Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function

Land degradation is a widely discussed and pressing global issue, as highlighted in the UN Sustainable Development Goals (SDGs). Understanding the extent of land degradation and its impact on agriculture requires precise research and an interdisciplinary approach due to the complexity of factors an...

Full description

Saved in:
Bibliographic Details
Main Author: Антон Сергеевич Строков
Format: Article
Language:English
Published: Russian Academy of Sciences, Institute of Economics of the Ural Branch 2024-12-01
Series:Экономика региона
Subjects:
Online Access:https://economyofregions.org/ojs/index.php/er/article/view/760
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832593867052941312
author Антон Сергеевич Строков
author_facet Антон Сергеевич Строков
author_sort Антон Сергеевич Строков
collection DOAJ
description Land degradation is a widely discussed and pressing global issue, as highlighted in the UN Sustainable Development Goals (SDGs). Understanding the extent of land degradation and its impact on agriculture requires precise research and an interdisciplinary approach due to the complexity of factors and indicators that characterize the issue. This paper focuses on one of Russia’s key agricultural regions, Samara Oblast, to examine how land degradation of agricultural soils affects crop production at the farm level. The dataset used in the study includes farm inputs (costs, land, and labour) and land quality variables, such as organic content (humus), levels of land degradation and soil erosion, as well as climate indicators, at the municipal level. To analyse the relationship between land degradation and agricultural output, the stochastic frontier analysis (SFA) was employed. This method not only estimates the parameters of a classic production function but also accounts for errors in the model by evaluating parameters related to risk and technical inefficiency. The results indicate that the proportion of degraded land in a district of the given region moderately reduces the maximum potential for crop production. In contrast, most inputs—such as production costs, cropland area, and labour—contribute positively to output. The study suggests that both the method and the estimates could be refined if data on land degradation, alongside other economic and environmental indicators, were collected and published annually.
format Article
id doaj-art-721ba35fdf59452fa845f3817f795ec5
institution Kabale University
issn 2072-6414
2411-1406
language English
publishDate 2024-12-01
publisher Russian Academy of Sciences, Institute of Economics of the Ural Branch
record_format Article
series Экономика региона
spelling doaj-art-721ba35fdf59452fa845f3817f795ec52025-01-20T08:41:27ZengRussian Academy of Sciences, Institute of Economics of the Ural BranchЭкономика региона2072-64142411-14062024-12-0120410.17059/ekon.reg.2024-4-12Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function Антон Сергеевич Строков 0RANEPA Land degradation is a widely discussed and pressing global issue, as highlighted in the UN Sustainable Development Goals (SDGs). Understanding the extent of land degradation and its impact on agriculture requires precise research and an interdisciplinary approach due to the complexity of factors and indicators that characterize the issue. This paper focuses on one of Russia’s key agricultural regions, Samara Oblast, to examine how land degradation of agricultural soils affects crop production at the farm level. The dataset used in the study includes farm inputs (costs, land, and labour) and land quality variables, such as organic content (humus), levels of land degradation and soil erosion, as well as climate indicators, at the municipal level. To analyse the relationship between land degradation and agricultural output, the stochastic frontier analysis (SFA) was employed. This method not only estimates the parameters of a classic production function but also accounts for errors in the model by evaluating parameters related to risk and technical inefficiency. The results indicate that the proportion of degraded land in a district of the given region moderately reduces the maximum potential for crop production. In contrast, most inputs—such as production costs, cropland area, and labour—contribute positively to output. The study suggests that both the method and the estimates could be refined if data on land degradation, alongside other economic and environmental indicators, were collected and published annually. https://economyofregions.org/ojs/index.php/er/article/view/760land degradation, soil erosion, production functions in agriculture, stochastic frontier analysis
spellingShingle Антон Сергеевич Строков
Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function
Экономика региона
land degradation, soil erosion, production functions in agriculture, stochastic frontier analysis
title Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function
title_full Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function
title_fullStr Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function
title_full_unstemmed Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function
title_short Assessing the Impact of Land Degradation on Agricultural Output Using a Stochastic Frontier Production Function
title_sort assessing the impact of land degradation on agricultural output using a stochastic frontier production function
topic land degradation, soil erosion, production functions in agriculture, stochastic frontier analysis
url https://economyofregions.org/ojs/index.php/er/article/view/760
work_keys_str_mv AT antonsergeevičstrokov assessingtheimpactoflanddegradationonagriculturaloutputusingastochasticfrontierproductionfunction