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Agriculture
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Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial...
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Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: closed (15 March 2026) | Viewed by 6585

Special Issue Editors

Dr. Dorijan Radočaj

E-Mail Website
Guest Editor
Department of Agricultural Engineering and Renewable Energy Sources, Faculty of Agrobiotehnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: GIS (geographic information systems); remote sensing; machine learning; predictive modeling and mapping; cropland suitability assessment; digital soil mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mladen Jurišić

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: GIS; precision agriculture; multicriteria analysis; farming and cropping systems; inventory of natural resources
Special Issues, Collections and Topics in MDPI journals
Dr. Ivan Plaščak

E-Mail Website
Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: GIS; precision agriculture; drones; geoinformation technologies; land use management; agricultural engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing demand for efficient and sustainable food production, the evaluation of cropland suitability using geospatial technologies has become a critical research topic. The history of this field is rooted in the development of land evaluation frameworks, evolving from traditional soil-based assessments to modern approaches that integrate remote sensing, GIS, and machine learning. Today, these methods enable more accurate, timely, and scalable assessments of land potential while addressing challenges posed by climate change, resource limitations, and environmental degradation.

This Special Issue will provide a platform for state-of-the-art research on spatial methods and models that advance cropland evaluation and yield prediction. The scope encompasses interdisciplinary contributions bridging agriculture, environmental science, and geoinformatics to support sustainable land use planning and precision farming practices.

We particularly welcome papers that explore novel applications of remote sensing, GIS, and big data analytics in cropland suitability mapping, yield prediction, and resource optimization. Research incorporating machine learning, multi-sensor data fusion, and phenological modeling is of high interest. Case studies demonstrating practical applications in diverse agroecosystems worldwide are strongly encouraged.

Dr. Dorijan Radočaj
Prof. Dr. Mladen Jurišić
Dr. Ivan Plaščak
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. Agriculture 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

  • cropland suitability
  • crop yield prediction
  • geospatial data
  • remote sensing
  • GIS
  • precision agriculture
  • machine learning
  • phenological modeling

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Published Papers (5 papers)

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Research

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20 pages, 2977 KB  
Article
Predicting AquaCrop-Simulated Durum Wheat Yield with Machine Learning: Algorithm Comparison and Agronomic Signal Convergence in the Capitanata Plain
by Pasquale Garofalo, Anna Rita Bernadette Cammerino and Maria Riccardi
Agriculture 2026, 16(8), 890; https://doi.org/10.3390/agriculture16080890 - 17 Apr 2026
Viewed by 429
Abstract
Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when [...] Read more.
Durum wheat production in the Mediterranean basin faces increasing climate variability and thus the need for computationally efficient tools to support agronomic decision-making at regional scale. Process-based crop models such as AquaCrop provide mechanistically sound yield estimates but require substantial computation time when screening large numbers of soil–climate–management combinations. The present study addresses this constraint by developing and evaluating five machine learning (ML) surrogate models—Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine for regression (SMOreg), RandomTree, and Reduced Error Pruning Tree (REPTree)—trained to emulate the AquaCrop-GIS response surface for durum wheat (Triticum durum Desf.) grain yield across the Capitanata plain (Southern Italy). A dataset of 342 instances was constructed by crossing 25 soil profiles, three sowing dates, and two irrigation regimes across 15 climatic grid cells (2014–2023), evaluated by stratified 10-fold cross-validation. The MLP achieved the highest accuracy (R = 0.983; R2 = 0.966; RMSE = 0.083 t ha−1); the four interpretable models were clustered at R = 0.891–0.907 (RMSE = 0.192–0.203 t ha−1). All models converged on consistent agronomic signals: standard sowing (1 November) yielded +0.53 t ha−1 over late sowing (15 November), supplemental irrigation added +0.17 t ha−1, and fine-textured soils produced superior yields. The convergence of directional signals across linear, kernel-based, and tree-based architectures confirms that ML surrogates trained on process-model outputs can efficiently emulate AquaCrop response surfaces and deliver actionable management guidance for durum wheat producers and agricultural planners in Mediterranean dryland farming systems. Full article
(This article belongs to the Special Issue Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data)
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20 pages, 1983 KB  
Article
Subsoil Geological Structure Associations with Yield and Wine Attributes of Merlot Grapevines
by Reuven Simhayov, Sergey Gurianov, Nimrod Inbar, Ziv Moreno and Yishai Netzer
Agriculture 2026, 16(5), 630; https://doi.org/10.3390/agriculture16050630 - 9 Mar 2026
Viewed by 413
Abstract
This study investigated the relationship between Subsoil Geological Structure (SSGS) and the yield, berry composition, and wine attributes of Merlot grapevines in a mountainous region. The research found significant differences in vine physiology, yield, and berry chemistry of grapevines between five adjacent rows, [...] Read more.
This study investigated the relationship between Subsoil Geological Structure (SSGS) and the yield, berry composition, and wine attributes of Merlot grapevines in a mountainous region. The research found significant differences in vine physiology, yield, and berry chemistry of grapevines between five adjacent rows, which corresponded with the underlying SSGS. The middle row, planted over filling material and a karst layer, had the highest yield (1.96 kg·vine−1), consistent with better water availability, but produced berries and wine with the lowest concentrations of anthocyanins, phenolics, and soluble solids, resulting in the lowest wine quality score (82.33 points). In contrast, the northernmost row planted over bedrock had the lowest yield (0.12 kg·vine−1), consistent with limited water availability, but produced highly concentrated berries, though extreme stress compromised overall wine balance. The southern row, positioned over filling material on bedrock with moderate water stress (stem water potential −1.4 MPa), achieved an optimal balance between yield and quality, producing wine with the highest sensory score (88.78 points) and favorable chemical composition. Geophysical methods, including electric resistivity tomography (ERT) and ground-penetrating radar (GPR), identified the subsurface structure, revealing the karst layer beneath high-yielding rows and consolidated bedrock beneath severely stressed rows. Chemical analyses of berries and wine confirmed the dilution effect of higher water availability on quality-determining compounds, providing mechanistic evidence linking SSGS to wine quality. This study demonstrates the utility of integrating geophysical, physiological, and enological approaches for understanding terroir effects and optimizing vineyard management in complex geological settings. Full article
(This article belongs to the Special Issue Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data)
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17 pages, 2662 KB  
Article
Sustainability of Oilseed Production Under Climate Change in the Canadian Prairies: A Machine-Learning-Enhanced Land Suitability Assessment for Canola and Flax
by Zahra Noorisameleh, David Sauchyn and Mohammad Zare
Agriculture 2026, 16(5), 604; https://doi.org/10.3390/agriculture16050604 - 5 Mar 2026
Viewed by 582
Abstract
Oilseed production in the Canadian Prairies is highly sensitive to interacting climatic, soil, and landscape constraints, which has important implications for long-term agricultural sustainability under climate change. Canola and flax are economically significant Prairie oilseeds with distinct sensitivities to temperature and moisture variability; [...] Read more.
Oilseed production in the Canadian Prairies is highly sensitive to interacting climatic, soil, and landscape constraints, which has important implications for long-term agricultural sustainability under climate change. Canola and flax are economically significant Prairie oilseeds with distinct sensitivities to temperature and moisture variability; however, region-wide, crop-specific suitability assessments remain limited. In this study, we developed a machine-learning-enhanced Land Suitability Rating System (LSRS) framework to evaluate both historical and projected suitability for canola and flax across the Prairie agricultural region. Random Forest regression models were trained using spatial crop-density data in combination with observed climate normals and updated soil and terrain variables. Variable-importance scores were used to derive empirical climate–soil weighting, resulting in climate contributions of 70% for canola and 75% for flax. Model performance demonstrated strong internal agreement with observed spatial patterns (R2 ≈ 0.99). Canola suitability was primarily associated with mid- to late-season precipitation and maximum temperatures, whereas flax suitability was more strongly influenced by growing-season minimum temperatures. Future suitability was simulated using CanDCS-M6 downscaled CMIP6 projections under SSP2-4.5, SSP3-7.0, and SSP5-8.5. Projected warming and regionally variable precipitation changes produced spatially heterogeneous shifts in suitability, including localized gains in northern areas and increased climatic risk across parts of the southern Prairies under higher-emission scenarios. The proposed framework integrates empirical machine-learning insights with an interpretable suitability rating system, providing a scalable and policy-relevant tool to support climate-informed adaptation and sustainable oilseed production planning. Full article
(This article belongs to the Special Issue Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data)
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25 pages, 3643 KB  
Article
Ecogeographic Characterization of Potential Tectona grandis L.f. (Teak) Exploitation Areas in Ecuador
by Edwin Borja, Miguel Guara-Requena, César Tapia and Danilo Vera
Agriculture 2025, 15(22), 2328; https://doi.org/10.3390/agriculture15222328 - 8 Nov 2025
Viewed by 1557
Abstract
Tectona grandis L.f. (teak) is a timber species of exceptional commercial value, widely cultivated in Ecuador for export to international markets. This study aimed to ecogeographically characterise current production and identify zones with high potential for exploitation, using tools from CAPFITOGEN v3.0 and [...] Read more.
Tectona grandis L.f. (teak) is a timber species of exceptional commercial value, widely cultivated in Ecuador for export to international markets. This study aimed to ecogeographically characterise current production and identify zones with high potential for exploitation, using tools from CAPFITOGEN v3.0 and the MaxEnt maximum entropy algorithm, based on data from 1023 plantations. The territory was classified into 26 ecogeographic categories, of which teak is present in 13. Categories 17, 19, and 21 were predominant, collectively accounting for 88.27% of the analysed plantations. Sixteen relevant variables (comprising four climatic, four edaphic, and eight geophysical factors) served as predictors in MaxEnt, with model validation demonstrating strong accuracy (AUC = 0.924). The most influential factors for teak suitability were precipitation seasonality, altitude, annual precipitation and September wind speed. Areas with elevated and high probabilities for teak exploitation were quantified at 6737.83 km2 and 10,154.70 km2, respectively, with Guayas, Los Ríos, and Manabí provinces showing the most favourable conditions. This integrative framework provides an evidence-based basis for land-use planning and resource management, supporting more sustainable and efficient development of Ecuador’s teak forestry sector. Full article
(This article belongs to the Special Issue Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data)
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Review

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22 pages, 3807 KB  
Review
Satellite Remote Sensing for Crop Yield Prediction: A Review
by Dorijan Radočaj, Mladen Jurišić, Ivan Plaščak and Lucija Galić
Agriculture 2026, 16(4), 417; https://doi.org/10.3390/agriculture16040417 - 12 Feb 2026
Cited by 2 | Viewed by 3085
Abstract
The rapid evolution of Earth observation satellite missions and computational methods made satellite remote sensing a foundation of state-of-the-art crop yield prediction. Therefore, the aim of this review is to analyze dominant drivers of crop yield prediction research based on satellite remote sensing, [...] Read more.
The rapid evolution of Earth observation satellite missions and computational methods made satellite remote sensing a foundation of state-of-the-art crop yield prediction. Therefore, the aim of this review is to analyze dominant drivers of crop yield prediction research based on satellite remote sensing, including dominant sensor types, satellite missions, crops, and specific research topics, as well as to identify present issues and research gaps. This review summarizes the bibliometric analysis of satellite-based crop yield prediction publications during 2000–2025, including 1174 articles that were indexed in the Web of Science Core Collection. Annual publication and citation trends, geographic patterns of research publications, prevalent satellite missions and sensor types, predominant crops used in research and trends in research themes were analyzed in the study. Findings show that there has been a consistent expansion of the study topic regarding publication count, with multispectral data, especially that of Sentinel-2, Landsat, and MODIS missions, being utilized in most of the literature in the field, while radar-based approaches are becoming increasingly important, providing complementary data to multispectral imagery. The review indicates a methodological shift in the models of simple regressions to machine learning, deep learning, and multi-sensor data fusion frameworks that use dense satellite imagery time series. Full article
(This article belongs to the Special Issue Cropland Suitability Evaluation Related to Crop Yield Based on Geospatial Data)
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