Математическое моделирование влияния агротехнических приёмов на урожай-ность масличного льна в условиях Северного Казахстана

Abstract

Abstract

Background and Aim. In the conditions of Northern Kazakhstan, the effectiveness of oilseed flax cultivation largely depends on agrotechnological factors such as the mineral nutrition system, sowing dates, and others. The development of mathematical models reflecting the influence of these factors on crop yields will optimize crop cultivation technologies and increase the productivity of agrocenosis in specific agro-climatic conditions. The aim of the study is a mathematical and statistical analysis of a model describing the dependence of oilseed flax yield on key agrotechnological parameters.

Materials and Methods. The study was conducted on the basis of the SKOS. The experiment included various options for mineral nutrition and two options for sowing dates. The analysis of variance (ANOVA) and a multivariate regression model were used as data processing methods. The construction of thermal correlation maps allowed us to identify the most significant factors affecting yields.

Results. The optimal nutrition level combined with early sowing ensured the highest yield of up to 16.2 c/ha. The developed mathematical model made it possible to predict yields with high accuracy depending on growing conditions, the coefficient of determination R²=0.692 showed that the model explains about 69% of the variability in yields from the use of fertilizers. The greatest contribution to yield is made by the mass of seeds per 1 m² (p≈0.247), the mass of seeds per 1 m² (r=0.76) and the mass of 1000 seeds (r=0.72) have the greatest relationship with yield.

Conclusion. Mathematical modeling can be used to optimize agrotechnological techniques in the cultivation of oilseed flax in Northern Kazakhstan. The results obtained contribute to improving the efficiency of crop cultivation and allow farmers to recommend the most productive farming strategies.

Keywords: oilseed flax; sowing dates; mineral fertilizers; mathematical modeling; statistical data processing.