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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.4 20241031//EN" "https://jats.nlm.nih.gov/archiving/1.4/JATS-archive-oasis-article1-4-mathml3.dtd">
<article xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" xml:lang="en"><front><journal-meta><issn publication-format="print">2072-6414</issn><issn publication-format="electronic">2411-1406</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.17059/ekon.reg.2024-4-12</article-id><title-group xml:lang="en"><article-title>Assessing the Impact of Land Degradation on Agricultural Output  Using a Stochastic Frontier Production Function </article-title></title-group><title-group xml:lang="ru"><article-title>Анализ влияния деградации земель на производство сельскохозяйственной продукции с помощью производственной  функции со стохастической границей</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3784-4974</contrib-id><name-alternatives><name xml:lang="en"><surname>Strokov</surname><given-names>Anton S. </given-names></name><name xml:lang="ru"><surname>Строков  </surname><given-names>Антон Сергеевич </given-names></name></name-alternatives><email>strokov-as@ranepa.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">RANEPA</institution></aff><aff><institution xml:lang="ru">РАНХиГС</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-12-29" publication-format="electronic"/><volume>20</volume><issue>4</issue><fpage>1161</fpage><lpage>1174</lpage><history><date date-type="received" iso-8601-date="2023-12-01"/><date date-type="accepted" iso-8601-date="2024-09-27"/></history><permissions><copyright-statement xml:lang="en">Copyright © 2024 Anton S. Strokov</copyright-statement><copyright-statement xml:lang="ru">Copyright © 2024 Антон Сергеевич Строков</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Anton S. Strokov</copyright-holder><copyright-holder xml:lang="ru">Антон Сергеевич Строков</copyright-holder><ali:free_to_read/><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><license-p>CC BY 4.0</license-p></license></permissions><self-uri content-type="html" mimetype="text/html" xlink:title="article webpage" xlink:href="https://www.economyofregions.org/ojs/index.php/er/article/view/760">https://www.economyofregions.org/ojs/index.php/er/article/view/760</self-uri><self-uri content-type="pdf" mimetype="application/pdf" xlink:title="article pdf" xlink:href="https://www.economyofregions.org/ojs/index.php/er/article/download/760/387">https://www.economyofregions.org/ojs/index.php/er/article/download/760/387</self-uri><abstract xml:lang="en"><p>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.</p></abstract><abstract xml:lang="ru"><p>Деградация земель – важная проблема современного общества, которая нашла своё отражение в Целях устойчивого развития ООН (ЦУР). Масштаб деградации земель и её влияние на сельскохозяйственную деятельность приводят к необходимости подробного исследования и применения междисциплинарного подхода с учетом специфических черт и индикаторов, характеризующих данное явление. В настоящей представлен анализ влияния деградации земель сельскохозяйственного назначения на выпуск продукции растениеводства на уровне ферм в одном из ключевых агропромышленных регионов России — Самарской области. В качестве данных использованы показатели, характеризующие средства сельскохозяйственного производства (затраты, площадь возделываемых земель, трудовые ресурсы), а также показатели качества почв, такие как содержание органических веществ (гумуса), доля деградированных земель, эрозия почв. Для выявления взаимосвязи между деградацией земель и выпуском сельскохозяйственной продукции использован метод производственной функции со стохастической границей, поскольку он не только позволяет оценить параметры классической производственной функции, но и учитывает ошибки модели в части рисков и технической неэффективности. Результаты исследования показали, что доля деградированных земель в конкретном регионе умеренно влияет на потенциал растениеводческой продукции. В то же время большая часть факторов производства, таких как затраты на производство, площадь посевов, трудовые ресурсы, напротив, позволяют увеличить производство. Используемый метод и полученные оценки могут быть улучшены, если появится возможность ежегодного сбора и публикации данных о деградации земель и других экономических и экологических индикаторах.</p></abstract><kwd-group xml:lang="en"><kwd>land degradation, soil erosion, production functions in agriculture, stochastic frontier analysis</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>деградация земель, эрозия почв, производственные функции в сельском хозяйстве, метод стохастической границы</kwd></kwd-group></article-meta></front><body/><back><ack xml:lang="en"><p>This research was conducted as part of the 2024 State Research Assignment of RANEPA. The author sincerely appreciates the support of colleagues Denis Ternovsky, Alexandra Elanskaya, and Ekaterina Shishkina from the Centre of Agricultural and Food Policy at RANEPA for their assistance with data collection and valuable advice on the paper’s content. Special thanks go to Igor Savin from the Russian Soil Institute named after V. Dokuchaev (Moscow, Russia) for providing municipal-level data on land degradation. I am also deeply grateful to Heinrich Hockmann for his mentorship and guidance during our collaborative work at IAMO (Halle-Saale, Germany) in the summer of 2012, where I first learned the fundamentals of stochastic frontier analysis. It took 11 years to gather the confidence to embark on independent research in this area. Finally, I extend my gratitude to the reviewer, whose insightful comments and suggestions helped refine the analysis and significantly improve this paper.</p></ack><ack xml:lang="ru"><p>Работа подготовлена в рамках государственного задания РАНХиГС за 2024 г. Автор выражает признательность  своим  коллегам Денису Терновскому, Александре  Еланской  и  Екатерине  Шишкиной  (Центр  агропродовольственной политики ИПЭИ РАНХиГС, Москва) за помощь в получении данных и полезные советы по содержанию статьи. Я также хочу поблагодарить Савина Игоря Юрьевича (Почвенный институт им. В.В. Докучаева, Москва) за предоставление данных по деградации земель на уровне муниципалитетов. Я также благодарен профессору Хайнриху Хокманну (ИАМО, Галле, Германия) за его знания и руководство во время совместной работы летом 2012 г., когда мне выпал шанс научиться всем сложностям работы с функциями со стохастической границей. Мне потребовалось 11 лет, чтобы начать свое собственное исследование по этой теме. Выражаю свою признательность рецензенту, чьи комментарии позволили углубить анализ и улучшить статью.</p></ack><ref-list><ref id="en-ref1"><label>1</label><mixed-citation xml:lang="en">Agheli, L. (2023). The Nexus between Economic Growth, Natural Resource Depletion and Foreign Direct Investment. Ekonomika regiona [Economy of regions], 19 (2), 537-547. https://doi.org/10.17059/ekon.reg.2023-2-18</mixed-citation></ref><ref id="en-ref2"><label>2</label><mixed-citation xml:lang="en">Aigner, D., Lovell, C. K., &amp; Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6 (1), 21–37. https://doi.org/10.1016/0304–4076(77)90052-5</mixed-citation></ref><ref id="en-ref3"><label>3</label><mixed-citation xml:lang="en">Belyaeva, M., Hockmann, H., &amp; Koch, F. (2014). Impact of regional diversity on production potential: an example of Russia. 142nd EAAE Seminar: “Growing Success? 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