Relative Performance of Ontario Soybean Cultivars and Differential Selection of Early Soybean Breeding Lines in Organic Versus Conventional Production Systems

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Relative Performance of Ontario Soybean Cultivars and Differential Selection of Early Soybean Breeding Lines in Organic versus Conventional Production Systems

Torin Dylan Boyle

A Thesis Presented to The University of Guelph

In partial fulfillment of requirements for the degree of Masters of Science in Plant Agriculture

Guelph, Ontario, Canada

ABSTRACT

RELATIVE PERFORMANCE OF ONTARIO SOYBEAN CULTIVARS AND DIFFERENTIAL SELECTION OF EARLY SOYBEAN BREEDING LINES IN ORGANIC VERSUS CONVENTIONAL PRODUCTION SYSTEMS

Torin Dylan Boyle Advisor: University of Guelph, 2016 Professor Istvan Rajcan

Organic production systems differ from the conventional for insect pest, weed, disease, and nutrient management. The objective of this thesis was to determine if soybean cultivars and breeding lines responded differently in organic vs. conventional production systems. An

F5/F6 generation breeding trial from two bi-parental soybean crosses and a replicated cultivar trials were conducted on an organic farm and conventional research station in the maturity group 0 zone of Southern Ontario. When thirty and thirty-three cultivars were tested in 2014 and 2015, respectively, significant crossover effects between environments indicated a differential cultivar performance between the production systems. GxT Biplots showed that the traits related to resource acquisition were associated with yield in the organic environment rather than nutrient re-mobilization. It was concluded that setting up a separate breeding program targeting an organic production system may lead to the development of a greater number of high yielding organic-adapted cultivars.

Dedication

I dedicate this thesis to the international organic agriculture movement. May we never forget the reasons why we work to foster a brighter future for healthy farms, food, people and communities.

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Acknowledgements Firstly, I would like to acknowledge my advisor Dr. Istvan Rajcan who has been a constant source of guidance, knowledge and encouragement, truly everything an advisor should be. I would also like to acknowledge and thank Dr. Ralph Martin for his enthusiasm and cooperation around the project as well. Dr. Rene Van Acker your input and insight has been a constant source of enlightenment, thank you.

The soybean crew will always hold a special place in my heart Colbey Templeman,

Chris Grainger, Lin Lao, Yesenia Salazar, Mei Wu, Martha Jimenez, Tim Currie, and the late

Wade Montminy, thank you all for the constant support and help.

My fellow graduate students, thank you for all the collaborations in classes and good times at the grad lounge. I received advice from numerous professors in the department and beyond about my project’s content and direction in both the planning and execution phase, thank you all.

My family, thank you for constantly inspiring me throughout my education and beyond you have been the tightly knit support network that I could always rely upon. Your contributions as a whole have led me to where I am today and will continue to shape who I am into the future. Thank you: Laura, Doug, Pat and Calan Boyle.

Finally, thank you Guelph and all the people who I have met and become friends with here. There are not a lot of places where social and environmental consciousness met a spirit of progress and entrepreneurism like they do in Guelph. Thank you for the everyday inspirations.

Table of Contents

CHAPTER 1: LITERATURE REVIEW: DEVELOPING AN ORGANIC SOYBEAN IDEOTYPE ...... 1

1.1: INTRODUCTION ...... 2

1.2: ORGANIC CROP BREEDING ...... 5

1.3: ORGANIC SOYBEAN IDEOTYPE DEVELOPMENT ...... 9

1.3.1: Biologically active organic matter fractions and nutrient dynamics ...... 9

1.3.2: Phosphorus deficits and use efficiency ...... 11

1.3.3: Potassium deficits and use efficiency...... 17

1.3.4: Symbiotic relationships and rhizosphere exudates ...... 19

1.3.5: Weed management, competition and suppressive ability ...... 25

1.3.6: Protein content, quality and processing factors ...... 32

1.3.7: Implications for an organic soybean ideotype ...... 35

1.4: HYPOTHESIS AND OBJECTIVES ...... 37

CHAPTER 2: ASSESSMENT OF MG 0 SOYBEAN (GLYCINE MAX L.) CULTIVARS IN BOTH AN ORGANIC AND CONVENTIONAL ENVIRONMENT FOR EARLY SEASON CANOPY DEVELOPMENT, ROOT MORPHOLOGY, NUTRIENT USE EFFICIENCY AND OTHER AGRONOMIC TRAITS ...... 38

2.1: ABSTRACT ...... 39

2.2: INTRODUCTION ...... 39

2.3: MATERIALS AND METHODS ...... 45

2.4- RESULTS ...... 55

2.4.1- Canopy Development Traits ...... 55

2.4.2- Root Morphology and Nodule Mass ...... 56

2.4.3- Nutrient Content and Use Efficiency ...... 56

2.4.4- Agronomic Traits ...... 57

2.4.5: Relationships between traits of interest ...... 58

2.5: DISCUSSION ...... 60

2.5.1: Canopy Development Traits ...... 60

2.5.2: Root Morphology and Nodule Mass ...... 63

2.5.3: Nutrient Content and Use Efficiency ...... 67

2.5.4: Agronomic Traits ...... 68

2.6: CONCLUSION ...... 72

CHAPTER 3: ASSESSMENT OF EARLY GENERATION SELECTION IN ORGANIC VS CONVENTIONAL ENVIRONMENTS USING TWO DIFFERENT SOYBEAN POPULATIONS ...... 93

3.1: ABSTRACT ...... 94

3.2: INTRODUCTION ...... 95

3.3: MATERIALS AND METHODS ...... 98

3.4: RESULTS ...... 104

3.4.1: F5 yield data ...... 104

3.4.2: F6 yield data ...... 105

3.4.3: SSR Marker Data ...... 108

3.5: DISCUSSION ...... 109

3.6: CONCLUSION ...... 116

GENERAL DISCUSSION AND CONCLUSIONS ...... 117

REFERENCES ...... 135

List of Tables Table 2.1: List of genotypes in the replicated yield trials including their genotype number and release year……………………………………………………………………………………75 Table 2.2: Results of soil nutrient tests including Potassium, Available P, Organic P, Fixed P, pH and Organic Matter from each location year and precipitation from March 1st to August31st of each growing season……………………………………………………………………………76 Table 2.3: P-values for both fixed and random effects of an ANOVAs (Analysis of Variance) for the difference between 30 Ontario MG 0 soybean cultivars grown in Moorefield or Elora 2014 for AUCPC, Root Length, Nodule Mass, NUE, DTM, or Oil Content. Only traits with significant genotype effects are reported for each location………………………………………………….77 Table 2.4: P-values for both fixed and random effects of ANOVAs (Analysis of Variance) for the difference between 33 Ontario MG 0 soybean cultivars combined over both Moorefield or Elora in 2014 for ASRV1C, ASRV3C, ASRV5C, N content, P content, PUE, KUE, NutUE, yield, plant height, lodging, protein yield, oil yield. Only traits with significant genotype effects are reported…………………………………………………………………………………………78 Table 2.5: P-values for both fixed and random effects of an ANOVAs (Analysis of Variance) for the difference between 33 Ontario MG 0 soybean cultivars grown in either Moorefield or Elora 2015 for root length, NUE, PUE, NutUE, yield, protein yield, oil yield and days to maturity. Only traits with significant genotype effects are reported for each location………………….....79 Table 2.6: P-values for both fixed and random effects of ANOVAs (Analysis of Variance) for the difference between 33 Ontario MG 0 soybean cultivars combined over both Moorefield or Elora in 2015 for N content, P content, K content, KUE and height. Only traits with significant genotype effects are reported…………………………………………………………………….80 Table 2.7: P-values for both fixed and random effects of an ANOVAs (Analysis of Variance) for the difference between 30 and 33 Ontario MG 0 soybean cultivars combined across Moorefield 2014 or Moorefield 2015, respectively, for root surface area and oil content…………………...80 Table 2.8: P-values for both fixed and random effects of ANOVAs (Analysis of Variance) for the difference between 30 and 33 Ontario MG 0 soybean cultivars combined over both Moorefield or Elora in 2014 and 2015, respectively. Only traits with significant genotype effects are reported including protein content and 100 seed weight……………………….………………………….81 Table 2.9: LS means for traits of interest for each location-year included in the trials, means with different letters following them are significantly’ different from each other at α=0.05 according to a Bonferroni adjusted LSD test………………………………………………………………..82 Table 2.10: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Moorefield 2014. Only significant correlation was reported…...83 Table 2.11: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Elora 2014. Only significant correlation where reported……….84

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Table 2.12: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Moorefield 2015. Only significant correlation where reported…85 Table 2.13: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Elora 2015. Only significant correlation where reported……….86 Table 3.1: P-values for the combined ANOVAs for the difference in yield between the genotypes and selection groups from populations IR0551 and IR0622 in 2014 from completely randomized un-replicated designs…………………………………………………………………………...124 Table 3.2: P-values for the ANOVAs for the difference in yield between the selection groups and the contrast from populations IR0551 and IR0622 in both Moorefield and Elora in 2014 from completely randomized un-replicated designs………………………………………………….124 Table 3.3: Average F5 yields for single row plots IR-055(OAC Sunny X S05-T6) and IR-062 (OAC Calypso x DH618) for all selections, different selection groups (organic selections, conventional selections and those selected in both sites) and BLUEs1 for only those selected in each location and the location average…………………………………………………………125 Table 3.4: Results of initial visual selections carried out in the F5 populations IR-055 (OAC Sunny X S05-T6) IR-062 (OAC Calypso X DH618) in 2014………………………………….125 Table 3.5: P-values for the combined ANOVAs for the difference in yield between the genotypes and selection groups from populations IR0551 and IR0622 in 2014 from completely randomized un-replicated designs………………………………………………………………………...…126 Table 3.6: P-values for the ANOVAs for the difference in yield between the selection groups and the contrasts from populations IR0551 and IR0622 in both Moorefield and Elora in 2015 from completely randomized un-replicated designs……………………………………………….....126 Table 3.7: Average F6 yields for non-replicated yield trials IR-055(OAC Sunny X S05-T6) and IR-062 (OAC Calypso x DH618) for the check cultivars, different selection groups (organic selections, conventional selections and those selected in both sites) and BLUEs1 for only those selected in each location and the average of all the selections…………………………………127 Table 3.8: Details of F6 selections based upon yield carried out in the F6 populations IR-055 (OAC Sunny X S05-T6) and IR-062 (OAC Calypso X DH618) in 2015……………………...128

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List of Figures Figure 2.1: 1) Visual of the camera set up in plot, 2) Cropped photo of V1 growth stage, 3) Cropped photo of V3 growth stage, 4) Cropped photo of V5 growth stage……………………..87 Figure 2.2: 1) Root Core sample in bag defrosting after being taken out of cold storage at -170C, 2) washing dirt off the root sample using a garden hose and screen, 3) Final root sample collected in a sieve before being placed in 50% ethanol for storage, 4) Final sample after being scanned in WinRhizo……………………………………………………………………………………...…88 Figure 2.3: Green leaf area (GLA) derived from digital image analysis plotted against leaf area index measured using a tabletop scanner with the linear regression line of GLA=183.9+0.51(LAI) and correlation coefficient of 0.967……………………………………89 Figure 2.4: Yield rank crossover effects and Kendall Tau-b Rank Correlation coefficient in 2014 between the organic location in Moorefield, ON and the Conventional location in Elora, ON………………………………………………………………………………………………..90 Figure 2.5: Yield rank crossover effects and Kendall Tau-b Rank Correlation coefficient in 2015 between the organic location in Moorefield, ON and the Conventional location in Elora, ON………………………………………………………………………………………………..91 Figure 2.6: GxT Biplot for 2014 and 2015 combined trait data selected using factor analysis in Elora ON constructed using GGE Biplot……………………………………………………...... 92 Figure 2.7: GxT Biplot for 2014 and 2015 combined trait data selected using factor analysis in Moorefield ON constructed using GGE Biplot……………………………………………….....93 Figure 3.1: Yield distribution of population IR055(OAC Sunny X S05-T6) F5 selections grown F6 yield trials in the organic location in Moorefield, ON in 2014…………………………..…129 Figure 3.2: Yield distribution of population IR055(OAC Sunny X S05-T6) F5 selections grown in F6 yield trials in the organic location in Elora, ON in 2015…………………………………130 Figure 3.3: Yield rank crossover effects in 2015 for selections from population IR055 (OAC Sunny x S05-T6) between the organic location in Moorefield, ON and the Conventional location in Elora, ON……………………………………………………………………………………131 Figure 3.4: Yield distribution of population IR062 (OAC Calypso X DH618) F5 selections grown in F6 yield trials in the organic location in Moorefield, ON in 2015……………...... 132 Figure 3.5: Yield distribution of population IR062 (OAC Calypso X DH618) F5 selections grown in F6 yield trials in the organic location in Elora, ON in 2015…………………………132 Figure 3.6: Yield rank crossover effects in 2015 for selections from population IR062 (OAC Calypso x DH618) between the organic location in Moorefield, ON and the Conventional location in Elora, ON…………………………………………………………………………...133 Figure 3.7: Dendrogram of genetic distance between selections made from population IR055 based off 13 polymorphic SSR markers………………………………………………………..134 ix

Figure 3.8: Dendrogram of the different cluster in the entire IR055population based off 13 polymorphic SSR markers……………………………………………………………………...135

List of Abbreviations ANOVA Anaylsis of Variance ARHL Average Root Hair Length ASRV1C ArcSin of canopy coverage at V1 ASRV3C ArcSin of canopy coverage at V3 ASRV5C ArcSin of canopy coverage at V5 AUCPC Area Under the Canopy Progress Curve CTWR Critical Timing of Weed Removal DIA Digital Image Analysis DTM Days to Maturity GDD Growing Degree Days GLA Green Leaf Area GxT Biplot Genotype by Trait Biplot IWM Intergrative Weed Management K Potassium KUE K use efficiency LAI Leaf area index MAS Marker Assisted Selection N Nitrogen NUE N use efficiency NutUE General Nutrient Use efficiency OMAFRA Ontario Ministry of Agriculture, Food and Rural Affairs P Phosphorus PCA Principal Components Analysis PEPP Phosphoenolpyruvate Phosphatase POM particulate soil organic matter POM-C carbon in the POM () PPB participatory plant breeding

PUE P use efficiency QTL Quantitative Trait Loci RCBD Randomized Complete Block Design RHD Root Hair Density RHLUR Root Hair Length per unit root length RIL Recombinant Inbred Line SOM soil organic matter USDA United States Department of Agricuture WSA weed suppressive ability WAE Weeks After Emergence

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Chapter 1:

Literature review: Developing an organic soybean ideotype

1.1: Introduction

High input industrialized agriculture has been the dominant form of agriculture in Canada since its adoption in the 1940’s (Troughton, 1985). The conventional model of agriculture has been credited repeatedly for being a significant cause of habitat and biodiversity loss, soil degradation, and air and water pollution. A general realization has been made that the conventional agricultural production model will not be able to sustain our planet into the future due to the environmental degradation resulting from these practices (Kirschenmann, 2009). One of the more popular systems that has emerged as an alternative to conventional agriculture has been organic agriculture. Organic farming is an integrated approach which seeks to manage agroecosystems to enhance their crop production services rather than relying on off-farm chemical inputs (Heckman et al., 2009). For this reason, common features of organic farms are longer and more complex rotations, mixed livestock and crop production, tight nutrient cycling, holistic farm management and ecological approaches to pest management (Heckman et al.,

2009).

Organic farming systems are often criticized for yielding lower than their conventional counterparts and raising concerns that they will not be able to adequately supply the world population projected in 2050 (Badgley et al., 2007). Models developed by Badgley et al. (2007) generated using ratios of organic to conventional yield suggest that worldwide organic farming adoption would not significantly reduce caloric production. However, Connor (2013) contests that looking at individual crops and comparing organic to conventional yields does not adequately reflect the productivity of organic compared to conventional production systems. In the largest meta-analysis comparing yield between organic and conventional production systems using over 1071 studies by Ponisio et al. (2015) noted a yield gap of 19.2% between

conventional and organic systems. When broken down into different crop types the organic yields were also consistently lower than conventional (Ponisio et al., 2015). In 2012, an international analysis revealed that 25% of the food produced in the world is lost through the food supply chain (Kummu et al., 2012). Perhaps a conversion to organic agriculture practices and reduced food waste could in conjunction met the demands of the world market into the future. Whether organic production systems can feed the world is still a matter of lively debate in the scientific literature.

Organic farming systems, once considered a marginal research area, have started to be investigated and developed more thoroughly by the scientific community (Niggli et al., 2008).

According to the USDA’s nearly 3 billion dollar Research Extension and Economics (REE)

Service the amount of funding being directed to sustainable or alternative agricultural research, of which organic is considered a sub group, is critically underfunded (DeLonge et al., 2016).

Funding related to organic research coming from the REE would only make up roughly 0.03% of the total budget (DeLonge et al., 2016). One of the goals identified in the “Vision for Organic

Food and Farming Research Agenda to 2025” was the need to close the productivity gap between organic and conventional systems achieved by eco-functional intensification (Niggli et al., 2008). Eco-functional intensification is the process of increasing the productivity of organic systems by better utilizing improved knowledge, practices and technologies to enhance on farm ecosystem services (Niggli et al., 2008).

In the recent meta-analysis by Ponisio et al. (2015), concluded that organic yields were significantly lower than conventional but they also discovered that improved cropping system diversification (improved crop rotation and in field polycultures) significantly improved yields in organic systems helping to narrow the yield gap. Considering the level of underfunding in

sustainable agriculture large gains could potentially be made in optimizing and improving organic production systems if more funding, research and development was devoted to closing this yield gap (DeLonge et al., 2016). Improved knowledge through research is the key to eco- functional intensification and one research topic which can meet this goal is breeding crops for improved performance in organic farming systems (Niggli et al., 2008). The current body of literature which compares conventional and organic yields would almost exclusively include the use of cultivars which have been bred for conventional use in the organic system (Ponisio et al.,

2015; Connor, 2013; Connor, 2008; Badgley et al., 2007; Murphy et al., 2007). Without using plant cultivars adapted to organic farming the organic systems may make them appear less productive in such comparisons (Murphy et al., 2007; Crespo-Herrera & Ortiz, 2015).

Soybean (Glycine Max (L.) Merr.) has the greatest production worldwide of any protein or oilseed crop (Cober, et al., 2010). Although most soybean is produced for oil extraction and the animal feed markets there still remains a food grade soybean market, which is on the rise as consumer acceptance of processed products like soymilk has increased in non-Asian markets

(Cober et al., 2010). The majority of organic soybeans grown across the world are intended for the specialty food grade market (Vollmann and Menken, 2012). In Ontario, there are more than

300 organic soybean growers that produce roughly 23,000 acres of organic soybeans per year.

Although the proportion of organic soybean growers is relatively small in Ontario compared to conventional growers, the growing demand for organic grains has been encouraging larger conventional farms to transition to organic (Hall and Mogyorody, 2001). Soybeans and other organic commodity crops grown in Ontario are exported to the USA where highly integrated value chains have been developed, which are necessary for organic processing, product sales and distribution (Hall and Mogyorody, 2001). Limited research has been carried out in efforts to

improve organic production systems functionality in Ontario and has instead focused on identifying biological and chemical differences in the soil between conventional and organic systems (Martin et al., 2007; Roberts, et al., 2008a).

1.2: Organic Crop Breeding

Breeding crops for organic production is a relatively new concept. The environment in organic systems is different from both conventional and low input systems in terms of rotation design, pest management, fertility management, biodiversity and cultivation (Lammerts van

Bueren & Myers, 2012). Organic farmers use fewer external inputs to deal with their management challenges and instead attempt to design robust systems, which internally regulate and prevent major management issues (Heckman et al., 2009). Conventional farmers have more crop demands whereas in organic environments farmers require crop cultivars, which are adapted to their production systems (Lammerts van Bueren & Myers, 2012). Because crop cultivars must suit the organic environment, cultivar selection becomes a critical decision for organic farmers in relation to fertility, pest and weed management and is frequently cited as a major management decision in production guides (Lammerts van Bueren & Myers, 2012; Atkinson et al., 2002;

Bond & Grundy, 2001; Zehnder et al., 2007). As most of the available information related to varietal performance comes from conventional yield trials it puts organic farmers at an inherent disadvantage. There may be an emphasis placed on yield stability in cultivar selection by organic farmers in the hopes that more stable cultivars will be able to withstand the inherent variability of the organic environment (Lammerts van Bueren and Myers, 2012). Selection of robust cultivars adapted to the organic environment lends itself well to the concept of problem avoidance by production system design, which is a feature in organic ideology. Most organic farmers in North

America are currently using modern cultivars that were bred for use in conventional production systems (Murphy et al., 2007).

To justify separate breeding programs for organic agriculture the performance and selection of breeding lines must be altered when tested in both organic and conventional environments. Thirty-five soft white winter wheat breeding lines in the F7-F9 stages were tested in five locations in WA, USA by Murphy et al., (2007). It has been asserted that selection of breeding lines in organic production systems really only needs to be carried out in the later stages of testing, perhaps from F7 to F10 (Crespo-Herrera and Ortiz, 2015). Direct selection under organic conditions produced soft white winter wheat breeding lines with higher yield than indirect selection in conventional environments in four of the five locations (Murphy et al.,

2007). A genotype x system interaction was observed for yield and test weight parameters in the same four locations (Murphy et al., 2007). The yield rank correlation coefficient was calculated for each location (Murphy et al., 2007). In the four locations with significant genotype x system interactions ranking cultivars in the conventional environment was not a dependable predictor of ranking within the organic environment (Murphy et al., 2007).

Differences in the selection were also observed by Reid et al. (2009) who examined the impact of selecting F6 spring wheat breeding lines in organic environments on the Canadian parries. The traits with the lowest Spearman Rank Correlations between lines tested in conventional and organic environment were yield, early season vigour, and weed biomass suppressive ability (Reid et al., 2009). Despite the differences in rank correlations for early season vigour and weed biomass suppressive ability neither of these traits were determined to be heritable, because the environmental variation contributing to these traits was much greater than their genotypic variation within the population examined (Reid et al., 2009). Murphy et al.

(2007) also predicted that breeding lines that were more appropriate for use in organic systems were likely to be removed from a conventional breeding program. Superior organic lines may have been purged from most conventional breeding programs when breeding began targeting intensive high input environments during the Green Revolution in the 1970s. The practice of regularly crossing elite by elite lines has led to the development of a modern narrow elite germplasm in soybeans in North America that yielded well in conventional environments. If breeding programs had targeted cultivar development for organic environments in the past 40 to

50 years the performance of organic farming systems and the makeup of a narrow elite organic germplasm could be different.

Direct selection of winter wheat lines in the organic environment resulted in a 14.5% higher average yield across the four environments and significant genotype x system interactions were observed (Murphy et al., 2007). Reid et al. (2009) found that using a 10% selection intensity on 80 individuals (8 lines being advanced) at the F6 generation would have resulted in only one line in common being advanced through both production systems. In a subsequent study, Reid et al. (2010) had concluded that in the F7 and F8 generations of multi-location yield trials that realized gains from selection were much lower in organic systems than conventional systems. They cited the inherent variability of the organic system as the source of slower progress in increasing yield than in the conventional environment (Reid et al., 2009). Both

Murphy et al. (2007) and Reid et al. (2009) concluded that breeding programs intent on producing cultivars of wheat for use in organic production systems should be carried out on land managed organically. As a caveat to these conclusions it is worthwhile pointing out that plant cultivars that were developed under conventional production systems do not always perform equally well in all conventional environments (Crespo-Herrera and Ortiz, 2015). Crespo-Herrera

& Ortiz (2015) asserted that cultivars produced in organic production systems would naturally follow a similar trend and not necessarily perform equally well in all organic environments.

Organic plant breeding may take on a less hierarchical approach in terms of its evaluation model than the conventional breeder led approach. Several authors have asserted that due to the relatively smaller amount of organic farmers relative to conventional and the large differences between individual organic farms a decentralized participatory plant breeding (PPB) approach may be more suitable (Shelton & Tracy, 2015; Lyon et al., 2015; Crespo-Herrera & Ortiz, 2015;

Vollmann & Menken, 2012). As organics is considered a more fringe or niche market and the expectation of lower sales of organic plant cultivars organic breeding tends to be under serviced by large breeding companies and the highly conventional and industrialized seed industry as a whole (Lyon et al., 2015). The progress in organic plant breeding has been historically and will most likely continue to be made by almost exclusively public sector researchers and breeders in conjunction with organic farmers or farmer-breeders (Lyon et al., 2015; Lammerts van Bueren &

Myers, 2012; Shelton & Tracy, 2015). Self-pollinating crops may lend themselves most easily to the participatory breeding model due to their relatively simple mode of pollination and crossing

(Shelton and Tracy, 2015). Shelton & Tracy (2015) used a PPB approach in conjunction with an organic farmer to investigate recurrent selection of full-sib families in an ear to row scheme for two open pollinated sweet corn populations on a farm in Farmington, Minnesota over 5 cycles of selection. Significant improvement was observed in the plant height, number of kernel rows and ear length over the cycles of recurrent selection employed in the study suggesting that these types of PPB schemes in conjunction with organic farmers can produce worthwhile gains from selection (Shelton and Tracy, 2015).

1.3: Organic Soybean Ideotype Development

To make breeding crop cultivars adapted to organic systems worthwhile the differences in the organic systems must impart enough of a selection pressure to change the performance of lines in a breeding program. In the case of wheat on the Canadian prairies this has been examined but not yet in soybeans in Ontario (Reid et al. 2009). Some of the key features of organic soybean production systems that could cause this change in selection pressure include: different organic matter levels, nutrient and soil biota dynamics, greater weed competition resulting in intensive cultivation and the end user or processor needs. These features need to be thoroughly examined by breeders who are conducting trials on conventional and organic sites to establish the main differences between the production systems. While acknowledging the need for plant cultivars developed specifically for organic production many of the overall breeding objectives for organic plant breeding are shared with conventional objectives (Crespo-Herrera and Ortiz, 2015). Once the differences between organic and conventional systems have been established they can be used to develop an organic ideotype that describes the ideal set of traits a plant could have to theoretically obtain optimal performance in an organic environment along with the standard traits and objectives investigated by all breeding programs such as improved yield, quality and disease resistance.

1.3.1: Biologically active organic matter fractions and nutrient dynamics

The result of more complex crop rotations and increased application of composted manures, as is common in organic production systems, is higher carbon additions to the soil environment increasing the particulate organic matter fraction (POM) carbon content and subsequently general total soil organic carbon (Fortuna et al., 2003). Mariangela & Francesco

(2010) examined long term cropping trials across the world to compare the benefits to soil

physical, chemical and biological characters from the application of organic amendments. It was noted that the application of organic amendments such as manure and compost, improved all aspects of soil quality as evaluated (Mariangela & Francesco, 2010). Marriott & Wander (2006) examined the difference between conventional and organic farming methods in terms of total soil organic matter (SOM) and biologically active SOM fractions. The majority of SOM represents inactive materials built up over a long period, which cannot be influenced by management practices (Christensen, 2001). Since total SOM does not adequately represent the influence of organic practices the biologically active SOM is measured instead as represented by the particulate soil organic matter (POM) fraction (Christensen, 2001). Marriott & Wander (2006) investigated nine long term cropping trials across the USA comparing organic to conventional systems examining the dynamics of carbon in the POM (POM-C) fraction. The complex organic rotations had POM-C content of approximately 2.25 gC/kg soil, significantly different from 1.75 gC/kg soil in the conventional rotations (Marriott and Wander, 2006). A strong correlation coefficent, 0.55 (P<0.001) between soil derived nitrogen(N) in plant biomass and POM-C content suggests that higher POM-C content relates to higher levels of SOM derived N uptake in crops (Nissen and Wander, 2003). The changes in nutrient availability are due to higher POM-C content and high carbon amendments common in organic productions system could impart a selective pressure on breeding lines being tested on organically managed land. It is possible that since more of the nutrients in an organic production system exist in the biologically active fraction of organic matter they are not as available for crop uptake as in conventional systems

(Messmer et al. , 2012). The implications for this are that crop cultivars that perform better in organic farming conditions may have to be able to achieve early establishment under low nutrient conditions and adapt to gradual slow release of mineralized nutrients over the crop’s

growth period rather than one bulk early season nutrient application as is common in conventional systems (Messmer et al., 2012). Since soybeans fix N through the symbiotic relationship they develop with rhizobia bacteria it is rarely a limiting nutrient in soybean production. Instead phosphorus (P) and potassium (K) are often considered the most limiting nutrients in soybean production.

1.3.2: Phosphorus deficits and use efficiency

Intuitively, problems arise in soybean production on Canadian organic farms where plant available P is often low (Martin et al., 2007). In the soil environment the majority of P is inaccessible as a plant nutrient because 20 to 80% is immobilized in the organic fraction of the soil and when in its soluble form it easily forms complexes with soil minerals (Schachtman et al.,1998). It is unclear what the relationship between POM-C fractions and plant P uptake is in

North American soils. Organic farmers often focus on cycling their nutrients such as P by integrating livestock into their farming operations. Farmers tend to utilize recycled nutrients in manure and rarely apply rock phosphate, an approved organic amendment for P, due to high cost and low P availability (Martin et al., 2007). However, even the tightest on-farm nutrient cycle must export some product, which will contain P. Nominal P supplied to organic production systems and continual nutrient export leads to a phenomenon termed here as “phosphorus mining”. P mining is evident in organic fields with low test P after only having converted to organic production for 5 years but is most prominent on farms that have been farming organically for 30+ years (Martin et al., 2007; Entz et al., 2001). With this in mind, it would be appropriate to consider the organic production system one in which P is limited. Examining the methods, by which plants acquire P from the soil environment and utilize stored P can be used to help us to understand what traits may be important to increasing P use efficiency (PUE) in

soybean cultivars bred for use in organic production systems. Increased root surface area through increased root hair development, differing root morphology or architecture and increased root exudates are some methods of increasing P uptake (Marschener, 1998). Another trait vital to increasing PUE in crop plants is increased P utilization efficiency in the crop often represented by reduced P concentrations in leaf tissues (Richardson et al., 2011).

The relationship between PUE and root architecture, referring to the distribution of roots in the soil profile, has been explored previously in soybeans. In Guangdong province, China, 308 soybean genotypes from the applied core germplasm including soybean’s ancestral viny progenitor species Glycine soja L., soybean landraces and modern bushing soybean cultivars were evaluated for root architectural traits (Jing et al., 2004). The genotypes were divided into three different groups with shallow, intermediate and deep rooting architecture (Jing et al.,

2004). Root architecture was related to domestication in that generally the progenitor Glycine soja had deeper rooting systems, the soybean landraces had intermediate rooting systems and modern soybeans had shallow rooting systems (Jing et al., 2004). Modern cultivars with shallow rooting depth were found to be the more PUE since they were superior at utilizing P in the upper layers of the soil profile, which is common in agroecosystems where nutrients are surface applied (Jing et al., 2004). Lammerts van Bueren et al. (2002) claims that cultivars bred for organic and low input systems may require deeper root systems to access nutrients further down in the soil profile. Lammerts van Bueren et al. (2002) claim would suggest that more nutrients would exist below the cultivated layer in organic production systems.

When conducting tests within layered soil columns Pothuluri et al. (1986) found that alfalfa (Medicago sativa L.) can uptake greater proportions of subsoil P when P levels are lower in the cultivated soil layer as they often are in organic production systems in Ontario (Roberts et

al., 2008). The findings of Pothuluri et al. (1986) would support the claims of Lammerts van

Bueren et al. (2002) but have not been confirmed directly in organic systems. Since most nutrients are still applied to the cultivated soil layer in organic production systems the less mobile nutrients like P are most likely accumulating there as they would in conventional systems

(Pothuluri et al., 1986). For deeper root systems to be more advantageous for P acquisition enough P must exist in the subsoil layers to counteract the trade-off from reduced root growth and thus reduced opportunity for P uptake in the cultivated soil layer. Jing et al. (2004) explain that current shallow soybean root systems were indirectly selected by humans during domestication in tandem with historical fertilization practices, which applied nutrients only to the cultivated soil layer.

Manschadi et al. (2014) suggests that increased PUE from shallow root architecture should be considered within the context of climate change for which models have predicted increases in drought occurrence. Since increased drought tolerance has been linked to deeper rooting cultivars a trade-off may exist between improved drought tolerance and PUE in soybeans

(Manschadi et al., 2014). This trade-off was discussed by Jing et al. (2004) who surveyed applied core soybean germplasm in China. They asserted that an indeterminate rooting type existed where an umbrella type root system with shallow roots to facilitate P uptake from surface soil layers, from which deeper roots emerge to access moisture in further soil layers (Jing et al.,

2004). In low P conditions shallower rooting cultivars had a significantly greater average root length than the deeper rooting Glycine soja with 728cm and 433cm , respectively (Jing et al.,

2004). Ao et al., (2010) compared root architectural traits and root morphological traits with plant P content in a F11 Recombinant Inbred Line (RIL) population derived from the cross of a modern soybean cultivar CN4(Glycine max) and ancestral accession XM6 (Glycine soja) in high

and low P conditions. Root morphology relates to the actual features of the roots while root architecture refers to their distribution in the soil profile (Ao et al., 2010). Root morphological traits such as root length, root surface area and root volume had the strongest correlation coefficients to PUE (0.591 , 0.710 and 0.702, respectively) rather than root architectural traits such as root depth or root width (0.215 and 0.526, respectively) (Ao et al., 2010). Root architectural traits may also be more environmentally dependent because Jing et al. (2004) found that under moisture stress rooting depth could change from intermediate to deep. It is unclear whether intermediate or shallow rooting depth is optimal for organic soybean cultivars but root morphological traits have been more strongly related to PUE when growing soybean in low P conditions (Ao et al., 2010).

Most of the P taken up by the plant is stored in the inorganic form for later use while a lower fraction is utilized in the organic form for metabolic reactions, much of which is continually recycled (Grant et al., 2001). This stored form of inorganic P is used to buffer P deficiencies later in the season (Grant et al., 2001). One measurement of improved PUE is measuring P uptake per dry matter production or per unit of yield, whereas lower levels indicate greater metabolic PUE or reduced P storage (Richardson et al., 2011). Early season P uptake is critical once the P reserves of the seed are exhausted by the developing seedling (Grant et al.,

2001). Thus, P deficiency may not be apparent in seedlings until P reserves have been fully utilized (Grant et al., 2001). Increasing the root length was correlated with greater plant P content in the early vegetative stage with a significant correlation (Jing et al., 2004). If greater root length relates to superior P nutrition during early plant establishment then it might contribute to increasing early vigour and early weed competition (Wolfe et al., 2008).

Larger seed size has also been related to early vigour in soybeans (Place et al., 2011a).

Thus greater seed reserves of P may improve early vigour but this contrasts with the concept of improving yield per unit of P uptake (Place et al., 2011a; Grant et al., 2001). Reduced P uptake per unit yield could reduce P content in the seed which may increase the demand for P during early season growth since seed reserves maybe exhausted more quickly. However, P deficencies often cause reduced soybean seed yield through reduced pod and seed devleopment but not nesscarily from reduced seed size (Crafts-Brandner, 1992). As yield reductions from P stress result in lower seed and pod production rather than reduced seed size, PUE could also be measured by number of pods per plant produced under P deficient conditions (Crafts-Brandner,

1992; Grant et al., 2001).

Zhang et al. (2009) conducted a QTL analysis of P deficiency tolerance in soybean seedlings in a population consisting of 152 F10 RILs originating from the cross Nannong94-156 x Bogao. They were able to detect QTL for enhanced soybean seedling shoot dry weight, root dry weight, P acquisition efficiency and PUE under low P conditions, which were all clustered between the two SSR markers Satt274-Satt703 (Zhang et al., 2009). In a QTL analysis of a cross of two soybean parents, one with a deep rooting p-inefficient phenotype and the other with a shallow rooting p-efficient phenotype, root length was correlated to shoot P content (Liang et al.,

2010). Liang et al. (2010) were also able to identify the punative QTLs Satt 519-Sat_128 for root length at low P levels, which were shared with putative QTLs for total root surface area and root dry weight. Root morphological traits (root length, root surface area and root volume) were also found to have greater broad-sense heritability (64.76%, 64.47% and 63.12%, respectively) than root architectural traits in low P conditions (Ao et al., 2010).

In Arabidopsis Thaliana L. low P conditions stimulated longer root hair growth as P concentrations decreased in in-vitro cultures (Bates and Lynch, 1996). In the case of soybean, variability within root hair traits exists (Wang et al., 2004). An F9 derived RIL population originating from a cross between the domestic soybean cultivar CN4(Glycine max) and accession

XM6 (Glycine soja) of soybean’s ancestral progenitor was evaluated for three root hair traits,

Root Hair Density(RHD), Root Hair Length per unit root length (RHLUR) and Average Root

Hair Length (ARHL) (Wang et al., 2004). ARHL had the greatest heritability in the broad sense

(Wang et al., 2004). The parent XM6 had higher P uptake than the CN4 parent when grown on low P soil (Wang et al., 2004). The XM6 parent had a significantly higher RHD and RHLUR, but a significantly lower ARHL than the CN4 parent (Wang et al., 2004). The root hair traits

RHD and RHLUR were both correlated with root P content within the F9 derived RIL population but not total P in the plant (Wang et al., 2004).

Developing soybean cultivars with improved PUE for use in organic production systems could lead to increased yields and improved nutrient cycling in the production system. However, these traits must be evaluated in the context of an organic production system because other stresses or production practices might alter their expression such as increased weed competition or the use of mechanical tillage for weed control. The simplest measurement of nutrient use efficiency (NutUE) is the ratio of yield to nutrient supplied from the soil (Manschadi et al.,

2014). Manschadi et al. (2014) described the nutrient use efficiency equation as:

푌𝑖푒푙푑(푘𝑔ℎ푎−1) 푁푢푡푟𝑖푒푛푡 푈푠푒 퐸푓푓𝑖푐푒푛푐푦(푁푢푡푈퐸) = 푁푢푡푟𝑖푒푛푡 퐴푐푐푢푚푢푙푎푡𝑖표푛 푎푡 푚푎푡푢푟𝑖푡푦 (푘𝑔푛푢푡푟푖푒푛푡ℎ푎−1)

퐻푎푟푣푒푠푡 퐼푛푑푒푥 = % 푁푢푡푟𝑖푒푛푡 푝푒푟 푝푙푎푛푡

The NutUE equation might be the easiest measure from a breeding perspective since yield is readily measured in most programs already and nutrient accumulation would be the only additional parameter needed. Components that should be addressed by a breeding program looking to develop PUE crop cultivars include: a) screening cultivars to determine the variability that exists for PUE traits in the program’s germplasm, b) using these traits to select parents for crossing to develop populations to select superior PUE genotypes, c) identifying which traits should be used to select for PUE genotypes on a larger scale in a timely manner, d) examining the different physiological traits which may confer improved PUE (Manschadi et al., 2014).

After these basic goals have been met further molecular methods should be employed to investigate the molecular and/or the transcriptional underpinning of PUE (Manschadi et al.,

2014).

1.3.3: Potassium deficits and use efficiency

Although organic farmers strive to establish highly efficient nutrient cycling systems which enhance on farm K cycling they are still allowed to apply off farm K inputs to their K needs (Mikkelsen, 2007). K inputs used by organic farmers cannot be chemically altered from their mineral salt forms (Mikkelsen, 2007). Another viable source of K in mixed organic livestock and cropping systems is via the import of feed and bedding material for the livestock onto the farm. (Mikkelsen, 2007). In the soil environment K can be divided into several different pools of varying availability depending upon how it is bonded to the soil Cation Exchange

Capacity (CEC), other soil particles such as clay particles in the 2:1 form and primary minerals

(Öborn et al., 2005). K is not actively incorporated into the organic structures of plants as the other macronutrients, N and P, and primarily acts as a signaling cation molecule in plants for osmoregulation but also as a catalyst for enzymatic reactions (Crocker, 2012). For this reason,

decomposition of organic materials does not act as a significant source of K in the soil system and the majority of K from organic sources such as manure are plant available within the first year of field application (Mikkelsen, 2007). Despite the options for organic management, there is generally a decline in available K on organic farms after transition and K can even become a limiting nutrient to crop production (Gosling & Shepherd, 2005; Öborn et al., 2005) .

Crop genotypes with an improved ability to grow and yield well on K deficient soils may be of benefit to organic farmers. Variation in K Use Efficiency (KUE) and K uptake efficiency under K deficient conditions has been reported for a number of crops including soybean (Rengel

& Damon, 2008). Root morphology also plays a role in KUE as it does in PUE since genotypes with a larger portion of small thin roots in their root systems have a higher KUE (Rengel &

Damon, 2008). Limited research has been done on KUE as compared to N or P use efficiency especially in the case of soybean. A difference in pod and seed production between five different soybean cultivars was observed in a greenhouse pot experiment (Sale and Campbell, 1987). Sale

& Campbell (1987) identified the soybean cultivar Bragg as being the most efficient at producing seed yield under K deficiency. In the case of common bean (Phaseolus vulgaris L.) Fageria et al.,

(2001) examined 10 bean genotypes in a greenhouse pot experiment and measured KUE as:

푃표푡푎푠푠𝑖푢푚 푈푠푒 퐸푓푓𝑖푐푒푛푐푦(퐾푈퐸)

(퐺푟𝑖푎푛 푤푡(푚푔) 푎푡 ℎ𝑖푔ℎ 퐾 − 퐺푟𝑖푎푛 푤푡(푚푔) 푎푡 푙표푤 퐾) = 푡표푡푎푙 푎푏표푣푒 푔푟표푢푛푑(푇퐴퐺)퐾 푢푝푡푎푘푒(푚푔) ℎ𝑖푔ℎ 퐾 − 푇퐴퐺 퐾 푢푝푡푎푘푒(푚푔)푙표푤 퐾

The investigators believed it would be best to identify cultivars that had both a high KUE but also were K responsive, in that they would be able to yield high under both K deficient and plentiful conditions (Fageria et al., 2001). In contrast to Manschadi et al. (2014) measurement of

NutUE this measurement gives the difference between yield (Grain wt) and nutrient uptake at

contrasting high and low fertilization rates (Fageria et al., 2001). Of the ten cultivars investigated they only identified a single genotype, Diamante Negro, as being both responsive to K application and also having high KUE (Fageria et al., 2001). The authors also acknowledged that genotypes which were KUE but non-responsive to K application could still be useful in breeding programs hoping to develop new KUE cultivars (Fageria et al., 2001). Woodend & Glass (1993) investigated the different methods of KUE measurement on 16 different wheat (Triticum aestivum L.) genotypes in a greenhouse hydroponic experiment in two separate trials, one with reduced K availability and the other with consistent K availability. They measured KUE on a vegetative K efficiency ratio (VKER=Fresh biomass(g)/ K content(mmol)) and an economic K efficiency ratio (EKER= Grain Produced(g)/K uptake in straw/grain(g)) (Woodend and Glass,

1993). When correlated VKER and other vegetative measures of KUE were not as useful or reliable predictors of EKER, however, only a poor correlation was observed between EKER and grain weight (Woodend and Glass, 1993). The measurement of EKER could be used in index selection along with yield to identify both high yielding and KUE wheat cultivars (Woodend and

Glass, 1993). Overall, KUE has not received as much attention in the scientific literature as N or

P use efficiency, however, it may be an important trait for helping to improve yields in organic production systems.

1.3.4: Symbiotic relationships and rhizosphere exudates

Plant cultivars utilized in organic systems which exhibit the slow release nutrient dynamics from biologically active SOM fractions or low nutrient availability would benefit from an enhanced ability to form symbiotic relationships with rhizosphere micro-organisms that increase nutrient availability (Messmer et al., 2012). Two symbiotic relationships of importance for soybean are their symbiosis with rhizobium bacteria to facilitate N fixation and arbuscular

mycorrhiza (AM) to assist in the acquisition of pools of P in the soil environment not normally plant available (Rengel, 2002). These symbiotic relationships are regulated by root exudates from the soybean plant, which activate signalling molecules in the rhizosphere. Along with indirectly increasing nutrient uptake through regulating symbiotic relationships root exudates can also directly increase nutrient availability by reacting with compounds in the rhizosphere

(Manschadi et al., 2014).

It has been suggested that organic systems provide an enhanced environment for AM colonization by providing more diverse rotations, the usage of slow release fertilizers and reduced biocide application (Gosling et al., 2006). Improved AM colonization of plant roots can enhance the uptake of phosphorus by the increasing the volume of soil available for P uptake but by also releasing organic anions and phosphatases to enhance release of P from organic sources in the soil environment (Dai et al., 2014). However, many studies have found that AM populations do not actively enhance the plant’s ability to uptake P or enhance plant P nutrition in organic systems (Gosling et al., 2006). Root exudates such as polyamines and flavonoids can play a crucial role in plant signaling to facilitate AM colonization (Rengel, 2002). Some researchers have suggested that the benefits of AM in organic production systems are not fully realized due to the use of modern plant cultivars with low responsiveness to AM colonization

(Gosling et al., 2006). Interestingly, it is important to mention that for AM association to be beneficial to crop productivity, available soil P must be in yield limiting conditions due to the carbon trade off for AM colonization; low soil P environment also induce increases in AM populations (Dai et al., 2014). Rengel (2002) also stated that breeding for enhanced AM symbiosis may be beneficial in environments where low P input occurs. Liu et al. (2008) found that soil P levels and soybean root architecture (i.e. shallow, intermediate or deep rooting)

affected AM colonization. In low P environments a relationship between root architecture and

AM colonization was demonstrated by soybean plants with deep and intermediate rooting depths having superior AM root colonization (Liu et al., 2008). Although root architecture seems to have an influence on AM colonization, root morphological traits as mentioned above had stronger correlation with P uptake than architectural traits and greater heritability in low P conditions (Ao et al., 2010). However, since AM populations and diversity already tend to be higher on organically managed land, the improvements to nutrient cycling and uptake provided by AM colonization may already be realized by production practices (Dai et al., 2014).

Improving AM colonization via breeding will prove to be difficult due to the challenge of phenotyping the trait and the concern that years of breeding targeting high input environments, which have relatively low AM abundance, may have bred out of the elite germplasm currently being employed in modern agricultural systems (Pswarayi and Fox, 2014).

In organic systems N is mostly acquired through biological N fixation, which is achieved through the symbiotic relationship between legumes and N fixing bacteria from the genus rhizobium (Vollmann and Menken, 2012). In the process of nodulation, soybean root hairs form a symbiotic relationship with soil bacteria, Bradyrhizobium japonicum Kirchner, to create a mutualistic site for N fixation, on a nodule (Rengel, 2002). Developing soybean lines which fix greater amounts of N is an appealing concept for organic plant breeding because it could supply greater level of N to organic crop rotations where it is generally scarce (Vollmann and Menken,

2012). One method for evaluating N fixation in legumes is measuring total N yield of the plant or the seed of plants grown under low N conditions (Herridge & Rose, 2000). Measuring total plant or seed N is generally considered a less accurate procedure for estimating N fixation from soybeans, because N uptake from the soil environment is not excluded in these tests (St. Clair et

al., 1988). However, the results from more exact procedures, such as 15N isotope labelled fertilizers, were highly correlated to total plant and seed N by St. Clair et al., (1988). St. Clair et al. (1988) suggested that under low N field conditions, such as those found in organic production systems, selection for improved N-fixation could be done with simple indirect measures such as total N content. Although, measurement methods for N fixation exist their usefulness is dependent upon the variation for this trait that exists in the population being examined (Herridge and Rose, 2000). Much of the soybean germplasm for enhanced N fixation originates from Korea and when trying to improve this trait for organic production systems in Ontario it may require the introgression of exotic germplasm (Herridge and Rose, 2000). During a breeding program run by

Herridge & Rose, (2000) the introgression of high N-fixing Korean soybean lines into high yielding Australian germplasm did not successfully produce enhanced N-fixation breeding lines with adequate yield or disease resistance to release as a cultivar. However, Herridge & Rose,

(2000) were attempting to produce a soybean cultivar which actively fixed N in the presence of moderate to high soil nitrate levels. In the case of organic systems, the main goal would be enhanced N fixation in low soil nitrate levels. Although the Australian breeding program failed to release a cultivar with higher-N-fixing capabilities it does not mean that similar germplasm would prove useless if it was introgressed into Ontario germplasm for intended use in organic systems with low available nitrate (Herridge and Rose, 2000). Low plant available phosphorus present in organic production systems could also reduce the efficiency of nitrogen fixation by reducing overall photosynthesis, reduced nucleotide biosynthesis and reduced nodule size

(Tsvetkova and Georgiev, 2003).

Plants exude a number of chemical compounds in the rhizosphere and one which may influence nutrient acquisition directly are organic anions (Manschadi et al., 2014). Citrate, malate

and oxalate are organic anion exudates released into the rhizosphere by some plant species that increase P availability as they occupy absorption sites on soil particles normally occupied by P or replace P in chelates formed with metals, thus freeing P existing in these previously unavailable nutrient pools (Richardson et al., 2011). When exposing soybean genotypes Baxi 10 and Bendi 2 (P efficient and inefficient, respectively) to both adequate P nutrition and P deficient treatments Dong et al. (2004) noted that they varied in their organic acid exudation levels. The two cultivars were subjected to multiple aluminium (Al) toxicity and P deficiency treatment combinations (Dong et al., 2004). In response to P deficiency Baxi 10 had greater and longer sustained oxalate exudation compared to Bendi 2 but they had similar malate exudation (Dong et al., 2004). Citrate was not associated with P deficiency but was with Al toxicity for these genotypes (Dong et al., 2004). Since acidic soils often exhibit both P deficiency and Al toxicity these stress tolerance traits most likely co-evolved in regions with low pH and from a breeding point of view it is important to determine which organic anions contribute to adaption to these individual stresses (Dong et al., 2004). A Japanese study examining 14 different soybean genotypes confirmed that citrate exudation is predominantly caused by Al toxicity (Yang et al.,

2000). Dong et al. (2004) also measured the activities of enzymes in the organic acid synthesis pathway and concluded that Al toxicity and P deficiency both induced phosphoenolpyruvate phosphatase (PEPP) activity and concluded that they must both affect other enzymes further upstream in the pathway they did not measure.

Organic acids release some forms of P bound in chelates and on adsorption sites on the

CEC but phosphatases release P arrested in the organic fraction of the soil (Richardson et al.,

2011). Since many of the nutrients in organic production systems existed in the organic form, increased exudation of phosphatases may contribute to enhanced P nutrition in organic crops.

Wang et al. (2009) inserted an overexpressed version of the gene AtPAP15 coding for an acid phosphatase involved in both mobilization of P stored in plant tissues and exudation in the rhizosphere into the soybean cultivar HN66. This transgenic cultivar exhibited greater acid phosphatase activity in both root exudates and leaf tissue (Wang et al., 2009). When transformed plants were grown with only an organic form of P (Phytate) available in to them in sand cultures they accumulated 20% more biomass than untransformed plants (Wang et al., 2009). In field experiments with low P the transformed plants also produced more pods per plant and more seeds per plant than untransformed plants (Wang et al., 2009). Although the use of genetically transformed plants is disallowed by organic production standards the results of this study are promising because they demonstrate that increased acid phosphatase production in soybean does relate to increased P acquisition efficiency and possibly enhanced metabolic P utilization (Wang et al., 2009).

Li et al. (2012) identified 35 acid phosphatase genes using BLAST searches of conserved sequence motifs of a known acid phosphatase gene PAP AY151271. BLASTp searches were performed using the protein sequences predicted for the 35 genes identified (Li et al., 2012).

Tissue specific expression of these 35 genes was then determined under both P deficiency and sufficiency by RNA extraction from different tissues followed by reverse-transcription and qRT-

PCR (Li et al., 2012). The leaves and roots both contained the most P deficient inducible acid phosphatase genes of the tissues examined in the study (Li et al., 2012). This demonstrated that although the AtPAP15 transformed soybean plants preformed superior in P deficient conditions a great number of genes still exist naturally in soybean, which react to P deficient conditions and may exhibit variability in their effectiveness within the soybean germplasm (Wang et al., 2009;

Li et al., 2012). These acid phosphatase genes should be examined when trying to determine the

genetic underpinnings of PUE soybean lines or when trying to develop molecular markers linked to QTL for increase PUE to be used in marker assisted selection (MAS) (Manschadi et al., 2014).

1.3.5: Weed management, competition and suppressive ability

Weed management in organic production systems takes an integrated approach, whereby the entire rotation scheme is designed to break weed cycles and reduce seed accumulation in the soil seed bank. The use of cultivars which have superior early vigour, are more competitive or still produce adequate yield under weed competition can contribute to integrative weed management (IWM) on organic farms (Vollmann and Menken, 2012). Although integrated cultural practices are vital for weed management in organic systems mechanical tillage

(including: pre-plant, pre-emergent and inter-row cultivation) will take precedence within any particular year in order to ensure proper management during the critical weed free period for that particular crop (Kluchinski and Singer, 2005). Lammerts van Bueren et al. (2002) notes that crop cultivars that recover quickly after mechanical tillage would be advantageous to organic farmers.

Organic soybean production can vary substantially between farms in terms of row spacing, and implements used for mechanical weed management. In Ontario row spacing in organic soybeans can vary from 38 cm to 76 cm where wider rows will allow more time for inter-row cultivation but narrower rows ensure rapid canopy closure by the crop (Kluchinski and Singer, 2005).

Traditionally, Ontario organic soybean growers grew soybeans in wider row spacing to help facilitate inter-row cultivation. However, many organic farmers in Ontario are moving from wider to narrower row spacing as more precise weeing equipment is developed.

Weeds compete with crops for nutrients, water and light. Plants have mechanisms in place to detect the other plants in their proximity and alter their morphology and metabolism accordingly in an effort to compensate and out compete neighbouring plants (Afifi and Swanton,

2012). One of the major signals detected by crop to control these regulatory mechanisms is the detection of the ratio of Red to Far Red (R:FR) light which is reflected from the leaf surface of weed species (Smith, 1982). The detection of a low R:FR by the plant results in the initiation of the shade avoidance response in corn and other crop plants (Page et al., 2010). The shade avoidance response can cause stem elongation, encourage apical dominance and reduced branching or tillering (Casal et al., 1987; Ballare et al., 1990; Page et al., 2009). Page et al.

(2010) suggested that the shade avoidance response has a yield cost due to resource diversion to compete with weed’s which are ultimately controlled effectively with herbicide in conventional high input systems. The detection of the low R:FR and subsequent initiation of shade avoidance has been demonstrated to reduce the fitness of corn plants in early growth reducing their leaf area and biomass 15 days after emergence compared to weed free controls (Page et al., 2009). Page et al. (2010) investigated how early weed competition affects the yield and vegetative production of corn plants grown in plastic pails outdoor with a regular fertigation and irrigation regime. The weed free treatments were grown to maturity without any competition from any other plants and the weedy treatments had the maize seedlings be surrounded by a ryegrass turf until the 10th lead tip stage (Page et al., 2010). They concluded that early season weed competition for light alone significantly reduced kernels per ear but did not significantly lower yield demonstrating that early season shade avoidance does carry a fitness cost in later growth and development (Page et al., 2010). The research reported by Page et al. (2010) was done under the underlying assumption that weeds would always be removed from the production system via selective herbicides, essentially not providing any true competition to the crop, thus the shade avoidance was the inappropriate response. However, in the context of organic production systems where mechanical and cultural weed control are not always effective as the application of herbicides it

is unclear if shade avoidance would carry a fitness cost to crops managed organically. Had Page et al. (2010) included a treatment where the weed competition was carried through until maturity to compare it to just the early season competition it would have been possible to determine if early season shade avoidance reduced yield in that instance.

In the case of soybeans, the critical weed free period begins after emergence and lasts up until the fourth node stage (V4) or 30 days after emergence and represents the time period in which weeds need to be controlled to achieve a 2.5% or lower reduction in yield due to weed competition and/or excessive weed biomass in the final crop (Van Acker et al., 1993). Excessive weed biomass in a final crop does not only affect overall yield but if weeds are still green at harvest it can cause staining on the seed coat. Since much of Ontario’s organic soybean crop is sold for human consumption staining represents a substantial reduction in quality for processors and may increase processing costs if staining is severe enough to require dehulling (Wood et al.,

2002). The Critical Timing of Weed Removal (CTWR) often occurs at the end of the critical weed-free period and lasts several weeks (Van Acker et al., 1993).

Knezevic et al. (2003) studied the effect that row spacing (19.05, 38.1, 76.2 cm) had on the CTWR in conventional soybean. They found that wider row spacing (76.2 cm) reduced the soybean’s natural tolerance to weed competition during the critical weed free period and that narrow rows increased this natural tolerance (Knezevic et al., 2003). Although organic farmers were traditionally growing soybeans in wider rows this practice may have been counteracting the natural competitive ability of the soybean cultivar they were growing making them even more reliant on mechanical tillage and less vigorous. Kluchinski & Singer (2005) examined the effectiveness of several mechanical weed management strategies on organic soybeans in wide

(76.2 cm) and narrow (20.32 cm) row widths with 11 different treatment combinations in New

Jersey, USA. There was no difference in plant density between the narrow and wide row treatments (Kluchinski and Singer, 2005). The narrow row spacing treatments did not receive any inter-row cultivation, instead they were cultivated using rotary hoe pre-emergence and the wider row spacing treatments received both rotary hoe treatments and inter-row cultivation

(Kluchinski and Singer, 2005). Although no significant differences in yield were observed by

Kluchinski & Singer (2005) between wide and narrow row treatments they suggested that wider row treatments provided greater yield stability because the time frame for weed control was increased allowing more flexibility in case of detrimental weather events. The main difference in the narrow and wider row treatments in Kluchinski & Singer’s (2005) study was inter-row cultivation but inter-row cultivation is feasible at row widths as low as 25.4 cm (Cloutier et al.,

2007). The effectiveness of different tillage implements is dependent upon the timing of their use in terms of weed development (Cloutier et al., 2007). For example, the use of a finger weeder would be appropriate in inter-row cultivation during early season crop development, cotyledon emergence to three leaf stage, and weed development but would become less effective past the three leaf stage of crop development (Cloutier et al., 2007). More research needs to be done in

Ontario to determine the optimum row spacing, implement usage and tillage treatments for organic soybean growers.

The identification of current and development of new soybean cultivars with improved competitive ability for organic production will depend on the traits used to screen for this quality.

Weed competition is often broken down into two main types: weed suppression and weed tolerance (Place et al., 2011a). Weed suppression is described as the ability of a plant to reduce weed growth through rapid early season growth both above and below ground to compete for light interception and nutrient uptake, respectively (Vollmann & Menken, 2012; Asif et al.,

2014). Weed tolerance is described as the plant’s ability to produce acceptable yields under weed competition (Place et al., 2011a). It is generally agreed upon that weed suppressive ability

(WSA) is the preferable trait out of the two because it can reduce weed seed set reducing the weed seed bank overtime and contribute more to IWM programs (Jannink et al., 1994).

However, Asif et al. (2014) suggested a combination of both WSA and weed tolerance traits in a single cultivar would be the most advantageous for organic production systems. Timing of measurements is critical when evaluating weed suppression traits because physiological changes over the growth period can influence measurements and should be done during the critical period of weed control roughly 2 to 7 weeks after emergence (WAE), (Place, et al., 2011b). Crop competitiveness is not governed by any particular trait per se but is the cumulative effect on its ability to exploit available resources quickly based on plant height, plant architecture, early leaf area development, rapid early root growth and nutrient uptake and lodging (Asif et al., 2014).

Jannink et al. (2000) evaluated 106 F5 lines originating from three soybean crosses in which parents differed for canopy area and determinate versus indeterminate growth habit in

Minnesota, USA. The progeny of the crosses were surveyed for specific leaf area, light interception 5 and 7 WAE, height 7 WAE and date of R2 in order to determine which of these criteria where most vital for determining WSA (Jannink et al., 2000). The study was conducted in a split plot design where white mustard seeds were planted after soybean emergence in one of the subplots while the rest the plot was kept weed free (Jannink et al., 2000). Mustard biomass was used to estimate WSA when harvested from the split plots 8 WAE and this was correlated with soybean growth traits of which height 7 WAE was the most prominent (Jannink et al.,

2000). Utilizing plot weed biomass and corresponding soybean biomass are very accurate methods for evaluating genotype competitive ability but are not feasible on a large scale (Place et

al., 2011b). Place et al. (2011a) screened groups of soybean genotypes for competitive ability in

North Carolina, USA and found the late height (7 WAE) and early height (3 WAE) to be significantly related to genotypes classified as soyfood processing cultivars, which had greater seed size. It was hypothesized that greater seed size could have contributed to greater seedling vigour resulting in increased height in the early(3 WAE) and late (7 WAE) periods of the critical weed free period (Place et al., 2011a). Jannink et al. (2000) predicted reductions in vital performance traits such as yield, days to maturity and lodging when selecting for the 35% most weed suppressive individuals or, using the indirect selection for WSA using the trait height 7

WAE. They utilized a restricted index selection technique where yield, days to maturity and lodging remained stable while selecting for height 7 WAE (Jannink et al., 2000). Index selection for WSA allowed Jannink et al. (2000) to predict that improved WSA could be made while maintaining vital performance traits such as later maturity, yield and lodging resistance. Jannink et al. (2001) examine the concept of a physiological trade-off between early growth and maturity.

Jannink et al. (2001) hypothesized that lines with faster growth may not maintain late season growth maturing earlier and early maturing lines may produce larger seeds which could be increasing their initial growth rate over later maturing lines. When comparing early and late maturing lines the study did not find any evidence to support a trade-off between rapid initial growth rate and sustained late season growth rate. When evaluating 16 soybean genotypes for competitiveness against 12 different weed species over two years in Minnesota, USA Bussan et al. (1997) found that a trade-off between yield and WSA did not necessarily exist. The cultivar

Parker maintained high yields in weed-free treatments but also suppressed weed biomass supporting the concept that increased WSA does not come at the expense of yield potential

(Bussan et al., 1997).

Leaf area index (LAI) is another trait commonly measured to estimate WSA. Although

LAI is a very accurate method of measuring WSA and associated traits it can be difficult, time consuming to measure and may require destruction of the plant material (Stewart et al., 2007).

Digital image analysis is another fast affordable method to estimate plant canopy cover (Stewart et al., 2007). Stewart et al. (2007) utilize digital image analysis to estimate cotton (Gossypium hirsutum L.) canopy cover and compared it to destructive measures of LAI through a regression analysis and found a strong correlation between the two. The method described by Stewart et al.

(2007) was utilized by Place et al. (2011a) and Place et al. (2011b) when surveying traits related to weed suppressive ability in existing soybean cultivars in North Carolina, USA. Place et al.

(2011b) utilized overhead photography at 3 and 5 WAE to estimate plant canopy coverage in a weedy and weed free split plot design where half a plot was overseeded with redroot pigweed

(Amaranthus retroflexus L.) to ensure weed competition was consistent. Using linear regression they found that plant canopy coverage measured at 3 WEA was the most accurate non- destructive method of estimating WSA compared to late and early light interception (measured at

4 and 6 WAE, respectively) and late plant canopy coverage (5 WAE) (Place et al., 2011b). Plant canopy coverage measured at 3 WEA was strongly correlated to reduced weed biomass (r=0.24)

(Place et al., 2011b). The results from these digital imaging analysis studies suggested that early plant canopy coverage may be a suitable trait to estimate WSA of soybean cultivars.

The development of soybeans lines with enhanced weed suppressive ability without sacrificing yield potential could make up a significant part of the IWM system utilized by organic farmers. It is important to note that although Place et al. (2011a), Place et al. (2011b),

Jannink et al. (2000), and Jannink et al. (2001) examined a number of different traits involved in

WSA and mentioned its importance for organic soybean growers none of them conducted their

trials in organic environments. Kendal τ Rank correlations for wheat yield(τ=0.28, P<0.01) in an

F6 RIL population originating from “AC Barrie” X “Attila” tested in multiple organic and conventional environments in Alberta, Canada demonstrated that the performance of breeding lines can be dependent upon the management system (Reid et al., 2010). This management effect has the potential to alter the significance of WSA traits that are evaluated in an organic production system even if they have been confirmed in a conventional environment.

1.3.6: Protein content, quality and processing factors

Worldwide soybeans are primarily utilized as an oilseed crop for the production of vegetable oil and the by-product soybean meal for animal feed (Vollmann and Menken, 2012).

However, a new food grade market expanded in North America during the early 2000’s as a response to reducing prices of conventional soybean for the crush market due to high production levels out of South America (Rao et al., 2002). Although much of the food grade market was being produced for export to Asian markets the North American soyfood market expanded rapidly during the beginning of the 21 century (Rao et al., 2002). The majority of organic soybean production is for the food-grade market where it is processed into soyfood products such as soymilk or tofu (Vollmann and Menken, 2012). The same seed component and quality parameters used for assessing conventional soybean cultivars apply to organic as well.

The quality requirements of food grade soybeans are not fully standardized and vary from region to region and by market class (Vollmann and Menken, 2012). The major two food grade market classes both contain high protein content and yellow hilum but differ in seed size; large seeded soybean cultivars are used for soymilk and tofu and small seeded, or natto soybean cultivars, are used for soybean based ferments like soya sauce or miso and sprout production

(Vollmann and Menken, 2012). Other quality factors are important in soyfood processing

including Lipoxygenase activity, sucrose content and protein profiles (Vollmann and Menken,

2012). Lipoxygenase (Lox) enzymes are responsible for the oxidation of polyunsaturated fatty acids in soybean seeds creating peroxides which contribute to off flavours in processed soyfoods

(Lenis et al., 2010). Three Lox genes exist in the soybean genome and each gene has a corresponding null mutant demonstrating simple Mendelian recessive inheritance and despite tight linkage of the Lox1 and Lox2 loci a triple lox null plant has been developed (Lenis et al.,

2010). In addition marker assays have been developed to identify each of the specific Lox null mutants (Lenis et al., 2010). If possible, future food grade soybeans should have these lox null mutation to improve quality and processing characteristics and MAS should be utilized to screen for lines containing these null mutations (Lenis et al., 2010).

Carbohydrate content has been relatively overlooked in soybean breeding because of the focus on oil and protein contents which are valued more by the processing sector (Maughan et al., 2000). The major carbohydrate in soybeans is sucrose and increased sucrose content is associated with improved taste in processed soyfoods (Maughan et al., 2000; Vollmann &

Menken, 2012). To determine the quantitative nature of sucrose content Maughan et al. (2000) conducted a QTL analysis of an F2 and F2:3 population originating from a cross between a high sugar content soybean line (V71-370) and a low sugar content Glycine soja line (PI407.162) using 178 molecular markers, a combination of SSRs and RAPDs. They found 17 markers, related to 7 distinct genomic regions, to be significantly associated with soybean seed sucrose content, many of which were co-localized with QTL from previous analyses of oil and protein content (Maughan et al., 2000). Maughan et al. (2000) suggested that either these genomic locations associated with seed components may represent a series of tighly linked loci related to all seed component traits or regions responsible for regulating seed component partioning.

Future research should focus on the development of dual high protein and high sucrose lines for use in the food grade market. The ratio of the two most plentiful proteins in soybeans,

11S/7S, form an important quality parameter in tofu production (Vollmann and Menken, 2012).

Tofu is produced by curdling soymilk using geling agents which cause the major protein consituents to bond together to entrap some water and lipids to produce a protein gel (Poysa and

Woodrow, 2002). Consumers perfer smoother texture in the tofu formed from this process

(Poysa and Woodrow, 2002). Kim & Wicker (2005) compared the textural parameters of tofu produced from two cultivars, Danbaekkong and Benning, with high and low 11S/7S protein ratios, respectively. Kim & Wicker (2005) found that lower 11S/7S portein ratios resulted in poorer textural qualities. However, tofu quality and yeild parameters are also dependent on processing procedures including the geling agent used (Poysa and Woodrow, 2002). Soybean cultivars with high 11S/7S portein ratios may be more favourable for the tofu market and processing. In an experiment to determine the effects and interactions of year, location and genotype interactions on seed quality and tofu production 10 soybean lines were tested at three locations across Southern Ontario over 2 years (Poysa and Woodrow, 2002). Poysa & Woodrow

(2002) developed a tofu index which combined all tofu quality parameters to compare genotypes with one measurement. Genotypic and year effects were the only ones to have a significant effect on tofu index (Poysa and Woodrow, 2002). Location had no impact on tofu index (Poysa and

Woodrow, 2002). Soybean quality parameters have not been compared between conventional and organic production systems. However, wheat quality parameters have been compared between organic and conventional systems over a 21 year period in Basel, Switzerland, and no significant differences were found in protein quality, 1000 seed weight, milling properties, or

baking tests (Paul et al., 2007). The only significant difference in quailty parameters between the systems was that the conventional had a higher protein content (Paul et al., 2007).

1.3.7: Implications for an organic soybean ideotype

The traits and concepts outlined here provide a framework for the University of Guelph’s soybean breeding program to develop an organic soybean ideotype with improved performance in Southern Ontario. An ideal soybean cultivar for enhanced performance on organic farms in

Ontario would display the following features: improved NutUE, the ability to suppress and compete with weeds, enhanced N fixation, enhanced rhizosphere exudates and signalling to encourage symbiotic relationships with micro-organisms, adequate seed components and quality parameters for food grade use, and maintain high yield under biotic and abiotic stresses. Many of the features of this organic soybean ideotype are governed by quantitative traits. Considering the narrow genetic diversity for much of the North American soybean germplasm, including the

University of Guelph’s, it is possible that variation for these traits has been bred out indirectly by selecting for performance in conventional high input environments.

It is then vital to conduct cultivar trials to evaluate the current Ontario soybean cultivars in organic and conventional environments to determine what the genetic variability for these traits of interest are, if they exhibit differential expression between environments and whether or not they are related to enhanced performance in either environment. The results from this research will help to create a context for future organic soybean breeding efforts by indicating which soybean cultivars are currently performing well on organic farms, which traits could be utilized as screening criteria in an organic soybean breeding program, if further production system specific germplasm improvement is needed and which cultivars are candidates for crosses to develop organic cultivars. Since most breeding programs select progeny from crosses in a

conventional environment throughout the entire process, lines which may have been productive in an organic environment could be removed in the early stages of testing and excluded from becoming new cultivars. The evaluation criteria used in conventional breeding programs may be inadequate for determining superior soybean lines in an organic breeding program.

Testing soybean breeding lines in both organic and conventional environments then comparing which lines are being advanced through the breeding program in each environment will determine if soybean breeding for organic production systems should be conducted on organically managed land. Many other investigators have examined the effects of selecting lines in organic versus conventional environments in the later generations (F7-F10) of a breeding program during the advanced yield trial phases (Reid et al., 2010; Reid et al., 2009; Murphy et al., 2007). Some have suggested that conducting all stages of breeding in organic locations may be more favourable (Lammerts van Bueren et al., 2011). Baenziger et al. (2011) suggested that only later generation testing (F6 and further) for organic production is needed in a breeding program. Few have investigated the effects on genomic inheritance of conducting selections in organic conditions in the early breeding stages (F5-F6).

Organic farming was once considered a marginal research area by the scientific community. This stigma is being shed as organic agriculture has risen to be one of the main challenges to our current or “conventional” paradigm within the farming and scientific community. The increased popularity and rising growership in the organic farming sector driven by the price premium command by organic products in the marketplace has also attracted researchers to this topic. Increasing the amount of scientific knowledge and development related to organic agriculture and farming systems will lead to further improvements and fine tuning to these systems. As researchers from different fields of science begin to converge upon this

intriguing topic the productivity of these farming systems can be enhanced. The increased price commanded by organic products such as soybeans in the commodity markets is contributing to this interest (McBride and Greene, 2009). Geneticists and breeders may play a crucial role in the future to improve the productivity of these farming systems by developing cultivars adapted to their unique challenges.

1.4: Hypothesis and Objectives

The hypotheses of this thesis were:

1) The relative performance and ranking of commercial soybean cultivars differs when grown under conventional and organic production systems;

2) Selection in early generations of breeding populations results in different genotypes being selected under conventional and organic production systems.

To address the above hypotheses, the following objectives have been selected:

1) To determine which of the current soybean cultivars developed in Ontario perform better in organic environments.

2) To determine the genetic variability existing within the current germplasm for traits of interest in organic production system and see which contribute to superior performance in organic systems.

3) To determine if selection of early generation F5/F6 soybean lines for organic production systems needs to be performed on organically managed land in Ontario.

Chapter 2:

Assessment of MG 0 soybean (Glycine max L.) cultivars in both an organic and conventional environment for early season canopy development, root morphology, nutrient use efficiency and other agronomic traits

2.1: Abstract

If there is a difference in how current cultivars perform in organic vs. conventional environments, it may be justified to breed cultivars specifically for organic producers. Traits considered important to organic soybean (Glycine Max (L.) Merr.) production (early season canopy development, root morphology, nodule production and nutrient use efficiency) were all measured throughout the growing season to see how they related to yield and other agronomic traits in both systems. Since there were only significant differences between genotypes for traits such as root morphology and canopy development in the organic environment, the contrasting conditions may be causing differential expression. Traits like early season canopy development, root length, nodule mass, and nutrient use efficiency were all related to yield in both environments. The principal components analysis for these traits and other agronomic traits showed that resource acquisition traits such as canopy development, root length, and nodule mass were more closely related to yield in the organic system than nutrient use efficiency.

Significant crossover effects in the cultivars yield between the two environments suggests that different soybean cultivars perform well in organic versus conventional environments.

2.2: Introduction

In the first half of the 1900’s during and after the great wars in Europe major changes began to occur in the agricultural world which saw the mass acceptance and implementation of chemical fertilizers, biocides, improved mechanization and modern dwarfing wheat and rice varieties in the western world and Latin American at first but later across the globe in the 1960’s in developing countries especially in South Asia and the Middle-East (Borlaug et al., 1969). This transition was termed the “Green Revolution” and caused the shift away from traditional low

input small holder agrarian styled farming systems which were previously typical in these regions (Borlaug et al., 1969). The organic agriculture movement developed simultaneously with the Green Revolution representing a counter movement to the major changes occurring in the domain of chemical agriculture led by figures such as: Sir Albert Howard, Jerome Rodale, Lady

Eve Balfour, Franklin H. King (Heckman, 2006). This movement applied the concepts of ecology to agricultural systems and attempted to contextualize agriculture as an ecosystem and rebelled against the reductionist scientific perspectives that where driving the transition to conventional agriculture (Heckman, 2006). This difference was exemplified in some of the very first organic research carried out by Lady Eve Balfour in England in the 1940’s who examined the use of organic materials as nutrient sources which contrasted with the conventional paradigm focusing on the mineral nutrition of plants through fertilizers (Watson et al., 2008). This counter movement truly began to polarize the agricultural debate into organic vs conventional houses in the period between the 1940’s-1980’s as Sir Albert Howard and Jerome Rodale published provocative books, like “The Organic Front” and “The War in the Soil”. (Heckman, 2006). It was during this time period that private institutions related to organic farming research were founded including the Rodale institute, Forschungsinstitut für biologischen Landba, and Lous

Bolk Institute since topics related to organic farming were not being examined elsewhere

(Watson et al., 2008).

As organic agriculture became more main stream and began to shed its stigma from the

1980’s into 2000’s increased institutional recognition of the legitimacy of these production methods and public support grew (Heckman, 2006). As the market for organic products grew substantially in this time period organic agriculture became increasingly institutionalized with the development of national standards in the USA and across the globe (Heckman, 2006). Many

believe that with the advent of organic standards the process by which organic agriculture is being coopted into the conventional agricultural system is being fully realized (De Wit and

Verhoog, 2007). The introduction of organic standards created more defined measures and benchmarks for what farmers must do to be considered organically certified. Interestingly, the organic standards are devoid of any philosophical foundation which were fostered by the movement founders but rather define organic by a list of normative statements which do not necessarily reflect the moral underpinnings of the movement. For example, the Canadian

Organic Standards are mostly defined by those agricultural practices, products and amendments which are not allowed by the organic standards and those products and amendments which are included in the list of permitted substances (CGSB, 2015).

Organic agriculture was largely dismissed by the academic establishment and only began to be explored more thoroughly by the scientific community through public pressure derived from dissent against chemical agricultural practices and interest in alternative production practices (Heckman, 2006). With agriculture being a major cause of the considerable and rapid environmental degradation occurring in the modern Anthropocene it is apparent that the conventional model of agricultural production born from our “Green Revolution” is in need of re-evaluation (Lewis and Maslin, 2015). The most popular alternative to the dominant conventional model of agriculture is organic agriculture, however, whether or not organic agriculture can truly reduce the environmental impact of farming while simultaneously feeding the population growth projected to nine billion by 2050 is a contentious topic in the literature

(Badgley et al., 2007; Murphy et al., 2007; Connor, 2008; Connor, 2013; Ponisio et al., 2015).

With the growth in organic market share and the interest in alternative agriculture by the scientific community organic farming systems are finding themselves evermore under the

scrutiny of researchers (Niggli et al., 2008). Despite this increase alternative or sustainable agriculture research, only receives a minute fraction of USDA’s Research Extension and

Economics funding budget (DeLonge et al., 2016). If one considers the large amount of scrutiny and optimization that conventional agricultural systems have been exposed to over the past 70 years and compare that to the level of research into organics, then the fact that organic yields are comparable to that of conventional is remarkable. One of the goals identified in the “Vision for

Organic Food and Farming Research Agenda to 2025” was the need to close the productivity gap between organic and conventional systems achieved by eco-functional intensification (Niggli et al., 2008). Niggli et al., (2008) defined Eco-functional intensification as the process of improving yields in organic agriculture by employing improved knowledge, optimized practices and sustainable technologies to enhance the services provided by agro-ecosystem.

Historically, improvement in conventional yield was mostly driven by changing agronomic practices and selection of crop germplasm achieved through increased use of off farm inputs and hierarchical plant breeding schemes (Evans, 1980). Logically, further improvements in organic productivity will come from the same two sources as in conventional agriculture. In order to achieve eco-functional intensification in organic production systems breeding crops adapted to the specific environmental conditions may be required (Niggli et al., 2008). All of the studies that compared yields between organic and conventional systems used plant cultivars, which were bred for conventional use in the organic systems (Ponisio et al., 2015; Connor, 2013;

Connor, 2008; Badgley et al., 2007; Mason et al., 2007; Murphy et al., 2007). In some instances organic growers are using cultivars which are considered “heritage” (developed before the introduction of Green Revolution technologies) and research has shown mixed results in terms of their performance in organic wheat production (Mason et al., 2007). Using older varieties or

those not developed for the challenges of organic production could be biasing the research into current yield gaps making organic agricultural systems appear less productive (Murphy et al.,

2007; Crespo-Herrera & Ortiz, 2015).

Breeding crops for organic production is a relatively new concept and organic production systems differ from conventional ones in terms of their rotations, pest and fertility management, biodiversity and their impact on biological systems (Lammerts van Bueren & Myers, 2012).

Organic production systems tend to be designed to internally regulate and mitigate major management problems whereas conventional farmers utilize numerous inputs that allow the growing environment to suit the demands of the crop (Lammerts van Bueren & Myers, 2012).

Organic farmers require plant cultivars which are adapted to the unique challenges that their systems present such as: increased weed pressure, increased pest pressure, lower nutrient availability (Lammerts van Bueren & Myers, 2012). Numerous organic production guides cite that varietal selection is of critical importance to devising an effective production plan for any crop in question (Lammerts van Bueren & Myers, 2012; Atkinson et al., 2002; Bond & Grundy,

2001; Zehnder et al., 2007). However, the majority of information available to organic farmers on cultivar performance is mostly coming from conventional yield trials which are well institutionalized in most of North America. Inherently, organic farmers are at a disadvantage since they do not have the same intellectual capital available to them that a conventional farmer might have.

To justify the effort to breed cultivars for organic agriculture the environment present in these systems must be sufficiently different from conventional environments to change the performance of breeding lines being evaluated. Organic production differs from conventional environments by: larger amounts of biologically active soil organic matter (SOM), nutrient and

soil biota dynamics, greater weed competition resulting in intensive cultivation and the end user or processor needs. These differences inform the type of cultivar which theoretically perform best in organic production systems, the organic ideotype.

Soybean is the largest producing protein or oilseed crop in the world (Cober, et al., 2010).

Food grade soybeans, that meet adequate quality parameters for use in making processed soyfoods, hold a smaller portion of the total world production, most of which is dedicated to crushing for oil extraction. (Cober et al., 2010). Most organic soybeans grown across the world are intended for the specialty food grade market (Vollmann and Menken, 2012). When defining an organic soybean ideotype qualities which would be of interest to a breeder include: improved nutrient use efficiency and nutrient acquisition, the ability to suppress and compete with weeds, enhanced nitrogen fixation, adequate seed components and quality parameters for food grade use, and maintain high yield under stresses.

More nutrients exist in the biologically active fractions of SOM in organic systems and these pools of nutrient are released via decomposition of these materials (Nissen & Wander,

2003; Marriott & Wander, 2006; Messmer et al. , 2012). Organic plant cultivars may need to be able to adapt to more gradual nutrient release. The major nutrients limiting soybean production are phosphorus and potassium since soybeans fix their own nitrogen from the atmosphere.

Phosphorus is usually in low supply in organic systems but genetic variation exists in the soybean genepool for methods of phosphorus scavenging such as improved root morphologies and increased internal nutrient use efficiency (Jing et al., 2004; Zhang et al., 2009; Ao et al.,

2010; Liang et al., 2010; Manschadi et al., 2014). Genetic variation also exists in soybean for mechanisms to release scare nutrients from the soil including root exudates some of which act directly to realease bound nutrients and others that act indirectly to foster arbuscular mycorrhiza

associations (Dong et al., 2004; Richardson et al., 2011; Li et al., 2012). Fewer options of weed control in organic systems means that those crops are going to be more likely to be exposed to weed pressure than convnetional crops but variation has been shown to exist in the soybean germplasm for weed suppresive ability and ability to tolerate weed pressure (Jannink et al., 1994;

Bussan et al., 1997; Jannink et al., 2000; Place et al., 2011a; Place, et al., 2011b; Vollmann &

Menken, 2012; Asif et al., 2014).

The amount of genetic variation for these traits of interest and how they relate to yield performance are explored by testing a group of food grade soybean genotypes in both an organic and a conventional environment in maturity group 0 in southern Ontario. Therefore, the objectives of this study were to: 1) identify soybean cultivars that currently perform well on organic farms; 2) the traits that could be utilized as selection criteria in an organic soybean breeding program; 3) the need for production system specific germplasm improvement, and 4) identify parental candidates for crosses to develop organic cultivars in the future.

2.3: Materials and Methods

Replicated yield trials were conducted in a Randomized Complete Block Design (RCBD) on an organic farm in Moorefield ON and a conventionally managed research station in Elora,

ON in 2014 and 2015, thus creating four location-years: Moorefield 2014, Elora 2014,

Moorefield 2015, Elora 2015. The soil type present at the Elora research station was a London

Loam (loam till) and the soil type present at the Moorefield site was a Huron Loam (clay loam till) (Hoffman et al., 1963). Thirty maturity group 0 soybean genotypes were grown in both environments in 2014 and thirty-three soybean genotypes were grown in both environments in

2015 (Table 1.1). Four-row plots spaced 38.1cm apart were planted 7m long then trimmed to 5m

for an area of 7.62m2. Plots were planted with a Hege self-propelled plot seeder. Plots in Elora

ON were planted at a seeding rate of 468,691 live seeds per hectare and 618,672 live seeds per hectare was used in Moorefield. Seeding rate was increased to the optimal seeding rates for organic soybean best management practices reported by Place et al. (2009) to enhance weed suppression. Seeding rate was not increased in the conventional environment because it was not shown to increase yield when investigated by Place et al. (2009). In Elora 2014 plots where planted on June 4th and plots emerged June 12th and in 2015, plots were planted on May 29th and emerged on June 10th. In Moorefield 2014 plots were planted on May 29th and emerged June 7th and in 2015, they were planted June 4th and emerged June 15th.

Fields at each location were soil sampled and nutrient tested for both phosphorus and potassium prior to planting in each year. The soil samples were sent to the University of

Guelph’s Laboratory Services and plant-available phosphorus (P) and potassium (K) was determined using the sodium bicarbonate method (Olsen et al., 1954) and ammonium acetate method (Carter, 1993), respectively. In addition, organic matter, pH, total P, and organic P were measured from the soil samples by the same lab using the Walkley-Black method, standard saturated paste method, nitric acid digest and NaOH/EDTA extraction, respectively (Walkley and Black, 1934; Thomas, 1996; Rowland and Haygarth, 1997; Bowman, 1993) . Total P, organic P and plant available P were all used to calculate the “fixed” P or the proportion of P not existing free in the soil or present in organic compounds.

퐹𝑖푥푒푑 푃 = 푇표푡푎푙 푃 − 푂푟푔푎푛𝑖푐 푃 − 푃푙푎푛푡 퐴푣푎𝑖푙푎푏푙푒 푃

The results of the soil testing and precipitation for each location-year are provided in

Table 1.2. Precipitation was only measured from March 1st to August 31st and was obtained from the closest Environment Canada weather station with complete data for the year to give a

measurement of rainfall prior to root core harvesting at the end of August. In Moorefield the station used was in Mount Forest and in Elora the closest station was at the Elora Shad Dam.

In Moorefield 2014, the sodium bicarbonate test estimated available P in the soil at 9.1 ppm whereas K was 94 ppm. According to the OMAFRA fertility recommendations the P and K content of the organic soil would have been yield limiting (OMAFRA, 2009). The soil test values in the organic environment would have called for 40kg/ha of phosphate and potash to be applied (OMAFRA, 2009) but no nutrients were applied in 2014. In Elora 2014 the P test value was 15ppm and the K was 77ppm. As a result, both phosphate and potash were applied at the recommended OMAFRA rate for the K soil tests in the 61 to 80 ppm in the conventional environment at a rate of 60 kg/ha (OMAFRA, 2009). Therefore, both the P and K in the conventional environment were raised well above the levels at which those nutrients would be limiting to soybean production.

Solid dairy cattle manure was applied to site in Moorefield in the fall of 2014 prior to the

2015 planting season at an unknown rate. Soil test P and K in Moorefield 2015 were 9.7 ppm and

100 ppm resulting in an OMAFRA recommendation of 30 kg/ha of phosphate and 40 kg/ha of potash to be applied per hectare (OMAFRA, 2009). The soil test results from the site in Elora

2015 were 15 ppm available P and 140 ppm available K both of which were considered in the low response range and not requiring additional phosphate or potash applications according to the OMAFRA guidelines (OMAFRA, 2009).

Weeds were controlled in Elora in both years using a pre-plant herbicide and a post emergence herbicide mix including: Assure II, Pinnacle, Basagran Forte. In Elora, post- emergence spraying was done July 10th 2014 and July 7th 2015. In Moorefield weeds were controlled using manual inter-row cultivation with 5” stirrup hoes from Johnny’s Select Seeds

(Winslow, ME) and elbow grease. In Moorefield 2014, inter-row cultivation occurred on June

20th and June 27th and in Moorefield 2015 inter-row cultivation occurred on June 24th and July

10th.

The land utilized at the organic site in Moorefield, ON was managed organically for the past 20 years and the rotation which preceded the yield trials in 2014 from the earliest to the previous year was hay, mixed vegetables, rye grain, oats with a cover crop of buckwheat. In

Elora 2014, the rotation preceding the yield trials was barley research plots in 2013 and soybeans in 2012. In Moorefield 2015, the yield trials were conducted in the same field but in a different section and the rotation preceding it was the same as in the previous year except for a summer fallow followed by a fall cover crop of tillage radish, which was employed in this area in 2014.

In Elora 2015, the crops preceding the replicated soybean trials were two years of corn preceded by soybeans.

Plot emergence was rated on a scale from 1 to 10 shortly after V1 soybean growth stage

(Fehr et al., 1971), 1 being only 10% of seeds planted emerged and 10 being 100% of seeds emerged. Flower color, leaf shape and pubescence were recorded throughout the season. Plots were harvested in Elora in both 2014 and 2015 with an SPC20 Almaco combine (Nevada, IA) which automatically collected moisture and weight for each plot which was used to calculate yield (kg/ha) at 13% moisture. In Moorefield in both years plots were harvested with a Classic

Plot Combine manufactured by Wintersteiger (Ankeny, IA). Grain moisture and weight and were collected manually for trials in Moorefield using a DICKEY-John (Auburn, IL) GAC 2100 and a

Sharp (Maharashtra, India) DIB-10 table top scale, respectively. Protein and oil content were measured using a GrainSpec B1126 from Foss Electric (Montgomeryville, PA). Height was measured from the base of the plant at the ground to the topmost point at maturity. Lodging was

rated on a scale from 1 to 5 with 1 being a fully standing crop and 5 being completely prostrate.

A Scout Pro SP601 scale from Ohaus Corporation (Troy Hills, NJ) was used to measure 100 seed weight. Oil and Protein content along with yield was used to calculate oil yield (kg/ha) and protein yield (kg/ha). Crops were scouted biweekly in the fall to record days to maturity.

Plant canopy coverage was measured using digital image analysis (DIA). Photos were taken at three time points in both environments in both 2014 and 2015. The time points at each environment were based off the growth and development stages of a standard genotype, OAC

Wallace. Photos were taken when OAC Wallace reached the V1, V3 and V5 development stages.

In Elora 2014, images were taken June 26th, July 4th, and July 11th. In Moorefield 2014, images were taken June 20th, July 2th, and July 9th. In Elora 2015, images were taken June 23th, July 8th, and July 17th. Whereas in Moorefield 2015, they were taken June 29th, July 13th, and July 20th.

Soybean Growing Degree Days (GDD) were monitored between each time point to compare the amount of heat energy available during early development between environments.

The soybean (GDD) calculated as:

푀푎푥𝑖푢푚 퐷푎𝑖푙푦 푇푒푚푝.(℃)+푀𝑖푛𝑖푚𝑖푢푚 퐷푎𝑖푙푦 푇푒푚푝.(℃ ) 푆표푦푏푒푎푛 퐺퐷퐷 = ( ) − 10℃ 2

Only the middle two rows of the four row plot were sampled using two custom made quadrants (76.2 cm x 65.6 cm) totaling a sampling area of exactly 1m2 per plot. Quadrants were placed 1m deep into each plot and 3m deep into each plot, in a systematic sampling scheme. A blue calibration square exactly 25cm2 was placed in each quadrant prior to image capture and the number of plants in each quadrant was counted and recorded. Two images were taken per plot, one of each quadrant and the plants within them. The Fujifilm FinePix XP70 digital camera was

placed on a Manfrotto 190 XPROB tripod and leveled using a leveling cube before each picture was taken. Each picture was taken at the same level of optical zoom (Figure 2.1).

The images were cropped to the area inside the quadrants using the path and extract tools in the program GIMP 2.8.14. Cropped images where loaded in ImageJ 1.49. The “color threshold” tool was used to highlight the pixels in the green color spectrum in the photo and then the saturation and brightness options where adjusted to optimize the mask. The mask was then measured using the “analyze particles” tool to estimate the number of pixels taken up by leaf area in each photo. The same process was used to measure the number of pixels in the blue spectrum to estimate the number of pixels in the 25 cm2 calibration square. These two pixel numbers were used to calculate the leaf area in cm2 in each photo:

퐺푟푒푒푛 푃𝑖푥푒푙 퐶표푢푛푡 퐺푟푒푒푛 퐿푒푎푓 퐴푟푒푎(퐺퐿퐴)(푐푚2) = (퐵푙푢푒 푃𝑖푥푒푙 퐶표푢푛푡/25푐푚2)

The GLA and the number of plants for the two quadrant photos per plot were then added together to represent one sample of GLA and plant count from each plot. GLA (cm2) at each time point was divided by the area of the quadrants (10,000cm2) to provide a scaling of 1 to 0 for canopy coverage. This canopy coverage was transformed using the ArcSin transformation, then converted to a percent, because the error was not normally distributed and the studentized residuals were not consistent across observations. This yielded three variables: ArcSin of canopy coverage at V1(ASRV1C), ArcSin of canopy coverage at V3(ASRV3C) and ArcSin of canopy coverage at V5(ASRV5C). The number of plants at each time point was used as the covariate for the variance analysis of canopy coverage.

The accuracy of DIA, which was used as a proxy for Leaf Area Index (LAI) to measure canopy development, was estimated in 2014 by sampling 6 border plots in each environment for

a total of 12 samples per time point (V1, V3 and V5). Images were taken and GLA was determined as described above and all plants in the quadrant were harvested and scanned using a

LI-3000 Area Metre from LI-COR Biosciences (Lincoln, NE) to measure LAI. The GLA measured in the photographs was correlated using Pearson’s correlation coefficient to the LAI measure in each time point and across all time points in SAS 9.4 using Proc Corr to determine the accuracy of the DIA.

Arcsine Percent Canopy Coverage was plotted for each time point (V1, V3 and V5) against the soybean GDD and an Area Under the Canopy Progress Curve (AUCPC) was calculated as follows:

푁푖−1 (퐺퐿퐴 +퐺퐿퐴 ) ∑ 𝑖 𝑖+1 ∗ (퐺퐷퐷 − 퐺퐷퐷 ) 2 𝑖+1 𝑖 𝑖=1

Area under the curve measurements are useful tools in describing the rates of development over time with a single quantitative number and comparing results across different locations with potentially different environmental conditions and growth rates. Using the GDD in each of the environments in this calculation helps account for differences in the time required for each development stage to be reached between locations. The number of plants at each time point was summed up and used as the covariate for the variance analysis of AUCPC.

Soil cores were collected once OAC Wallace reached the R5 development stage using a single root auger from Eijkelkamp Inc.(Giesbeek, Netherlands) with a diameter of 8 cm. Two 20 cm deep soil cores were taken per plot totaling a sample volume of 2,010.62 cm3 per plot. One core was taken directly on the row including at least one tap root and the other directly proximal to the row. These cores were bulked and kept at -150C until further washing and processing.

Frozen cores were left out overnight to thaw prior to washing. Soil was mixed with water in a bucket and passed through an 18-by-16 mesh size screen to separate the organic debris, roots and nodules from the soil. Large debris was removed with tweezers and smaller floating organic debris was removed using a sieve and placing the sample in a bucket of water. The nodules were separated from the root sample and both were placed in 50% ethanol solution and stored at -

150C. The root samples were spread out on scanning trays filled with water and scanned using an

EPSON Expression 1640XL table top scanner (Prefecture, Japan) and the images were analyzed in the program WinRhizo by Regent Insturments Inc. (Ville de Quebec, QC) to measure the root morphology traits including: total root surface area, total root length, total root volume (Figure

2.2). Nodules were rinsed and placed in paper envelopes then dried at 600C for 24 hours to 0% moisture then weighed using an AB135-S analytical balance from Mettler Toledo©

(Mississauga, ON) to measure nodule dry mass. Total root length and total root surface area was transformed using the natural logarithm because the error was not normally distributed and the studentized residuals were not consistent across observations. Nodule dry mass was transformed using the square root function because the error was not normally distributed and the studentized residuals were not consistent across observations.

Four whole plants were sampled systematically from 4 positions in the four row plot when the benchmark genotype, OAC Wallace, reached the R5 soybean growth stage at each environment; two plants per inner row at 1m and 3m from the beginning of the plot rows. This was carried out concurrently with soil core collection. This plant tissue sample was bulked in paper bags then dried at 700C for 3 days to 0% moisture. The sample was weighed using a Sharp

DIB-10 table top scale and the dry biomass was recorded. Plant tissue was ground and sieved and a sample taken for further analysis. Nitrogen(N) content was measured by combustion in a

sealed system and all nitrogenous compounds were reduced to N2 gas and measured by thermal conductivity using the LECO FP428 Nitrogen and Protein Determinator. In the case of P and K, plant tissue was microwaved, acid digested and diluted by 10 using Nanopure water. The sample was then run through Varian 820-MS ICP to measure P and K content.

Nutrient accumulation in each plot was obtained by taking the nutrient content per plant and multiplying it by the estimated number of plants in the plot. Plant count in a plot was calculated by taking emergence (scaled from 0 to 1) as the proportion of seeds that grew into full plants multiplied by the number of seeds planted in each plot (500 in Elora and 660 in

Moorefield). Nutrient accumulation (mg/plot) was scaled to kg of nutrient per hectare. Nutrient use efficiency calculated here was according to a method by Manschadi et al. (2014) who recommended the use of Nutrient accumulation at physical maturity rather than at R5. Nitrogen

(NUE), Phosphorus (PUE), and Potassium (KUE) Use Efficiency was calculated by:

푘푔 퐶푟표푝 푌𝑖푒푙푑 ( ) 푁푢푡푟𝑖푒푛푡 푈푠푒 퐸푓푓𝑖푐푒푛푐푦 = ℎ푎 푘푔 푁푢푡푟𝑖푒푛푡 퐴푐푐푢푚푢푙푎푡𝑖표푛 푎푡 푅5( ) ℎ푎

NUE, PUE and KUE were then add together to provide a single variable to represent

General Nutrient Use Efficiency (NutUE):

푁푈퐸 + PUE + KUE = 퐺푒푛푒푟푎푙 푁푢푡푟𝑖푒푛푡 푈푠푒 퐸푓푓𝑖푐푒푛푐푦(푁푢푡푈퐸)

Analyses of Variance was conducted in PROC GLM Procedure in SAS to determine whether or not the trait data could be combined across location-years according to either a

Levene’s test of Homogeneity of Variance (if the data was non-normally distributed) or

Bartlett’s test of Homogeneity of Variance (if the data was normally distributed). Traits were tested first to see if they could be combined across all four location-years (Moorefield 2014,

Elora 2014, Moorefield 2015 and Elora 2015) then across the environments within each individual year (Moorefield 2015 and Elora 2015). Lastly, the tests were conducted to see if the data was combinable within each individual environment but across years (Moorefield 2014 and

Moorefield 2015).

Once each variable was grouped (across all location-years, between some or by analyzing each location-year individually) the analysis of variance was conducted in PROC Mixed

Procedure. The genotypes and any covariate used in the analysis were considered fixed effects since the genotypes were selected specifically out of a pool of other possible genotypes. Random effects used in the model included: Block, Environment, Year, Environment*Genotype,

Year*Genotype, Year*Environment*Genotype.

Phenotypic correlations were obtained by calculating the Least Squares (LS) Means for each trait with a significant genotype effect in each location-year using Proc Mixed with genotypes as a fixed effect and block as a random effect. Pearson’s correlation coefficient were calculated between all the LS means in each location-year separately in using Proc Corr in SAS

9.4. Rank correlation between yield rankings was conducted using the Kendall Tau-b correlation coefficient in SAS 9.4 using Proc Corr.

Relationships between traits were also investigated by constructing GxT biplots using

Principal Components Analysis (PCA) in the program GGE Biplot©. To compare the effects of the different traits between the organic and conventional environments the raw data from 2014 and 2015 was combined in both Moorefield and Elora and only genotypes 1 to 30 were used for the analysis. Variables initially included were yield (kg/ha), nodule mass (g), root length (cm), root surface area (cm2), area under the canopy progress curve, nitrogen content, phosphorus content, potassium content, days to maturity, lodging (1-5), height (cm), 100 seed weight (g), oil

content (%), protein content (%) and NutUE. Proc Factor in SAS 9.4 was used to generate Kaiser measures of sampling accuracy for all the traits being considered for PCA. Those traits which had Kaiser’s measures below 50 were dropped and the remaining traits were retested and dropped accordingly until no traits below 50 remained. The final traits were then loaded into

GGE Biplot organized in the 3-way format in excel by environment, block, genotype then each subsequent trait. GxT biplots were then generated using the 4way menu and selecting “Geno by

Trait Biplots” option. The default SD (Standard Deviation) data scaling method and the tester based data centering methods were used in the construction of the biplots. Trait vectors and genotypes were plotted on the GxT biplots to demonstrate their interrelationships; the smaller the angle between trait vectors the closer related the traits.

2.4- Results 2.4.1- Canopy Development Traits

The correlations between GLA and LAI were high in all growth stages (V1, V3, and V5) and across all growth stages as follows: V1 r=0.97, at V3 r=0.88, at V5 r=0.89, across all growth stages r=0.97 (Figure 2.1).

None of the data sites could be combined for AUCPC according to a Levene’s test. In the case of AUCPC there was not a significant difference between the genotypes in Elora 2014,

Moorefield 2015 and Elora 2015. In Moorefield 2014 there was a significant Genotype effect for

AUCPC and the covariate of total number of plants had a strong effect on the analysis according to the ANOVA (Table 2.3). In 2014 the two sites were combined for the ASRV1C, ASRV3C and ASRV5C and the ANOVAs for ASRV1C, ASRV3C and ASRV5C all demonstrated significant genotype effects and significant plant count covariates (Table 2.4).

AUCPC was selected out of the four canopy development traits for inclusion in the principal components analysis for both Moorefield and Elora because it combined the most data from all the canopy development measures. In the GxT Biplot for Moorefield 2014 and 2015 the trait vectors of yield and AUCPC nearly overlapped (Figure 2.5). In contrast, in the GxT biplot for Elora 2014 and 2015 AUCPC was not as closely associated with yield but would still be considered related. Across both years in Moorefield (Figure 2.5), it appears the AUCPC was also closely related to root length, and nodule mass whereas in Elora (Figure 2.4) it appears to be related to nodule mass but to have almost no relationship to root length.

2.4.2- Root Morphology and Nodule Mass

None of the data sites could be combined for total root length or nodule dry mass but in the case of total root surface area in Moorefield 2014 and 2015 it could be combined. There were no differences between the entries for any root trait examined in Elora 2014 or Elora 2015. In contrast, Moorefield 2014 and 2015 had a significant genotype effect for root length with p=0.0114 (Table 2.3) and p=0.0315 (Table 2.5), respectively. A significant genotype effect

(p=0.0464) was observed in both years in Moorefield for the natural logarithm of root surface area (Table 2.7). No significant genotype*year effect was detected for the natural logarithm of root surface area in Moorefield 2014 and 2015 (Table 2.7). Neither Moorefield 2015 nor Elora

2015 had a significant genotype effect for the square root of nodule dry mass (Table 2.10). There was a significant genotype effect for nodule dry mass in Elora 2014 and nodule dry mass in

Moorefield 2014 (Table 2.3).

2.4.3- Nutrient Content and Use Efficiency

N and P content could be combined between the locations in 2014 and 2015 but not across years. K content could only be combined between the two locations in 2015 but not in

2014. In 2014 and 2015 there was a significant genotype effect across Moorefield and Elora for

N content (Table 2.4 and 2.6, respectively). In the case of P content in 2014 there was a significant genotype effect and dry biomass acted as significant covariate (Table 2.4). In 2015 P content had a significant genotype effect, p=0.018, across Elora and Moorefield (Table 2.6). K content had a significant genotype effect in both Elora and Moorefield in 2014 (Table 2.3). In

2015, there was a significant genotype effect between Moorefield and Elora for K content (Table

2.6).

In 2014, KUE and PUE were both combined across Moorefield and in 2015 only KUE was combined across Moorefield and Elora. There was a significant genotype effect in Elora

2014, Moorefield 2014 and Moorefield 2015 (Tables 2.3 and 2.5) for NUE. In the case of PUE,

KUE and NutUE in 2014 there was a significant genotype effect and genotype*environment effect across Moorefield and Elora (Table 2.4). For PUE in 2015 there was a significant genotype effect at both Moorefield and Elora (Table 2.5). In 2015 there was a significant genotype effect (p=0.0004) for KUE across Moorefield and Elora (Table 2.6). In Elora and

Moorefield 2015 there was a significant genotype effect for NutUE in both locations (Table 2.5).

2.4.4- Agronomic Traits

In the case of yield, the data was only combined across Elora and Moorefield in 2014. In

2014 there was a significant genotype effect a significant genotype*environment effect and the covariate, emergence rating, was significant for yield (Table 2.4). In the case of yield in Elora 2015 and Moorefield 2015 there was a significant genotype effect and emergence covariate in both locations (Table 2.5). The oil content was combined between both years in Moorefield. In Elora

2014 there was a significant Genotype effect (p=<0.0001) in the variance analysis for oil content

(Table 2.3). In Moorefield over 2014 and 2015 there was a significant genotype effect (p=0.0003)

for the variance analysis for oil content (Table 2.7). Protein content was combined across Elora and Moorefield in both 2014 and 2015. Across both years and locations protein content exhibited a significant genotype effect and a year*environment*genotype interaction effect (Table 2.8).

Plant height data could be combined across Moorefield and Elora in both 2014 and 2015 and in both situations there were significant genotype effects (Table 2.4 and 2.6). Lodging was combined across Moorefield and Elora in 2014. There was a significant genotype effect for lodging in 2014 across Moorefield and Elora (Table 2.4).

For 100 seed weight all the data was combined across all environments and years. There was a significant genotype effect, and a significant year*environment*genotype interaction effect for 100 seed weight across Moorefield and Elora in 2014 and 2015 (Table 2.9).

In the case of protein yield and oil yield the data was only combined between Elora and

Moorefield in 2014 and there was a significant genotype effect, a significant covariate and a significant genotype*environment interaction for both traits (Table 2.4). In both Elora 2015 and

Moorefield 2015 there was a significant genotype effect and a significant covariate with emergence

(Table 2.5). In the case of oil yield the data was only combined between Elora and Moorefield in

2014. In Elora 2015 there was a significant genotype effect and a significant covariate with emergence (p=0.0082) but in Moorefield 2015 only genotype effect was significant (Table 2.5).

DTM could not be combined across any location-years and there was a significant genotype effect for each location-year (Tables 2.3 and 2.5).

2.4.5: Relationships between traits of interest

In Moorefield 2014, all significant phenotypic correlations are reported in Table 2.10.

Yield was positively correlated to AUCPC, ASRV1C, ASRV3C and ASRV5C root length, nodule mass, NUE, PUE, KUE and height. In Elora 2014, all significant phenotypic correlations

are reported in Table 2.11 and yield was positively correlated with ASRV1C, ASRV3C, nodule mass, N content, NUE, PUE, KUE and height. In Moorefield 2015 yield was correlated with

NUE, PUE, KUE and Height (Tabe 2.12). In Elora 2015 yield was positively correlated with N content, PUE and plant height (Tabe 2.13)..

Some correlations were consistent across multiple location years. In both Moorefield and

Elora 2014 ASRV1C was correlated to NUE, PUE, KUE and height and ASRV3C was negatively correlated with N content (Table 2.10 and 2.10). Nodule mass was correlated to NUE,

ASRV1C and ASRV3C in both Moorefield and Elora 2014. Root Length was correlated to protein yield in both Moorefield 2014 and Moorefield 2015. In every location-year K content was positively correlated with DTM. N content was positively correlated with yield in Elora in both years and each time N content had a significant genotype effect it was positively correlated with protein content. Protein and oil content were negatively correlated in Moorefield 2014,

Elora 2014 and Moorefiled 2015.

Yield rank correlations between genotypes in Elora and Moorefield were significant in both 2014 (Figure 2.2; r=0.57) and 2015 (Figure 2.2; r=0.31) and some large crossover effects were observed in both 2014 and 2015. In 2014, the two highest yield ranking genotypes, the 1st being S03-W4 and 2nd being DH530, in Moorefield ranked the 9th and the 19th in Elora, respectively (Figure 2.2). Some genotypes had a stable yield between the environments; OAC

Sunny which was 1st highest yielding in Elora 2014 also ranked 3rd highest yielding in

Moorefield 2014 (Figure 1.2). In 2015, the four highest yielding lines in Elora (1st OAC Calypso,

2nd OAC Wellington, 3rd OAC Belgrave and 4th OAC Prodigy) all ranked in the bottom half of the group in Moorefield (29th OAC Calypso, 17th OAC Wellington, 26th OAC Belgrave and 19th

OAC Prodigy) (Figure 2.3). However, some of the top yielding lines in Moorefield (e.g., Eider,

OAC Nation, Colby, OAC Blythe) still ranked in the top 8 lines at Elora in 2015 (Figure 2.3).

In Elora the traits yield, nodule mass, root length, root surface area, AUCPC, N content,

P content, P content, DTM, lodging, plant height, oil content,and NutUE were retained for principal components analysis and GxT biplot construction after factor analysis. In Moorefield the traits yield, nodule mass, root length, root surface area, area under the canopy progress curve,

N content, P content, K content, plant height, protein content and NutUE were retained for principal component analysis and GxT biplot construction after factor analysis. The GxT Biplot constructed for Elora 2014 and 2015 (Figure 2.4) demonstrates a tight relationship between yield and NutUE since their vectors are very closely aligned. AUCPC, nodule mass, plant height, and root length were related but had relatively larger angles compared to NutUE in Elora. The GxT

Biplot constructed for Moorefield in 2014 and 2015 (Figure 2.5) showed that vectors for

AUCPC, root length and nodule mass were all closely clustered with the vector for yield.

2.5: Discussion 2.5.1: Canopy Development Traits

The significant genotype effects for the canopy development traits in both Moorefield

2014 (AUCPC, ASRV1C, ASRV3C and ASRV5C) and Elora 2014 (ASRV1C and ASRV3C) suggested that there were significant differences between the genotypes for their rates of canopy development. AUCPC had no significant genotype effect in any of the other environment years besides Moorefield 2014, suggesting that it may not be a very consistent predictor of early season vigour. ASRV1C, ASRV3C and ASRV5C all had significant genotype effects across both

Elora and Moorefield in 2014, which suggests they might represent a more consistent measure of early season canopy development as they might be under a stronger genetic control compared to

AUCPC. ASRV3C was particularly interesting since it was the only variable out of the three that had a significant genotype*environment effect suggesting that this growth stage might be the best one to examine when looking for differences in the rate of canopy development between the organic and conventional environments. In each instance plant counts used as covariates were highly significant suggesting that their use as an adjustment for canopy coverage is valuable and improves the accuracy of this analysis. When examining the correlations between AUCPC,

ASRV1C, ASRV3C and ASRV5C and other traits of interest in Moorefield 2014 there was never an instance where AUCPC was correlated to a trait without at least one of ASRV1C,

ASRV3C or ASRV5C being correlated to it as well.

The 100 seed wt. was positively correlated to ASRV1C and ASRV3C in Elora 2014 but not in Moorefield 2014. A stronger relationship between canopy coverage and seed size was reported by Place et al. (2011) who examined a cultivar for traits related to early canopy development using multiple linear regression models. Place et al. (2011) found seed size to be the most influential trait on early season weed suppression when testing 27 genotypes in a split plot with both weedy and non-weedy sub plots in conventional production systems. Martin and

Williams (2015) also found a strong relationship between seed size and plant canopy and leaf area development 3 weeks after planting but they noted that this association diminished 6 weeks after planting. In contrast, none of the canopy development traits in Moorefield 2014 were significantly correlated with 100 seed weight despite Moorefield having larger average seed weight compared to Elora in both years (Table 2.9). The 100 seed weight also had significant genotype effects across all location years and a significant year*environment*genotype effect. It is possible that some aspect of the organic environment could be limiting the rate of early season canopy development such as early season nutrient uptake and nitrogen fixation since the canopy

coverage was strongly correlated to root length, nodule mass and several nutrient use efficiency and content traits in Moorefield 2014.

There were no significant genotype effects for AUCPC or canopy coverage at V1, V3, or

V5 in 2015, which implies that there was a strong year effect that confounded these observations in the second year. Particularly in Moorefield 2015 there was a significant rainfall event immediately after planting, which caused significant slaking and subsequent crusting in some plots resulting in non-uniform emergence. This may have contributed to the larger environmental influence on the canopy development traits in Moorefield 2015 confounding the genotype effects in this environment year. Elora 2015 had the fewest significant genotype effects of all traits investigated and the highest average yield. Elora 2015 may have been a location-year in which optimal climatic and soil conditions produced maximal yields but obscured any genotypic effects for weed competitive traits or nutrient stress related traits.

Out of the 4 canopy development traits examined in Moorefield 2014 ASRV5C was correlated to the highest number of distinct traits. In Elora 2014 only ASRV1C and ASRV3C had significant genotype effects but correlated with the same agronomic traits. ASRV3C seemed to be the most informative measure for early canopy development and its relationship to other agronomic traits it was consistently significant in both Elora 2014 and Moorefield 2014 and had a significant genotype*environment effect between these sites. We utilized a flagstone genotype

(OAC Wallace) to measure soybean growth development and when it reached V3 in each environment the canopy images were collected. This growth and development stage occurred within the range of 24 to 29 days post-emergence. Place (2011) concluded that using DIA to capture soybean canopy closure at 3 weeks post emergence (similar to the timing of our V3

growth stage benchmark) was best for predicting weed suppressive ability for different soybean genotypes.

2.5.2: Root Morphology and Nodule Mass

There was no significant difference between the genotypes examined in either year in the conventional environment in Elora for root length or surface area. However, there was a difference in the organic environment in both years, which suggested that conditions present in the organic environment might be eliciting a differential response in the genotypes with respect to these traits. Soil tests indicated in both 2014 and 2015 that P and K were both at levels that would stimulate a yield response from additional P or K being added in the form of phosphate or potash in Moorefield. The significant genotypic effects seen in Moorefield might have been driven by genotype specific responses to low P and K, which might have caused some differential root growth in the top soil layer to improve P and K acquisition by some genotypes.

Altered basal root gravitropism is one of the main responses to low P availability in annual dicots according to Lynch & Brown (2001). Generally, in P-deficient conditions the angle of basal roots coming from the main tap root is reduced (they grow more horizontal in the soil profile) to improve nutrient acquisition from the top soil where P is in great abundance (Lynch and Brown, 2001). Another response to low P is to increase adventitious rooting from the underground part of the hypocotyl (Lynch & Brown, 2001; Miller et al., 2003). Bonser et al.

(1996) tested 16 common bean (Pheseolus vulgaris L.) genotypes in high and low P conditions and measured basal root angle during the first few days of growth. They found that changes in basal root growth in response to P deficiency were dependent upon the genotype and duration of

P deficiency (Bonser et al., 1996). In common bean Miller et al. (2003) investigated 4 genotypes and compared their relative development of basal, adventitious and lateral roots in response to low and high P conditions in both the field and sand cultures. They found that low P conditions generally increased the percent of dry root weigh portioned towards adventitious and basal roots and there was a genotype by P level interaction for the portioning to basal roots (Miller et al.,

2003).

Differential changes based on genotype response to low P to the basal root angle during early growth and enhanced allocation of resources to adventitious root development could have changed the density of root systems later on at R5 when root coring occurred (Miller et al.,

2003). This could explain why we observed significant differences between genotypes in the low

P Moorefield site in 2014 and 2015 for root length with p=0.0114 (Table 2.3) and p=0.0315

(Table 2.5) but not in the conventional P adequate site. As the initial angle of basal root development can set up the future architecture of the root system a reduction in basal root angle in early growth, as was also observed in the soybean genotype Williams under low P, could have significantly altered total root length at R5 in the top soil layer in our trials (Bonser et al., 1996).

Root Length was correlated with NUE in Moorefield 2014 (Table 2.10) and both root length and root surface area were correlated with K content and PUE in Moorefield 2015 (Table

2.12), however, there appears to be no consistent association with any of the nutrient content traits or nutrient use efficiency traits. It is possible that some genotypes are able to respond to the nutrient deficiencies in Moorefield by increasing total root length or total root surface area in the top 20 cm of soil differently compared to other genotypes. Not withstanding, in the conventional environment, where there would have been adequate levels of P and K for the plants to utilize,

genotypes would have relatively similar root systems since they did not have to search for nutrients in the same way as they did in the organic environment.

In Moorefield 2014 root length was correlated to a larger number of traits compared to root surface area (Table 2.10), including yield, oil yield, protein yield and NUE. In Moorefield

2015, both root length and root surface area were correlated for the same traits, including protein yield, K content and PUE (Table 2.12). In the GxT Biplot for Elora root length and root surface area were closely related, their vectors being very close together, but unrelated to yield (Figure

2.4). In the Moorefield GxT Biplot root length was closely associated with yield and other traits such as AUCPC and nodule mass (Figure 2.5). If increased root length was contributing to enhanced nutrient foraging in the organic environment, where nutrients are in a limited state, intuitively root length could be a major contributor to increased organic yield. However, enhanced root growth in response to low P comes at a metabolic cost since the plant will partition more photosynthetic resources to root growth which will reduce and slow shoot growth and development (Lynch & Brown, 2001; Lynch & Ho, 2005; Hernandez et al., 2007). A depression in shoot growth and development, which would subsequently reduce the amount of structures available for air exchange, carbon assimilation and photosynthesis, induced by an increase in root growth, to improve top soil nutrient foraging, could explain the lower yields in the organic system relative to the conventional (Table 2.9) (Nielsen et al., 2001).

In the conventional environment, greater root length might also come at a yield cost when nutrients are not in a limiting state. When 4 common bean genotypes were tested in high, medium and low P conditions their overall carbon assimilation 28 days after planting was highest in the high P conditions which also had the lowest partition of carbon (between 21 to 31%) assimilated towards the root system (Nielsen et al., 2001). The total carbon assimilation was

lowest in the low P treatment but had the highest partition of photo-assimilate towards the root system(38 to 43%) 28 days after planting (Nielsen et al., 2001). Both Elora 2014 and Elora 2015 actually had moderately high total average root length (Table 2.9), which suggests that although a larger % of carbon may have been partitioned to root systems in the organic environment there may have been a larger general carbon assimilation capacity in the conventional system still allowing them to have a dense root system.

Root surface area could be combined across Moorefield 2014 and 2015 and there was no genotype*environment interaction suggesting that this trait has a stronger genetic component to its expression than root length. However, root surface area was not associated with yield in

Moorefield or Elora according to the GxT Biplot despite being correlated to protein yield in

Moorefield 2015 (Figures 2.4 and 2.5). Although root surface area might be under a greater genotypic control than root length it does not appear to be related to yield in either Moorefield or

Elora.

In both Moorefield 2014 and Elora 2014 nodule dry mass was positively correlated with

NUE and yield but it was only correlated with N content in Moorefield 2014. In the Elora GxT biplot nodule dry mass was associated with AUCPC but was not closely associated with yield

(Figure 2.4). In Moorefield, nodule dry mass clustered closely with AUCPC but also with root length and yield (Figure 2.5).

Since there was manure applied to the soil at the beginning of the 2015 growing season in

Moorefield it was expected that there were higher levels of nitrogen available in the soil. As soybeans take up nitrogen from the soil preferentially rather than nodules this could be the reason why there was no significant genotype effect in Moorefield 2015 (Herridge and Rose,

2000). Moorefield 2015 also had the lowest average nodule mass of any of the location years

(Table 2.9). Despite it having the lowest nodule mass of any location year Moorefield 2015 also had an N content at R5 that was not significantly different from the highest N content observed in Elora 2015 according to a Bonferroni test (Table 2.9). This suggested that there was a relatively large amount of N accumulation despite the lower nodulation. Moorefield 2015 also had the lowest precipitation from March 1st to August 31st of any of the location-years (Table

1.2), which may have resulted in much lower nodulation in the top 20 cm of the soil that were sampled (Serraj et al., 1999). The latter could explain the absence of a significant genotype effect in the Moorefield 2015 location-year for several traits we examined. In Elora 2015, nodule mass was on average highest for any location-year (Table 2.9) but there was no significant genotype effect. There was a similar pattern observed for root surface area and oil content in this location- year in that they had the highest averages of any location-year but no significant genotype effect

(Table 2.9). It is possible that optimal environmental conditions for nodulation, root development and yield production were present in Elora 2015, which eclipsed the genotype effects for these traits.

2.5.3: Nutrient Content and Use Efficiency

The nutrient content traits appear to have a relatively strong genetic component since the combined analysis of variance indicated significant genotype effects for several traits. N and P content in Moorefield and Elora showed no significant genotype*environment interaction in either year. For K content, the combined ANOVA between the locations in 2015 showed no significant genotype*environment effect. For N content there was a consistent pattern in both

Moorefield 2014 and Elora 2014 showing a negative correlation with oil content but positive correlation with protein content. N content was also positively correlated to protein content in

Elora 2015. Much of the N that exists in above ground leaf tissues is remobilized to seed protein

stores during the seed fill period since N is a major component of amino acids the building blocks of protein (Fabre & Planchon, 2000; Wilcox & Shibles, 2001). As there is an inverse relationship between oil and protein content production in the seed at a ratio of 1 unit of oil to 2 units of protein a higher N content that increased protein production would have the inverse effect on oil content (Clemente and Cahoon, 2009).

The K content, was consistently positively correlated with DTM in each site year and correlated to plant height in both Moorefield 2015 and Elora 2015. Dry biomass of the plant tissue sample at R5 was used as a covariate in each of the variance analyses for nutrient content traits but was only significant in the variance analysis across Moorefield and Elora 2014 suggesting that dry biomass was an unimportant covariate to use for nutrient content traits.

Because whole plants were sampled to measure nutrient content at the beginning of seed fill it gives a relatively accurate measure of the nutrients available for remobilization to seed reserves.

It appears the KUE is under the strongest genetic control of all the nutrient use efficiency as there was a consistent genotype effect but also over both environments in both years a significant genotype*environment effect in 2014 only. In almost every environment where NUE,

PUE or KUE had significant genotypic effects they had strong positive correlations to yield, oil yield and protein yield.

2.5.4: Agronomic Traits

Combined analysis of yield across locations in 2014 showed a significant genotype*environment interaction suggesting the environment might be contributing significantly to the differences in yield. The rank correlation between the genotype rankings in

2014 was r =0.57, which means the rankings in genotypes between environments in 2014 was only concordant about 60% of the time. In 2015 the rank correlation coefficient was r =0.31, so

the genotype rankings were only concordant about 30% of the times. When examining the

Bonferroni test of the mean yield between location-years it was observed that Elora 2014,

Moorefield 2014 and Moorefield 2015 were all grouped together and had similar average yields

(2742 kg/ha, 2697 kg/ha and 2830 kg/ha, respectively) (Table 2.9). The major deviation was

Elora 2015, which had the highest yield of all location-years at 3562 kg/ha (Table 2.9). The higher concordance between the environments in 2014 was consistent with having similar average yields. In 2014, there was no significant difference between yield at the organic environment and conventional environment but there was a difference in which genotype was providing the highest overall yield in each environment according to the rank correlation. In

2015 the organic location was obviously lower yielding than the conventional location but there was an even stronger difference in which genotype was providing optimal yield in each environment according to the rank correlation coefficient. Emergence was consistently an informative covariate for adjusting yield in all location-years (Tables 2.4 and 2.5).

When examining the traits associated with yield through GxT biplots they could be classified as those associated with resource acquisition such as AUCPC for light resource acquisition and nutrient acquisition via root length (typically, P and K) and nodule mass (N).

Others could be classified as those associated with nutrient remobilization, specifically NUTUE.

It appears that in Elora the trait associated with the uptake and conversion of nutrients to yield components (NutUE) was the trait most closely associated with yield. In Elora, the traits that were investigated associated with resource acquisition were also associated with yield but not to the same extent. In contrast, the traits most related to resource acquisition (AUCPC, nodule mass and root length) all seemed to be highly associated with yield in the Moorefield location. NutUE was reference by Lammerts van Bueren et al. (2011) being described as both the efficacy of the

root system to acquire nutrients and remobilize them into yield as being vital traits to focus upon for organic cultivars. The tight association between AUCPC and yield was expected in the organic site as compared to the conventional as it was a trait which was consistently cited by sources in the organic breeding literature (Lammerts van Bueren et al., 2011; Place et al., 2011a;

Place et al., 2011b; Vollmann and Menken, 2012; Lammerts van Bueren and Myers, 2012;

Crespo-Herrera and Ortiz, 2015). From our observations in soybean, it would seem that nutrient remobilization is not as critical an aspect for organic cultivars as the initial nutrient acquisition.

Extensive amounts of research and development have been invested into developing modern plant varieties to ensure that they are able to remobilize nutrients and senescence in response to day length with impeccable timing (Gregersen, 2011). Intuitively, the efficacy of the crop to remobilize nutrients will be important for both organic and conventional farmers but the limiting factor in organics may be initial nutrient uptake.

In the case of oil content, it could be combined between Moorefield 2014 and 2015 but not in Elora and there was no significant genotype effect in Elora 2015. Interestingly, Elora 2015 was the highest yielding location-year and also had the highest oil content of any location. It is possible that ideal conditions for maximal yield and oil production occurred at this environment in 2015, which could be confounding the genotypic effects. Protein content was combined between the environments in both years and locations, which suggests it might have a stronger genotypic effect compared to oil content. However, there was a significant 3-way interaction between year*environment*genotype in the variance analysis for protein content suggesting that year and location factors were both influencing the phenotype. Moorefield 2015 had the highest average protein content and Elora 2015 had the lowest protein content. Oil content and protein content where negatively correlated in every location-year (Elora 2014, Moorefield 2014 and

Moorefield 2015) except Elora 2015 where there was no genotype effect for oil content.

Intuitively, oil yield and protein yield represent a biochemical trade off for resource allocation in the plant and it only makes sense that one increases at the expense of the other (Wilcox &

Shibles, 2001;Clemente & Cahoon, 2009; Rotundo & Westgate, 2009).

In a meta analysis of the Uniform Soybean Test Northern Region from the USDA-ARS they examined how temperature, drought and nitrogen application affected protein and oil content (Rotundo and Westgate, 2009). In the case of protein content, it increased when nitrogen was added in field studies (Rotundo and Westgate, 2009). This report is consistent with reduced nodulation but high N content in Moorefield 2015 as a result of the manure application prior to the 2015 planting season.

In the GxT biplots, protein content was excluded according to the factor analysis of all the variables of interest in the Elora location but oil content was retained. In contrast, in

Moorefield oil content was excluded from the GxT biplot according to the factor analysis and protein content was retained. This suggested that overall protein content was associated with the variables tested in Moorefield to a greater extent than oil content and the opposite was true in the

Elora location. The GxT biplot for Elora suggests that oil content was negatively associated with lodging, DTM, root length, P content, K content and root surface area (Figure 2.4). In

Moorefield protein content was associated most strongly with K content then also with N content, P content and root surface area (Figure 2.5). It seems that protein content is more associated with nutrient uptake and root morphology traits than oil content was.

Plant height exhibited a strong genetic component across both years and with no genotype*environment interaction observed. Plant height was consistently positively correlated with yield, oil yield and protein yield in every single location-year. Interestingly, the highest

yielding location-year, Elora 2015, had the smallest mean plant height of any of the location years and the lowest yielding location-year, Moorefield 2014, had the highest mean plant height of any location (Table 2.9). In both years Moorefield had a higher average plant height than in

Elora (Table 2.9). Significant weed pressure in the early season in Moorefield may have induced shade avoidance reactions from the cultivars causing them to grow taller faster potentially causing a fitness cost to due to greater carbon partitioning to stem growth rather than leaf development (Page et al., 2010). Lodging only had a significant genotype effect in 2014 and was positively correlated with both root length and root surface area in Moorefield 2014 but not to any other trait in Elora 2014.

Protein and oil yield followed the same general pattern for the variance analysis. The data was only combined between the locations in 2014 in which there was a significant genotype*environment. In 2015 the data was no combinable and both the locations had a significant genotype effect. Both protein and oil yield generally had strong correlations with the same traits that correlated well with yield. However, they deviate from overall yield in that they occasionally correlated with traits which previously seemed unrelated to yield. For example, root length and root surface area in Moorefield 2015 were only correlated with protein yield but no other yield trait. In Moorefield 2015, P content was positively correlated with oil yield but no other yield trait.

2.6: Conclusion

The first objective of the thesis was to determine the soybean cultivars that currently perform well on organic farms. Although strong year effects meant the genotypes that performed the best in each location were inconsistent, several genotypes had a moderate and

stable yield in the organic site over both years including OAC Sunny, OAC Lakeview and Etna.

These cultivars might make good parents for future organic breeding projects addressing the fourth objective. The second objective was to identify the traits that could be utilized as selection criteria in an organic soybean breeding program. The principal components analysis and GxT

Biplots indicated that traits related to resource acquisition such as AUCPC, nodule mass and root length, were closely related to yield in Moorefield. For this observation it can be inferred that if one selected for yield alone in the organic environment they would also be indirectly selecting for these traits. Although resource acquisition traits were also related to yield in the Elora, GxT

Biplots were not as strongly associated with yield as nutrient use efficiency. The third objective was to determine if variation for traits of interest to the organic soybean ideotype existed within the current Ontario soybean germplasm and determine the need for production system specific germplasm improvement. From the results of the ANOVA for several canopy development, root morphology and nutrient use efficiency traits it appears that genetic variation existed for most traits of interest for the organic soybean ideotype. However, to observe these traits they need to be examined in the organic environment for cultivar differences to be evident as in the case of the root morphological traits. Although variation exists within the current germplasm further introduction of new genetic material could lead to further improvement for traits related to the organic ideotype. When the genotypes were grown in Elora they generally had fewer impediments to optimizing solar energy interception, nutrient uptake, and nitrogen fixation and had higher LS means for traits related to the organic ideotype. The conventional environment was modified to suit crop demands, which could have caused the absence of significant genotype effects for resource acquisition traits such as root length or AUCPC.

Table 2.1: List of genotypes in the replicated yield trials including their genotype number and release year

Genotype Number Genotype Release Year 1 OAC Bayfield 1993 2 OAC Clinton 1999 3 OAC Champion 2001 4 OAC Wallace 2003 5 OAC Prodigy 2005 6 OAC 01-26 2006 7 OAC Ayton 2006 8 OAC Gretna 2006 9 OAC Lakeview 2006 10 DH420 2007 11 DH530 2008 12 OAC Ginty 2009 13 OAC Drayton 2010 14 OAC Madoc 2010 15 OAC Perth 2010 16 OAC Blythe 2011 17 OAC Wellington 2011 18 OAC Belgrave 2012 19 OAC Nation 2012 20 S05-T6 2008 21 S03-W4 2000 22 Naya 2007 23 Etna 2007 24 90M30 n/a 25 OAC Calypso 2011 26 DH618 2009 27 OAC Sunny 2012 28 OAC 13-58C-ChCdn NYR1 29 OAC 13-60C-ChCdn NYR 30 OAC 13-61C-ChCdn NYR 31 Colby 2005 32 Eider n/a 33 Savanna n/a 1 Not yet released, genotypes 28-30 are advanced experimental lines

Table 2.2: Results of soil nutrient tests including Potassium, Available P, Organic P, Fixed P, pH and Organic Matter from each location year and precipitation from March 1st to August31st of each growing season

Available Available P Organic P Fixed P OM 2 Precipitation Location Year pH K (ppm) (ppm) (1) (ppm) (1) (ppm) (1) (%) (mm)3

Moorefield 2014 94 9.1 (1.4) 182(28) 458.9(70.6) 7.6 4.6 408.2 2015 100 9.7(1.4) 198(29.1) 472.3(69.5) 7.6 4.6 321.6 Elora 2014 77 15(1.9) 270(35.5) 475(62.5) 7.8 3.6 472 2015 140 15(1.8) 263(32.9) 522(65.3) 7.8 4 444.4

1% of total soil phosphorus

2 Orgamic Matter (OM)

3 Data was obtained from the nearest Environment Canada weather stations with a complete data set, Fergus shad dam was used for the site in Elora and the Mount Forrest station was used for Moorefield and is the total from March 1st to August 31st

Table 2.3: P-values for both fixed and random effects of an ANOVAs (Analysis of Variance) for the difference between 30 Ontario MG 0 soybean cultivars grown in Moorefield or Elora 2014 for AUCPC, Root Length, Nodule Mass, NUE, DTM, or Oil Content. Only traits with significant genotype effects are reported for each location.

Moorefield 2014

Nodule K Content Effect AUCPC1 Root Length2 NUE4 DTM5 mass3 Fixed Effects Genotype <0.0001 0.0114 0.0025 0.0018 <0.0001 <.0001 Covariate <0.00016 Random Effects

Block 0.3186 0.1827 0.1919 0.2735 0.2214 0.3727 Residual <0.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Elora 2014 Oil Nodule K8 Content NUE Content DTM mass7 (%) Fixed Effects Genotype 0.0009 <0.0001 <0.0001 <.0001 <0.0001 Random Effects

Block 0.3958 0.1646 0.1752 0.2048 0.1921 Residual <.0001 <.0001 <.0001 <.0001 <.0001

1Area Under the Canopy Progress Curve, 2Transformed using the natural logarithm function, 3Transformed using the natural logarithm function, 4Nitrogen Use Efficiency, 5Days to Maturity, 6 Covariate for AUCPC was the sum of plant counts over the three time points, 7Transformed using the square root function, 8Potassium

Table 2.4: P-values for both fixed and random effects of ANOVAs (Analysis of Variance) for the difference between 33 Ontario MG 0 soybean cultivars combined over both Moorefield or Elora in 2014 for ASRV1C1, ASRV3C2, ASRV5C3, N content, P content, PUE, KUE, NutUE, yield,plant height, lodging, protein yield, oil yield. Only traits with significant genotype effects are reported.

2014

(cm)

Content

PUE Yield Yield

KUE

Height

(kg/ha) Protein (kg/ha) (kg/ha)

NutUE

Lodging

Oil Yield Oil

ASRV1C ASRV3C ASRV5C

P N Fixed Effects

Genotype 0.0006 0.002 0.0019 0.0003 <0.0001 0.0003 0.0002 0.0051 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Covariate <0.000110 <0.000110 <0.000110 . . . . <0.000112 . . 0.009311 0.000112 <0.000112

Random Effects

Environment 0.2639 . . . 0.2403 . 0.3763 0.4057 0.3659 0.2553 . 0.3357 .

. . Block(Environment) 0.1079 0.1283 0.1856 0.2596 0.3109 0.1203 0.0985 0.1112 . 0.2036 0.2133

Genotype* 0.161 0.0476 0.2184 Environment 0.3413 0.4226 0.0271 0.0142 0.0234 0.0128 0.1245 0.2284 0.0130 0.0162

Residual <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

1ArcSin of canopy coverage at V1, 2ArcSin of canopy coverage at V3, 3ArcSin of canopy coverage at V5, 4Nitrogen, 5Phoshporus, 6Phosphorus Use Efficiency, 7Potassium Use Efficiency, 8General Nutrient Use Efficiency, 9Rated on a scale form 1-5, 10 Plant count, 11Plant tissue sample, 12 Emergence Rating

Table 2.5: P-values for both fixed and random effects of an ANOVAs (Analysis of Variance) for the difference between 33 Ontario MG 0 soybean cultivars grown in either Moorefield or Elora 2015 for root length, NUE, PUE, NutUE, yield, protein yield, oil yield and days to maturity. Only traits with significant genotype effects are reported for each location.

Moorefield 2015

Protein Oil Root Yield NUE2 PUE3 NutUE4 Yield Yield DTM Length1 (kg/ha) (kg/ha) (kg/ha) Fixed Effects

Genotype 0.0315 <0.0001 0.0007 0.0002 <.0001 <.0001 <.0001 <.0001 . . . . Covariate 0.03315 0.04235 . . Random Effects

Block 0.1968 0.1854 0.1821 0.1809 . . . . Residual <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Elora 2015

Protein Yield Oil Yield PUE NutUE Yield DTM (kg/ha) (kg/ha) (kg/ha) Fixed effects

Genotype 0.0373 0.0455 <.0001 <.0001 <.0001 <.0001 . . Covariate 0.00995 0.03095 0.00825 . Random Effects

Block . . . . . 0.2264 Residual <.0001 <.0001 <0.0001 <0.0001 <0.0001 <.0001 1Transformed using the natural logarithm, 2 Nitrogen Use Efficiency, 3Phosphorus Use Efficiency Phosphorus, 4General Nutrient Use Efficiency, 5 Covariate was emergence rating,

Table 2.6: P-values for both fixed and random effects of ANOVAs (Analysis of Variance) for the difference between 33 Ontario MG 0 soybean cultivars combined over both Moorefield or Elora in 2015 for N content, P content, K content, KUE and height. Only traits with significant genotype effects are reported.

2015

N Content P Content K Content KUE Height Fixed Effects

Genotype 0.0100 0.0180 0.0004 0.0004 <.0001 Random Effects

Environment 0.3223 0.2716 0.2531 0.2531 0.2753 Block(Environment) 0.1689 0.0926 0.0860 0.0860 0.1254 Genotype* Environment 0.0689 . . . 0.3297 Residual <.0001 <.0001 <.0001 <.0001 <.0001

Table 2.7: P-values for both fixed and random effects of an ANOVAs (Analysis of Variance) for the difference between 30 and 33 Ontario MG 0 soybean cultivars combined across Moorefield 2014 or Moorefield 2015, respectively, for root surface area and oil content.

Moorefield

Root Surface Oil Content Area1

Fixed Effects

Genotype 0.0464 0.0003 Random Effects

Environment 0.2832 0.2640 Block(Environment) 0.1068 . Genotype* Environment 0.1375 0.0663 Residual <.0001 <.0001 1 transformed using the natural logarithm

Table 2.8: P-values for both fixed and random effects of ANOVAs (Analysis of Variance) for the difference between 30 and 33 Ontario MG 0 soybean cultivars combined over both Moorefield or Elora in 2014 and 2015, respectively. Only traits with significant genotype effects are reported including protein content and 100 seed weight.

Protein Content 100 seed Weight

Fixed Effects

Genotype 0.0008 0.0006

Random Effects Year . . Environment(Year) 0.1778 0.1170 Block(Environment) 0.4035 . Year*Genotype 0.0501 . Environment*Genotype 0.4398 . Year*Environment*Genotype 0.0048 <.0001

Table 2.9: LS means for traits of interest for each location-year included in the trials, means with different letters following them are significantly’ different from each other at α=0.05 according to a Bonferroni adjusted LSD test

Location-year Traits Elora Elora Moorefield Moorefield 2014 2015 2014 2015

Yield (kg/ha) 2742.8 b 3562.78 a 2697.36 b 2830.53 b Nodule Mass (g) 0.6859 b 0.8877 a 0.4342 c 0.2505 d Root Length (cm) 3795.76 b 4128.47 ab 3174.44 c 4275.71 a Root Surface Area (cm2) 450.38 b 504.87 a 432.38 b 509.46 a AUCPC 3721.49 b 3085.38 c 5949.47 a 3538.99 b N content (%) 32.65 b 38.23 a 32.3 b 37.51 a P content (%) 3.37 b 3.62 a 2.75 d 3.12 c K content (%) 13.5 c 24.87 a 13.06 c 18.56 b Days to Maturity 126.26 b 123c 128.59 a 129.7 a Height 75.58 b 70.76 c 80.58 a 76.25 b 100 Seed Weight (g) 18.7 c 18.38 c 19.93 b 20.79 a Oil Content (%) 19.43 c 20.6 a 19.57 c 19.92 b Protein Content (%) 42.77 ab 42.27 b 42.53 ab 42.89 a NUE1 5.13 c 10.36 a 7.16 b 4.89 c PUE2 8.84 b 13.76 a 9.13 b 8.43 b KUE3 2.24 a 2 b 1.93 b 1.42 c NUTUE 16.22 bc 26.12 a 18.22 b 14.75 c

1 Nitrogen Use Efficiency, 2 Phosphorus Use Efficiency, 3 Potassium Use Efficiency

Table 2.10: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Moorefield 2014. Only significant correlation was reported.

Oil Oil

PUE

Root Root

Area NUE

KUE

Mass

Yield Yield

DTM

Height

Length Nodule (kg/ha) (kg/ha) (kg/ha)

Protein Protein

Surface Surface

Content Content

Lodging

Oil Yield

ASRV1C ASRV3C ASRV5C

P content

N N content K K content

AUCPC1 0.88 0.94 0.9 ns ns 0.55 -0.42 ns ns ns ns ns 0.59 ns ns 0.47 ns 0.59 0.6 ns

ASRV1C2 . 0.82 0.9 0.41 ns 0.51 ns ns ns 0.58 0.49 0.5 0.78 ns ns 0.4 ns 0.77 0.8 ns

ASRV3C3 . . 0.87 0.39 ns 0.61 -0.5 ns ns 0.38 ns ns 0.7 ns ns 0.53 ns 0.69 0.69 ns

ASRV5C4 . . . 0.47 ns 0.67 -0.38 ns ns 0.47 0.39 0.39 0.71 ns ns 0.49 ns 0.7 0.71 ns

Root Length5 . . . . 0.95 0.56 ns ns ns 0.42 ns ns 0.46 ns ns 0.47 0.39 0.42 0.48 ns

Root Surface Area6 . . . . . 0.49 ns ns ns ns ns ns ns ns ns 0.49 0.37 ns ns ns

Nodule Mass7 ...... -0.54 ns ns 0.45 ns ns 0.54 ns ns 0.52 ns 0.52 0.52 ns

N Content8 ...... ns ns ns ns -0.45 -0.39 -0.45 0.54 ns ns -0.44 ns ns

P Content9 ...... ns ns ns ns ns ns ns ns ns ns ns ns

K Content10 ...... ns ns ns ns -0.37 ns ns ns ns ns 0.47

NUE11 ...... 0.91 0.84 0.86 . ns ns ns 0.87 0.83 ns

PUE 12 ...... 0.94 0.83 0.37 ns ns ns 0.84 0.8 ns

KUE13 ...... 0.79 0.53 ns ns ns 0.82 0.74 ns

Yield (Kg/ha) ...... ns ns 0.47 ns 0.99 0.99 ns

Oil Content (%) ...... -0.68 ns ns 0.43 0.21 -0.62

Protein Content (%) ...... ns ns ns 0.12 ns

Height ...... ns 0.4 0.5 ns

Lodging ...... ns ns ns

Oil Yield (Kg/ha) ...... 0.97 -0.42

Protein Yield (Kg/ha) ...... ns

DTM14 ......

1Area under the Canopy Progress Curve, 2ArcSine of Canopy Coverage at V1 Growth Stage, 3ArcSine of Canopy Coverage at V3 Growth Stage, 4ArcSine of Canopy Coverage at V5 Growth Stage, 5Root length was transformed using the natural Logarithm, 6Root surface area was transformed using the natural Logarithm, 7Nodule Mass was transformed using the natural logarithm, 8 Nitrogen Content at R5, , 9 Phosphorus Content at R5, 10 Potassium Content at R5, 11Nitrogen Use Efficiency, 12Phosphorus Use Efficiency, 13Potassium Use Efficiency, 14Days to Maturity

Table 2.11: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Elora

2014. Only significant correlation where reported.

ontent ontent

(%)

PUE

NUE

KUE

DTM

Height

(kg/ha) (kg/ha)

Protein Protein

Lodging

Oil Yield Oil Yield

ASRV3C

P P content

N content N

K content K

Oil C

Content (%) Content

Nodule Mass Nodule

Yield (kg/ha) Protein Yield Yield Protein

ns ns ns ns ns ns ARSV1C1 0.89 0.5 -0.41 0.71 0.66 0.66 0.74 0.48 0.76 0.77 . ns ns ns ns ns ns ARSV3C2 0.63 -0.36 0.7 0.66 0.66 0.72 0.43 0.72 0.72 . . ns ns ns ns ns ns ns ns Nodule Mass3 0.44 0.51 0.51 0.56 0.55 0.52 . . . ns ns ns ns ns ns ns N Content4 0.52 -0.39 -0.38 0.42 -0.42 ns . . . . ns ns ns ns ns ns ns ns ns ns ns ns P Content5 . . . . . ns ns ns ns ns ns ns ns K Content6 0.14 -0.45 0.68 ...... ns ns ns ns NUE7 0.93 0.92 0.82 0.56 0.91 0.9 ...... ns ns ns ns PUE8 0.96 0.86 0.61 0.89 0.92 ...... ns ns ns ns KUE9 0.8 0.59 0.84 0.86 ...... ns ns ns ns Yield (kg/ha) 0.62 0.95 0.93 Oil Content ...... ns ns ns -0.73 -0.46 -0.54 (%) Protein ...... ns ns ns ns ns Content (%) Plant Height ...... ns 0.58 0.69 0.41 (cm) ...... ns ns ns Lodging Oil Yield ...... ns 0.96 (kg/ha) Protein Yield ...... ns (kg/ha) ...... DTM10 1ArcSine of Canopy Coverage at V1 Growth Stage, 2 ArcSine of Canopy Coverage at V3 Growth Stage, 3Nodule Mass was transformed using the natural logarithm, 4Nitrogen Content at R5, 5Phosphorus Content at R5, 6 Potassium Content at R5, 7Nitrogen Use Efficiency, 8Phosphorus Use Efficiency, 9Potassium Use Efficiency, 10Days to Maturity

Table 2.12: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in

Moorefield 2015. Only significant correlation where reported.

PUE

Root

Area NUE

KUE

Yield Yield Yield

DTM

Height

(kg/ha) (kg/ha) (kg/ha)

Protein Protein

Surface

Content

OilYield

P content K content OilContent ns ns ns ns ns ns ns Root Length 0.95 0.39 0.51 0.39 0.36 0.43 Root Surface . ns ns ns ns ns ns ns Area 0.39 0.46 0.46 0.4 0.49 P Content . . ns ns ns ns ns ns ns 0.35 -0.54 ns -0.37 . . . ns ns ns ns ns ns ns ns K Content 0.36 0.64 NUE . . . . ns ns ns 0.85 0.51 0.65 ns 0.63 0.57 PUE . . . . ns ns ns . 0.45 0.64 0.37 0.61 0.61 KUE ...... ns ns ns 0.37 ns 0.41 ns ...... ns ns ns Yield 0.49 0.98 0.97 Oil Content ...... ns ns ns ns (%) -0.52 Protein ...... ns ns ns ns Content (%) Plant Height ...... ns (cm) 0.44 0.56 0.6 Oil Yield ...... ns ns (kg/ha) 0.92 Protein Yield ...... ns (kg/ha) DTM ......

1Root Length was transformed using the natural logarithm, 2Nitrogen Content at R5, 5Phosphorus Content at R5, 6 Potassium Content at R5, 7Nitrogen Use Efficiency, 8Phosphorus Use Efficiency, 9Potassium Use Efficiency, 10Days to Maturity

Table 2.13: Phenotypic Pearson’s correlation coefficients between LS means of all traits with significant genotype effects in Elora

2015. Only significant correlation where reported.

Oil Oil

(%)

KUE

Yield Yield Yield

DTM

Height

(kg/ha) (kg/ha) (kg/ha)

Protein Protein Protein Content N Content1 ns ns 0.46 ns ns ns ns K Content2 ns 0.4 ns 0.41 0.35 ns 0.46 PUE3 0.85 0.4 ns 0.36 . ns ns KUE4 . ns ns ns ns ns ns Yield (kg/ha) . . . 0.56 0.96 0.95 0.57 Protein . . . . ns ns ns Content Height . . . . 0.56 0.51 0.52 Oil Yield . . . . . 0.94 0.5 (kg/ha) . . . . . Protein Yield . 0.42 (kg/ha) 1Nitrogen Content at R5, 2Potassium Content at R5, 3Phosphorus Use Efficiency, 4Potassium Use Efficiency

Figure 2.1: 1) Visual of the camera set up in plot, 2) Cropped photo of V1 growth stage, 3) Cropped photo of V3 growth stage, 4) Cropped photo of V5 growth stage

Figure 2.2: 1) Root Core sample in bag defrosting after being taken out of cold storage at -170C, 2) washing dirt off the root sample using a garden hose and screen, 3) Final root sample collected in a sieve before being placed in 50% ethanol for storage, 4) Final sample after being scanned in WinRhizo

5000 Regression(GLA=183.9+0.51(LAI)

4500 r=0.967)

) 2 4000

3500

3000

2500

2000

1500 Green Leaf Area (cm Area Leaf Green

1000

500

0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Leaf Area Index (LAI) (cm2)

Figure 2.3: Green leaf area (GLA) derived from digital image analysis plotted against leaf area index measured using a tabletop scanner with the linear regression line of GLA=183.9+0.51(LAI) and correlation coefficient of 0.967

0 S03-W4, 1 OAC Sunny, 1 DH530, 2 OAC 13-58C-ChCdn, 2 OAC Sunny, 3 OAC 13-60C-ChCdn, 3 OAC 13-61C-ChCdn, 4 OAC Drayton, 4 5 OAC Calypso, 5 OAC 13-61C-ChCdn, 5 OAC Lakeview, 6 OAC Calypso, 6 Etna, 7 S05-T6, 7 OAC Ginty, 8 90M30, 8 OAC 13-60C-ChCdn, 9 S03-W4, 9 10 90M30, 10 OAC Nation, 10 OAC Nation, 11 OAC Belgrave, 11 OAC Drayton, 12 Etna, 12 OAC Prodigy, 13 OAC Lakeview, 13 OAC 13-58C-ChCdn, 14 OAC Perth, 14 15 S05-T6, 15 OAC Ginty, 15 Kendall Tau-b OAC Perth, 16 OAC Blythe, 16 OAC Madoc, 17 OAC Wallace, 17 R=0.57 DH420, 18 OAC Prodigy, 18 OAC Belgrave, 19 DH530, 19 p=<0.0001 20 Naya, 20 OAC Champion, 20 OAC Wellington, 21 DH420, 21 OAC Champion, 22 Naya, 22 OAC Wallace, 23 OAC Madoc, 23 OAC Blythe, 24 OAC Clinton, 24 25 OAC Bayfield, 25 OAC Bayfield, 25 DH618, 26 OAC Wellington, 26 OAC Ayton, 27 DH618, 27

Yield 30) Rankings to (1 Yield OAC Clinton, 28 OAC Ayton, 28 OAC Gretna, 29 OAC 01-26, 29 30 OAC 01-26, 30 OAC Gretna, 30 Moorefield Elora

Figure 2.4: Yield rank crossover effects and Kendall Tau-b Rank Correlation coefficient in 2014 between the organic location in Moorefield, ON and the Conventional location in Elora, ON.

0 Eider, 1 OAC Calypso, 1 OAC Drayton, 2 OAC Wellington, 2 OAC Nation, 3 OAC Belgrave, 3 Colby, 4 OAC Prodigy, 4 5 OAC Blythe, 5 OAC Blythe, 5 Etna, 6 Colby, 6 OAC Sunny, 7 Eider, 7 OAC Lakeview, 8 OAC Nation, 8 DH618, 9 OAC Sunny, 9 10 OAC Champion, 10 OAC Perth, 10 OAC 13-58C-ChCdn, 11 OAC Lakeview, 11 OAC 01-26, 12 S05-T6, 12 OAC 13-60C-ChCdn, 13 Etna, 13 OAC Ginty, 14 OAC 13-60C-ChCdn, 14 15 OAC Perth, 15 OAC Ginty, 15 OAC 13-61C-ChCdn, 16 OAC 01-26, 16 OAC Wellington, 17 OAC Drayton, 17 Kendall Tau-b Savanna, 18 OAC 13-61C-ChCdn, 18 OAC Prodigy, 19 OAC Clinton, 19 20 OAC Bayfield, 20 OAC Champion, 20 R=0.31 OAC Madoc, 21 S03-W4, 21 OAC Clinton, 22 Naya, 22 p=0.01 Naya, 23 90M30, 23 OAC Ayton, 24 Savanna, 24 25 S05-T6, 25 OAC 13-58C-ChCdn, 25 OAC Belgrave, 26 DH530, 26 OAC Gretna, 27 DH618, 27 DH530, 28 OAC Madoc, 28 OAC Calypso, 29 OAC Ayton, 29 30 90M30, 30 OAC Wallace, 30

Yield 33) Rankings to (1 Yield S03-W4, 31 OAC Bayfield, 31 DH420, 32 OAC Gretna, 32 OAC Wallace, 33 DH420, 33

35 Moorefield Elora

Figure 2.5: Yield rank crossover effects and Kendall Tau-b Rank Correlation coefficient in 2015 between the organic location in Moorefield, ON and the Conventional location in Elora, ON.

Y=Yield(kg/ha), HT=Height(cm), LODG=Lodging (1-5), DTM=Days to Maturity, Oil=Oil Content (%), RL=Root Length(cm), SA=Root Surface Area(cm2), NUTUE=General Nutrient Use Efficency, AUCPC= Area Under the Canopy Progress Curve, NM=Nodule Mass(g), N=Nitrogen Content at R5(%), P=Phopshorus Content at R5(%), K=Potassium Content at R5(%)

Figure 2.6: GxT Biplot for 2014 and 2015 combined trait data selected using factor analysis in Elora ON constructed using GGE Biplot

Y=Yield(kg/ha), HT=Height(cm), Prot=Protein Content (%), RL=Root Length(cm), SA=Root Surface Area(cm2), NUTUE=General Nutrient Use Efficency,AUCPC= Area Under the Canopy Progress Curve, NM=Nodule Mass(g), N=Nitrogen Content at R5(%), P=Phopshorus Content at R5(%), K=Potassium Content at R5(%)

Figure 2.7: GxT Biplot for 2014 and 2015 combined trait data selected using factor analysis in Moorefield ON constructed using GGE Biplot

Chapter 3:

Assessment of early generation selection in organic vs conventional environments using two different soybean populations

3.1: Abstract

Breeding plants specifically for organic production systems has been generating interest recently in the research community. Soybean is an important crop in organic farmer’s rotations and organic age has been growing with the increased demand for processed soy-foods. Selection during the early generations, F4 to F6, of breeding represents the most rapid reduction in diversity in most soybean breeding programs. Two populations, IR062 (OAC Calypso x DH618) and

IR055 (OAC Sunny x S05-T6), were grown at both organic and conventional sites in their F5 and

F6 generations in 2014 and 2015, respectively. The populations were subjected to a 25% selection pressure based upon visual qualities in both environments during the F5 generation and selections made in both environments were advanced to the next year of trials. The F6 generation was selected based upon yield using two options, a 25% selection pressure and selecting those that yielded higher than the check cultivars. The IR055 population was genotyped using 13 polymorphic markers and a dendrogram of the population was constructed. The yield distributions of the populations at each environment showed that the IR055 population was higher yielding at the organic location and the IR062 population was higher yielding in the conventional location. When examining the dendrogram no obvious genetic bias was observed between the selections made in the organic environment and those made in the conventional environment in the IR055 population. The highest yielding lines in the conventional location did end up ranking within the top ten yielding lines in the organic location in the F6 generation for both populations. In the context of an entire breeding program selection in the conventional environment alone will result in the loss of high yielding organic germplasm.

3.2: Introduction

It has been suggested that it maybe worthwhile to start breeding plant cultivars which are more specifically adapted to the unique challenges faced by organic farmers (Haring et al., 1999;

Lammerts van Bueren et al., 2002; Murphy et al., 2007; Wolfe et al., 2008; Reid et al., 2009;

Reid et al., 2010; Vollmann and Menken, 2012; Crespo-Herrera and Ortiz, 2015; Shelton and

Tracy, 2015). Conventional farmers have available to them a suite of management options, in the form of chemical fertilizers and various forms of biocides, to modify and regulate the environment to suit the demands of the crops they grow. In contrast, organic farmers seek to design robust systems in which major management problems are prevented by utilizing on farm ecosystem services, biological controls and inputs allowed by the regional organic regulations

(Sandhu et al., 2010). In the absence of the same external inputs used by conventional farmers organic crops may encounter more stresses in terms of nutrient deficiencies, weed competition and pest pressures.

Predominantly, modern plant cultivars, which are bred in the conventional environment, are being grown by organic farmers. The large rate of yield improvements in conventional agriculture over the past 100 years has been driven by two major avenues: improved agronomic practices and inputs, and improved breeding to optimize plant cultivars for the environment they will be grown in (Evans, 1980). The “Vision for Organic Food and Farming Research Agenda to

2025” identifies the need to close the productivity gap between organic and conventional systems via eco-functional intensification (Niggli et al., 2008). Eco-functional intensification is the process of increasing the productivity of organic systems by better utilizing improved knowledge, practices and technologies to enhance on farm ecosystem services (Niggli et al.,

2008). It is reasonable to assume that the major avenues of improved yields can be the same for

organic production systems as they were in conventional agriculture and these systems will be optimized via improved research and extension in the coming years and plant breeding plays a major role in this process. However, to justify the resource allocation to separate breeding programs for organic agriculture the performance and selection of breeding lines must be altered when tested in both organic and conventional environments.

Investigations to determine if the organic environment has a significant impact on breeding have focused on the latter stages of testing self-pollinated crops (F7-F10) such as wheat

(Crespo-Herrera and Ortiz, 2015). Previously, Murphy et al., (2007) tested thirty-five soft white winter wheat breeding lines in the F7-F9 stages in five locations in Washington, USA. When examining the effects of direct selection and indirect selection for yield in organic systems

Murphy et al., (2007) found that at four of the five sites direct selection in organic systems produced higher yielding lines for the organic site and significant genotype*environment interactions were observed. Reid et al. (2009) investigated the impact of the organic environment on selection of 79 F4 derived F6 spring wheat breeding lines from a population derived from the cross AC Barrie x Attila in Edmonton, AB Canada. When applying three different selection pressures to their population in both conventional and organic environments based on yield, Reid et al. (2009) advanced 1 line, 4 lines and 8 lines in common between production systems using

10%, 15% and 20% selection pressures, respectively. Heritability measures for yield and other agronomic traits in the population from AC Barrie x Attila suggested that similar progress could be made by breeding in both organic and conventional environments (Reid et al., 2009). In a subsequent study, where Reid et al. (2010) conducted multi-environment yield trials for the F7 and F8 generations of the AC Barrie x Attila population, they found that realized gain from

selection was much lower in the organic environments and attributed it to the inherent variability of the organic production system (Reid et al., 2010).

Since organic agriculture holds such a small share of the overall soybean growership resulting in lower predicted sales of organic plant cultivars, organic farmers tend to be under serviced by the private plant breeding and seed industry (Lyon et al., 2015). This means that organic plant breeding will most likely have to be serviced by public sector researchers and breeders in conjunction with organic farmers (Lyon et al., 2015; Lammerts van Bueren & Myers,

2012; Shelton & Tracy, 2015). The majority of soybeans in Ontario are grown for oil extraction and the leftover by-product is used for animal feed, however, there remains a strong conventional food grade soybean market, which is on the rise as consumer acceptance of processed products such as soymilk has increased in non-Asian markets (Cober et al., 2010). Most organic soybeans produced in the world are destined to be processed into soyfood products such as tofu or soymilk

(Vollmann and Menken, 2012). Providing improved food grade soybeans for organic growers in

Ontario may help this burgeoning industry.

The use of multiple breeding populations and high selection pressure in many large scale breeding program means that those lines, which may have performed best in organic production systems may have been purged from the germplasm through the hierarchical selection process.

This process has been going on since the mass adoption of conventional agricultural methods and subsequent targeted breeding for high input environments during the Green Revolution in the

1970s. Furthermore, the practice of regularly intercrossing elite genotypes generated from these hierarchical breeding schemes has led to the development of a modern narrow elite germplasm in many breeding programs around the world, which is geared towards high yields in conventional environments. Ultimately, Murphy et al. (2007) and Reid et al. (2010) concluded that selection

for organic plant cultivars should be conducted on organically managed land. It is important to note that plant cultivars, which were developed in conventional environments do not always perform uniformly in all conventional environments (Crespo-Herrera and Ortiz, 2015).

Intuitively, plant cultivars developed in organic environments would follow a similar trend in terms of regional adaptation (Crespo-Herrera and Ortiz, 2015). The hypothesis of this chapter is that selection in the early generations of breeding populations results in different genotypes being selected under conventional and organic production systems. The objectives of the chapter are: 1) to determine if selection of early generation F5/F6 soybean lines for organic production systems needs to be performed on organically managed land in Ontario; 2) to determine if selection of breeding lines in the two production systems causes a genetic bias between the groups of lines selected in organic vs conventional environments.

3.3: Materials and Methods

Two F4 soybean RIL populations, IR055 (OAC Sunny x S05-T6) and IR062 (OAC

Calypso x DH618), were selected based on one of the parents being a food grade cultivar. These

RIL F4:5 where grown in non-replicated completely randomized yield trials on an organic farm in

Moorefield ON and a conventionally managed research station in Elora, ON in 2014 and 2015.

This produced four location-years: Moorefield 2014, Elora 2014, Moorefield 2015, Elora 2015.

In 2014, IR055 was composed of 179 F5 lines and IR062 was composed of 152 F5 lines. F5 trials were planted June 4th 2014 at both Moorefield and Elora with a modified corn planter in 76.2 cm spaced single row plots 3.5m long with 50 seeds per plot (187, 476 seeds/ha). Plots emerged

June 20th 2014 at both sites. At the end of the 2014 growing season a 25% selection pressure was imposed upon each population in both Moorefield and Elora. Visual selection conducted by an experienced plant breeder (Dr. Istvan Rajcan) and myself based on characteristics such as: 98

growth habit, number of pods, pod distribution on the stem, lodging, branching and maturity.

Selections in each population were classified into three groups those made in the organic site only, those made in the conventional site only and those made in both sites independently. These groups were named ‘organic’, ‘conventional’ and ‘cross’, respectively. Those selected in the opposing environment were advanced to the next year in each location to compare their performance across the environments in the F6 generation. Selected lines were advanced to F6 in

2015, in an un-replicated completely randomized design and tested with replicated check cultivars and the parents of the cross. Check cultivars used in the F6 yield trials included: OAC

Champion, OAC Prescott, OAC Wallace, Factor, Colby, DH 530 and OAC Calypso. F6 yield trial plots were composed of four row plots spaced 38.1cm in-between rows and were planted 7m long then trimmed to 5m for an area of 7.62m2. Plots were planted with a Hege self propelled plot seeder. Plots in Elora ON were planted at a seeding rate of 468,691 live seeds per hectare and 618,672 live seeds per hectare were used in Moorefield. In Elora 2015 plots were planted on

May 29th and emerged on June 10th and in Moorefield 2015 plots were planted June 4th and emerged June 15th.

In Moorefield 2014 the sodium bicarbonate test estimated available P in the soil at 9.1 ppm whereas K was 94 ppm. According to the OMAFRA fertility recommendations the P and K content of the organic soil would have been yield limiting (OMAFRA, 2009). The soil test values in the organic environment would have called for 40 kg/ha of phosphate and potash to be applied but no nutrients were applied in 2014 (OMAFRA, 2009). In Elora 2014 the P test value was 15 ppm and K was at 77 ppm. As a result, both phosphate and potash fertilizers were applied at the recommended OMAFRA rate for the K soil tests in the 61 to 80 ppm in the conventional environment at a rate of 60kg/ha (OMAFRA, 2009). This raised both the P and K in the

conventional environment well above the levels at which those nutrients would be limiting to soybean production.

Prior to planting in 2015 dairy cattle manure was applied to site in Moorefield 2015 at an unknown rate. Soil test P and K in Moorefield 2015 were 9.7 ppm and 100 ppm resulting in an

OMAFRA recommendation of 30 kg/ha of phosphate and 40 kg/ha of potash be applied per hectare (OMAFRA, 2009). The soil test results from the site in Elora 2015 were 15 ppm available P and 140 ppm available K both of which are considered in the low response range and did not recommend additional phosphate or potash applications according to the OMAFRA guidelines (OMAFRA, 2009).

Weeds were controlled in Elora in both 2014 and 2015 using a pre-plant herbicide and a post emergence herbicide mix including: Assure II, Pinnacle, Basagran Forte. In Elora, post- emergence spraying was done on July 10th 2014 and on July 7th 2015. In Moorefield weeds were controlled using manual inter-row cultivation with 5” stirrup hoes from Johnny’s Select Seeds and elbow grease. In Moorefield 2014 the first round of inter-row cultivation occurred on July

2nd and the second was completed on July 11th, July 14th and July 20th to 21st. In Moorefield 2015 for population IR062 inter-row cultivation occurred on June 24th and on July 24th and for population IR055 inter-row cultivation occurred on July 2nd and on July 25th.

The land utilized at the organic site in Moorefield, ON, was managed organically for the past 20 years and the rotation which preceded the trials in 2014 was: hay, mixed vegetables, rye grain, and oats (cover crop of buckwheat). In Elora 2014, the rotation preceding the replicated yield trials was barley research plots and soybeans. In Moorefield 2015, the yield trials were conducted in the same field but in a different section and the rotation preceding it was the same as in the previous year except a summer fallow followed by a fall cover crop of tillage radish that

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was employed in this area in 2014. In Elora 2015, the crops preceding the soybean trials was two years of corn preceded by soybeans.

Plots were harvested in Elora 2014, Moorefield 2014 and Elora 2015 with a small plot combine from Wintersteiger (Ankeny, IA). Grain moisture and weight and were collected manually for trials in Moorefield using a DICKEY-john(Auburn, IL) GAC 2100 and a table top scale, respectively, to calculate yield in kg/ha at a standard 13% moisture. Plots in Elora 2015 were harvested with a SPC20 Almaco (Nevada, IA) combine which automatically collected moisture and weight for each plot which was used to calculate Yield(kg/ha) at 13% moisture.

Planting errors in Elora and Moorefield 2015 resulted in partial and half plots being planted. These errors were removed from the data set in the location that they occurred and from the corresponding location data set to make the data balanced. An observation was considered an outlier and removed if it’s absolute value of the internally studentized residuals were higher than

Lunds critical value of 3.23 (α = 0.05) (Lund, 1975). Removed outliers were treated as missing data points in the Analysis of variance (ANOVA). ANOVA was conducted using proc mixed for both 2014 data from the F5 generation and 2015 data from the F6 generation. Contrasts were used to compare the difference between the selections made within a location and those made outside the location in both 2014 and 2015 for each population. In 2015, contrasts were also used to compare all selections to the checks in each population in each location. Estimate statements were used to obtain Best Linear Unbiased Estimates (BLUEs) for all selections and for the location specific selections in each population in each location for both years. LS means were calculated for the selection groups in a population at a location when the ANOVA demonstrated a significant selection group effect.

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Leaf samples of individuals from the population IR055 were harvested from the field using foreceps and placed in a 2 ml 96 well plate then stored at -18oC. Leaf samples were ground by placing a steel grinding bead in each well along with 400 ul of lysis buffer then sealed using a plate seal and tape and placed in a plate homogenizer at 20 Hz for 80 seconds. The 96 well 2 ml plate was incubated at 65oC on a shaker platform for an hour then centrifuged at 500 g for 5 minutes in the Eppendorf centrifuge 5804 with the A-2-DWP swing bucket rotor. One hundred ul of Lysate was transferred to a 400 ul 96 well plate and 200 ul of binding buffer is added and mixed by using a 12 channel pipette. Lysate buffer mixture was transferred to a Pure Plate 96 well DNA Binding Plate from OmniGenX (Santa Clara, CA, USA) in a Multi-Well Plate

Vacuum Manifold from PALL Life Sciences (Port Washington, NY, USA) and vacuumed for 2 minutes at 23 Hg. A volume of 400 ul of first wash buffer was added to the DNA Binding Plate then vacuumed for 3 minutes at 23 Hg with a sealing film on top. An aliquot of 750 ul of second wash buffer was added to the DNA binding plate then vacuumed for 3 minutes 23 Hg with a sealing film on top. DNA binding plate was then lined up with a Polymerized Chain Reaction

(PCR) plate and secured using elastic bands. DNA Binding Plate was Centrifuged for 1 minute in the Eppendorf centrifuge 5804 with the A-2-DWP swing bucket rotor. DNA Binding plate with

PCR Plate was incubated for at 56oC for 30 minutes. A new PCR plate was secured to the bottom of the DNA Binding plate and 100 ul of 56oC molecular grade water was added to the DNA binding plate then centrifuged for 5 minutes at 5000 g to elute the DNA. Extracted DNA was stored in a 96-well plate at 4oC. Parental DNA from OAC Sunny and S05-T6 was screened for polymorphisms with a set of SSR marker loci from the University of Guelph’s SSR Marker

Library using PCR and M13 fluorescent labelled primers.

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A master mix composed of 3 μl of 20% Trehalose, 4.06 μl of Molecular Biology Grade

H2O, 1.5 μl 10 x PCR buffer, 1 μl 3mM Deoxyribonucleotide triphosphate (dNTPs), 1.5 μl 50 mM Magnesium Chloride (MgCl2) Sigma, 0.12 μl of 4 μM forward primer, 0.48 μl of 4 μM primer reverse, 0.48 μl of M13 fluorescent dye labeled forward primer, 4 μM 0.4 μl of 2.5U/μl

Taq DNA Polymerase, and 3 μl of 10 ng/μl DNA will be prepared for each PCR reactions. The

M13 fluorescent dye labeled primer is either VIC, FAM, NED or RED displaying green, blue, yellow and red fluorescence when analysed, respectively. In a 96 well PCR plate, every well had

3 μl of genomic DNA and 12 μl of the master mixture added. The PCR plate was then spun in a centrifuge briefly to remove any persisting air bubbles (Hermle Z180M, Labnet, Edison, NJ,

USA). The PCR reactions were carried out in a Mastercycler® pro Thermal Cyclers (Eppendorf).

The amplification program consisted of 2 min at 95ºC then 35 cycles each cycle composed of 1.5 minute denaturation at 95ºC, 1.5 minutes annealing at 47ºC, and 1.5 minutes of extension at

68ºC. The last extension step occurred at 72 ºC for 5 minutes then cooled and held at 4 ºC. The

PCR products were then pool-plexed loaded into the AB 3730 DNA Analyzer from Applied

BiosystemsTM (Foster City, CA, USA) to for fragment analysis. The size of the PCR products was compared to the standard ladder (500-Liz) using the program GeneMarker® (Softgenetics,

State College, PA, USA). Fragment sizing was determined using the local southern sizing algorithm.

The alleles at each marker for each line were determined using the software

GeneMapper® v3.7 and each line was scored as either containing the allele from OAC Sunny or

S05-T6. The lines were scored based upon their length in base pairs (bp) and any line ± 3 bp of the parent allele was categorized as a positive detection for that particular allele. Genotypic allele data was formatted in Microsoft excel and loaded into GGT 2.0 (Van Berloo, 2008) to calculate a

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genetic distance matrix for all the lines calculated. The matrix was loaded into MEGA version 4

(Tamura et al., 2007) and analysed using the “Phylogeny” function with the Unweighted Pair

Group Method with Arithmetic Mean (UPGMA) option to produce a dendrogram displaying the degree of relatedness between all population members and the subset of selections.

3.4: Results 3.4.1: F5 yield data

Combined ANOVA across environments for the differences in yield (kg/ha) among the

58 selected genotypes in population IR055 in 2014 showed a significant genotype effect and no genotype*environment effect in both Moorefield and Elora (Table 3.1). Combined ANOVA for the difference in yield between selection groups (organic selections, conventional selections and cross selections) in population IR055 in 2014 showed no significant differences between selection groups and no significant selection group*environment interactions in both Moorefield and Elora (Table 3.1). The ANOVA for differences in yield between the selection groups in population IR055 showed no significant selection group effect in either Moorefield or Elora in

2014. The contrasts comparing the differences between location specific selections and those selected outside the location were also not significant in either Moorefield or Elora in 2014

(Table 3.2).

Combined ANOVA for the differences in yield (kg/ha) between the 67 selected genotypes in population IR062 revealed a significant genotype effect and no genotype*environment effect in both Moorefield and Elora in 2014 (Table 3.1). Combined

ANOVA for the difference in yield between selection groups (organic selections, conventional selections and cross selections) in population IR062 showed no differences between selection groups or selection group*environment interactions in both Moorefield and Elora in 2014 (Table

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3.1). There was a significant selection group effect when variance analysis was conducted on the differences in yield in population IR062 in Moorefield 2014 (Table 3.2). The contrast of the differences between Moorefield 2014 specific selections and those selected in the conventional environment in the IR062 population had no significant effect (Table 3.2). The selection group effect was not significant for the ANOVA of the differences in yield in population IR062 in

Elora 2014 (Table 3.2). The contrast for the difference in yield between Elora 2014 specific selections and those selected in the organic environment, Moorefield 2014, in the IR062 population was significant (Table 3.2).

In Table 3.3 arithmetic means for selection groups were reported when the populations did not have significant selection group effect in a particular site and Least Squares (LS)means are reported for the populations which had significant selection group effects. Best Linear

Unbiased Estimates (BLUEs) are provided for the overall mean of each population in each site and for site specific selections (Table 3.3). The visual selections for both IR055 and IR062 were analysed (Table 3.3). When utilizing a 25% selection pressure population IR055 had a greater number of lines in common than IR062, 24 versus 8 lines, between the two sites.

3.4.2: F6 yield data

Combined ANOVA for the difference in yield between 58 selected genotypes from population IR055, the parents of the population, and 7 check cultivars grown in Moorefield and

Elora 2015 showed no significant genotype or environment effect, however, there was a significant genotype*environment effect (Table 3.5). Combined ANOVA for the difference in yield between the three selection groups from population IR055 and the group of check cultivars grown in Moorefield and Elora 2015 showed no genotype effect, environment effect or genotype*environment effect (Table 3.5). There was no selection group effect for yield between

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the selection groups from population IR055 and the group of checks and parents grown in

Moorefield 2015. However, the contrasts comparing all selection groups directly to the check cultivars and organic selections to the check cultivars were significant for IR055 in Moorefield

2015 (Table 3.6). In Elora 2015 the variance analysis for the difference in yield between the selection groups and check and parents group had a significant selection group effect (Table 3.6).

For IR055 population in Elora 2015, the two contrasts comparing all selection groups to the check group and all the selections made in the conventional environment to the checks were both significant (Table 3.6).

Combined ANOVA for yield between the 63 selected genotypes from population IR062,

6 check cultivars and the two parents of the population grown in both Moorefield and Elora in

2015 showed no significant genotype effect; however, a significant environment and genotype*environment effect was observed (Table 3.5). The combined ANOVA for yield between selection groups from IR062, and a group of the checks and parents of the population showed no significant selection group effect, environment effect or genotype*environment

(Table 3.5). ANOVA for yield between selection groups from population IR062 and the group of checks and parents grown in Moorefield 2015 showed no significant selection group effect. None of the contrasts comparing all selections and only organic selections to the group of checks were significant either (Table 3.6). In Elora 2015 the selection group effect for population IR062 was significant. There was also a significant contrast comparing all selections to the checks and a significant contrast comparing all conventional selections to the group of checks in Elora 2015

(Table 3.6).

In Table 3.7 the LS means for the different selections groups in populations IR055 and

IR062, the check means, and BLUEs for all the selections and site specific selections are

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reported. In the organic site only the mean of the organic selection group was higher than the check mean for population IR055 (Table 3.7). No selection group in the conventional site in either population had a higher mean yield than the check mean (Table 3.7).

The F6 yield distributions for members of population IR055 in the organic location

(Figure 3.1) showed that the distribution was skewed towards higher yields with many of the lines yielding above 3400 kg/ha and three lines, all of which were selected in the organic site and yielding over 4300 kg/ha. The type of selection (organic, cross and conventional) were pretty evenly distributed in terms of their yields throughout the F6 population (Figure 3.1). The IR055

F6 population yield distribution in the conventional location appears relatively normal with selection types evenly distributed throughout the population (Figure 3.2). Only five lines yielded over 4000 kg/ha from IR055 in the conventional site, three of which were selected in both environments and two selected in the organic environment (Figure 3.2). The cross over effects between the organic site in Moorefield and the conventional site in Elora for population IR055 are displayed with the selection group for each line being color coded (Figure 3.3). The first and second ranked lines in IR055 were the same in both locations and represented a cross selection and an organic selection, respectively (Figure3.3). Although the two highest yielding lines were the same in both locations significant crossover effects were observed throughout the population.

(Figure 3) and the rank correlation was very low (Ʈ=0.08, Table 3.7).

The population yield distribution of IR062 in the organic location appears normal with the selection type evenly distributed throughout the population (Figure 4). Out of the three highest yielding lines, that yielded above 4200 kg/ha for IR062 in the organic environment, only one was originally selected in the organic environment (Figure 4). For the F6 yield distribution for IR062 in the conventional location, the population is skewed towards higher yields and had 4

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lines that yielded over 4600 kg/ha, all of which were selected in the conventional environment

(Figure 5). Crossover effects between the organic and conventional sites in population IR062 and the selection group as indicated by color are provided (Figure 6). The 1st and 2nd highest yielding lines, 4-IR12-062 and 56-IR12-062 in the organic location, were selected and ranked 1st and 3rd at the conventional site in Elora (Figure 6). However, several of the lines ranking within the top ten in the organic site were only selected in the organic site and yielded low in the conventional site (Figure 6).

The results of different selection schemes, a 25% selection pressure and selecting anything higher than the check mean on the F6 populations are provided (Table 3.8). When selecting in both environments roughly 1/3 of the selections being advanced in each environment were the same (Table 3.8). If selection had been done only in each individual environment, one line out of the 10 being advanced to the F7 would have been in common between the environments in the IR055 population and no lines in common would have been advanced in the

IR062 population (Table 3.8).

3.4.3: SSR Marker Data

Thirteen polymorphic SSR loci were discovered after screening the parental DNA with

.136 SSR markers. The dendrogram constructed from the genotypic SSR data for the selections and parents of population IR055 separated each of the parents into the two major groups (Figure

7). Selection group membership did not appear to determine its association to either of the two major groups (Figure 7). Within the sub groups there are pockets were lines selected in the organic site tended to group together and lines selected in the conventional site tended to group together (Figure 7). The dendrogram of the selections in the context of the entire IR055 population is shown in Figure 8. Once again, both OAC Sunny and S05-T6 are separated by the

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two major groups in the dendrogram and there were specific pockets where groups of similar lines were selected (Figure 8). In the dendrogram of all members of the IR055 population there were also groups of related lines which tended not to have been selected at all (Figure 8).

3.5: Discussion

In the F5 generation I observed a significant difference between the selected genotypes in both populations based on a combined ANOVA across both Moorefield and Elora. However, there was no difference between the selection groups in a combined analysis in 2014 across both locations (Table 3.2). When testing for differences between selection groups in the individual sites for each population there was no difference between the groups in the IR055 population for either Moorefield or Elora. The IR055 population also had a greater number of overlapping selected lines in the population (58.5% of lines overlapping) compared to IR062. The only population that showed a difference between selection groups in 2014 was IR062 in Moorefield.

The LS means of the different selection groups (organic, conventional and cross) in IR055 showed the lines from the cross group were higher yielding compared to the conventional or the organic selections (Table 3.3). When examining the arithmetic means in Table 3.3 for population

IR055 they followed a similar pattern with the yield of the cross selections being higher on average than either the conventional or organic selections. The cross selection group is even higher yielding on average compared to the BLUE for site specific selections in the IR055 population. In contrast, the cross was not highest yielding in the IR062 population when grown in Elora 2014 (Table 3.3) but the difference is only 60.6 kg/ha which is lower than the differences between the cross selection group and other groups in IR055. This suggested that when lines are selected within both of these environments they tend to be on average the highest yielding individuals regardless of environment. This supports that general adaptation between the

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two production systems is achievable as some varieties will likely perform well in both environments; this is promising since high yielding organic lines will not necessarily be lost in early selection phases. Murphy et al., (2007) concluded that indirect selection for wheat yield in a conventional environment would be less efficient than selection in organic environments for yield in terms of genetic correlations for the F6 generation but did not factor in the increased costs of conducting early generation breeding trials separately on organic land. Interaction between genotypes and their environment is common in plant breeding, it is not exclusive to the differences between conventional and organic production systems, but it is desirable to identify genotypes that have broad adaptation to multiple different environments (Crespo-Herrera and

Ortiz, 2015). Conducting selections in both environments simultaneously will help identify these broadly adapted genotypes.

When grown in Elora 2014 the yield of the organic selection group in the IR055 population was comparable to that of the conventional selection group (18.2 kg/ha difference).

However, the organic selections out yielded the conventional selections in the IR062 population in Elora 2014 by 308 kg/ha. Although there was no significant difference at α=0.05 the p-value for the difference in selection group effect was very close to significance (p=0.0568) (Table 3.2).

In addition, the contrast between the conventional selections and the organic selections in Elora

IR062 showed a significant difference. The BLUEs for all selections in the location were very close for each of the populations at each of the sites with populations at the organic sites being slightly lower yielding than at the conventional sites (Table 3.3). It appears that the population seems to determine the degree of difference between selection groups within the environment in the F5 generation with IR062 having much larger differences between selection groups in each location and less overlap across selections (Table 3.3).

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At the F6 generation there was no significant genotype effect in either population but there was a significant genotype*environment effect in both IR055 and IR062. This result suggested that the environment may have influenced the performance of the lines (Table 3.5).

For Moorefield and Elora, the selection group effect for IR062 and IR055 was not significant and there was no selection group*environment (Table 3.5). There was no selection group effect in

Moorefield 2015 for either IR055 or IR062, however, there was a significant selection group effect for both populations in Elora 2015. The significant selection group effects were likely due to the higher average yields of the check cultivars in Elora 2015 yielding 4003 kg/ha and 4146 kg/ha in the IR055 and IR062 tests, respectively (Table 3.7). According to the LS means test the three selection groups (cross, organic, and conventional) were not significantly different from each other in either population in Elora but were different from the checks (Table 3.7).

In Moorefield 2015, the contrasts comparing all the selections versus the checks and the site specific selections vs the checks were significant (Table 3.6). Both the BLUEs of the site specific selections (3630 kg/ha) and all selections (3601 kg/ha) were higher than the check mean for IR055 in Moorefield 2015 (Table 3.7). IR055 in Moorefield 2015 is the only instance in which the mean yield of the selections actually out yielded that of the checks and was significantly different according to the contrast between all selections and the check mean.

When comparing the performance of IR055 and IR062 between Moorefield and Elora it is apparent that there is a cross over effect between the locations for these two populations.

IR055 was the higher yielding population in Moorefield with an average of 3601 kg/ha compared to 3380 kg/ha in Elora. The BLUE for all selections in IR062 in Elora 2015 was 3745 kg/ha compared to 3025 kg/ha in Moorefield. When Reid et al. (2009) examined 79 F4 derived wheat

RILs over 3 growing seasons the population mean never once yielded higher in the organic

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location than the conventional location. When Murphy et al. (2007) tested 35 wheat genotypes from varied sources at five different pairs of organic and conventional sites only in one instance did the organic location yield more than the conventional and the difference was not significant.

Since the IR055 population was performing better in the organic environment and the IR062 population was performing better in the conventional environment it is possible that we can design specific crosses for organic environments. Interestingly, there was a large difference in population performance despite the parents of each cross coming from high yielding elite conventional germplasm resources. It has been previously asserted in the literature that production system specific germplasm introductions maybe required to develop the best performing organic cultivars (Wolfe et al., 2008). From our results in soybeans in Ontario it appears that members of the current elite germplasm are performing well in an organic setting.

In Figure 1 the yield distribution of IR055 in Moorefield seems skewed towards the higher yielding or the right side as compared to the more normal yield distribution of the IR055 in Elora (Figure 3.2). It is obvious that a greater number of individuals in IR055 were higher yielding in Moorefield. When examining the distribution of the selection groups in both

Moorefield and Elora for IR055 it appears that selection groups did not tend to group at the end of the spectrum in either location. Instead, the members of each selection group seem to be rather evenly distributed (Figure 3.1 and 3.2). In Figure 3 the crossover effects between the yield ranking of selections from IR055 at the two locations showed that two lines, 158-IR12-055 and

2-IR12-055, were the 1st and 2nd highest yielding in both environments. The rank correlation for the IR055 population was not significant and relatively low at Ʈ =0.08 (Table 3.7). Low rank correlations between varieties tested on organic vs conventional sites where also observed between 4 out of 5 pairs of sites by Murphy et al. (2007). This suggests that even though the

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selection groups seem evenly distributed for yield distribution the high yielding lines which are performing well in each environment in the IR055 population were not necessarily the same ones.

The dendrogram of the selections in the IR055 population (Figure 3.7) has small localized groupings of specific selection groups but there is no obvious divide or clustering of selection groups. The clusters in the dendrogram of the entire IR055 population showed definite pockets where related lines tended to be selected but there was once again no obvious division between organic selections and conventional selections (Figure 3.8). Interestingly, there are definite pockets where negative selection has occurred for a group of related lines which were never selected from the F5 generation (Figure 3.8).

In the case of the IR062 population the yield among the lines in the organic environment was much more normally distributed (Figure 3.1) and the one from the conventional environment is more skewed to towards higher yields (Figure 3.2). In the case of the IR062 there does not seem to be any particular grouping of the selections in either Moorefield or in Elora. The crossover diagram for IR062 between Moorefield and Elora shows that the 1st and 2nd highest yielding lines in the organic environment, 4-IR12-062 and 56-IR12-062, were also the 3rd and 1st highest yielding lines in Elora, respectively (Figure 3.6). However, the higher yielding organic selections in Moorefield had stronger crossover effect and yielded lower in Elora with some of the top 10 selections in Elora being ranked at the bottom in Moorefield (Figure 3.6). Similar crossover effects between wheat genotypes that were high yielding in the organic environment were observed at 4 pairs of organic and conventional locations investigated by Murphy et al.

(2007).

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There were marked differences between both the locations and the populations in terms of selections. If we considered the top 25% of selections being made independently in both

Moorefield and Elora out of 10 lines advanced in each location only one line would have been in common between the sites. Using the same scenario for the IR062 population there would be no lines to overlap and be advanced in both sites (Table 3.8). The standard method of selecting lines in the F6 generation in the University of Guelph’s soybean breeding program is to advance only those that match or out yield the average check cultivar. This method might be unrealistic since there was such a large difference in the yield of the checks in the organic site compared to the conventional site. The LS mean for the checks was much lower in the organic site in both IR055 and IR062, 3292 kg/ha and 2982 kg/ha, compared to the conventional site, 4003 kg/ha and 4146 kg/ha, respectively. As a result, if the check mean was used as a benchmark for line advancement in the organic site about 80% of the lines would be advanced in the IR055 population and 50% of the lines would be advanced in IR062. If this method were employed in the conventional site only about 5% and 30% of lines would be advanced in populations IR055 and IR062, respectively. It appears that using a specific selection pressure might be a better approach to selection in the organic site since it would greatly reduce the number of lines being advanced in the program, making it more manageable.

Ultimately, it must be determined if using the yield data from the populations in the conventional environment to conduct indirect selection for yield in the organic site is worthwhile. If the population IR055 had been selected only in the conventional site, Elora, using a 25% selection pressure (selecting 10 lines) then only two individuals would be advanced that ranked within the top 10 on the organic site, Moorefield. In the case of population IR062 selecting exclusively in Elora for the top 25% (eight lines) would have resulted in three

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individual lines being advanced that ranked within the top 25% of lines in Moorefield. If the criteria was used for advancing lines higher yielding than the checks in the conventional only, then two lines would be advanced in IR055 and 11 would be advanced in IR062. One of the two lines advanced in the IR055 population would be 158-IR12-055 which was the highest yielding line fromm this population in both environments. Out of the 11 lines being advanced in the

IR062 population four would have ranked within the top 25% of lines in Moorefield.

My data shows that not all material which was high yielding in the organic environment would be purged from a breeding program if it was conducted solely in the conventional environment between the F5 and F6 generations. However, the F5 to F6 generations are still at a relatively early stage of any breeding program. On average a single soybean cultivar is released from the University of Guelph’s soybean breeding program each year and hundreds of crosses are made which makes the probability of selecting a high yielding organic line seem extremely remote. It is also important to note that a considerable number of high yielding organic lines would most likely have been lost. If the selection was based on the check mean, two lines would have been advanced into F7 trials from the IR055 population in the conventional environment and 11 lines would have been advanced from the IR062 population. However, in the organic environment IR055 was on average higher yielding than the IR062 population. In the context of the general germplasm of a breeding program, if selection only occurred in the conventional location it would be selecting away from high yielding organic germplasm. If a breeder were to seek to maximize the number of high yielding organic lines being advanced to replicated F7 trials it seems more advantageous to conduct these early generation trials in organic environments in order to maximize the chances of developing a high yielding organic cultivar. This conclusion was shared by Reid et al. (2009) who also determined that to it would be appropriate to select for

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organic yield on organically managed land in the case of wheat on the Canadian Prairies.

However, in the context of a program with limited resources which has to also cater to larger group of conventional soybean growers relative to the organic ones it could be more cost effective to conduct these trials on a conventional site. Conventional data could be used to conduct indirect selection for organic yield in the F6 generation and advance F7 lines to replicated multi-environmental trials, which may include at least a single organic site. This is similar to the system that is suggested by Baenziger et al. (2011) who initiates testing and selection of wheat breeding lines in the F6 generation while conducting testing for disease resistance and other traits which are important to both production systems in the early generations.

3.6: Conclusion

In conclusion, our results suggest the selection pressure caused by the organic and conventional environments is population specific. IR055 seems to be better adapted to the organic environment than IR062. There was no obvious genetic bias observed in the selections in population IR055 but with only 13 polymorphic markers being used the power is relatively low.

The high yielding lines in the conventional location do tend to rank within the top ten yielding lines in the organic location in the F6 generation. However, in the context of an entire breeding program selection in the conventional environments alone will result in the loss of high yielding organic germplasm. Using the selection criteria based on the check mean will not be suitable in the organic environment since the checks developed for conventional environments were low yielding in the organic environment. Thus, a percent selection pressure would be more appropriate when conducting selection in the organic environment.

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General Discussion and Conclusions

This thesis examined the traits that could be important for organic soybean production and development of an organic soybean ideotype using a cultivar trial. It was postulated that the ideal organic soybean cultivar for organic farmers would display the following features: improved NutUE and nutrient uptake, the ability to suppress and compete with weeds, enhanced

N fixation, enhanced rhizosphere exudates and signalling to encourage symbiotic relationships with micro-organisms, adequate seed components and quality parameters for food grade use, and maintain high yield under biotic and abiotic stresses. Although few groups had researched these traits in direct relation to organic production systems the research has been carried out in conventional environments only.

In the cultivar trials, I measured the rate of crop canopy development, took soil cores to measure nodule mass and root morphology traits, and measured NutUE by estimating nutrient uptake by the R5 development stage to the ratio of yield produced in both the organic and conventional production sites. There were significant differences between the cultivars in the organic vs. conventional sites for the traits related to yield but this relationship was not always consistent. The Genotype x Trait (GxT) Biplot conducted for both years for these various traits for the organic and conventional environments separately, suggested that the traits related to plant’s resource acquisition like AUCPC, nodule mass and root length seemed to be more closely related to yield in the organic system than the conventional system. In the conventional system

GxT Biplot, NUTUE, which is a measure of the efficiency of the remobilization of nutrients to produce yield, was more closely related to yield. This result represents a major contrast in the stress encountered by organic vs conventional crops. With fewer and more difficult to access nutrients in the soil profile, the major limiting factor for production was not the efficiency of

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remobilization of nutrients to the seed to produce yield but their initial uptake (Lynch and Ho,

2005). With an increased focus on topsoil foraging an inherit trade off occurs whereby more carbon is partitioned to the root system reducing the amount of photosynthate contributing directly to yield (Lynch and Brown, 2001). Since a weed free environment is not the norm in organic production systems the ability to rapidly close the plant canopy may be of greater importance to the organic crop since fewer management options exist for growers to control weeds and it will help to maximize light interception faster reducing later weed competition

(Swanton and Murphy, 2013). Increased stress due to both weed competition and reduced nutrient availability could substantially reduce nodulation (Kasperbauer et al., 1984; Israel,

1987). Therefore, the cultivars that were able to more effectively manage these stresses in the organic environment may have also substantially improved their nodulation, carbon economy and overall yield.

In our trials we were unable to identify a particular cultivar which consistently yielded higher in the organic than in the conventional system, likely due to year-to-year variations in environments. However, the expectation may have been unrealistic since year effects can drastically alter the performance of soybean cultivars in conventional systems as well. The traits that were analyzed (early season canopy development, root length and nodule mass) would make criteria for screening for superior soybean cultivars for organic systems technically difficult as they are relatively time consuming and sometimes difficult to measure. If these methods could be modified for a high throughput fashion they might be able to form screening criteria for a breeding program. However, under the assumption that these traits are highly related to yield, which is suggested in our PCA, it can be inferred that selecting for them could be done simultaneously when selecting for yield alone. Since yield is truly the sum of all relevant traits

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and their gene expression in a given environment (Slafer, 2003), the results suggest that selecting for high yielding soybean lines in an organic production system will also select for these traits we have investigated.

In the selection experiment, soybean breeding lines in the F5-F6 generations were selected in the organic vs. conventional environments independently. In the two breeding populations that were used, selection pressure imposed by the organic environment was population dependent. In the F6 generation, population IR055 appeared to be better adapted overall to the organic system with a higher overall yield and a greater number of lines yielding over 4300 kg/ha than the IR062 population. In a conventional breeding program only 2 lines from the F6 IR055 population outperformed the average check yield to be advanced to the F7 multiple environment tests. In contrast, 48 (82.8%) of the lines from the IR055 population were higher yielding than the average of the replicated check varieties in the organic site. This finding has larger implications given the cyclical and hierarchical nature of a breeding program. If germplasm which is not ideal for organic production systems is continually being selected in the conventional programs and elite selected lines are used in the next round of crosses, the overall germplasm in the program could lose the most highly productive lines in the organic systems.

The check cultivars were so low yielding in the organic environment that using a percent selection pressure would be a more useful selection tool. Despite the large population differences some of the top yielding lines in the conventional environment were still high yielding in the organic environment in the F6 generation. This suggests that selection in the conventional environment would not completely purge useful organic germplasm from a breeding program.

No significant differences in genetic structure of the lines selected in organic vs. conventional

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system were observed, however, the number of markers tested was relatively low thereby reducing the power of the analysis.

Resource allocation constraints and overall goals should be considered when making decisions about conducting early generation breeding trials in organic production systems. From our results my opinion is that if the overall goal of a breeding program was to maximize the chances of producing a good organic soybean cultivar early generation testing in organic sites would be worthwhile. However, a full cost benefit analysis would be required to judge the value in sales of soybean varieties directed to organic farmers, the increase in yield and profit gained by farmers and processors then weigh it against the cost associated with running separate soybean breeding trials at different stages of a breeding program. If there are currently 23,000 acres of organic soybeans being grown in Southern Ontario such a cost benefit analysis would have to include different scenarios of projected growth in acreage over the course of a ten-year organic breeding program and even further into the future.

Due to resource restrictions these trials where limited in size as only a single organic and a single conventional location was used over two years, which reduces the scope of inference for these trials. Although considerable effort was made to make sure that the organic farm selected for the trials displayed the typical characteristics of an organic production system it is impossible to claim that one particular farm typifies organic practices across a wide region due to the many differences between individual farms. Further research should focus on conducting cultivar trials in multiple organic and multiple conventional sites investigating only one of the traits, which showed consistent differences to make the study more manageable. The two populations of soybeans should be advanced to the F7 generation utilizing a 25% selection pressure and then

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tested in replicated multi-environmental organic and conventional trials to further investigate differences in selection pressure.

I would recommend a few improvements on the methods used for my research to an graduate researchers or other interested parties. Firstly, taking three separate canopy development photos was probably superfluous and similar results and data could have been acquired by simply taking photos at the V3 development stage rather than all V1, V3 and V5. In

2014 we found a that the Arcsine of Canopy coverage at V3 was highly correlated to AUCPC and it also displayed a significant GxE interaction effect. This suggests that this measure might typify the differences in canopy development between production systems more than other stages of growth. Our results generally agree with Place et al. (2011a) who concluded that taking canopy development photos at 3 WAE provided the best estimate of weed suppressive ability and our results from the 2014 growing season would support this since the V3 stage did occur at about 3 WAE in each of our sites. In Elora and Moorefield 2014, there were 22 days and 25 days between emergence and the V3 growth stage, respectively. However, utilizing a growth stage as a benchmark for taking observations is probably more effective to accommodate differences in temperature, and climate between locations. Secondly, the enormous task that is required to collect soil core samples could be made easier by reducing the total number of cores collected.

Rather than collecting both a core on the row and directly beside the row a subsequent researcher should consider the increased efficiency of the task that is achieved by taking a single core per plot. In addition, if a great deal of soil cores are being processed at once consider developing a system for root washing such as the one described by Benjamin and Nielsen (2004) whereby up to 24 samples could be processed at once.

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Organic farming was once considered a marginal research area by the scientific community.

This initial stigma is being shed as organic agriculture has risen to be one of the main challenges to our current or “conventional” paradigm within the farming and scientific community.

Increasing the amount of scientific knowledge and development related to organic agriculture and farming systems will lead to further improvements and fine tuning to these systems. As researchers from different fields of science begin to converge upon this intriguing topic the productivity of these farming systems can be enhanced. Geneticists and breeders will play a crucial role in the future to improve the productivity of the organic farming systems by developing varieties adapted to their unique challenges.

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Table 3.1: P-values for the combined ANOVAs for the difference in yield between the genotypes and selection groups from populations IR0551 and IR0622 in 2014 m completely randomized un- replicated designs

IR055 IR062 IR055 IR062 Fixed Effects Selection Group Genotype 0.0049 <0.0001 0.4223 0.2701

Random Effects Environment - 0.2798 Environment - - Genotype * Selection Group* - - - 0.2368 Environment Environment Residual <.0001 <.0001 Residual <.0001 <.0001 1 RIL Population derived from OAC Sunny X S05-T6, 2 RIL Population derived from OAC Calypso X DH618

Table 3.2: P-values for the ANOVAs for the difference in yield between the selection groups and the contrast from populations IR0551 and IR0622 in both Moorefield and Elora in 2014 from completely randomized un-replicated designs

Moorefield Elora IR055 IR062 IR055 IR062 Fixed Effects

Selection Group 0.6001 0.0129 Selection Group 0.4153 0.0568

All Organic vs All Conventional vs Conventional 0.6425 0.1510 Organic 0.4701 0.0414 Selections Selections Random Effects

Residual <.0001 <.0001 Residual <.0001 <.0001

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Table 3.3: Average F5 yields for single row plots IR-055(OAC Sunny X S05-T6) and IR-062 (OAC Calypso x DH618) for all selections, different selection groups (organic selections, conventional selections and those selected in both sites) and BLUEs1 for only those selected in each location and the location average. Yields (kg/ha) IR-0551 IR-0622 Population Selection Group Moorefield Elora Moorefield3 Elora Selected Organic 1829.3 1820.8 1569.7b 1696.9 (SD4) (±437.82) (±444.64) (±101.62) (±491.75) Least Squares or Arithmetic Means Selected 1829.3 1838.6 1687.1b 2005.2 Conventional (SD) (±599.16) (±301.65) (±103.36) (±470.46) Selected in Both 1961.8 1970.8 2244.9a 1945.8 (SD) (±436.26) (±421.79) (±196.79) (±590.9) 1873.5 1895.6 1882.6 All Selections (SE5) 1833.9 (±81.47) (±65.15) (±77.61) (±72.39) BLUEs6 Site Specific 1895.6 1904.7 1907.3 1975.5 Selections (±77.61) (±63.07) (±110.74) (±98.75) (SE) Ʈ7 0.22* 0.322*** 1 RIL Population derived from OAC Sunny X S05-T6, 2 RIL Population derived from OAC Calypso X DH618, 3 LS means are being reported for the Moorefield IR-062 population because there was a significant selection effect for this site and Standard Errors are reported rather than Standard Deviation, 4Standard Deviation, 5 Standard Error, 6Best Linear Unbiased estimate, 7 Kendall Tau-b rank correlation co-efficient

Table 3.4: Results of initial visual selections carried out in the F5 populations IR-055 (OAC Sunny X S05-T6) IR-062 (OAC Calypso X DH618) in 2014

Populations Initial Number Number of Lines in Total Number Number of Lines Selections common of Lines of Lines advanced to F6 IR-055 175 162 41 (25.3%) 24 (58.5%) 58 IR-062 152 152 38 (25%) 8 (21%) 67

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Table 3.5: P-values for the combined ANOVAs for the difference in yield between the genotypes and selection groups from populations IR0551 and IR0622 in 2014 from completely randomized un-replicated designs

IR055 IR062 IR055 IR062 Fixed Effects

Genotype 0.3603 0.2945 Selection Group 0.8760 0.5425

Random Effects Environment 0.4468 <.0001 Environment . 0.2429 Genotype * 0.0008 0.0215 Selection Group* 0.1101 0.4054 Environment Environment Residual <.0001 <.0001 Residual <.0001 <.0001

Table 3.6: P-values for the ANOVAs for the difference in yield between the selection groups and the contrasts from populations IR0551 and IR0622 in both Moorefield and Elora in 2015 from completely randomized un-replicated designs

Moorefield Elora IR055 IR062 IR055 IR062 Fixed Effects

Selection Group 0.1450 0.9760 Selection Group <.0001 0.0200

All Selections vs All Selections vs 0.0265 0.8193 <.0001 0.0033 Checks Checks Organic Selections Conventional 0.0219 0.8985 <.0001 0.0064 vs Checks Selections vs Checks Random Effects

Residual <.0001 <.0001 Residual <.0001 <.0001

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Table 3.7: Average F6 yields for non-replicated yield trials IR-055(OAC Sunny X S05-T6) and IR-062 (OAC Calypso x DH618) for the check cultivars, different selection groups (organic selections, conventional selections and those selected in both sites) and BLUEs1 for only those selected in each location and the average of all the selections. Yields (kg/ha) IR-0551 IR-0622 Population Selection Group Moorefield Elora Moorefield Elora Checks 3292.3 4003.1a 4146.6a 2982 (±152.68) (SE3) (±115.78) (±89.31) (±106.13)

Organic selections 3662.5 3467.9b 3048.9 3801.48b (SE) (±131.71) (±103.88) (±121.04) (±83.82) Least Squares (LS) Conventional Means 3543.5 3323.7b 3056.5 3917.85b Selections (±131.71) (±103.88) (±127.83) (±88.53) (SE) Cross Selections 3597.6 3350.5b 3517b 2971 (±291.51) (SE) (±110.85) (±87.43) (±201.88) 3601.03 3380.7 3025.5 3745.4 All Selections (SE) (±72.25) (±56.99) (±112.81) (±78.41) BLUEs4 Site Specific 3630.01 3337.1 3717.4 Selections 3010 (±156.84) (±86.07) (±67.89) (±109.94) (SE) Ʈ5 0.08 0.09 1RIL Population derived from OAC Sunny X S05-T6, 2RIL Population derived from OAC Calypso X DH618, 3Standard Error, 4Best Linear Unbiased estimate, 5Kendall Tau-b rank correlation co-efficient

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Table 3.8: Details of F6 selections based upon yield carried out in the F6 populations IR-055 (OAC Sunny X S05-T6) and IR-062 (OAC Calypso X DH618) in 2015

Initial Location Selection Scenario Final Lines advanced Lines advanced Number Number (selection higher than of Lines in F6 pressure) checks in F6 IR055 (OAC Sunny x S05-T6) All Selections 58 58 14(24.1%) 48 (82.8%) Moorefield Organic Selections 41 41 10(24.4%) 34 (82.9%) All Selections 58 58 14(24.1%) 4 (6.9%) Elora Conventional Selections 41 41 10(24.4%) 2 (4.9%) IR062 (OAC Calypso x DH618) All Selections 67 60 15(25%) 31 (51.7%) Moorefield Organic Selections 38 34 8(23.5%) 15 (44.1%) All Selections 67 60 15(25%) 16 (26.7%) Elora Conventional Selections 38 31 8(25.8%) 11 (35.5%) IR-062 IR-055 All IR-055 Specific IR-062 All Specific Selections Selections Selections Selections Number of Selections in 4/14 1/10 4/15 0/8 Common

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OAC Sunny: 3188 Kg/ha Conventional Cross Organic S05-T6: 2218 Kg/ha Check Mean: 3237 Kg/ha Population Mean: 3600 Kg/ha 3

Frequency 4 3

2 2 3 6 6 1 1 2 2 2 2 2 1 1 1

Yield Range (Kg/ha)

Figure 3.1: Yield distribution of population IR055(OAC Sunny X S05-T6) F5 selections grown in F6 yield trials in the organic locaiton in Moorefield, ON in 2014

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OAC Sunny: 3800 Kg/ha Conventional Cross Organic S0 5 - T6: 3451 Kg/ha Check Mean: 4039 Kg/ha Population Mean: 3377 Kg/ha

3 3

5 4 4

Frequency 4

2 3 5 3 6 6

3 2 2 1 1

> 2 5 0 0 2500- 2800 2800- 3100 3100- 3400 3400- 3700 3700- 4000 < 4 0 0 0 Yield Range (Kg/Ha

Figure 3.2: Yield distribution of population IR055(OAC Sunny X S05-T6) F5 selections grown in F6 yield trials in the organic locaiton in Elora, ON in 2015

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Selection Group: •Cross •Conventional •Organic

0 158-IR12-055, 1 158-IR12-055, 1 2-IR12-055, 2 2-IR12-055, 2 130-IR12-055, 3 168-IR12-055, 3 36-IR12-055, 4 119-IR12-055, 4 144-IR12-055, 5 107-IR12-055, 5 162-IR12-055, 6 18-IR12-055, 6 110-IR12-055, 7 155-IR12-055, 7 41-IR12-055, 8 82-IR12-055, 8 107-IR12-055, 9 36-IR12-055, 9 10 13-IR12-055, 10 104-IR12-055, 10 171-IR12-055, 11 117-IR12-055, 11 55-IR12-055, 12 40-IR12-055, 12 48-IR12-055, 13 34-IR12-055, 13 39-IR12-055, 14 113-IR12-055, 14 168-IR12-055, 15 7-IR12-055, 15 40-IR12-055, 16 81-IR12-055, 16 104-IR12-055, 17 128-IR12-055, 17 146-IR12-055, 18 23-IR12-055, 18 91-IR12-055, 19 67-IR12-055, 19 20 82-IR12-055, 20 14-IR12-055, 20 60-IR12-055, 21 12-IR12-055, 21 78-IR12-055, 22 55-IR12-055, 22 14-IR12-055, 23 91-IR12-055, 23 18-IR12-055, 24 60-IR12-055, 24 34-IR12-055, 25 29-IR12-055, 25 175-IR12-055, 26 160-IR12-055, 26 119-IR12-055, 27 48-IR12-055, 27 29-IR12-055, 28 110-IR12-055, 28 132-IR12-055, 29 170-IR12-055, 29 30 129-IR12-055, 30 76-IR12-055, 30 131-IR12-055, 31 75-IR12-055, 31 11-IR12-055, 32 130-IR12-055, 32 151-IR12-055, 33 63-IR12-055, 33 113-IR12-055, 34 144-IR12-055, 34 76-IR12-055, 35 1-IR12-055, 35 160-IR12-055, 36 32-IR12-055 112-IR12-055, 37 20-IR12-055, 37 Yield Rankings (1 58) Rankings to (1 Yield 63-IR12-055, 38 11-IR12-055, 38 27-IR12-055, 39 165-IR12-055, 39 40 32-IR12-055, 40 127-IR12-055, 40 12-IR12-055, 41 41-IR12-055, 41 126-IR12-055, 42 112-IR12-055, 42 128-IR12-055, 43 142-IR12-055, 43 117-IR12-055, 44 151-IR12-055, 44 150-IR12-055, 45 162-IR12-055, 45 67-IR12-055, 46 39-IR12-055, 46 77-IR12-055, 47 132-IR12-055, 47 142-IR12-055, 48 146-IR12-055, 48 7-IR12-055, 49 131-IR12-055, 49 50 81-IR12-055, 50 77-IR12-055, 50 165-IR12-055, 51 175-IR12-055, 51 170-IR12-055, 52 150-IR12-055, 52 1-IR12-055, 53 126-IR12-055, 53 23-IR12-055, 54 129-IR12-055, 54 127-IR12-055, 55 78-IR12-055, 55 75-IR12-055, 56 27-IR12-055, 56 155-IR12-055, 57 13-IR12-055, 57 20-IR12-055, 58 171-IR12-055, 58 60 Moorefield Elora

Figure 3.3: Yield rank crossover effects in 2015 for selections from population IR055 (OAC Sunny x S05-T6) between the organic location in Moorefield, ON and the Conventional location in Elora, ON.

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OAC Calypso: 2824 Kg/ha Conventional Cross Organic DH618: 3227 K g/ ha Check Mean: 2890 K g/ ha Population Mean: 3070 K g/ ha

2 9 6

5 1 Frequency 1 2 1 7 1 4 4 1 4 3 3 3 3 2 1 1 1

Yield Range (Kg/ha) Figure 3.4: Yield distribution of population IR062 (OAC Calypso X DH618) F5 selections grown in F6 yield trials in the organic location in Moorefield, ON in 2015

OAC Calypso: 4504 Kg/ha Conventional Cross Organic DH618: 3960 Kg/ha Check Mean: 4109 Kg/ha Population Mean: 3839 Kg/ha

Frequency 6 2 2 1 1 8 2 4 4 5 3 4 1 1 1 > 2 8 0 0 2800- 3100 3100- 3400 3400- 3700 3700- 4000 4000- 4300 4300- 4600 < 4 6 0 0 Yield Range (Kg/ha)

Figure 3.5: Yield distribution of population IR062 (OAC Calypso X DH618) F5 selections grown in F6 yield trials in the organic location in Elora, ON in 2015

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Selection Group: •Cross •Conventional •Organic 0 4-IR12-062, 1 56-IR12-062, 1 56-IR12-062, 2 80-IR12-062, 2 47-IR12-062, 3 4-IR12-062, 3 37-IR12-062, 4 107-IR12-062, 4 91-IR12-062, 5 58-IR12-062, 5 17-IR12-062, 6 41-IR12-062, 6 112-IR12-062, 7 63-IR12-062, 7 33-IR12-062, 8 71-IR12-062, 8 68-IR12-062, 9 12-IR12-062, 9 10 94-IR12-062, 10 37-IR12-062, 10 64-IR12-062, 11 43-IR12-062, 11 24-IR12-062, 12 53-IR12-062, 12 96-IR12-062, 13 124-IR12-062, 13 97-IR12-062, 14 65-IR12-062, 14 63-IR12-062, 15 78-IR12-062, 15 36-IR12-062, 16 68-IR12-062, 16 78-IR12-062, 17 33-IR12-062, 17 80-IR12-062, 18 62-IR12-062, 18 71-IR12-062, 19 8-IR12-062, 19 20 29-IR12-062, 20 39-IR12-062, 20 40-IR12-062, 21 9-IR12-062, 21 133-IR12-062, 22 17-IR12-062, 22 103-IR12-062, 23 64-IR12-062, 23 92-IR12-062, 24 139-IR12-062, 24 101-IR12-062, 25 36-IR12-062 12-IR12-062, 26 113-IR12-062, 26 6-IR12-062, 27 13-IR12-062, 27 65-IR12-062, 28 100-IR12-062, 28 8-IR12-062, 29 98-IR12-062, 29 30 145-IR12-062, 30 50-IR12-062, 30 108-IR12-062, 31 6-IR12-062, 31 113-IR12-062, 32 40-IR12-062, 32 41-IR12-062, 33 29-IR12-062, 33 85-IR12-062, 34 82-IR12-062, 34 124-IR12-062, 35 47-IR12-062, 35 53-IR12-062, 36 103-IR12-062, 36 14-IR12-062, 37 136-IR12-062, 37 98-IR12-062, 38 85-IR12-062, 38 110-IR12-062, 39 94-IR12-062, 39 40 82-IR12-062, 40 108-IR12-062, 40 141-IR12-062, 41 112-IR12-062, 41 139-IR12-062, 42 24-IR12-062, 42

Yield 60) Rankings to (1 Yield 50-IR12-062, 43 147-IR12-062, 43 79-IR12-062, 44 14-IR12-062, 44 147-IR12-062, 45 87-IR12-062, 45 13-IR12-062, 46 99-IR12-062, 46 84-IR12-062, 47 84-IR12-062, 47 99-IR12-062, 48 143-IR12-062, 48 146-IR12-062, 49 101-IR12-062, 49 50 39-IR12-062, 50 133-IR12-062, 50 62-IR12-062, 51 146-IR12-062, 51 100-IR12-062, 52 91-IR12-062, 52 87-IR12-062, 53 92-IR12-062, 53 143-IR12-062, 54 10-IR12-062, 54 136-IR12-062, 55 79-IR12-062, 55 43-IR12-062, 56 97-IR12-062, 56 9-IR12-062, 57 96-IR12-062, 57 107-IR12-062, 58 141-IR12-062, 58 10-IR12-062, 59 110-IR12-062, 59 60 58-IR12-062, 60 145-IR12-062, 60 Moorefield Elora

Figure 3.6: Yield rank crossover effects in 2015 for selections from population IR062 (OAC Calypso x DH618) between the organic location in Moorefield, ON and the Conventional location in Elora, ON

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Figure 3.7: Dendrogram of genetic distance between selections made from population IR055 based off 13 polymorphic SSR markers

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Figure 3.8: Dendrogram of the different cluster in the entire IR055population based off 13 polymorphic SSR markers

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