Crops: Our current work is focused primarily on grain legumes, cereals, and leafy greens.
Nutrients: We are examining both macro- and micronutrients (including vitamins and minerals), and [reduction of] anti-nutrients (compounds that are harmful to human and/or animal health), among others. We are additionally interested in links between nutritional quality and consumer-facing traits (e.g. pigmentation and flavor of the edible portion of the crop), and in mitigating or reversing tradeoffs between nutritional quality and productivity.
Methods: We work with statistical genetics/computational genomics methodologies to identify the genetic basis of—and/or predict breeding values for—crop nutritional quality traits. We also integrate these methodologies with models of crop growth and development, and data representing other tissue and time scales of relevance to plant biology. Our long-term goals are as follows: 1) to contribute to fundamental understanding of the accumulation and retention of crop nutrients for human and/or animal health; 2) to help enable the integration of crop biochemistry and physiology into the breeding process, in a routine and cost-effective manner; and 3) to work, including through transdisciplinary partnerships, to develop nutritionally dense crop varieties that are also highly productive and otherwise acceptable to growers, processors, and consumers. We are consistently expanding our methodological tool kit, both within and across disciplines, as needed to meet these goals and to obtain a comprehensive understanding of the underlying mechanisms (and phenomena) of crop nutritional quality and productivity, including under conditions of abiotic stress.
Summary of current projects (authored by lab members):
Breeding program: We are actively breeding lima beans, garbanzos, and other food legumes for yield, seed size and quality, and other aspects of regional adaptation. Please feel free to subscribe to the UC Dry Bean Blog (https://ucanr.edu/blog/uc-dry-bean-blog) if you are interested in receiving updates and/or to access recent field day handouts. Long-term work in this area has been thanks to the support of the California Dry Bean Advisory Board.
Common bean simulated digestion: Research in our lab funded by the USDA Pulse Crop Health Initiative (PCHI) is focused on the GxE examination of at-harvest and bioaccessible nutrients in common bean. This project utilizes robotic stomach chambers to dynamically simulate in vitro digestion for the release of bioaccessible nutrients to determine the nature and extent of changes in bean quality profiles from samples with contrasting seed coat color patterns. Additionally, this project aims to develop and compare simulated digestion models for use with small sample masses for use in breeding and product placement.
Breeding resources for lima beans: Lima beans are a direct-for-consumption dry bean with broad adaptation. More research is needed to adapt the more diverse germplasm to U.S. growing environments. We have evaluated a germplasm collection of diverse and elite accessions and are integrating agronomic, adaptation (photosensitivity, growth habit, etc.), and quality traits (nutritional, sensory, etc.) with genotypic data to further improve lima bean production in the US. We are also working to investigate consumer views of lima beans and sensory evaluations to examine and enable further improvement on their culinary and nutritional qualities. This work is funded by the USDA NIFA Specialty Crop Research Initiative.
Integration of sensing, crop modeling, and breeding/genomics: Many staple crops that are important for food, nutritional, and economic security in low- and middle-income countries (LMICs) have not experienced the same large gains in yield and quality over the last decades as crops such as maize and soybean. Further, these crops are faced with increasing risk and uncertain growing conditions due to climate change. GxExM Innovation through Intelligence for Climate Adaptation (GEMINI) aims to develop a state-of-the-art breeding toolkit, building on the latest techniques in AI-enabled sensing, 3-D crop modeling, and molecular breeding, to create an inflection point in the productivity and quality curves of crops that are central in LMICs. A companion project has been focused on improving nutritional quality, productivity, and high-temperature stress tolerance in Phaseolus beans and cowpea, with a focus on multi-environment field evaluation, genetic mapping, and genomic prediction, with a focus on co-developing pre-breeding resources with the grain legume breeding community.
Evaluation of sorghum agronomic and grain compositional traits under well-watered and pre-anthesis drought conditions: Sorghum is the world’s fifth most important cereal crop, and is a staple crop in parts of Africa. Sorghum is often grown under limited or sporadic water conditions which affects multiple traits important to those that cultivate it. In a USDA NIFA-funded project, we have been studying both agronomic and nutritional quality traits in sorghum grain under pre-anthesis drought conditions in the Central Valley of California. Grain composition was phenotyped using near-infrared spectroscopy with wet-chemistry reference analyses conducted on a subset of samples per environment-treatment combination. We are performing genome-wide association studies to identify candidate genes as well as genomic prediction and evaluating which accessions had favorable agronomics in the Central Valley.
Sensing- and genomics-enabled prediction of chlorophylls/carotenoids in leafy greens: Lettuce (Lactuca sativa) and spinach (Spinacia oleracea) are widely consumed leafy greens that contribute essential vitamins, minerals, and phytonutrients to human diets. Both crops are important dietary sources of chlorophylls and carotenoids, pigments that not only contribute to leaf coloration but also play critical roles in photosynthesis and in human health upon ingestion. This research aims to characterize natural variation in carotenoid and chlorophyll concentrations and their genetic basis in spinach and lettuce, in collaboration with the UC Davis breeding programs for those crops. This project also seeks to develop tools to increase the throughput of nutritional quality characterization in leafy greens. These investigations are leveraging genome-wide association studies and genomics- and sensing-enabled prediction to identify candidate parents, genomic regions, and develop predictive models that can be readily incorporated in breeding for nutritional quality and abiotic stress tolerance.
Pistachio macronutrients: This project has been focused on high-throughput phenotyping of kernel macronutrients via near-infrared spectroscopy (NIRS) and integration with genetic data in collaboration with the UC Davis pistachio breeding program.
Please feel free to contact us for more information regarding current and pending projects and opportunities.