Statistical characteristics of cereal crop yield variability depending on climate
Climate is the main stress factor that affects crop production in the Tomsk Region because agriculture in the region has low adaptive capacity due to the significant influence of adverse weather conditions. The paper presents the results of a long-term study data from 2001 to 2015 on climatic indicators and the gross yield of spring wheat and oats cultivated on gray podzolic soils in the sub-taiga zone of the Tomsk Region. Correlation analysis revealed the sequence of influence of agrometeorological parameters on the yield of the studied crops. The most significant climate indicators were determined, which affected crop productivity during the growing season (April - September) using factor analysis. Both single-factor and multifactor models of dependence the grain productivity on limiting climatic factors were constructed through multiple nonlinear regression. The proposed functional equations explain 80-87 % of the dependent variable spread. The coefficient of determination was calculated. It showed that multifactor models are higher than a single factor or a model with two initial parameters. A comprehensive analysis of the differences between the initial values and the values predicted by the model showed the adequacy of the constructed relationships. The statistical significance of the regression equations is confirmed at the 5 % level, which allows them to be applied with the source data's frame values. A statistical evaluation of the models indicates that crop yield models' error does not exceed 10 %. The obtained results allow using climatic indicators in assessing potential agro-ecological resources of the studied areas.