Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco

Abstract

In Morocco, no operational system actually exists for the early prediction of the grain yields of wheat (Triticum aestivum L.). This study proposes empirical ordinary least squares regression models to forecast the yields at provincial and national levels. The predictions were based on dekadal (10-daily) NDVI/AVHRR, dekadal rainfall sums and average monthly air temperatures. The Global Land Cover raster map (GLC2000) was used to select only the NDVI pixels that are related to agricultural land. Provincial wheat yields were assessed with errors varying from 80 to 762kgha−1, depending on the province. At national level, wheat yield was predicted at the third dekad of April with 73kgha−1 error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84kgha−1 error, at least 1 month before harvest. At the provincial and national levels, most of the yield variation was accounted for by NDVI. The proposed models can be used in an operational context to early forecast wheat yields in Morocco.