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Agronomy
Special Issues
Landscape-Scale Modeling of Agricultural Land Use
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Landscape-Scale Modeling of Agricultural Land Use

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Soil and Plant Nutrition".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1956

Special Issue Editors

Dr. Xiumei Tang

E-Mail Website
Guest Editor
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: ecosystem service supply; region division; land use; poi
Dr. Zsolt Pinke

E-Mail Website
Guest Editor
Centre for Agricultural Research, Institute for Soil Sciences, 1116 Budapest, Hungary
Interests: climate change; cereal response; food security; land use planning

Special Issue Information

Dear Colleagues,

Understanding agricultural land-use change at a landscape scale is critical for addressing pressing global challenges, including food security, biodiversity conservation, and climate change adaptation. Historically, modeling approaches have evolved from statistical analyses to sophisticated computational simulations to capture the complex drivers of land-use decisions.

This Special Issue aims to collate cutting-edge research on spatially explicit models that simulate the patterns, processes, and outcomes of agricultural land-use change. We seek contributions that advance the methodological frontier and provide actionable insights for sustainable landscape management.

We encourage submissions leveraging innovative approaches, such as integrating AI and machine learning with process-based models, coupling ecological and socio-economic drivers, and incorporating remote sensing and big data to achieve high-precision, predictive simulations at large scales.

We welcome original research, reviews, and case studies focused on the following: development of novel modeling frameworks; analyses of land-use impacts on ecosystem services; scenario exploration for sustainable intensification; and studies that effectively bridge the science-policy gap for informed decision-making.

Dr. Xiumei Tang
Dr. Zsolt Pinke
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land-use change
  • agricultural landscape
  • spatial modeling
  • sustainability
  • ecosystem services

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

34 pages, 14975 KB  
Article
Identifying Critical Threshold Responses of Ecosystem Services in Arid Areas: A Synergistic Approach of Causal Inference and Machine Learning
by Xiumei Tang, Yukun Zhang, Peiyu Du, Zhe Hao, Heju Huai, Wen Liu, Dongyuan Zhang and Jianhong Qiu
Agronomy 2026, 16(8), 804; https://doi.org/10.3390/agronomy16080804 - 14 Apr 2026
Viewed by 528
Abstract
Arid region ecosystems are among the most fragile ecological types worldwide. They depend heavily on limited water resources and are strongly influenced by intensive human activities, leading their ecosystem services to exhibit nonlinear and threshold responses to driving factors. Identifying the thresholds of [...] Read more.
Arid region ecosystems are among the most fragile ecological types worldwide. They depend heavily on limited water resources and are strongly influenced by intensive human activities, leading their ecosystem services to exhibit nonlinear and threshold responses to driving factors. Identifying the thresholds of ecosystem services under the combined influence of natural and socio-economic interactive drivers is of great significance for regional ecological risk warning and differentiated management. Taking the Tarim River Basin as a case study, this research establishes an integrated analytical framework that combines causal inference, interaction term construction, interpretable machine learning (XGBoost-SHAP), and piecewise linear regression. The framework is used to evaluate the variations in four types of ecosystem services in 2000, 2010, and 2023, to analyze the interactive effects of driving factors, and to identify their thresholds influencing ecosystem service functions. The results indicate that (1) different types of ecosystem service functions exhibited distinct trends from 2000 to 2023, with habitat quality and water yield showing declining tendencies, while soil conservation and Windbreak and sand fixation demonstrated gradual increases; (2) Causal Screening and interaction modeling revealed that the interaction between precipitation and population density (Pre × Pop) served as the key synergistic driver of changes in the four ecosystem service functions. Both the ecosystem services and the coupled natural–social driving processes exhibited pronounced nonlinear characteristics, with evident trend shifts occurring within specific threshold intervals. (3) The precise coupling thresholds of different ecosystem services under natural–social drivers were identified, intuitively revealing the coupling threshold characteristics of various ecosystem services; (4) The integration of causal inference with interpretable machine learning enhances the reliability of threshold identification, revealing the heterogeneous response mechanisms of different services and providing a quantitative basis for the zoning regulation and differentiated management of regional ecosystems. The findings offer a transferable methodological framework to support ecological governance in arid regions. Full article
(This article belongs to the Special Issue Landscape-Scale Modeling of Agricultural Land Use)
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20 pages, 1989 KB  
Article
Reconstructing Millennial-Scale Spatiotemporal Dynamics of Japan’s Cropland Cover
by Meijiao Li, Caishan Zhao, Fanneng He, Shicheng Li and Fan Yang
Agronomy 2025, 15(12), 2834; https://doi.org/10.3390/agronomy15122834 - 10 Dec 2025
Viewed by 936
Abstract
Historical cropland cover change reconstruction is essential for understanding long-term agricultural reclamation dynamics, particularly for modeling carbon and nitrogen cycles and assessing their climatic impacts. Such reconstructions also provide critical regional benchmarks for improving global land-use datasets. In this study, we integrated historical [...] Read more.
Historical cropland cover change reconstruction is essential for understanding long-term agricultural reclamation dynamics, particularly for modeling carbon and nitrogen cycles and assessing their climatic impacts. Such reconstructions also provide critical regional benchmarks for improving global land-use datasets. In this study, we integrated historical documents and land survey records spanning the Heian period (794–1185 CE) to the present with modern remote sensing data to develop a spatially explicit methodology for reconstructing Japan’s cropland extent over the past millennium. Our analysis revealed four distinct phases of cropland area change, (1) slow expansion (800–1338 CE), (2) gradual decline (1338–1598 CE), (3) rapid growth (1598–1940 CE), and (4) sharp contraction (1940–2000 CE), with significant regional variations. Spatially, cropland progressively expanded from the core Kansai and Kantō regions toward the southwestern and northeastern frontiers. Cropland cover changes in Japan over the past millennium were driven by a combination of socio-political factors—such as technological innovations in agriculture, feudal conflicts, demographic shifts, agricultural industrialization, and urbanization—as well as natural conditions, including topography, climate, and soil texture. Validation against year-2000 remote sensing data demonstrated high accuracy, with 69.12% of grid cells showing ≤20% absolute difference and only 0.15% exceeding ±80% deviation. Full article
(This article belongs to the Special Issue Landscape-Scale Modeling of Agricultural Land Use)
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