Location: Dale Bumpers National Rice Research Center
Project Number: 6028-21000-012-000-D
Project Type: In-House Appropriated
Start Date: Mar 26, 2023
End Date: Mar 25, 2028
Objective:
1. Develop new climate-resilient rice with stable and high-quality yield, reduced irrigation needs, and lower methane emissions for sustainable U.S. agricultural production, and safer food supplies using new technologies.
2. Characterize and strengthen the rice germplasm collection and the blast fungal collection for genetic diversity to foster adaptation to climate change and to reduce vulnerability to biotic stress.
3. Identify the genes and gene networks that underlie beneficial rice traits to produce rice with increased economic productivity, resistance to biotic and abiotic stress, and enhanced value for human nutrition and processing. Identify the genes and gene networks that underlie beneficial rice traits to produce rice with increased economic productivity, resistance to biotic and abiotic stress, and enhanced value for human nutrition and processing.
4. Identify for critical environments the optimum gene combinations for climate-resilient, stable agronomic performance, and biotic/abiotic stress tolerance using artificial intelligence/machine learning approaches to analyze genomic information and high-throughput phenotyping data.
Please see copy of upload Project Plan for all subobjectives; due to character limit they will not fit in this field.
Approach:
The approach includes 1) developing germplasm and tools for breeding climate-resilient rice for sustainable U.S. agricultural production, 2) exploring diverse genetic resources for novel traits and genes to foster adaptation to climate change and to reduce vulnerability to biotic stress, 3) identifying the genes and gene networks that underlie beneficial traits to produce rice with increased economic productivity, resistance to biotic and abiotic stress, and enhanced value for human nutrition and processing, and 4) identifying optimum gene combinations for climate-resilient rice with stable agronomic performance and biotic/abiotic stress tolerance using artificial intelligence (AI) /machine learning to analyze genomic information and high-throughput phenotyping data. Marker assisted selection and rapid generation advance will be used to develop conventional and specialty breeding materials for release to US breeding programs. Efficient new methods will be developed for phenotyping traits associated with climate resilience and grain quality. Interactions between soil microbes, greenhouse gas emissions, grain quality and quantity under different irrigation systems will be evaluated. Mapping populations will be developed and evaluated to discover novel alleles from rice wild relatives that provide adaptation to abiotic and biotic stress. The USDA’s world rice collection of over 19,000 cultivars will be mined to discover useful novel alleles through the characterization of sub collections including a Tropical japonica Core (TRJC) collection, an aus subpopulation collection, weedy rice, and rice wild relative collections of O. glaberrima, O. barthii and O. australiensis. In addition, the US rice blast (Magnaporthea oryzea) field isolates will be characterized to guide the deployment of blast resistance genes. Several mapping populations will be used to identify genes and gene networks including a Japonica Multi-parent Advanced Generation Inter-Cross (MAGIC) population for yield components, an aus nested association mapping (NAM) population for water deficit and high temperature stress tolerance, and biparental populations for grain mineral accumulation. Genes and gene interactions involving biotic stress will be investigated using yeast two hybrid (Y2H) screening, and tradeoffs between biotic and abiotic stress responses will be investigated by gene expression analysis of varieties with differential blast resistance genes. Machine learning and artificial intelligence methods will be developed and deployed for high-throughput phenotyping using UAV or ground-based imaging systems. A database will be developed for genomic selection and a rapid cycle recurrent selection (RCRS) breeding pipeline will be established for fast rice breeding. The RCRS pipeline will be used to breed germplasm with increased quantitative resistance to sheath blight and cold tolerance. Outcomes from this research will include new public germplasm, new methods for accurate and efficient rice breeding, and genetic markers linked to traits that can be used in marker assisted breeding.
