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

In Morocco, no operational system actually exists for the early prediction of the grain yields of bread wheat. 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 map GLC2000 was used to select only the NDVI pixels that are related to agricultural lands. Provincial yields were assessed with errors varying from 80 to 762 kg.ha-1, depending on the province. At national level, yield was predicted at the third dekad of April with 73 kg.ha-1 error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84 kg.ha-1 error. At the province and country levels most of the yield variation was accounted for by NDVI. The proposed models can be used in an operational context to forecast bread wheat yields in Morocco.