Land and Food Systems, Faculty of
DSpace
UBCV
Furey, George Neil
Master of Science - MSc
University of British Columbia
Management of our agroecosystems faces a dual problem where we need to produce more food, fiber and fuel with the same amount of land while limiting the impact of this production on the earth’s biodiversity and environment. A solution to this problem is to manage agroecosystems for augmented biodiversity to increase the provision of Ecosystem Services (ES). Increasing on-farm diversity can improve ES beyond the provision of food to help reduce the use of environmentally costly inputs through ES provision such as growing nitrogen-fixing plants to supply nitrogen to food crops in lieu of other inputs. Cover crops are plants that can be grown temporal or spatially isolated from food crops with limited negative impacts on yield and can provide significant positive impacts to both the farmer’s profitability and the environment. Increasing the biodiversity of cover crops is one key strategy to sustainably intensify agriculture by helping to improve yields while limiting the environmental impact of agricultural production. To determine if increasing cover crop biodiversity leads to greater ES provision, experiments were conducted that manipulated cover crop functional diversity in three settings, a field trial at the UBC Farm, a pot experiment in the UBC Horticultural Greenhouse, and a litter-bag study at the UBC Farm. A functional group framework was applied to cover crops using a grass (Rye Secale cereale L.), a legume (Lana Vetch Vicia villosa ssp. dasycarpa Roth.) and a forb (Chicory Cichorium intybus L.) in mixtures of varying degrees of diversity. The general conclusion from these analyses was that mixtures were capable of providing a comparable magnitude of ES to the best monoculture while providing a range of ES greater than any one monoculture. In monoculture, rye was the best for carbon fixation (biomass production and CO₂ uptake) and weed suppression; Lana vetch was the best for improving soil fertility (nitrogen and phosphorus release); and chicory was the best for soil cover (leaf area) and residue decomposition. There was sufficient evidence to support the use of cover crop mixtures to provide ES that reduce the environmental impact of agricultural production with potential to improve crop yields.
Functional Diversity In Cover Crops And Ecosystem Services by George Neil Furey B.A., Memorial University of Newfoundland, 2011 B.Sc., Memorial University of Newfoundland, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Plant Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2015 © George Neil Furey 2015 ii Abstract Management of our agroecosystems faces a dual problem where we need to produce more food, fiber and fuel with the same amount of land while limiting the impact of this production on the earth’s biodiversity and environment. A solution to this problem is to manage agroecosystems for augmented biodiversity to increase the provision of Ecosystem Services (ES). Increasing on-farm diversity can improve ES beyond the provision of food to help reduce the use of environmentally costly inputs through ES provision such as growing nitrogen-fixing plants to supply nitrogen to food crops in lieu of other inputs. Cover crops are plants that can be grown temporal or spatially isolated from food crops with limited negative impacts on yield and can provide significant positive impacts to both the farmer’s profitability and the environment. Increasing the biodiversity of cover crops is one key strategy to sustainably intensify agriculture by helping to improve yields while limiting the environmental impact of agricultural production. To determine if increasing cover crop biodiversity leads to greater ES provision, experiments were conducted that manipulated cover crop functional diversity in three settings, a field trial at the UBC Farm, a pot experiment in the UBC Horticultural Greenhouse, and a litter-bag study at the UBC Farm. A functional group framework was applied to cover crops using a grass (Rye Secale cereale L.), a legume (Lana Vetch Vicia villosa ssp. dasycarpa Roth.) and a forb (Chicory Cichorium intybus L.) in mixtures of varying degrees of diversity. The general conclusion from these analyses was that mixtures were capable of providing a comparable magnitude of ES to the best monoculture while providing a range of ES greater than any one monoculture. In monoculture, rye was the best for carbon fixation (biomass production and CO2 uptake) and weed suppression; Lana vetch was the best for improving soil fertility (nitrogen and phosphorus release); and chicory was the best for soil cover (leaf area) and residue iii decomposition. There was sufficient evidence to support the use of cover crop mixtures to provide ES that reduce the environmental impact of agricultural production with potential to improve crop yields. iv Preface This thesis is a result of field and greenhouse experimentation at the University of British Columbia - Vancouver at the Centre for Sustainable Food Systems – UBC Farm and the UBC Horticultural Greenhouse. The author is responsible for designing research, collecting data, data analyses and writing manuscripts presented herein. v Table Of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table Of Contents ......................................................................................................................... v List Of Tables ............................................................................................................................... ix List Of Figures .............................................................................................................................. xi List Of Abbreviations ................................................................................................................. xv Acknowledgements .................................................................................................................... xvi Dedication .................................................................................................................................. xvii Chapter 1: Introduction ............................................................................................................... 1 1.1 Introduction ........................................................................................................................ 1 1.1.1 Context of research ..................................................................................................... 1 1.1.2 Cover crops, biodiversity and function in agroecosystems ........................................ 3 1.1.3 Agroecology and the application of ecological theory ............................................... 4 1.2 Research questions ............................................................................................................. 5 1.2.1 Hypothesis ................................................................................................................... 5 1.3 Research objectives ............................................................................................................ 6 Chapter 2: Functionally Diverse Cover Crops And Ecosystem Services Under Organic Management .................................................................................................................................. 7 2.1 Introduction ........................................................................................................................ 7 2.2 Materials and methods ..................................................................................................... 10 2.2.1 Study site ................................................................................................................... 10 vi 2.2.2 Cover crop selection ................................................................................................. 10 2.2.3 Experimental design .................................................................................................. 11 2.2.4 Treatments ................................................................................................................. 11 2.2.5 Sample preparation and analysis ............................................................................... 12 2.2.6 Greenhouse gas emission .......................................................................................... 12 2.2.7 Inverse yield density models ..................................................................................... 13 2.2.8 Statistical analyses .................................................................................................... 13 2.3 Results .............................................................................................................................. 15 2.3.1 Total land output ....................................................................................................... 15 2.3.2 Weed suppression as a proportion of control ............................................................ 17 2.3.2.1 Inverse yield density models for rye and vetch ................................................. 19 2.3.3 Tissue concentrations ................................................................................................ 21 2.3.3.1 Tissue nitrogen ................................................................................................... 21 2.3.3.2 Tissue carbon ..................................................................................................... 22 2.3.4 Carbon flux ............................................................................................................... 24 2.4 Discussion ........................................................................................................................ 25 Chapter 3: Functionally Diverse Cover Crops Mixtures And Carbon Fixation Under Greenhouse Conditions .............................................................................................................. 31 3.1 Introduction ...................................................................................................................... 31 3.2 Materials and methods ..................................................................................................... 35 3.2.1 Culture ....................................................................................................................... 35 3.2.2 Germplasm ................................................................................................................ 36 3.2.3 Experimental design .................................................................................................. 36 vii 3.2.4 Sampling procedure .................................................................................................. 38 3.2.5 Carbon flux measurements ....................................................................................... 38 3.2.6 Data analyses ............................................................................................................ 39 3.3 Results .............................................................................................................................. 40 3.3.1 Total land output ....................................................................................................... 40 3.3.2 Leaf area .................................................................................................................... 42 3.3.3 Carbon flux ............................................................................................................... 45 3.3.3.1 Net ecosystem exchange (NEE), gross ecosystem photosynthesis (GEP) and net ecosystem productivity (NEP) .......................................................................................... 45 3.3.3.2 Ecosystem respiration (Re) ................................................................................. 46 3.4 Discussion ........................................................................................................................ 48 Chapter 4: Functional Group Diversity Of Cover Crop Mixtures, Nutrient Release And Decomposition Of Above-Ground Tissues ................................................................................ 54 4.1 Introduction ...................................................................................................................... 54 4.2 Materials and methods ..................................................................................................... 57 4.2.1 Study site ................................................................................................................... 57 4.2.2 Field incubation set-up .............................................................................................. 58 4.2.3 Soil temperature and moisture content ..................................................................... 60 4.2.4 Measurement of nutrient release ............................................................................... 60 4.2.5 Calculation of decomposition ................................................................................... 61 4.2.6 Nitrogen budget ........................................................................................................ 61 4.2.7 Statistical analyses .................................................................................................... 62 4.3 Results .............................................................................................................................. 63 viii 4.3.1 Soil moisture and temperature .................................................................................. 63 4.3.2 Total nitrogen ............................................................................................................ 65 4.3.3 Nitrogen release over time ........................................................................................ 68 4.3.4 Nitrogen budget ........................................................................................................ 71 4.3.5 Percent decomposition .............................................................................................. 72 4.3.6 Short term nutrient release ........................................................................................ 74 4.4 Discussion ........................................................................................................................ 77 Chapter 5: Conclusion ................................................................................................................ 83 5.1 Introduction ...................................................................................................................... 83 5.2 General conclusions ......................................................................................................... 83 5.3 Recommendations ............................................................................................................ 85 5.3.1 Field production at the UBC Farm ............................................................................ 85 5.4 Directions for future research .......................................................................................... 86 5.5 Conclusion ....................................................................................................................... 88 Bibliography ................................................................................................................................ 90 Appendices ................................................................................................................................. 100 Appendix A Circle packing references for greenhouse study plant arrangements ................. 100 Appendix B R-script for CO2 Flux analysis ........................................................................... 101 ix List Of Tables Table 1: Levels of each factor as represented by plant density. Columns represent low, medium, and high density for each crop. ....................................................................................... 12 Table 2: Regression summary for TLO. ....................................................................................... 15 Table 3: Regression summary for weed suppression. ................................................................... 17 Table 4: Regression summary for inverse yield density model of rye. ........................................ 19 Table 5: Regression summary for inverse yield density model of vetch. ..................................... 20 Table 6: Tissue concentrations of rye, vetch and bicultures of rye and vetch (g element kg-1 dry weight tissue) .................................................................................................................. 21 Table 7: Regression model summary of total carbon. .................................................................. 23 Table 8: Fitted Values with standard error for NEE (µmolCO2m-2s-1), RE (µmolCO2m-2s-1), GEP (µmolCO2m-2s-1) and NEP (mgCm-2h-1). ........................................................................ 25 Table 9: Levels of each factor as represented by plant density. ................................................... 37 Table 10: Mean dry matter (g) ± standard deviation for each level of plant density. .................. 41 Table 11: Summary ANOVA for the fitted model of the log of Total Land Output. ................... 41 Table 12: T-test comparison between bicultures and monoculture of rye and chicory at a range of densities. ......................................................................................................................... 42 Table 13: Mean leaf area (cm2) ± standard deviation for each level of plant density. ................ 42 Table 14: Summary ANOVA for the fitted model of the log of Total Leaf area: ........................ 43 Table 15: T-test comparison between bicultures and monoculture of rye and chicory at a range of densities. ......................................................................................................................... 44 Table 16: Summary ANOVA for the fitted model of Net Ecosystem Exchange: ........................ 45 Table 17: Summary ANOVA for the fitted model of Gross Ecosystem Photosynthesis: ............ 45 x Table 18 Summary ANOVA for the fitted model of Net Ecosystem Productivity: ..................... 45 Table 19: Fitted mean values across three time points at three densities of rye for NEE, NEP and GEP. ................................................................................................................................ 46 Table 20: Summary ANOVA for the fitted model of Ecosystem Respiration: ............................ 46 Table 21: T-test comparisons between bicultures and monoculture of rye and chicory at a range of densities. ..................................................................................................................... 47 Table 22: Initial nutrient concentration where N is Nitrogen, P is Phosphorus and C is Carbon (g element kg-1 dry weight tissue). ...................................................................................... 59 Table 23: Fresh and dry weight of tissue at start of experiment placed in mesh bags (grams). ... 60 Table 24: Regression summary for cumulative total nitrogen. ..................................................... 70 Table 25: Circle packing in a circle references. .......................................................................... 100 xi List Of Figures Figure 1: The effect of the density of rye on TLO. Red points indicate fitted points connected by the fitted line. .................................................................................................................. 16 Figure 2: Box plot of Tukey post hoc test for TLO. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. ........................... 16 Figure 3: The effect of the density of rye on weed suppression. Red points indicate fitted points connected by the fitted line. ............................................................................................ 18 Figure 4: Box plot of Tukey post hoc test for weed suppression. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. ........................................................................................................................................ 18 Figure 5: The effect of the density of rye on the inverse yield per plant of rye (dry mass (g)). Red line indicates fitted line based on equation presented. ................................................... 19 Figure 6: The effect of the density of vetch on the inverse yield per plant of vetch (dry mass (g)). Different lines indicated adjusted intercept for each block. ........................................... 21 Figure 7: Box plot of Tukey post hoc test for total nitrogen. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. ........................................................................................................................................ 22 xii Figure 8: The effect of the density of rye on total carbon. Red points indicate fitted points connected by the fitted line. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. ................................................................... 23 Figure 9: Box plot of Tukey post hoc test for total carbon. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. ........................... 24 Figure 10 The interaction between the density of rye and the density of chicory on fitted values for the model of log(TLO). Different lines indicate chicory density. ............................ 41 Figure 11: The interaction between the days after sowing and the density of rye on fitted values for the model of log(leaf area). Different lines indicate Rye density. ............................ 43 Figure 12: The interaction between the density of rye and the density of chicory on fitted values for the model of log(leaf area). Different lines indicate chicory density. ....................... 44 Figure 13: The interaction between the days after sowing and the density of rye on fitted values for the model of respiration. Different lines indicate Rye density. ................................ 47 Figure 14: Soil temperature and water for the full course of the experiment. Start indicates when probes were placed and End represents when probes were removed. The period between End and before the next Start had no probes present. Arrows represent weeks with high rainfall and no probes present (i.e., Week 2 and 14). ..................................................... 64 Figure 15: Weekly precipitation (mm) data from Totem Field Climate station for the course of the experiment. Bars represent total weekly precipitation. ............................................. 65 xiii Figure 16: Box plot of Tukey post hoc test for cumulative total nitrogen. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ........................................ 66 Figure 17: Box plot of Tukey post hoc test for cumulative total nitrogeng-1 dry weight. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ....................... 67 Figure 18: Box plot of Tukey post hoc test for ammonium release after one week. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ....................... 68 Figure 19: A: Mean nitrate (± standard deviation) release over 15 weeks across six treatments. B: Mean ammonium (± standard deviation) release over 15 weeks across six treatments. ....................................................................................................................... 69 Figure 20: Cumulative nitrogen release over the course of the experiment (15 weeks). Points represent fitted values from the model for each treatment level. .................................... 71 Figure 21: A: Percentage N captured by PRS probes. B: Percentage captured by probes with remaining residue N. Asterisk indicates two-tailed t-test different from 50% with p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ............................................................................ 72 xiv Figure 22: Box plot of Tukey post hoc test for percentage decomposed. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ........................................ 73 Figure 23: Percentage of deviation from expected decomposed tissue compared to zero; Asterisk indicates p<0.05. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. .................................................................................................................... 74 Figure 24: Box plot of Tukey post hoc test for phosphorus release (phosphate). Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ........................................ 75 Figure 25: Box plot of Tukey post hoc test for sulfur release (sulphate). Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ........................................ 76 xv List Of Abbreviations ANOVA Analysis of variance BNF Biological Nitrogen Fixation CO2 Carbon dioxide cm Centimeter DAS Days after Sowing GEP Gross Ecosystem Photosynthesis ES Ecosystem Services ha hectare kg kilogram NEE Net Ecosystem Exchange NEP Net Ecosystem Productivity NS Non-significant RH Relative Humidity Re Ecosystem Respiration SEM Standard Error of Mean SD Standard Deviation TLO Total Land Output UBC University of British Columbia xvi Acknowledgements I am grateful for the opportunity to have conducted this research and gained the skills to become a scientist. I appreciate the support and guidance from my supervisor Dr. Andrew Riseman to produce the research presented in this thesis in his effective questioning, patience in editing and kindness. I am thankful for my supervisory committee of Dr. Gary Bradfield and Dr. Sean Smukler for their good advice and time guiding my project. I also appreciate the guidance in my program from Dr. Mahesh Upadhyaya. I am thankful for support from lab members Tejendra Chapagain, Molly Campbell and Alisa Koebel. I also would not have been able to conduct the research presented in this thesis without the support from the greater UBC research community. I appreciate the use of equipment from Dr. Black and help from the UBC Biometeorology and Soil Physics Group. I appreciate the guidance from emeritus professors Dr. Art Bomke and Dr. Peter Jolliffe in sharing their expertise and experience. My project would not have been possible without help from all of the staff at the UBC Farm and guidance for my research projects from Tim Carter, Véronik Campbell and Jacob Slosberg. I am thankful to have received support from my family and friends in pursuing my studies at UBC. I am grateful for the support from my parents, George and Janet, my brother Greg, my uncles Pat and Mike and their families, as well as my friends, Darrin Marshall, Matthew So, Tim Rusted, Daniel Albrechtsons and Jon Furlong. I am also grateful for financial support from the Faculty of Land and Food System, the Eddie family, and NSERC. I am also thankful for the opportunity provided to me by Drs. Sean and Margaret Brosnan at Memorial University of Newfoundland who taught me the fundamentals of science research and set me up for a positive experience at UBC. xvii Dedication To those who have stood before me in their stewardship of nature, my grandparents George and Margaret Furey and my uncle Bill Furey. To the future generations to follow, we are doing the best that we can to preserve it for you. 1 Chapter 1: Introduction 1.1 Introduction 1.1.1 Context of research With increasing carbon dioxide emissions and rapid biodiversity loss, ecologists research the consequences of these global issues on decreased ecosystem functioning (Cardinale et al. 2012). However, agroecosystems, ecosystems managed for agricultural outputs, offer an opportunity to ask the counterpoint question, does increasing biodiversity in agroecosystems increase ecosystem function? Agroecology, the application of ecological principles to agriculture, can inform farm management decisions that seek to balance environmental integrity with farm productivity. Viewing agricultural fields as ecosystems and managing for increased biodiversity can improve ecosystem services to farmers thereby reducing input requirements, reducing environmental impacts while increasing food-crop yields. Biodiversity can be defined as the diversity of species in an ecosystem or the diversity of traits that play a functional role in an ecosystem. Grouping plants by functional traits into functional groups offers a simplified framework to measure the effects of biodiversity on ecosystem function. This research intends to apply a functional group diversity framework to assess the impacts of cover crop functional groupings on ecosystem function and whether this framework has value in designing cover crop mixtures. The addition of a non-leguminous forb functional group to the traditional grass:legume mix will serve as the base system for assessment. Metrics measured included aboveground and belowground biomass, nutrient release, carbon fixation, decomposition, and weed suppression. 2 Increasing biodiversity is predicted to increase ecosystem functioning. Planting purposely-designed species mixtures (i.e., polycultures, intercropping, linear habitats) is one method that can both increase on-site biodiversity while also having the potential to increase ecosystem function. Applying biodiversity-function theory, i.e., the relationship between biodiversity and ecosystem function, to agroecosystems can provide a framework for assessing polycultures for their effect on ecosystem function and ecosystem service provision. Ecosystem function can be defined as, “the rate, level, or temporal dynamics of ecosystem processes” (Tilman 2001), while ecosystem services can be defined as “the set of ecosystem functions that are useful to humans” (Kremen 2005). Biodiversity is broadly defined as the diversity of life, usually expressed by difference in species, genes, traits found at a given level-of-analysis. Biodiversity is well established as crucial in sustaining ecosystem function and certain ecosystem services (Cardinale et al. 2012). However, there is strong evidence that both biodiversity and ecosystem services are declining worldwide (Butchart et al. 2010, Millennium Ecosystem Assessment 2005). Conventional agriculture, i.e., intensive monocultured fields with synthetic chemical inputs, represents agroecosystems that are a key cause in the decline of biodiversity as they both exclusively occupy large expanses of arable land and pollute native ecosystems through chemical contamination (Tscharntke et al. 2005, Tilman et al. 2002). However, agroecosystems managed under agroecological practices, the application of ecological principles to agriculture, use biodiversity to enhance ecosystem services through various techniques and strategies (Altieri 1999). As all agroecosystems are managed landscapes designed to maximize ecosystem function and provide ecosystem services to society (i.e., food fibre, fuel), they provide an ideal platform for researching how biodiversity affects ecosystem function regardless of farming practices. My 3 research asks whether increasing on-farm plant biodiversity through the use of designed cover crop mixtures of different functional groups will increase ecosystem function while also maintaining a high level of crop productivity and other ES. 1.1.2 Cover crops, biodiversity and function in agroecosystems Cover crops are non-food agricultural plantings originally used to reduce soil erosion between crops. However the range of ecosystem services provided are increasingly being recognized to include N fixation, residual N capture (N scavenging), soil conditioning, addition of organic matter, weed suppression, and habitat for beneficial insects. They have typically been single species plantings chosen for a specific outcome. For example, a legume species is usually chosen when higher N levels are needed for the following crop. More recently, cover crop plantings have become more complex by combining two and three species together, thereby expanding the potential number of ecosystem services provided (Collins et al. 2007, O'Reilly et al. 2012, Schipanski and Drinkwater 2011). However, choosing the most appropriate species is a complex question that involves not only climatic and farm management issues but also which ecosystem services are most desired. Management decisions based on agroecological theory strive for every farm component to serve multiple purposes and integrate multi-functionality as much as possible. With this in mind, using biodiversity-function theory to guide development of more complex cover crop mixes offers theory to guide management practices to help fulfill the ecosystem service needs for both farm production and environmental integrity. 4 1.1.3 Agroecology and the application of ecological theory This research draws evidence and insight from biodiversity-function research conducted in grasslands to assess whether increasing on-farm functional biodiversity provides greater ecosystem services through enhanced ecosystem function. While most of the grassland research has assessed species richness and its effect on ecosystem function, the importance of functional traits, i.e., the characteristics that lead to specific ecosystem function, are increasingly recognized as a key factor in determining ecosystem function and ecosystem service provision (Díaz et al. 2007, Díaz and Cabido 2001, Conti and Díaz 2013). A simple way to change the functional traits in an ecosystem is to add or remove an entire functional group, i.e., a group of organisms that share common functional traits (Hooper and Vitousek 1998). In an agroecosystem, this could be accomplished by changing the species combination in a cover crop mixture. Cover crops and herbaceous plants found in similar ecosystems can be grouped into categories of monocotyldons ‘grasses’; and two types of dicotyldons, ‘legumes’ that provide nitrogen fixation and ‘non-leguminous forbs’ that do not provide nitrogen fixation (Fornara and Tilman 2008, McLaren and Turkington 2010, Clark 2012). Therefore, by including or excluding a particular species, functional traits can be manipulated and assessed for ecosystem function and service provision. Biodiversity research on grasslands has also demonstrated that increasing the species richness of a plant community will increase ecosystem function, including biomass production (Tilman et al. 2012). Increasing the functional group diversity of cover crops could increase ecosystem services provided to the farm by promoting a wider variety of ecosystem functions. For example, the traditional cover crop mix of winter wheat and hairy vetch represent the functional groups of a grass (ecosystem functions: nitrogen scavenging and weed suppression), and a 5 legume (ecosystem functions: nitrogen fixation and improve residue N mineralization) (Odhiambo and Bomke 2001). I assessed the impact of adding another functional group to the mixture, a non-leguminous forb. Non-leguminous forbs can increase biomass through increased resource use, decreased species invasion (i.e., weed suppression), and improved nitrogen cycling to subsequent crops. In grassland communities, forbs were shown to decrease light penetration through the canopy and increase N-uptake leading to decreased species invasion and greater biomass production (Frankow-Lindberg 2011). Furthermore, functional groups were shown to resist invasion of the same functional group suggesting that forbs could compete with broad-leaved weeds that fill a similar niche (Frankow-Lindberg 2011). Forbs have been shown to have fine roots that enhance root decomposition and N-cycling that increase available N as well as deep roots that can scavenge N from deep in the soil profile (Fornara and Tilman 2008, Thorup-Kristensen 2006). The addition of functional groups beyond the traditional grass:legume biculture can improve the variety of traits in a mixture to enhance ES provision. While there is a long list of functions that could be measured, I selected the following: 1) aboveground biomass production, 2) nutrient cycling, 3) decomposition, 4) weed suppression, and 5) carbon fixation. 1.2 Research questions The following section outlines my hypothesis, objectives, and specific research questions. 1.2.1 Hypothesis Ho: Increasing the functional group diversity (through polycultures) of a cover crop to include a grass, a legume, and a non-leguminous forb will not exhibit increased above-ground biomass, increased carbon fixation, increased nutrient cycling, increased decomposition rates, or increased 6 weed suppression compared to monocultures of each functional group or a grass:legume biculture. H1: Increasing the functional group diversity (through polycultures) of a cover crop to include a grass, a legume, and a non-leguminous forb will exhibit increased above-ground biomass, increased carbon fixation, increased nutrient cycling, increased decomposition rates, or increased weed suppression compared to monocultures of each functional group or a grass:legume biculture. 1.3 Research objectives 1. To determine if increasing the functional group diversity of cover crops will increase ecosystem function by way of increased above-ground biomass, increased carbon fixation, increased nutrient cycling, increased decomposition rates, and increased weed suppression; 2. To determine the performance of the following cover crops in monoculture, Fall Rye Secale cereale L., Lana Vetch Vicia villosa ssp. dasycarpa Roth., Forage Chicory Cichorium intybus L. on above-ground biomass, carbon fixation, nutrient cycling, decomposition rates, and weed suppression; 3. To evaluate synergies and antagonisms among the three cover crops over the range of ecosystem services measured. 7 Chapter 2: Functionally Diverse Cover Crops And Ecosystem Services Under Organic Management 2.1 Introduction Agriculture faces the problem of increasing food production while limiting environmental degradation and biodiversity loss. Sustainable intensification (SI) is a concept that has been suggested to address this problem whereby yields are increased without additional environmental damage (Garnett et al. 2013). SI does not provide a recipe for a solution, but espouses a greater use of farm-based ecosystem services (ES) (Poppy et al. 2014). Accepting that biodiversity may be a driver to enhance ES (Cardinale et al. 2012), it follows that research into biodiversity and ecosystem functioning is an important aspect of SI. Increasing plant diversity is one option to manage for multiple services as it has been shown to increase the multifunctionality (i.e., the presence of multiple ecosystem functions) in grassland ecosystems and may be mirrored in agricultural contexts (Zavaleta et al. 2010). While land conversion to agriculture had led to a decrease in functional diversity in grassland ecosystems, increasing diversity through inclusion of cover crops is one option to restore traits leading to increased ecosystem function (Lin et al. 2011). Increasing the functional diversity of cover crops is then one way to potentially increase the suite of ES provided by agriculture lands. In addition, cover crops have limited negative impacts on food crops, due to their spatial or temporal isolation from production, while potentially having positive impacts on environmental protection and crop yields (Schipanski et al. 2014). Understanding the role of functional diversity in cover crops mixtures designed to provide multiple ecosystem services is a tangible strategy to help achieve SI. 8 Management to increase the functional diversity of cover crops seeks to orchestrate the different traits among plant species and cultivars to provide the combination of ecosystem services desired. Cover crops can provide ES such as suppressing weeds, preventing soil erosion, regulating green-house gas emissions, as well as providing nitrogen to subsequent crops through N-fixation and N-scavenging (Schipanski et al. 2014). The manipulation of cover crop mixtures has included different combinations of grasses and legumes as well as perennials and other annuals (Odhiambo and Bomke 2001, Schipanski and Drinkwater 2011, Brainard et al. 2011, Ranells and Wagger 1997). A common mixture that holds promise for SI is Fall rye (Secale cereale L.) and Hairy vetch (Vicia villosa Roth.). Grasses such as rye can suppress winter weeds whereas legumes such as vetch can provide nitrogen to subsequent crops (Hayden et al. 2014). Including legumes in a mixture with rye also improves nitrogen mineralization and nitrogen availability to subsequent crops, potentially reducing the need for additional inputs (Odhiambo and Bomke 2000). Including vetch in a mixture with rye can change the timing of release of available nitrogen to subsequent crops, but may not be sufficient in quantity to provide adequate nitrogen in certain climates (Lawson et al. 2013). Certain bi-culture mixtures of grasses/legumes cover crops were also shown to produce more biomass and greater soil organic carbon than monoculture counterparts (Wang et al. 2012). For a variety of ecosystem services that are relevant to farmers, functionally diverse mixtures can provide a greater suite of services than monocultures of any one species. Research into the interactions of functional diverse mixtures has potential to improve the suite of ES provided and help achieve SI. While we know much about the traditional role of grass/legume mixtures, other functional groups found to be efficacious in grassland ecosystem deserve assessment under agricultural settings. Furthermore, recognizing that SI requires 9 flexibility, different species, cultivars, and other sources of genetic diversity should be examined to better match local conditions and farmer needs. Specifically, non-leguminous forbs have been found to play an important role in grassland ecosystems and may have a role in agriculture. In grassland communities, they decrease light penetration through the canopy thereby suppressing species invasion and increase nitrogen-uptake leading to greater biomass production (Frankow-Lindberg 2011). Therefore, including a forb could expand the range of ES available to farmers. They could improve the suppression of non-mustard broadleaf weeds, reported as a problem with rye-hairy vetch mixtures (Hayden et al. 2012), by both light interception and general competition within the same niche (Frankow-Lindberg 2011). They can also increase available nitrogen to the subsequent crops through enhanced root decomposition and nitrogen-cycling (Fornara and Tilman 2008, Fornara et al. 2009). Furthermore, chicory (Cichorium intybus L.), a deep rooted forb, has been shown to access nitrogen from deep in the subsoil and cycling it to the surface providing nitrogen for more shallow rooted crops (Thorup-Kristensen 2006). Considering the importance of supplying a range of novel functional traits through cover crops, a wide diversity of plants is needed to meet the specific needs of farmers in different regions (Snapp et al. 2005). In addition, functional trait selection in cover crops has been identified as a key area to improve ES provision through selective breeding underpinning novel cover crop traits (Wilke and Snapp 2008). Through both an inclusion of novel functional group combinations and novel functional traits, cover crops can improve the suite of ecosystem services available to farmers. To assess both novel cover crops and the effect of increasing functional group diversity on multiple ecosystem services, a field trial was conducted at the Centre for Sustainable Food Systems at the UBC Farm in Fall 2013. A mixture of three cover crops, a grass, legume, and forb were grown as mixtures and monocultures. Furthermore, a variety of densities were 10 included to improve our understanding of each component’s contribution on ES provision. Assessing mixtures at multiple densities could provide an indication of which species have a density-dependent effect for each service. My field trial aimed to answer the following questions: • What ES do rye, Lana vetch, and chicory best provide as fall cover crops in the Pacific Northwest? • How does changing the species mixture and density affect the magnitude and range of ecosystem services provided? • Does a polyculture provide greater provision of ecosystem services, in terms of magnitude and range, compared to monocultures of individual species? 2.2 Materials and methods 2.2.1 Study site Research was conducted between Fall 2013 and Spring 2014 at the Centre for Sustainable Food System at UBC Farm located in Vancouver, BC Canada. The site is located 49 15’ 3” N and 123 14’ 20” W with an altitude of 100 m above sea-level. The soil is a coarse-textured, sandy loam with an average pH of 5.8, organic matter content of 119 g kg-1 dry soil and total nitrogen 3.6 g kg-1 dry soil (Chapagain and Riseman 2014b). The experimental field’s recent history includes winter cover crop of rye, hairy vetch, crimson clover and annual organic vegetable production including carrots and head-lettuce. 2.2.2 Cover crop selection Species were selected based on the following criteria: regional suitability, biomass production and complementary growth forms. Specifically, rye (Secale cereale L.) was chosen 11 for its ability to produce biomass and suppress weeds. A subspecies of hairy vetch, Lana vetch (Vicia villosa ssp. dasycarpa Roth.) was selected for its known ability to trellis with a grass, and its potential for strong spring growth in this region. To expand on the traditional grass/legume mixture, Forage chicory (Cichorium intybus L. cv Puna), a non-leguminous perennial forb, was chosen for its prostrate growth form and ability to form a deep taproot. Chicory also represents a forb not in the family Brassicaceae as other common forb cover crops such as mustard and canola. A non-Brassica forb is highly desirable in this system due to its inability to harbour Brassica disease that could affect the Farm’s field crops. 2.2.3 Experimental design Prior to planting, the field was disked to a depth of 20 cm and rotovated to a depth of 15 cm. Lana vetch was inoculated with Rhizobium leguminosarum biovar viceae. The three cover crop species were hand planted using a Jang Push seeder JP-1 (Mechanical Transplanter) at varying densities as described below on September 19th and 20th, 2013 (Table 1). Plots were 2.1 x 3.5 m (7.35 m2) and comprised of 14 rows with 15 cm spacing. Two-species plots alternated species rows while three species mixtures were planted in the order of rye, Lana vetch, chicory. Rye and Lana vetch were seeded at a depth of 3.81 cm whereas chicory was seeded at a depth of 1.27 cm. 2.2.4 Treatments Treatments were constructed using a substitute design where two-species mixtures contained one half or their respective monoculture densities and three species mixtures contained one-third of their monoculture rate. Three densities for each species (Table 1) were used in a full factorial design, with one additional plot at the highest density mixture, for a total of 28 plots per 12 replicate. The experiment had two replications arranged in a randomized block design for a total of 56 plots. The field layout consisted of experimental plots, 0.5 m pathways surrounding all plots and a 0.5 m buffer surrounding the outside edge of the field. A buffer was planted at the highest density three species mixture to control for the edge effect. Table 1: Levels of each factor as represented by plant density. Columns represent low, medium, and high density for each crop. Plant Actual Density (plants m-2) Actual Rate (kg ha-1) Desired Rate (kg ha-1) Rye 0 28 58 0 8.75 18.31 0 40 80 Lana vetch 0 16 24 0 6.475 9.71 0 13 26 Chicory 0 78 118 0 1.49 2.26 0 2.4 4.8 2.2.5 Sample preparation and analysis Above ground plant tissue was randomly sampled using a 0.25 m2 quadrat and clipped to ground level on May 6th, 2014 (DAS = 235). Plant tissue was separated by crop species with the remaining considered weeds. Tissue was dried at 60 ° for 72 hrs and ground to a powder (1mm mesh) using a Wiley Mill model 4 (Thomas Scientific). 2.2.6 Greenhouse gas emission Measurements of Net Ecosystem Exchange (NEE) and Ecosystem Respiration (Re) were measured using a clear chamber as described previously (Chapagain and Riseman 2014a). Samples were taken on March 23, 2014 (DAS=191) and April 30th, 2014 (DAS=229). Gross Ecosystem Photosynthesis (GEP) was derived from the NEE and Re data. CO2 flux was not linear and a quadratic term was included in the model to account for non-linearity as follows: 𝐶𝑂?(𝜇𝑚𝑜𝑙𝑚??) = 𝑏? + 𝑏?𝑋? + 𝑏?𝑋?? Where X1 is time in seconds 13 A tangent to the line at 5 seconds was used to calculate CO2 flux to avoid initial noise in placing the chamber on the plants. The relative humidity increased as plant matter increased but remained less than 4% and was considered not significant (Welles et al. 2001). 2.2.7 Inverse yield density models Inverse yield density models were used to evaluate competition (Spitters 1983). The yield per area is divided by the number of plants to get yield per plant. Densities used in models were measured on October 30th, 2013 (DAS=41). The reciprocal of the yield per plant is used as the response variable to transform the non-linear relationship between plant density and yield per plant into a linear relationship. The parameters in the regression were used to assess the relative strength of intra- and inter-specific competition. 2.2.8 Statistical analyses Statistical analyses were performed using R 3.0.2 with additional packages (R Core Team 2013, Hothorn et al. 2008, Wickham 2009, 2007, 2011, Hlavac 2014, Auguie 2012, Pinheiro et al. 2015, Fox and Weisberg 2011). Linear regression was used to model the response variables using fall observed density as a continuous variable for each species, respectively. All regressions were checked for meeting required assumptions of normality and heteroscedasticity. No chicory tissue was analyzed for carbon or nitrogen and both control and chicory monocultures were removed from carbon and nitrogen results. A high-density rye and low-density chicory plot was removed as an outlier from regression analysis of TLO and weed suppression due to planting error. Non-linearity was detected in most models and a quadratic term was included when necessary. Generalized least squares regression was used with several 14 variance models using package nlme in modeling the inverse yield density model for rye. A high-density rye, low-density chicory plot was removed as an outlier from the regression analysis of the inverse yield density for rye due to planting error. A low-density rye, low-density vetch plot and a high-density rye, high-density vetch plot were removed as outliers from the regression analysis of the inverse yield density of vetch due to zero vetch density sampled. A second analysis was performed omitting density to allow comparisons between bicultures and monocultures of each respective species. The factorial design was not possible without control for levels and instead, plots were considered as categorical variables of each respective species. Chicory was omitted from the analysis as a categorical variable, as it produced negligible biomass, so those experimental units were then considered as either monocultures or bicultures of rye or Lana vetch or removed from the analysis when no rye or Lana vetch was present. In analysis for NEE, GEP, and NEP 2 points were removed due to sampling error on DAS 191, and 1 point on DAS 2291. In analysis for Re, 4 points were removed from analysis due to sampling error on DAS 2292. A two-way ANOVA was conducted with Treatment of biculture rye:vetch, monoculture of rye, monoculture of vetch and Block. A Tukey post-hoc Multiple Comparison procedure was performed to determine within-group differences significant at p<0.05. 1 Two high-density vetch plots on DAS 191 and a high-density rye, high-density vetch plot on DAS 229 2 Two high-density rye, high-density vetch plots; one high-density rye, low-density vetch plot; and one low-density rye, low-density vetch plot. 15 2.3 Results 2.3.1 Total land output Target densities were not achieved due to improper calibration of the seeder with observed rates approximately one-half to a one-quarter of target plants m-2 causing biomass values to be low in certain plots. There was a main effect for the density of rye and the quadratic form of the density of rye on Total Land Output (TLO) (i.e., the sum of all above ground plant tissue) (Table 2). The density of chicory and vetch did not have a detectable effect on TLO. Regression results of the density on TLO are displayed (Figure 1). A one-way ANOVA (F(2,47)=16.368, p<0.001) identified a significant main effect for Treatment on TLO. Tukey post-hoc test showed that rye and the biculture of rye:vetch were the equivalent and significantly higher than vetch monoculture (Figure 2). Table 2: Regression summary for TLO. Dependent variable: Total Land Output (g) Rye 11.056*** (1.695) Rye2 -0.387*** (0.094) Intercept 28.859*** (5.612) Observations 55 R2 0.575 Adjusted R2 0.559 Residual Std. Error 26.438 (df = 52) F Statistic 35.221*** (df = 2; 52) Note: *p<0.1; **p<0.05; ***p<0.01 16 Figure 1: The effect of the density of rye on TLO. Red points indicate fitted points connected by the fitted line. Figure 2: Box plot of Tukey post hoc test for TLO. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. y = 28.86 + 11.05x − 0.39x2R2 = 0.57502000400060000 5 10 15 20Plants per 0.25m2 ryeTotal Land Output (kg/ha)a a b200040006000Biculture Rye VetchTreatmentTotal Land Output (kg/ha)Mean Total TLO17 2.3.2 Weed suppression as a proportion of control There was a main effect for density of rye and the quadratic form of density of rye on weed suppression (Table 3). The density of chicory and vetch did not have a detectable effect on the weed suppression. Regression results are displayed (Figure 3). A one-way ANOVA (F(2,47)=8.3412, p<0.001) identified a significant main effect for Treatment on weed suppression. Tukey post-hoc test showed that rye and the biculture of rye:vetch were the equivalent and significantly lower than vetch monoculture (Figure 4). Table 3: Regression summary for weed suppression. Dependent variable: Proportion Weed of control Rye -0.072*** (0.013) Rye2 0.003*** (0.001) Intercept 0.712*** (0.043) Observations 55 R2 0.469 Adjusted R2 0.449 Residual Std. Error 0.203 (df = 52) F Statistic 22.999*** (df = 2; 52) Note:*p<0.1; **p<0.05; ***p<0.01 18 Figure 3: The effect of the density of rye on weed suppression. Red points indicate fitted points connected by the fitted line. Figure 4: Box plot of Tukey post hoc test for weed suppression. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. y = 0.71 − 0.072x + 0.0026x2R2 = 0.4690.250.500.751.000 5 10 15 20Plants per 0.25m2 ryeProportion of weeds compared to controla a b0.30.60.9Biculture Rye VetchTreatmentWeed suppression as a proportion of Control per 0.25m2 (g)Mean Total Weed19 2.3.2.1 Inverse yield density models for rye and vetch There was a significant main effect for the density of rye on the inverse yield per plant of rye (Table 4). The main effect for density of rye indicates that there is detectable level of intraspecific competition. The density of chicory and vetch did not have a detectable effect on the inverse yield per plant of rye indicating that the density of these plants had no detectable competitive effect on rye growth. Regression results are displayed (Figure 5). Table 4: Regression summary for inverse yield density model of rye. Dependent variable: Inverse yield per plant Rye Rye 0.011*** (0.002) Intercept 0.038*** (0.009) Observations 37 Log Likelihood 55.434 Akaike Inf. Crit. -104.869 Bayesian Inf. Crit. -100.203 Note: *p<0.1; **p<0.05; ***p<0.01 Figure 5: The effect of the density of rye on the inverse yield per plant of rye (dry mass (g)). Red line indicates fitted line based on equation presented. y = 0.38 + 0.011x0.10.20 5 10 15 20Plants of rye per 0.25m2(Yield per plant Rye)−120 There was a significant main effect for the density of Lana vetch and Block on the inverse yield per plant of Lana vetch (Table 5). The main effect for density of vetch indicated that there is a detectable level of intraspecific competition. The main effect for Block indicates that yield per plant varied among blocks due to seasonal field variation. The density of chicory and rye did not have a detectable effect on the inverse yield per plant of vetch indicating that the densities of these plants had no detectable impact on vetch growth. Regression results are displayed (Figure 6). Table 5: Regression summary for inverse yield density model of vetch. Dependent variable: Inverse yield per plant Vetch Vetch 0.018*** (0.006) Block2 0.063** (0.029) Intercept 0.052 (0.032) Observations 36 R2 0.268 Adjusted R2 0.224 Residual Std. Error 0.085 (df = 33) F Statistic 6.051*** (df = 2; 33) Note: *p<0.1; **p<0.05; ***p<0.01 21 Figure 6: The effect of the density of vetch on the inverse yield per plant of vetch (dry mass (g)). Different lines indicated adjusted intercept for each block. 2.3.3 Tissue concentrations Table 6: Tissue concentrations of rye, vetch and bicultures of rye and vetch (g element kg-1 dry weight tissue) Treatment Carbon Nitrogen C:N Ratio Rye 420 9.7 43.3 Lana Vetch 420 33.6 12.5 Biculture 420 17.5 24.9 2.3.3.1 Tissue nitrogen There was no effect for the density of rye or the density of vetch on total aboveground nitrogen (F(2,47)=2.318,p=0.109). However, one-way ANOVA (F(2,47)=6.636,p<0.01) identified a significant main effect for Treatment on total nitrogen. Post hoc comparisons showed that the rye:vetch biculture was higher in total nitrogen than the rye monoculture, but not y = 0.1145 + 0.018xR2 = 0.26830.00.10.20.30.40.52.5 5.0 7.5 10.0 12.5Plants of vetch per 0.25m2(Yield per plant vetch)−1BlockBlock 1Block 222 different from the vetch monoculture (Figure 7). Rye and vetch monocultures were also not different from each other (Figure 7). Figure 7: Box plot of Tukey post hoc test for total nitrogen. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. 2.3.3.2 Tissue carbon There was a main effect for the density of rye and the quadratic form of the density of rye on total aboveground carbon (Table 7). The density of chicory and vetch did not have a detectable effect on the total carbon. Regression results are displayed (Figure 8). A one-way ANOVA (F(2,47)=16.379,p<0.001) identified a significant main effect for Treatment on total carbon. Tukey post-hoc test showed that rye and the biculture of rye:vetch were equivalent, but significantly higher than vetch monoculture (Figure 9). b a ab306090Biculture Rye VetchTreatmentTotal Nitrogen (kgha−1 )Mean Total N23 Table 7: Regression model summary of total carbon. Dependent variable: Total Carbon Rye 4.005*** (0.679) Rye2 -0.146*** (0.033) Intercept 16.166*** (2.611) Observations 50 R2 0.482 Adjusted R2 0.460 Residual Std. Error 10.626 (df = 47) F Statistic 21.896*** (df = 2; 47) Note: *p<0.1; **p<0.05; ***p<0.01 Figure 8: The effect of the density of rye on total carbon. Red points indicate fitted points connected by the fitted line. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. y = 16.16 + 4x − 0.014x2R2 = 0.48232040600 5 10 15 20 25Plants of rye per 0.25m2Total Carbon 0.25m2 (g)24 Figure 9: Box plot of Tukey post hoc test for total carbon. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. Black points indicate outliers. 2.3.4 Carbon flux The was a main effect for Treatment on NEE on both sampling days, 191 DAS (F(2,45)=7.253,p<0.001) and 229 DAS (F(2,46)=12.967,p<0.001). Tukey post-hoc test showed that rye and the biculture of rye:vetch were equivalent and significantly lower than vetch monoculture (Table 6). However, the was no main effect for Treatment on Re on either sampling day, 191 DAS (F(2,47)=0.133,p=0.875) or 229 DAS (F(2,43)=1.336,p=0.2735). Fitted mean values are presented (Table 6). The was a main effect for Treatment on GEP at 191 DAS (F(2,45)=6.5427,p<0.001) and 229 DAS (F(2,42)=10.728,p<0.001). Tukey post-hoc test showed b b a100020003000Biculture Rye VetchTreatmentTotal Carbon (kgha−1 )Mean TotalC25 that rye and the biculture of rye:vetch were equivalent and significantly higher than vetch monoculture (Table 6). The was a main effect for Treatment on NEP on 191 DAS (F(2,45)=7.253,p<0.001) and 229 DAS (F(2,46)=12.967,p<0.001). Tukey post-hoc test showed that rye and the biculture of rye:vetch were equivalent and significantly higher than vetch monoculture (Table 6). Table 8: Fitted Values with standard error for NEE (µmolCO2m-2s-1), RE (µmolCO2m-2s-1), GEP (µmolCO2m-2s-1) and NEP (mgCm-2h-1). DAS 191 229 191 229 191 229 191 229 Treatment NEE NEE RE RE GEP GEP NEP NEP Biculture -13.66(1.14)a -38.70(2.38)a 6.95(0.75)a 8.57(0.67)a 20.61(1.17)b 47.28(2.99)b 590.19(49.40)b 1672.04(102.70)b Rye -14.79(1.73)a -43.68(4.34)a 6.55(1.2)a 7.30(0.64)a 21.34(2.23)b 50.97(4.70)b 638.74(74.56)b 1886.79(187.71)b Vetch -6.34(1.61)b -20.54(2.39)b 6.32(0.8)a 7.08(0.83)a 12.66(1.49)a 27.62(2.92)a 274.09(69.77)a 887.53(103.31)a Note: Different letter denotes significant difference at p<0.05 2.4 Discussion Achieving sustainable intensification in agriculture will require improved management of a wide range of ES provided by farmland beyond the provision of food to include those that reduce the environmental impact of food production. Increasing plant functional diversity in cover crops was shown to be an effective strategy to harness ES beneficial for the environment (e.g., CO2 fixation) and subsequent crops (e.g., soil fertility). The present study compared various cover crop mixtures to monoculture counterparts over a range of ecosystem services. The common trend among ecosystem service provision is that bicultures of rye:vetch were equal to the best monoculture of either rye or vetch and that adding a forb is inconclusive based on the planting date used for chicory in this climate in this farming system. At an earlier planting date, the ES provision of chicory such as greater soil cover and nitrogen cycling could be realized. As expected, Rye produced greater biomass with a lower tissue concentration of nitrogen, whereas vetch produced lower biomass but with higher tissue nitrogen separating the ES where they are 26 the best monoculture respectively. However, only changing the density of a rye monoculture changed the magnitude of the ecosystem services of carbon fixation (biomass and CO2 flux) and weed suppression. Nevertheless, there remains a positive role for mixtures of rye and vetch where the density of rye can be varied to modulate ES. The mixtures of rye:vetch provided equivalent biomass, weed suppression and carbon fixation compared to the best monoculture. However, there was no evidence of transgressive overyielding where a more diverse mixture produced more ES than the respective monocultures. For example, the carbon flux values do not show greater carbon fixation in mixtures, but similarly to biomass, produced comparable values to the best monoculture. I therefore conclude that mixtures generally provided equivalent ES provision to the best monoculture. A review of the literature highlights the range of biomass outcomes found in rye:vetch mixtures; biomass may be greater in the rye monoculture than the mixture (Ranells and Wagger 1997), greater in the mixture than in either monoculture (Sainju et al. 2005), or equivalent or greater biomass than either monoculture with a land equivalency ratio greater than 1.0 (Hayden et al. 2014, Teasdale and Abdul-Baki 1998). Unfortunately, there have been only a limited number of studies comparing carbon flux in monocultures and mixtures in cover crops. Using similar equipment at the same site, a barley-pea intercrop (grass:legume) was shown to have greater carbon fixation (NEE and GEP) compared to either crop as monoculture (Chapagain and Riseman 2014a). Weed suppression in mixtures of rye:vetch has also been shown equivalent to the monoculture of rye suggesting that the benefits of the other plant in the mixture can be gained without a cost to weed suppression (Teasdale and Abdul-Baki 1998, Hayden et al. 2012). The mechanism underlying the equivalency of ES provision (i.e., biomass production and weed suppression) in monocultures and mixtures is likely due to the variation in growth habits (i.e., vetch’s ability to 27 trellis on rye) allowed for greater resource utilization such as lighter interception. This is supported by my inverse yield density results that indicated there was no competition between rye or vetch and supports niche differentiation among the species where both plants were able to obtain required resources when intercropped. For all carbon and biomass based ES, mixtures provide a clear benefit without a loss in magnitude of any service or a loss of any particular service. Monocultures were able to provide strong carbon fixation in the form of biomass production and a negative NEE. More specifically, rye seeding density was identified as the best management option to modulate these ES. While rye biomass was slightly lower than other reports for this region (Odhiambo and Bomke 2001, Hatakka 2010), perhaps reflecting the low densities observed, even at low density, there was still a considerable increase in ES provision. My NEE values from the rye monocultures, (-14 to -43 µmol CO2 m-2s-1) are similar to those reported using eddy covariance technology of a rye cover crop in Minnesota of ~-25 µmol CO2 m-2s-1 (Baker and Griffis 2009). The more negative values obtained in this study are likely due to warmer temperatures leading to greater plant growth. My Lana vetch biomass was also lower than yields obtained in neighboring Washington State and with that of Hairy vetch at the same site, suggesting that for biomass production, this subspecies is not better than Hairy vetch in our environment (Wayman et al. 2014, Hatakka 2010). Of the three species assessed, unsurprisingly, rye was the best for sequestering carbon, biomass production, and weed suppression due to its strong winter growth and a significant effect of its density on these ES. The effect of density being important in ES provision allows for management decisions to adjust rye planting density for a desired response. For example, the strong weed suppression at a low rye density suggests that a small amount of rye has a large impact. This suggests that a farmer could use a low rye 28 density for weed suppression and a legume for nitrogen fixation without negative interaction. This is important to consider as high carbon rye residues can immobilize nitrogen impacting crops that follow in the rotation (Odhiambo and Bomke 2000). These data also suggest that at low rye densities, the marginal increase in yield at harvest was decreasing suggesting the highest densities were plateauing as it approached a constant final yield (Weiner and Freckleton 2010). In conclusion, the ES provision of biomass, carbon, and weed suppression was most impacted by rye with chicory and vetch each contributing very little. Aboveground nitrogen was positively impacted by using a rye:vetch biculture. It produced more available nitrogen than the rye monoculture and comparable levels to the vetch monoculture indicating a net gain in ES provision. By combining rye and vetch, the C:N ratio changed to a more optimal level, between 25-30, improving the availability of resources for nitrogen mineralization. Two different N-based ES were provided, N fixation by the vetch and N scavenging by the rye. However, the timing of nitrogen release to the subsequent crop should also be considered in addition to total nitrogen. In the present study, the biculture had greater tissue nitrogen concentration than the rye monoculture that offers one indication that it would have a quicker and greater release of nitrogen. While vetch has higher nitrogen content in its aboveground tissue, it decomposes rapidly releasing a flush of nitrogen typically not aligned with crop needs. Rye, on the other hand, scavenges nitrogen, but can prevent it from being accessible as microbial communities can immobilize nitrogen as the residues decompose. Combining the two species in a mixture can gain the benefits of scavenging nitrogen, fixing nitrogen, and improved nitrogen release. The initial total nitrogen and C:N ratio are suggested indicators of how nitrogen will be released over time and the biculture mixture had a tissue concentration greater than the critical threshold for a net release of nitrogen in this region whereas the rye 29 monoculture was below (Odhiambo and Bomke 2000). Specifically, rye:vetch mixtures can improve nitrogen release to be more aligned with crop needs (Lawson et al. 2013). Mixing rye and vetch together is a viable strategy to modulate carbon and nitrogen for a greater net N release and better synchronicity with crop uptake. The comparable above-ground nitrogen content of rye and vetch monocultures was based on rye’s lower tissue nitrogen content but greater biomass production compared with vetch, however, neither plant density was predictive of total above ground nitrogen. However, neither rye nor vetch plant density had a detectable effect on nitrogen related ES as it did with biomass and weed suppression. Vetch density was not significant, perhaps because of its growth pattern of producing multiple stems per plant allowing considerable growth from one plant. A single vetch plant has been shown to produce large amounts of biomass in a control plot in a previous study (Art Bomke, Personal communication). Rye density was also not significant which may reflect its low tissue nitrogen content. Despite producing a lot of biomass, modulating density was not predictive of nitrogen and plant nitrogen may be better predicted by soil nitrogen conditions. Therefore, farmers have a lot of flexibility in choosing a density used in their cover crops to manage for aboveground nitrogen. They should plant vetch at a density that ensures a uniform stand rather than highly dense patches, i.e., ensuring there are no bare patches without a vetch plant. However, regardless of the density of rye or vetch, both are a viable choice to manage aboveground nitrogen. The use of cover crop mixtures in the present study provided a greater range of ecosystem services, at comparable magnitude, to their best monoculture counterparts. The only limitation identified in using mixtures is the cost of seed and learning curve in managing for mixtures in the field. The poor establishment of chicory at the used planting date limits 30 conclusions for forbs in this system. While limited new knowledge is provided on the role of forbs, there was evidence of a clear difference in the ecosystem functioning of grass (i.e., carbon and biomass) and legume (i.e., nitrogen) in the present study. Farm managers can use the density dependent response to guide the custom design of cover crop mixtures to simultaneously improve aboveground nitrogen, weed suppression, carbon fixation and soil fertility in their specific system. 31 Chapter 3: Functionally Diverse Cover Crops Mixtures And Carbon Fixation Under Greenhouse Conditions 3.1 Introduction Agriculture is both a significant source of carbon dioxide (CO2) emissions and a potential area for its capture. It is a system that manages ecosystem services, such as the provision of food, fibre, and fuel for human needs and CO2 regulation. As such, managing agricultural practices to optimize ecosystem services to include carbon sequestration can be approached through two processes: 1) increasing net plant biomass production known as net primary productivity (NPP) and, 2) sequestering the carbon in the produced biomass (Lal 2010). In addition, a key strategy to effectively manage ecosystems for enhanced CO2 fixation is to increase the diversity of functional traits underlying these processes (Cardinale et al. 2012). Specifically, it is reported that increasing the functional biodiversity of prairie grasslands can play a crucial role in ecosystem processes leading to increased plant biomass production (Tilman et al. 2012, Isbell et al. 2011). Functional biodiversity refers to grouping plants by functional roles such as grasses or legumes (Tilman 2001). Increasing the functional diversity of a plant community has been shown to produce synergistic results. For example, C4 grasses and legumes have displayed complementarity in a diverse assemblage of species that produced greater biomass and soil carbon increases over monocultures of each group (Fornara and Tilman 2008). The effect of plant functional diversity leading to greater plant biomass production and increased CO2 fixation has also been observed in highly managed systems for biofuel production (Tilman et al. 2006), supporting its further application to agricultural systems. Indeed, there is a renewed 32 interest in applying ecological principles of functional diversity through intercropping to improve the sustainability of food production systems (Brooker et al. 2015). As agriculture’s primary purpose is to produce food, any strategy to increase biodiversity to reduce CO2 emissions should have limited negative on impact yield or farm economics without providing a clear benefit to off-set their cost. Furthermore, there are many other factors affecting agricultural CO2 emissions other than ecosystem processes including emissions from farm machinery, various inputs, and the removal of biomass for food consumption. Current management strategies to reduce CO2 emissions from these sources include reduced tillage, organic matter additions such as biochar, and incorporation of crop residues. Also, an effective strategy should be considered in combination with farm finances and fit seamlessly with normal farm operations. Within this context, cover crops are a promising management option that can be implemented with current farm equipment and that have the potential to provide a range of ecosystem services that can offset their cost (Schipanski et al. 2014). Including cover crops in a crop rotation, sequentially following and preceding food crop production, increases the biodiversity of the farm system and produces plant biomass than can be incorporated into the soil to sequester carbon. Cover crops can positively affect the net balance of carbon exchange known as Net Ecosystem Exchange (NEE) by increasing the off-season Gross Ecosystem Photosynthesis (GEP) helping turn agricultural fields into carbon sinks. A meta-analysis highlighted that including cover crops in a crop rotation is an effective strategy to increase soil organic carbon and is capable of sequestering approximately 0.12 Pg C yr-1 globally (Poeplau and Don 2015). However, results from individual trials have varied. For example, a winter wheat cover crop following soybean production was shown to be a carbon sink during the winter at a magnitude sufficient to offset CO2 emissions during summer production (Gebremedhin et al. 33 2012). However, a rye cover crop following maize/soybean production was shown to be a net source of CO2 emissions, likely due to the decomposition of cover crop residues (Bavin et al. 2009). While on average, the presence of a cover crop generally increases plant biomass leading to increased CO2 fixation, the variation among individual experiments highlights the need for further investigation on how specific cover crops regulate CO2 emissions and the role of functional biodiversity on the underlying processes. Planting functionally diverse cover crops is one management option that has the potential to increase plant biomass production leading to increased CO2 fixation while also providing additional ecosystem services (i.e., weed suppression, nitrogen scavenging, etc.). The conventional practice is to group plants based on relevant functional traits into broad functional groups such as grasses or legumes. Typically, a cover crop mixture is chosen because it offers the possibility of providing more than one ecosystem service in one planting. Typical cover crop mixtures include annual grasses and annual legumes, with the grass providing weed suppression and nutrient scavenging while the legume provides nitrogen fixation (Brainard et al. 2011, 2012, Hayden et al. 2012). There is also evidence that bicultures of a grass and legume display synergistic effects by producing more biomass and sequestering more carbon than comparable monocultures. For example, Sainju et al. (2006) using this functional group classification, found that a rye (i.e., C3 grass) and Hairy vetch (i.e., legume) biculture was more effective in increasing soil organic carbon compared with a monoculture of either or a bare soil control. Wang et al. (2012) found that bicultures including a grass and a legume were more effective at producing above-ground biomass and increasing soil organic carbon than their monoculture counterparts. Therefore, using a cover crop comprised of two functional groups is shown to produce greater impacts over monoculture planting; an outcome perfectly aligned with grassland ecological 34 theory. However, grassland studies that included greater than two functional groups observed even greater gains and support research into whether additional functional group diversity of cover crop mixtures can yield further benefits. To advance cover crop research, it is imperative to move beyond the standard cover crop mixtures containing annual grasses and annual legumes. Including additional functional diversity in the form of non-leguminous forbs (hereafter forbs) or perennials may improve CO2 fixation due to increased biomass allocation to roots and potential complementarity among growth forms. Research on intercropping agricultural plants suggests that mixtures may be more efficient in capturing both above and below ground resources (Vandermeer 1989). For aboveground resource capture, Trenbath (1986) postulated that greater above-ground light interception could be achieved by mixed planting of a high photosynthetic capability erect-leaved plant with low photosynthetic capacity prostrate growing plant (Keating and Carberry 1993). For below ground resources, Li et al (2014b) postulated that more nutrients could be captured by mixed plantings through mobilization and that this could be one mechanism driving the diversity effect of over-yielding. Application of these ideas could be achieved by intercropping prostrate growing perennials with different root structures, such as perennial legumes or forbs, with an erect-leaved C3 grasses. For example, it is common practice to plant species in the genus Trifolium with grass cover crops for its complementarity (Schipanski and Drinkwater 2012). Increasing functional group diversity of cover crops to include prostrate growing perennial plants has the potential to increase resource capture and enhance ecosystem service provision. There are few studies on the impact of including forbs in cover crop mixtures for increased carbon fixation. Broadleaved perennial forbs, with their ground shading canopies and 35 deep root systems, may fill an unoccupied niche in current grass:legume mixtures as rye:vetch mixtures were found inefficient at suppressing non-mustard broadleaved weeds (2012). Therefore, including broadleaf forbs in a rye:vetch mixture could fill available above ground niches through greater light interception leading to improved biomass production and weed suppression. They could also fill below ground niches with their varied root architectures leading to the cycling of belowground nutrients from deeper to shallower in the profile (Thorup-Kristensen 2006). Therefore, increasing the functional diversity in the conventional grass:legume mixture to include a prostate growing, perennial forb may allow greater resource capture through complementarity enhancing ecosystem service provision. To gain a better understanding of the biomass production and NEE potential for mixtures of functionally diverse cover crops without environmental constraints, a greenhouse experiment was conducted with the following objectives: • To assess functional group combinations for fixing atmospheric carbon through biomass production and leaf area; • To assess the impact including perennial forb in a mixture with a C3 grass and legume on NEE, leaf area and biomass production; 3.2 Materials and methods 3.2.1 Culture This study was conducted at the University of British Columbia Horticultural Greenhouse. Seeds were sown in a potting mixture containing 75% peat and 25% perlite and germinated on mist benches for one week and then hardened off under HPS lamps for one week. Daily fertigation was applied with a solution maintained at a pH of 6.4, electrical conductivity 36 0.9 mS, and containing NO3- (6.1 mM/L), Cl (0.03 mM/L), SO4- (0.3 mM/L), HCO3 (0.3 mM/L), P (0.74 mM/L), NH4+ (1.4 mM/L), K (1.9 mM/L), Na (0.3 mM/L), Ca 0.7 (mM/L), Mg (1.6 mM/L), Si (0.10 µM/L), Fe (5.7 µM/L), Mn (2.7 µM/L), Zn (3.0 µM/L), B (13 µM/L), Cu (6.0 µM/L), and Mo (0.3 µM/L). Two-week-old seedlings were transplanted to 7.6 litre pots (2-gallon) and grown under 600W HPS lamps set on a 16-hour supplementary window when sunlight was less than 500W. Ambient temperature was set to 18 C during the night and 22 C during the day with an average humidity of 30%. If observed, pests were controlled with natural enemies under integrated pest management strategies. 3.2.2 Germplasm Rye (Secale cereale L.) was chosen as the C3 grass as it forms erect stems and produces a large amount of biomass. Vetch (Vicia villosa Roth.) was chosen as the legume for its known synergism with rye as it trellises on the erect stems potentially capturing more light. More specifically, the subspecies Lana vetch (Vicia villosa ssp. dasycarpa Roth.) was selected for its early spring growth and maturity. Prior to planting Lana vetch was inoculated with Rhizobium leguminosarum biovar viceae. Chicory (Cichorium intybus L. cv Puna) was chosen as the perennial forb for its prostrate growth pattern, long taproot, current use as a forage crop (Rumball 1986), and potential for complementarity with rye and Lana vetch. 3.2.3 Experimental design Plants were transplanted based on circle packing theory using solved optimal arrangements (i.e., regardless of number of plants, the maximum space was provide equally; See Appendix 1). Pots were arranged on one bench in a completely randomized design. The 37 experiment was conducted as a response surface design at three separate densities for each species and at three harvest dates to correct for confounding issues of density in mixtures (Jolliffe 2000). Plant mixtures were comprised of three densities of each species (Table 9) using a response surface design with all possible combinations in a complete factorial treatment arrangement (Jolliffe 2000). To allow for destructive harvesting on three dates (21, 28, 35 DAS), 81 total experimental units were planted at the beginning of the experiment. On each date, 27 pots were measured and destructively harvested. Each harvest date was an un-replicated complete factorial. A preliminary trial was run to determine the optimal duration of the experiment within the constraints of the CO2 chamber (i.e., the plants grow too large to fit inside the chamber as explained in Section 4.2.4). Thirty-five DAS was determined as an optimal duration (data not shown). All pots were re-randomized biweekly and rotated to avoid any orientation effects. At the end of the experiment, the rye roots in monoculture had fully explored the pot, but there was no crowding or roots appearing at the drainage holes. At termination, rye had reached 4 on Feekes scale with erect leaf sheaths, but no node visible, while Lana vetch and chicory were vegetative with no visible buds or stems. There was some variation in vetch growing outside the pot footprint despite rotation and bi-weekly replacement inside the pot lip. Lana vetch roots were inspected for nodules at the end of each sampling date and nodules were present, but in an immature state. Table 9: Levels of each factor as represented by plant density. Plant Density (kg ha-1) Density (Plants per pot) Rye 0 29.44 58.89 0 3 6 Lana vetch 0 12.87 25.74 0 1 2 Chicory 0 1.83 3.65 0 3 6 38 3.2.4 Sampling procedure Sampling occurred after watering and drainage to field capacity, approximately 30 minutes after irrigation. A 60 second CO2 flux sample was taking using the chamber described in Section 4.2.3. The above ground tissue was then clipped to ground level and stored at 4 C in an airtight bag with a moist paper towel. A subsequent CO2 flux sample was taken on the bare soil for a period of 30 seconds. The above ground tissue was measured for one sided leaf area using a leaf area meter (LI-3100, LI-COR). Leaf area for the grass was measured as the blade separate from the sheath as per standard protocol (Pérez-Harguindeguy et al. 2013). Above ground tissue was dried at 60 C for 72 hours and weighed. 3.2.5 Carbon flux measurements Measurements of Net Ecosystem Exchange (NEE) and Ecosystem Respiration (Re) were measured using a clear chamber as described by (Chapagain and Riseman 2014a). As potting soil was used, the contribution by heterotrophic organisms was considered negligible and respiration was considered to represent autotrophic respiration (Ra). Gross Ecosystem Photosynthesis (GEP) was derived from the NEE and Re data. NEP was calculated as the negative NEE converted to mg C m-2 h-1 using the conversion of 43.2 (Jassal et al. 2005). CO2 flux was not linear and a quadratic term was included in the model to account for non-linearity as follows: 𝐶𝑂?(𝜇𝑚𝑜𝑙𝑚??) = 𝑏? + 𝑏?𝑋? + 𝑏?𝑋?? Where X1 is time in seconds 39 A tangent to the line at 5 seconds was used to calculate CO2 flux to avoid initial noise in placing the chamber on the plants. The relative humidity increased as plant matter increased but remained less than 4% and was considered not significant (Welles et al. 2001). 3.2.6 Data analyses Data analyses were conducted using R version 3.0.2 with additional packages (R Core Team 2013, Hothorn et al. 2008, Wickham 2009, 2007, 2011, Hlavac 2014, Auguie 2012, Pinheiro et al. 2015, Fox and Weisberg 2011). Models were built treating each plant density and time as a factor at three levels. Stepwise forward regression was used to determine the optimum model using Bayesian Information Criterion (BIC), as a parsimonious model was preferred for ease of interpretation with a large factorial design. Furthermore, we were less interested in significance of parameters assuming that all plants were producing dry matter and photosynthesizing and more interested in understanding which plants in the mixture explained the most variance. For Total Land Output and total leaf area, the zero points were removed and the data were log-transformed to reduce heteroscedasticity. Sampling error prompted the removal of two experimental units (Treatment 18 R6V2C0D13, Treatment 48 R3V2C0D1) from all CO2 flux analysis and inconsistent ambient sunlight prompted the removal of three experimental units from the calculation of NEE, GEP and NEP (Treatment 10 R3V2C3D3, Treatment 11 R6V1C3D3, Treatment 19 R6V1C0D3). All other response variables met required assumptions. Comparisons between polycultures to monocultures were completed using t-tests with manual contrasts with the multcomp R package using single-step procedure. Comparisons 3 First letter = Plant (R=rye, V=vetch, C=chicory), number equals plants per pot, D1 = 21 DAS, D2 =27 DAS, D3 = 35 DAS. 40 were performed between monocultures and bicultures containing the same number of plants of one species (i.e., a 6 rye plant 3 chicory plant (R6C3) was compared to a 6 rye plant monoculture (R6) and a 3 chicory plant monoculture (C3)). When an interaction was detected between a plant density and time, the final time point was used for comparison to avoid inflating Type-1 error. When one plant factor was not significant, due to the factorial design, three-species mixtures were averaged with two-species mixtures, and the two-species mixtures were averaged with monocultures providing additional degrees of freedom for t-test comparisons. 3.3 Results 3.3.1 Total land output To allow comparison to other studies, summary data for dry matter are presented (Table 10). Combined values for each pot used to model TLO demonstrated significant effects for Rye, Chicory and Time (Table 11). As expected, the effect for Time indicates that TLO increased positively as the experiment progressed and plants grew. A significant interaction between Rye and Chicory indicates a negative impact from combining these two species (Table 11). At C3, there was limited reduction on TLO as the density of Rye increased (Figure 10). However, at C6, there was a reduction in TLO as Rye density increased indicating a negative effect on TLO at the highest density of both plants (Figure 10). Post-hoc analysis comparing bicultures to monocultures demonstrates that bicultures had similar TLO to the rye monocultures with the exception of the R3 monoculture to the R3C6 biculture (denoted by an asterisk), but significantly higher better than all chicory monocultures (Table 12). 41 Table 10: Mean dry matter (g) ± standard deviation for each level of plant density. Density Level 1 Density Level 2 Days after sowing 21 28 35 21 28 35 Rye 1.06 (0.36) 4.18 (0.32) 9.23 (1.57) 2.06 (0.32) 7.72 (1.58) 13.81 (3.45) Lana vetch 0.24 (0.04) 0.55 (0.19) 1.07 (0.96) 0.34 (0.10) 1.30 (0.43) 0.78 (1.14) Chicory 0.42(0.09) 2.08 (0.46) 5.69 (2.14) 0.70 (0.12) 3.14 (0.93) 8.40 (2.23) Table 11: Summary ANOVA for the fitted model of the log of Total Land Output. Log(TLO) = Rye + Chicory + Time + Rye*Chicory Parameter SumSq Df p Rye 21.3 2 <0.0001 Lana vetch 0 0 ns Chicory 4.4 2 <0.0001 Time 63.8 2 <0.0001 Rye:Chicory 4.7 4 <0.0001 Rye:Time 0 0 ns Chicory:Time 0 0 ns Residuals 4.1 67 Figure 10 The interaction between the density of rye and the density of chicory on fitted values for the model of log(TLO). Different lines indicate chicory density. 0.00.51.01.52.0Density of RyeLog(Plant Dry mass) (g)0 3 6 Density of Chicory36042 Table 12: T-test comparison between bicultures and monoculture of rye and chicory at a range of densities. Comparison log(TLO) (g) log(TLO) (g) Difference Standard Error t p R6C3-R6C0 2.0780 1.9338 0.1441 0.1172 1.229 0.7957 R6C6-R6C0 2.0463 1.9338 0.1126 0.1172 0.960 0.9257 R3C6-R3C0 1.8411 1.5124 0.3287 0.1172 2.804 0.0465* R6C3-R0C3 2.0779 0.9697 1.1082 0.1172 9.453 <0.001 R3C6-R0C6 1.8411 1.2846 0.5565 0.1172 4.747 <0.001 R6C6-R0C6 2.0463 1.2846 0.7617 0.1172 6.498 <0.001 R3C3-R3C0 1.6737 1.5124 0.1613 0.1172 1.376 0.7005 R3C3-R0C3 1.6737 0.9697 0.7040 0.1172 6.005 <0.001 Note: R=rye, C=chicory, number=density of plants per pot. * indicates comparison different from trend 3.3.2 Leaf area To allow comparison to other studies, summary data for leaf area are presented (Table 13). The model for total leaf area had significant effects for Rye, Chicory and Time (Table 14). There was a significant interaction between Rye and Time which indicated that as time increased from 28 to 35 DAS, rye did not continue with a steady increase in leaf area (Figure 11). There was a significant interaction between Rye and Chicory that indicated that as density increased for each plant, it reduced the leaf area change as density increased for the other plant (i.e., the increase in leaf area from R3 to R6 was less as a result of the density of chicory) (Figure 12). Post hoc analysis demonstrated that at most densities, the chicory monoculture had equal total leaf area to the biculture of rye and chicory with the exception of C3 compared to R6C3 biculture (denoted by an asterisk) whereas the rye monocultures had significantly less total leaf area compared to all bicultures (Table 15). Table 13: Mean leaf area (cm2) ± standard deviation for each level of plant density. Density Level 1 Density Level 2 Days after sowing 21 28 35 21 28 35 Rye 255.23 (93.49) 945.07 (110.98) 1710.51 (399.99) 513.33 (76.39) 1545.75 (284.09) 2560.89 (437.30) Lana vetch 61.62 (27.21) 143.23 (44.33) 228.25 (186.02) 89.63 (19.72) 339.16 (126.79) 189.07 (266.26) Chicory 199.16 (45.57) 884.50 (202.87) 1954.93 (672.10) 330.53 (22.04) 1385.71 (349.15) 3116.07 (605.18) 43 Table 14: Summary ANOVA for the fitted model of the log of Total Leaf area: Log(Total leaf area) = Rye + Chicory + Time + Rye*Chicory + Rye*Time Sum Sq Df Pr(>F) Rye 14.21 2 <0.0001 Chicory 13.12 2 <0.0001 Lana vetch 0 0 ns Time 52.89 2 <0.0001 Rye:Chicory 8.05 4 <0.0001 Rye:Time 0.72 4 <0.01 Chicory:Time 0 0 ns Residuals 2.59 63 Figure 11: The interaction between the days after sowing and the density of rye on fitted values for the model of log(leaf area). Different lines indicate Rye density. 5.56.06.57.07.58.0Days After SowingLog(Leaf area)cm221 28 35 Density of Rye63044 Figure 12: The interaction between the density of rye and the density of chicory on fitted values for the model of log(leaf area). Different lines indicate chicory density. Table 15: T-test comparison between bicultures and monoculture of rye and chicory at a range of densities. Comparison log(total leaf area) cm2 log(total leaf area) cm2 Difference Standard error t p R6C3-R6C0 8.3516 8.0441 0.3075 0.0956 3.216 0.0153 R6C6-R6C0 8.4918 8.0441 0.4477 0.0956 4.682 0.0001 R3C6-R3C0 8.3688 7.7604 0.6084 0.0956 6.363 <0.0001 R6C3-R0C3 8.3516 7.8761 0.4755 0.1250 3.805 0.0025* R3C6-R0C6 8.3688 8.2633 0.1055 0.1250 0.844 0.9671 R6C6-R0C6 8.4918 8.2633 0.2285 0.1250 1.828 0.3993 R3C3-R3C0 8.1626 7.7604 0.4022 0.0956 4.206 0.0007 R3C3-R0C3 8.1626 7.8761 0.2865 0.1250 2.292 0.1657 Note: R=rye, C=chicory, number=density of plants per pot. * indicates comparison different from trend 5.05.56.06.57.07.5Density of RyeLog(Leaf area)cm20 3 6 Density of Chicory63045 3.3.3 Carbon flux 3.3.3.1 Net ecosystem exchange (NEE), gross ecosystem photosynthesis (GEP) and net ecosystem productivity (NEP) The model for NEE had significant main effects for Rye and Time (Table 16). The main effect for Time indicated that as time increased, the magnitude of NEE increased regardless of species or combination. A negative NEE indicates a net carbon sink. The significant main effect for Rye indicated that as the density of rye increased from 3 plants to 6 plants, the magnitude of NEE increased. A similar trend occurred for both GEP and NEP with significant main effects for Rye and Time (Table 17 & 18). Fitted values for all three variables are reported (Table 19). Table 16: Summary ANOVA for the fitted model of Net Ecosystem Exchange: NEE = Rye + Time Sum Sq Df Pr(>F) Rye 2828.9 2 <0.0001 Time 3066.9 2 <0.0001 Residuals 3140.5 71 Table 17: Summary ANOVA for the fitted model of Gross Ecosystem Photosynthesis: GEP = Rye + Time Sum Sq Df Pr(>F) Rye 5021 2 <0.0001 Time 5549 2 <0.0001 Residuals 3809 71 Table 18 Summary ANOVA for the fitted model of Net Ecosystem Productivity: NPP = Rye + Time Sum Sq Df Pr(>F) Rye 5279417 2 <0.0001 Time 5723522 2 <0.0001 Residuals 5860951 71 46 Table 19: Fitted mean values across three time points at three densities of rye for NEE, NEP and GEP. Days after sowing 21 21 21 28 28 28 35 35 35 Density of Rye 0 3 6 0 3 6 0 3 6 NEE (µmolCO2m-2s-1) -0.86 -9.36 -15.86 -10.91 -19.40 -25.90 -16.44 -24.94 -31.44 NEP (mgCm-2h-1) 37.34 404.39 685.29 471.16 838.21 1119.02 710.29 1077.34 1358.15 GEP (µmolCO2m-2s-1) 0.55 11.83 20.54 12.45 23.73 32.44 21.79 33.06 41.77 Note: Mean photosynthetically active radiation (µmol photons m-2s-1) and temperature (°C) at 21 DAS 362.07, 22.87; 28 DAS 306.078, 22.88; 35 DAS 291.64, 24.42 3.3.3.2 Ecosystem respiration (Re) There was a significant main effect for Rye, Time and Chicory in the model for Re (Table 20). There was also a significant interaction between Rye and Time which indicated that rye respiration increased markedly from 28 days to 35 days after sowing as density of rye increased (Figure 13). The main effect for Chicory indicated that as the density of chicory increased, there was an increase in the positive magnitude of respiration (i.e., released more C). Post hoc analysis showed that the highest density biculture (R6C6) produced greater respiration than the R6 monoculture and that R3C6 also produced more respiration that than the R3 monoculture (denoted by an asterisk in Table 21). All other rye monocultures produced equivalent respiration to rye bicultures with the same number of rye plants. All chicory monocultures had significantly less respiration than the bicultures of rye and chicory. A summary of all t-tests is provided (Table 21). Table 20: Summary ANOVA for the fitted model of Ecosystem Respiration: Re = Rye + Time + Chicory + Rye*Time Sum Sq Df Pr(>F) Rye 328.85 2 <0.0001 Time 438.07 2 <0.0001 Chicory 29.71 2 0.0117 Rye*Time 92.50 5 <0.0001 Residuals 3139 68 47 Figure 13: The interaction between the days after sowing and the density of rye on fitted values for the model of respiration. Different lines indicate Rye density. Table 21: T-test comparisons between bicultures and monoculture of rye and chicory at a range of densities. Comparison Respiration (µmolCO2m2s-1) Respiration (µmolCO2m2s-1) Difference Standard Error t p R6C3-R6C0 12.3586 11.1277 1.2309 0.4917 2.5033 0.0542* R6C6-R6C0 12.4722 11.1277 1.3445 0.4812 2.7941 0.0255 R3C6-R3C0 8.9646 7.6201 1.3445 0.4812 2.7941 0.0255* R6C3-R0C3 12.3586 3.8261 8.5325 0.8335 10.2372 <0.0001 R3C6-R0C6 12.4722 3.9398 8.5325 0.8335 10.2372 <0.0001 R6C6-R0C6 8.9646 3.9398 5.0249 0.8335 6.0288 <0.0001 R3C3-R3C0 8.8510 7.6201 1.2309 0.4917 2.5033 0.05421 R3C3-R0C3 8.8510 3.8261 5.0249 0.8335 6.0288 <0.0001 Note: R=rye, C=chicory, number=density of plants per pot. * indicates comparison different from trend 24681012Days after sowingRespiration (µmolCO2m2 s−1 )21 28 35 Density of Rye63048 3.4 Discussion Using mixtures of functionally diverse cover crops is one strategy to increase the range of ES provided to farmers and sustainably intensify agriculture. The results of this study offer insight into using a functional group framework for optimizing ecosystem functions of carbon fixation (biomass and CO2 uptake) and leaf area leading to ES of greenhouse gas regulation and soil cover. In general, mixtures provided an improvement and expansion of ES provision comparable to the best monoculture. However, only one species within a mixture typically dominated the impact on an individual ecosystem service (i.e., rye for carbon fixation and chicory for leaf area). The use of cover crop mixtures increased the range of ES provided suggesting that mixtures offer a premium without a loss of service. The best biculture for ES provision was rye:chicory and not rye:vetch suggesting that grass:forb functional group combination is preferable for the ES measured under our high light and high nutrition greenhouse conditions. Chicory was able to produce a large amount of leaf area in the presence of the strong growth of rye, which may indicate an open niche in the low canopy. Contrary to expectations, the mixture of rye:chicory had higher biomass and leaf area compared to the commonly used rye:vetch mixtures. The best mixture for biomass production was a grass:forb (i.e., rye:chicory) and offers a viable alternative the common grass:legume biculture (rye:vetch). Although, the rye:chicory biculture produced the most biomass, there was no evidence of transgressive overyielding or synergism and actually produced a negative impact on each other’s growth at high densities. This result has also been reported with fall-sown wheat cover crop (C3 grass) undersown with chicory, to be grown as pasture, where the mixture caused both reduced grain yields and poor pasture establishment (Li, G. D. et al. 2014a). However, the TLO of the present study indicated some degree of 49 complementarity between rye and chicory at a low chicory density. At a low chicory density, the rosette forming growth of chicory may support Trenbath’s (1986) suggestion of canopy complementarity with an erect-leaf plant with a capture of more light in the understory of the rye stand; unfortunately, I did not measure light interception at ground level. The growth pattern of the grass:forb mixture combined with the greater leaf area is useful in applied settings. The application of this information may result in greater soil cover leading to reduced erosion and weed pressure reducing inputs and supporting sustainable soil management. A niche low in the canopy could otherwise be exploited by broadleaf weeds which are a known problem in rye:vetch mixtures (Hayden et al. 2012). If functional groups resist invasion to similar groups (Frankow-Lindberg 2011), there may be strong rationale to include a broad leaved forb into the mix. The dominant most productive mixture being rye:chicory rather than rye:vetch or rye:vetch:chicory could be do greenhouse conditions limiting the advantage of vetch N-fixation or chicory may have limited vetch’s ability to trellis on rye. Typically, experiments have found rye:vetch mixtures to produce equal or more biomass than their best monoculture (Sainju et al. 2005, Hayden et al. 2014, Teasdale and Abdul-Baki 1998). Under similar greenhouse conditions, a rye and Hairy vetch biculture produced equivalent biomass to a rye monoculture; however, the Hairy vetch biomass contribution reduced from 18% to 6% from low to high fertility rates (Mwaja et al. 1995). These results share two similarities to the present study: 1) in a rye:vetch mixture, rye will dominate biomass production under high nutrient conditions and 2) that vetch contributes only a negligible amount to biomass in bicultures at the same seeding rates as the monoculture. The data from this experiment support an argument for the provision of an increased range of ES at comparable magnitudes for a grass:forb biculture vs. a grass:legume biculture under high light, high nutrition conditions of the greenhouse. 50 While mixtures provided comparable magnitude and multiple services, farmers seeking to manage their fields to maximize one service are best to use a monoculture. The results of this study highlight rye and chicory as important monocultures for carbon fixation and leaf area, respectively. The limited production of Lana vetch in this study does not support this cultivar’s use for carbon fixation or leaf area. While Lana vetch typically matures early (Wilke and Snapp 2008), it also typically produces 2 Mg ha-1 less biomass than Hairy vetch, Vicia villosa cv Purple Bounty, or common vetch, Vicia sativa (Wayman et al. 2014). Early maturity is useful under no-till conditions, but for the goals of this study (i.e., carbon fixation assessment), it appears a poor choice. It is well-known that in the field, rye grows well under cold conditions and that chicory cv Puna can display winter dormancy (Clark 2012, Rumball 1986). The high GEP and biomass production of the cool season C3 grass (rye) compared to the warm season C3 forb (chicory) under greenhouse conditions was unexpected considering chicory is more often used during summer production similar to the experimental conditions. A possible explanation is that chicory may be better suited to high light/low water conditions during hot, dry summers with its deep taproot whereas rye can perform better with high light/high water under the greenhouse conditions. The higher GEP and biomass production for rye cannot be explained due to the greater leaf area or the growth strategies of vetch or chicory. The highest leaf area was a result of the horizontal, rosette growth form of chicory suggesting that it is the dominant functional group for this variable, but did not provide it with greater GEP. Forbs have been shown to have a more horizontal growth pattern compared to the upright growth of monocotyledons (Anten et al. 1995). However, both chicory and vetch are dicotyldons, but with different growth patterns from rosette forming to climbing. There was no evidence that the climbing strategy of vetch caused greater leaf area through its access to light higher in the canopy. The higher GEP and 51 biomass production for rye can also not be attributed to differences in photosynthetic pathways between the dominant C4 grasses and C3 grasses and forbs. By some unknown mechanism, rye was capable of converting available resources into biomass better than chicory or vetch. Physiological differences between C3 grass cover crops and forb cover crops, such as the stomata conductance, could explain this observation and deserves further investigation. The results (greenhouse) are deemed valid as our fitted Net Ecosystem Exchange measurements were similar to field observations using eddy covariance technology of a fall rye cover crop in Minnesota (NEE= -31 to ~-25) (Baker and Griffis 2009). However, these values were more negative (i.e., a greater carbon sink) than those reported under field conditions during peak summer growth of a barley monoculture (-4.16) (Chapagain and Riseman 2014a). Within the limitation of greenhouse conditions, rye remains the best option for increasing carbon fixation and chicory as the best option for increasing leaf area. Mixtures increased the magnitude of carbon evolution and provide a strong indication of greater belowground root activity. The observation that the highest root respiration was in the R6C6 mixture compared to the R6 monoculture suggests that adding chicory increased the amount of root respiration without decreasing rye root respiration below monoculture rates. Considering the relative role of each plant, the presence of chicory in the final model for respiration, but not in the model for NEE, GEP or NEP, is aligned with the prediction that chicory, as a perennial with a taproot, has a greater relative contribution to respiration rather than the photosynthesis component of NEE. A relative contribution towards respiration over photosynthesis is also likely to increase considering Alloush et al.’s (2003) finding of a continual decrease in the shoot:root ratio from 28-42 days of growth to reach ~2.25 (7 days longer than the present study). Rye has also been shown to have 13% of its biomass contribution from 52 belowground sources with an average shoot:root ratio 7.69 (Sainju et al. 2005). Chicory appears to be partitioning greater resources to its roots, although rye is still the dominant source of root respiration. Roots have been argued to be a key area for research for carbon sequestration (Kell 2012), and a rye and chicory mixture is a promising option for future research in increasing belowground biomass production. A caveat to consider is that the maintenance root respiration and growth respiration may be different for the plants of this study and that respiration is not a perfect predictor of root growth. Nevertheless, measuring respiration offers an alternative to disentangling roots in plant mixtures and can provide insight on belowground carbon dynamics. My results are different from studies of functional groups in native grasslands where forbs and legumes both had greater root respiration compared with C3 grasses (Reich et al. 2003). Forbs were also found to be the main driver of soil respiration in a grassland mesocosm study compared with grasses or sedges (Johnson et al. 2008). It appears that under these experimental conditions (i.e., greenhouse conditions and with short duration), the responses are significantly different than seen from native species. In the present study system, the C3 grass (rye) was the dominant plant for respiration with the forb (chicory) making a modest contribution. The ultimate decision on what cover crop plants to grow depends on the priorities of the farmer and which ecosystem services are most relevant to their system. The results of this study demonstrate difference in both functional group choice and density of planting supporting a role for cover crop management in ecosystem service provision. In management of cover crops for the carbon fixation, rye was the best choice out of the three crops assessed. However, the inclusion of perennial forbs may provide benefit for greater leaf area leading to greater soil cover and weed suppression, but should be used at a low rate so to not compete with the growth of a C3 53 grass. The study has described the different roles for each functional group in relation to ES provision and supports management decisions using mixtures designed for specific ES. 54 Chapter 4: Functional Group Diversity Of Cover Crop Mixtures, Nutrient Release And Decomposition Of Above-Ground Tissues 4.1 Introduction Increasing the functional diversity of plants is one strategy to achieve sustainable intensification of agriculture through improving nutrient cycling and management. An ecosystem-based approach integrating plant functional diversity can access and mobilize previously unavailable nutrients offering an alternative to synthetic fertilizer use and its associated environmental pollution (Drinkwater and Snapp 2007). Increasing functional biodiversity through designed species combinations, based on the ecological theories of complementarity and facilitation, can provide these desired benefits (Malézieux et al. 2009). There are many options to increase plant biodiversity in agriculture such as increasing the intraspecific diversity through cultivar choice, spatially through intercropping, or temporally through crop rotations and cover crops. Cover crops are a management option that can provide many benefits including temporal segregation from production crops reducing negative impacts, custom mixtures providing an array of ecosystem services, and yield increases through enhanced soil fertility (Schipanski et al. 2014). Due to their flexibility, cover crops represent a key strategy to improve nutrient cycling and to meet the goals of sustainable intensification. Cover cropping is an effective strategy regardless of the degree of biodiversity. Single species cover crops are often used to provide important ES such as improving nutrient availability, cycling and mobilization. One of the most common uses of cover crops is to regulate nitrogen cycling through scavenging and fixation. Tonitto et al. (2006) found that both single species non-legume or legume cover crops effectively scavenged nitrogen while 55 leguminous crops also increased nitrogen supply. Winter rye, used as a non-legume cover crop, was predicted to scavenge N and reduce NO3- leaching by a magnitude of 20% if used across the Midwest United States (Kladivko et al. 2014). Leguminous cover crops can go beyond scavenging nitrogen and add additional N to the system through biological N-fixation (BNF). The use of the leguminous cover crops Hairy vetch, Crimson clover and Persian clover were shown to fix over 100 kg ha-1 of nitrogen with Hairy vetch yielding the highest (Mueller and Thorup-Kristensen 2001). This BFN can then increase fertility to subsequent crops and reduce costs of other fertilizers (Parr et al. 2011). Differences in the decomposition of cover crop residues can lead to changes in this nutrient release with grasses releasing less available nitrogen than legumes (Ruffo and Bollero 2003). Cover crops are also capable of regulating nutrients beyond nitrogen. Some plants release chemicals into the rhizosphere that mobilize inaccessible P that can then become available to neighboring or subsequent plants (Li, L. et al. 2014b). Specifically, the presence of legumes has been shown to increase soil phosphorus availability to surrounding and subsequent crops (Arrobas et al. 2015, Kamh et al. 1999). Through these many benefits, monoculture cover crops have a proven record of improving nutrient cycling in agroecosystems. Increasing functional diversity by planting species mixtures can expand and enhance ES provision. Specifically, intercropping grasses and legumes can lower the carbon:nitrogen (C:N) ratio and reduce nitrogen immobilization from the decomposition of high carbon grass residues (Odhiambo and Bomke 2000). Grasses and legumes in bicultures can also slow the rate of decomposition leading to improved release of available N compared to their respective monocultures (Ranells and Wagger 1996). Also, grass:legume cover crop mixtures have demonstrated synergism resulting in increased nitrogen fixation efficiency over a legume 56 monoculture (Brainard et al. 2012, Kurdali et al. 1996). In a grass:legume biculture, the grass component may suppress weeds reducing competition on the legume and allowing for greater nitrogen fixation (Hayden et al. 2012). Grass:legume bicultures are a proven strategy to increase the range of ecosystem services provided to the farmer with few to no risks to yield. To maximize benefits from functional diversity, it is imperative to further diversify the functional traits within the mixtures and include functional groups beyond the grasses and legumes. Loss of biodiversity has been associated with reduced ecosystem function (Cardinale et al. 2012), suggesting an application of the inverse in agriculture. Adding the non-leguminous forbs (hereafter forbs) functional group to traditional mixtures may lead to greater ecosystem services provided to farmers. Thorup-Kristensen et al. (2006) reported that the deep-rooted forb, chicory Cichorium intybus L., cycled nitrogen to the surface from deep in the soil profile making it available to subsequent crops. Chicory has also been used in forage mixtures as it is a source of minerals to dairy cattle (Harrington et al. 2006). It also has the ability to mine and retain phosphorus due to its extensive root system (Liu et al. 2015). Other forb cover crops, such radish, phacelia, and mustard, have also been shown to scavenge nitrogen better than cereal grasses (rye or oats) (Allison and Armstrong 1998) with mustards and phacelia able to produce greater above-ground biomass and scavenge more N compared to an oat cover crop (Stivers-Young 1998). As nitrogen that is scavenged or fixed must be released to subsequent crops to be useful to crop yields, the decomposition of tissue in mixtures is also important. Synergies between components in mixtures can be referred to as non-additive effects when mixtures decompose differently compared to their respective monocultures. Using a similar functional group framework and methods, mixtures were shown to have both positive and negative non-additive effects in an alpine herbaceous community (McLaren and Turkington 2011). For both 57 nutrient cycling and release from decomposing residues, mixtures with increased functional group diversity can affect the resulting ES. Therefore, including forbs in the conventional grass:legume mix may expand and enhance the release of nutrients to subsequent crops by increasing the variety of above-ground and belowground functional traits. The present study was conducted to improve our understanding of the impacts of functional group diversity on the release of nutrients during decomposition. Specifically, I assessed nutrient release among grass, legume, and forb combinations to determine whether specific mixtures: • affected the magnitude, timing and range of nutrients released from above-ground decomposing tissue; • affected the amount of decomposed aboveground tissue after 15 weeks. 4.2 Materials and methods 4.2.1 Study site The study was conducted at the Centre for Sustainable Food Systems at the UBC Farm from May 13th, 2014 to August 26th 2014 (15 weeks). The location of the trial was in an alley between organic bean production. Previous site history had a cover crop mixture of rye, White clover, and Crimson clover that were incorporated six weeks prior to the beginning of this study. Soil in the experimental plots had pH = 6.0, EC = 0.3 mmhos cm-1, total C = 4%, organic matter = 7.0 %, total nitrogen = 0.2 %, and available phosphorus = 69.3 ppm. 58 4.2.2 Field incubation set-up The cover crops used in this study were fall rye Secale cereale L. cv common, Lana vetch Vicia villosa ssp. dasycarpa Roth, and forage chicory Cichorium intybus L. cv Puna4. Initial tissue nutrient concentrations are presented in Table 22. Six treatments were designed to simulate commonly used cover crop mixtures and included: rye monoculture, chicory monoculture, vetch monoculture, rye:vetch (RV) biculture, and rye:vetch:chicory polyculture (RVC), and a blank soil control. Tissue weights and species ratios were based on literature values, local observed values, and greenhouse observations as described in Table 23. Mixture weights were based on regional production values of ~5000 kg ha-1 rye and ~2000 kg ha-1 Lana vetch. Chicory was based on 927 kg ha-1 as it displayed limited winter growth in this region and this value better reflects yields under forage conditions. Mixture proportions of dry weight were 80% rye : 20% Lana vetch in the biculture and 70% rye : 10% Lana vetch : 20% chicory in the polyculture. Cover crop tissue was harvested from established plots on May 6th 2014, transferred to air-tight plastic bags with a moist paper towel and stored at 4 C prior to burial on May 13th 2014. A sub-sample of each species, in triplicate, was dried at 60 C for 72 hours to calculate percent dry weight. Wet weight and dry weight values are listed (Table 23) to highlight the differences in total wet and dry weights between plant species. A local laboratory, Pacific Soil Analysis analyzed a sub-sample of dried tissue for nutrients. Fiberglass screen, 1 mm mesh-size, was cut to 0.2 m x 0.228 m, folded in half, and sealed along three edges using epoxy glue (Fast set 16, Scigrip). Fresh tissue was clipped into 5 cm long pieces to simulate field disking and placed in 4 The chicory used in this study contracted a spot fungus that potentially limited its growth and affected its decomposition and nutrient release. 59 mesh bags based on dry-weight targets (Table 23). Four PRS Probes (Western Ag, Saskatoon, SK, Canada), two anion and two cation, were placed alternating in each bag and then filled with the appropriate mixture. Sixty grams of soil were then added to ensure tissue contact with the probe membranes. The bags were spaced 15 cm apart in a trench of 15 cm depth with probes on the bottom side. A mesh sheet made of the same material of the bags was placed over each block and backfilled with soil level to the ground. The plots were keep clear of plants by bi-weekly weeding. The soil was then irrigated to field capacity to ensure good contact with the membranes. No additional irrigation was applied throughout the experiment. The PRS-Probes were replaced after one week, washed with deionized water and stored at 4 C. Every second week, fresh probes were placed under the tissue in each bag. Western Ag analyzed the probes for nutrient contents (Western Ag n.d.). At the end of the experiment, remaining residue was sieved through a 2mm screen, dried at 60 C for 72 hours, and weighed. Pacific Soil Analysis analyzed a subsample for total carbon and total nitrogen. Table 22: Initial nutrient concentration where N is Nitrogen, P is Phosphorus and C is Carbon (g element kg-1 dry weight tissue). Treatment C:N ratio N P C Soil 0 0 0 0 Chicory 21.80 18.90 4.30 412.00 Vetch 10.38 42.00 8.90 436.00 Rye 41.32 10.60 3.00 438.00 R:V 25.86 16.92 3.18 437.00 R:V:C 28.08 15.38 3.84 432.70 60 Table 23: Fresh and dry weight of tissue at start of experiment placed in mesh bags (grams). Treatment Dry.Rye Dry.Vetch Dry.Chicory Wet.Rye Wet.Vetch Wet.Chicory Total.Dry Total.Wet Kg.dryweightha-1 Soil 0 0 0 0 0 0 0 0 0 Chicory 0 0 4.24 0 0 29.08 4.24 29.08 927 Vetch 0 9.03 0 0 62.3 0 9.03 62.3 1974 Rye 22.89 0 0 105.48 0 0 22.89 105.48 5002 R:V 18.25 4.59 0 84.11 31.68 0 22.84 115.79 4992 R:V:C 15.92 2.33 4.31 73.38 16.06 29.53 22.56 118.98 4931 4.2.3 Soil temperature and moisture content Soil temperature and moisture content were monitored over the course of the experiment. Moisture content was measured in each block using a Watermark 900M (Irrometer, Riverside CA, USA). Soil temperature was measured using a HOBO data logger (Model H08, Onset, Bourne, MA, USA) with one sensor centered within each block at the same depth as the mesh bags. Precipitation was measured at the nearby Totem Field weather station. 4.2.4 Measurement of nutrient release PRS-probes were used to capture nutrient release from plant residues during decomposition. A full profile of anions and cations was measured for the first week of the experiment while only nitrate and ammonium were measured biweekly for the remainder of time. The probes were placed in the bags for the first week and removed after one week. New probes were placed on every odd week and no probes were present every even week. A control was included in each block for comparison to native soil conditions. 61 4.2.5 Calculation of decomposition Decomposition was based on initial and final carbon tissue concentrations of plant residues to avoid contamination of mineral soil on final weight values. Predicted and actual values for decomposition were calculated using a method designed to detect non-additive effects in mixtures using monoculture rates where a positive value indicate a synergy and negative values indicated negative interactions (McLaren and Turkington 2011) as follows: Proportion decomposed = ( ™????? ™???? ? ™??? ™???? )™????? ™???? Deviation from expected decomposition = ( ™?????? ™???☤?????? ™ ™?????? ? ™ ⌤???? ™???? ? ™ ⌢?? ™?␥ ™ ™ ™?␥?␧?? )( ™ ⌤???? ™???☤?????? ™?␥ ™ ™?????????? ) A t-test was performed comparing the predicted proportion of decomposed to zero. A non-additive effect was concluded if the p-value was less than 0.05. 4.2.6 Nitrogen budget A nitrogen budget was calculated based on the assumption that nitrogen release was uniform over the surface area of each bag. The rate of release per probe area per time was extrapolated to the entire surface of each bag, converted to grams, and multiplied by the total. The total, in grams, was divided by the initial N concentration in the tissue to get an estimate of the percent nitrogen captured by the probes. Nitrogen in the residual tissue was added to the estimated amount captured by the probes and divided by the initial N concentration to get a total 62 N budget. At best, the estimate should account for 50% of the total nitrogen available as probes were only present for 50% of the experimental period. The equations to calculate the nitrogen budget are presented as follows: 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑀𝑒𝑠ℎ 𝑏𝑎𝑔𝑠𝐴𝑟𝑒𝑎 𝑜𝑓 𝑃𝑟𝑜𝑏𝑒 𝑢𝑛𝑖𝑡𝑠 = 0.04567𝑚?10𝑐𝑚? ∗ 1𝑚?10 000 𝑐𝑚? = 45.76 𝑇𝑜𝑡𝑎𝑙 𝑁 𝑓𝑟𝑜𝑚 𝑃𝑅𝑆 𝑝𝑟𝑜𝑏𝑒𝑠 𝜇𝑔 1 𝑔1 000 000 𝜇𝑔 ∗ 45.76 = 𝑇𝑜𝑡𝑎𝑙 𝑁 𝑝𝑒𝑟 𝑏𝑎𝑔 𝑔 𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝐶𝑎𝑝𝑡𝑢𝑟𝑒 = 𝑇𝑜𝑡𝑎𝑙 𝑁 𝑝𝑒𝑟 𝑏𝑎𝑔 𝑔𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑁 𝑖𝑛 𝑝𝑙𝑎𝑛𝑡 𝑡𝑖𝑠𝑠𝑢𝑒 𝑝𝑒𝑟 𝑏𝑎𝑔 𝑔 𝑇𝑜𝑡𝑎𝑙 𝑁 = 𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑐𝑎𝑝𝑡𝑢𝑟𝑒𝑑 𝑔 + 𝑁 𝑟𝑒𝑚𝑎𝑖𝑛𝑖𝑛𝑔 𝑖𝑛 𝑡𝑖𝑠𝑠𝑢𝑒 𝑔 𝑇𝑜𝑡𝑎𝑙 𝐵𝑢𝑑𝑔𝑒𝑡 = 𝑇𝑜𝑡𝑎𝑙 𝑁 (𝑔)𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑁 𝑖𝑛 𝑝𝑙𝑎𝑛𝑡 𝑡𝑖𝑠𝑠𝑢𝑒 𝑝𝑒𝑟 𝑏𝑎𝑔 (𝑔) 4.2.7 Statistical analyses Data analyses were conducted using R version 3.0.2 (R Core Team 2013, Hothorn et al. 2008, Wickham 2009, 2007, 2011, Hlavac 2014, Auguie 2012, Pinheiro et al. 2015, Fox and Weisberg 2011). The model used consisted of a two way ANOVA with Treatment and Block. Dependent variables included initial one-week values for all nutrients and cumulative measurements for nitrate and ammonium for the whole experimental period. Proportion decomposed at the end of the experiment was also a dependent variable. For initial nutrient release, a MANOVA was performed to detect differences among all measured nutrients. Subsequent ANOVA analyses were performed for each nutrient. P values were adjusted using a false discovery rate correction (Benjamini and Hochberg 1995), to avoid inflating Type 1 error and when the adjusted p values for the ANOVAs were less than 0.05, a Tukey Multiple 63 Comparison procedure was completed to detect within group differences. For cumulative ammonium and nitrate release, a linear mixed effect model was used. The initial model included Treatment, Time and Block. Stepwise forward regression was used with Bayesian Information Criterion to find the optimum model. Interactions between Treatment or Time with Block were not considered as it is an assumption of a randomized block design. Package “Linear and Nonlinear Mixed Effects Models” (nlme) was used with a random effect included for Time as well as variance models of VarIdent for Treatment and VarExp for Time to account for unequal variance. A non-linear trend was detected over time and a quadractic term was included for Time. The final model used was: Total N = Treatment + Time + Time2 + Time*Treatment + Time2*Treatment 4.3 Results 4.3.1 Soil moisture and temperature Soil temperature steadily increased over the course of the experiment averaging 21 C (max 36 C, min 12 C) (Figure 14). There was rainfall when probes were not present in Weeks 2,6,8,14 (Figure 15). Soil moisture data collected in situ showed considerable wetting during Weeks 1, 2, 3, 7, 11, 14 indicating that in Weeks 2 and 14 (no probe present) unmonitored nitrogen leaching likely occurred (Figure 14) 64 Figure 14: Soil temperature and water for the full course of the experiment. Start indicates when probes were placed and End represents when probes were removed. The period between End and before the next Start had no probes present. Arrows represent weeks with high rainfall and no probes present (i.e., Week 2 and 14). 20301−Start 1−End 3−Start 3−End 5−Start 5−End 7−Start 7−End 9−Start 9−End 11−Start 11−End 13−Start 13−End 15−Start 15−EndTime of experiment (Weekly start and end dates)Temperature in CelsiusBlockBlock1Block2Block3Soil Temperature010203040501−Start 1−End 3−Start 3−End 5−Start 5−End 7−Start 7−End 9−Start 9−End 11−Start 11−End 13−Start 13−End 15−Start 15−EndTime of experiment (Weekly start and end dates)Soil Water in CentibarsBlockBlock 1Block 2Block 3Soil Water65 Figure 15: Weekly precipitation (mm) data from Totem Field Climate station for the course of the experiment. Bars represent total weekly precipitation. 4.3.2 Total nitrogen A two-way ANOVA found significant main effects for Treatment (p<0.001) and Block (p=0.0327) on cumulative total nitrogen (nitrate + ammonium) at the end of experiment. Post-hoc comparisons demonstrated that the mean value for RVC, RV, and Vetch were different from the soil control, whereas Rye and Chicory were not (Figure 16). RVC had a mean value greater than Rye or Chicory, but not Vetch or RV. RV biculture had a mean greater than Rye, but not greater than Vetch. Vetch monoculture was greater than Rye and Chicory monocultures (Figure 16). There was a marginal difference between RVC and RV (p=0.0795). 051015201 2 3 4 5 6 7 8 9 10 11 12 13 14 15WeekTotal Weekly Precipitation (mm)66 There was a significant main effect for Treatment on total nitrogen per gram of tissue as determined by ANOVA (F(4,10)=15.32), p=<0.001). Post hoc comparisons showed that Chicory and Vetch treatments were the highest, but not different from each other (Figure 17). Vetch was also not different from RVC mixture. Rye, RV and RVC were all not different from each other (Figure 17). When divided by dry weight, chicory and vetch changed their relative ranking compared to the treatments containing rye. Figure 16: Box plot of Tukey post hoc test for cumulative total nitrogen. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ab a bcca c10002000soil chicory rye vetch ryevetch ryevetchchicoryTreatmentSum of Biweekly Total Nitrogen over 15 weeks(µg/10cm2)Mean Total N67 Figure 17: Box plot of Tukey post hoc test for cumulative total nitrogeng-1 dry weight. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. c a abc ab100200300chicory rye vetch ryevetch ryevetchchicoryTreatmentSum of Biweekly Total Nitrogen over 15 weeks(µg/10cm 2/gram of dry tissue)Mean Total N68 4.3.3 Nitrogen release over time There was a significant main effect for Treatment on ammonium release in the first week (ANOVA ((F(5,12)=10.77), p=<0.001))) but not for nitrate ((F(5,12)=2.845) p=0.107). Post hoc comparisons showed that Vetch was greater than all other treatments while other treatments were not different from the soil control or each other (Figure 18). Two patterns emerged for nitrogen release over time where ammonium had a high initial rate of release until Week 3 whereas nitrate had a low initial rate of release that gradually increased over time (Figure 19). Figure 18: Box plot of Tukey post hoc test for ammonium release after one week. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. a a aba a0100200300soil chicory rye vetch ryevetch ryevetchchicoryTreatmentAmmonium release after 1 week (µg/10cm2)Mean Total NH469 Figure 19: A: Mean nitrate (± standard deviation) release over 15 weeks across six treatments. B: Mean ammonium (± standard deviation) release over 15 weeks across six treatments. There were significant main effects for Time, Time2 and Treatment for cumulative total nitrogen release over the 15-week period (Table 24). However, there were also significant interactions between Time and Treatment as well as Time2 and Treatment. These indicate that the rate of release over time changed as treatment level changed. Examining the modeled responses for trends (Figure 20), there were two groups, vetch, RV, RVC and rye, soil and chicory, differentiated by their rate of release relative to the control. While not different from soil, Rye’s mean value was lower than the soil control at several times, and is discussed further chicory rye ryevetchryevetchchicory soil vetch03006009001200030060090012001 3 5 7 9 11 13 15 1 3 5 7 9 11 13 15 1 3 5 7 9 11 13 15Weeks of ExperimentNitrate release(µg/10cm2/1week)Achicory rye ryevetchryevetchchicory soil vetch010020030001002003001 3 5 7 9 11 13 15 1 3 5 7 9 11 13 15 1 3 5 7 9 11 13 15Weeks of ExperimentAmmonium release(µg/10cm2/1week)B70 in Section 5.3.1. Chicory appears lower than soil, but then begins to release N following week 11. Table 24: Regression summary for cumulative total nitrogen. DF F-value p-value Intercept 1 204.46 <0.001 Treatment.D 5 101.38 <0.001 Time 1 132.73 <0.001 Time2 1 9.91 0.025 Treatment.D*Time 5 70.28 <0.001 Treatment.D*Time2 5 6.76 <0.001 loglik -829.37 AIC 1710.75 BIC 1784.49 71 Figure 20: Cumulative nitrogen release over the course of the experiment (15 weeks). Points represent fitted values from the model for each treatment level. 4.3.4 Nitrogen budget Nitrogen budget, as derived from the probes’ recovery rates and residual tissue, is presented (Figure 21). As captured by the probes, chicory was not different from 50%. However, N captured from all other treatments was less than 50% (Figure 21). When recalcitrant tissue N was included, the percentage captured from Chicory, Rye and RVC were all equivalent to 50% (Figure 21). Rye and RVC changed from less than 50% to near equivalent when the residue was added indicating there was considerable N remaining in the tissue. RV had greater than 50% indicating that it captured more N than expected, assuming nitrogen release TotalN = Treatment + Time + Time2 + TreatmentTime + TreatmentTime20250500750100012501500175020002250250027501 3 5 7 9 11 13 15Weeks of ExperimentCumulative Total Nitrogen release(µg/10cm2/1 week)Treatment.Dchicoryryeryevetchryevetchchicorysoilvetch72 was equivalent in off-weeks to weeks with probes present. Vetch had less than 50% indicating that less N was captured than expected suggesting that some N is unaccounted and perhaps released when probes were absent. Figure 21: A: Percentage N captured by PRS probes. B: Percentage captured by probes with remaining residue N. Asterisk indicates two-tailed t-test different from 50% with p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. 4.3.5 Percent decomposition There was a significant main effect for Treatment on percent decomposition as determined by ANOVA (F(4,10)=8.1186), p=<0.01). Post hoc analysis demonstrated that Chicory, RVC, Rye, and Vetch had similar percentage of tissue decomposed as well as RVC, * *2040506080100chicory rye vetch ryevetch ryevetchchicoryTreatmentTotal Percentage Captured with Remaining Plant NTotal Percent Captured with Remaining Plant NA* ** *2040506080100chicory rye vetch ryevetch ryevetchchicoryTreatmentPercentage of Initial Captured from ProbesMean Percentage CapturedB73 Rye, Vetch and RV (Figure 22). Chicory had the highest decomposition at 100% with RV mixture the lowest at 75%. Non-additive effects on decomposition were present for RV, p=0.04979 but not for RVC, p=0.6406 (Figure 23). RV had a negative non-additive effect indicating that the combination reduced decomposition compared to their monoculture counterparts (Figure 23). Figure 22: Box plot of Tukey post hoc test for percentage decomposed. Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. b ab ab a ab708090100chicory rye vetch ryevetch ryevetchchicoryTreatmentPercentage DecomposedMean Percentage Decomposed74 Figure 23: Percentage of deviation from expected decomposed tissue compared to zero; Asterisk indicates p<0.05. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. 4.3.6 Short term nutrient release The results for nutrient release after one week were mixed with no difference for any element except for sulfur and phosphorus, which displayed large differences among treatments. There were no treatment effects displayed in a one-way ANOVA for calcium, magnesium, potassium, iron, boron or aluminum (p>0.05). However, there was a significant main effect for treatment on phosphorus release (F(5,12)=6.674), p=<0.01). Post hoc comparisons showed that only vetch and RVC were significantly different from the soil control for P release (Figure 24). Vetch was also significantly different from rye for P (Figure 24). RVC, Chicory, and RV were similar while Chicory and Vetch were similar for P (Figure 24). There was also a main effect for treatment for sulfur release (F(5,12)=18.21), p=<0.001). Post hoc comparisons showed that Chicory had a greater S release compared to all others treatments while Chicory and RVC were *−15−10−50ryevetch ryevetchchicoryTreatmentDeviation from Expected Decomposition (%)Mean Deviation from Expected75 the only treatments different from the soil control (Figure 25). The high S from Chicory was a result of a seed coating discovered post experiment. Figure 24: Box plot of Tukey post hoc test for phosphorus release (phosphate). Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. ac ab acca bc024soil chicory rye vetch ryevetch ryevetchchicoryTreatmentPhosphorus release after 1 week (µg/10cm 2)Mean Total P76 Figure 25: Box plot of Tukey post hoc test for sulfur release (sulphate). Different letters indicated a difference at p<0.05. Mean values are presented as red points. The vertical line represents range of data, the middle line represents the median and the outline of the box represents the upper and lower quartile respectively. c ab ababa b102030soil chicory rye vetch ryevetch ryevetchchicoryTreatmentSulfur release after 1 week (µg/10cm 2)Mean Total S77 4.4 Discussion One strategy to achieve the sustainable intensification of agriculture is to increase the functional diversity of plants in the farm system. Appling the ecological theory of functional groups to cover crop selection can improve our understanding of the functional roles of different plants in ecosystem functions that then provide specific ES to farmers. In my system, increasing the functional diversity changed the magnitude, timing, and range of nutrients released from aboveground tissue as well the magnitude of its decomposition. These results support previous research on grass:legume bicultures on nutrient release and decomposition plus add a new understanding on the potential of using forbs in a cover crop mixture. Using more functionally diverse mixtures positively affected nitrogen release in both magnitude and timing. More diverse mixtures released comparable amounts of nitrogen while also shifting release times allowing for better alignment to crop production. In the present study, cumulative total nitrogen was comparable among the best monoculture, vetch and all mixtures RV and RVC. Furthermore, compared to the vetch monoculture, both mixtures delayed nitrogen release early in the season to better synchronize with crops that need nitrogen later in the season or past the point where no crops are present (i.e., during the first week after incorporation). There was comparable N release even when the vetch (high N) residue proportion was decreased from 20% to 10% and chicory was included at 20% of the total in the RVC mixture. There is a trend in the data where the RVC polyculture had a greater increase in N release after the high rainfall in week 11 compared to the RV biculture. This reflects the pattern of N-release in chicory monoculture where it was slow to release N relative to vetch, but released comparable nitrogen per g of dry tissue. Including forbs in grass:legume mixtures offers another option to modulate nitrogen release by delaying release until later in the season with potentially less N 78 immobilization. Bicultures of a grass and legume have been used previously to reduce the immobilization of nitrogen from high carbon grass residue and slow the release of nitrogen by legume residues (Odhiambo and Bomke 2000, Ranells and Wagger 1996, Rosecrance et al. 2000). The mechanism appears to be a difference in the type of available nitrogen (i.e., ammonium vs nitrate) released and interactions with the microbial community. Prominent in the vetch monocultures, the quick release of ammonium and subsequent conversion to NO3- may be described as rapid nitrification. Rapid nitrification has been reported with hairy vetch and sweet clover (Melilotus officinalis (L.) Pall.) supporting the findings that the initial release from legume cover crops is ammonium followed by nitrification and conversion to nitrate (Bair et al. 2008). A RV or RVC mixture can reduce the initial flush of ammonium and slow its release to better align with crop uptake. It has been shown that chicory is capable of scavenging available nitrogen (Thorup-Kristensen 2006), and could fill part of this role in a mixture. In locations where chicory can be planted early enough to over-winter, increasing functional diversity by replacing some proportion of rye for chicory can help maintain N scavenging without the potential immobilization of nitrogen from pure rye residues. The degree of functional group diversity of a cover crop mixture affects the nitrogen availability to subsequent crops. In monocultures, rye and chicory nitrogen mineralized slowly compared to vetch which mineralized much more quickly. There was also a trend that suggests that rye monocultures may have immobilized nitrogen as it decomposed throughout the season while chicory only immobilized N early in the season and then released it later as residues further decomposed. Further investigation into nitrogen mineralization of above ground chicory tissue is of interest as it showed comparable total nitrogen release per g of dry tissue to vetch and higher than rye while its C:N ratio was in-between. During cover crop residue decomposition, 79 two pools of nitrogen have been suggested, one that slowly mineralizes and one that rapidly mineralizes (Ranells and Wagger 1996). The initial N concentration for rye per dry weight (10.6 g kg-1) was below critical thresholds for net release reported by others of 14 g kg-1 (Odhiambo and Bomke 2000), 16 g kg-1 (Lawson et al. 2013) and 18 g kg-1 (Kuo et al. 1996) whereas chicory was slightly above (18.9 g kg-1) these thresholds suggesting that chicory is more likely to provide a net release. The difference in dry weight used in the treatments can explain the comparable release despite differences in N concentration in that chicory had less tissue, but greater release per g of dry weight. However, all mixtures showed a net release of nitrogen above the critical threshold for this region supporting their use as net providers of nitrogen (Odhiambo and Bomke 2000). A caveat to consider is that chicory’s shoot architecture may have impacted mineralization rates as it did not produce stems, only rosette leaves whereas rye produced stems and leaves. Quemada and Carbera (1995) reported different cover crop N mineralization rates when comparing stems and leaves of various crops. Nevertheless, there is evidence that low diversity or monoculture cover crops can provide a benefit to farmers seeking either a quick release of nitrogen, as in vetch, or a slow release, as in rye or chicory. Availability of phosphorus, but no other nutrients, was also impacted by cover crop diversity. Specifically, the release pattern of P suggests a role of legumes as nutrient mobilizers. Lana vetch was the only monoculture that released more P than the soil control supporting a legume’s role in P release in addition to the aforementioned N release. Chicory, which has been used as a source of micronutrients and minerals in forage settings, contrary to expectation, did not provide a release of any minerals greater than the soil during the 1 week measured. The release of P from Lana vetch residues supports previous findings that legumes can increase mobilization of soil phosphorus and that its residues can be a source of available phosphorus for 80 subsequent crops (Arrobas et al. 2015, Kamh et al. 1999, Dube et al. 2014). The present study did not identify whether Lana vetch mobilized inaccessible phosphorus or simply preferentially accessed available soil phosphorus in greater quantity than chicory or rye. However, Lana vetch tissue did have higher phosphorus content per dry weight compared to chicory or rye. The lack of difference for minerals or micronutrients does not align with results from forage studies where chicory was found to be a source of minerals different from perennial rye grass (grass) and white clover (legume) (Harrington et al. 2006). Furthermore, forbs generally have been shown to be a source of micro and macro minerals in forage swaths compared to grasses and legumes (Pirhofer-Walzl et al. 2011). The short duration of nutrient measurement (i.e., 1 week) in the present study likely does not reflect the total release during the field season and further investigation is warranted. In conclusion, monoculture Lana vetch played the strongest role for non-nitrogen nutrient release with comparable benefits in a mixture of RVC. Functionally diversity of the cover crop mixture also affected the decomposition of residues. However, mixtures had either a negative or no synergistic affect relative to their monoculture counterpart’s decomposition. RV had negative non-additive effects whereas the RVC mixture did not. The negative non-additive effect of mixing rye and vetch could induce a higher proportion of tissue to become recalcitrant compared to what was expected compared to monocultures. Ruffo et al. found no evidence for positive or negative non-additive effects from decomposition of a rye:vetch biculture but did find rye slower to decompose than vetch (2003). Plant ecology research on the effect of functional group diversity using similar methods found both positive and negative non-additive effects on decomposition leading to contrasting effects in different years suggesting that these effects are real and not experimental error (McLaren and Turkington 2011). There are numerous mechanisms that have been suggested for biodiversity’s 81 effect on decomposition and elucidating any one factor is beyond the scope of this study (Hättenschwiler et al. 2005). However, one mechanism that supports my observations is ‘microenvironment effects’. While these are often associated with positive effects (Hättenschwiler et al. 2005), they may explain my observations of negative non-additive effects. The heterogeneity of the mixture to include fibrous, hard to decompose rye tissue with the non-fibrous, high nitrogen vetch residues could create patchy microenvironments that locally limit decomposition. Another option to explain the reduced decomposition in the RV biculture is humification. In forest soils, increasing nitrogen has been shown to slow decay and increase the formation of humus (Prescott 2010). It is possible that the increased nitrogen from the vetch tissue created conditions that increased humus rather than further decomposed the tissue. If this result can be replicated, it suggests that rye:vetch biculture could increase soil organic matter potentially storing greater carbon in the soil and improving soil fertility. There may also be inherent differences in the tissues used in this study such as the carbohydrate to lignin ratio that could better explain the decomposition results (Wagger 1989b). Nevertheless, the application of the functional group framework appears to have captured differences in the aboveground plants tissue that lead to differential decomposition. Decomposition of aboveground tissue from each functional group in monoculture showed differences in the proportion of remaining tissue remaining after 15 weeks. There was similar percent decomposition for all monocultures. While not different at p<0.05, chicory appears to be a high-quality litter with very high decomposition. Chicory is seen as a highly palatable and digestible forage crop that may relate similarly to its decomposition considering similar enzymes, such as those involved in the digestion of cellulose, are at work in both cow rumen and in the field. It presents a cover crop option when no persistent residues are desired but as such, 82 not a strong soil builder. At termination, the similarity in proportion remaining tissue between rye and vetch likely did not capture the change over time of release and more likely reflects an end point as others have found increased rates of decomposition in legumes over grasses (Murungu et al. 2010, Wagger 1989a). To better understand the pattern of decomposition, the lignin to N ratio and polyphenol content should be measured as they are shown to be important indicators of decomposition (Prescott 2010). In the context of the present study, there was limited detectable difference between monoculture crops and their decomposition. The results of this study improve our understanding of mixtures of cover crops on nutrient release from decomposing above-ground tissue by demonstrating that there are inherent differences in functional groups and their mixtures. Mixtures were shown, with the exception of decomposition and sulfur, to provide comparable magnitude of ES to the best monoculture while providing a range of ES better than any one monoculture. The addition of chicory to the traditional rye:vetch mixture was shown to be comparable with a marginal increase in phosphorus and a change in decomposition. The functional group framework provided an a priori means to group a suite of functional traits linked to a detectable change in ES provision. Increasing the functional group diversity of cover crops is a meaningful strategy to reduce the reliance on external inputs and move towards the sustainable intensification of agriculture. 83 Chapter 5: Conclusion 5.1 Introduction The sustainable intensification of agriculture can be helped through increasing the functional diversity of plants to promote a range of Ecosystem Services beyond the provision of food. Functionally diverse mixtures of cover crops can offer supporting and regulating ES that reduce the need for inputs, improve soil fertility, and suppress weeds while regulating greenhouse gas emissions and nutrient cycling. The functional group framework of using a grass, legume and forb has demonstrated differences in the magnitude and range of ES provided depending on both the density and composition of cover crop mixtures. This is promising as it provides farmers with additional options to best meet their needs and goals. Cover crop mixtures are an excellent option to increase biodiversity in agriculture to help achieve high yields and environmental integrity. 5.2 General conclusions From all experiments, there is insufficient evidence to reject the null hypothesis that increasing functional group diversity will not provide an increased level of ES compared to biculture or monocultures of each species. The results demonstrate that a mixture can provide ES at a magnitude comparable to the best monoculture and expand the range of ES that are not found in any one monoculture. Consider for example that while rye was the dominant producer of biomass in the field and in the greenhouse, at a comparable levels to the biculture of rye:vetch in the field and rye:chicory in the greenhouse, a pure stand of rye was shown to produce less leaf area than a rye:chicory biculture and less available nitrogen than a monoculture of vetch, or RV 84 biculture or RVC polyculture. Therefore, viewing ES provision over the range of services supports the management decision to increase the functional diversity of cover crops. The evidence collected also provides insight into the relative role of these cover crops in monoculture. For carbon fixation, biomass, and weed suppression, rye is the best choice in our environment. For the provision of nitrogen and phosphorus release from aboveground residues, Lana vetch is the best choice. For the decomposition of residues and production of leaf area, chicory is the best choice. Farmers seeking to manage for any one of these factors specifically are best to plant a monoculture stand of the aforementioned crops. The results of this thesis also confirm previous findings on the functional role of rye and vetch, as well as biculture mixtures of the two. It is well established that there are synergies between rye and vetch for a variety of ecosystem processes. These can be explained by differences in growth form and tissue nutrient concentrations. The vigorous upright growth of rye can suppress weeds coupled with the trellising ability of vetch to create a synergy where rye stems allow vetch to gain access to light higher in the canopy. This finding was supported in this study through field observations as well as in the inverse yield density models supporting evidence that these plants do not compete with each other. In addition, the tissue concentrations of rye are high in carbon and the tissues of vetch are high in nitrogen. These differences allow mixtures to display improved N release compared to rye monocultures. This finding was supported in the present study in Chapter 4 where the bicultures and polycultures provided more available N than rye monoculture. There were also results that differed from previous research. The limited growth of the rye:vetch biculture in the greenhouse presented a situation where rye:vetch was not the best biculture for biomass and instead, the rye:chicory biculture produced the most biomass. Although different from field conditions, the greenhouse study demonstrates 85 that different functional groups can dominate plant biomass production, depending on conditions (e.g., high nitrogen fertilization). In conclusion, the known benefits of a grass:legume biculture were affirmed while further investigation into a grass:forb mixture is supported. This thesis adds new knowledge on the use of chicory as a cover crop as well as in mixtures. The grass:forb biculture was shown to have high biomass and leaf area. This is a beneficial combination to maximize light interception, soil cover and weed suppression. This combination also has potential to improve on the grass:legume biculture under certain conditions such as high soil nitrogen or when a legume is not required. While the agronomic use of chicory as a fall cover crop at the UBC Farm was not successful, there is information supporting its application. With an earlier planting date and modified cultivar selection (e.g., cvs. Chico or Forager), the increases in leaf area observed in the greenhouse could be realized in the field. Chicory also decomposed in its entirety. Considering how rye scavenges nitrogen but produces copious amounts of recalcitrant tissue, chicory may offer an option to achieve the same nitrogen scavenging while producing easily decomposed residues. Chicory was also capable of releasing comparable N (per kg of dry tissue) to Lana vetch suggesting that it can be a strong source of nitrogen to subsequent crops. Therefore, both the chicory monoculture and the rye:chicory biculture present new options for farmers seeking to manage their fields for ES provision. 5.3 Recommendations 5.3.1 Field production at the UBC Farm My results offer direction for future cover crop research and use at the UBC Farm. The high weed suppression and low nitrogen availability of rye could be used as a tool to reduce weed pressure during summer production. Purslane (Portulaca oleracea L.) is the main weed in 86 the alleys requiring considerable farm labor and resources to manage (Personal Communication, Tim Carter Farm Manager). To address this, the UBC Farm should consider adopting a combined strategy of cover crop mixtures and strip tillage. Strip tillage is a type of conservation tillage process where the section of the field for crop production is tilled with the alley left undisturbed. A winter cover plan that uses a high density rye monoculture in the alleys and a grass:legume polyculture where production beds will be could produce the needed ES. Removing legumes from the cover crop mixtures in the alleys could suppress weeds through direct competition and reduced available nitrogen. Also, conservation tillage combined with cover crop mixtures could be an excellent option in the sandy soils at the UBC Farm. Chicory presents an interesting option that requires further agronomic fine-tuning to be a viable fall cover crop in line with field production at the UBC Farm. As mentioned, the cultivar ‘Puna’ does not appear viable in current farm production as a fall cover crop with a mid-September planting date. If ‘Puna’ is to be used at the UBC, it should be primarily as a summer cover crop or forage. 5.4 Directions for future research This thesis provides ample background theory and methods to further study increasing biodiversity in agriculture through the use of cover crop mixtures. Theoretically, the functional group framework proved to be useful in delineating a posteriori differences in ecosystem services based on a priori categories of grass, legume and forb. Further design and optimization of this functional group framework with a wider range of species is warranted. There are several specific areas for future inquiry. The methods used to measure carbon flux should be repeated with a larger sample size and extended sampling dates to obtain a more accurate carbon budget. 87 This thesis provides a clear, easy-to-use script in the R environment for another student to complete this work (Appendix B). The methods used to measure nutrient cycling should be expanded to measure P and K timed with crop uptake. There is promising evidence that Lana vetch could provide additional P to subsequent crops that could help crop production. Considering the statistical methods used, this author feels that the focus on a variety of densities was not as important as replication of the mixture combinations. Future research should focus on site and mixture replication rather than density per se. More experimental units may be better to improve the statistical power than a range of densities. In conclusion, in both the theory and methods used, there is adequate knowledge to continue the history of cover crop research and intercropping started by Dr. Bomke and Dr. Jolliffe and continued by the Riseman lab. Specific projects that could be designed to add to the findings in this thesis should be focused on testing the mixtures at other sites including those in the upper Fraser valley and Delta. A significant limitation of this study was that the soil and conditions of the UBC Farm are largely different from those in nearby agricultural areas. Furthermore, the presence of grazing waterfowl in Delta presents a unique challenge to be addressed by cover crops. Chicory with its deep taproot may be able to help improve soil physical properties, such as soil compaction in clay soils. Whereas my field study at the UBC Farm had a mid-September planting, previous work by Bomke had a planting date of mid-August. A mid-August planting date could allow chicory to form adequate growth to resist waterfowl grazing as it has been bred for the purpose of forage. This author strongly encourages a follow up study using Forage Chicory, Cichorium intybus L., as a cover crop in the Delta region. There are also specific projects that could be used to better align nitrogen release with the crop uptake. This thesis provided evidence that species composition and dry matter quantity of 88 aboveground cover crops residues affect nitrogen release. Agronomic research into the period of crop nitrogen need and the in situ nitrogen release from cover crops under exact field conditions is warranted. There is not sufficient data to inform farm management on the status of available nitrogen for subsequent crops based on current UBC Farm sowing and tillage practices. PRS-probe technology could be used to monitor total N release during the production season to gain a better understanding of N release under the irrigation and tillage practices used. This N release research is warranted as it can improve yields and provide insight into the impacts of farm inputs such as manure or fish-based fertilizer. As organic amendments are costly, this research could lead to cost savings if it can be determined whether 1) cover crops can meet production crop nutritional needs in the absence of additional inputs and, 2) cover crops’ N release aligns with crop needs or is leached to the subsoil. In closing, there is still much to learn to fine-tune cover crop production at the UBC Farm to increase yields and reduce inputs and meet the goals of sustainable intensification. 5.5 Conclusion This study offers insight into the general role of functional group diversity and cover crop mixtures designed increase the range and magnitude of ES provision. These results provide evidence that rye, a C3 grass, will be the dominant factor for photosynthesis in a mixture of a Lana vetch, a legume, and chicory, a perennial forb. However, there are other benefits of the mixture when considering the full suite of ecosystem functions. Farmers may be less concerned about total biomass production from Lana vetch and more concerned about nitrogen fixation. It might also be useful in some situations to have both a high total leaf area and high biomass production, found in the bicultures of rye and chicory, when total soil cover is desired. In 89 addition, focusing on only one ES limits the benefits provided by a more diverse cover crops mixtures. Viewing the results of each response variable in isolation limits the potential multifunctional benefits provided by intercropping functional diverse cover crops. 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Density of plants Reference Treatments 1 V1 2 (Pirl 1969) V2 3 (Pirl 1969) R3, C3 4 (Pirl 1969) R3V1, C3V1 5 (Pirl 1969) R3V2, C3V2 6 (Pirl 1969) R6, C6 7 (Pirl 1969) R6V1, C6V1 8 (Pirl 1969) R6V2, C6V2 9 (Pirl 1969) R6C3, C3R6 10 (Pirl 1969) R6C3V1, R3C6V1 11 (Graham et al. 1998) R6C3V2, R3C6V2 12 (Fodor 2000) R6C6 13 (Fodor 2003) R6C6V1 14 (Goldberg 1971) R6C6V2 101 Appendix B R-script for CO2 Flux analysis Data<-data.frame((read.table("filepath.dat", header=FALSE, sep= ","))) colnames(Data) <- c("120 L","Year_RTM L","Day_RTM L","Time.mins","secs","BatteryV H","CO2_V H", "CO2_RAW H","CO2.ppm.1","T_HMP H","RH_HMP H","PanelT H","SoilT_1 H","PAR") # rename columns ############# Data$Mins=Data$Time.mins*60 # convert minutes to seconds Data$Time=Data$Mins+Data$secs # add seconds #store data in csv file write.csv(Data, file="filepath.csv") ########## #Calculating the slope of carbon flux for one plant treatment #plant2 plant2 <- subset(Data, Data$Time>56480 & Data$Time<56700) # using your recorded time separate out where your flux measurement was taken plot(plant2$Time, plant2$CO2.ppm.1, main="Plant 2", xlab="Seconds", ylab="C02 Concentration(ppm)", ) # plot the data plant2$Time <- plant2$Time-plant2$Time[1] #gives you an x beginning with zero plant2.slope <- lm(plant2$CO2.ppm.1 ~ plant2$Time + I(plant2$Time^2)) # save the slope of the line plant2C <- round(coef(plant2.slope), 5) # provides slope value mtext(bquote(y == .(plant2C[3])*x^2+ .(plant2C[2])*x + .(plant2C[1])), adj=1,padj=0) # adds text to your plot plant2Q <- 2*plant2.slope$coefficients[3]*(5.3)+plant2.slope$coefficients[2] # derivative at 5 seconds to get true slope of quadratic line plot(plant2$Time, plant2$CO2.ppm.1, main="Plant 2", xlab="Seconds", ylab="C02 Concentration(ppm)", ) # replot your data points(plant2$Time, predict(plant2.slope), col="red") # provides the fitted line mtext(bquote(y == .(plant2C[3])*x^2+ .(plant2C[2])*x + .(plant2C[1])), adj=1,padj=0) # provides the formula #Resp2 Resp2 <- subset(Data, Data$Time>56880 & Data$Time<56990) plot(Resp2$Time, Resp2$CO2.ppm.1, main="Respiration 2", xlab="Seconds", ylab="C02 Concentration(ppm)") Resp2$Time <- Resp2$Time-Resp2$Time[1] Resp2.slope <- lm(Resp2$CO2.ppm.1 ~ Resp2$Time) Resp2C <- round(coef(Resp2.slope), 5) plot(Resp2$Time, Resp2$CO2.ppm.1, main="Respiration 2t", xlab="Seconds", ylab="C02 Concentration(ppm)") abline(Resp2.slope) mtext(bquote(y == .(Resp2C[2])*x + .(Resp2C[1])), adj=1,padj=0)
Thesis/Dissertation
eng
Plant Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivs 2.5 Canada
Graduate
Functional diversity in cover crops and ecosystem services
Text