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AgriEngineering
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Agrometeorology and Agricultural Water Management: Technology Advances and Applications in...
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Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3973

Special Issue Editors

Prof. Dr. Felipe Schwerz

E-Mail Website
Guest Editor
Departament of Agricultural Engineering, Federal University of Lavras, Professor Edmir Sá Santos Roundabout, 3037, Lavras 37200-900, MG, Brazil
Interests: agrometeorology; soil-water-plant-atmosphere system; irrigation management; plant-weather relations; agricultural systems
Prof. Dr. Diego Bedin Marin

E-Mail Website
Guest Editor
Institute of Science, Technology and Innovation, Federal University of Lavras, São Sebastião do Paraíso 37950-000, MG, Brazil
Interests: UAV; precision irrigation; remote sensing; digital and precision agriculture; spectral analysis and machine learning
Prof. Dr. Victor Buono da Silva Baptista

E-Mail Website
Guest Editor
Department of Water Resources, School of Engineering, Lavras Federal University (UFLA), Professor Edmir Sá Santos Roundabout, 3037, Lavras 37200-900, MG, Brazil
Interests: water and soil engineering; energy efficiency of pressurized irrigation systems; irrigation equipment evaluation; water application uniformity

Special Issue Information

Dear Colleagues,

Agriculture worldwide is increasingly facing complex challenges and uncertainties due to climate variability and resource constraints. The substantial fluctuations in food production are closely linked to the high variability of meteorological conditions, particularly water scarcity. Extreme weather events pose significant threats to agricultural and forest ecosystems, making it increasingly difficult to sustain food production for a growing global population amid shifting climatic patterns and heightened competition for natural resources.

The impacts of agrometeorology on agriculture are already evident across various regions, with rising temperatures, frost events, altered precipitation patterns, and frequent extreme weather phenomena contributing to uncertainty in crop productivity. These climatic shifts influence crop growth, development, and overall survival, underscoring the critical role of water availability in the agricultural sector. One of the key challenges affecting water and food security is inefficiency in agricultural water distribution and management systems, leading to significant water losses. Another major concern is the excessive application of irrigation water beyond plant requirements, exacerbating resource depletion. To address these challenges, a suite of advanced irrigation management strategies and precision agricultural practices can enhance water-use efficiency and resilience in food production systems. The integration of agrometeorological data, cutting-edge instrumentation for the real-time monitoring of agricultural water demand, remote sensing technologies, precision irrigation (smart farming), decision-support systems, and soil conservation techniques offers promising solutions for climate adaptation and sustainable agricultural water management.

In this context, this Special Issue highlights the critical role of agrometeorology and agricultural water management in optimizing cropping systems. It also explores the challenges and applications of innovative agricultural practices and water conservation strategies aimed at enhancing the resilience and sustainability of agricultural production while ensuring higher productivity under changing climatic conditions.

Topics of interest include, but are not limited to, the following:

  • Plant–weather relations;
  • Water stress;
  • Agricultural water management;
  • Irrigation management;
  • Precision irrigation (smart farming);
  • Water losses;
  • Weather factors' effect on plants;
  • Water use and crop responses;
  • Impacts of climate and climate change on agricultural crops;
  • Intercropping systems;
  • Remote sensing and water relations;
  • Artificial intelligence for climate risk management in agriculture;
  • Plant–soil interactions;
  • Soil conservation practices.

Prof. Dr. Felipe Schwerz
Prof. Dr. Diego Bedin Marin
Prof. Dr. Victor Buono da Silva Baptista
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. AgriEngineering is an international peer-reviewed open access monthly 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 1600 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

  • agrometeorology
  • extreme weather events
  • climate resilience
  • evapotranspiration
  • irrigation management
  • precision irrigation
  • remote sensing
  • sustainable irrigation strategies
  • water productivity
  • water conservation
  • cover crops
  • agricultural practices
  • tillage and no-till system
  • plant–soil interactions
  • intercropping systems
  • agricultural systems

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Published Papers (4 papers)

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Research

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27 pages, 4697 KiB  
Article
Study of Changing Land Use Land Cover from Forests to Cropland on Rainfall: Case Study of Alabama’s Black Belt Region
by Salem Ibrahim, Gamal El Afandi, Amira Moustafa and Muhammad Irfan
AgriEngineering 2025, 7(6), 176; https://doi.org/10.3390/agriengineering7060176 - 4 Jun 2025
Viewed by 279
Abstract
This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: [...] Read more.
This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: the WRF Control Run, which maintained unchanged LULC, and the WRF Sensitivity Experiment, which converted 56.5% of forested areas into cropland to assess the impact on storm dynamics. Quantitative comparisons of predicted rainfall from both simulations were conducted against observed data. The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. In contrast, the modified scenario yielded an RMSE of 2.01, suggesting lower reliability. The Mean Bias (MB) values were 1.32 for the control run and 1.58 for the modified scenario, revealing notable discrepancies in accuracy. The coefficient of determination (R2) was 0.247 for the control run and 0.270 for the modified scenario. The Nash–Sutcliffe Efficiency (NSE) value was 0.1567 for the control run but dropped to −0.2257 following LULC modifications. Sensitivity analyses revealed a 60% increase in heat flux and a 36% rise in precipitation, underscoring the significant impact of LULC on meteorological outcomes. While this study concentrated on the Black Belt region, the methodologies employed could apply to various other areas, though caution is advised when generalizing these results to different climates and socio-economic contexts. Further research is necessary to enhance the model’s applicability across diverse environments. Full article
(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)
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26 pages, 3632 KiB  
Article
Enhancing Temperature Data Quality for Agricultural Decision-Making with Emphasis to Evapotranspiration Calculation: A Robust Framework Integrating Dynamic Time Warping, Fuzzy Logic, and Machine Learning
by Christos Koliopanos, Alexandra Gemitzi, Petros Kofakis, Nikolaos Malamos and Ioannis Tsirogiannis
AgriEngineering 2025, 7(6), 174; https://doi.org/10.3390/agriengineering7060174 - 3 Jun 2025
Viewed by 289
Abstract
This study introduces a comprehensive framework for assessing and enhancing the quality of hourly temperature data collected from a six-station agrometeorological network in the Arta plain, Epirus, Greece, spanning the period 2015–2023. By combining traditional quality control (QC) techniques with advanced methods—Dynamic Time [...] Read more.
This study introduces a comprehensive framework for assessing and enhancing the quality of hourly temperature data collected from a six-station agrometeorological network in the Arta plain, Epirus, Greece, spanning the period 2015–2023. By combining traditional quality control (QC) techniques with advanced methods—Dynamic Time Warping (DTW), Fuzzy Logic, and XGBoost machine learning—the framework effectively identifies anomalies and reconstructs missing or erroneous temperature values. The DTW–Fuzzy Logic approach reliably detected spatial inconsistencies, while the machine learning reconstruction achieved low root mean squared error (RMSE) values (0.40–0.66 °C), ensuring the high fidelity of the corrected dataset. A Data Quality Index (DQI) was developed to quantify improvements in both completeness and accuracy, providing a transparent and standardized metric for end users. The enhanced temperature data significantly improve the reliability of inputs for applications such as evapotranspiration (ET) estimation and agricultural decision support systems (DSS). Designed to be scalable and automated, the proposed framework ensures robust Internal Consistency across the network—even when stations are intermittently offline—yielding direct benefits for irrigation water management, as well as broader agrometeorological applications. Full article
(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)
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21 pages, 6578 KiB  
Article
Canopy Transpiration Mapping in an Apple Orchard Using High-Resolution Airborne Spectral and Thermal Imagery with Weather Data
by Abhilash K. Chandel, Lav R. Khot, Claudio O. Stöckle, Lee Kalcsits, Steve Mantle, Anura P. Rathnayake and Troy R. Peters
AgriEngineering 2025, 7(5), 154; https://doi.org/10.3390/agriengineering7050154 - 14 May 2025
Viewed by 414
Abstract
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very [...] Read more.
Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article
(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)
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Review

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21 pages, 3523 KiB  
Review
Smart Irrigation Technologies and Prospects for Enhancing Water Use Efficiency for Sustainable Agriculture
by Awais Ali, Tajamul Hussain and Azlan Zahid
AgriEngineering 2025, 7(4), 106; https://doi.org/10.3390/agriengineering7040106 - 4 Apr 2025
Viewed by 2694
Abstract
Rapid population growth, rising food demand, and climate change have created significant challenges to meet the water demands for agriculture. Effective irrigation water management is essential to address the world’s water crisis. The transition from conventional, frequently ineffective gravity-driven irrigations to contemporary, pressure-driven [...] Read more.
Rapid population growth, rising food demand, and climate change have created significant challenges to meet the water demands for agriculture. Effective irrigation water management is essential to address the world’s water crisis. The transition from conventional, frequently ineffective gravity-driven irrigations to contemporary, pressure-driven precision irrigation methods are explored in this article, addressing the difficulties associated with water-intensive irrigation, the possibility of updating conventional techniques, and the developments in smart and precision irrigation technologies. This study comprehensively analyses published literature of 150 articles from the year 2005 to 2024, based on titles, abstract, and conclusions that contain keywords such as precision irrigation scheduling, water-saving technologies, and smart irrigation systems, in addition to providing potential solutions to achieve sustainable development goals and smart agricultural production systems. Moreover, it explores the fundamentals and processes of smart irrigation, such as open- and closed-loop control, precision monitoring and control systems, and smart monitoring methods based on soil data, plant water status, weather data, remote sensing, and participatory irrigation management. Likewise, to emphasize the potential of these technologies for a more sustainable agricultural future, several smart techniques, including IoT, wireless sensor networks, deep learning, and fuzzy logic, and their effects on crop performance and water conservation across various crops are discussed. The review concludes by summarizing the limitations and challenges of implementing precision irrigation systems and AI in agriculture along with highlighting the relationship of adopting precision irrigation and ultimately achieving various sustainable development goals (SDGs). Full article
(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)
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