Search

Abstract: The worldwide increase in the demand for food and the limited available resources to produce it make it necessary to develop tools which allow estimation of crop production, thereby helping to manage the way food is produced, stored and distributed. GRAMI, a model developed to simulate the growth and yield of grain crops and capable of using remotely sensed information, was applied to the semiarid region of Southern Spain. The aim of this study was to demonstrate a methodology for using the GRAMI model, along with satellite remote sensing data, to estimate regional crop yields, and to assess the accuracy of the resulting yield estimates. Crop-specific model parameters (light-use efficiency, crop phenological stage and yield partitioning factor) were evaluated using information collected from 29 durum and bread wheat experimental plots in order to verify the performance of the model in this region spectral radiometry measurements were taken for each plot throughout the growing season to obtain experimental relationships between the normalized difference vegetation index (NDVI) and leaf area index (LAI). This relationship was used to estimate crop LAI for the within-season calibration of GRAMI from satellite remote sensing data. Forty-nine commercial wheat fields were chosen in 2008 and 2009 to validate the model. Information from meteorological stations was used in running the model, and satellite image data were used along with the LAI–NDVI relationship to provide estimates of crop LAI for within-season calibration of the model. Yield data for comparison with model estimates were obtained for each field from the farmers. For the validation study, the average amount that the yield of an individual field was over- or under-estimated was 884 and 852kgha−1 for the 2008 and 2009 seasons, respectively. The absolute errors between the average estimated and average observed yield values were 5.44% and 6.86% for the 2008 and 2009 seasons, respectively. For each of the 2 years, the average estimated yield was not significantly different from its corresponding average observed yield. Based primarily on its ability to accurately estimate the average yield for a set of fields and its reliance on readily available weather and remote sensing data, the GRAMI model, verified for a region and calibrated using satellite image data, appears to be a practical and appropriate option for operationally monitoring regional crop yields with a reasonable degree of accuracy. [Copyright &y& Elsevier]