Application of Weighted Regression for the Prediction of Soft Wheat Production in France

Abstract

An accurate prediction of the production level for certain individual crops is always an important topic for the crop sector and the government decision-makers. From a perspective of the global market, these statistics are needed to make accurate price predictions, which in turn serve to make business decisions. With the development of computer science and mathematics and the easier access to the open agricultural datasets, the statistical learning methods can serve as an alternative for this purpose. In this article, the weighted statistical learning methods will be applied to predict the soft wheat production in France for the period 1995-2010 with the related methodological records. In term of prediction error, the weighted regression methods are proved to be more effective with a 5.5% relative prediction error. Besides, some simple data preprocessing methods are tested to make the predictive model simpler and more robust.