198-2 An In-Depth Study of Growth, Stover and Grain Predictions of Hybrid-Maize and CERES-Maize in Rainfed Conditions in Northwestern Indiana.
Poster Number 1017
Scientists face an unprecedented challenge of increasing food production by 100% within the next fifty years to meet demand while responding to the undetermined effect and impact of climate change. To respond to these challenges, scientists are turning to models to assist them in mapping out the complex interactions between environmental conditions, management strategies and crop genotype. The overall goals of this study are to evaluate the Hybrid-Maize (HM) model in the wet, rainfed conditions representative of northern Indiana and compare its performance to the DSSAT model. The specific objectives were to: study model performance for simulating early growth, stover and grain yield and evaluate the impact of grain filling temperatures on gaps between predictions and observations. Weather, soil and management inputs were carefully gathered to ensure accuracy in model input data. Predicted growth, yield and stover were compared against the measured data collected at Purdue’s Agronomy Center of Research and Education (ACRE). Both models were found to be good predictors of grain yield (n-RMSE=18%). The HM model was a fair predictor of stover biomass (n-RMSE=23%) but it significantly over-predicted early growth (RMSE=1.19 Mg dry matter ha-1). In contrast, DSSAT was an excellent predictor of stover yield (n-RMSE=9%) and early growth (RMSE=0.31 Mg dry matter ha-1). Excess water stress was significantly correlated to gaps in biomass predictions for HM at growth (V6) and stover growth stages but was not correlated with grain yield gaps. Both HM and DSSAT simulated grain yields were significantly correlated with average grain filling temperatures. Years with colder grain filling temperatures had larger yield gaps. In conclusion, HM is a good predictor of grain yield while DSSAT a good predictor of early growth biomass, stover and grain yield. Future research directions include: 1) adjusting the temperature-based empirical equations used in predicting grain yield by accounting for cooler weather conditions during grain fill for both models and 2) expanding HM water stress function to include excess water stress.