By Scout Nelson
North Dakota State University (NDSU) is using Predictive Crop Performance (PCP) to make its breeding programs more efficient and ensure the sustainability of public crops. This method uses data, modeling, and technology to forecast how crops will grow and perform in different environments.
According to Richard Horsley, head of NDSU’s Plant Sciences Department and barley breeder, the goal is to make North Dakota’s public crops—such as barley, dry beans, flax, oats, and pulses—more resilient to climate challenges, pests, and diseases. The initiative is part of NDSU’s Food, Energy, and Water Security program.
Funding from the U.S. Department of Agriculture (USDA) supports genome sequencing and data collection, while the National Science Foundation (NSF) provides resources for developing data analytics tools. These technologies help scientists predict which crop lines are most likely to thrive under changing conditions.
Using genomic selection, breeders can now identify high-performing lines earlier in the process. Horsley’s team developed a 3.5K AgriSeq SNP array to select 500 promising barley lines annually for yield trials, cutting costs and improving results. As a result, 88% of tested barley lines now have lower wort β-glucan levels, improving malt quality.
Similarly, researchers Phil McClean and Juan Osorno created a 3.9K SNP array for dry beans, allowing predictions for traits like canning quality, disease resistance, and yield potential.
Graduate student Mat Souza noted that field data and analytics directly help breeders make better decisions. The NDSU Agricultural Data Analytics Team continues developing these digital tools, enabling public breeders to match the efficiency of large multinational companies.
By combining genomic and phenotypic data, NDSU’s Predictive Crop Performance project is transforming crop improvement, ensuring North Dakota farmers have access to resilient, high-quality crops that thrive in real-world conditions.
Photo Credit: gettyimages-creischl
Categories: North Dakota, Crops, Equipment & Machinery, Weather