Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
Plant, Soil and Microbial Science
Non Technical Summary
Michigan is the second largest dry bean producing state in the US in terms of the number of farms growing dry beans, number of acres harvested, and quantity harvested, according to the Census of Agriculture. Michigan is also the leading producer of organic dry bean in the US. It is also the most important food legume consumed worldwide because of its major role in nutrition, sustainability, food security, and income generation throughout the developing world. Dry bean production also plays a significant role in the rural economies where it is grown. However, to keep pace with the ever-growing population and to counter the increased impact of climate change, global crop production must be doubled by 2050, which current yield trends are insufficient to achieve in this timeframe. The yield potential of this crop is rarely achieved due to several biotic and abiotic factors. Through sciences-based approaches, this project aims to improve the economic and environmental impact of dry bean production in Michigan, the US, and worldwide through innovative breeding technologies. This project is important because it address climate change, sustainable agriculture, and rural economic development for local and larger communities. Our project will utilize faster and more accurate tools to delivery improved dry bean varieties with competitive yield and canning quality, improved diseases resistances, and efficient symbiotic nitrogen fixation. Combined all these attributes will deliver improved economic profit to producers and the canning industry, reduce the environmental impact of dry bean production, and contribute to global good security worldwide by collaborating with international groups.First, this project will collect data using conventional and unmanned aerial systems (UAS) to develop improved methods for phenotyping dry bean traits. We will compare conventional phenotyping methods to our new methods using UAS using regression analysis and other machine learning approaches. We will then collect genotype data using next generation sequencing and the BARCBean6k_2 BeadChip to perform linkage mapping. This will be used to validate the consistency of detecting QTL using these new approaches on previously collected traits using conventional methods. Second, we will perform estimate the genomic prediction accuracy using conventional and high-throughput approaches on a black bean mapping population. We will also compile genotypic and phenotypic data on yield trials for black beans across years to perform genomic predictions. Third, to detect QTL associated with disease resistance and canning quality in dry beans we will perform linkage mapping on specific mapping populations segregating for these traits. Molecular markers associated with significant QTL will be used for marker assisted selection (MAS) in the Michigan State dry bean breeding program and openly available for other dry bean breeders. Fourth, we will develop crosses that maintain genetic diversity while maintaining yield and quality. Our methods will include developing genomic prediction models that include optimal methods to convert genetic diversity into genetic gain while maintaining genomic prediction accuracy. We will also mine for genetic diversity using genomic selection on the USDA dry bean germplasm collection. Finally, we will increase the symbiotic nitrogen fixation (SNF) or dry bean cultivars. To do so we will evaluate elite dry bean varieties under low nitrogen (N) and indirectly select for high yielding cultivars that are stable across environments and years.Climate change, food security, loss of arable land, environmental degradation, and habitat loss are just a few of the major challenges that agriculture faces today. Our project aims to tackle these issues using advanced breeding technologies aimed at accelerating the rate of genetic gain in yield and canning quality in dry beans. By developing competitive dry bean varieties to producers and the canning industry we will bring economic profit and sustainable methods for dry bean production in the Great Lakes Area. Internationally, we will collaborate with groups to improve yield and other important cooking related traits vital for food security in those regions where beans are a staple.
Animal Health Component
90%
Research Effort Categories
Basic
10%
Applied
90%
Developmental
(N/A)
Goals / Objectives
The overarching goal of this project is to achieve faster genetics gain in dry bean by integrating modern breeding technologies in a conventional dry bean breeding program. To achieve this, our major goals are (1) Develop a high-throughput phenotyping pipeline to increase selection accuracy; (2) Develop genomics-assisted breeding pipeline to the accuracy, increase selection coefficient, and decrease length of breeding cycle; (3) Increase selection intensity through marker-assisted selection for biotic and quality traits; (4) Increasing additive genetic variance; (5) Develop improved dry bean varieties with increased SNF.
Project Methods
For all objectives prior experimental design will be conducted depending on the type of analysis required to meet each objective. We will collect either phenotypic or genotypic or both and perform data assessment including outlier analysis.For Objective 1-2: Phenotypic and genotypic data will be collected and analyzed using appropriate statistical models. We will estimate best linear unbiased predictors (BLUPs) and best linear unbiased estimator (BLUEs) for use in association mapping and genomic selection. Regression analysis and machine learning methods using cross validation will be used to evaluate new methods in objective 1.For Objectives 3-4: Phenotypic and genotypic data will be collected and analyzed using appropriate statistical models. For the identification of QTL markers associated with biotic and quality traits we will use mixed models to estimate BLUEs and perform a linkage map analysis. For genomic prediction models we will use BLUPS and determine accuracy of the prediction models.For Objective 5: We will conduct experiments across different environments using a randomized complete block in a split-plot arrangement. We will perform an appropriate linear model to make statistical inferences.Efforts: All objectives will be undertaken through the formal and informal education of graduate students. Students will take courses to learn plant breeding theory and routinely participate in an active dry bean breeding program as well as conduct research from this project.Evaluations: We will evaluate the success of the project through key milestones and specific tasks for each objective. We will also quantify our success on our deliverables for each objective.