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Agronomy
Special Issues
Precision Agriculture and Crop Models for Climate Change Adaptation
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Precision Agriculture and Crop Models for Climate Change Adaptation

A Special Issue of Agronomy (ISSN 2073-4395) belonging to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 6645

Editors

Dr. Katalin Somfalvi-Tóth

E-Mail Website
Guest Editor
Institute of Agronomy, Department of Agronomy, Kaposvár Campus, Hungarian University of Agriculture and Life Sciences, Guba Sandor utca 40, 7400 Kaposvár, Hungary
Interests: agrometeorology; crop modeling; climate projections; stress analysis; climate adaptation
Prof. Dr. Gábor Milics

E-Mail Website
Guest Editor
Institute of Agronomy, Department of Precision Agriculture and Digital Farming, Hungarian University of Agriculture and Life Sciences, Páter K. utca 1, 2100 Gödöllő, Hungary
Interests: precision agriculture; remote sensing; crop monitoring; digital farming

Special Issue Information

Dear Colleagues,

Climate change is reshaping agriculture: rising temperatures, erratic extremes, shifting habitats, tightening water budgets, and accelerating soil erosion are all converging while a growing population demands not only more food, but higher nutritional quality and safety. At the same time, advances in crop modeling and precision agriculture enable site-specific, real-time, and anticipatory management—turning data into resilient decisions.

This Special Issue invites studies that—whether rooted in field or laboratory experiments or in crop modeling—quantify climate-related risk and design actionable adaptation/mitigation strategies. These strategies can range from plot and farm to national, continental, or even global levels, and across time horizons from historical baselines through to near-term or end-century projections. This Special Issue will explore precision farming solutions where the data and experience provided can contribute to adapting to the challenges of climate change.

We welcome contributions from across the field that promote climate change adaptation and mitigation in agriculture. This includes studies that integrate in situ measurements, as well as remote/proximal sensing and management data to improve yield, quality, and nutritional traits, stability, resource use efficiency (water, plant nutrition, and energy), and soil health.

Dr. Katalin Somfalvi-Tóth
Prof. Dr. Gábor Milics
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. 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

  • climate change
  • precision agriculture
  • crop models
  • adaptation
  • soil conservation
  • data-based agriculture
  • phenology
  • crop yield

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Published Papers (6 papers)

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Research

Jump to: Review

18 pages, 12540 KB  
Article
Designing Rice Cropping Schedules Using a Heading Date Prediction Model: An Integrated Approach for Climate Adaptation, Workload Leveling, and Spatial Optimization
by Yusaku Aoki, Atsushi Mochizuki and Chikara Kuwata
Agronomy 2026, 16(12), 1157; https://doi.org/10.3390/agronomy16121157 - 12 Jun 2026
Viewed by 497
Abstract
In large-scale rice farming systems, the design of efficient cropping schedules is essential for improving labor management and operational efficiency. However, climate change, including rising temperatures and increased frequency of extreme weather events, has altered crop growth dynamics, making it difficult to achieve [...] Read more.
In large-scale rice farming systems, the design of efficient cropping schedules is essential for improving labor management and operational efficiency. However, climate change, including rising temperatures and increased frequency of extreme weather events, has altered crop growth dynamics, making it difficult to achieve optimal management using conventional experience-based scheduling. In addition, the need to distribute operations across numerous fields and optimize labor allocation has increased the complexity of schedule design. In this study, we propose a decision-support method for designing rice cropping schedules using a heading date prediction model and climatological temperature data. The method adjusts transplanting dates based on predicted heading and maturity dates and determines operation periods through both forward and backward scheduling. A case study conducted on a large-scale farming system in Chiba Prefecture demonstrated that the proposed method effectively dispersed the distribution of heading and maturity dates, leading to improved temporal distribution of operations. The standard deviation of heading dates decreased from 11.7 to 8.7 days, indicating a reduction in peak labor demand. The novelty of this study lies in extending a heading date prediction model from growth prediction to practical applications in cropping schedule design and visualization. This approach enables a transition from experience-based planning to data-driven decision-making and contributes to labor distribution in large-scale farming under climate change conditions. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Models for Climate Change Adaptation)
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23 pages, 1668 KB  
Article
Precision-Based Assessment of Environmental Water and Thermal Balance in Basin-Mulched Date Palm Orchards Under Arid Conditions
by Abdulaziz Alharbi and Mohamed Ghonimy
Agronomy 2026, 16(5), 539; https://doi.org/10.3390/agronomy16050539 - 28 Feb 2026
Cited by 2 | Viewed by 584
Abstract
Precision field measurements were conducted to evaluate the mechanism of organic basin mulching on water and thermal dynamics in arid date palm orchards in central Saudi Arabia. Partly mulched zones (20 m radius) and fully mulched basins were compared with adjacent bare soil [...] Read more.
Precision field measurements were conducted to evaluate the mechanism of organic basin mulching on water and thermal dynamics in arid date palm orchards in central Saudi Arabia. Partly mulched zones (20 m radius) and fully mulched basins were compared with adjacent bare soil using micrometeorological sensors and microlysimeters. In partly mulched areas, soil heat flux (G) decreased by 68.3% while sensible heat flux (H) increased up to 86.9% during late spring, indicating enhanced energy redistribution. Bare soil exhibited slightly negative latent heat flux (λE) in early spring, reflecting vapor adsorption, whereas fully mulched basins substantially reduced evaporation, with Water Conservation Efficiency Index (WCEĪ) values of 0.33 in spring and 0.27 in summer, corresponding to 33% and 27% water savings, respectively. Root-zone thermal moderation, quantified by the Root-Zone Thermal Moderation Index (RTMI), confirmed effective buffering of subsurface temperatures by 6–7 °C across 2–10 cm depths, despite slightly elevated surface temperatures. These results demonstrate that basin mulching stabilizes soil moisture, moderates diurnal thermal fluctuations, and optimizes soil–atmosphere energy partitioning under arid conditions. By integrating direct lysimeter measurements with continuous energy flux observations and index-based analysis, this study provides novel, field-based insights into the dual role of organic mulching in enhancing water conservation and thermal regulation in arid date palm orchards. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Models for Climate Change Adaptation)
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Review

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21 pages, 3838 KB  
Review
Forecasting Models for Plant Diseases: Advances, Applications and Future Perspectives
by Anran Fan, Lichun Wang, Senli Jia, Chenfang Wang, Tao Ji, Jorge Antonio Sánchez-Molina, Wei Zhang and Hui Wang
Agronomy 2026, 16(16), 1603; https://doi.org/10.3390/agronomy16161603 - 19 Aug 2026
Viewed by 663
Abstract
Plant disease forecasting plays an important role in modern crop protection by enabling early disease prediction and supporting optimized management decisions. With the rapid development of digital agriculture, artificial intelligence, and environmental monitoring technologies, forecasting systems have evolved from traditional empirical and mechanistic [...] Read more.
Plant disease forecasting plays an important role in modern crop protection by enabling early disease prediction and supporting optimized management decisions. With the rapid development of digital agriculture, artificial intelligence, and environmental monitoring technologies, forecasting systems have evolved from traditional empirical and mechanistic models to machine learning, deep learning, multi-source data fusion, and hybrid forecasting frameworks. Unlike previous reviews that mainly focused on specific model types, decision support systems, or disease recognition technologies, this review provides a comprehensive synthesis of different forecasting approaches and their practical applications. The strengths and limitations of various models are comparatively analyzed in terms of predictive performance, interpretability, fungicide reduction potential, and practical applicability. In addition, recent advances in climate-driven disease forecasting, precision disease management, and intelligent decision support systems are discussed. Finally, current challenges and future directions, including AI-mechanistic model integration, multi-disease forecasting, IoT and remote sensing data fusion, and climate-adaptive forecasting systems, are highlighted to support the development of sustainable and intelligent crop protection strategies. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Models for Climate Change Adaptation)
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16 pages, 5612 KB  
Review
Resilience of Agricultural Water Resource Systems in Yellow River Irrigation Districts
by Jingwei Yao, Cheng Chen, Xingye Han, Peiqing Xiao, Julio Berbel and Wenyi Yao
Agronomy 2026, 16(16), 1590; https://doi.org/10.3390/agronomy16161590 - 18 Aug 2026
Viewed by 309
Abstract
Yellow River irrigation districts must maintain food production under variable inflows, rigid diversion quotas, sedimentation, groundwater depletion, and soil salinization. This systematic review synthesized 79 journal articles from Web of Science and CNKI to clarify how resilience can be assessed and managed at [...] Read more.
Yellow River irrigation districts must maintain food production under variable inflows, rigid diversion quotas, sedimentation, groundwater depletion, and soil salinization. This systematic review synthesized 79 journal articles from Web of Science and CNKI to clarify how resilience can be assessed and managed at the irrigation-district scale. The evidence indicates that resilience is a time-dependent combination of resistance, recovery, adaptability, and transformability within a coupled water source–canal–field–drainage–ecology–institution system. Although composite indices and hydrological–crop models have advanced, three gaps remain: operational thresholds rarely connect indicators to failure and recovery; farmer and institutional feedbacks are weakly represented; and assessments seldom translate into executable schedules. We, therefore, propose an irrigation-district-specific framework that couples water, sediment, salt, crops, ecology, and governance across basin–district–field scales without transferring risk between scales. Management priorities differ spatially: upstream districts require coordinated water–salt control; middle-reach well–canal systems require surface-water substitution and groundwater recovery; and downstream diversion districts require multi-source allocation and adaptive intake. A digital twin-based closed loop—continuous monitoring, forecasting, optimization, operational commands, and feedback correction—can translate diagnosis into canal rotation, recharge, drainage, and emergency actions. This review provides operational indicators and a decision-oriented research agenda for resilient irrigation modernization. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Models for Climate Change Adaptation)
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21 pages, 1405 KB  
Review
A Review of Agricultural Drought Monitoring, Policy, and Farmer Adaptation Under Climate Vulnerability in Hungary
by Mahrokh Shafiei, Ledianë Durmishi, Tibor Farkas, Iman Mirmazloum, István Waltner and Györgyi Gelybó
Agronomy 2026, 16(13), 1212; https://doi.org/10.3390/agronomy16131212 - 23 Jun 2026
Cited by 1 | Viewed by 1248
Abstract
Hungary is experiencing more frequent and severe droughts due to climate change, with 60% of its arable land in the vulnerable Great Hungarian Plain. Drought events in 2012 and 2022 reduced maize yields by more than 50% in some regions. This review synthesizes [...] Read more.
Hungary is experiencing more frequent and severe droughts due to climate change, with 60% of its arable land in the vulnerable Great Hungarian Plain. Drought events in 2012 and 2022 reduced maize yields by more than 50% in some regions. This review synthesizes studies (2000–2025) on remote sensing capabilities, climate change impacts, and farmer adaptation in Hungarian agriculture. Remote sensing technologies (Sentinel, Landsat, MODIS) and indices (NDVI, VCI, LST, TCI) achieve high accuracy (often >80%) in drought detection under validated conditions, yet technical and financial barriers limit uptake among smallholder farmers. Climate projections indicate that a 2 °C temperature rise by 2050 will expand drought-affected areas. Farmer adaptation varies sharply by farm size: large farms (>100 ha) adopt precision agriculture (65% uptake), while smallholders (<10 ha) rely on crop rotation and drought-resistant varieties. Although substantial support is provided through the EU Common Agricultural Policy, institutional fragmentation and weak extension services—which reach only 32% of farmers—undermine its effectiveness. Bridging this gap requires integrating accessible remote sensing tools with targeted smallholder support and reformed extension services. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Models for Climate Change Adaptation)
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20 pages, 4170 KB  
Review
Enhancing Agricultural Water System Resilience Under Climate Change: A Socio-Ecological Framework and Future Pathways
by Wenmin Zhang, Jingwei Yao, Julio Berbel, Wenyi Yao, Zhenzhou Shen, Hao Hu, Shuangjiang Li and Peiqing Xiao
Agronomy 2026, 16(12), 1141; https://doi.org/10.3390/agronomy16121141 - 10 Jun 2026
Cited by 1 | Viewed by 593
Abstract
Climate change intensifies hydrological variability and threatens agricultural water security. This review synthesizes literature on agricultural water system resilience under climate change through a structured critical narrative approach informed by PRISMA/SALSA reporting principles. We examine four linked domains: resilience concepts and indicators, assessment [...] Read more.
Climate change intensifies hydrological variability and threatens agricultural water security. This review synthesizes literature on agricultural water system resilience under climate change through a structured critical narrative approach informed by PRISMA/SALSA reporting principles. We examine four linked domains: resilience concepts and indicators, assessment methods under uncertainty, climate impact and vulnerability evidence, and adaptation/governance pathways. The synthesis indicates a broad shift from engineering-centered water-supply approaches toward socio-ecological resilience frameworks that combine infrastructure, ecosystem processes, farmer behavior, and institutions. Methodologically, deterministic optimization is increasingly complemented by stochastic, robust, integrated-assessment, remote-sensing, and machine-learning approaches, although data requirements, uncertainty propagation, and interpretability remain important constraints. Evidence suggests that crop water demand and irrigation requirements may increase substantially under high-emission scenarios, with acute risks in arid and semi-arid regions. Effective adaptation is unlikely to rely on single technologies alone; precision irrigation, nature-based solutions, climate services, and infrastructure investments require complementary demand-side rules, water accounting, equity safeguards, and participatory governance to avoid maladaptation such as the irrigation-efficiency rebound effect. We identify priority research needs in transparent review protocols, uncertainty quantification, cross-scale governance, farmer decision-making, digital inclusion, and monitoring systems. The review provides a moderated conceptual framework and policy-oriented research agenda for strengthening agricultural water resilience. Full article
(This article belongs to the Special Issue Precision Agriculture and Crop Models for Climate Change Adaptation)
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