(Difference between revisions)
| Revision as of 14:41, 10 June 2006 Peter (Talk | contribs) ← Previous diff |
Revision as of 14:44, 10 June 2006 Peter (Talk | contribs) (→'''Gathering data and getting them right. ''') Next diff → |
||
| Line 13: | Line 13: | ||
| ='''Gathering data and getting them right. '''= | ='''Gathering data and getting them right. '''= | ||
| - | '''Chapter 5.''' The two basic modelling options: gridding before modelling, and modelling before gridding. Advantages and disadvantages in terms of errors, labour and accuracy of forecasts. | + | '''Chapter 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. |
| - | '''Chapter 5.''' Selection of reference periods: a compromise between statistical significance and agronomic significance. | + | '''Chapter 7.''' Selection of reference periods: a compromise between statistical significance and agronomic significance. |
| - | '''Chapter 5.''' Practical introduction to Geostatistics and the spatial interpolation of agroclimatic and other variables. This contains a description of LocClim and SEDI | + | '''Chapter 8.''' Practical introduction to Geostatistics and the spatial interpolation of agroclimatic and other variables. This contains a description of LocClim and SEDI |
| - | '''Chapter 5.''' Development of practical and simplified PET and radiation computation procedure. | + | '''Chapter 9.''' Development of practical and simplified PET and radiation computation procedure. |
| - | '''Chapter 5.''' Preparation of ten-daily PET maps (36 dekads per calibration year) | + | '''Chapter 10.''' Preparation of ten-daily PET maps (36 dekads per calibration year) |
| - | '''Chapter 5.''' 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 | + | '''Chapter 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 |
| - | '''Chapter 5.''' Analysis of time series of climate and crops to identify trends, if they are present. Construction of detrended crop yield time series | + | '''Chapter 12.''' Analysis of time series of climate and crops to identify trends, if they are present. Construction of detrended crop yield time series |
| - | '''Chapter 5.''' Preparation of polygons for main crop growing areas in the country and define cropping practices and conditions (planting/transplanting, soil features, irrigation water amounts...) | + | '''Chapter 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...) |
| - | Using satellite imagery | + | ='''Using satellite imagery'''= |
| - | 14) Development of a standard procedure to define actual phenology (in particular crop planting date), based on local practice and satellite imagery | + | '''Chapter 14.''' Development of a standard procedure to define actual phenology (in particular crop planting date), based on local practice and satellite imagery |
| - | 15) Extract Normalised Difference Vegetation Index (NDVI) images for the country from the global data | + | '''Chapter 15.''' Extract Normalised Difference Vegetation Index (NDVI) images for the country from the global data |
| - | Running the FAO water balance model | + | ='''Running the FAO water balance model '''= |
| - | 16) Read all data prepared above into the AgroMetShell crop simulation software (AMS) | + | '''Chapter 16.''' Read all data prepared above into the AgroMetShell crop simulation software (AMS) |
| - | 17) Run AMS for the historical time period, extract average water balance parameters over main crop growing areas | + | '''Chapter 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 | + | '''Chapter 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. | + | '''Chapter 19.''' Calibrate crop yields against water balance outputs and other variables against and validate the coefficients. |
| - | Forecasting Yield | + | ='''Forecasting Yield'''= |
| - | 20) Using equations derived under 19) above, compute crop yield maps and derive tables of agricultural statistics from the maps (the forecasts) | + | '''Chapter 20.''' Using equations derived under 19) above, compute crop yield maps and derive tables of agricultural statistics from the maps (the forecasts) |
| - | 21) Prepare write-up of the products above as inputs to national crop monitoring bulletins | + | '''Chapter 21.''' Prepare write-up of the products above as inputs to national crop monitoring bulletins |
Revision as of 14:44, 10 June 2006
Contents |
Introduction
Chapter 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
Chapter 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.
Chapter 3. Presentation of Potential Evapotranspiration (PET) and its role in the calculation of crop water budgets and crop forecasting
Chapter 4. Introduction to Remote Sensing (CCD and NDVI) and its role in crop forecasting
Chapter 5. Introduction to data formats and GIS.
Gathering data and getting them right.
Chapter 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.
Chapter 7. Selection of reference periods: a compromise between statistical significance and agronomic significance.
Chapter 8. Practical introduction to Geostatistics and the spatial interpolation of agroclimatic and other variables. This contains a description of LocClim and SEDI
Chapter 9. Development of practical and simplified PET and radiation computation procedure.
Chapter 10. Preparation of ten-daily PET maps (36 dekads per calibration year)
Chapter 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
Chapter 12. Analysis of time series of climate and crops to identify trends, if they are present. Construction of detrended crop yield time series
Chapter 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...)
Using satellite imagery
Chapter 14. Development of a standard procedure to define actual phenology (in particular crop planting date), based on local practice and satellite imagery
Chapter 15. Extract Normalised Difference Vegetation Index (NDVI) images for the country from the global data
Running the FAO water balance model
Chapter 16. Read all data prepared above into the AgroMetShell crop simulation software (AMS)
Chapter 17. Run AMS for the historical time period, extract average water balance parameters over main crop growing areas Chapter 18. Prcatical introduction to multiple regression techniques and the selection of variables through a principal components analysis Chapter 19. Calibrate crop yields against water balance outputs and other variables against and validate the coefficients.
Forecasting Yield
Chapter 20. Using equations derived under 19) above, compute crop yield maps and derive tables of agricultural statistics from the maps (the forecasts)
Chapter 21. Prepare write-up of the products above as inputs to national crop monitoring bulletins