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Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions

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  • Park, S.J.
  • Hwang, C.S.
  • Vlek, P.L.G.

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  • Park, S.J. & Hwang, C.S. & Vlek, P.L.G., 2005. "Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions," Agricultural Systems, Elsevier, vol. 85(1), pages 59-81, July.
    • Handle: RePEc:eee:agisys:v:85:y:2005:i:1:p:59-81
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    References listed on IDEAS

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    1. Aggarwal, P. K., 1995. "Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications," Agricultural Systems, Elsevier, vol. 48(3), pages 361-384.
    2. Bouman, B. A. M. & van Keulen, H. & van Laar, H. H. & Rabbinge, R., 1996. "The `School of de Wit' crop growth simulation models: A pedigree and historical overview," Agricultural Systems, Elsevier, vol. 52(2-3), pages 171-198.
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    Cited by:

    1. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    2. Gniewko Niedbała, 2019. "Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed," Sustainability, MDPI, vol. 11(2), pages 1-13, January.
    3. Stricevic, Ruzica & Cosic, Marija & Djurovic, Nevenka & Pejic, Borivoj & Maksimovic, Livija, 2011. "Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower," Agricultural Water Management, Elsevier, vol. 98(10), pages 1615-1621, August.

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