For most climate vulnerability, impact, and adaptation (VIA) analyses in agriculture or related sectors, researchers and the typical stakeholders that they target for their information products, require measures of crop yields and climate impacts at decision-relevant environmental and geo-political scales (e.g. counties, states, nations, watersheds, and river basins). Though detailed biophysical crop models are likely the most promising way to quantify these yield and impact measures, these models are generally focused on reproducing phenology and productivity from detailed field-scale measures and have typically been less tested and less successful at reproducing larger scale measures. We are developing a gridded version of the DSSAT family of models
In order to explore the variability in crop yield measures introduced by the differences in commonly used historical weather products, we simulated maize over the conterminous US using 4 different historical data products spanning 1980-2009, comprised of different combinations of observation- and reanalysis-based data products (NCEP CFSR, NOAA CPC, and NASA SRB). Using observed historical yield statistics from USDA NASS we are developing a number of validation metrics based on RMSE differences and time-series correlation measures, along with temperature and precipitation indices, to assess the applicability of each data product for agricultural yield and impact simulations and estimate their accuracies at a variety of spatial scales. We focus specifically on the additional error introduced in the simulations by using reanalysis-based precipitation data in place of observation-based products, with the goal of extending this analysis globally, including regions where observation-based products are scarce or non-existent.
Comparison of the dierences between crop yield simulations with pure reanalysis based data (CFSR; top right) and with observation-based products for precipitation and solar (CPC and SRB; top left), and the dierence of the two (bottom). Pure reanalysis-based simulations underpredict crop yields across wide swaths of the US corn belt, especially the northwestern portion.
We have also begun to apply this framework to regional and global scale climate impact simulations. The figure below shows results of simulations of climate impact measures for the cUSA using CCSM3 output for scenario A2, with CO2 fertilization turned on. CO2 parameters are based on recent work by Ken Boote using USDA-ARS SAP4.3 (4% grain yield/biomass increase for doubling of CO2). We are currently expanding this framework globally and to soy, wheat, and rice as part of the AgMIP/ISI-MIP climat impact model intercomparison project.
Rain fed high-input yields compared to the 1980-2009 baseline period from pDSSAT simulations using Bias Corrected and Statistically Downscaled (BCSD) CCSM3 output for emissions scenario A2. 2010-2039 against baseline (top left), 2040-2069 against baseline (top right), and 2070-2099 against baseline (bottom).