Soybean breeding program

Additional information Our research projects are designed to address local and or regional problems, because crop production is directly influenced by the environments that surround the crop.

The soybean crop in Ohio has a set of major challenges which are of lesser problems to other states of the USA. In many ways "all agricultural problems are local or regional" like all politics are local or regional. Such problems can not be solved by research programs located at a faraway place from the problem area.

Due to its unique location, the Ohio soybean growers face some unique problems that are not priority for soybean researchers in other states. To be clear, it will very difficult for other states to solve such problems even if they want to. These two programs collaborate with each other and various departments of The OSU, including plant pathology and entomology to solve the local and regional soybean problems.

Our common goal is to make the Ohio soybeans the best in the nation. Menu Agricultural Research Service U. Search small Search. Dennis White, Research Technologist, Mr. Travis Wegner, Research Technologist, Mrs. Cultivars and germplasm from this project are used as parental lines for future variety development efforts at other universities, USDA, research institutions, and commercial seed companied throughout the world.

Publications Graef, George, B. LaVallee, P. Tenopir, M. Tat, B. Schweiger, A. Kinney, J. Van Gerpen, and T. A high-oleic and low-palmitic acid soybean: agronomic performance and evaluation as a feedstock for biodiesel. Plant Biotechnology Journal 7: Yan, Lin, George L. Graef, Philip G. Reeves, and LuAnn K. Selenium bioavailability from soy protein isolate and tofu in rats fed a torula yeast-based diet.

Food Chem. The tests include a total of entries in 69 tests throughout the USA for Maturity Groups 0 through V at 39 environments, involving 23 Ph. Publications Bastidas, A. Setiyono, A. Dobermann, K. Cassman, R. Elmore, G. Graef, and J. Crop Sci. The tests include a total of entries in 45 tests throughout the USA for Maturity Groups 0 through V at 38 locations, involving 23 Ph.

Denmis White, Research Technologist Mr. Leandro Castaneda, Research Technologist, M. Student Mr. Travis Wegner, Reserach Technologist, M. Student Mrs. Publications Ziems, A. Response of soybean cultivars to Bean pod mottle virus infection. Plant Dis.

The tests were conducted throughout the US in 65 tests at 38 locations for Maturity Groups 0 through V, involving 24 Ph. Cultivars and germplasm from this project are used as parental lines for future variety development efforts at toher universities, USDA, research institutions, and commercial seed companied throughout the world. Publications Vasconcelos, M. Eckert, V. Arahana, G.

Graef, M. Grusak, and T. Molecular and phenotypic characterization of transgenic soybean expressing the Arabidopsis ferric chelate reductase gene, Fro2. Planta Baenziger, P. Russell, G. Graef, and B. Improving lives: 50 years of crop breeding, genetics and cytology C Graef, G. Castaneda, and T. Ejemplos de algunos esfuerzos en fitomejoramiento para valor-anadido en soja. In Rodolfo Rossi ed. Conferencias plenarias, foros, workshops.

Alabama Soybean Checkoff. Arkansas Soybean Promotion Board. Atlantic Region Soybean Board. Crop Protection Network. Delaware Soybean Board. Eastern Region Soybean Board. Georgia Soybeans. Illinois Soybean Association. Indiana Soybean Alliance. Iowa Soybean Association. Iowa Soybean Research Center.

Kansas Soybean Commission. Kentucky Soybean Board. Maryland Soybean Board. Michigan Soybean Committee. Mid-South Soybean Board. Minnesota Soybean Research Promotion Council. Mississippi Soybean Promotion Board. Missouri Soybean Merchandising Council. Nebraska Soybean Board. New Jersey Soybean Board. North Carolina Soybean Producers Association.

North Central Soybean Research Program. North Dakota Soybean Council. Considering that taller and bigger plants do not result in higher yields when ranking the top BLUPs, several lines that were selected based on ACC may have had poor yield potential. In addition, the lack of correlation of yield and ACC in PR may have been a result of this unusual canopy growth.

Therefore, despite the evidence that one trait can be used to indirect select for yield, the breeder needs to consider the environmental influence on the trait phenotypes at the time of selection. In our case, we could have used a threshold for ACC before doing the selections, avoiding the very high values of canopy coverage, or restricted selection dates to earlier points in development. Thus, despite the difference in mean performance among the selection categories in the PYT stage, we have demonstrated that ACC alone or combined with yield Yield ACC are valuable secondary traits for selection in the PR stage.

Poor yield measurements due to harvesting errors, weather, and plot damage, lead to inaccurate representations of yield potential. Adjusting yield for early season ACC compensates for these inadequacies and is a better predictor of the real yield potential. This is in agreement with Jarquin et al.

Additionally, digital canopy coverage has a one to one relationship to LI, which in turn is an important factor for yield potential equation [ 32 , 33 , 48 ]. Therefore, up to a certain point, increases in LI, through ACC, will result in increases in yield when the other parameters in the yield equation are kept the same. In this study, we have shown that the efficiency of selecting high yielding soybean lines can be improved by taking advantage of an HTP trait.

Field-based HTP using UAS is robust, simple, and cost-effective and can measure a wide range of phenotypes that can be converted into useful secondary traits [ 2 , 49 ]. Breeding teams need to evaluate carefully the value of these secondary traits in increasing genetic gain either in a phenotypic selection or as part of pedigree or genomic prediction schemes [ 2 , 14 ].

In addition, we recommend testing different scenarios to ensure if the greater response is using the secondary trait alone or in combination with yield.

However, if not in the literature, an investigation of heritability and genetic correlation to yield should be carried out to evaluate the potential of the trait. One of the most important tasks of a plant breeder is to find among the available selection criteria a combination that can promote the desirable genetic gain for the traits of interest within their breeding program. Field HTP must be integrated into a wider context in breeding programs than trait estimation, evaluation of platforms, and genetic association studies.

We compared their performance in advancing selected lines in the following generations common in a soybean breeding program. We have demonstrated that the secondary trait ACC measured using an aerial HTP platform can be used for selection, alone or in combination with yield, in early stages of soybean breeding pipelines. Further studies are needed to assess environmental effects on canopy coverage phenotypic variation in order to have optimized recommendations on the use of ACC for selecting high yielding lines in different scenarios.

The datasets generated and analyzed during the current study are not publicly available as they are part of the Purdue Soybean Breeding program but are available from the corresponding author on reasonable request. Introduction to quantitative genetics. Burnt Mill: Longman; Google Scholar. Theor Appl Genet. Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol. Enhancing genetic gain in the era of molecular breeding.

J Exp Bot. Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.

A review of imaging techniques for plant phenotyping. High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge. J Integr Plant Biol. Fiorani F, Schurr U. Future scenarios for plant phenotyping. Annu Rev Plant Biol. Plant phenomics, from sensors to knowledge. Curr Biol. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci. Increasing predictive ability by modeling interactions between environments, genotype and canopy coverage image data for soybeans.

Article Google Scholar. Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. Plant Genome. Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices.

Crop Sci. Richards RA. Selectable traits to increase crop photosynthesis and yield of grain crops. Bernardo RN. Breeding for quantitative traits in plants. Woodbury: Stemma Press; Genetic improvement: conventional and molecular-based strategies. Soybeans Improv Prod Uses. Thin plate spline regression model used at early stages of soybean breeding to control field spatial variation.

J Crop Improv. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant Methods. Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. Plant Sci. Temporal importance of greater light interception to increased yield in narrow-row soybean.

Agron J. Interception of solar radiation and dry matter production by various soybean planting patterns.