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Modelling of the effect of dry periods on yielding of spring barley

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  • Szulczewski, Wieslaw
  • Zyromski, Andrzej
  • Biniak-Pieróg, Malgorzata
  • Machowczyk, Anna

Abstract

The paper presents a weather-yield model developed for the purpose of estimating spring barley yield on the basis of dry spells occurring in individual periods between the phenological phases of that plant. For that purpose research material on spring barley, originating from the years 1976-1997, was used as well as diurnal sums of precipitation. Five periods were considered in the analysis: sowing-emergence, emergence-tillering, tillering-heading, heading-milk ripeness and milk ripeness-full ripeness. In the study a model of changes in the amount of water available for plant during rainless periods was used. Five measures were adopted for characterisation of the approximation error: correlation coefficient, mean relative error, relative root mean square error, model efficiency and coefficient of residual mass. The analyses performed demonstrated that yield reduction is significantly influenced by rainless periods that occur in the sowing-emergence and tillering-heading inter-phase periods. The adopted criteria for yield reduction estimation show considerable similarity for the emergence-tillering and heading-milk ripeness inter-phase periods. At the same time, their influence on yield reduction is three-fold lower than during the sowing-emergence and tillering-heading inter-phase periods. Analyses performed with the use of the developed model indicate that yield size is affected by rainless periods of duration longer than 30% of the inter-phase period.

Suggested Citation

  • Szulczewski, Wieslaw & Zyromski, Andrzej & Biniak-Pieróg, Malgorzata & Machowczyk, Anna, 2010. "Modelling of the effect of dry periods on yielding of spring barley," Agricultural Water Management, Elsevier, vol. 97(5), pages 587-595, May.
  • Handle: RePEc:eee:agiwat:v:97:y:2010:i:5:p:587-595
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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. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
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    1. Mulka, Rafał & Szulczewski, Wiesław & Szlachta, Józef & Mulka, Mariusz, 2016. "Estimation of methane production for batch technology – A new approach," Renewable Energy, Elsevier, vol. 90(C), pages 440-449.
    2. Żyromski, Andrzej & Szulczewski, Wiesław & Biniak-Pieróg, Małgorzata & Jakubowski, Wojciech, 2016. "The estimation of basket willow (Salix viminalis) yield – New approach. Part I: Background and statistical description," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1118-1126.

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