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Automated calibration of farm-scale mixed linear programming models using bi-level programming

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  • Britz, Wolfgang

Abstract

We calibrate Linear and Mixed Integer Programs with a bi-level estimator, minimizing under First-order-conditions (FOC) conditions under a penalty function considering the calibration fit and deviations from given parameters. To deal with non-convexity, a heuristic generates restart points from current best-fit parameters and their means. Monte-Carlo analysis assesses the approach by drawing parameters for a model optimizing acreages under maximal crop shares, a land balance and annual plus intra-annual labour constraints; a variant comprises integer based investments. Resulting optimal solutions perturbed by white noise provide calibration targets. The approach recovers the true parameters and thus allows for systematic and automated calibration.

Suggested Citation

  • Britz, Wolfgang, 2020. "Automated calibration of farm-scale mixed linear programming models using bi-level programming," Discussion Papers 303683, University of Bonn, Institute for Food and Resource Economics.
    • Handle: RePEc:ags:ubfred:303683
    DOI: 10.22004/ag.econ.303683
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    Cited by:

    1. Britz, Wolfgang & Ciaian, Pavel & Gocht, Alexander & Kanellopoulos, Argyris & Kremmydas, Dimitrios & Müller, Marc & Petsakos, Athanasios & Reidsma, Pytrik, 2021. "A design for a generic and modular bio-economic farm model," Agricultural Systems, Elsevier, vol. 191(C).

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    Research Methods/ Statistical Methods;

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