Forecasting Oil Crops Yields on the Regional Scale Using Normalized Difference Vegetation Index

Early prediction of crop yields on large cropland areas is of a great importance for operational planning in the agrarian sector of economy and ensuring food security. Large-scale forecasts became possible owing to the introduction of remote sensing technologies in the systems of precision agriculture, providing the information on crops conditions both on a certain field and large croplands. The study on the forecasting of major oil crop yields, namely, sunflower (Helianthus annuus L), winter rape (Brássica nápus) and soybean (Glycine max), on the regional level in Kherson oblast of Ukraine was conducted using historical yielding data and monthly MODIS Terrain NDVI smoothed time series imagery with 250 m resolution of the period from 2012 to 2019. The statistical data on the crop yields were linked to the corresponding values of monthly NDVI to determine the type of inter-relationship and work out the regression models for the oil crops yield prediction based on the remotely sensed vegetation index. The highest correlation between the yields of the oil crops and NDVI with the best prediction accuracy were obtained by using the index values at the period of April for winter rape, July for sunflower, and August for soybean. The developed regression models have reasonable accuracy with the mean absolute percentage errors of predictions reaching 25.23 percent for sunflower, 18.28 percent for winter rape, and 13.24 percent for soybean. The models are easy in use and might be recommended for introduction in theory and practice of precision agriculture.