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A META-ANALYSIS OF HOW MANAGEMENT PRACTICES AFFECT SOYBEAN YIELD AND QUALITY

December 2019

December 2019

Thesis for: Master of Science
Advisor: Frederick E. Below

Authors:

CHS Inc

Soybean [Glycine max (L.) Merr.] is a major cultivated crop, providing protein and oil for human and animal feed. While soybean yields in the United States have increased over the years, grain oil concentrations have remained relatively constant while protein concentrations have decreased. The objective of this work was to determine if management practices (nitrogen (N), phosphorus (P), and potassium (K)) fertilization; foliar protection, and reduced row spacing) could simultaneously increase yield as well as seed protein and oil concentrations. A meta-analysis was performed on 50 soybean crop management projects conducted between 2012 and 2018, which included five field sites around Illinois. These trials measured yield, seed protein and oil concentrations, weather (precipitation and temperature), soil constituents (CEC, organic matter, P and K levels), and recorded planting and harvest dates. A meta-analysis of the mean differences was used to examine the impact of management practices on yield, and moderators to explain the heterogeneity levels between studies were included. Nitrogen or P fertilization, reduced row spacing and foliar protection all increased yield, while K fertilization tended to decrease yield. Seed protein concentration was not affected by N or K fertilization, but was altered by P fertilization depending on the method of application. Seed protein concentration decreased when the P fertilizer was banded beneath the crop row, but tended to increase when the P fertilizer was broadcasted on the soil surface. Reduced row spacing and foliar protection decreased seed protein concentration. Banded P fertilization, reduced row spacing, and foliar protection all increased seed oil concentration. Soil organic matter level and planting date were moderators that explained the variation in the responses to N fertilization of soybean yield and protein concentration, respectively. In regards to P fertilization, soil P level was a moderator of the yield response, while soil CEC was a moderator of the seed oil concentration response. Yield and seed quality responses iii to reduced row spacing were both moderated by soil CEC. In response to foliar protection, yield was moderated by soil organic matter, while seed protein and oil concentrations both had location as a moderator. These data show that N and P fertilization, reduced row spacing, and foliar protection can individually increase soybean yield, and that banded P fertilization, reduced row spacing and foliar protection can increase seed oil concentration, but no management practice evaluated in this review was able to simultaneously increase yield and seed protein concentration. iv ACKNOWLEDGEMENTS

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A META-ANALYSIS OF HOW MANAGEMENT PRACTICES AFFECT SOYBEAN

YIELD AND QUALITY

VITOR RAMPAZZO FAVORETTO

THESIS

Submitted in partial fulfillment of the requirements

for the degree of Master of Science in Crop Sciences

in the Graduate College of the

University of Illinois at Urbana-Champaign, 2019

Urbana, Illinois

Master’s Committee:

Professor Frederick Below, Chair

Research Assistant Professor Carrie Butts-Wilmsmeyer

Professor Brian Diers

ABSTRACT

Soybean [Glycine max (L.) Merr.] is a major cultivated crop, providing protein and oil for

human and animal feed. While soybean yields in the United States have increased over the years,

grain oil concentrations have remained relatively constant while protein concentrations have

decreased. The objective of this work was to determine if management practices (nitrogen (N),

phosphorus (P), and potassium (K)) fertilization; foliar protection, and reduced row spacing) could

simultaneously increase yield as well as seed protein and oil concentrations. A meta-analysis was

performed on 50 soybean crop management projects conducted between 2012 and 2018, which

included five field sites around Illinois. These trials measured yield, seed protein and oil

concentrations, weather (precipitation and temperature), soil constituents (CEC, organic matter, P

and K levels), and recorded planting and harvest dates. A meta-analysis of the mean differences

was used to examine the impact of management practices on yield, and moderators to explain the

heterogeneity levels between studies were included. Nitrogen or P fertilization, reduced row

spacing and foliar protection all increased yield, while K fertilization tended to decrease yield.

Seed protein concentration was not affected by N or K fertilization, but was altered by P

fertilization depending on the method of application. Seed protein concentration decreased when

the P fertilizer was banded beneath the crop row, but tended to increase when the P fertilizer was

broadcasted on the soil surface. Reduced row spacing and foliar protection decreased seed protein

concentration. Banded P fertilization, reduced row spacing, and foliar protection all increased seed

oil concentration. Soil organic matter level and planting date were moderators that explained the

variation in the responses to N fertilization of soybean yield and protein concentration,

respectively. In regards to P fertilization, soil P level was a moderator of the yield response, while

soil CEC was a moderator of the seed oil concentration response. Yield and seed quality responses

iii

to reduced row spacing were both moderated by soil CEC. In response to foliar protection, yield

was moderated by soil organic matter, while seed protein and oil concentrations both had location

as a moderator. These data show that N and P fertilization, reduced row spacing, and foliar

protection can individually increase soybean yield, and that banded P fertilization, reduced row

spacing and foliar protection can increase seed oil concentration, but no management practice

evaluated in this review was able to simultaneously increase yield and seed protein concentration.

ACKNOWLEDGEMENTS

I would like to start by thanking my parents, whom beyond placing me in the world, gave

me an education that permitted me to be where I am. Next, I thank my host parents, because it was

in the experience that they offered me that I decided to be an agronomy major. I want to thank the

Universidade Estadual de Londrina in Parana, Brazil for the free college degree in Agronomy and

the University of Illinois at Urbana-Champaign (UIUC), especially the Department of Crop

Sciences for the graduate program. I also want to give thanks to Giovani Fontes, for having me as

a roommate through the period of my master’s studies. To my advisor Professor Frederick Below,

for accepting me as a student in his program and the opportunity to work in diverse projects,

because with that broader experience, I found direction in pursuing my career and also to professor

Romulo Lollato from Kansas State University, who encouraged me to go to graduate school. I

heavily thank my partner Marli de Moraes Gomes for all the emotional support and understanding.

Special thanks to all the personnel of the Crop Physiology laboratory of the University of Illinois

at Urbana-Champaign, from all the undergraduate interns and visiting scholars to the graduate

student colleagues, technician and the Research Specialist for all the support in the evaluations in

field to the inputs in writing and statistics, which Dr. Butts-Wilmsmeyer deserves a highlight. To

each person that was present in these two years of graduate school, because the experience from

each of you contributed to the road that brought me here. Finally, I would like to thank the United

States Soybean Board, from the American Soybean Association for the direct funding of this

research and all the other indirect sponsors that contributed to the studies used for the database of

this project.

TABLE OF CONTENTS

INTRODUCTION .......................................................................................................................... 1

METHODS ................................................................................................................................... 10

RESULTS ..................................................................................................................................... 19

DISCUSSION ............................................................................................................................... 24

CONCLUSIONS........................................................................................................................... 36

TABLES AND FIGURE .............................................................................................................. 37

REFERENCES ............................................................................................................................. 47

APPENDIX: SUPPLEMENTAL TABLES ................................................................................. 68

INTRODUCTION

Soybean (Glycine max [L.] Merr.) is a leguminous plant, in the biological family Fabaceae

(Leguminosae). Its center of origin is in the northwest of China, specifically the Manchuria region

(EMBRAPA, 2005; Missao, 2006), where domestication of soybean started three millenniums

ago (North Carolina Soybeans Producers Association, 2019). The ancient cultivated soybean had

a creeping growth habit and grew along wetlands near lakes and rivers (Aprosoja, 2014). Through

breeding, the Chinese started its cultivation before English travelers and oriental immigrants

(Missao, 2006) dispersed it to the south of China, as well as to Korea and to Japan in the 3rd

century B. C. (Aprosoja, 2014).

In the United States, a Georgia British colonist introduced soybean in 1765, but cultivation

expanded greatly eighty-six years later, with the distribution of seeds to farmers in Illinois and

other states in the Corn Belt (North Carolina Soybeans Producers Association, 2019). Soybean

became a commodity worldwide in 1919, after the First World War, and its world production

chain was established with the creation of the American Soybean Association (Aprosoja, 2014).

Currently, soybean is a major globally-cultivated crop, with worldwide production

increasing every year. Soybean cultivation reached 352.6 million metric tons in 2017, representing

an increase of 5.1% in relation to 2016 (FAO, 2019). Consequently, the consumption of soybean

around the world increased 4.54% from 1987 to 2009 (Lazzarotto and Hirakuri, 2010). One reason

for this historic increase in soybean production in the world is the various uses for its grain,

especially its protein and oil, which are components of high economic importance (Brumm and

Hurburgh, 1990; Hurburgh, 1994). The United States is one of the main producers of soybean

worldwide, producing 120.52 million metric tons in the 2018/19 season, or 33.6% of the world

total (USDA-FAS, 2019). Of the U.S. production in 2018 that was not exported, 47.4% was

crushed (USDA-NASS, 2019), a process that creates two co-products: meal and oil.

Soybean meal is one of the major protein sources for animal feed, due to its balanced amino

acid profile and high digestibility level, making it universally accepted as the most important

protein ingredient in animal diets (Willis, 2003). Compared to other animal-feed protein sources,

soybean has the highest quality, providing an economic advantage to its use (Grieshop and Fahey,

2001). The United States production of soybean meal was 44,626 metric tons in 2017/18 and has

been increasing since 2015 (USDA-NASS, 2019). The marketing of soybean meal is highly

dependent on its protein concentration meeting a required minimum (Rotundo et al., 2016)

following oil extraction from the crushed grain (Brumm and Hurburgh, 1990). The desired

concentrations of protein and lipids in the soybean grain are 400 g kg-1 and 200 g kg-1 of the dry

matter, respectively (De Moraes et al., 2006), which is equivalent to 348 and 174 g kg-1 of the

grain at 130 g kg-1 moisture. Since the beginning of the 21st century, soybean grain protein

concentration has been decreasing. Protein concentration also has been lower than the desired

concentration since 2012, when the reported levels at 130 g kg-1 moisture were 343 g kg-1 for

protein and 185 g kg-1 for oil (Miller-Garvin and Naeve, 2017). The productivity of American

soybeans has been increasing annually, achieving yield of 3331 kg ha-1 in 2017 (Miller-Garvin and

Naeve, 2017). However, because the protein concentration in the grain has been decreasing, the

concentration in its meal coproduct has simultaneously been tending to decrease. Therefore, to

supply the same amount of protein, more of the less-protein concentrated meal is needed, which

increases feed costs and influences the final price of the resulting animal meat (Plume, 2017). The

lower protein concentration in the grain also hiders the United States exports of soybean, resulting

in lost market share in China to other countries that have a higher level of grain protein (Plume,

2018).

The other coproduct of the crushed grain, soybean oil, also is directly related to its

concentration in the original soybean grain. Similar to meal, its production has increased and

reached approximately 10.8 million metric tons in 2017/18 (USDA-NASS, 2019). Besides direct

human nutrition as a food additive, the process to obtain oil generates lecithin, which is a versatile

coproduct. Lecithin is used as an emulsifying agent in the food industry (Hymowitz and Newell,

1981), but it is also used in the production of paints, insecticides, cosmetics and textiles (Scott and

Aldrich, 1970; Wolf and Cowen, 1971). Recently, transesterification technology has allowed

soybean oil to be used as biodiesel (Kinney and Clemente, 2005). According to Kinney and

Clemente (2005, p. 1139), “approximately 3.6% of the U.S. soybean oil production is targeted for

industrial applications (approximately 288 million kg), of which 1% (4.5 million kg) is used for

biodiesel”. Thus, a continued increase in demand for soybean oil is expected to occur. Historically,

soybean grain oil concentration has been increasing concurrently with the yield increase (Miller-

Garvin and Naeve, 2017). However, since 2010, the production of the oil has been less than the

demand (USDA-ERS, 2019). In 2017/18, the gap in the production of soybean oil compared to the

amount used was less than the previous season, decreasing from –133,734 to -23,122 metric tons

(USDA-ERS, 2019), with processing of stored grain helping to overcome the shortfall. With the

potential for further increased use of soybean oil by industry, there is a need to continue increasing

the soybean grain oil concentration concurrently with yield.

The final soybean grain quality (protein and oil concentrations), similar to yield, is driven

by a combination of the seed genetics, the growing environment, the agronomic management

practices, and their interactions (De Bruin and Pedersen, 2008; Assefa et al., 2019). Some of the

soybean agronomic management practices available include the location, genetics, nutrient

fertilization, planting row configuration, and foliar protection. Since the mid-1990s, the U. S.

Soybean Board has led an initiative to increase the compositional quality of the U. S. soybean to

meet the domestic market need through identifying and improving the associated traits (Durham,

2003). However, the influence of each factor on the resulting grain quality characteristics is not

entirely known (Rao et al., 2002; Assefa et al., 2018).

Several studies reported the influence of the plant growth environment on the resulting

soybean grain quality (Grieshop et al., 2003; Rotundo et al., 2016; Mourtzinis et al., 2018; Assefa

et al., 2018, 2019). The consensus among these studies is that soybeans cultivated in warmer areas,

with adequate water throughout the season tended to have higher concentrations of both protein

and oil in the grain. This greater compositional grain quality may be linked to the center of origin

of the crop, which has humid summers with tropical heat (Box and Choi, 2003). Thus, expanding

soybean cultivation to areas in the country that are colder and/or have water limitations during the

growing season impacts the average quality of U. S. soybeans.

In addition, Assefa et al. (2019) relate planting date with the environment for plant growth,

because it determines the time that the crop will be exposed to the environment and the period that

the nutrients are available to the plant. In the same study, the response to planting date was also

linked to latitude, in which soybean planted after the 145th day of the year tended to have less

yield and grain oil concentration than those planted earlier when grown in the mid to high latitude

ranges in the United States (35-45° N).

Different ranges in temperature also could be reflected in high yield variability as well as

in the amino acid profile of the soybean grain (Carrera et al., 2011). In addition, soil characteristics

could influence the growth of soybean, such as cation exchange capacity (CEC) and organic

matter. Villamil et al. (2012) studied soybean yield from on-farm data in Illinois and found that

soil CEC and organic matter levels had a negative relationship with grain yield.

Modern techniques have increased the efficiency of the breeding process, optimizing

variety development by private institutions, consequently introducing more varieties with higher

yield potential (Sleper and Shannon, 2003). Genetic traits linked to high protein grain have been

identified (Wilcox and Cavins, 1995; Cober and Voldeng, 2000; Sebolt et al., 2000). However,

current breeding programs have focused primarily on yield potential, while the grain quality

became a secondary factor of minimal interest. Thus, one approach to achieving greater grain

quality is for companies to develop new high-yielding varieties that also generate high grain

quality in the multitude of production environments available.

Even with modern breeding techniques, new varieties take a significant amount of time to

be launched into the market. Therefore, another solution to the low-quality profile of the grain

could be done with in-season management of the current existing varieties. Modern soybean

systems are focused on high yields, which are linked to rapid canopy closure, comprised of more

leaves and more photosynthetic tissue (Arce et al., 2009). These soybean systems must have

sufficient nitrogen to assist in the conversion of solar radiation into new biomass and grain yield

(Salvagiotti et al., 2008).

Nitrogen is one of the most required elements by plants and it is a component of proteins

(Souza and Fernandes, 2006). Soybean can use many forms of nitrogen, including atmospheric

nitrogen because of its symbiotic relationship with Bradyrhizobium japonicum bacteria (Macák

and Candráková, 2013). The peak nitrogen fixation rate occurs in the late plant reproductive stages

(Zapata et al., 1987). Demand prior to that peak needs to be supplied by another nitrogen source

(Salvagiotti et al., 2008) to avoid remobilization from other tissues, which could limit yield (Kessel

and Hartley, 2000). Therefore, external provision of this nutrient could be a viable alternative for

a system focused on the combination of higher yields and quality.

Besides nitrogen, phosphorus is necessary in many processes of plant metabolism, such as

energy transfer, synthesis of nucleic acids, and cellular membrane stability (Araujo and Machado,

2006), and also helps in the fixation of atmospheric nitrogen (Vance et al., 2003). In addition,

Bender (2015) found that 80% of the accumulated phosphorus from a modern soybean plant is

removed with the grain, and if not replenished in the soil, may result in future yield limitations.

Farmaha et al. (2012) observed significant effects of phosphorus fertilization on soybean protein

and oil concentrations and yield under different tillage systems. Phosphorus fertilization was able

to increase protein and oil in the grain when applied before sowing in a study performed in Pakistan

(Abbasi et al., 2012). In the same study, potassium fertilization was able to increase yield and

quality of soybean grain at both supply levels, 40 and 80 kg ha-1. In addition, in the United States,

soybeans are commonly fertilized with potassium at a higher rate than any other nutrient because

of the perception among growers that it is the most important nutrient to soybean (USDA-ERS,

2017).

Besides nutrient fertilization, other agronomic management procedures could be done to

enhance yield and quality of the soybean crop. Reducing row spacing is a valuable practice that is

associated with earlier canopy closure (Ball et al., 2000; Silva et al., 2013; Andrade et al., 2019).

Reducing row spacing also permits more light interception (Çalişkan et al., 2007; Zhou et al., 2011;

Silva et al., 2013) and is associated with an increase the leaf area index per plant (Zhou et al., 2011;

Malek et al., 2012), which can lead to more yield at the end of the season. However, it has been

reported that reducing the row spacing less than 30 cm did not result in a yield increase (Moreira

et al., 2015; Ferreira et al., 2019), suggesting that there might be minimum row spacing threshold

for optimum soybean growth and yield. Regarding grain quality, narrowing the row spacing has

led to mixed results, ranging from not affecting it (Al-Tawaha and Seguin, 2006; Bellaloui et al.,

2014; Flajšman et al., 2019) to increasing the quality (Moreira et al., 2015; Werner et al., 2017).

The plant canopy is mainly composed of leaves, which have photosynthesis as their major

function (Taiz and Zeiger, 2010). Thus, protecting the integrity of leaves from foliar diseases and

feeding from insects using fungicidal and/or insecticidal products is a viable practice to increase

soybean yield. Yield gains from applications of those products have been found, especially when

disease pressure was high for a specific pathogen (Delaney et al., 2018; Molina et al., 2019;

Willbur et al., 2019). Other studies have shown a yield benefit of these products regardless of the

disease pressure presumably due to growth regulator effect (Bender, 2015; Beyrer, 2018).

However, foliar applications are not a guarantee of higher yields (Swoboda and Pedersen, 2009).

Assefa et al. (2019) reported that fungicide and insecticide applications resulted in increased grain

oil concentration and had a tendency to increase soybean grain protein level. Therefore, the

management practice of foliar protection could be a viable strategy towards increased soybean

yield and quality. However, a better understanding of the use of foliar protectants is needed, since

fungicide alone has been found to be as effective as a mixture with an insecticide (Ng et al., 2018).

Management practices on soybean production have been widely studied. Thus,

summarizing the findings of an array of studies regarding the effect of management practices on

yield and grain quality could be a comprehensive way to evaluate overall management effect.

Meta-analysis is a statistical tool used to synthetize and quantify the evidence present in many

studies for a certain treatment. It became popular in 1980’s in medicine and social sciences as an

alternative to narrative reviews (Borenstein et al., 2009a; Hedges and Olkin, 2014). It was

introduced to other fields such as ecology and biology in the early 1990’s (Jarvinen, 1991;

Gurevitch et al., 1992) and started being used in agronomy in the early 2000’s (Marra and Kaval,

2000; Ainsworth et al., 2002; Miguez and Bollero, 2005). Similarly to an analysis of variance, an

overall effect of the treatment is reported, which results from a weighted mathematical calculation

of all the studies included in the meta-analysis, providing an objective, clear and replicable result

(Borenstein et al., 2009a).

In agronomy, it is known that each study is different because it is performed in a specific

year and location, thus, variance between studies is expected, and the usage of the random-model

effects in agronomic meta-analysis is preferred because it accounts for and quantifies that

variability (Borenstein et al., 2009b; Mengersen et al., 2013), also known as heterogeneity (I2). In

areas that are expected to have variability among studies, quantifying that variability and

explaining it is crucial for a complete meta-analysis (Koricheva et al., 2013). Dividing the data

into sub-groups and/or including moderators (similar to an analysis of covariance) that quantifies

the differences between studies could explain the heterogeneity (Borenstein et al., 2009a; Steward

et al., 2013). However, minimizing the number of moderators is a good way not to over fit the

model and introduce bias (Steward et al., 2013).

The moderator or subgroup could be statistically relevant or not, depending on its

probability value (p-value). For moderators, other statistical values could be of importance to

assess the most relevant moderator when the p-value among them are similar. The Akaike

information criteria (AIC) is informative when comparing different models (with different

moderators) indicating the best model among others as the one that minimizes the AIC value

(Sakamoto et al., 1988). Also, since moderators are included in a regression model, regression

estimators, such as R2 could also be used, and in the case of meta-regression it means the amount

of the original heterogeneity present in the model that came from between-study variance (I2) that

could be explained when the moderator was added to the model (Viechtbauer, 2010).

In summary, taking into consideration that modern soybean varieties have a greater focus

on yield potential and that soybean cultivation is moving to colder areas in the United States and

Canada, the protein level of United States soybean is decreasing each year. The lower quality

impedes both animal production, since soybean meal is an important source of protein for animal

feed, and the international trade of the commodity, since other countries could offer a higher

quality soybean for similar prices.

Developing new varieties to overcome the issue of low grain protein concentration could

be a solution, but that path must make financial sense for the breeding companies. A more viable

alternative is to manage soybeans during the season not only for yield, but also for the grain quality

aspect. Research needs to be done with the primary intention of determining how agronomic

management affects yield and grain quality.

Therefore, the objectives of this study were to determine if agronomic management

practice(s) could simultaneously increase soybean yield and grain quality, and if so, which

practice(s) would be the most influential in altering yield and quality characteristics. To

accomplish the objectives, meta-analytic methods were used on data archived from studies of the

Crop Physiology Laboratory of the University of Illinois.

METHODS

The Crop Physiology Laboratory has a vast database of soybean experiments from 2012 to

2018, using five locations in the state of Illinois: DeKalb (41°55′53″N 88°45′01″W, 268 m above

sea level); Yorkville (41°39′57″N 88°26′31″W, 200 m above sea level); Champaign (40°06′54″N

88°16′22″W, 233 m above sea level); Rushville (40°07′16″N 90°33′47″W, 205 m above sea level);

and Harrisburg (37°44′02″N 88°32′45″W, 121 m above sea level). All experiments consisted of

replicated treatments arranged in randomized complete block design and measured for yield

(metric tons (T) hectare-1 with 0 g kg-1 moisture concentration), harvested with and AlmacoTM plot

harvester, and a sample was analyzed using a NIR transmittance analyzer (Infratec 1241; FOSS,

Denmark) to obtain grain protein and oil concentrations (g kg-1) standardized to a moisture

concentration of 130 g kg-1. Additional information collected for all trials included: planting and

harvest dates; average temperature (Celsius) and total precipitation (mm); soil cation exchange

capacity (CEC) (Meq 100 g-1); organic matter (OM) (g kg-1); pH; and preplant soil phosphorus and

potassium levels (mg kg-1) The experiments all had accurate records of their protocols, with

treatment descriptions, number of replications and soybean variety(ies) used. The collected results

were stored in a main database each year making the data easily retrievable.

Selection of experiments

For this study, the seven years of experiments up to and including 2018 were considered,

encompassing 70 experiments. Soybean studies with at least three replications each were selected

that had evaluated at least one of the following management practices: dry fertilizer applications,

fungicide and/or insecticide application, and/or row spacing. Ultimately, six experiments were

selected that spanned 50 site-year combinations: “Soybean Management Yield Potential”;

“Soybean Omission Plots”; “Phosphorus Source, Rate and Placement Soybean”; “Soybean

Response to Nitrogen”; “Soybean Fertigation”; and “Soybean Relay”. These experiments are

briefly explained below and the locations, agronomic management(s), and data used are

summarized in Appendix A.

Soybean Management Yield Potential

The Soybean Management Yield Potential experiment was performed from 2016 through

2018. It was planted at three locations in Illinois (Yorkville, Champaign and Harrisburg), resulting

in eight site-years. The goal of this trial was to categorize different soybean varieties regarding of

their yield response to phosphorus fertilization and foliar protection. A resulting offensive variety

would be one that increases yield with both inputs compared to a defensive variety as one that

would have a stable yield regardless of the inputs. A split-plot randomized complete block design

with four replicates was used with the whole plot being fertility and the split plot being foliar

protection, with the randomization restricted to the thirty different varieties tested every year.

Phosphorus (P) fertilization, banded directly underneath the crop row before planting, at a rate of

84 kg ha-1 of phosphorus was provided by the products MicroEssentials SZ™ (MESZ) (12-40-0-

10S-1Zn) (Mosaic, Minneapolis, MN) in 2016 but was substituted with MicroEssentials S10™

(MES10) (12-40-0-10S) (Mosaic, Minneapolis, MN) in the 2017 and 2018 trials. Foliar

applications of fungicide at the R3 growth stage were accomplished using Quadris Top® SB™

(Azoxystrobin + Difenoconazole; Syngenta, Greensboro, NC) at 874 ml ha-1 for 2016, and for

2017 and 2018 Trivapro™ (Benzovindiflupyr +Azoxystrobin + Propiconazole at 1000 ml ha-1;

Syngenta, Greensboro, NC) was used. The insecticide was applied at R3 with or without the

fungicide Endigo® ZC™ (Lambda-cyhalothrin + Thiamethoxam at 292 ml ha-1 (Syngenta,

Greensboro, NC) for the respective treatments all three years.

Soybean Omission Plots

Conducted from 2012 to 2018 at five locations in Illinois (DeKalb, Yorkville, Rushville,

Champaign and Harrisburg), this experiment totaled 19 site-years. Multiple agronomic practices

were investigated as part of this study, including row spacing, fertilization with phosphorus and/or

potassium before planting, and foliar protection with fungicides and/or insecticides at the R3

growth stages. In years that had multiple inputs with a similar mode of action, treatments were

combined for data analysis, theoretically increasing the statistical power. This experiment was

conducted as a split-plot RCBD, with row spacing as the main block and replication as the split

plot. Brief details of the major agronomic managements evaluated each year are as follows:

2012 and 2013 - Phosphorus fertilization: MESZ to provide 84 kg of P2O5 ha-1, banded

before planting. Foliar protection: Fungicide (Quilt Xcel™ at 1,022 ml ha-1 and Priaxor™ at 292

ml ha-1) and insecticide (Endigo ZC™ at 292 ml ha-1 and Fastac™ at 278 ml ha-1 [Alpha-

cypermethrin; Florham Park, NJ]) applied at plant growth stage R3 either individually or

combined. Reduced row spacing: 50.8 cm compared to 76.2 cm.

2014 - Phosphorus fertilization: MESZ to provide 84 kg of P2O5 ha-1, banded before

planting. Potassium fertilization: Aspire™ (0-0-58-0.5B) (Mosaic, Minneapolis, MN) to provide

84 kg of K2O ha-1, broadcasted before planting. Foliar protection: Fungicide (Priaxor™ at 292 ml

ha-1) and insecticide (Endigo ZC™ at 292 ml ha-1 and Fastac™ at 278 ml ha-1) applied at R3 either

individually or combined. Reduced row spacing: 50.8 cm compared to 76.2 cm.

2015 - Phosphorus fertilization: MESZ to provide 84 kg of P2O5 ha-1, banded before

planting. Potassium fertilization: Aspire™ to provide 84 kg of K2O ha-1, broadcasted before

planting. Foliar protection: Fungicide (Priaxor™ at 292 ml ha-1 and Quadris Flowable™ at 438 ml

ha-1 [Azoxystrobin; Syngenta, Greensboro, NC]), insecticide (Endigo ZC™ at 292 ml ha-1 and

Fastac™ at 278 ml ha-1) and adjuvant (Masterlock™ at 292 ml ha-1 [WinField United, Arden Hills,

MN] and FS Aqua Supreme™ at 175.2 ml ha-1 [FS System, Bloomington, IL]) applied together

at R3. Reduced row spacing: 50.8 cm compared to 76.2 cm.

2016 - Phosphorus fertilization: MESZ to provide 84 kg of P2O5 ha-1, banded before

planting. Foliar protection: Fungicide (Priaxor™ at 292 ml ha-1 and Quadris Top SBX™ at 511 ml

ha-1 [Azoxystrobin + Difeconazole; Syngenta, Greensboro, NC] ), insecticide (Endigo ZC™ at 292

ml ha-1 and Fastac™ at 278 ml ha-1) and adjuvant (Masterlock™ at 292 ml ha-1) applied together

at R3. Reduced row spacing: 50.8 cm compared to 76.2 cm.

2017 - Phosphorus fertilization: MES10 or diammonium phosphate (DAP) to provide 84

kg of P2O5 ha-1. The DAP was broadcasted before planting, while the MES10 was either banded

or broadcasted before planting. Foliar protection: Fungicide (Priaxor™ at 292 ml ha-1 and

Trivapro™ at 1,000 ml ha-1) and insecticide (Endigo ZC™ at 292 ml ha-1 and Fastac™ at 278 ml

ha-1) applied together at R3. Reduced row spacing: 50.8 cm compared to 76.2 cm.

2018 - Phosphorus fertilization: MES10 or DAP to provide 84 kg of P2O5 ha-1, with DAP

broadcasted before planting, while MES10 was either banded or broadcasted before planting.

Foliar protection: Fungicide (Priaxor™ at 292 ml ha-1 and Trivapro™ at 1,000 ml ha-1) and

insecticide (Endigo ZC™ at 292 ml ha-1 and Fastac™ at 278 ml ha-1) applied together at R3.

Reduced row spacing: 50.8 cm compared to 76.2 cm.

Phosphorus Source, Rate and Placement Soybean

This experiment was conducted for three years (2014-2016) at Champaign, resulting in

three site-years. It tested different rates (0, 56, 112 and 168 kg ha-1) of phosphorus using

MicroEssentials SZ (MESZ) for all three years, with or without: Titan™ (in 2014) [Bacillus

licheniformis; Loveland products, Greenville, MS]; Titan™, Zync LS™ [0-0-0-7S-10Zn;

WinField United, Arden Hills, MN], or Levesol™ (in 2015); and Levesol™ [20 g kg-1 nitrogen

and chelating agents; CHS, Inver Grove Heights, MN] or Titan (in 2016). These fertilizers were

evaluated using two different application methods (either broadcast or banded). For 2016, a

monoammonium phosphate (MAP) with Zync LS™ was also added as a fertilizer mix treatment.

All treatment combinations had six replications in 2014 and 2015, and nine replications in 2016.

The design was a randomized complete block (RCBD). Only the broadcast versus banded

applications of 112 kg ha-1 of MESZ were used for meta-analysis, as the other treatments were

unique to this project.

Soybean Response to Nitrogen

Conducted from 2013 to 2016, with a design change and expansion from 2013 to 2014, the

trial was implemented at five locations (DeKalb, Yorkville, Champaign, Rushville and

Harrisburg). Of the locations, Champaign and Harrisburg were held constant over the years, while

Rushville and Yorkville were only in 2013 and 2016 respectively, and DeKalb was used from 2013

to 2015, resulting in 13 site-years. The experiment tested the response of soybean to different

nitrogen fertilizer sources consisting of urea (45-0-0) and Environmentally Smart Nitrogen (ESN;

44-0-0; Nutrien, Saskatoon, Canada) in 2013, and the other years evaluating urea, ESN,

ammonium nitrate (AN) (34-0-0), ammonium sulfate (AMS) (21-0-0-24S) and urea-ammonium

nitrate (UAN) (28-0-0). These nitrogen sources were assessed at different application times (before

planting, V3, R1 and R3), usually broadcasted. In 2013, a banded before planting treatment was

also tested, but was excluded from final data analysis, due to its uniqueness. The fertilizer rate was

held constant at 112 kg of N ha-1. The design used was an RCBD, with 2013 being unbalanced,

because only one untreated control was used across all treatments and 2014-2016 balanced, with

one control for each different time of application. After the statistical analysis, all treatment groups

without nitrogen were combined to form an overall control.

Soybean Fertigation

While this experiment was conducted from 2015 to 2018, only the 2015 data was used. It

was conducted at Champaign, in a specialized field for fertigation of the University of Illinois

farms. The general objective of the trial through the years was to show the behavior of different

soybean varieties, with and without fertigation, in different management practices. The specific

treatments changed year by year. The trial was a split-plot RCBD, with the fertigation zones being

the main blocking factor. While there were different treatments in the overall experiment, only the

results from the fungicide plus insecticide application at R3 treatment (Priaxor™: at a rate of 585

ml ha-1 and Fastac™: fluxapyroxad and pyraclostrobin at a rate of 278 ml ha-1 (BASF, Florham

Park, NJ), respectively) was used for data analysis.

Soybean response to Phosphorus Fertilizer Distance

Conducted at Champaign in 2014 and Champaign and Harrisburg in 2015, the trial tested

soybean yield and grain quality response to different fertilizer application methods and different

distance of the band from the planting row. Phosphorus as MESZ was either broadcasted or banded

at 0, 7.6, 15, 22.9, 30.5 or 38.1 cm distance from the planting row to provide a total of 84 kg of

P2O5 ha-1. Treatments were arranged in an RCBD with eight replications and placement (band vs.

broadcast) as the main blocking factor. Data from the broadcasted treatment at 396,000 plants ha-

1 was evaluated.

Soybean Relay

Conducted in 2016, at three Illinois locations (Yorkville, Champaign and Harrisburg),

resulting in three site-years. These trials investigated the yield response of soybean to different

placements (broadcasted, banded below the seed, or banded 15 cm from the seed in one or two

bands) of a phosphorus fertilizer (MESZ to provide a total of 84 kg of P2O5 ha-1), and also with or

without a starter of 10-34-0. The experiment was an RCBD with six replications. Data from the

broadcast or in the 15 cm one-band phosphorus fertilization without starter treatments were used

for analysis.

Statistical analysis

All agronomic management treatments evaluated had respective control plots. Overall,

there were 50 site-years assessed, which contributed at least one management factor to the analysis

(Appendix A). All site-years were analyzed using the PROC MIXED, PROC UNIVARIATE and

PROC GLM procedures of SAS software (Version 9.4, SAS System for Windows, SAS institute

Inc., Cary, NC, USA) to generate means and standard deviation values resulting from each

management treatment, as well as to check assumptions of normality and homoscedasticity. The

outliers were subsequently removed from this original group, leaving at least three replications per

treatment.

Data extraction

Each site-year had the experiment type, location and year identified. After the primary

statistical analysis, the means, standard deviations and number of observations (replications) of

the control (untreated) and the management factor of interest were extracted for the following

response variables: yield (metric tons (T) ha-1 (0 g kg-1 moisture)); protein and oil concentrations

(at 130 g kg-1 moisture); product and rate used (when appropriate); and application method.

Further, other information was extracted to serve as moderators in the model: planting and harvest

dates; average temperature (Celsius) and total precipitation (mm) during the period of the crop

growth for each site year; soil cation exchange capacity (CEC) (Meq 100 g-1); soil organic matter

(OM) (g kg-1); soil pH; and preplant soil phosphorus and potassium levels (mg kg-1).

Quantitative data synthesis

A random effects meta-analysis model was chosen based on the nature of the studies prior

to the analysis, and not based on the results from heterogeneity indexes (Borenstein et al., 2009b),

using the “meta” package (Schwarzer et al., 2015) in R 3.5.1(R Core Team, 2018). Different

management practices were divided into respective sub-groups (i. e. different fertilizer sources per

nutrient) prior to the analysis (Table 1). The agronomic management sub-groups that led to

significant differences in yield or grain quality were maintained.

For each management practice, heterogeneity values from the meta-analyses were

measured using three parameters: 1) The Q test, which tests the hypothesis of having heterogeneity

among studies in the meta-analysis; 2) the I2 value which quantifies how much of the variation

observed cannot be explained by the model and; 3) the T2 which estimates the true variance from

the effect size. Based on this analysis, four potential moderators were selected for each

management practice based on their impact in the literature and agronomic knowledge. The

“metafor” package (v2.0-0; Viechtbauer, 2010) was then used to determine the influence of the

moderators on the observed heterogeneities. The moderators for each management practice (Table

2) were tested individually. Finally, the models created for each moderator were compared among

themselves by the Akaike Information Criteria (AIC), p value and R2 values (Sakamoto et al.,

1988). The model that had a significant p value (α = 0.05), a high R2 value, and a lower AIC value

was considered the best model, since it explained most of the observed heterogeneity associated

with each management practice.

RESULTS

This study involved five locations, representing a broad cultivation area of Illinois, and

seven years of research. The difference in the number of observations for each of the response

variables within a management practice (Table 3) was due to abnormal values identified with

outlier analyses that were difficult to be normalized. Normally distributed effect values from the

data set is a requirement for meta-analyses using the random-effects model (Borenstein et al.,

2009a). Therefore, data from site-years that could not be normalized by transformation were not

included in the final meta-analysis, and this resulted in a different number of observations per

variable within an agronomic management factor.

Nitrogen fertilization

Overall, applying nitrogen to the soybean crop as a broadcasted application prior to

planting increased yield by 190 kg ha-1 (Table 4). None of the nitrogen fertilizer sources evaluated,

(i.e., urea, ESN, ammonium nitrate, ammonium sulfate, urea ammonium nitrate, or Limus urea),

was better than the others at increasing yield (Table 5). However, fertilizing with ammonium

nitrate tended to increase yield the most (240 kg ha-1), while urea with the urease inhibitor Limus

increased yield the least (120 kg ha-1) (data not shown).

The heterogeneity in the model was high for the response of soybean grain yield to nitrogen

fertilization, since it has a low p-value in the Q test (<0.0001), not much of the variation from the

overall effect is true (0.03), and 92.3% of the variation could not be explained by the model (I2)

(Table 6). Therefore, further analysis was performed using potential moderators of the N fertilizer-

yield interaction, including soil cation exchange capacity (CEC), soil organic matter (OM),

planting date (PD), and year of the experiment. Of the potential moderators, soil organic matter

(OM) differences explained most of the yield variability in response to N application (Table 7). In

regards to grain quality, nitrogen application did not significantly alter the concentration of either

protein or oil (Figure 1), and there were no differences between N fertilizer source (Table 5).

The heterogeneity values for the nitrogen fertilization model with the sub-groups for both

grain quality aspects were high, with the nitrogen sources having a Q statistics p < 0.0001 for both

protein and oil (Table 6). Because of this high variability, moderator analysis was performed, using

the same potential moderators as for yield (CEC, OM, PD, year). For both grain protein and oil

concentrations, planting date acted as a significant moderator to N fertilization, with the highest

R2 value and lowest AIC (Table 7).

Phosphorus fertilization

An increase of 110 kg ha-1 in grain yield was observed when at least 84 kg P2O5 ha-1 of

phosphorus fertilization was applied prior to planting soybean (Table 4). There were no differences

in yield due to the different P placement strategies, but there were for protein and oil concentrations

(Table 5).

The variability in the model was high for the yield response to phosphorus fertilization

(Table 6). Thus, a moderator analysis was performed using soil CEC, initial soil P level (P), total

precipitation during the growing season (TPr), and the year as the potential moderators. The best

moderator of the soybean yield response to phosphorus fertilization was the initial soil P level (P)

(R2=13.5, AIC= 4.6, p-value= <0.01 (Table 7)).

Grain quality was dependent upon P placement strategy (Table 5). When P was applied

banded under the seedling row, compared to unfertilized plots, the protein concentration in the

grain significantly decreased by 1 g kg-1, and the oil increased by 0.7 g kg-1 (Table 9). In contrast,

when phosphorus was broadcasted on the surface of the soil, the resulting grain protein and oil

concentrations tended to be opposite from those when fertilizer was banded.

The overall variability of grain quality response to P fertilization was high (Table 6), with

the broadcasted application sub-group partially explaining the variability (Table 5). Since the

fertilizer application sub-groups statistically differed from each other, moderator analysis was

done on each placement strategy separately. The same moderators were used as for the yield

response to phosphorus fertilization. For banded P fertilization, CEC and year were found to

significantly modulate final grain oil and protein levels, while the soil CEC influenced grain

protein (lower AIC value and higher R2), year modulated grain oil concentrations (Table 7).

However, for broadcast P placement, year significantly affected grain protein level, while soil CEC

affected grain oil concentrations; as both modulator variables had higher R2 values and lower AICs

(Table 7).

Potassium fertilization

Overall, potassium fertilization tended to decrease yield by 30 kg ha-1 (Table 4). Because

it was the management practice with the lowest number of observations (Table 3), restricted to two

years (2014 and 2015), no sub-group analysis was performed. The variability in the yield response

to K fertilization was high according to the heterogeneity indicators (Table 6). Thus, an analysis

using soil CEC, soil OM, trial location, and soil potassium level (K) as moderators was performed.

Soil CEC and location were found to significantly affect the yield response to fertilizer K, with

location being the most influential, because of its lower AIC and higher R2 values (Table 7).

For the grain quality aspects, potassium fertilization only had a modest tendency to increase

protein concentration (+0.5 g kg-1) and did not alter oil level (Figure 1). There were, however, only

a few data points available for the grain quality parameters for potassium fertilization (Table 3),

and as a result, the heterogeneity was high for both parameters (Table 6). Additional analysis was

performed to explain this heterogeneity using the same potential moderators as used for yield.

While the response to K fertilization in grain protein concentration was moderated primarily by

soil CEC (due to a higher R2 value) none of the moderators tested influenced the response in grain

oil concentration.

Row spacing

Reducing space between planting rows from 76 to 51 cm increased yield by 340 kg ha-1

(Table 4). To explain the high variability in yield response to this management practice (Table 6),

soil CEC, soil OM, total precipitation (TPr) and average temperature (T) of the crop season were

selected as potential moderators. Except for average temperature during the crop season (T) all of

the other selected moderators influenced the yield response to row spacing, especially soil CEC

(R2 = 60.2) (Table 7).

Reduced row spacing, however, decreased grain protein concentration by 3.1 g kg-1 (Table

4) with a corresponding increase in oil level of 0.9 g kg-1. To explain the high heterogeneity,

additional analysis using the same potential moderator values as for yield was performed. While

no moderator was found to significantly influence the response in grain composition to reduced

row spacing, soil CEC accounted for 7% of the variability in protein concentration and 17.8% of

the grain oil concentration variability, as shown by the respective R2 values (Table 7).

Foliar protection

Protecting leaf area with an application of either fungicide or insecticide at R3 increased

yield by 150 kg ha-1 (Table 4), although there were no yield differences between applying fungicide

or insecticide individually versus when they were combined (Table 5). Applying only fungicide,

however, tended to increase yield more (180 kg ha-1) then applications of insecticide alone (140

kg ha-1) or the combination of the two (150 kg ha-1) (data not shown). Additionally, analysis by

foliar application product was not able to reduce the yield variability (Table 5). Thus, location, soil

OM, planting date (PD) and precipitation (TPr) were tested as moderators of the yield response to

foliar protectants. Both location and OM resulted in low p-values (0.04 and <0.01, respectively)

and similar R2 values; but since OM had the lower AIC value, it was considered the best moderator

of the yield response to foliar protectants (Table 7).

For grain quality, foliar protectant applications at R3 reduced protein concentrations by 1.3

g kg-1 (Table 4). In contrast, grain oil concentration was increased by an average of 0.9 g kg-1 from

an R3 application (Figure 1). The high variability in the grain composition responses to foliar

protection (Table 6), had the same moderators as the associated responses in yield (location and

OM) (Table 7). For both protein and oil levels, location was determined to be the best moderator

of the response to foliar protection, since it had a higher R2 value (23.5% and 31.4% for protein

and oil respectively) (Table 7) and the AIC values between the two moderators were similar.

DISCUSSION

Nitrogen fertilization

Mourtzinis et al. (2018) evaluated the effect of nitrogen application on soybean yields

across the United States and concluded that nitrogen fertilizer increased yields by an average of

60 kg ha-1 when nitrogen was applied once to the crop, regardless of the application method. The

results presented here are similar, in which the application of nitrogen before planting increased

yields by 190 kg ha-1. While the production year was the main cause of yield variation in the

previous study (Mourtzinis et al. 2018). In the current study, organic matter was the main

explanation of yield variation in response to N fertilizer applications. Other multiple-year studies

(Lawn and Brun, 1974; Mendes et al., 2008; Cluj-napoca and Turda, 2013; Macák and

Candráková, 2013; Bobrecka-Jamro et al., 2018; McCoy et al., 2018) have also found a positive

response in soybean yield when nitrogen was applied. Generally, the yield response to N

application time varied among the three years, from a 3% increase up to a single 23.5% increase

for nitrogen applied immediately prior to planting (Bobrecka-Jamro et al., 2018). According to the

authors, the weather was moderate, with warmer temperatures and adequate and equally

distributed rainfall over the years, while the soil was low in nitrogen content, with average levels

of organic matter. Their data suggest that nitrogen fertilization increased the number of pods per

plant, in a directly proportional manner with the nitrogen dose applied and increased the thousand

grain weight by 4.8 grams.

A common characteristic among the previous studies that reported increased soybean

yields in response to nitrogen mineral fertilization was soil pH between 6.5-7.5 (Lawn and Brun,

1974; Mendes et al., 2008; Cluj-napoca and Turda, 2013; Macák and Candráková, 2013;

Bobrecka-Jamro et al., 2018; McCoy et al., 2018). At pH levels close to 7, nitrogen fertilizer as

ammonia is a weak base, and therefore is present in its protonated form (ammonium gas), which

can be passively absorbed by plants (Souza and Fernandes, 2006). In contrast, other studies that

have reported soybean yield decreases in response to nitrogen inputs (Gaydou and Arrivets, 1983;

Ferreira et al., 2016; Kaschuk et al., 2016) had more acidic soils (pH approximately 5.0-5.5).

In the current study, the variation in the yield response to nitrogen fertilization was related

to the organic matter concentration in the soil, where more organic matter in the soil usually led to

greater yields in response to nitrogen fertilization. This relationship, although significant, was

weak (R2 = 16%) indicating that a single moderator factor was not able to account for the variation

in soybean yield in response to nitrogen inputs.

Previous studies of soybean response to nitrogen used different N sources, with urea being

the most common, and this may have caused the difference in yield response observed in the

current study. Therefore, to determine if N fertilizer source was the basis for the variation in the

yield response, further analysis of the nitrogen source subgroups (urea, ESN, ammonium nitrate,

ammonium sulfate, urea ammonium nitrate and Limus urea) was performed. However, no

significant differences between N sources (p-value 0.782) in the yield response to nitrogen were

observed (Table 5) and as a result, accounting for the different sources did not decrease the

heterogeneity observed (data not shown). Thus, further research is needed that focuses on other

environmental factors, such as organic matter and pH in the soil, when studying the effect of

nitrogen fertilization on soybean. Furthermore, using nitrogen fertilizer to increase yield in

soybean may not always be economical, and the grower and agronomist need to consider the return

on investment when deciding whether to fertilize soybean with nitrogen (McCoy et al., 2018).

With regard to changes in the grain protein concentration in response to nitrogen

fertilization, several studies agree with the findings presented here that there was no effect of

nitrogen fertilization on grain protein concentration (Gaydou and Arrivets, 1983; Macák and

Candráková, 2013; Dozet et al., 2016; Ferreira et al., 2016; Moreira et al., 2017). This finding

might be explained by the observation that nitrogen from biological fixation is partitioned

preferentially to the grain (Hanway and Weber, 1971; Warembourg and Fernandez, 1985; Israel

et al., 1987; Pipolo et al., 2015). Moreira et al. (2017) reported that the nitrogen concentration in

the grain at R5 was not affected by applications of different sources of foliar nitrogen at the R3 to

R4 growth stage, nor was the protein level in the mature grain. In contrast, biological nitrogen

fixation during the reproductive growth stages has reportedly contributed to a higher concentration

of grain protein (Zapata et al., 1987; Leffel et al., 1992; Purcell et al., 2004).

Increased protein concentration in the grain from nitrogen inputs varies depending on the

year and other environmental factors (Bobrecka-Jamro et al. 2018). Grain quality can be highly

modified by water availability, as well as the distribution during the crop season (Popovic et al.,

2016; Sliwa et al., 2015). In response to three nitrogen fertilization doses (no nitrogen, 35 kg N ha-

1 and 105 kg N ha-1) in three different water environments (no irrigation; two irrigations of 25 mm

at R2 and R4 and two irrigations of 50 mm at the same stages), Basal and Szabó (2018) observed

an increase in grain protein concentration from the nitrogen inputs only in the environment with

two irrigations of 25 mm at R2 and R4, with the highest nitrogen dose generating a 22 g kg-1

greater protein level compared to the control.

In the current study, the variation in grain protein in response to nitrogen fertilization was

dependent upon the planting date (Table 7), in which an earlier planting date was associated with

a positive response. Since the experiments for this study were conducted in Illinois, which has

historically less precipitation in the late months of summer (August and September) (Illinois State

Water Survey, 2019), planting soybean earlier may have prevented seed development and filling

from occurring during this drier period, therefore leading to a greater grain protein level.

The oil concentration in the grain was not affected by the nitrogen inputs, similar to

previous studies (Gaydou and Arrivets, 1983; Macák and Candráková, 2013; Dozet et al., 2016;

Ferreira et al., 2016; Moreira et al., 2017; Bobrecka-Jamro et al., 2018). There was, however, a

tendency that nitrogen fertilization led to a slight decrease in grain oil level, which could be

explained by the inverse relationship between protein and oil that is often observed (Macák et al.,

2010). This inverse relationship was also observed in the explanation of variance, where the most

significant moderator was also the planting date (Table 7). Thus, a late planting date led to a greater

oil concentration in the grain in response to nitrogen inputs.

Phosphorus fertilization

Preplant phosphorus applications increased soybean yield by 110 kg ha-1 (Figure 1) when

compared to the plots that were not treated, which yielded 4.86 Mg ha-1 on average (Appendix B).

Similarly, for soybean grown in Illinois during 2014 and 2015, the addition of 84 kg of P2O5 ha-1

as MicroEssentials SZ in a band 4-6 inches below the crop row increased yield by 410 kg ha-1

(Beyrer, 2018). Yield was previously shown to increase in tandem with the addition of 90, 180 and

360 kg of P2O5 ha-1, with a maximum yield increase of 450 kg ha-1 (Gaydou & Arrivets 1983).

Likewise, Buah et al. (2000) observed a positive yield response to applied phosphorus at every

rate tested, and this increase was independent of the on application method (banded or broadcast).

This finding is in agreement with the current study, where subgrouping by fertilizer placement was

not statistically significant for the yield response (Table 5). Phosphorus level in the soil was the

moderator that was best able to account for the variation in the yield response to phosphorus

fertilization (Table 8), with a proportional relationship to the yield response. This finding is in

contrast to previous findings that soybean planted in soils with low level of phosphorus usually

results in a higher yield response to phosphorus fertilizers (Buah et al., 2000).

In contrast to yield, the grain protein and oil concentration responses to P fertilizers were

dependent upon the placement (Table 5). When phosphorus was broadcasted, the grain protein

level tended to increase by 1 g kg-1, but when P was banded, the grain protein level decreased by

1 g kg-1 (Table 9). Studies in Pakistan also observed a maximum increase of 83.4 g kg-1 in soybean

grain protein with phosphorus applications of up to 120 kg of P2O5 ha-1 when compared to the

control (Abbasi et al., 2010). While Gaydou and Arrivets (1983) also observed grain protein

increases due to phosphorus fertilizer nether this study or Abbasi et al. (2010) had details regarding

the placement of the phosphorus fertilizer, although it could be assumed that the fertilizer was

broadcast. Farmaha et al. (2012), found similar trends in protein yield, in no-till systems where the

broadcasted fertilizer led to a higher protein yield than the banded fertilizer for all phosphorus rates

tested.

The response in grain oil to phosphorus application was inverse to the protein level,

because of the nature of those two characteristics (Macák et al., 2010); oil in the grain was

increased by banding phosphorus but tended to decrease when P was broadcasted (Table 8). This

trend for grain oil level to decrease with P fertilization was also observed by Gaydou and Arrivets

(1983), while Abbasi et al. (2010) observed an increase in grain oil concentration from phosphorus

application.

The moderators that contributed to the variation for each grain quality response to P

fertilization were the same (CEC and year), but they were reversed for each placement. At this

point no previous studies were found examining the linkage between soil CEC or year and grain

quality, but it can be inferred that both moderators influenced the quality response to phosphorus

fertilization.

Potassium fertilization

Interestingly, the most common fertilization practice for soybean production (potassium

fertilization) did not generate any changes in soybean yield or grain quality in this study (Figure

1). The lack of significant response to potassium fertilization could be a consequence of the low

number of observations for this management practice (Table 3), which when analyzed in a random

model could result in greater between-studies variance (Borenstein et al., 2009c). The yield

response to potassium fertilization in the current study tended to be negative, decreasing yield by

30 kg ha-1. A lack of yield response to potassium fertilization was also reported by Yin and Vyn

(2002) in Canada with different placements in the fall and the spring, and under different tillage

practices. A similar lack of response to potassium fertilization was reported by Buah et al. (2000).

Minimal research has examined the effect of potassium fertilization on soybean grain

quality. One report by Gaydou and Arrivets (1983) for a site with a low soil potassium level

(highest value was 0.14 meq 100 g-1 in the 0-20 cm soil layer), showed that adding potassium

fertilizer increased grain oil concentration, while protein decreased. This finding is in contrast with

the results from this study, and maybe because the average potassium level in the soils were higher

(Appendix C). A study by Abbasi et al. (2010) found no advantage of potassium fertilization for

increasing either total grain protein or oil, and Farmaha et al. (2012) observed a negative effect of

potassium fertilization on both grain quality parameters.

Row spacing

Planting soybean in a 51 cm between-row spacing instead of a 76 cm spacing increased

yield by 340 kg ha-1, the most of any of the management factors studied (Figure 1). Across the

United States, the potential for an average 7% yield gain from the adoption of narrow rows has

been reported, with the potential to increase yields by 15% in Illinois (Andrade et al., 2019).

Similarly a study in Indiana evaluated three row spacings (19, 38 and 76 cm) and observed a yield

advantage of narrowing the row spacing when water was not limited (Hanna et al., 2008). In New

York, a two-year study that evaluated three different row spacings (19, 38 and 76 cm) and four

seedling rates (321, 371, 420 and 469 thousand seeds ha-1) observed an average yield increase of

510 kg ha-1 when reducing the row spacing from 76 to 19 cm across all seedling rates (Cox and

Cherney, 2011). The greatest yield increase, of 790 kg ha-1, was observed at the lowest seedling

rate of 321 thousand seeds ha-1 (Cox and Cherney, 2011). In Tennessee, yield increase responses

were up to 506 kg ha-1 were reported by reducing the row spacing from 76 cm to 38 cm in a droght

year (Walker et al., 2010).

The yield increases due to reduced row spacing have also been observed outside the United

States. In Argentina, yield increased 798 kg ha-1 when the row spacing was reduced from 50 to 25

cm and the number of plants increased by 50%, with a notable increase in seeds per area and no

decrease in grain mass (Di Mauro et al., 2019). In the south of Brazil (Rio Grande do Sul), yields

from 17 and 34 cm row spacings were greater than from the 51 cm row spacing, and similar to the

finding of Di Mauro et al. (2019), grain mass was not affected (Hoffmann et al., 2019). In contrast,

Tourino et al. (2002) found no difference in yields from row spacing alterations, regardless of plant

density. Similarly, Ferreira et al. (2019) and Moreira et al. (2015) did not observe a yield advantage

when the row spacing was reduced in Londrina, Brazil. The row spacing reductions in those studies

were from 50 cm to 20 and 30 cm respectively, which may suggest a lower limit to how narrow

the row spacing can be for yield increases to be observed, probably due to an increase of plant

competition as row width narrows (Çalişkan et al., 2007).

This advantage from narrow rows on soybean yield could be explained by plants being

more distributed over the area, having more plant-to-plant space to develop, therefore fostering

better plant development. Narrow row spacing have been associated with faster canopy closure

(Ball et al., 2000; Silva et al., 2013; Andrade et al., 2019) and greater light interception (Çalişkan

et al., 2007; Zhou et al., 2011; Silva et al., 2013), which can lead to more yield at the end of the

season.

To explain the variation observed in the yield response to narrow rows (Table 7), total

precipitation was considered as a possible moderator, since drought years has been found to

decrease the yield response to narrow rows (Hanna et al., 2008; Walker et al., 2010; Cox and

Cherney, 2011), especially when the drought occurs during the reproductive stages (Norsworthy

and Shipe, 2005). Although total precipitation during the season was a moderator of yield in

narrow rows, soil CEC explained more of the model variation (R2=60.2%) (Table 8). As a result,

soils with lower CEC values had a greater yield increase when row spacing was reduced.

Grain protein and oil concentrations exhibited the greatest mean changes in response to

row spacing, compared to the other management practices evaluated, with changes of - 3.1 g kg-1

and + 0.9 g kg-1 respectively (Table 4). While studies in Tennessee (Bellaloui et al., 2014) and in

Canada (Al-Tawaha and Seguin, 2006) did not find significant effects of reduced row spacing on

soybean protein, the interaction of row spacing and seedling rates has reportedly altered the

concentration of grain protein, with the direction of this alteration dependent on the year and the

variety (Bellaloui et al., 2014). In Slovenia, a three-year study (2015-2017) also did not observe

an effect of reduced row spacing on grain protein concentration (Flajšman et al., 2019). Above-

ground plant competition appears to not interfere with seed protein synthesis or concentration in

the grain (Umburanas et al., 2018), since it does not affect biological nitrogen fixation, which goes

preferentially to the grain (Hanway and Weber, 1971; Warembourg and Fernandez, 1985; Israel

et al., 1987; Pipolo et al., 2015). In more tropical climates, decreasing row spacing from 50 to 20

cm between rows increased grain protein by 6 g kg-1 (Werner et al., 2017), or tended to increase

nitrogen concentration in the grain in response to the reduction in row spacing (Moreira et al.,

2017).

When grown in a narrow row spacing, the soybean plant has greater leaf area and more

light interception in the early development stages, increasing the production of photosynthesis

products available to fill the grain (Ball et al., 2000). However, since the nitrogen in the grain

comes primarily from biological nitrogen fixation, and given the fact that reducing row spacing

does not enhance this mechanism, the grain was probably filled more with photosynthesis products

(such as oil) then with nitrogen. Thus, a decrease in the grain protein concentration was observed

(Figure 1).

An increase in grain oil concentration has also been observed in response to reduced row

spacing (Flajšman et al., 2019), while other authors did not report any effects of reduced row

spacing on grain oil (Al-Tawaha and Seguin, 2006; Bellaloui et al., 2014; Werner et al., 2017). Oil

and protein tend to be inversely related in grain crops (Macák et al., 2010), possibly explaining the

positive response of oil level to reduced row spacing observed in this current study.

The variation observed for the response in grain quality parameters to reduced row spacing

was high (Table 6) and exhibited the largest confidence intervals (Figure 1). While soil CEC for

both grain protein and oil levels was selected as the best moderator of the response to reduced row

spacing (Table 8), none of the tested moderators were found to have a significant influence.

Foliar protection

The application of foliar protectants (fungicide and/or insecticide) at the R3 growth stage

increased soybean yield by 150 kg ha-1 (Figure 1). Bender (2015) and Beyrer (2018), also found

increased yields in response to foliar protectants applied to soybean in Illinois of 134 and 222 kg

ha-1, respectively. A multiple site-year study in Iowa comparing the response to foliar fungicide in

small-plot and on farm research also reported increased yields in both trial types in response to a

foliar application at the R2-R3 growth stage (Kandel et al., 2018). Another on-farm study with

multiple site-years in Ohio reported increased yields due to the fungicide application in 4 out of

10 site-years, and a tendency for increased yields in all the other site-years but one (Ng et al.,

2018). The same trial also evaluated the yield response due to the application of insecticides and/or

fungicides, and showed no significant response to either insecticide alone, nor the addition of an

insecticide to a fungicide application (Ng et al., 2018). Similarly, the current study showed that

the sub-groups of different foliar protectant product types (fungicide only, insecticide only, or

insecticide and fungicide) were not significantly different from each other and could not explain

the variation observed in yield.

When conditions for foliar diseases were not present, fungicide applications did not affect

soybean yield, as reported from a multiple site-year study in Iowa (Swoboda and Pedersen, 2009).

Other meta-analytical reviews on the impact of fungicides on specific diseases of soybean (i.e.,

soybean rust (Delaney et al., 2018), target spot (Molina et al., 2019) and sclerotinia stem rot

(Willbur et al., 2019)) all concluded that yield gain from foliar applications is greater under

conditions with higher disease pressure. Fungicides are used primarily for protecting the leaves

and maintaining their green area, thereby maintaining photosynthesis. Photosynthetic assimilates

are essential for plant growth and grain storage components, translating to higher yields.

Location, planting date, and total precipitation are environmental factors that can affect the

disease pressure of a crop and were considered as moderators to explain the variability in the

current study. Soil organic matter, however, was the most significant factor acting as a moderator

of the yield response to foliar protectants. Similarly, lower levels of organic matter in the soil is

generally considered as being less optimal for growth, and as a result when foliar protectants are

applied in combination with this condition, a greater yield response might be expected.

With regard to grain quality, when foliar protectants were applied the grain protein

concentration decreased (-1.3 g kg-1), with a corresponding increase in oil (0.9 g kg-1) (Table 4).

In a review of 21 studies across 11 states in the United States to assess the variation of soybean

grain composition, foliar fungicide and insecticide applications improved grain oil by 3 g kg-1 and

tended to increase protein by the same amount (Assefa et al., 2019). However, foliar applications

had minimal influence on grain composition when pooled with other managements to assess the

overall effect of management on soybean grain protein and oil (Assefa et al., 2019). In a multi site-

year study in Missouri, fungicide and insecticide applied together decreased protein concentration

by 4 g kg-1 compared to the control with no foliar protectants applied (Nelson et al. 2010). The

grain oil concentration was increased by the fungicide plus insecticide application, as well as by

the fungicide by 2 and 1 g kg-1 respectively (Nelson et al. 2010). Other study, showed that higher

doses of herbicide and insecticide combinations decreased both grain protein and oil levels, while

the lowest dose did not affect protein but increased oil concentration (Seddiqui and Ahmed, 2006)

The high heterogeneities observed for both grain quality parameters in response to foliar

protection application (Table 6) were primarily explained by the location moderator (R2 values of

23 and 31% for protein and oil respectively), and secondarily, by soil organic matter level.

CONCLUSIONS

All management practices were geared toward yield increase, with the exception of

potassium fertilization. None of the management practices evaluated, however, was able to

simultaneously increase soybean yield and grain quality. In general, when yield was increased,

grain protein level decreased and oil level increased.

Soybean grain quality has a greater chance to be enhanced by agronomic management in

soils with high cation exchange capacity values. However, when the management practice is

phosphorus fertilization, the response is dependent on the application method of the fertilizer,

specifically whether the fertilizer is broadcast or banded beneath the row.

TABLES AND FIGURE

Table 1. Selected management practices with respective subgroups from soybean

management experiments conducted between 2012-2018.

Management Practice

Sub-groups

Nitrogen fertilization

Nitrogen source (urea; ESN†;

ammonium nitrate; ammonium sulfate;

urea ammonium nitrate; Limus urea‡)

Phosphorus fertilization

Application method (banded or

broadcasted)

Foliar protection

Product (fungicide and insecticide;

fungicide; insecticide)

†Urea (440 g kg-1 nitrogen) with a polymer coating, to control the release of

nitrogen from the granule

‡Urea coated with a urease inhibitor to minimize volatilization loss from surface

application

Table 2. Variables selected as moderators for each management

practice for soybean grown from 2012-2018.

Management Practice

Moderators†

Nitrogen fertilization

CEC; OM; PD; Year

Phosphorus fertilization

CEC; P; TPr; Year

Potassium Fertilization

CEC; K; Location; OM

Row Spacing

CEC; OM; TPr; T

Foliar protection

Location; OM; PD; TPr

† CEC = Cation exchange capacity (Meq 100 g-1);

K = Soil potassium level (mg kg-1);

Location = city; OM = Soil organic matter level (g kg-1);

PD = Planting date;

TPr = Total precipitation (mm);

T = Average temperature (Celsius).

Table 3. Total number of data points for yield, protein and oil in each

soybean management practice (nitrogen, phosphorus and potassium

fertilizations, reduced row spacing and foliar protection) using

50 site-years of experimentation from 2012-2018.

Management Practice

Variables

Total

Yield

Protein

Oil

Nitrogen fertilization

173

Phosphorus fertilization

106

Potassium fertilization

Row spacing

Foliar protection

Total

161

137

125

423

Table 4. Soybean yield (Mg ha-1) and grain protein and oil (g kg-1) overall responses and

respective 95% confidence interval (C.I.) lower and upper limits for each management practice.

†Yield values presented with 0 g kg-1 of moisture

‡Grain concentration presented with 130 g kg-1 of moisture

Management

Practice

Yield†

Grain concentration‡

Protein

Oil

Response

C. I.

Response

C. I.

Response

C. I.

Mg ha-1

---------------------------g kg-1-------------------------

Nitrogen

fertilization

0.19

[0.15; 0.23]

0.1

[-0.5; 0.7]

-0.1

[-0.4; 0.3]

Phosphorus

fertilization

0.11

[0.06; 0.16]

-0.6

[-1.1; -0.1]

0.5

[0.2; 0.8]

Potassium

fertilization

-0.03

[-0.11; 0.05]

0.5

[-1.7; 2.7]

-0.1

[-1; 0.7]

Row spacing

0.34

[0.22; 0.45]

-3.1

[-5.9; -0.2]

0.9

[0.3; 1.4]

Foliar

protection

0.15

[0.12; 0.19]

-1.3

[-1.8; -0.8]

0.9

[0.5; 1.4]

Table 5. Between sub-group differences p-value, for nitrogen (N) and

phosphorus (P) fertilization and foliar protection, when each specific

subgroup was added to the model for soybean responses to various

management practices.

Management Practice

Sub-group

Variables

Yield

Protein

Oil

N fertilization

Source†

0.782

0.199

0.462

P fertilization

Placement‡

0.356

0.021*

0.003*

Foliar protection

Product§

0.743

0.415

0.701

† Different sources were: urea, ESN, AN, AMS, UAN and Limus urea.

‡ Different placements were: broadcast on the surface or banded 5 cm

to the side and under the row.

§ Different products were: fungicide only, insecticide only or both.

* Significant difference between sub-groups at p = 0.05.

Table 6. Q statistic p-value, estimate of the variance of the true effect (T2)

and the percentage of excess dispersion to the total dispersion (I2) values for

each management practice tested (nitrogen, phosphorus and potassium fertilization;

reduced row spacing and foliar protection), used to asses heterogeneity for yield

and protein and oil grain concentration of soybean.

Management Practice

Grain Concentration

Yield

Protein

Oil

Nitrogen fertilization

p < 0.0001

T2 = 0.03

T2 = 0.04

T2 = 0.02

I2 = 92.3%

I2 = 89.5%

I2 = 87.5%

Phosphorus fertilization

p = 0

T 2 = 0.03

T2 = 0.02

T2 = 0.006

I2 = 99.2%

I2 = 98.4%

I2 =98.4%

Potassium fertilization

p < 0.0001

p = 0.0005

T2 = 0.01

T2 = 0.06

T2 = 0.007

I2 = 96.6%

I2 = 98.1%

I2 =86.8%

Row spacing

p = 0

p < 0.0001

T2 = 0.06

T2 = 0.27

T2 = 0.007

I2 = 99.9%

I2 = 100%

I2 =98.8%

Foliar protection

p = 0

T2 = 0.01

T2 = 0.02

T2 = 0.01

I2 = 98.6%

I2 = 98.7%

I2 =99.3%

Table 7. The p-value for each moderator, Akaike information criterion (AIC), and R2 values for

the response in yield and grain protein and oil concentration to each management factor with

non-significant subgroup (nitrogen fertilization; potassium fertilization; reduced row spacing;

and foliar protection).

† CEC = Cation exchange capacity (Meq 100 g-1); K = Soil potassium level ( mg kg-1);

L = Location (city); OM = Soil organic matter level (g kg-1); PD = Planting date; TPr = Total

precipitation (mm); T = Average temperature (Celsius).

‡ p = p-value

Manage-

ment

Mode-

rator†

Yield

Grain concentration

Protein

Oil

p‡

AIC

Nitrogen

CEC

0.26

-23.2

0.4

0.26

7.3

0.8

0.20

-48.5

2.1

<0.01

-32.3

16.5

0.38

7.7

0.77

-47.1

0.21

-23.5

0.7

0.03

4.0

8.8

0.03

-51.5

5.9

Year

0.23

-23.4

0.4

0.65

8.4

0.32

-47.9

Potassium

CEC

<0.01

-2.5

57.5

0.20

8.0

26.3

0.38

4.1

0.95

1.7

0.42

7.3

0.40

4.1

<0.01

0.1

77.8

0.84

7.8

0.76

4.4

0.15

>-0.1

18.7

0.45

7.3

0.58

4.3

Row

spacing

CEC

<0.01

-2.6

60.2

0.17

32.5

7.0

0.12

-10.3

17.8

<0.01

0.8

50.2

0.58

34.0

0.23

-9.5

6.1

TPr

0.03

8.2

18.6

0.29

33.2

1.2

0.23

-9.5

7.0

0.43

11.6

0.89

34.3

0.14

-10.2

14.2

Foliar

protection

0.04

-31.8

15.1

0.01

40.5

23.5

<0.01

16.2

31.4

<0.01

-40.2

16.4

0.01

40.0

16.5

<0.01

15.6

23.5

0.39

-36.2

0.14

44.3

3.9

0.14

19.7

5.5

TPr

0.76

-35.5

0.59

46.1

0.56

21.3

Table 8. The p value for each, Akaike information criterion (AIC), and R2 values for soybean

yield response to phosphorus fertilization, accounting for the sub-group (banded and

broadcasted) in the grain quality (protein and oil concentration) responses.

Res-

ponse

Moderators†

CEC

TPr

Year

p‡

AIC

Yield

0.49

10.7

<0.01

4.6

13.5

0.60

10.9

0.22

9.7

1.2

Pro-

tein

----------------------------------------------Banded----------------------------------------------

0.04

13.0

12.2

0.47

45.4

0.58

46.6

0.05

43.5

10.1

---------------------------------------------Broadcasted------------------------------------------

0.42

6.9

0.75

7.4

0.46

6.9

0.22

6.1

6.6

Oil

----------------------------------------------Banded----------------------------------------------

0.04

11.6

13.3

0.43

14.6

0.42

14.6

0.03

11.1

16.0

---------------------------------------------Broadcasted------------------------------------------

0.02

-9.2

41.3

0.70

-5.0

0.39

-5.6

0.26

-6.0

1.0

† CEC = Cation exchange capacity (Meq 100g-1); P = Soil; phosphorus level (mg kg -1); TPr =

Total precipitation (mm).

‡ p = p-value

Table 9. Soybean grain quality components (protein and oil concentrations)

overall responses and respective 95% Confidence Interval (C.I.) lower and

upper limits for each phosphorus fertilizer placement strategy.

Phosphorus

placement

Protein

Oil

Response

C. I.

Response

C. I.

--------------------------g kg-1---------------------------

Banded

-1.0

[-1.6;-0.5]

+0.7

[0.4; 1.1]

Broadcasted

+1.0

[-0.6;+2.6]

-0.2

[-0.6; 0.3]

† Bigger boxes have more weight over the overall effect (not shown).

‡ Lines represent the upper and lower limits of the 95% Confidence Interval.

§Represented as the difference between experimental and control groups, respectively being:

fertilized and unfertilized for fertilization factors; 51 and 76 cm for row spacing, and; applied

and unapplied for foliar protection.

Figure 1. Forest plot of the overall effects of management practices (nitrogen, phosphorus and

potassium fertilizations; row spacing and foliar protection) on soybean yield, grain protein and

oil concentrations in comparison with the respective untreated controls.

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APPENDIX: SUPPLEMENTAL TABLES

Table 10. Included experiments, years and location of trials used in the meta-analysis.

The productivity parameters measured and the agronomic management practices

examined in each trial are noted with check marks.

Year

Location

Productivity Parameters

Agronomic Management

Yield

(T ha-1)

Protein

(g kg-1)

Oil

(g kg-1)

Fertility

Reduced

row

spacing

Foliar

protection

Management Yield Potential

2016

Champaign

✓

Yorkville

✓

2017

Champaign

✓

Harrisburg

✓

Yorkville

✓

2018

Champaign

✓

Harrisburg

✓

Yorkville

✓

Omission Plots

2012

Champaign

✓

DeKalb

✓

Harrisburg

✓

Rushville

✓

2013

Champaign

✓

DeKalb

✓

Harrisburg

✓

Rushville

✓

2014

Champaign

✓

DeKalb

✓

Harrisburg

✓

2015

Champaign

✓

DeKalb

✓

Harrisburg

✓

2016

Champaign

✓

Harrisburg

✓

Yorkville

✓

2017

Champaign

✓

2018

Champaign

✓

Table 10. (continued).

Year

Location

Productivity Parameters

Contribution to the review

Yield

(T ha-1)

Protein

(g kg-1)

Oil

(g kg-1)

Fertility

Reduced

row

spacing

Foliar

protection

Phosphorus Source, Rate and Placement

2014

Champaign

✓

2015

Champaign

✓

2016

Champaign

✓

Soybean Response to Nitrogen

2013

Champaign

✓

DeKalb

✓

Harrisburg

✓

Rushville

✓

2014

Champaign

✓

DeKalb

✓

Harrisburg

✓

2015

Champaign

✓

DeKalb

✓

Harrisburg

✓

2016

Champaign

✓

Yorkville

✓

Harrisburg

✓

Soybean Fertigation

2015

Champaign

✓

Soybean Response to Fertilizer Distance

2014

Champaign

✓

2015

Champaign

✓

Harrisburg

✓

Soybean Relay

2016

Champaign

✓

Table 11. Average values of yield (Mg ha-1) and grain protein and

oil concentration (g kg-1) for the untreated control plots for each

management practice.

†Yield values presented with 0 g kg-1 of moisture

‡Grain concentration presented with 130 g kg-1 of moisture

Management Practice

Yield†

Grain‡

Protein

Oil

Mg ha-1

g kg-1

Nitrogen fertilization

4.16

346.7

191.4

Phosphorus fertilization

4.86

349.3

190.7

Potassium fertilization

4.74

347.6

185.8

Row spacing

4.60

352.1

188.9

Foliar protection

4.63

353.7

189.2

Table 12. Average preplant values of soil organic matter (OM), cation

exchange capacity (CEC), pH, phosphorus (P), and potassium (K) levels

by location.

Location

CEC

g kg-1

Meq/100g

------- mg kg-1------

DeKalb

21.2

6.5

132

Yorkville

26.0

6.2

206

Champaign

19.6

6.0

133

Rushville

10.9

5.8

188

Harrisburg

18.4

6.3

171

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Full-text available

Apr 2019

Soybeans sowing in different plants’ spatial distribution can influence the phytosanitary management of this crop and, consequently, impact on grains yield. This study was carried out to evaluate the effect of plants arrangements on infestation and control of caterpillars, the deposition of spray syrup as well as assess the agronomic performance of soybean cultivated in the Brazilian Cerrado. The assay was performed during two consecutive seasons in a randomized complete block design with four replications. Soybean cultivation was implemented in 0.50 m spacing between rows, crossed (0.50 m x 0.50 m), twin rows (0.25 m / 0.75 m), and narrow (0.25 m). In the reproductive stage of plants, both crossed and narrow arrangements showed higher caterpillars’ incidence. There was a more evident risk of caterpillar incidence in arrangements that promoted better equidistance among plants. This risk was mitigated when taking into account both control and overlap of syrup, which could be incremented into inferior canopy with the enhancement of application rate. The increase in application rate from 75 to 150 L ha-1 promoted superior spray deposition volumes. Increases in grain yield was noted in the narrow arrangement.

Assessing Variation in US Soybean Seed Composition (Protein and Oil)

Article

Full-text available

Mar 2019

Soybean [Glycine max (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R²∼0.70) and a 0.01 to 0.04 Mg ha⁻¹ decline in seed yield per week, mainly in northern latitudes (40–45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha⁻¹ positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10–50 kg N ha⁻¹) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha⁻¹. At southern latitudes (30–35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

Genotype × Environment Interactions and Yield Stability of Food‐Grade Soybean Genotypes

Article

Jan 2002

Soybean [ Glycine max (L.) Merr.], an important component of the Asian diet, is gaining popularity as a source of vegetable protein and phytochemicals in the USA. However, soybean cultivars with desirable agronomic traits and biochemical components that enhance the quality of soyfoods have not been identified for cultivation in the USA. Twelve soybean genotypes, including three from Japan, were evaluated for their agronomic performance, genotype × environment (GE) interactions, and yield stability at four locations in the USA from 1994 to 1997. At maturity, seed yield, biomass, harvest index (HI), and 100‐seed dry weight were determined using plants harvested from the middle two rows of each plot. Genotypic differences for the traits examined were significant. The mean seed yield across locations and years ranged from 2.0 to 3.0 Mg ha ⁻¹ . The Japanese cultivars had larger seeds but were outyielded by the American genotypes by ≈10% and up to 35% by ‘Hutcheson’. The genotype effects were significantly larger than the location × year effects for plant height, seed weight, and HI, but not for biomass or seed yield. Biomass and HI were important determinants of seed yield. S90–1056, V81–1603, V71–370, ‘Enrei’, ‘Nakasennari’, ‘Ware’, and ‘York’ were stable for seed weight across years. Hutcheson, S90–1056, York, MD86–5788, Nakasennari, and BARC‐8 showed yield stability across environments and years. S90–1056, York, and Nakasennari were stable for both seed weight and seed yield; therefore, they could be used for commercial production in the USA or for breeding soybean cultivars suitable for tofu preparation.

Introduction to Meta‐Analysis

Book

Jun 2021

Bacterial Seed Inoculation and Row Spacing Affect the Nutritional Composition and Agronomic Performance of Soybean

Article

Mar 2019

Sowing of bacterial inoculated seeds and using different cultivar-specific row spacing are 2 well-known agricultural practices in soybean production. However, the connection between different bacterial seed inoculations and row spacing has not previously been investigated in a single study. A 3-year field experiment (2015–2017) was carried out on soybean cv. ES Mentor to assess the effect of 4 rhizobia inoculation treatments (un-inoculated control, C; factory-inoculated seed, F; fresh pre-sowing seed treatment with commercial inoculant, I; and a combination of treatments F and I, FI) and 3 row spacings (12.5 cm, 25 cm and 37.5 cm) on the protein, oil, crude fibre and ash content. The seed, protein and oil yields were determined as well as a thousand seed weight, plant height, pod number and harvest index. There was no interaction between plant spacing and inoculation; however, the inoculation treatments enhanced protein content of seeds by 1.2–1.7%, and increased yields of seed, protein and oil by a maximum of 6.8%, 8.3% and 5.9%, respectively, compared to the un-inoculated control, which produced an average seed yield of 4098 kg/ha. The inoculation treatments also had a moderate influence on biometric measurements. Row spacing had a pronounced effect on seed, protein and oil yields, with plants in 12.5 cm and 25 cm row spacings generating higher yields than those in 37.5 cm row spacings. Correlation analysis showed a significant positive association between seed yield and pod number, and a significant negative correlation between protein and oil content.

Meta-analytic and economic approaches for evaluation of pesticide impact on Sclerotinia stem rot control and soybean yield in the North Central U.S.

Article

Mar 2019
PHYTOPATHOLOGY

As complete host resistance in soybean has not been achieved, Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum continues to be of major economic concern for farmers. Thus, chemical control remains a prevalent disease management strategy. Pesticide evaluations were conducted in Illinois, Iowa, Michigan, Minnesota, New Jersey, and Wisconsin from 2009 to 2016, for a total of 25 site-years (n = 2057 plot-level data points). These studies were used in network meta-analyses to evaluate the impact of 10 popular pesticide active ingredients, and seven common application timings on SSR control and yield benefit, compared to not treating with a pesticide. Boscalid and picoxystrobin frequently offered the best reductions in disease severity and best yield benefit (P < 0.0001). Pesticide applications (one or two-spray programs) made during the bloom period provided significant reductions in disease severity index (DIX) (P < 0.0001) and led to significant yield benefits (P = 0.0009). Data from these studies were also used in nonlinear regression analyses to determine the effect of DIX on soybean yield. A three-parameter logistic model was found to best describe soybean yield loss (pseudo-R2 = 0.309). In modern soybean cultivars, yield loss due to SSR does not occur until 20-25% DIX, and considerable yield loss (-697 kg ha-1 or -10 bu a-1) is observed at 68% DIX. Further analyses identified several pesticides and programs that resulted in greater than 60% probability for return on investment under high disease levels.

Exploring soybean management options for environments with contrasting water availability

Article

Jan 2019

Soybean is commonly cultivated under rainfed conditions being water availability the main constraint. We evaluated the performance of different managements under contrasting water availability to test possible trade‐offs among managements, and to determine physiological variables explaining these yield differences. Four treatments were designed through specific combinations of cultivar, row spacing and stand density. They were classified as stress tolerance or yield potential strategies and were evaluated under two contrasting water availability treatments. Treatments ranged from 349 to 954 mm total water availability. Water stress treatments yielded 72% and 59% of the well‐watered treatment each year, similar to frequent soybean water stress levels for our production region. Management treatments showed significant yield differences (p < 0.05), but the management × water availability interaction did not (p = 0.42). No management option helped reduce negative water stress effects. Highest yields were achieved using 0.25 m row spacing, a stand density of 60 pl per m2, and a high yield potential genotype. Yield variations were explained by differences in harvested seeds per unit area (R2 = 0.75; p < 0.001) and total N uptake at maturity (R2 = 0.93; p < 0.001) across environments. Because management strategies specifically tailored to cope with water shortages showed limited value, farmers need to target yield potential management options.

Assessing the influence of row spacing on soybean yield using experimental and producer survey data

Article

Nov 2018
FIELD CROP RES

Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during critical crop stages for yield determination. Theoretically, this should enhance seed yield. However, in prior studies, the impact of narrowing row spacing on soybean yield has been inconsistent. To explore on a broader scale the potential factors underlying this inconsistency, we evaluated the yield difference between narrow (NR; ≈38 cm) and wide (WR; ≈76 cm) row spacing using two sources of yield and management information: (i) data collected from 4879 soybean production fields via a multi-year, multi-state survey of soybean producers in the North Central US region; and (ii) data extracted from 129 site-year experiments that quantified NR-WR yield difference. The producer fields were allocated to their respective climate-soil domains to enable analysis of the NR-WR yield difference within each domain. The experimental trial data originated from three US geographic regions: south, central, and north. Key crop developmental stages in each trial were estimated using a soybean crop simulation model to discern if changes in crop phenology or any weather variable occurring before versus after a specific crop stage modulated the magnitude of the NR-WR yield difference. Analysis of experimental trial data indicated that, while NR yields were overall higher than WR yields, the NR-WR yield difference varied by region: 540 (south), 104 (central), and 240 kg ha−1 (north); the respective NR yields were greater than WR yields in 92%, 68%, and 84% of the cases. In the north and south regions, the NRWR yield difference increased when the crop cycle length decreased as a consequence of later sowing date, earlier cultivar maturity group, and/or higher temperature. The relatively smaller (and occasionally negative) NR-WR yield difference detected in the central region was likely the result of environmental conditions that favored canopy closure irrespective of row spacing. In contrast to the analysis of the experimental database, no consistent NR-WR yield differences were detected in the producer field database. We hypothesize that the apparent absence of a significant NR-WR effect in the producer dataset is likely associated with the background management used with narrow spacing, together with yield losses due to wheel damage and greater disease pressure. This complementary approach using both producer and experimental data can help evaluate if practices documented in experimental trials to enhance yield realize equivalent yield increases in producer fields and, if not, explore underlying causes for the discrepancy.

A META-ANALYSIS OF HOW MANAGEMENT PRACTICES AFFECT SOYBEAN YIELD AND QUALITY

Abstract

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