Phenology is critical in simulating crop production and hydrology and must be sufficiently robust to respond to varying environments, soils, and management practices. Phenological algorithms typically focus on the air temperature response function and rarely quantify the phenological responses to varying water deficits, particularly for versions of the Environmental Policy Integrated Climate model (EPIC)‐based plant growth component used in many agroecosystem models. Three EPIC‐based plant growth components (Soil Water Assessment Tool [SWAT], Wind Erosion Prediction System [WEPS], and the Unified Plant Growth Model [UPGM]) have been incorporated into the spatially distributed Agricultural Ecosystems Services model [AgES], and only the UPGM includes a phenological response to varying water deficits. These three plant components were used to evaluate the phenological responses of winter wheat (Triticum aestivum L.) to varying water deficits and whether having a water stress factor in UPGM improves the simulation of phenology. A 3‐yr irrigation study and a 4‐yr study across a rainfed landscape were used in the evaluation. Only the UPGM simulated all five of the developmental stagesmeasured. The UPGM was the only component that simulated a phenological response to variable water deficits, resulting in better prediction of phenology. For example, the RMSE (days) and relative error (RE, days) decreased and index of agreement (d) increased in predicting maturity from SWAT (RMSE = 18.4; RE = 9.2; d = 0.34) to WEPS (RMSE = 6.2; RE = 1.0, d = 0.63) to the UPGM (RMSE = 6.1; RE = 0.1; d = 0.70). Incorporating phenological responses to varying water deficits improves the accuracy and robustness of predicting phenology, which is particularly important in spatially distributed agroecosystem models.
Core Ideas
Phenology is critical in accurately simulating crop production and hydrology.
The AgES watershed model evaluated three EPIC‐based plant growth components.
Only UPGM was able to simulate phenological responses to varying water deficits.
The results promote more robust simulation of phenology in varying environments.
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