Michael de Paly and Niels Schuetze and Andreas Zell

Determining Crop-Production Functions using Multi-Objective Evolutionary Algorithms

Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, 2010, pp. 1870 - 1877


Abstract

The scarcity of water compared with the abundance of land constitutes the main drawback within agricultural production. Besides the improvement of irrigation techniques a task of primary importance is solving the problem of intra-seasonal irrigation scheduling under limited seasonal water supply. An efficient scheduling algorithm has to take into account the crops' response to water stress at different stages throughout the growing season. Furthermore, for large scale planning tools compact presentations of the relationship between irrigation practices and grain yield, such as crop water production functions, are often used which also rely on an optimal scheduling of the considered irrigation systems. In this study, two new optimization algorithms for single-crop intra-seasonal scheduling of deficit irrigation systems are introduced which are able to operate with general crop growth simulation models. First, a tailored evolutionary optimization technique (EA) searches for optimal schedules over a whole growing season within an open-loop optimization framework. Second, a neuro-dynamic programming technique (NDP) is used for determining optimal irrigation policy. In this paper, different management schemes are considered and crop-yield functions generated with both the EA and the NDP optimization algorithms compared.


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BibTeX

@inproceedings{DePaly10,
  author = {Michael de Paly and Niels Schuetze and Andreas Zell},
  title = {Determining Crop-Production Functions using Multi-Objective Evolutionary
	Algorithms},
  booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC)},
  year = {2010},
  pages = {1870 - 1877},
  address = {Barcelona, Spain},
  month = jul,
  doi = {10.1109/CEC.2010.5586147},
}

@article{DePaly10HydroInf,
  author = {Niels Schuetze and Michael de Paly and Uri Shamir},
  title = {Novel simulation-based algorithms for optimal open-loop and closed-loop},
  journal = {Journal of Hydroinformatics},
  year = {2010},
  abstract = {The scarcity of water compared with the abundance of land constitutes
	the main drawback within agricultural production. Besides the improvement
	of irrigation techniques a task of primary importance is solving
	the problem of intra-seasonal irrigation scheduling under limited
	seasonal water supply. An efficient scheduling algorithm has to take
	into account the crops' response to water stress at different stages
	throughout the growing season. Furthermore, for large scale planning
	tools compact presentations of the relationship between irrigation
	practices and grain yield, such as crop water production functions,
	are often used which also rely on an optimal scheduling of the considered
	irrigation systems. In this study, two new optimization algorithms
	for single-crop intra-seasonal scheduling of deficit irrigation systems
	are introduced which are able to operate with general crop growth
	simulation models. First, a tailored evolutionary optimization technique
	(EA) searches for optimal schedules over a whole growing season within
	an open-loop optimization framework. Second, a neuro-dynamic programming
	technique (NDP) is used for determining optimal irrigation policy.
	In this paper, different management schemes are considered and crop-yield
	functions generated with both the EA and the NDP optimization algorithms
	compared.},
  doi = {10.2166/hydro.2011.073},
  url = {http://www.iwaponline.com/jh/up/jh2011073.htm}
}