Winter Wheat Phenology Simulations Improve when Adding Responses to Water Stress

Abstract

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.

Estrategias para la seguridad hídrica ante los cambios de precipitación por efectos del cambio climático

“…El aumento de las precipitaciones solo se dará por fenómenos como el Niño y la Niña, así como la situación presentada en 2013 (figura 1) que ocasionaron pérdidas en la agricultura por lluvias intensas (McNeeley et al, 2018), esto en contraste con Parry et al (2007), quienes mencionan que la producción de alimentos en algunas regiones del mundo será estable para los años 2030 y 2050, mientras Hasegawa et al (2018) proyectan un incremento en la inseguridad hídrica y alimentaria, con aumentos sustanciales de los precios y hambre en las regiones más pobres, que se combinarán con olas de calor durante las sequías y causarán daños a la agricultura, extinción de especies y escasez de agua (Esparza, 2014;McMaster et al, 2019;Miralles, Gentine, Seneviratne & Teuling, 2019;Naumann et al, 2018;Sánchez-Balseca, Muñoz-Rodríguez y Aldás-Sandoval, 2019).…”

Crop Models Improve Growth Simulation by Including Phenological Response to Water Stress

“…Of the three models (SWAT, WEPS, and UPGM) evaluated in this study, the SWAT version 2009 [13] adopted the EPIC crop growth submodel with few modi cations of the above processes. Each model provides a different approach to simulating phenology, as described by McMaster et al [10][11], based on the thermaltime approach to simulating phenological development (Eqs. 2, 3).…”

“…We associate the double-ridge (DR) event with the beginning of reproductive partitioning. Jointing (J) is assumed to occur 180 GDDs after DR [11,[14][15]. Unlike EPIC, in which LAI is increased on a HU-based curve, WEPS accumulates and partitions biomass to various plant parts and then calculates the resulting LAI from leaf biomass using a crop-speci c leaf area conversion factor [9].…”

“…McMaster et al [11] assessed performance of the three crop models in simulating the timing of ve winterwheat developmental stages under various levels of water stress. Start of senescence (S) and physiological maturity (M) were compared among SWAT, WEPS and UPGM.…”

“…They found that UPGM simulated J, S, and M as well or better than WEPS and always better than SWAT. Unanswered by McMaster et al [11] is the question of whether the demonstrated superiority in simulating phenological timing translates into improved simulation of crop yield and biomass growth components across varying environments. The objective of the present study is to evaluate whether the improved phenology model in UPGM also improves the simulation of other important winter wheat growth and development processes (i.e., yield, aboveground biomass, canopy height, harvest index, and leaf area index [LAI]) under various levels of water stress compared to SWAT and WEPS.…”