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-Introduction 
-1) Quick overview of FAO food security work, the raison d'être of the crop forecasting and presentation of the overall crop forecasting philosophy adopted by FAO  
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-2) Introduction into the principles of crop modelling (including basic crop model overview) and their implementation in AgroMetShell and the CMBox. The principle of indicators like ETa and the WSI index.  
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-3) Presentation of Potential Evapotranspiration (PET) and its role in the calculation of crop water budgets and crop forecasting  
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-4) Introduction to Remote Sensing (CCD and NDVI) and its role in crop forecasting  
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-5) Introduction to data formats and GIS. 
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-Gathering data and getting them right.  
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-6) The two basic modelling options: gridding before modelling, and modelling before gridding. Advantages and disadvantages in terms of errors, labour and accuracy of forecasts.  
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-7) Selection of reference periods: a compromise between statistical significance and agronomic significance.  
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-8) Practical introduction to Geostatistics and the spatial interpolation of agroclimatic and other variables. This contains a description of LocClim and SEDI  
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-9) Development of practical and simplified PET and radiation computation procedure.  
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-10) Preparation of ten-daily PET maps (36 dekads per calibration year)  
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-11) Preparation of ten-daily rainfall maps (36 dekads/year). If necessary, develop a technique to derive/interpolate rainfall based on Global Telecommunications System (GTS of WMO) and Japanese meteorological satellite images  
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-12) Analysis of time series of climate and crops to identify trends, if they are present. Construction of detrended crop yield time series  
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-13) Preparation of polygons for main crop growing areas in the country and define cropping practices and conditions (planting/transplanting, soil features, irrigation water amounts...) 
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-Using satellite imagery 
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-14) Development of a standard procedure to define actual phenology (in particular crop planting date), based on local practice and satellite imagery  
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-15) Extract Normalised Difference Vegetation Index (NDVI) images for the country from the global data  
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-Running the FAO water balance model  
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-16) Read all data prepared above into the AgroMetShell crop simulation software (AMS)  
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-17) Run AMS for the historical time period, extract average water balance parameters over main crop growing areas 
-18) Prcatical introduction to multiple regression techniques and the selection of variables through a principal components analysis 
-19) Calibrate crop yields against water balance outputs and other variables against and validate the coefficients.  
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-Forecasting Yield 
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-20) Using equations derived under 19) above, compute crop yield maps and derive tables of agricultural statistics from the maps (the forecasts)  
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-21) Prepare write-up of the products above as inputs to national crop monitoring bulletins 

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