To develop climate resilient crops, breeders must first understand how traits such as yield vary across environments and whether genetic variations can influence a crop’s sensitivity to climate conditions. With this knowledge, breeders and growers can use genomic data to predict crops’ resiliency and select plants for breeding that are best suited to expected and unexpected future climate conditions.
Cornell researchers, led by Dr. Kelly Robbins, are planting diverse varieties of maize and alfalfa crops with known differences in growth patterns in two contrasting growth environments to determine the locations’ effects on crop performance over time. Partnering researchers at Cornell, Virginia Tech, New Mexico State University, BASF and Limagrain will focus on alfalfa, maize, wheat, soybean, cotton and canola due to the economic importance of these crops for food, fiber and feed.
The research team is using aerial imaging to assess observable traits of crops through each stage of their growth. Crops are genotyped to detect the genetic differences that affect the observable traits. With this information, the researchers can better understand the interactions of environments and genomes in the plants’ development, allowing breeders to select crops that adapt to stress. The research also identifies which observable features help predict growth and development success. Finally, the researchers are developing statistical models and software to predict crop performance more accurately, which will be publicly shared.
“The use of high-throughput phenotyping, which provides rapid, non-destructive characterization of a plant’s genetic traits that are physically expressed, now enables us to efficiently collect data on plant growth and development throughout the growing season,” said Robbins. “Using this data, we can develop more detailed genetic models of interactions between plants and dynamic environmental data. These models will increase our understanding of the genetic factors that drive plant responses to constantly changing environmental conditions, improving our ability to predict crop performance under future environmental conditions.”