Open access
Date
2008Type
- Journal Article
ETH Bibliography
yes
Altmetrics
Abstract
The adequate representation of crop response functions is crucial for agronomic as well as agricultural economic modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. In this article, the perspective is expanded also by considering different regression methods. This is motivated by the 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 (Zea mays L.) yields. It shows that the use of robust regression narrows the range of optimal input levels across different functional forms and reduces potential costs of misspecification compared to least squares estimation. Thus, differences between functional forms are reduced by applying robust regression. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000010430Publication status
publishedExternal links
Journal / series
The Open Agriculture JournalVolume
Pages / Article No.
Publisher
Bentham Science PublishersSubject
Production function estimation; production function comparison; robust regression; crop responseOrganisational unit
03327 - Lehmann, Bernard09564 - Finger, Robert / Finger, Robert
Related publications and datasets
Has part: http://hdl.handle.net/20.500.11850/4866
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ETH Bibliography
yes
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