DOI: 10.5061/dryad.7ms8db6
title: Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions

description: Crop growth models and remote sensing are useful tools for predicting crop
 growth and yield, but each tool has inherent drawbacks when predicting
 crop growth and yield at a regional scale. To improve the accuracy and
 precision of regional corn yield predictions, a simple approach for
 assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)
 products into a crop growth model was developed, and regional yield
 prediction performance was evaluated in a major corn-producing state,
 Illinois, USA. Corn growth and yield were simulated for each grid using
 the Crop Environment Resource Synthesis (CERES)-Maize model with minimum
 inputs comprising planting date, fertilizer amount, genetic coefficients,
 soil, and weather data. Planting date was estimated using a phenology
 model with a leaf area duration (LAD)-logistic function that describes the
 seasonal evolution of MODIS-derived leaf area index (LAI). Genetic
 coefficients of the corn cultivar were determined to be the genetic
 coefficients of the maturity group [included in Decision Support System
 for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum
 difference between the maximum LAI derived from the LAD-logistic function
 and that simulated by the CERES-Maize model. In addition, the daily water
 stress factors were estimated from the ratio between daily leaf
 area/weight growth rates estimated from the LAD-logistic function and that
 simulated by the CERES-Maize model under the rain-fed and auto-irrigation
 conditions. The additional assimilation of MODIS data-derived water stress
 factors and LAI under the auto-irrigation condition showed the highest
 prediction accuracy and precision for the yearly corn yield prediction (R2
 is 0.78 and the root mean square error is 0.75 t ha-1). The present
 strategy for assimilating MODIS data into a crop growth model using
 minimum inputs was successful for predicting regional yields, and it
 should be examined for spatial portability to diverse agro-climatic and
 agro-technology regions.
description: Weather
 dataWeather data in
 IllinoisIL_weather.zipSOIL dataSOIL data by county in IllinoisSOIL.SOLFile name descriptionWater stress estimation procedure and fortran codeWater stress estimation procedure and fortran codewater stress estimation fortran codeWater stress factors estimation by ISTAGEWater stress factors.f90Cultivation informationplanting date, maturity group, weather and soil data by gridinputs.zipEstimated corn yieldsEstimated corn yields by gridoutputs.zipReported and predicted corn yields at the AD levelReported and predicted corn yields at the agricultural district (AD) level with different data assimilation and simulation conditions from 2000 to 2013 in Illinois, USAReported and predicted corn yields at the state levelReported and predicted corn yields at the state level with different data assimilation and simulation conditions from 2000 to 2013 in Illinois, USA.Statistical analyses of predicted corn yields at the AD levelStatistical analyses of predicted corn yields at the agricultural district (AD) level with different data assimilation and simulation conditions from 2000 to 2013 in Illinois, USA.Statistical analyses of predicted corn yields at the state levelStatistical analyses of predicted corn yields at the state level with different data assimilation and simulation conditions from 2000 to 2013 in Illinois, USA.