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Income prediction in the agrarian sector using product unit neural networks

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  • García-Alonso, Carlos R.
  • Torres-Jiménez, Mercedes
  • Hervás-Martínez, César

Abstract

European Union financial subsidies in the agrarian sector are directly related to maintaining a sustainable farm income, so its determination using, for example, the farm gross margin is a basic element in agrarian programs for sustainable development. Using this tool, it is possible the identification of the agrarian structures that need financial support and to what extent it is needed. However, the process of farm gross margin determination is complicated and expensive because it is necessary to find the value of all the inputs consumed and outputs produced. Considering the circumstances mentioned, the objectives of this research were to: (1) select a representative and reduced set of easy-to-collect descriptive variables to estimate the gross margin of a group of olive-tree farms in Andalusia; (2) investigate if artificial neural network models (ANN) with two different types of basis functions (sigmoidal and product-units) could effectively predict the gross margin of olive-tree farms; (3) compare the effectiveness of multiple linear, quadratic and robust regression models versus ANN; and (4) validate the best mathematical model obtained for gross margin prediction by analysing realistic farm and farmer scenarios. Results from ANN models, specially the product-unit ones, have provided the most accurate gross margin predictions.

Suggested Citation

  • García-Alonso, Carlos R. & Torres-Jiménez, Mercedes & Hervás-Martínez, César, 2010. "Income prediction in the agrarian sector using product unit neural networks," European Journal of Operational Research, Elsevier, vol. 204(2), pages 355-365, July.
    • Handle: RePEc:eee:ejores:v:204:y:2010:i:2:p:355-365
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    References listed on IDEAS

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    1. Amores, Antonio F. & Contreras, Ignacio, 2009. "New approach for the assignment of new European agricultural subsidies using scores from data envelopment analysis: Application to olive-growing farms in Andalusia (Spain)," European Journal of Operational Research, Elsevier, vol. 193(3), pages 718-729, March.
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    1. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    2. Ali Taghi-Molla & Masoud Rabbani & Mohammad Hosein Karimi Gavareshki & Ehsan Dehghani, 2020. "Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 641-654, June.
    3. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    4. Carravilla, M. A. & Oliveira, J. F., 2013. "Operations Research in Agriculture: Better Decisions for a Scarce and Uncertain World," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(2), pages 1-10, June.

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