Cereal yield forecasting in semi-arid region of Algeria using MODIS-NDVI

Abstract

The prediction of cereals yields today is very important for global food security and helps decision-makers in the import-export operations of countries, especially with the rise world population. The advent of remote sensing technologies in precision farming systems has made cereal yield predictions possible, providing valuable insights into the temporal and spatial variations in cereal conditions across both large and small-scale crop lands. Among the various vegetation indices used to analyze these conditions, the normalized difference of vegetation index (NDVI) has emerged as a key indicator. The main objective of this study is to evaluate the possibility of using MODIS-NDVI data to forecast the yield of cereal crops (wheat and barley) in semi-arid region of Algeria (Setif). Additionally, identify the optimal timing for reliable and accurate crop yield forecasts. The remote sensing data utilized in this study covered the growing seasons from February to June, from 2002 to 2022. The results indicated a strong correlation between cereal grain yield and NDVI from late February to mid-March, with R² values ranging from 0.55 to 0.82 for the two cereal species. The RMSE of the NDVI based prediction model ranged from 0.01 t ha-1 to 0.276 t ha-1. The approximate average increase in the grain yield of barley and wheat lies between 0.659 to 0.746 t ha-1 with an increase of 0.1 in NDVI value. These results demonstrate the effectiveness of using MODIS-NDVI data for cereal yield forecasting in semi-arid region of Algeria, offering valuable predictions two to three months before the harvest.