand also decrease. This is a reflection of the differences in parent materials as well as natural geomorphic processes. The nutrient concentrations (all P fractions, carbon, nitrogen) and the fine-textured particles go up with distance downstream. Again this may be a reflection of natural geomorphic processes, but may also be influenced by land use. Similady to the contributing area trends, shifts from forest to agriculture tends to show similar patterns. However, the sediments sampled in the mid to lower portions of the watershed were not significantly different from each other, with the exception of Bray P2 phosphorus. The levels of available P in the Mouth contributing area were significandy higher than in all other areas. This is shown in figure 5.64. This shift is related to changes in land use and P inputs to the soil. These results correspond well to the findings of the P Index. 80 While the spatial distribution of the sediment variables is an indicator of environmental issues, the accepted method is to show enrichment of transported sediments. Table 5.63 shows the enrichment ratios for several variables. The ratio is calculated as the concentration of any given variable in the eroded materials to that in the contributing soils (Avnimelech and McHenry, 1984). Table 5.63 Enrichment ratios of selected variables in the sediments from the Elk Creek watershed. N Minimum Maximum Mean Std. Deviation pH 10 .94 1.34 1.1000 .1363 P2 10 .58 7.56 1.9630 2.2076 PI 10 .05 1.49 .3440 .4165 TP 10 .08 3.50 .6880 1.0294 %C 10 .03 4.16 .7380 1.2288 %N 10 .16 2.23 .7610 .6238 Al 10 .28 2.65 1.0970 .7105 Fe 10 .41 2.30 1.1420 .5684 Mn 10 .41 5.27 1.2850 1.4286 %Sand 9 .72 2.67 1.3711 .6882 %Silt 9 .16 2.87 .9578 .8026 %Clay 9 .00 3.71 1.1200 1.0671 Adsorption 9 .09 1.86 .5922 .5277 Maximum Adsorption 10 .33 1.42 .7660 .3098 at 45 mg/L 81 The enrichment ratio is analyzed for its variance around the union point of 1.00. Any levels above one indicate enrichment of sediment by nutrients and any results below one indicate that depletion has occurred in the sediment. In a lake system where turbulence is low, enrichment in carbon, nitrogen, phosphorus and fine textured particles is expected (Avnimelech and McHenry, 1984). However, in a stream situation where the fluvial forms and processes are constandy changing, the sediments are generally depleted in comparison to soil. Measures of pH, Al, and silt showed litde to no change from soil to sediment. The less-than-one enrichment ratios of Bray PI, total phosphorus, carbon, nitrogen and the adsorption maxima indicate depletion and show the effectiveness of aquatic plants and moving water at removing nutrients. These ratios are depicted graphically in figures 5.65, 5.66 and 5.67. The eroded carbon particles that are small in size tend to float and are easily removed from the system, while the more soluble nitrogen is generally consumed by aquatic plants and organisms or removed from the system. The reduction in organic materials associated with sediment may be the cause of the lessened adsorption capacity. 82 The positive enrichment in iron and manganese is also accounted for by eroded soil particles, generally found in clay-organic matter chelates. At the higher pH values found in the stream water the metal ions become less readily available and are more difficult to remove. The enrichment in metals may also be a reflection of the parent materials in the bed sediments, and from naturally occurring metals found in the Brunisolic forest soils of the headwaters area. Bray P2 was also enriched in sediments. This shows the difference between the labile PI, which was depleted by plant uptake or removal by water, and the more residual P2 that is associated with metals. The differences in enrichment is shown in figure 5.68, which gives the 1:1 soil to sediment ratio lines to act as a benchmark to show enrichment or depletion. 100 P2/P1 in Soil 200 TJ 0) 0. 0 < 40 %AOP (in soil) 60 80 Figure 5.68 Enrichment ratios of available phosphorus. Figure 5.69 Enrichment ratio of available organic phosphorus. 83 Figure 5.69 confirms the de-enrichment of PI, as the highly soluble organic portion of the available P is also de-enriched. The %AOP in sediments is readily adsorbed by aquatic plants or removed in solution. Water Quality Indicators Chapter 2 presented a picture of the Elk Creek watershed including an assessment of the water quality spatially and seasonally. This next section explores the relationships between the water quality indicators and the soil variables. These variables include the soil components, the different phosphorus fractions, the adsorption maxima, the two indexes, and the sediments. Strong relationships indicate a link between land and water, and as a result have environmental implications. Table 5.64 presents the Spearman correlation coefficients between selected water quality indicators and soil components. It is interesting to note from the table that the variables that often play a key role within soil are not as critical in soil-water relationships. Normally one of the stronger variables in soil, pH only has a weakly positive correlation with most of the water quality indicators. The strength of the relationship is greater for the dry-season averages, and this is due to the timing of the soil sampling, which was at the end of the dry season period. The wet season average for pH in water is slightly more acidic and is most likely a result of the additional rains flushing the basic cations from the soil and the creeks. The strong positive relationships between the water quality criteria and the base saturation of the soil agree with this conclusion. Another important variable in soil is the organic matter content The measures of carbon and nitrogen have generally held strong relationships between soil and sediment components. However, the relationships between these two variables and water quality are quite weak if they exist at all. This would imply that it is not the amount of organic matter in soil that influences water quality. From the more 84 consistent and moderate relationships between the C : N ratio and the water quality variables, it appears that it is the content of the organic matter that exerts more influence. The negative R 2 values indicate that as the C : N ratio decreases, the nutrient concentrations in water increase. From an environmental perspective this would imply that maintaining the ratio at the upper desirable limits (ratios of 12 to 15) would reduce the movement of excess nutrients into the aquatic environment. Texture is another variable in soil that has an influence over many of the other properties. However, all three of the main texture classes correlated very poorly with the water quality data. A n R 2 = 0.28 for percent sand and dry season ammonia-N, and R 2 = -0.45 for percent clay and wet season dissolved oxygen are the exceptions. A high sand content would encourage the mobile, soluble ammonia to leach into the water system, and an increase in clay may influence the dissolved oxygen content by association with nutrient-rich, eutrophication-inducing particles. Table 5.64 Spearman correlations between selected soil components and water quality indicators. All correlations are significant at the 0.05 level. Soil W.Q. P H %BS % C % N C : N % Sand % Clay N 0 3 (w) 0.34 0.47 -0.52 N 0 3 (d) 0.48 0.54 0.32 0.24 NH 3(w) 0.37 0.52 -0.52 NH 3 (d) 0.52 0.72 0.37 -0.37 0.28 TP(w) 0.34 0.47 0.50 pH(w) -0.30 -0.48 0.54 P H(d) -0.47 -0.67 0.35 0.34 DO(w) -0.35 0.53 0.56 -0.45 DO(d) -0.37 0.56 0.34 Fecal (w) 0.33 0.47 -0.52 Fecal (d) 0.27 0.39 -0.55 85 Table 5.65 Spearman correlations between selected sediment components and water ruality indicators. All correlations are significant at the 0.05 level. Sed. W.Q. PH %C % N %Sand % Silt % Clay NO3 (w) -0.59 0.57 0.38 -0.44 0.56 0.58 NO3 (d) 0.45 NH3(w) -0.59 0.58 -0.42 0.55 0.57 NH3(d) 0.39 TP(w) -0.60 0.57 0.45 0.60 pH(w) 0.62 -0.58 -0.40 0.40 -0.57 -0.59 PH(d) -0.39 DO(w) 0.62 -0.58 -0.58 DO(d) Fecal (w) -0.59 0.57 0.47 0.58 Fecal (d) -0.66 0.53 0.67 0.52 0.65 The sediment and water quality values correlated with each other quite well. This is due in part to the small sample size of both data sets. However, the data found in table 5.65 give an insight to the qualitative relationships between sediments and water. It shows that a decrease in the pH of the sediments relates to an increase in nitrite and nitrate, ammonia-N, and total phosphorus. These relationships are stronger during the wet-season. The high levels of plant productivity during the summer months mask relationships between the variables, as most of the available nitrogen is removed from the system. Relationships between the carbon and nitrogen content in sediment and the water quality data also exist. The correlations show positive relationships between %C and % N with the nitrogen-based nutrients, total P, and fecal matter content. The carbon and nitrogen contents had negative relationships with pH and dissolved oxygen. Evidently as the nutrient concentrations increase, plant productivity and the eutrophic cycles consume oxygen from the aquatic system. The texture of the sediment also relates well to the concentration of nutrients, with the finer particles associated with the nitrate, ammonia, and phosphorus 86 concentrations while sand decreases the concentration. These relationships are similar to those found in soil. The specific relationships between soil and sediment P concentrations and water quality is of special importance to this study. Table 5.66 is the correlation matrix of the soil and sediment P fractions with selected water quality indicators. From the table it is evident that it is the available P fraction that relates with water quality to the greatest extent. The correlations between the variables and the wet season values tend to be stronger than those from the dry season. This can be accounted for by the plant uptake during the summer months that reduces the variability in the data, and by increased inputs by runoff, leaching and erosion in the winter months. Regardless of the season, the correlations between available P and the water quality data imply that a certain amount of matter is moving from the soil to the aquatic system, carrying with it nutrient rich materials. Table 5.66 Correlation matrix for P fractions in soil and sediment with water quality indicators. All corrrelations are signification at the 0.05 level. Water Quality Soil Sediment PI P2 AOP TP PI P2 OP TP NOx (w) 0.76 0.56 0.56 0.41 0.61 NOx (d) 0.19 0.42 0.38 0.53 NH3 (w) 0.63 0.59 0.55 0.40 0.62 NH3 (d) 0.53 0.61 0.49 0.62 -0.45 TP(w) 0.79 0.570 0.56 0.33 0.61 TP(d) 0.77 0.71 0.41 DO(w) -0.63 -0.60 -0.58 -0.43 -0.49 -0.62 DO(d) -0.32 -0.46 -0.60 -0.43 0.70 Fecal (w) 0.62 0.56 0.39 Fecal (d) 0.60 0.52 0.66 0.48 0.59 87 In addition to the phosphorus fractions, the adsorption maxima also correlated well with the water quality data. The Langmuir *b" values showed positive correlations with the nitrate and nitrite averages for both wet and dry season (R2 = 0.32, 0.24). It also correlated positively with total phosphorus in water at the same strength, and slightly stronger with pH in water at R 2 = 0.42 (wet) and 0.49 (dry). These positive correlations infer that with greater phosphorus adsorption capacity the water quality is enriched with nutrients. This can be explained using figure 5.45 that shows adsorption is generally highest in soils rich in carbon, basic cations, and higher pH. The same criteria that increase adsorption are also those that would increase the nutrient content of water should the soil be eroded from the surface or undergo excessive leaching. From the adsorption maxima values the degree of phosphorus saturation was calculated (DPS). This ratio of the amount of available P to the potential adsorption capacity is used to indicate areas where P is in excess of a critical limit of 20 to 40%, established by region. As stated earlier, the majority soils of the watershed were well below even the stringent 20% limit. The calculated DPS values did not correlated significandy with any of the measured water quality indicators. This may be taken as a reflection of the low vulnerability for P loss described by the index, or more likely as an indication of the lack of fit for the model in this region. Conversely, the calculated P Index correlated quite well with several of the water quality indicators. Table 5.67 gives the Spearman R 2 values for the P Index with water quality. 88 Table 5.67 Correlations between P Index rankings and water quality indicators. All correlations are significant at 0.05 level. WQ Indicator NOx (w) NH3 (w) NH3 TP (w) TP pH « pH (d) D O (w) Fecal (w) Fecal (d) R2 for P Index 0.53 0.82 0.55 0.81 0.66 -0.79 -0.47 -0.80 0.82 0.90 The high R 2 values are a good indication of the effectiveness of the index. In particular, the high positive score with total phosphorus and the high negative score with dissolved oxygen show that the assessment of vulnerability may reflect P loss already taking place. By taking into account both input and transport factors the model has accurately predicted areas where phosphorus loss from a site may negatively impact the aquatic environment. In addition to the high correlations, the value of the model can be seen in figure 5.70. This figure gives several of the measures of P taken in soil, sediment, and water with the P index score. The increase in P index rating is influenced by the increase in available P in soil, and both of these vulnerability indicators correspond well with the environmental references of sediment P and total P in water. Upper Elk Mid Elk Contributing Area Mouth I Amount adsorbed from 45ppm sol'n. I Soil Bray P2 I Sediment Bray P2 Total P in water (dry season) (ug/L) — • — P Index Score Figure 5.70 Various P fractions with the P Index Score by contributing area. 8 9 As stated earlier, the P index is also reflective of land use intensity. However, from the analysis of difference in water quality by land use, it is important to include the transport factors as well. The Kruskal-Wallis test for differences between means show very distinct differences between water samples taken adjacent to forest and agricultural areas. In every measured water quality indicator, for both the wet and dry seasons, the measurements in forested areas were significantly different from all measurements taken adjacent to agricultural land uses. There were no significant differences between the different agricultural land uses such as com, pasture or horticulture. However, with the small number of sampling stations it is likely that differences would be found with a greater number of measurements taken. 90 Chapter 6 SUMMARY A N D CONCLUSIONS Problems related to water quality and fish habitat in the Elk Creek watershed are due in part to phosphorus inputs from the soil to the aquatic ecosystem. With urban development slated for the hillsides area in the near future and agricultural intensification already beginning in the mouth area of the watershed, it is crucial to determine the current natural and anthropogenic factors that influence the movement of this nutrient. This thesis aimed to characterize the P status of the Elk Creek watershed with respect to its potential as a source for aquatic pollution. Links between phosphorus and soil characteristics, land use, and spatial distribution were identified. Two P-indexing systems were also evaluated in an effort to determine areas vulnerable to P loss. All of these criteria were then related to two environmental indices; sediment and water quality evaluations. 6.1 Soil Fertility Summary Soil samples were taken from 45 sites located throughout the watershed during the fall of 2000. The overall soil fertility was rated as adequate to good. The mean percent base saturation and pH readings were below desirable levels, although these mean values were influenced by the low readings in forest soils which are naturally low in pH and exchangeable cations. Spatially, significant differences existed primarily between the hillside area and the lowlands. This spatial variation also corresponded with the differences between land use types, predominandy forest and agricultural land covers. The sandy soils 91 of the golf course were also significantly different, due to the complete lack of similarities between the imported sands and the naturally occurring soils of the watershed. 6.2 Land Use Summary Compilation and analysis, with the help of field surveys, windshield surveys, and GIS, revealed the land use trends over the last six years. Little change has occurred in the headwaters area, with only small additions of rural non-farm housing in the forest. The residential area at the foot of the slope has also experienced little change, although construction is slated to begin in the near future. Although the agricultural land base remains constant, agricultural intensification is apparent through the growth of the horticultural industry in the mouth area. Other changes in the agricultural area reflect the traditional rotations between com and grass land covers. The overall animal density for the agricultural portion of the watershed was determined to be 1.3 AU/ha. Amongst just the farms that housed animals the density was 2.8 AU/ha. P inputs to the watershed were concentrated in the agricultural and recreational areas. Organic inputs ranged from 14 to 44 kgP/ha/yr, with a mean rate of 28 kgP/ha/yr. The inorganic P inputs ranged from 0.7 to 4.6 kgP/ha/yr, with a mean of 1.24 kgP/ha/yr. Sixty-two percent of those surveyed applied chemical fertilizer, while only 36% applied manure. Twenty-six percent surveyed applied both. Spatially, both manure and chemical fertilizer inputs increase with distance downstream. These findings correspond well with the estimated increase in land use intensity. 92 6.3 Phosphorus Status Summary Analysis of available P indicates that the majority of sites sampled had concentrations that were adequate to good in terms of soil fertility. There is an excess of soil test P in the mouth area of the watershed. Intensively managed sites, as expected, had higher availability indices (Bray PI and Bray P2). Through Spearmans' correlation analyses, it was determined that the fertilizer and manure inputs, %BS, Mn, pH, and %C exerted the greatest influence over the amount of available P in soil. The organic P fraction was estimated using the Bowman and Moir (1993) extraction, and a surrogate calculation was used to give the amount of organic P in the available fraction as well as the total organic P. While this technique provided a data set that was adequate for comparative purposes, assessment techniques for available organic phosphorus are poor. Spatially, the %AOP was found to decrease with distance from the headwaters area. The high A l and Fe content and low pH of the forest soils appear to be effective at retaining and preventing the loss of the organic P. 6.4 Vulnerability Indices The adsorption procedure used in this thesis was an adaptation of the method proposed by Nair et. al (1984). In general, the sorption data fit the Langmuir model. Exceptions to this are the forest Bfj horizons, which have high sorptive capacities because of the presence of sesquioxides, and the soils high in organic matter (>30% O.M. by mass), which by definition are not capable of meeting the uniform sorption layer that defines the Langmuir model. The soils that did fit the model had maximum sorption capacities that ranged from 8.0 to 327 mg/kg. This range in capacity agreed with other findings in the literature, but was much lower than the capacities presented by Yuan and Lavkulich for nearby British Columbia Spodsols. 93 The adsorption capacities were then fit to the Degree of Phosphorus Saturation (DPS) index. Ranging from 0.1 to 27.9% saturation, the index showed that there is no evidence of excess P beyond sorptive capacity and crop needs, and therefore is of little concern for the leaching of P. However, the DPS model does not agree well with the availability indices (eg. Bray). The other index used in this thesis was the P Index. This ranking of vulnerability to P loss placed sites into four categories. The soils of the watershed fit only into the lower three categories, ranging from low to high. No soils were evaluated as very high. Spatially, the P Index showed strong variation through the watershed, and agreed well with the degree of land cultivation. 6.5 Environmental Indicators An analysis of the bed sediments in the ditches and creeks of the watershed, as well as a water quality assessment provided the means to determine the usefulness of the models. The sediment analysis showed similar spatial trends to the soils, with an increase in P concentrations with distance downstream. As expected, the sediments exhibited reasonable relationships with adjacent soils, and fit the general enrichment concept for creeks. Most nutrients were depleted from the sediments by aquatic plant uptake or through removal by water. The Bray P2 phosphorus values were the exception to this depletion, as the sediments were enriched in this residual P. This is explained by the correlation with iron and aluminum content in sediments, which would be effective at retaining P. The water quality assessment also showed reasonable relationships with both the soils and sediment General trends included increases in most soluble nutrients (N0 3 , N H 3 , and TP) and decreases in pH and D O in areas that had been shown 94 to be vulnerable to P loss, either by the P availability indicators, land use intensity, or by the P Index. The fecal coliform populations also showed spatial relationships with areas high in manure applications. There was good agreement between the soil and water quality assessments. This was evidenced primarily through the spatial trends, as Bray P, organic P, water quality indicators, and sediment P all show the same partem of increasing impacts downstream. There was also good linkages between the P Index and water quality variables. In particular, the strong relationships between the rankings and the wet season water quality variables of NH 3 , TP, fecal colifoms, and dissolved oxygen, show the effectiveness of the model's predictive capacity. Areas that are highly vulnerable to P loss are most likely to lose P during the heavy rains of the winter months, thus influencing the wet season water quality to the greatest extent. 6.6 Conclusions The above summaries lead to several conclusions, addressed to agencies or individuals wishing to pursue research or management objectives within and outside the watershed. 1. The availability indices of P (Bray) are useful for soil fertility and crop requirement needs, and may also be used as a very rough indication of potential environmental contributions. However, in areas receiving high organic matter (manure) inputs, the STP analysis would benefit from the addition of an organic P assessment in order to fully address the available P status. 2. Currently, organic P is poorly understood. Throughout the literature and within this study are indications that it may be largely responsible for the 95 movement of P into the aquatic environment. Improved organic P testing techniques are needed in order to confirm these theories. 3. The adsorption maxima assessment technique was a lengthy procedure that needs to be calibrated and adjusted for variations in soil properties (eg. forest soils versus soils under agricultural cultivation). However, through the determination of the P sorption a better understanding of the local soils was gained. Management techniques that maintain or improve the carbon content and the pH are tools that can be used to help retain P against movement to the aquatic ecosystem. 4. The Degree of Phosphorus Saturation index needs further calibration for the watershed and the Lower Fraser Valley. 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Journal of Environmental Science and Health 30:841-857. 102 Appendix 1 L A N D USE SURVEY / FIELD NOTES NON-FARM SITE RECORD: soil / sediment Date: Location: Site Description Notes: Sample Area: Exposure of Subsoil: Evidence of Deposition: Slope Class: Slope Percentage: Slope Length: Site Position on Slope: Mode of Deposition: Land Use: Vegetation: Additional Notes and Profile Description: 103 ELK CREEK WATERSHED FARM SURVEY 1. Name: 2. Location: 3. What is the size of the farm? 4. Type of farm (general): FARM INFORMATION Table 1. Field Crops Fidd Crops Yield Manure Fertilizer Amount Applied Timing Chemical Fertilizer Amount Applied Timing Com H P C O Grass/Hay H P C O Horticulture H P C O Trees H P C O Other H P C O Table 2. Animals Animals # Grow own feed? % of home-grown feed Dairy cattle Beef cattle Poultry (meat / eggs) Horses Pigs Goats Sheep Other 1. What was the crop rotation for the last three years? 2. Do you test your soils? Y / N 3. How do you determine fertilizer amounts? 104 FARM SITE RECORD: soil / sediment Date: Location: Field Y i e l d Manure A m o u n t T i m i n g Chemica l A m o u n t T i m i n g Crops Fertilizer A p p l i e d Fertilizer A p p l i e d Com Grass/Hay Horticultural Crop: Tree: Other: Sample Area Size: Exposure of Subsoil: Evidence of Deposition: Mode of Deposition: Proximity to stream and riparian vegetation: Additional Notes and Profile Description: 105 Appendix 2 PHOSPHORUS ADSORPTION PROCEDURE 1. Prepare 0.01 M CaCl 2 (1.11 g CaCl 2 L"1) 2. Prepare 0.01 M K H 2 P 0 4 (1.37 g K H 2 P 0 4 L"1). Dilute to the following concentrations, making up to volume with CaCl 2 solution: Initial Dissolved Inorganic P Concen. (mnol/L) 0 6.45 16.13 32.26 161.3 323.0 Initial Dissolved KHoPO, Concen. fmnol/L) 0 28.34 70.88 141.75 708.6 1419.28 Amount of 0.01M needed (mL) 0 2.8 7.09 14.2 70.9 141.9 3. Weight 6,1.0g samples of each air-dry soil sample, ground to <30 mesh, into 50 mL centrifuge tubes. 4. Add to the tubes 20 mL of solution, each of the six samples with one of the concentrations listed above. 5. Add 0.4g (20 g L"1) chloroform to inhibit microbial activity and stopper all tubes. 6. Place in the end-over-end shaker and equilibrate mixture for 24h. 7. Centrifuge filtrates at 13800 x g for 12 minutes. 8. Filter through a Whatman No.41 filter paper to ensure removal of organic debris. 9. Analyze clear solution for orthophosphate. 106 Appendix 3 SOIL FERTILITY D A T A 107 Sample Org/Min/ CA LUT pH Ca Mg Na K CEC Sand meq/100g meq/100g meq/100g meq/100g meq/100g 1 1 0 1 3.79 19.960 1.296 0.100 0.448 140.30 2 3 4 6 5.89 3.031 0.658 0.042 0.364 2.72 3 3 4 6 5.41 1.422 0.370 -0.009 0.179 2.20 4 3 4 6 5.84 2.732 0.261 0.022 0.173 2.38 5 2 0 4 4.94 15.220 2.140 0.105 0.716 51.62 6 2 4 4 5.1 10.354 0.432 0.052 0.079 23.18 7 2 7 4 5.12 7.398 1.811 0.080 0.767 21.93 8 2 7 3 5.22 8.558 2.284 0.069 1.502 25.15 9 2 7 8 5.23 13.224 2.778 0.095 1.208 32.70 10 2 7 8 5.2 9.481 2.613 0.100 0.818 22.94 11 2 6 4 5.14 6.574 1.728 0.041 0.959 17.50 12 2 6 4 5.01 8.346 2.181 0.064 0.505 21.95 13 2 6 3 4.95 6.886 1.235 0.048 0.524 20.02 14 2 5 4 5.03 13.224 1.132 0.092 0.134 27.56 15 2 6 4 4.72 0.898 1.461 0.090 0.179 24.61 16 2 6 3 4.94 13.224 1.831 0.107 0.230 33.19 17 2 6 7 4.67 3.942 0.967 0.033 0.863 18.86 18 2 6 3 4.63 6.924 0.885 0.045 0.377 20.58 19Ahe 2 3 2 3.48 1.023 0.370 0.075 0.211 42.13 19Bf 2 3 2 3.99 0.087 0.140 0.052 0.141 44.37 20Ahe 2 3 2 3.23 -0.044 0.095 0.059 0.192 20.89 20Bf 2 3 2 3.76 -0.039 0.146 0.036 0.115 45.94 21Ae 2 3 2 3.45 0.861 0.350 0.049 0.173 28.71 21 Bf 2 3 2 4.05 1.198 0.473 0.049 0.460 43.89 22Bf 2 3 2 3.92 2.383 0.370 0.066 0.237 41.79 23Ahe 2 2 2 3.34 1.609 0.370 0.062 0.320 37.07 23Bf 2 2 2 3.92 0.898 0.230 0.046 0.153 41.79 24 2 7 8 5.85 11.228 1.276 0.053 0.639 15.48 25 2 7 8 5.9 10.853 1.214 0.041 0.601 14.90 26 2 7 8 5.64 8.258 1.070 0.026 0.703 15.87 27 2 7 8 5.4 9.356 1.358 0.039 0.614 19.64 28 2 5 1 4.86 0.936 0.741 0.116 0.051 18.92 29 1 6 4 4.24 14.346 2.387 0.102 0.243 75.24 30 1 6 3 4.27 21.208 4.383 0.240 0.703 107.19 31 2 6 7 4.53 15.594 2.037 0.119 0.243 53.37 32 2 5 6 4.65 5.976 0.741 0.042 0.511 21.56 33 2 7 8 5.36 9.606 1.173 0.101 0.416 19.77 34 2 7 8 5.3 0.961 1.646 0.062 0.435 12.92 35 2 7 8 5.59 0.998 1.852 0.099 0.806 18.66 36 3 3 2 6.65 1.198 0.329 0.011 0.480 11.17 37 2 2 6 5.95 7.398 1.914 0.033 0.435 16.95 38 2 2 1 5.7 3.056 0.535 0.029 0.237 12.36 39 2 4 6 5.44 5.140 0.514 0.028 0.230 10.53 40 2 6 4 4.48 5.115 1.132 0.046 0.499 28.20 41 2 6 7 4.66 6.437 1.049 0.045 0.454 30.41 42 2 0 6 4.59 5.601 0.823 0.033 0.435 24.30 43 2 5 7 4.59 13.723 2.531 0.127 0.774 47.42 44 2 7 7 4.93 6.300 0.946 0.042 0.326 15.70 45 2 7 4 5.14 7.198 1.337 0.077 0.595 17.17 3b 2 6 9 4.7 5b 2 6 9 4.83 7B 2 6 9 5.36 13b 2 6 9 5.44 16b 3 6 9 6.06 17b 2 5 9 5.66 24/25/26b 2 6 9 5.69 29/30/31 b 3 3 9 6.45 36b 3 4 9 6.36 40/41 b 2 0 9 5.29 Sample B.S.% %C %C %N C:N Al Fe Mn % Sand % Silt % Clay new Ratio % in soil % in soil % in soil 1 15.54% 33.56 33.10 2.54 13.01 0.33 0.54 0.01 43.57 22.73 33.70 2 89.22% 0.51 0.52 0.03 15.07 0.33 0.73 0.02 97.80 2.20 0.00 3 134.02% 0.29 0.22 0.02 12.70 0.50 1.08 0.02 98.75 1.25 0.00 4 134.02% 0.31 0.28 0.02 14.78 0.41 0.93 0.03 96.24 1.25 2.51 5 35.22% 7.88 7.45 0.57 13.02 1.18 • 1.20 0.05 18.25 67.73 14.01 6 47.11% 3.32 3.19 0.27 11.88 1.42 1.66 0.06 26.14 56.33 17.53 7 45.85% 2.24 2.34 0.22 10.84 0.79 0.96 0.04 40.01 45.64 14.35 8 49.36% 2.54 2.51 0.24 10.30 0.76 0.95 0.04 27.09 55.06 17.85 9 52.92% 3.22 3.34 0.23 14.55 0.84 1.13 0.08 19.74 61.35 18.91 10 56.72% 1.95 1.94 0.13 14.42 0.92 1.20 0.07 21.92 61.21 16.87 11 53.16% 1.89 1.83 0.16 11.23 0.61 0.81 0.05 29.63 52.61 17.76 12 50.55% 2.67 2.64 0.25 10.58 0.91 1.12 0.04 25.02 56.13 18.85 13 43.43% 3.57 3.48 0.30 11.55 0.72 1.22 0.06 49.91 37.35 12.74 14 52.90% 3.33 3.49 0.29 11.98 0.82 0.98 0.05 11.02 64.07 24.92 15 10.68% 2.58 2.66 0.24 10.87 1.11 1.28 0.05 15.11 61.26 23.63 16 46.38% 4.09 4.53 0.41 10.94 1.58 2.22 0.05 21.04 57.87 21.09 17 30.78% 3.15 3.18 0.28 11.24 1.84 1.96 0.06 36.76 43.04 20.20 18 40.00% 2.76 2.74 0.24 11.57 1.25 1.81 0.11 46.29 37.11 16.59 19Ahe 3.99% 5.79 6.14 0.30 20.51 1.15 1.51 0.07 25.28 51.94 22.78 19Bf 0.95% 5.71 5.60 0.26 21.51 1.18 2.62 0.00 34.78 55.51 9.71 20Ahe 1.44% 2.93 2.82 0.13 21.36 0.52 0.47 0.01 21.74 62.43 15.83 20Bf 0.56% 5.08 4.72 0.22 21.14 0.55 0.90 0.00 45.37 44.16 10.47 21Ae 4.99% 3.53 3.57 0.19 18.66 0.47 0.60 0.00 18.34 61.19 20.47 21 Bf 4.97% 4.73 4.50 0.25 18.14 0.61 1.02 0.02 35.15 46.83 18.01 22Bf 7.31% 5.95 5.28 0.23 23.11 0.33 0.75 0.00 42.04 43.91 14.05 23Ahe 6.37% 4.69 5.93 0.23 25.57 0.36 0.56 0.00 29.35 63.41 7.25 23Bf 3.18% 5.74 5.28 0.21 24.84 0.49 1.29 0.03 41.10 54.10 4.80 24 85.25% 1.51 5.93 0.13 45.58 0.65 0.94 0.05 28.47 56.88 14.65 25 85.32% 1.22 5.28 0.10 53.83 0.72 0.88 0.06 26.14 60.98 12.88 26 63.37% 1.9 1.53 0.17 8.87 0.68 0.94 0.05 45.72 40.49 13.78 27 57.87% 2.21 1.16 0.18 6.60 0.84 1.62 0.07 35.93 50.74 13.33 28 9.75% 2.76 1.99 0.23 8.69 0.90 1.73 0.74 24.27 57.07 18.66 29 22.70% 18.66 19.00 1.53 12.43 0.60 0.87 0.04 32.34 43.70 23.96 30 24.75% 31.56 30.40 2.52 12.06 0.41 0.85 0.02 48.54 29.21 22.25 31 33.71% 9.17 8.65 0.74 11.74 1.02 1.14 0.05 7.95 82.78 9.27 32 33.72% 2.48 2.43 0.21 11.36 0.69 1.30 0.05 33.76 52.34 13.90 33 57.13% 1.74 1.75 0.15 12.04 0.64 1.20 0.05 18.11 65.94 15.95 34 24.02% 1.28 1.25 0.11 11.13 0.61 1.17 0.06 43.55 44.05 12.40 35 20.12% 1.8 1.64 0.15 11.25 0.56 0.93 0.66 23.85 63.24 12.91 36 18.06% 1.5 1.55 0.10 15.58 0.80 1.55 0.06 81.19 16.68 2.14 37 57.70% 2.14 2.18 0.20 10.68 0.71 0.80 0.73 38 31.20% 2.16 1.89 0.13 14.41 0.34 0.51 0.06 39 56.13% 4.23 3.43 0.24 14.17 0.64 1.13 0.03 59.65 23.00 17.36 40 24.08% 4.67 4.52 0.43 10.56 0.52 0.83 0.08 23.07 48.50 28.42 41 26.26% 3.88 3.89 0.37 10.66 0.71 1.04 0.11 36.10 46.98 16.92 42 28.36% 3.42 3.53 0.32 11.07 0.87 0.94 0.03 24.06 60.84 15.10 43 36.18% 10.12 9.30 0.83 11.16 0.98 1.11 0.04 17.19 71.96 10.85 44 48.50% 2.29 1.98 0.17 11.33 0.52 0.86 0.06 42.89 40.24 16.87 45 53.63% 2.14 2.23 0.20 11.39 0.54 0.77 0.07 50.51 38.20 11.29 3b 2.36 2.18 0.16 13.74 0.62 0.94 0.03 16.62 60.80 22.58 5b 2.38 2.07 0.15 13.84 0.77 1.57 0.04 58.30 30.20 11.21 7B 2.53 2.91 0.18 15.98 1.01 1.51 0.06 49.49 34.10 16.40 13b 0.37 0.35 0.03 12.68 0.50 0.91 0.03 24.70 59.40 15.90 16b 0.56 0.48 0.04 10.86 0.89 1.63 0.04 87.61 6.93 5.46 17b 0.78 0.87 0.08 10.38 0.71 0.98 0.03 27.24 54.99 17.77 24/25/26b 6.01 5.94 0.04 154.65 1.60 2.00 0.04 29/30/31 b 1.28 0.60 0.05 11.17 1.33 2.28 0.06 87.44 4.61 7.95 36b 0.78 0.78 0.07 10.71 0.46 1.01 0.13 90.07 3.59 6.34 40/41 b 0.98 0.73 0.06 12.05 0.34 0.50 0.03 48.69 33.35 17.96 |0~lb Appendix 4 PHOSPHORUS D A T A 108 ample P2 P1 Total P NaOH + Total % Avail. %AOP % TOP ppm in soil ppm in soil % in soil EDTA Avail. P P 1 20.05 6.28 0.1538 63.86 83.91 0.05 76.11 4.15 2 147.03 56.14 0.0402 6.26 153.29 0.38 4.08 1.56 3 158.2 43.46 0.0371 3.62 161.82 0.44 2.24 0.98 4 113.84 29.27 0.0297 2.22 116.06 0.39 1.91 0.75 5 99.48 79.21 0.2557 78.30 177.78 0.07 44.04 3.06 6 216.9 102.75 0.1664 51.24 268.14 0.16 19.11 3.08 7 323.8 202.87 0.1354 44.48 368.28 0.27 12.08 3.29 8 342.8 175.59 0.1822 51.11 393.91 0.22 12.98 2.81 9 458.1 367.7 0.2226 64.42 522.52 0.23 12.33 2.89 10 285.2 207.79 0.1587 40.50 325.70 0.21 12.43 2.55 11 184.06 106.04 0.1081 31.91 215.97 0.20 14.78 2.95 12 201.41 100.67 0.1194 37.71 239.12 0.20 15.77 3.16 13 13.95 24.7 0.1052 28.22 42.17 0.04 66.92 2.68 14 60.9 17.38 0.0917 21.53 82.43 0.09 26.12 2.35 15 92.64 29.82 0.0933 25.48 118.12 0.13 21.57 2.73 16 40.72 7.4 0.0975 27.00 67.72 0.07 39.87 2.77 17 132.39 96.67 0.1273 43.36 175.75 0.14 24.67 3.41 18 189.23 129 0.1207 43.44 232.67 0.19 18.67 3.60 19Ahe 14.04 4.26 0.0640 27.35 41.39 0.06 66.08 4.27 19Bf 11.85 5.3 0.1125 35.50 47.35 0.04 74.97 3.16 20Ahe 19.98 15.75 0.0191 12.29 32.27 0.17 38.08 6.42 20Bf 6.43 3.89 0.0784 22.62 29.05 0.04 77.87 2.88 21Ae 16.67 3.32 0.0444 18.94 35.61 0.08 53.19 4.27 21 Bf 8.53 5.34 0.1133 40.85 49.38 0.04 82.73 3.61 22Bf 12.73 6.63 0.0917 40.30 53.03 0.06 75.99 4.40 23Ahe 76.65 34.46 0.0571 25.99 102.64 0.18 25.32 4.55 23Bf 39.96 18.02 0.1934 72.52 112.48 0.06 64.47 3.75 24 157.17 70.27 0.1026 27.08 184.25 0.18 14.70 2.64 25 136.55 58.56 0.0875 21.10 157.65 0.18 13.38 2.41 26 278 234.3 0.1204 43.88 321.88 0.27 13.63 3.64 27 164.61 90.46 0.1145 31.15 195.76 0.17 15.91 2.72 28 80.22 3.46 0.0902 15.46 95.68 0.11 16.16 1.71 29 41.73 13.71 0.2546 108.80 150.53 0.06 72.28 4.27 30 42.56 26.79 0.3554 145.88 188.44 0.05 77.41 4.10 31 55.7 16.82 0.1721 54.44 110.14 0.06 49.43 3.16 32 286.9 209.79 0.1555 56.86 343.76 0.22 16.54 3.66 33 89.03 27.7 0.1023 20.64 109.67 0.11 18.82 2.02 34 88.4 17.55 0.0699 12.95 101.35 0.14 12.78 1.85 35 162.12 70.65 0.1154 32.39 194.51 0.17 16.65 2.81 36 74.9 15.14 0.0469 5.19 80.09 0.17 6.48 1.11 37 140.54 58.07 0.0982 33.87 174.41 0.18 19.42 3.45 38 33.78 13.17 0.0368 13.65 47.43 0.13 28.78 3.71 39 124.38 12.63 0.0638 16.04 140.42 0.22 11.42 2.51 40 80.25 38.47 0.1699 59.90 140.15 0.08 42.74 3.53 41 99.45 82.53 0.1595 54.29 153.74 0.10 35.31 3.40 42 488 260.3 0.1443 42.68 530.68 0.37 8.04 2.96 43 57.31 22.43 0.1916 66.33 123.64 0.06 53.65 3.46 44 330.4 269.6 0.1303 46.25 376.65 0.29 12.28 3.55 45 254.2 191.13 0.1216 41.43 295.63 0.24 14.01 3.41 3b 169.12 64.58 0.0526 8.65 177.77 0.34 4.87 1.64 5b 93.15 11.64 0.0409 9.75 102.90 0.25 9.48 2.38 7b 186.8 11 0.1086 28.56 215.36 0.20 13.26 2.63 13b 105.5 5.7 0.0769 14.75 120.25 0.16 12.27 1.92 16b 166.34 2.2 0.0558 3.26 169.60 0.30 1.92 0.58 17b 85.43 11.37 0.0313 2.84 88.27 0.28 3.22 0.91 24/25/26b 161.63 28.5 0.0607 12.34 173.97 0.29 7.09 2.03 29/30/31 b 39.5 2.32 0.1044 24.95 64.45 0.06 38.71 2.39 36b 85.48 5.01 0.0359 2.25 87.73 0.24 2.56 0.63 40/41 b 115.44 7.29 0.0763 18.30 133.74 0.18 13.68 2.40 (08 Appendix 5 ADSORPTION ISOTHERMS 109 ro ro E in C N c ro .c *-> co CO V E E x ro E c o o CO T3 ro T3 0) ro E CO LU £ n 13 J3 U I — "xT C D < N C O - 3 - C D C O I O T - C \ I C O 0 ) ( D < D < D < D < D ( D a ) a ) 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . E E E E E E E E E c o c o c o c o c o c o c o c o c o HMHt l I L O XT O L O CO o C O L O CM o C s i E c g ro L . c Q> O c o o c o o CO (B^/Buj) p a q j o s p v j u n o u i v o in LO I i 1 i 1 1 i 1 , f f i — S 1 o o o o o o o o o o o l O O L O O L O O U I O L O O l O • S J - ^ O O C O C M C M T - T - 1 (6^/6LU) paqjospv junouiv Appendix 6 P I N D E X RESULTS 110 Sample # RUSLE (ton/ac/yr) A A Erosion by Runoff Flooding Dist. To R K LS C P T/A/yr Rating Sprinkler Class Hazard Surface W. 1 86 0.37 0.15 0.02 1 0.10 0.00 0.00 0.00 0.75 0.75 2 86 0.01 0.06 0.02 1 0.00 0.00 0.75 0.00 0.00 0.75 3 86 0.01 0.107 0.02 1 0.00 0.00 0.75 0.00 0.00 0.75 4 86 0.01 0.06 0.02 1 0.00 0.00 0.75 0.00 0.00 0.75 5 86 0.42 0.15 0.02 1 0.11 0.00 0.00 0.00 0.75 3.00 6 86 0.43 0.15 0.3 1 1.66 1.50 0.00 0.00 0.75 3.00 7 86 0.65 0.15 0.3 1 2.52 1.50 0.00 0.00 1.50 3.00 8 86 0.56 0.15 0.3 1 2.17 1.50 0.00 0.00 1.50 1.50 9 86 0.57 0.096 0.02 1 0.09 0.00 0.75 0.00 0.75 0.75 10 86 0.57 0.096 0.02 1 0.09 0.00 0.75 0.00 0.75 0.75 11 86 0.53 0.15 0.3 1 2.05 1.50 0.00 0.00 0.75 1.50 12 86 0.52 0.15 0.3 1 2.01 1.50 0.00 0.00 0.75 1.50 13 86 0.52 0.133 0.3 1 1.78 1.50 0.00 0.00 0.75 6.00 14 86 0.48 0.15 0.3 1 1.86 1.50 0.00 0.00 0.75 1.50 15 86 0.48 0.15 0.3 1 1.86 1.50 0.00 0.00 0.75 1.50 16 86 0.5 0.15 0.3 1 1.94 1.50 0.00 0.00 0.75 3.00 17 86 0.37 0.437 0.31 1 4.31 3.00 0.00 1.00 0.00 3.00 18 86 0.33 0.437 0.3 1 3.72 3.00 o.oo - 1.00 0.00 3.00 19Ahe assumed 0.00 0.00 2.00 0.00 0.00 19Bf assumed 0.00 0.00 2.00 0.00 0.00 20Ahe assumed 0.00 0.00 2.00 0.00 0.00 20Bf assumed 0.00 0.00 2.00 0.00 0.00 21Ae assumed 0.00 0.00 2.00 0.00 0.00 21 Bf assumed 0.00 0.00 2.00 0.00 0.00 22Bf assumed 0.00 0.00 2.00 0.00 0.00 23Ahe assumed 0.00 0.00 2.00 0.00 0.00 23Bf assumed 0.00 0.00 2.00 0.00 0.00 24 86 0.4 0.096 0.02 1 0.07 0.00 0.00 0.00 1.50 6.00 25 86 0.41 0.096 0.02 1 0.07 0.00 0.00 0.00 1.50 6.00 26 86 0.4 0.096 0.02 1 0.07 0.00 0.00 0.00 1.50 3.00 27 . 86 0.6 0.096 0.02 1 0.10 0.00 0.00 0.00 1.50 3.00 28 86 0.54 0.096 0.02 1 0.09 0.00 0.00 0.00 0.75 1.50 29 86 0.37 0.15 0.3 1 1.43 1.50 0.00 0.00 0.75 1.50 30 86 0.24 0.15 0.3 1 0.93 0.00 0.00 0.00 0.75 1.50 31 86 0.5 0.15 0.3 1 1.94 1.50 0.00 0.00 0.75 1.50 32 86 0.48 0.15 0.02 1 0.12 0.00 0.00 0.00 0.75 3.00 33 86 0.56 0.096 0.02 1 0.09 0.00 0.00 0.00 1.50 3.00 34 86 0.42 0.15 0.02 1 0.11 0.00 0.00 0.00 1.50 3.00 35 86 0.56 0.096 0.02 1 0.09 0.00 0.00 0.00 1.50 3.00 36 assumed 0.00 0.00 2.00 0.00 1.50 37 86 0.4 0.305 0.02 1 0.21 0.00 0.00 2.00 0.00 0.75 38 86 0.32 0.437 0.02 1 0.24 0.00 0.00 2.00 0.00 3.00 39 86 0.2 0.15 0.02 1 0.05 0.00 0.75 0.00 0.00 1.50 40 86 0.4 0.305 0.31 1 3.25 3.00 0.00 1.00 0.75 3.00 41 86 0.39 0.305 0.31 1 3.17 3.00 0.00 1.00 0.75 3.00 42 86 0.4 0.096 0.02 1 0.07 0.00 0.00 0.00 0.75 0.75 43 86 0.41 0.15 0.31 1 1.64 1.50 0.00 0.00 0.75 1.50 44 86 0.42 0.15 0.31 1 1.68 1.50 0.00 0.00 0.75 0.75 45 86 0.32 0.15 0.3 1 1.24 1.50 0.00 0.00 0.75 0.75 Subsurf. T Factors STP STP calc. Fert. P Fert. P Org. P Org. P S Factors Total Site Drainage Total Bray P1 Bray P1 App. Rate App. Meth. App. Rate App. Meth Total Rating Class 0.00 1.13 6.28 0.00 0.00 0.00 0.00 0.00 0.00 1.13 1.00 0.50 1.38 56.14 1.61 6.53 1.00 1.18 0.00 8.19 9.57 1.00 0.50 . 1.38 43.46 0.35 0.57 1.00 1.18 0.00 2.45 3.83 1.00 0.50 1.38 29.27 0.00 0.57 1.00 1.18 0.00 2.11 3.48 1.00 1.00 3.31 79.21 3.92 0.34 1.00 1.35 3.00 9.03 12.34 2.00 1.00 5.56 102.75 6.28 0.34 1.00 1.66 0.00 8.69 14.25 1.00 2.00 6.63 202.87 16.29 2.23 1.00 0.00 3.00 21.46 28.09 3.00 2.00 5.50 175.59 13.56 2.23 1.00 1.77 3.00 20.50 26.00 3.00 4.00 3.69 367.70 32.77 9.34 1.00 1.77 0.00 42.05 45.73 3.00 4.00 3.69 207.79 16.78 9.34 1.00 0.00 0.00 24.29 27.97 3.00 1.00 4.44 106.04 6.60 0.00 0.00 0.00 3.00 9.60 14.04 2.00 1.00 4.44 100.67 6.07 0.00 0.00 0.65 3.00 9.72 14.16 2.00 0.50 7.56 24.70 0.00 0.00 0.00 0.65 3.00 3.65 11.21 1.00 0.50 4.19 17.38 0.00 1.87 1.00 0.65 3.00 5.56 9.74 1.00 0.50 4.19 29.82 0.00 1.87 1.00 0.91 3.00 5.82 10.01 1.00 0.50 5.31 7.40 0.00 1.87 1.00 0.91 3.00 5.82 11.13 1.00 0.00 7.75 96.67 5.67 6.00 1.00 0.91 3.00 14.58 22.33 2.00 0.00 7.75 129.00 8.90 6.00 1.00 0.57 3.00 • 17.47 25.22 3.00 0.00 2.00 4.26 0.00 0.00 0.00 0.57 0.00 0.57 2.57 1.00 0.00 2.00 5.30 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 15.75 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 3.89 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 3.32 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 5.34 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 6.63 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 34.46 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 0.00 2.00 18.02 0.00 0.00 0.00 0.00 0.00 0.00 2.00 1.00 1.00 6.13 70.27 3.03 5.00 1.00 0.00 3.00 10.28 16.40 2.00 0.50 5.88 58.56 1.86 5.00 1.00 0.00 3.00 9.11 14.98 2.00 4.00 5.38 234.30 19.43 5.00 1.00 0.00 3.00 26.68 32.06 3.00 1.00 3.88 90.46 5.05 5.00 1.00 0.00 3.00 12.30 16.17 3.00 0.00 1.69 3.46 0.00 0.00 0.00 0.00 0.00 0.00 1.69 1.00 0.00 3.94 13.71 0.00 5.00 1.00 0.00 3.00 7.25 11.19 2.00 0.00 1.69 26.79 0.00 5.00 1.00 0.00 3.00 7.25 8.94 2.00 0.00 3.94 16.82 0.00 5.00 1.00 0.00 3.00 7.25 11.19 2.00 4.00 4.81 209.79 16.98 0.00 0.00 0.00 0.00 16.98 21.79 2.00 0.50 3.63 27.70 0.00 5.00 1.00 0.00 3.00 7.25 10.88 1.00 0.50 3.63 17.55 0.00 5.00 1.00 0.00 3.00 7.25 10.88 1.00 1.00 3.88 70.65 3.07 5.00 1.00 0.00 3.00 10.32 14.19 2.00 0.00 3.13 15.14 0.00 0.00 0.00 0.00 0.00 0.00 3.13 1.00 0.50 2.81 58.07 1.81 0.35 1.00 0.74 0.00 3.30 6.12 1.00 0.00 4.25 13.17 0.00 0.00 0.00 0.74 0.00 0.74 4.99 1.00 0.50 1.94 12.63 0.00 0.57 1.00 0.00 0.00 0.93 2.87 1.00 0.00 8.31 38.47 0.00 2.00 1.00 1.28 3.00 6.28 14.60 2.00 0.00 8.31 82.53 4.25 2.00 1.00 1.28 3.00 10.54 18^85 2.00 4.00 3.13 260.30 22.03 0.00 0.00 0.00 0.00 22.03 25.16 3.00 0.50 4.19 22.43 0.00 0.80 1.00 0.00 3.00 4.10 8.29 2.00 4.00 5.38 269.60 22.96 4.00 1.00 0.00 3.00 29.46 34.84 3.00 4.00 5.38 191.13 15.11 0.50 1.00 0.00 3.00 18.99 24.36 12.70041 45.73273 3.00 1.693878 Appendix 7 WESTERN WASHINGTON WORKSHEET PHOSPHORUS INDEX WORKSHEET - WESTERN OREGON AND WASHINGTON (Draft 6) August 11, 2000 Producer: County: Tract No. Field No(s). Date: Soil Map Unit(s) Soil Test P ppm Lab. Method Sample Depth _ Crop Rotation: Nutrient Application Method(s) P H O S P H O R U S L O S S R A T I N G Weighted Rating Value T R A N S P O R T F A C T O R S Factor Weight None (0) Low 0) Medium (2) High (4) Very High (8) Current Planned Soil Erosion -tons/ac/yr (RUSLE) 1.50 < 1 (0) 1-3 (1.5) 4-6 (3.0) 7-15 (6.0) > 15 (12.0) Soil Erosion from Sprinkler Irrigation 0.75 No sprinkler irrigation (0) Application rate < infiltration rate No visible runoff at field borders (0.75) Application rate = infiltration rate Utile lo no visible runoff at field borders (1.5) Application rate > infiltration rate Visible runoff at field borders (3.0) Application rate > infiltration rate Excessive runoff visible at field borders. Rills and gullies present. (6.0) Runoff Class 1.00 Negligible (0) Very tow or low (1.0) Medium (2-0) High (4.0) Very High (8.0) Flooding Hazard 0.75 None or very rare (0) Rare (0.75) Occasional (1-5) Frequent (3.0) Very Frequent (6.0) Distance to perennial surface waters / buffer widths 0.75 > 500 feet OR buffer > 30 ft. wide (or meets NRCS standards) next to sudace waters (0) 300 - 500 feet OR buffer 20-30 ft. wide next to surface waters (0.75) 200 - 299 feet OR buffer 10 -19 ft wide next to surface waters (1.5) 100-199 feet AND buffer < 10 ft. wide next to surface waters (3.0) < 100 feet AND No buffer next to surface waters (6.0) Subsudace Drainage 0.50 No Tile Drains (0) Tile drains present Soil Test P (Bray P1) < 60 ppm (0.5) Tile drains present Soil Test P (Bray P1) 61 -140 ppm d-0) Tile drains present Soil Test P (Bray PI) 141-190 ppm (2.0) Tile drains present Soil Test P (Bray P1) > 190 ppm (4.0) P H O S P H O R U S L O S S R A T I N G Weighted Rating Value S O U R C E F A C T O R S Factor Weight None (0) Low (1) Medium (2) High (4) Very High (8) Current Planned Soil Test P -ppm (Bray Pt) 1.00 (Soil Test P - 40) / 10 ( -40)/10= Asslon 0 points fl Soil Test P < 40 Dom Soil Test P Commercial P Fertilizer Application Rate 0.75 (Ibs/ac P,0< / 50) x 0.75 I / 50) x 0.75 = Ibs/ac P20B Commercial P Fertilizer Application Method 0.50 None Applied (0) Injected / banded deeper than 2 inches OR Incorporated within 5 days of application from March through September (0.5) Incorporated within 5 days of application from October through February OR Surface applied March through August d-0) Incorporated more than 5 days after application OR Surface applied September through October (2.0) Surface applied November through February (4.0) Organic P Source Application Rate 1.00 Ibs/ac P,Cu/50 /50 = Ibs/ac P,Os Organic P Source Application Method 1.00 None Applied (0) Injected deeper than 2 inches OR Incorporated within 5 days ol application from March through September (1.0) Incorporated within 5 days of application from October through February OR Surface applied March through August (2.0) Incorporated more than 5 days after application OR Surface applied September through October (4.0) Surface applied November through February (8.0) Total Rating Value TFS + SFS Site Vulnerability Class < 13.0 Low 13.0-25.0 Medium 25.1 -50.0 High > 50.0 Very High Current Planned Transport Factors Subtotal (TFS) Source Factors Subtotal (SFS) Total Rating Value (TFS + SFS) Site Vulnerability Class 01 

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