Life Sciences Commons^™

Method Developments To Identify Loci And Selection Patterns Associated With Genotype By Environment Interactions In Soybean, Mary M. Happ

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

For many complex traits such as grain yield, genotype by environment (GxE) interactions are a prevalent source of phenotypic variation. Exploring the capacity of different methodologies to help describe and quantify the GxE interaction landscape for grain yield is an important step in informing plant breeders what the most viable strategies for management and exploitation of GxE may be. In this endeavor, we compared the results from multiple genome wide association studies (GWAS) that used either stability estimators as a phenotype to capture GxE variance, or directly mapped GxE in a mixed model for yield. Leading into this study, a …

Arkansas Soybean Performance Tests 2022, J. F. Carlin, R. B. Mulloy, R. D. Bond

Arkansas Agricultural Experiment Station Research Series

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/ or marketing seed within the state, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers.

Genomic Selection For Yield And Seed Composition Stability In An Applied Soybean Breeding Program, Benjamin Harms

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Stability traits are of primary importance in plant breeding to ensure consistency in phenotype across a range of environments. However, selection efficiency and accuracy for stability traits can be hindered due to the requirement of obtaining phenotype data across multiple years and environments for proper stability analysis. Genomic selection is a method that allows prediction of a phenotype prior to observation in the field using genome-wide marker data and phenotype data from a training population. To assess prediction of stability traits, two elite-yielding soybean populations developed three years apart in the same breeding program were used. The individuals in each …

A Leaf-Level Spectral Library To Support High-Throughput Plant Phenotyping: Predictive Accuracy And Model Transfer, Nuwan K. Wijewardane, Huichun Zhang, Jinliang Yang, James C. Schnable, Daniel P. Schachtman, Yufeng Ge

Department of Agronomy and Horticulture: Faculty Publications

Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets …

Recombination Hotspots In Soybean [Glycine Max (L.) Merr.], Samantha Mcconaughy, Keenan L. Amundsen, Qijian Song, Vince Pantalone, D. Hyten

Department of Agronomy and Horticulture: Faculty Publications

Recombination allows for the exchange of genetic material between two parents, which plant breeders exploit to make improved cultivars. This recombination is not distributed evenly across the chromosome. Recombination mostly occurs in euchromatic regions of the genome and even then, recombination is focused into clusters of crossovers termed recombination hotspots. Understanding the distribution of these hotspots along with the sequence motifs associated with them may lead to methods that enable breeders to better exploit recombination in breeding. To map recombination hotspots and identify sequence motifs associated with hotspots in soybean [Glycine max (L.) Merr.], two biparental recombinant inbred lines …

Fine Mapping And Cloning Of The Major Seed Protein Quantitative Trait Loci On Soybean Chromosome 20, Christina E. Fliege, Russell A. Ward, Pamela Vogel, Hanh Nguyen, Truyen Quach, Ming Guo, João Paulo Gomes Viana, Lucas Borges Dos Santos, James Specht, Thomas Clemente, Matthew E. Hudson, Brian W. Diers

Department of Agronomy and Horticulture: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a unique crop species because it has high levels of both protein and oil in its seed. Of the many quantitative trait loci (QTL) controlling soybean seed protein content, alleles of the cqSeed protein-003 QTL on chromosome 20 exert the greatest additive effect. The high-protein allele exists in both cultivated and wild soybean (Glycine soja Siebold & Zucc.) germplasm. Our objective was to fine map this QTL to enable positional-based cloning of its underlying causative gene(s). Fine mapping was achieved by developing and testing a series of populations in which the chromosomal region surrounding …

Field Validation Of A Farmer Supplied Data Approach To Close Soybean Yield Gaps In The Us North Central Region, José F. Andrade, Spyridon Mourtzinis, Juan I. Rattalino Edreira, Shawn P. Conley, John Gaska, Herman J. Kandel, Laura E. Lindsey, Seth Naeve, Scott Nelson, Maninder P. Singhi, Laura J. Thompson, James E. Specht, Patricio Grassini

Department of Agronomy and Horticulture: Faculty Publications

CONTEXT: Producer-reported data can be used to identify suites of management practices that lead to higher yield and profit. However, a rigorous validation of the approach in relation to its potential impact on farmer yield and profit is lacking.

OBJECTIVE: This study aimed to validate a producer-data approach on its capability to guide on-farm evaluation of management practices with greatest potential for increasing producer yield and profit. We show proof of concept using soybean in the North Central US region as a case study.

METHODS: We used a combination of regression tree analysis and a spatial framework to determine practices …

Arkansas Soybean Research Studies 2020, Jeremy Ross

Arkansas Agricultural Experiment Station Research Series

The 2020 Arkansas Soybean Research Studies includes research reports on topics pertaining to soybean across several disciplines from breeding to post-harvest processing. Research reports contained in this publication may represent preliminary or only data from a single year or limited results; therefore, these results should not be used as a basis for long-term recommendations. Several research reports in this publication will appear in other University of Arkansas System Division of Agriculture’s Arkansas Agricultural Experiment Station publications. This duplication is the result of the overlap in research coverage between disciplines and our effort to inform Arkansas soybean producers of the research …

Rhizosphere Microbiomes In A Historical Maize-Soybean Rotation System Respond To Host Species And Nitrogen Fertilization At The Genus And Subgenus Levels, Michael A. Meier, Martha G. Lopez-Guerrero, Ming Guo, Marty Schmer, Josh Herr, James Schnable, James R. Alfano, Jinliang Yang

Department of Agronomy and Horticulture: Faculty Publications

Root-associated microbes are key players in plant health, disease resistance, and nitrogen (N) use efficiency. It remains largely unclear how the interplay of biological and environmental factors affects rhizobiome dynamics in agricultural systems. In this study, we quantified the composition of rhizosphere and bulk soil microbial communities associated with maize (Zea mays L.) and soybean (Glycine max L.) in a long-term crop rotation study under conventional fertilization and low-N regimes. Over two growing seasons, we evaluated the effects of environmental conditions and several treatment factors on the abundance of rhizosphere- and soil-colonizing microbial taxa. Time of sampling, host plant species, …

Comparing A Mixed Model Approach To Traditional Stability Estimators For Mapping Genotype By Environment Interactions And Yield Stability In Soybean [Glycine Max (L.) Merr.], Mary M. Happ, George L. Graef, Haichuan Wang, Reka Howard, Luis Posadas, David L. Hyten

Department of Agronomy and Horticulture: Faculty Publications

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype’s range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield “stability” values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies …

A Bumper Crop Of Snps In Soybean Through High-Density Genotyping-By-Sequencing (Hd-Gbs), Davoud Torkamaneh, Jerome Laroche, Brian Boyle, David L. Hyten, Fancois Belzile

Department of Agronomy and Horticulture: Faculty Publications

No abstract provided.

Development Of Mping-Based Activation Tags For Crop Insertional Mutagenesis, Alexander Johnson, Edward Mcassey, Stephanie Diaz, Jacod Reagin, Priscilla S. Redd, Daymond R. Parrilla, Hanh Nguyen, Adrian Stec, Lauren A.L. Mcdaniel, Thomas E. Clemente, Robert M. Stupar, Wayne A. Parrott, C. Nathan Hancock

Department of Agronomy and Horticulture: Faculty Publications

Modern plant breeding increasingly relies on genomic information to guide crop im- provement. Although some genes are characterized, additional tools are needed to effectively identify and characterize genes associated with crop traits. To address this need, the mPing element from rice was modified to serve as an activation tag to in- duce expression of nearby genes. Embedding promoter sequences in mPing resulted in a decrease in overall transposition rate; however, this effect was negated by using a hyperactive version of mPing called mmPing20. Transgenic soybean events carrying mPing-based activation tags and the appropriate transposase expression cassettes showed evidence of transposition. …

Arkansas Soybean Performance Tests 2020, J. F. Carlin, R. D. Bond, R. B. Morgan

Arkansas Agricultural Experiment Station Research Series

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers.

Development Of Mping-Based Activation Tags For Crop Insertional Mutagenesis, Alexander Johnson, Edward Mcassey, Stephanie Diaz, Jacob Reagin, Priscilla S. Redd, Daymond R. Parrilla, Hanh T. Nguyen, Adrian Stec, Lauren A.L. Mcdaniel, Thomas E. Clemente, Robert M. Stupar, Wayne A. Parrott, C Nathan Hancock

Department of Agronomy and Horticulture: Faculty Publications

Modern plant breeding increasingly relies on genomic information to guide crop improvement. Although some genes are characterized, additional tools are needed to effectively identify and characterize genes associated with crop traits. To address this need, the mPing element from rice was modified to serve as an activation tag to induce expression of nearby genes. Embedding promoter sequences in mPing resulted in a decrease in overall transposition rate; however, this effect was negated by using a hyperactive version of mPing called mmPing20. Transgenic soybean events carrying mPing-based activation tags and the appropriate transposase expression cassettes showed evidence of transposition. Expression analysis …

Arkansas Soybean Research Studies 2019, Jeremy Ross

Arkansas Agricultural Experiment Station Research Series

The 2019 Arkansas Soybean Research Studies includes research reports on topics pertaining to soybean across several disciplines, from breeding to post-harvest processing. Research reports contained in this publication may represent preliminary or only a data set from a single year or limited results; therefore, these results should not be used as a basis for long-term recommendations. Several research reports in this publication will appear in other University of Arkansas System Division of Agriculture’s Arkansas Agricultural Experiment Station publications. This duplication is the result of the overlap in research coverage between disciplines and our effort to inform Arkansas soybean producers of …

Assessing Approaches For Stratifying Producer Fields Based On Biophysical Attributes For Regional Yield-Gap Analysis, Spyridon Mourtzinis, Patricio Grassini, Juan I. Rattalino Edreira, José F. Andrade, Peter M. Kyveryga, Shawn P. Conley

Department of Agronomy and Horticulture: Faculty Publications

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (‘TEDs’) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop …

Soybean Response To Water: Trait Identification And Prediction, Shawn Jenkins

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

The rising demand for soybean [Glycine Max (L.) Merrill] taken in consideration with current climatic trends accentuates the importance of improving soybean seed yield response per unit water (WP). To further our understanding of the quantitative WP trait, a multi-omic approach was implemented for improved trait identification and predictive modeling opportunities. Through the evaluation of two recombinant inbred line populations jointly totaling 439 lines subjected to contrasting irrigation treatments, informative agronomic, phenomic, and genomic associations were identified. Across both populations, relationships were identified between lodging at maturity (r = -0.58, H = 0.86), canopy to air temperature differential …

Insufficient Nitrogen Supply From Symbiotic Fixation Reduces Seasonal Crop Growth And Nitrogen Mobilization To Seed In Highly Productive Soybean Crops, Nicolas Cafaro La Menza, Juan Pablo Monzon, John L. Lindquist, Timothy J. Arkebauer, Johannes M.H. Knops, Murray Unkovich, James E. Specht, Patricio Grassini

Department of Agronomy and Horticulture: Faculty Publications

Nitrogen (N) supply can limit the yields of soybean [Glycine max (L.) Merr.] in highly productive environments. To explore the physiological mechanisms underlying this limitation, seasonal changes in N dynamics, aboveground dry matter (ADM) accumula- tion, leaf area index (LAI) and fraction of absorbed radiation (fAPAR) were compared in crops relying only on biological N2 fixation and available soil N (zero-N treatment) versus crops receiving N fertilizer (full-N treatment). Experiments were conducted in seven high-yield environments without water limitation, where crops received optimal management. In the zero-N treatment, biological N2 fixation was not sufficient to meet the N demand of …

Arkansas Soybean Research Studies 2018, Jeremy Ross

Arkansas Agricultural Experiment Station Research Series

Arkansas is the leading soybean-producing state in the mid-southern United States. Arkansas ranked 11th in soybean production in 2018 when compared to the other soybean-producing states in the U.S. The state represents 3.7% of the total U.S. soybean production and 3.7% of the total acres planted in soybean in 2018. The 2018 state soybean average was 50.5 bushels per acre, half a bushel lower than the state record set in 2017. The top five soybean-producing counties in 2018 were Mississippi, Desha, Phillips, Arkansas, and Poinsett Counties (Table 1). These five counties accounted for 33.7% of soybean production in Arkansas in …

Arkansas Soybean Performance Tests 2019, J. F. Carlin, R. D. Bond, J. A. Still

Arkansas Agricultural Experiment Station Research Series

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers.

Integration Of Cover Crops Into Midwest Corn-Soybean Cropping Systems And Potential For Weed Suppression, Joshua S. Wehrbein

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Cover crops have potential to provide benefits to agricultural systems, such as improved soil productivity, nutrient scavenging, weed suppression, and livestock forage. There are several challenges associated with cover crop integration into traditional Midwest corn-soybean cropping systems. One of these challenges is timely establishment in the fall, which is limited by the relatively late harvest of corn and soybean. Cover crop effectiveness is related to the amount of biomass produced, thus maximizing the growth period in the fall is desired. To address this challenge, we evaluated the potential to utilize early-season soybean maturity groups (MGs) to allow for earlier soybean …

Generating High Density, Low Cost Genotype Data In Soybean [Glycine Max (L.) Merr.], Mary M. Happ, Haichuan Wang, George L. Graef, David L. Hyten

Department of Agronomy and Horticulture: Faculty Publications

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using …

Response Surface Analysis Of Genomic Prediction Accuracy Values Using Quality Control Covariates In Soybean, Diego Jarquin, Reka Howard, George L. Graef, Aaron Lorenz

Department of Agronomy and Horticulture: Faculty Publications

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and …

Arkansas Soybean Research Studies 2017, Jeremy Ross

Arkansas Agricultural Experiment Station Research Series

No abstract provided.

Arkansas Soybean Research Studies 2016, Jeremy Ross

Arkansas Agricultural Experiment Station Research Series

Arkansas is the leading soybean-producing state in the mid-southern United States. Arkansas ranked 11th in soybean production in 2016 when compared to the other soybean-producing states in the U.S. The state represents 3.4% of the total U.S. soybean production and 3.8% of the total acres planted to soybean in 2016. The 2016 state soybean average was 47 bushels per acre, 2.5 bushels per acre less than the state record soybean yield set in 2014. The top five soybean-producing counties in 2016 were Mississippi, Phillips, Poinsett, Crittenden, Arkansas Counties. These five counties accounted for 34.7% of soybean production in Arkansas in …

Gene Expression Profiling Of Iron Deficiency Chlorosis Sensitive And Tolerant Soybean Indicates Key Roles For Phenylpropanoids Under Alkalinity Stress, Brian M. Waters, Keenan Amundsen, George L. Graef

Department of Agronomy and Horticulture: Faculty Publications

Alkaline soils comprise 30% of the earth and have low plant-available iron (Fe) concentration, and can cause iron deficiency chlorosis (IDC). IDC causes soybean yield losses of $260 million annually. However, it is not known whether molecular responses to IDC are equivalent to responses to low iron supply. IDC tolerant and sensitive soybean lines provide a contrast to identify specific factors associated with IDC.We used RNA-seq to compare gene expression under combinations of normal pH (5.7) or alkaline pH (7.7, imposed by 2.5mM bicarbonate, or pH 8.2 imposed by 5mM bicarbonate) and normal (25μM) or low (1μM) iron conditions from …

Genetic Architecture Of Soybean Yield And Agronomic Traits, Brian W. Diers, Jim Specht, Katy Martin Rainey, Perry Cregan, Qijian Song, Vishnu Ramasubramanian, George Graef, Randall L. Nelson, William Schapaugh, Dechun Wang, Grover Shannon, Leah Mchale, Stella K. Kantartzi, Alencar Xavier, Rouf Mian, Robert M. Stupar, Jean-Michel Michno, Yong-Qiang Charles An, Wolfgang Goettel, Russell Ward, Carolyn Fox, Alexander E. Lipka, David Hyten, Troy Cary, William D. Beavis

Department of Agronomy and Horticulture: Faculty Publications

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. …

Assessing Explanatory Factors For Variation In On-Farm Irrigation In Us Maize-Soybean Systems, Katherine E.B. Gibson, Haishun S. Yang, Trenton E. Franz, Dean E. Eisenhauer, John B. Gates, Paolo Nasta, Bhupinder S. Farmaha, Patricio Grassini

Department of Agronomy and Horticulture: Faculty Publications

Irrigation exhibits large variation across producer fields, even within same region and year. A knowledge gap exists relative to factors that explain this variation, in part due to lack of availability of high-quality irrigation data from multiple field-years. This study assessed sources of variation in irrigation using a large database collected during 9 years (2005–2013) from ca. 1400 maize and soybean producer fields in Nebraska, central USA (total of 12,750 field-year observations). The study area is representative of ca. 4.5 million ha of irrigated land sown with maize and soybean. Influence of biophysical (weather, soil, and crop type) and behavioral …

Sifting And Winnowing: Analysis Of Farmer Field Data For Soybean In The Us North-Central Region, Spyridon Mourtzinis, Juan I. Rattalino Edreira, Patricio Grassini, Adam C. Roth, Shaun N. Casteel, Ignacio A. Ciampitti, Hans J. Kandel, Peter M. Kyveryga, Mark A. Licht, Laura E. Lindsey, Daren S. Mueller, Emerson D. Nafziger, Seth L. Naeve, Jordan Stanley, Michael J. Staton, Shawn P. Conley

Department of Agronomy and Horticulture: Faculty Publications

Field trials are commonly used to estimate the effects of different factors on crop yields. In the present study, we followed an alternative approach to identify factors that explain field-to-field yield variation, which consisted of farmer survey data, a spatial framework, and multiple statistical procedures. This approach was used to identify management factors with strongest association with on-farm soybean yield variation in the US North Central (NC) region. Field survey data, including yield and management information, were collected over two crop growing seasons (2014 and 2015) from rainfed and irrigated soybean fields (total of 3568 field-year observations). Fields were grouped …

Arkansas Soybean Performance Tests 2017, R. D. Bond, J. A. Still, D. G. Dombek

Arkansas Agricultural Experiment Station Research Series

Soybean variety and strain performance tests are conducted each year in Arkansas by the University of Arkansas System Division of Agriculture’s Arkansas Crop Variety Improvement Program. The tests provide information to companies developing varieties and/or marketing seed within the State, and aid the Arkansas Cooperative Extension Service in formulating variety recommendations for soybean producers.