Ensemble Climate and Crop Model Predictions of Maize Yield in the Northeast U.S. to the End of the 21st Century for Two Contrasting Greenhouse Gas Emission Pathways.

According to a recent report of the Intergovernmental Panel on Climate Change, global greenhouse gas emissions are the highest in history and a leading cause of climate variability. A warming climate may also affect the quantity and distribution of precipitation. The changing temperature and precipitation patterns will affect crop growth and development depending upon when and where the crop is grown. To gain insight on potential local impacts of a warming climate on maize yield under two emission pathways (RCP4.5 -lower emission and RCP8.5 -higher emission), we chose three spatially distinct latitudinal coordinates (Syracuse, NY; State College, PA and Landisville, PA) in the northeastern United States. Further, we used an ensemble of three location-specific calibrated crop models (DSSAT, IFSM and APSIM) and climate projections downscaled from nine global climate models to evaluate the impact of climate forcing through RCP4.5 and RCP8.5 pathways for the period from 2000 to 2100. The annual mean temperature difference between the past (1981-2000) and future (2001-2100) show an increasing trend of 0.3 and 0.5°C per decade under RCP4.5 and RCP8.5, respectively, for the three locations. Annual cumulative precipitation differs between locations and RCP and with an increasing trend (median trend averaged across 3 locations under RCP8.5 and 4.5 are 1.7 and 1.3 mm year^-1, respectively). The maturity length (physiological maturity) for full (2500 GDD) and short-season (2000-2350 GDD) maize show a declining trend. The slope of the trend (averaged across three locations) is 2.4 and 4 day decade^-1 for full-season maize under RCP4.5 and 8.5, respectively, and 2 and 3 day decade^-1 for short-season maize under RCP4.5 and 8.5, respectively. Analysis of the effects of climate forcing through these two emission pathways on maize yield will be reported for the chosen locations. This type of data will help in the preparation of adaptation strategies at the local level to meet the future food and feed needs of a growing human population.