Neural network forecasting in dairy farming

Gilyan V. Fedotova, Ivan F. Gorlov, Marina I. Slozhenkina and Natali I. Mosolova

International Journal of Trade and Global Markets, 2023, vol. 18, issue 2/3, 207-216

Abstract: Purpose/objectives: the paper is devoted to a study of dairy farming in the south of Russia. Methodology: to construct a forecast for the industry development, the online academic platform Deductor was applied and perceptrons of the cattle population were built based on retrospective dynamics data on the total cattle population, dairy cattle population, and milk yield volume. Results: Evaluation of the predicted development of dairy farming in the Volgograd region with respect to 11 input parameters, using a neural network modelling tool, made it possible to present industry indices for 2022. Conclusions/relevance: the peculiarity of this work is testing a promising neural network forecasting technique under conditions of high stability, since all the considered parameters did not go beyond the confidence intervals of scatter diagrams, and the error values were zero for all intervals of the predicted values.

Keywords: dairy farming; neural network forecast; dairy raw material; cattle population; dairy cows. (search for similar items in EconPapers)
Date: 2023
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