Application of multiple linear regression for multi-criteria yield prediction of winter wheat

Abstract

The aim of the work was to produce three independent models for prediction and simulation of winter wheat yield, which were marked in the following way: ReWW15_04, ReWW31_05 and ReWW30_06. The produced models enable to make yield forecasts for April 15, May 31 and June 30, directly before harvest in the current agrotechnical season. For the construction of prediction models the Multiple Linear Regression (MLR) method was used. The models are based on meteorological data (air temperature and rainfall) and information on mineral fertilisation. The data were collected from 2008-2015 from 301 production fields located in Poland, in the Wielkopolskie Voivodeship. Evaluation of the quality of forecasts based on MLR models was verified by determining forecast errors using RAE, RMS, MAE and MAPE error gauges. An important feature of the produced prediction model consists in the possibility of making a prediction in the current agrotechnical year on the basis of current weather and fertilizer information.

Authors and Affiliations

Gniewko Niedbała

Keywords

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  • EP ID EP515025
  • DOI -
  • Views 85
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How To Cite

Gniewko Niedbała (2018). Application of multiple linear regression for multi-criteria yield prediction of winter wheat. Journal of Research and Applications in Agricultural Engineering (ISSN 1642-686X), 63(4), 125-131. https://europub.co.uk/articles/-A-515025