Why is the direct use of crop models by farmers still limited?

Authors: BEEGUM, SAHILA - University Of Nebraska-Lincoln; Timlin, Dennis; Roper Iii, Wayne; Fleisher, David; Han, Eun Jin; RAY, CHITTARANJAN - University Of Nebraska-Lincoln

Submitted to: ACS Agricultural Science and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2026
Publication Date: 9/9/2026
Citation: Beegum, S., Timlin, D.J., Roper Iii, W.R., Fleisher, D.H., Han, E., Ray, C. 2026. Why is the direct use of crop models by farmers still limited?. ACS Agricultural Science and Technology. https://doi.org/10.1021/acsagscitech.6c00845.
DOI: https://doi.org/10.1021/acsagscitech.6c00845

Interpretive Summary: Crop models are computer programs that simulate how crops grow under different weather, soil, and management conditions. Scientists have spent more than 60 years improving these models, making them more accurate and better able to represent the processes that affect crop growth and yield. Although crop models were developed to help farmers make better management decisions and improve productivity and profitability, they are still not widely used on farms. This viewpoint explores the reasons behind this gap and recommends that future crop-model research should focus not only on improving prediction accuracy but also on making models simpler, easier to use, and better suited to farmers' everyday decision making.

Technical Abstract: Crop models were developed with a vision for improving agricultural productivity and sustainability through simulations that help predict management outcomes. For more than six decades, many private and public research institutions have invested in the development of crop models to increase their utility in applied agricultural problem solving. Over the years, crop models have evolved from simple empirical yield estimators that correlated final yield directly with basic weather or management indicators to sophisticated frameworks for simulating individual processes of weather, soil, water, nutrients, management, genetics, and their interactions. Today, crop models are applied across varying scales, from analyzing crop responses in small experimental plots to production at global scales. (1) Crop models continue to receive institutional support, and current priorities include improving predictions of extreme weather impacts during critical growth stages, supporting crop breeding, integrating real-time sensing, developing virtual crop models that continuously update with real-world observations, and combining crop science with artificial intelligence. (2) Modern crop models can explore thousands of combinations of environments, managements, and genotypes that would be impossible to test experimentally, yet a fundamental question remains: If crop models have become increasingly sophisticated and accurate, why are they still rarely used directly by farmers for day-to-day decision-making? The answer may lie in the difference between the problems crop modelers attempt to solve and the problems farmers face in practice.