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Earth
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Smart and Precision Farming for Climate-Resilient Water and Land Management
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Smart and Precision Farming for Climate-Resilient Water and Land Management

A special issue of Earth (ISSN 2673-4834).

Deadline for manuscript submissions: 1 October 2026 | Viewed by 3187

Editors

Dr. Dimitrios E. Tsesmelis

E-Mail Website
Guest Editor
Department of Agriculture, University of Patras, 30200 Messolonghi, Greece
Interests: precision agriculture; smart farming; natural resource management; GIS; IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pantelis E. Barouchas

E-Mail Website
Guest Editor
Department of Agriculture, University of Patras, 30200 Messolonghi, Greece
Interests: soil inorganic carbon (SIC); soil and water conservation engineering; soil spectroscopy; heavy metals accumulation; soil genesis; innovations and techniques in soil and plant analysis; non-destructive techniques for soil testing
Special Issues, Collections and Topics in MDPI journals
Dr. Georgios Bourantas

E-Mail Website
Guest Editor
Department of Agriculture, University of Patras, 30200 Messolonghi, Greece
Interests: fluid mechanics; computational fluid dynamics; porous media flow
Dr. Kleomenis Kalogeropoulos

E-Mail Website
Guest Editor
Department of Surveying & Geoinformatics Engineering, University of West Attica, 12243 Aigaleo, Greece
Interests: geographic information science; geoinformatics; geographic information systems; cartography; geodemography; geoarchaeology; spatial analysis in physical geography and human geography; modeling of natural phenomena and disasters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate variability is exacerbating drought, heat stress, desertification and freshwater availability in agricultural regions worldwide. In this scenario, the agricultural sector must increase productivity, reduce environmental impacts and ensure economic sustainability simultaneously. Technology-based smart and precision farming, using sensing, geospatial, decision support, automation and data-driven irrigation and fertilizer use, can offer solutions in this area.

The Special Issue, titled ‘Smart and Precision Farming for Climate-Resilient Water and Land Management,’ encourages original articles, review papers, methodological contributions and case studies that explore applications of digital agriculture, precision agronomy and smart monitoring/forecasting platforms that might serve for sustainable water resource management, soil preservation, carbon-efficient farming and more effective climate change resilience at the field scale, farm scale, basin scale and regional scale. Applications that connect technology to real-world problems in agronomy, the environment and policy are especially invited. Agriculture occupies the land–water–climate triangle. The unpredictability of rainfall, increasing occurrences of climate-related extremes, waterlogged conditions caused by over-irrigation, increasing soil salinization, and the acceleration of evapotranspiration rates are pushing the conventional management system beyond its limits.

“Average-year” planning on the usual traditional basis is no longer adequate. Here, there is a need for adaptive systems. The systems should be able to detect stress in near-real time, predict risk before a loss occurs and offer suggestions for actions that save water, protect soils and preserve yield. Technologies for smart and precision agriculture have developed at an incredible pace in the mentioned area. These include the following: proximal and remote sensing of crop conditions and soil water status; the carrying out of “Fert-irrigations” through “variable rating irrigations”; decision-support systems through Edge computing as well as through Cloud computing;  the conducting of “GNSS-guided” operations on the farm; the installation of “Internet of Things” sensors; “Climate, Hydrology and Crop Growth” blends through “AI/ML” techniques and the creation of “Digital Twin” models for the “Farms” as well as for the “Watershed”.

This Special Issue aims to showcase the science, technology and practice of cutting-edge science at this juncture. Of special interest are research that (i) estimate benefits (water saved, yield maintained, emissions reduced), (ii) address uncertainties explicitly (e.g., probabilistic seasonal forecasts, probabilistic drought alerts, climate scenarios) and/or (iii) demonstrate real-world scaling and deployment.

Dr. Dimitrios E. Tsesmelis
Prof. Dr. Pantelis E. Barouchas
Dr. Georgios Bourantas
Dr. Kleomenis Kalogeropoulos
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Earth 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 1400 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

  • precision agriculture
  • smart farming
  • climate resilience in farming
  • carbon farming
  • remote sensing for crops
  • digital soil
  • decision support systems in agriculture
  • smart irrigation management

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Published Papers (3 papers)

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Research

22 pages, 811 KB  
Article
Mapping the Future of Nature-Based Solutions in Sustainable Agriculture: A Structural Topic Modeling and Socio-Ecological Scenario Analysis
by Xiaohe Liang, Jiayu Zhuang, Jiajia Liu, Qi Wang and Ailian Zhou
Earth 2026, 7(4), 123; https://doi.org/10.3390/earth7040123 - 24 Jul 2026
Viewed by 399
Abstract
Nature-based solutions (NbS) are increasingly recognized as important strategies for advancing agricultural sustainability and supporting the United Nations Sustainable Development Goals (SDGs). Much research has highlighted the biophysical potential of NbS; however, the literature remains fragmented regarding their socio-economic and governance implications. This [...] Read more.
Nature-based solutions (NbS) are increasingly recognized as important strategies for advancing agricultural sustainability and supporting the United Nations Sustainable Development Goals (SDGs). Much research has highlighted the biophysical potential of NbS; however, the literature remains fragmented regarding their socio-economic and governance implications. This study synthesizes existing research, identifies major knowledge structures, and develops a forward-looking research agenda. We analyzed 1198 academic articles using Structural Topic Modeling (STM) to identify ten latent topics. These topics were subsequently mapped onto the adapted Agro-Ecotopia model based on two dimensions: Ecosystem State & Control and Governance Logic & Goal Alignment. This approach established an Agro-NbS analytical framework and identified four exploratory scenarios: Green Regulation, Engineered Ecotopia, Vulnerable Wilderness, and Grassroots Resilience. These scenarios characterize diverse pathways of agricultural NbS development, highlighting potential spatial trade-offs and emerging system vulnerabilities associated with social equity. Furthermore, this study extends traditional biophysical assessments by emphasizing the importance of polycentric governance and Traditional Ecological Knowledge (TEK) in addressing complex climate challenges. The findings contribute to the understanding of agricultural Socio-Ecological Systems (SESs) and provide insights for policymakers seeking to promote a more equitable transition toward sustainable agriculture. Full article
(This article belongs to the Special Issue Smart and Precision Farming for Climate-Resilient Water and Land Management)
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41 pages, 7468 KB  
Article
A Comparative Analysis of Dynamic Time Warping and Machine Learning Models for Crop Classification: Case Study of Limarí River Basin, Chile
by Aldo A. Tapia and Andrew Bennett
Earth 2026, 7(4), 122; https://doi.org/10.3390/earth7040122 - 24 Jul 2026
Viewed by 678
Abstract
Crop monitoring is an important aspect of agricultural management, as it provides insights into cultivated area, crop health, growth patterns, and yields potential. Mapping cultivated areas and identifying crop types was historically conducted through field surveys and manual mapping, which are time-consuming and [...] Read more.
Crop monitoring is an important aspect of agricultural management, as it provides insights into cultivated area, crop health, growth patterns, and yields potential. Mapping cultivated areas and identifying crop types was historically conducted through field surveys and manual mapping, which are time-consuming and labor-intensive. Remote sensing classification has transformed large-scale land cover mapping, including crop identification. This work aims to: (1) compare the performance of Dynamic Time Warping (DTW) and two machine learning families (artificial neural networks and decision trees) for crop classification using Sentinel-2 data; (2) assess whether reflectance data, spectral indices, or both yield better classification results; and (3) evaluate the effect of hyperparameters on model performance. Among the DTW variants evaluated, dynamic time warping without a time constraint performed the best, with an overall accuracy of 0.921 using the combination of both reflectance and spectral indices. Most machine learning methods outperformed DTW. Although the convolutional neural network reached the highest single accuracy (0.948), the transformer was selected as the best model overall (accuracy of 0.944), as it combined a comparable accuracy with the lowest sensitivity to hyperparameter variations, making it a reliable option when testing machine learning architectures applied to crop mapping. This work also provides insights for model architecture development based on an exhaustive hyperparameter search for the machine learning models. Full article
(This article belongs to the Special Issue Smart and Precision Farming for Climate-Resilient Water and Land Management)
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Graphical abstract

27 pages, 22570 KB  
Article
Development of High-Resolution Agroclimatic Zoning Method to Determine Micro-Agroclimatic Zones in Greece
by Nikolaos-Fivos Galatoulas, Dimitrios E. Tsesmelis, Angeliki Kavga, Kleomenis Kalogeropoulos and Pantelis E. Barouchas
Earth 2026, 7(2), 61; https://doi.org/10.3390/earth7020061 - 9 Apr 2026
Cited by 1 | Viewed by 1169
Abstract
Climate variability and rising water scarcity are major challenges to agricultural sustainability, particularly in Mediterranean climates with high spatial heterogeneity. Agroclimatic zoning is a fundamental analytical tool for digital agriculture and climate-resilient agriculture. The current effort proposes an integrated agroclimatic and micro-agroclimatic zoning [...] Read more.
Climate variability and rising water scarcity are major challenges to agricultural sustainability, particularly in Mediterranean climates with high spatial heterogeneity. Agroclimatic zoning is a fundamental analytical tool for digital agriculture and climate-resilient agriculture. The current effort proposes an integrated agroclimatic and micro-agroclimatic zoning approach for Greece, based on the Aridity Index (AI), CORINE Land Cover 2018 land-use data, and topographic factors. Daily precipitation and reference evapotranspiration data from 139 meteorological stations and 382 rain gauges were spatially interpolated using Empirical Bayesian Kriging, identifying eight agroclimatic classes adapted to the country’s specific conditions. The results indicate a high degree of variability in space, with most agricultural areas being classified as dry to sub-humid, suggesting higher irrigation requirements and sensitivity to drought. Micro-agroclimatic zones have been identified by combining agroclimatic classes, land use, and elevation. Consequently, the derived zones can be used as groundwork for designing methodologies towards more efficient agrometeorological monitoring through the improved localization of IoT agrometeorological stations. Validation with the Köppen–Geiger climate classification reveals high spatial and statistical agreement (χ2 = 248,454.09, df = 49, p < 0.001), proving the climatic validity of the proposed approach and its higher sensitivity to local water balance conditions. Full article
(This article belongs to the Special Issue Smart and Precision Farming for Climate-Resilient Water and Land Management)
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