Assessing long-term weather variability impacts on annual grain yields using a maize simulation model

Authors: White, Kathryn; Fleisher, David; Cavigelli, Michel; Timlin, Dennis; Schomberg, Harry

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/23/2025
Publication Date: 5/2/2025
Citation: White, K.E., Fleisher, D.H., Cavigelli, M.A., Timlin, D.J., Schomberg, H.H. 2025. Assessing long-term weather variability impacts on annual grain yields using a maize simulation model. Agricultural and Forest Meteorology. 370. Article e110593. https://doi.org/10.1016/j.agrformet.2025.110593.
DOI: https://doi.org/10.1016/j.agrformet.2025.110593

Interpretive Summary: Low rainfall and heat stress during critical periods of crop growth affect corn grain yields each year. Large differences in growing season weather between years makes it difficult to determine how these weather factors interact and when they might have the largest impact on yields. Understanding the effects of growing season weather variability on crop development and yield is essential to help identify management strategies that limit yield losses in poor growing years. Scientists from the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD used a state-of-the-art crop and soil computer simulation model developed by the ARS to evaluate the effects of growing season weather on corn grain yield from an ongoing 24-year cropping system field study. Simulation results were used to identify how corn physiological development interacted with growing season weather to influence yields. The modeled yield response to weather and management factors reflected those observed in the long-term study showing that the model was accurately simulating the field experiment. Results revealed that differences in the amount of rainfall and the number of high temperature days during pollination and early grain formation accounted for 62% of the variability in yields over 24 years. This information could help adapt crop management strategies to avoid periods of water and heat stress during certain growth phases. This adaptation could be through adjusting planting dates or choosing varieties that mature at a different rate to avoid periods of water and heat stress during these growth phases. These results will be beneficial to producers and crop advisors as they adjust to variable weather conditions (climate change) to ensure farm productivity and profitability.

Technical Abstract: Process-based model simulation studies using legacy data can be used to expand LTAR (Long-Term Agroecosystem Research) thereby enabling exploration of factors that would otherwise be difficult to measure in the field. In addition, management strategies to improve yield stability in response to long-term weather variability, including climate extremes, can be readily evaluated. MAIZSIM is a coupled crop and soil simulation model that simulates processes at an hourly time-step, allowing for detailed assessment of crop genetic x environment x management interactions. The model was evaluated using 24 years of management and yield data from the ARS Farming Systems Project (FSP) in Beltsville, MD. We also compared model performance relative to measured relationships between growing season weather and FSP yield. The model was calibrated using 2 parameters (staygreen, juvenile leaf number) to assess model sensitivity. The validated model fit was good (Index of Agreement = 0.92, Mean Bias Error = 51 kg ha-1), but low measured yields were overpredicted and high measured yields were underpredicted. The effect of interannual variability in growing season weather was comparable between measured and modeled yields, revealing that the model simulated the long-term agronomic trends associated with annual weather patterns at the FSP. Commonality analysis revealed that cumulative precipitation from 9 to 13 weeks and heat stress from 8 to 13 weeks after planting were critical periods that accounted for 62% of the explained (R2 = 0.84) annual simulated yield variation. Results suggest that adapting management strategies (cultivar selection, planting rate, planting date) to avoid critical period water and heat stress could help to minimize yield losses, particularly under future climate scenarios with more variable precipitation patterns and higher growing season temperatures.