The adequate representation of crop response functions is crucial for agronomic as well as agricultural economicmodeling and analysis. So far, the evaluation of such functions focused on the comparison of different functionalforms. In this article, the perspective is expanded also by considering different regression methods. This is motivated bythe fact that exceptional crop yield observations (outliers) can cause misleading results if least squares regression is applied.In order to address this problem we also apply robust regression techniques that are not affected by such outliers.We evaluate the quadratic, the square root and the Mitscherlich-Baule function using the example of Swiss corn (Zeamays L.) yields. It shows that the use of robust regression narrows the range of optimal input levels across different functionalforms and reduces potential costs of misspecification compared to least squares estimation. Thus, differences betweenfunctional forms are reduced by applying robust regression.
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