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Full-Text Articles in Life Sciences
Arkansas Soybean Research Studies 2018, Jeremy Ross
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 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
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
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
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 …