Winter wheat crop models improve growth simulation by including phenological response to water-deficit stress

Authors: Mankin, Kyle; Edmunds, Debora; McMaster, Gregory; Fox, Jr, Fred; Wagner, Larry; Green, Timothy

Submitted to: Environmental Modeling and Assessment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/22/2023
Publication Date: 11/10/2023
Citation: Mankin, K.R., Edmunds, D.A., Mcmaster, G.S., Fox, F.A., Wagner, L.E., Green, T.R. 2023. Winter wheat crop models improve growth simulation by including phenological response to water-deficit stress. Environmental Modeling and Assessment. https://doi.org/10.1007/s10666-023-09939-5.
DOI: https://doi.org/10.1007/s10666-023-09939-5

Interpretive Summary: Crop growth models typically simulate growth of vegetative biomass and/or grain yield in response to soil, environment and management conditions. They also typically assume there is sufficient water available so that crop growth is not limited and that the timing of crop growth stages is the same regardless water stress. This study assessed whether these are good assumptions for simulating winter wheat growth and yield under a range of water-stress conditions. We compared three models that make different assumptions about growth-stage timing and the impacts of water stress on that timing. We found that crop yield simulation was similar among models regardless of their assumptions, but that simulation of biomass, final crop height, and harvest index were improved by including a detailed, direct simulation of crop growth stage timing. This study suggests that models that directly simulate the timing of growth stages under water stressed conditions will be critical to understand the impact of environmental conditions, including effects of climate change, on crop production where water stress and irrigation limitation are concerns. Future work is underway to develop and test a similar approach for simulating growth-stage timing and water stress for 10 other important grain and forage crops.

Technical Abstract: CONTEXT. Crop models can provide insights into the impacts of climate and management on crop growth and yield, but most currently are limited by overly simplistic assumptions about phenological development and response to water stress. OBJECTIVE. We assessed winter wheat growth and yield performance of three crop models with lineage to the EPIC crop submodel. METHODS. SWAT adopted the EPIC approach with few modifications, WEPS added new biomass accumulation, partitioning, and canopy approaches linked to key phenological development stages, and UPGM added to WEPS a detailed phenology component simulating responses to water stress. The models were evaluated with default parameters and compared to experimental data for winter wheat (Triticum aestivum L.) from two sites and a range of water-stress conditions for yield, aboveground biomass, biomass partitioning, canopy height, harvest index, and leaf area index. RESULTS AND CONCLUSIONS. All models simulated yield very well (index of agreement [d] = 0.93), but differences in model performance were increasingly evident for biomass (d = 0.91 [WEPS] to 0.86 [SWAT]), final canopy height (d = 0.68 [UPGM] to 0.44 [SWAT]), and harvest index (d = 0.61 [WEPS] to 0.43 [SWAT]). Errors in biomass simulation were most evident in the grain-filling period late in the growing season. Both WEPS and UPGM exhibited improved simulation of biomass and other response variables by including more explicit simulation of phenological response to water stress. SIGNIFICANCE. The consistent improvement in winter wheat growth and yield simulation achieved with detailed phenology simulation provides an incentive to develop and test detailed phenology simulation components for other crops: currently 11 crops are simulated in UPGM, although the phenological parameters are uncalibrated. Better modeling linkages of water-stressed phenological development with other physiological processes will be critical to inform crop production where water stress and irrigation limitation are concerns.