Improved Crop Canopy and Water Balance Dynamics for Agroecosystem Modeling Using DayCent

Abstract

Core Ideas Green leaf weight ratio (GLWR) of annual crops can be approximated by piece‐wise linear equations over the entire growing season.Our modified green leaf area index (GLAI) method is simple yet both general and robust for simulation of GLAI of annual grain crops.Incorporation of this new approach in DayCent model yielded substantial improvements in prediction of canopy dynamics, evapotranspiration and crop production. Field experimental data of maize and soybean from two locations in the US Corn Belt and winter wheat in northern Oklahoma were used to develop a modified empirical method for simulating green leaf area index (GLAI) in the DayCent model. The method is based on the change of green leaf weight ratio (GLWR) as a function of phasic development in grain crops, in which GLWR decreases as growing degree days increase. In the previous version of DayCent, GLWR was assumed constant for annual crops and GLAI increased proportional to above‐ground biomass from post‐emergence until reaching full canopy and was then assumed to remain constant until harvest. The simulated results from the modified DayCent model compared well with field measurement of GLAI and aboveground biomass for all three crops (avg. R2 of approximately 0.8). The new GLAI algorithm yielded a substantial improvement in simulated canopy dynamics as well as evapotranspiration and soil–water content, especially in late growing season period, with the improved representation of canopy senescence. The overall R2 for canopy cover improved from 0.66 to 0.93 for maize and 0.52 to 0.88 for soybean; the R2 for weekly ET in late growing season increased from 0.53 to 0.82 for the Nebraska site. Simulation of biomass and grain yield by the modified model was also improved relative to the prior DayCent version for maize (R2 increased from 0.47 to 0.69).