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Some years ago the MARS-FOOD group was established to support the Food Aid and Food Security policies of the European Commission.The activities are aimed at improving methods and information on crop yield prospects. Russia, Central Asia, and non-European Mediterranean countries (MECA region). Eastern Africa (IGAD sub-region) and the MERCOSUR region in South America were selected as pilot areas.Crop growth indicators are produced based on low resolution remote sensing data, global meteorological modelling outputs (ECMWF model) and crop growth  simulation models (CGMS and FAO-WSI).Crop yield forecasting is done using predictors selected from the crop growth  indicators. Dekadal  SPOT-VEGETATION data are used as a basis for calculation of remote sensing indicators of crop growth. The Normalized Difference Vegetation Index (NDVI) and results of Dry Matter Production modelling (DMP) applying the Monteith approach (Monteith, 1972) are used as a main source of remote sensing indicators for the MECA region. The indicators are used in aggregated for sub-national administrative unit form applying crop mask. Some indicators are derived for a network of representative points. The current dekadal indicators are compared with previous year decadal values or with long-term average decadal data. Additionally relative time mosaics of indicators are used as a tool for crop growth monitoring (Savin, Negre, 2002). We analyze additionally seasonal cumulative values of indicators by comparing seasonal time profiles. As a result, near 10 remote sensing indicators can be derived for each crop for each decad of growing season in aggregated form and the same amount for representative points. Crop yield forecasting starts from an attempt to build simple regression equation between statistical crop yield and crop growth indicators. We found that regression with high  R² can be built for many administrative units of MEGA region. During the second phase of crop yield prediction the similarity analysis is applied. The aim of analysis is to define a year-analogue for indicator time profiles. This operation is conducted mainly for the administrative units where regression analysis does not give acceptable results. The last phase is devoted to comparison of indicator's value with previous year or long-term average value. Final yield prediction is made by expert taking into consideration the results of all phases of indicators analysis. The crop yield can be predicted quantitatively based only on remote sensing indicators for many administrative units of the region. For some units only a sign of crop yield changes can be predicted. In same cases it is impossible to predict crop yield based only on remote sensing indicators.The time when crop yield prediction can be made differs from region to region. For the most part of administrative units of the region the best time for crop yield prediction is allocated near crop flowering. However, for some units the best time is shifted to ealier or to later period of crop growing season. The results of the crop growth monitoring and yield prediction are summarized in the form of agro-meteorological bulletins, issued bimonthly for Russia and Central Asia, and for the Mediterranean countries.