Harnessing enviromics to predict climate-impacted high-profile traits to assist informed decisions in agriculture

Modern agriculture is a complex system that demands real-time and large-scale quantification of trait values for evidence-based decisions. However, high-profile traits determining market values often lack high-throughput phenotyping technologies to achieve this objective; therefore, risks of undermining crop values through arbitrary decisions are high. Because environmental conditions are major contributors to performance fluctuation, with the contemporary informatics infrastructures, we proposed enviromic prediction as a potential strategy to assess traits for informed decisions. We demonstrated this concept with wheat falling number (FN), a critical end-use quality trait that significantly impacts wheat market values but is measured using a low-throughput technology. Using 8 years of FN records from elite variety testing trials, we developed a predictive model capturing the general trend of FN based on biologically meaningful environmental conditions. An explicit environmental index that was highly correlated (r = 0.646) with the FN trend observed from variety testing trials was identified. An independent validation experiment verified the biological relevance of this index. An enviromic prediction model based on this index achieved accurate and on-target predictions for the FN trend in new growing seasons. Two applications designed for production fields illustrated how such enviromic prediction models could assist informed decision along the food supply chain. We envision that enviromic prediction would have a vital role in sustaining food security amidst rapidly changing climate. As conducting variety testing trials is a standard component in modern agricultural industry, the strategy of leveraging historical trial data is widely applicable for other high-profile traits in various crops.