Abstract
The article presents a statistical model of the impact of agromeliorative factors, including methods and modes of irrigation on the productivity of beet root crops in the combination of drip irrigation and fine sprinkling (MAV). The experiments were carried out according to a three – factor scheme providing for the regulation of the phytoclimate (factor A): A1 - drip irrigation; A2-drip irrigation together with the management of the phytoclimate by MAV. Hydrothermal regulation of the phytoclimate was carried out using additional equipment with an interval of 1 hour during the entire vegetation period, provided that the air temperature was higher than the biologically optimal 26°C. the parameters of controlling the lowest humidity of HB (factor B) were taken: B1 – 70 %; B2 – 80 %. On the basis of the dispersion statistical analysis of the results of field studies, the following statistically significant shares of their participation in the formation of the crop were established: factor A – 23%, factor B – 29%, factor C – 44%. The revealed joint influence of factors A and C on the variability of the crop of root crops, the share of which was two percent, exceeds the value of the influence of other pair interactions.
Highlights
Improving resource-saving irrigation technologies, in particular combined irrigation [1], requires multi-factor field studies that reveal the degree of their influence
The problem of statistically reliable mathematical modeling of crop productivity based on the results of multi-factor field experiments is due to its significant variability when cultivated under similar agrometeorological conditions
To take into account the complex influence of the irrigation regime and agrotechnical factors, the most reliable method is the construction of statistical models using multivariate analysis of variance (MAV)
Summary
Improving resource-saving irrigation technologies, in particular combined irrigation [1], requires multi-factor field studies that reveal the degree of their influence. The problem of statistically reliable mathematical modeling of crop productivity based on the results of multi-factor field experiments is due to its significant variability when cultivated under similar agrometeorological conditions. This is caused by the mutual influence of various biological, agrotechnological, and climatic factors [2,3]. According to a number of authors [4,5,6,7,8], it is difficult to construct reliable mathematical models of variation in yield levels for new technologies being developed, in particular combined irrigation In this regard, to take into account the complex influence of the irrigation regime and agrotechnical factors, the most reliable method is the construction of statistical models using multivariate analysis of variance (MAV) To take into account the complex influence of the irrigation regime and agrotechnical factors, the most reliable method is the construction of statistical models using multivariate analysis of variance (MAV).
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