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The present note tries to summarise some of the considerations which the crop forecaster should keep in mind when deriving multiple regression equations (so-called Yield Functions) which will eventually be used for forecasting crop yields. The process by which the coefficients of a yield function are derived are known as calibration15. The rules below are purely empirical or based on common sense: The present note tries to summarise some of the considerations which the crop forecaster should keep in mind when deriving multiple regression equations (so-called Yield Functions) which will eventually be used for forecasting crop yields. The process by which the coefficients of a yield function are derived are known as calibration15. The rules below are purely empirical or based on common sense:
-* Use only variables which are known to be meaningful for the crop under consideration. When there are good reasons to suspect that the response of+* Use only variables which are known to be meaningful for the crop under consideration. When there are good reasons to suspect that the response of crop production to a given variable is not linear, use a quadratic term in addition to the linear term.
-crop production to a given variable is not linear, use a quadratic term in addition to the linear term.+
* Retain only those variables for which the coefficients are significantly different from 0. This is to say that the regression coefficients must be significantly larger (absolute values) than their standard errors. This can be tested statistically (ratio of coefficient to its error), but common sense is usually enough. * Retain only those variables for which the coefficients are significantly different from 0. This is to say that the regression coefficients must be significantly larger (absolute values) than their standard errors. This can be tested statistically (ratio of coefficient to its error), but common sense is usually enough.
-* The sign of the coefficients must correspond to what is known about the response of the crop to the variable considered. This applies also to the+* The sign of the coefficients must correspond to what is known about the response of the crop to the variable considered. This applies also to the quadratic terms.
-quadratic terms.+
* The coefficients must be spatially coherent, which is to say that they must vary smoothly over adjacent districts * The coefficients must be spatially coherent, which is to say that they must vary smoothly over adjacent districts
* The quality of a regression equation is given, in addition to the statistics (R, R2, coefficients significantly different from 0), by the average error of estimated yields. * The quality of a regression equation is given, in addition to the statistics (R, R2, coefficients significantly different from 0), by the average error of estimated yields.

Revision as of 10:40, 22 September 2006

Using Yield functions

The present note tries to summarise some of the considerations which the crop forecaster should keep in mind when deriving multiple regression equations (so-called Yield Functions) which will eventually be used for forecasting crop yields. The process by which the coefficients of a yield function are derived are known as calibration15. The rules below are purely empirical or based on common sense:

respiration) and qualitative ones (e.g. male sterility induced by high temperatures).

Some additional advice

“automatic”) addition of variables to techniques with deletion of variables;

series);


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