Modeling of arable in land fund structure using mathematical methods (on the example of Belgorod region)
DOI:
https://doi.org/10.18413/2712-7443-2020-44-3-319-322Keywords:
correlation analyzer, factor loads, land fund, modeling, regression equation, Belgorod regionAbstract
There is a practice of transforming the land fund in our world. It depends on the economic and political course, contributes to the solution of management tasks, taking into account the quantitative state of land and with a forecast for the future. To solve these problems, it is necessary to assess the tightness of the connection, find a specific mathematical function and obtain an interval forecast for the value of the dependent. The existing models of the spatial dynamics of the land fund aimed either at a statistical description or at a spatial-transitional description. In this article describes the method of land fund transformation in the Belgorod region. The author constructed an equation for the dependence of the arable land area on other land holdings, also appreciated of the density and connection was made, an interval forecast of the arable land area by 2025 was obtained. Based on the results of the established correlation, factor loads and regression analysis, an equation for the dependence of the areal characteristics of arable land and other land holdings participating in the study was constructed. The connection equation is recognized as a model, since both the parameters and the equation as a whole are statistically significant, which means that the resulting model of transformation of the land fund can be used for forecasting purposes.
Downloads
References
Белгородская область в цифрах. Краткий статистический сборник. Белгородстат, 2019. 252 с.
Григорьева О.И., Лихневская Н.В., Зеленская Е.Я. Динамика структуры земельного фонда Белгородской области в период с 1955 г. по 2019 г. Свидетельство о государственной регистрации базы данных, охраняемой авторскими правами. № охранного документа 2020620329. Дата регистрации 20.02.2020. № заявки 2020620146. Дата приоритета 10.02.2020 // Программы для ЭВМ. Базы данных. Топологии интегральных микросхем. 2020. № 2. С. 1. [Электронный ресурс]: https://www1.fips.ru/registers-doc-view/fips_servlet?DB=DB&rn=8601&DocNumber=2020620329&TypeFile=html.
Фишер Р.А. 1958. Статистические методы для исследователей. М., Госстатиздат, 159 c.
Буряк Ж.А., Терехин Э.А. 2019. Оценка агроклиматического потенциала территории юга среднерусской возвышенности (на примере Белгородской области). Научные ведомости Белгородского государственного университета. Серия: Естественные науки, 43 (3): 286–293. https://doi.org/10.18413/2075-4671-2019-43-3-286-293.
Григорьева О.И. 2015. Геоинформационный анализ почвенно-геоморфологических связей в целях рациональной организации агроландшафтов на бассейновых принципах. Научные ведомости Белгородского государственного университета. Серия: Естественные науки, 3 (200): 157–166.
Дегтярь А.В., Григорьева О.И. 2018. Изменение лесистости Белгородской области за 400-летний период. Научные ведомости Белгородского государственного университета. Серия: Естественные науки, 42 (4): 574–586. https://doi.org/10.18413/2075-4671-2018-42-4-574-586.
Лисецкий Ф.Н., Землякова А.В., Нарожняя А.Г., Терехин Э.А., Пичура В.И., Буряк Ж.А., Самофалова О.М., Григорьева О.И. 2014. Геопланирование сельских территорий: опыт реализации концепции бассейнового природопользования на региональном уровне. Вісник Одеського національного університету. Географічні та геологічні науки, 19 (3): 125–137.
Лисецкий Ф.Н., Китов М.В., Цапков А.Н. 2016. Реализация мероприятий по почвоводоохранному обустройству агроландшафтов в Белгородской области. В кн.: Материалы Всероссийской науч. конф. с международным участием и XXXI пленарного межвузовского координационного совещания «Современные проблемы эрозионных, русловых и устьевых процессов», Архангельск, 25-30 сент. 2016 г. ООО «Издательский центр А3+»: 126–128.
Baker W.L. 1989. A review of models of landscape change. Landscape Ecology, 2: 111–133.
Feng Y., Lei Zh., Tong X., Gao Ch., Chen Sh., Wang J., Wang S. 2020. Spatially-explicit modeling and intensity analysis of China's land use change 2000-2050. Journal of Environmental Management, 263: 110407. https://doi.org/10.1016/j.jenvman.2020.110407.
Feng O., Chen Sh., Tong X., Lei Zh., Gao Ch., Wang J. 2020. Modeling changes in China’s 2000-2030 carbon stock caused by land use change. Journal of Cleaner Production, 252 (10): 119659. https://doi.org/10.1016/j.jclepro.2019.119659.
Grigoreva O.I., Marinina O.A., Zelenskaya E.Ya. 2020. Spatial and temporal changes in the land resources of the Belgorod region from 1954 to 2017 under the influence of anthropogenic factors. Biosciences, Biotechnology Research Communications, 13 (1): 60–67.
Hanta E., Bakker M. 2011. Abandonment and expansion of arable land in Europe. Ecosystem, 14: 720–731. https://doi.org/10.1007/s10021-011-9441-y.
Liu D., Zheng X., Wang H. 2020. Land-use simulation and decision-support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417: 108924. https://doi.org/10.1016/j.ecolmodel.2019.108924.
Lü Y., Fu B., Feng X., Zeng Y., Liu Y., Chang R., Sun G., Wu B. 2012. A policy-driven large scale ecological restoration: quantifying ecosystem services changes in the Loess Plateau of China. PLoS ONE, 7 (2): e31782. https://doi.org/10.1371/journal.pone.0031782.
Muller M.R., Middleton J. 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9: 151–157.
Semwal R.L. 2005. The Terai Arc Landscape in India. Securing protected areas in the face of global change. New Delhi, WWF-India, 47 p.
Singh S.K., Laari P.B., Mustak Sk., Srivastava P.K., Szabó S. 2017. Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India, Geocarto International, 33 (11): 1202–1222. https://doi.org/10.1080/10106049.2017.1343390.
Singh S.K., Srivastava P.K., Szilárd S., Petropoulos G.P., Gupta M., Islam M. 2016. Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets. Geocarto International, 32 (2): 113–127. https://doi.org/10.1080/10106049.2015.1130084.
Soma A.S., Kubota T., Aditian A. 2019. Comparative Study of Land Use Change and Landslide Susceptibility Using Frequency Ratio, Certainty Factor, and Logistic Regression in Upper Area of Ujung-Loe Watersheds South Sulawesi Indonesia. International Journal of Erosion Control Engineering, 11 (4): 103–115. https://doi.org/10.13101/ijece.11.103
Tang J., Wang L., Yao Z. 2007. Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm International. Journal of Remote Sensing, 28 (15): 3255–3271.
Turner M.G. 1987. Spatial simulation of landscape changes in Georgia: a comparison of 3 transition models. Landscape Ecology, 1: 29–36.
Pearson E.S., Hartley H.O. 1962. Abridged from Table 12. Biometrika tables for statisticians. London: Cambridge University Press, 263 p.
Weng Q. 2002. Land use change analysis in the Zhujiang delta of China using satellite remote sensing, GIS and stochastic modeling. Journal of Environmental Management, 64: 273–284.
Yu Z., Lu Ch., Cao P., Tian H. 2018. Longterm terrestrial carbon dynamics in the Midwestern United States during 1850–2015: Roles of land use and cover change and agricultural management. Global Change Biology, 24 (6): 1–18. https://doi.org/10.1111/gcb.14074.
Abstract views: 163
Share
Published
How to Cite
Issue
Section
Copyright (c) 2020 REGIONAL GEOSYSTEMS
This work is licensed under a Creative Commons Attribution 4.0 International License.
