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| Depending on the circumstances, the crop forecasting network can be based on: | Depending on the circumstances, the crop forecasting network can be based on: | ||
| - | [[Image:graph5.jpg|thumb|'''Type 1. Rainfall stations in Africa'''|400px|right]] | + | [[Image:graph5.jpg|thumb|'''Modeling Type 1. Rainfall stations in Africa'''|400px|right]] |
| * An ''irregularly'' spaced network based on real-world stations. As an example below part of the rainfall recording stations in Africa (''Type 1''). This type of network has a preference over the second type whenever station data (to run the water balance) are available. The main advantages of this type of monitoring network are: | * An ''irregularly'' spaced network based on real-world stations. As an example below part of the rainfall recording stations in Africa (''Type 1''). This type of network has a preference over the second type whenever station data (to run the water balance) are available. The main advantages of this type of monitoring network are: | ||
| ** Input and output of the water balance model can be checked against the real situation in the field. | ** Input and output of the water balance model can be checked against the real situation in the field. | ||
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| : Although the water balance is calculated on a irregularly spaced network, ouput data can be transformed into grids or images using interpolation techniques. This enables the estimation of water balance parameters for all corners of the monitoring area. | : Although the water balance is calculated on a irregularly spaced network, ouput data can be transformed into grids or images using interpolation techniques. This enables the estimation of water balance parameters for all corners of the monitoring area. | ||
| * A ''regularly'' spaced grid (''Type 2''). In this case, grid points do not coincide with meteorological stations. This type of monitoring network is particularly usefull if ground data are scrase or inaccurate and wheather data with sufficient accuracy are available on a grid or image basis. | * A ''regularly'' spaced grid (''Type 2''). In this case, grid points do not coincide with meteorological stations. This type of monitoring network is particularly usefull if ground data are scrase or inaccurate and wheather data with sufficient accuracy are available on a grid or image basis. | ||
| - | [[Image:graph6.jpg|thumb|'''Type 2. Grid example for Afghanistan (0.5 degree distance)'''|400px|right]] | + | [[Image:graph6.jpg|thumb|'''Modeling Type 2. Grid example for Afghanistan (0.5 degree distance)'''|400px|right]] |
Revision as of 12:57, 15 June 2006
Two basic modelling options
A crop monitoring program has to be based on data from different locations inside the country of region that needs to be monitored.
Technically these monitoring locations are points. Points can be pinpointed at the earth’s surface with coordinates. The coordinate system that is used in crop forecasting is called a geographic coordinate system whereby the coordinates are expressed in Longitude - Latitude pairs.
Example : the geographic coordinates of Pnom Penn (Cambodia) are:
- Longitude: 104.917445
- Latitude : 11.558831
Normally expressed as 104.917445, 11.558831 (longitude always first). Read more: Geographic Coordinates
Depending on the circumstances, the crop forecasting network can be based on:
Modeling Type 1. Rainfall stations in Africa
- An irregularly spaced network based on real-world stations. As an example below part of the rainfall recording stations in Africa (Type 1). This type of network has a preference over the second type whenever station data (to run the water balance) are available. The main advantages of this type of monitoring network are:
- Input and output of the water balance model can be checked against the real situation in the field.
- Station weather data are more accurate than gridded data in the vicinity of the stations themselves.
- Usually requires less computer power due to lower number of monitoring points.
- Although the water balance is calculated on a irregularly spaced network, ouput data can be transformed into grids or images using interpolation techniques. This enables the estimation of water balance parameters for all corners of the monitoring area.
- A regularly spaced grid (Type 2). In this case, grid points do not coincide with meteorological stations. This type of monitoring network is particularly usefull if ground data are scrase or inaccurate and wheather data with sufficient accuracy are available on a grid or image basis.
Modeling Type 2. Grid example for Afghanistan (0.5 degree distance)
Within the crop forecasting tools these locations areWhen setting up a crop monitoring program two basic approaches exist
gridding before modelling, and modelling before gridding. Advantages and disadvantages in terms of errors, labour and accuracy of forecasts.
