The article is devoted to the results of modeling the combination of agricultural production and harvesting of wild food resources. Agricultural enterprise models that are able to expand food production activities through the use of wild plants are proposed. A prerequisite for the application of the developed linear programming models with uncertain parameters is the availability of sufficient reserves of wild food resources located at relatively small distances from the farm. This condition is acceptable for many farms in the Irkutsk region. As optimization problems, linear parametric problems with interval and random estimates are used. With sustainable agriculture, the uncertain values are yield, labor costs and prices for harvesting and selling wild plants. The models used to manage the activities of agricultural producers of the Irkutsk region showed additional opportunities for the development of the regional agro-industrial complex.
The aim of the work is to build and use problems of parametric and stochastic programming to solve problems of optimizing the production of agricultural products under conditions of biological risks. To achieve the goal, two tasks were solved: determining the patterns of variability of some series characterizing possible risks in crop production, and building mathematical models to optimize crop production, taking into account the damage caused by plant pests. The paper proposes models for optimizing the production of crop products under conditions of biological risks. Some indicators of such tasks are described using probabilistic estimates. To optimize the production of agricultural products under the influence of biological risks, mathematical models with probabilistic estimates, as well as a parametric programming model, were built. Examples of approbation of models in the planning of production of crop production are given. The work used materials on the number of rodents, invasions of locust pests for the long-term period 2009-2020. according to the Irkutsk region. In addition, information on the yield of agricultural crops in the municipalities of the southern territory of the region, as well as data on the activities of CJSC Irkutsk Semen, located in the Irkutsk region, are involved. To model series on the number of rodents, areas of distribution of acridoids and the number of their larvae per unit area, methods of probability theory and mathematical statistics were used (technologies for constructing probability distribution laws, regression analysis and assessment of the quality of models). The variability of agricultural crop yields was assessed using the methods of constructing trends and factor dependencies. When optimizing the production of crop products, methods for constructing and solving extreme problems were used. Models of parametric and stochastic programming are proposed. At the same time, the experience of developing applied models for optimizing the production of agricultural products with and without taking into account risks was applied.
The paper considers factor models that allow predicting the yield of agricultural crops. It is shown that the main climatic parameters that affect the effective feature are the air temperature and precipitation during the initial growing season. In this case, the factors of heat supply and moisture supply can represent values for both a month and another interval close to this duration. In addition to air temperature and precipitation, the yield of grain crops is affected by time. Models can reflect the relationship of the effective feature with factors at the level of experimental fields, agricultural organizations, and municipal districts. The presence of significant regression dependencies, which can be linear and nonlinear, reduces the uncertainty of the problem of optimizing agricultural production by reducing random and interval parameters. A model of parametric programming is presented, taking into account the expressions that characterize the relationship between the yield of grain crops and meteorological parameters in two variants, in order to obtain optimal plans for the production of agricultural products by the commodity producer. An example of the implementation of an optimization model for a real economy is considered. The proposed model is designed to support decision-making in conditions of uncertainty. The work is carried out according to statistical data on the yield of wheat, barley and oats in the Usolsky, Cheremkhovsky and Irkutsk districts for 1997-2018; based on the yield of variety plots in the Usolsky, Irkutsk, Bratsky and Nukutsky districts for 2000-2018 according to the data of the State Export Commission; based on the yield of LLC "Sibirskaya Niva" for the period 2005-2018. In addition, daily air temperatures and daily precipitation in the period May–August for 1997-2018 were used for meteorological points: Usolye-Sibirskoye, Cheremkhovo, Irkutsk and Bratsk.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.