Agric. Econ. - Czech, 2014, 60(3):123-132 | DOI: 10.17221/77/2013-AGRICECON

Crop plan optimization under risk on a farm level in the Czech RepublicOriginal Paper

Jitka JANOVÁ
Department of Statistics and Operational Research, Faculty of Business and Economics, Mendel University in Brno, Brno, Czech Republic

The precise agricultural operations research models claiming complex solutions and specialized software tools may appear too complicated to truly support the decision making process on the farm level. The specific balance between the simplifications made and the correspondence to the real problem must be found when building decision support for a local farmer. In this paper, a decision support model for agricultural production planning in the Czech Republic is developed covering both the randomness of parameters entering the problem and the complex crop succession requirements. Crucially, the model can be approached using software tools commonly available to farmers. The structure of the simplified model is described in detail, its validity proved by the recently suggested simulation procedures and the general applicability of the model structuring for a local agriculture decision support is discussed.

Keywords: crop plan, crop rotation constraints, optimization, stochastic programming.

Published: March 31, 2014  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
JANOVÁ J. Crop plan optimization under risk on a farm level in the Czech Republic. Agric. Econ. - Czech. 2014;60(3):123-132. doi: 10.17221/77/2013-AGRICECON.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Ahumada O., Villalobos J. R. (2009): Application of planning modelsin the agri-food supply chain: A review. European Journal of Operational Research, 195: 1-20. Go to original source...
    2. Ambrožová P. (2013): Validation of optimization models for decision management of cooperative farms. [Diploma theses.] Mendel University, Brno.
    3. Bachinger J., Zander P. (2007): ROTOR, a tool for generating and evaluating crop rotations for organic farming systems. European Journal of Agronomy, 26: 130-143. Go to original source...
    4. Benjamin L.R., Milne A.E., Parsons D.J., Cussans J., Lutman P.J.W. (2009): Using stochastic dynamic programming to support weed management decisions over a rotation. Weed Research, 49: 207-216. Go to original source...
    5. Bjorndal T., Herrero I., Newman A., Romero C., Weintraub A. (2012): Operations research in the natural resource industry. International Transactions in Operational Research, 19: 39-62. Go to original source...
    6. Boehlje M. (1999): Stuctural changes in the agricultural industries: How do we measure, analyze and understand them? American Journal of Agricultural Economics, 81: 108-141. Go to original source...
    7. Bohle C., Maturana S., Vera J. (2009): A robust optimization approach to wine grape harvesting scheduling. European Journal of Operational Research, 200: 245-252. Go to original source...
    8. Castellazzi M.S., Wood G.A., Burgess P.J., Morris J., Conrad K.F., Perry J.N. (2008): A systematic representation of crop rotations. Agricultural Systems, 97: 26-33. Go to original source...
    9. Cramer G.L., Jensen C.W., Southgate D.D. (2000): Agricultural Economics and Agribusiness. John Wiley & Sons, Inc., New York; ISBN 0-471-38847-5.
    10. Detlefsen N.K., Jensen A.L. (2007): Modelling optimal crop sequences using network flows. Agricultural Systems, 94: 566-572. Go to original source...
    11. Dogliotti S., Rossing W.A.H., van Ittersum M.K. (2003): ROTAT, a tool for systematically generating crop rotations. European Journal of Agronomy, 19: 239-250. Go to original source...
    12. dos Santos L.M.R., Michelon P., Arenales M.N., Santos R.H.S. (2011): Crop rotation scheduling with adjacency constraints. Annals of Operations Research, 190: 165-180. Go to original source...
    13. Elton E.J., Gruber M.J. (1997): Modern portfolio theory, 1950 to date. Journal of Banking and Finance, 21: 1743-1759. Go to original source...
    14. Freund R.J. (1956): The introduction of risk into a programming model. Econometrica, 24: 253-263. Go to original source...
    15. Hardaker J., Pendey S., Patten L. (1991): A farm planning under uncertainty: A review of alternative programming models. Review of Marketing and Agricultural Economics, 59: 9-22.
    16. Hardaker J.B., Huirne R.B.M., Anderson J.R. (2004): Coping with Risk in Agriculture. CAB International, Washington; ISBN 0-85199-831-3 Go to original source...
    17. Hazell P.B.R., Norton R.D. (1986): Mathematical Programming for Economic Analysis in Agriculture. Macmillan Publishing Company, New York.
    18. Janová J. (2012): Crop planning optimization model: the validation and verification processes. Central European Journal of Operations Research, 20: 451-462. Go to original source...
    19. Janová J., Ambrožová P. (2009): Optimization of production planning in Czech agriculture co-operative via linear programming. Acta Universitatis Agriculturae et Silvicurturae Mendeleianae Brunensis, 57: 99-104. Go to original source...
    20. Jatoe J.B.D., Yiridoe E.K., Weersink A., Clark J.S. (2008): Economic and environmental impacts of introducing land use policies and rotations on Prince Edward Island potato farms. Land Use Policy, 25: 309-319. Go to original source...
    21. Klein Haneveld W.K., Stegeman A.W. (2005): Crop succession requirements in agriculture production planning. European Journal of Operational Research, 166: 406-429. Go to original source...
    22. Kraus A., Litzenberger R. (1976): Skewness preference and the valuation of risky assets. Journal of Finance, 21: 1085-1100. Go to original source...
    23. Lee C.F. (1977): Functional form, skewness effect and the risk return relationship. Journal of Financial and Quantitative Analysis, 12: 55-72. Go to original source...
    24. Lien G., Hardaker J.B., Van Asseldonk M.A.P.M., Richardson J.W. (2009): Risk programming and sparse data: how to get more reliable results. Agricultural systems, 101: 42-48. Go to original source...
    25. Lowe T.J., Preckel P.V. (2004): Decision technologies for agribusiness problems: A brief review of selected literature and a call for research. Manufacturing & Services Operations Management, 6: 201-208. Go to original source...
    26. Maatman A., Schweigman C., Ruijs A., van der Vlerk M.H. (2002): Modeling farmer's response to uncertain rain fall in Burkina Faso: A stochastic programming approach. Operations Research, 50: 399-414. Go to original source...
    27. Myers P., McIntoshC.S., Patterson P.E., Taylor R.G., Hopkins B.G. (2008): Optimal crop rotation of Idaho potatoes. American Journal of Potatoes, 85: 183-197. Go to original source...
    28. Parsons D.J., Benjamin L.R., Clarke J., Ginsburg D., Mayes A., Milne A.E., Wilkinson D.J. (2009): Weed Manager - A model-based decision support system for weed management in arable crops. Computers and Electronics in Agriculture, 65: 155-167. Go to original source...
    29. Rubinstein M. (2002): Markowitz's 'portfolio selection': A fifty-year retrospective. The Journal of Finance, 57: 1041-1045. Go to original source...
    30. Santos L.M., Costa A.M., Arenales M.N., Santos R.H.S. (2010): Sustainable vegetable crop supply problem. European Journal of Operational Research, 204: 639-647. Go to original source...
    31. Seppelt R. (2000): Regionalised optimum control problems for agroecosystem management. Ecological Modelling, 131: 121-132. Go to original source...
    32. Torkamani J. (2005): Using whole-farm modeling approach to assess prospective technologies under uncertainity. Agricultural Systems, 85: 138-154. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content