Winter wheat phenology simulations improve when adding responses to water stress

Authors: McMaster, Gregory; Edmunds, Debora; MARQUEZ, ROGER - Lockheed Martin; HALEY, SCOTT - Colorado State University; Buchleiter, Gerald; BRYNE, PATRICK - Colorado State University; Green, Timothy; Erskine, Robert - Rob; LIGHTHART, NATHAN - Colorado State University; KIPKA, HOLM - Colorado State University; Fox, Jr, Fred; Wagner, Larry; Tatarko, John; MARAGUES, MARC - Colorado State University; Ascough Ii, James

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/21/2018
Publication Date: 1/15/2019
Citation: McMaster, G.S., Edmunds, D.A., Marquez, R., Haley, S.D., Buchleiter, G.W., Bryne, P.F., Green, T.R., Erskine, R.H., Lighthart, N.P., Kipka, H., Fox, F.A., Wagner, L.E., Tatarko, J., Maragues, M., Ascough II, J.C. 2019. Winter wheat phenology simulations improve when adding responses to water stress. Agronomy Journal. 3:1-11. https://doi.org/10.2134/agronj2018.09.0615.
DOI: https://doi.org/10.2134/agronj2018.09.0615

Interpretive Summary: Agroecosystem and hydrologic models typically simulate many biogeochemical processes for different land uses, environments, soils, and management practices across a landscape. Phenology is critical in accurately simulating crop production and hydrology, and must be sufficiently robust to respond to varying environments, soils, and management practices. While extensive phenological research has focused on the temperature response function, limited work on quantifying the phenological responses to varying water deficits has been done, particularly for versions of the 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 (AgES) model, and only 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. Two data sets from northeastern Colorado, USA were used in the evaluation: 1) 24 genotypes were grown in plots for three years under variable irrigation ranging from rainfed to full irrigation, and 2) one genotype was grown four years across a rainfed landscape. Five developmental stages were measured: jointing (J), flag leaf complete (FLC), heading (H), anthesis start (A), and physiological maturity (M). UPGM simulates the five measured developmental stages. The SWAT and WEPS components only simulated beginning of canopy senescence (estimated from the beginning of LAI decline, which should coincide with FLC stage) and M. In addition, WEPS also simulated the beginning of reproductive growth (which occurs slightly before J). All simulations used default crop parameter values. As expected, UPGM was the only component that simulated a phenological response to variable water deficits and this resulted in better prediction of phenology. Incorporating phenological responses to varying water deficits improves the accuracy and robustness of predicting phenology in agroecosystem simulation models.

Technical Abstract: Phenology is critical in accurately 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 temperature response function and rarely include quantifying the phenological responses to varying water deficits, particularly for versions of the 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 (AgES) model, and only UPGM includes a phenological response to varying water deficits. These three plant components were used to evaluate the phenological responses of winter wheat to varying water deficits, and whether having a water stress factor in UPGM improves the simulation of phenology. A three-year irrigation study and a four-year study across a rainfed landscape were used in the evaluation. Only UPGM was able to simulate the five developmental stages measured. UPGM was the only component that simulated a phenological response to variable water deficits and this resulted in better prediction of phenology. For example, the Root Mean Square Error (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 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.