Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions

PloS One
Ho-Young BanByun-Woo Lee

Abstract

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. I...Continue Reading

References

Aug 3, 2004·Journal of Experimental Botany·J S Boyer, M E Westgate
Jul 1, 1968·Plant Physiology·J S Boyer
Mar 2, 2013·Frontiers in Plant Science·Moustafa EldakakJai S Rohila

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Methods Mentioned

BETA
PLAS

Software Mentioned

FWTools
ExelisVIS
Sbuild
ISTAGE
Simple Universal Crop Growth Simulator ( SUCROS )
DSSAT4
EnKF
WOFOST
DSSAT
MATLAB

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