This paper considers the problem of zone delineation management and crop planning. The problem consists of selecting which crops to plant in different management zones in order to minimize the total costs subjected to a given demand requirement. From a hierarchical point of view, the process starts by generating a partition of an agricultural field into homogeneous management zones, according to a given soil property. Then, the best crop rotation must be assigned to each management zone, applying agronomic practices in a site-specific manner in each zone. This hierarchical approach establishes two decision making levels of planning. At each level, a two-stage stochastic optimization model is proposed, representing the uncertain behavior of a soil property and crop yields by using a finite set of scenarios. Next, we combined them into a new two-stage stochastic program, solving an integrated approach by simultaneously determining an optimal zoning and allocation. Results from a set of evaluated instances showed the relevance of the proposed methodology and the benefits of the hierarchical approach over the integrated one.
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The authors would like to thank the anonymous reviewers for their valuable comments and suggestions on earlier versions of this paper. We also appreciate the work performed by research assistants Francisco Peñailillo and Gonzalo Agusto. This research was partially supported by DGIIP from Universidad Técnica Federico Santa María (Grants USM 28.15.20 and PIM 172) and DGIP from Universidad del Bío-Bío (Project DIUBB No. 161418 3/R). The authors also wish to acknowledge the Ibero-American Program for Science and Technology for Development (CYTED 516RT0513).
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Albornoz, V.M., Véliz, M.I., Ortega, R. et al. Integrated versus hierarchical approach for zone delineation and crop planning under uncertainty. Ann Oper Res 286, 617–634 (2020). https://doi.org/10.1007/s10479-019-03198-y
- Two-stage stochastic program
- Crop planning
- Management zones
- Crop rotation problem
- Hierarchical production planning
- Precision agriculture