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Arkansas Soybean Research Studies 2017, Jeremy Ross
Arkansas Soybean Research Studies 2017, Jeremy Ross
Arkansas Agricultural Experiment Station Research Series
No abstract provided.
Cover Crops Have Negligible Impact On Soil Water In Nebraska Maize–Soybean Rotation, J. Burdette Barker, Derek M. Heeren, Katja Koehler-Cole, Charles Shapiro, Humberto Blanco-Canqui, Roger W. Elmore, Christopher A. Proctor, Suat Irmak, Charles A. Francis, Tim M. Shaver, Ali T. Mohammed
Cover Crops Have Negligible Impact On Soil Water In Nebraska Maize–Soybean Rotation, J. Burdette Barker, Derek M. Heeren, Katja Koehler-Cole, Charles Shapiro, Humberto Blanco-Canqui, Roger W. Elmore, Christopher A. Proctor, Suat Irmak, Charles A. Francis, Tim M. Shaver, Ali T. Mohammed
Biological Systems Engineering: Papers and Publications
One perceived cost of integrating winter cover cropping in maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] rotation systems is the potential negative impact on soil water storage available for primary crop production. The objective of this 3-year study was to evaluate the effects of winter cover crops on soil water storage and cover crop biomass production following no-till maize and soybean rotations. Locations were near Brule (west-central), Clay Center (south-central), Concord (northeast), and Mead (east-central), Nebraska, United States. Treatments included crop residue only (no cover crop) and a multi-species cover crop mix, both broadcast-seeded before …
Genetic And Phytopathological Studies On Charcoal Rot Resistance In Soybean [Glycine Max (L) Merr.], Marcos Paulo Da Silva
Genetic And Phytopathological Studies On Charcoal Rot Resistance In Soybean [Glycine Max (L) Merr.], Marcos Paulo Da Silva
Graduate Theses and Dissertations
Charcoal rot of soybean (Glycine max (L.), caused by Macrophomina phaseolina is a disease of economic significance in the United States. The identification and quantification of the resistance is difficult, and very little is known about the genetics and markers linked to the charcoal rot (CR) resistance genes. Current assay methods can be time consuming, and data may vary between tests. The objectives of this study were to 1) create a robust seed plate assay (SPA) for CR resistance by comparing results with cut-stem and CFUI assays; 2) correlate and compare field data disease assessments with SPA; 3) identify QTLs …
Arkansas Soybean Research Studies 2016, Jeremy Ross
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
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
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
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
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 …