Submit to Special Issue Submit Abstract to Special Issue Review for AgriEngineering Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 48713

Share This 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; remote sensing in agriculture; precision agriculture; AI in agriculture; spectral technology in agriculture; crop yield forecasting

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 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. 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 1800 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

20 pages, 5201 KB

Open AccessArticle

Application of Fuzzy Logic to Predict Instantaneous Water Use Efficiency in a Forage Grass Under Organic and Mineral Fertilization and Water Deficit Conditions

by Maria Pereira de Araújo, Alessandro Torres Campos, Milson Evaldo Serafim, Bruna Campos Amaral, Luzia Batista Moura, Romário de Sousa Almeida, Bruno Montoani Silva, Leônidas Canuto dos Santos, Tadayuki Yanagi Junior, Sarah Emília Ieno Reis, Victor Buono da Silva Baptista, Diego Bedin Marin and Felipe Schwerz

AgriEngineering 2026, 8(7), 255; https://doi.org/10.3390/agriengineering8070255 - 23 Jun 2026

Viewed by 422

Abstract

Pastures are the primary food source for cattle, yet their productivity is often limited by management practices and water scarcity. In this context, approaches capable of representing nonlinear relationships and handling uncertainties can support sustainable water management. The objective of this study was to develop and compare fuzzy inference systems (FISs) to predict the instantaneous water use efficiency (iWUE) in a forage species subjected to organic and mineral fertilization under different levels of water deficit. The models were built in MATLAB R2024a using Mamdani and Sugeno inference methods. Input variables (fertilization and water deficit) were represented by triangular, trapezoidal, and Gaussian membership functions, while the output variable (iWUE) was modeled using triangular, trapezoidal, and Gaussian membership functions in the Mamdani system and singleton functions in the Sugeno system. Different defuzzification strategies were evaluated, resulting in 21 fuzzy systems. The results showed satisfactory model performance, with coefficients of determination above 0.90 and strong agreement between observed and simulated values. The Mamdani system with trapezoidal membership functions and centroid defuzzification achieved the best predictive performance (R² = 0.9846, NSE = 0.9887, RMSE = 0.0923). The response surface generated by the best-performing fuzzy system indicated a smaller reduction in iWUE under organic fertilization compared to mineral fertilization as water deficit intensified. The developed fuzzy systems demonstrated potential to represent the interaction between nutritional management and water availability, supporting decision-making in forage production systems. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Figure 1

42 pages, 6619 KB

Open AccessArticle

Multi-Scenario Optimization of Cropping Patterns Under Variable Water Availability in Lao Irrigation Systems

by Khambay Phomphakdy, Rapeepat Techarungruengsakul, Ratsuda Ngamsert, Haris Prasanchum, Jirawat Supakosol, Kantiya Sanusan, Ounla Sivanpheng, Phetyasone Xaypanya and Anongrit Kangrang

AgriEngineering 2026, 8(6), 238; https://doi.org/10.3390/agriengineering8060238 - 11 Jun 2026

Viewed by 449

Abstract

Sustainable irrigation planning under increasing water scarcity requires quantitative optimization tools to balance land and water resources. This study develops a linear programming (LP)-based framework to determine optimal cropping patterns under variable seasonal water availability in three irrigation projects in Lao PDR: Nam Tong 2 (1000 ha; ≈48.16 million m³ (MCM)), Nam Hin (80 ha; ≈0.73 MCM), and Xe Salalong (1530 ha; ≈30.80 MCM). Six major crops were analyzed for each project, with crop water requirements ranging from 4000 to 12,000 m³ ha⁻¹ and gross revenues from 1200 to 41,322 US$ ha⁻¹. Eight irrigation scenarios were constructed by combining land suitability (suitable vs. unsuitable), crop water requirement levels, and gross revenue assumptions. The model maximizes total gross revenue subject to seasonal water and land constraints. The results indicate that under limited water availability (e.g., 5.35–6.20 MCM in Nam Tong 2), crops with lower water demand (≤6000 m³ ha⁻¹) and higher economic return per unit of water are prioritized, improving water-use efficiency. As water availability increases, high-value but water-intensive crops expand until land suitability becomes the dominant constraint. Expanding irrigation on unsuitable land produces diminishing economic returns. The framework enhances the realism of irrigation planning and supports economically efficient, water-sustainable crop allocation in water-scarce regions. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Figure 1

25 pages, 6936 KB

Open AccessArticle

Evaluating the PhenoGlad Model as a Decision-Support Tool for Gladiolus Production in Tropical and Subtropical Environments

by Priscila Maria Silva Francisco, José Carlos Sorgato, Jéssica Celeste Mônico Ramos, Lucas Coutinho Reis, Luan Marlon Ribeiro, Marcio Roberto Rigotte, Mateus Augusto Donegá, Dislaine Becker, Regina Tomiozzo, Lilian Osmari Uhlmann and Nereu Augusto Streck

AgriEngineering 2026, 8(6), 202; https://doi.org/10.3390/agriengineering8060202 - 25 May 2026

Viewed by 291

Abstract

The expansion of floriculture into climatic transition regions requires precise tools to mitigate thermo-hydric risks. Gladiolus (Gladiolus × grandiflorus Hort.) is sensitive to temperature extremes, requiring strategic planning of planting schedules and heat stress mitigation. The objective in this study was to evaluate the PhenoGlad model for its ability to simulate developmental stages and heat stress damage in eight gladiolus cultivars across multiple environments and planting dates in the state of Mato Grosso do Sul, Midwest Brazil. Field experiments were conducted in five municipalities during the autumn, winter, and spring growing seasons. Model performance was evaluated by starting the simulation at planting or at emergence, using the statistics root mean square error (RMSE), bias index (BIAS), Willmott’s index of agreement (d), and the correlation coefficient (r). Simulations starting at emergence reduced the error in predicting the timing of developmental stages (from 5.34 to 3.16 days). For leaf development, the model was highly accurate, with an RMSE lower than one leaf for different planting dates, sites, and cultivars. Furthermore, the model accurately predicted extreme heat stress events (daily maximum temperatures > 34 °C associated with low relative humidity), which resulted in severe damage and inhibition of reproductive development in the field. In conclusion, the PhenoGlad model is a robust decision-support system and agricultural engineering tool for production scheduling and climate loss mitigation in tropical floriculture. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Graphical abstract

21 pages, 8632 KB

Open AccessArticle

A Simple Turbulent Exchange Approach for Estimating Reservoir Evaporation in Managing Water for Irrigation Using Remote Sensing and Ground Measurements

by Thanushan Kirupairaja and A. Salim Bawazir

AgriEngineering 2026, 8(5), 169; https://doi.org/10.3390/agriengineering8050169 - 28 Apr 2026

Viewed by 742

Abstract

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Figure 1

33 pages, 5528 KB

Open AccessArticle

Multisensor Monitoring of Soil–Plant–Atmosphere Interactions During Reproductive Development in Wheat

by Sandra Skendžić, Darija Lemić, Hrvoje Novak, Marko Reljić, Marko Maričević, Vinko Lešić, Ivana Pajač Živković and Monika Zovko

AgriEngineering 2026, 8(3), 119; https://doi.org/10.3390/agriengineering8030119 - 20 Mar 2026

Viewed by 998

Abstract

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha⁻¹. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Figure 1

13 pages, 3026 KB

Open AccessArticle

Water Distribution Uniformity of Traveling Gun Sprinklers: Day–Night Wind and Towpath Alignment

by Henrique Fonseca Elias de Oliveira, José Henrique Nunes Flores, Lessandro Coll Faria, Samuel Beskow, Giuliani do Prado, Gustavo Borges Lima, Jhon Lennon Bezerra da Silva, Marcos Vinícius da Silva and Alberto Colombo

AgriEngineering 2026, 8(3), 102; https://doi.org/10.3390/agriengineering8030102 - 8 Mar 2026

Viewed by 1117

Abstract

Wind is a primary driver of nonuniform water application in traveling gun sprinklers, yet design guidance often treats wind only as speed. This study quantifies how diurnal wind regimes (day vs. night) and wind incidence relative to the towpath (φ) affect application-rate patterns and the Christiansen uniformity coefficient (UC) as a function of towpath spacing expressed as a fraction of wetted diameter (WD). Class-specific sprinkler patterns were generated with the Simulation Model for Sprinkler Irrigation (SIA) and combined with local daytime and nighttime wind-frequency data to build composite application-rate fields; these drove traveler simulations that computed cross-track depth, lateral overlap across spacings, and UC for representative wind speeds (0–6 m s⁻¹) and φ (0°, 45°, 90°). Nighttime operation yielded higher UC, with a day–night crossover near ~50% WD and an average UC gain of ~9.5 percentage points; typical gains were +6 to +9 points between 55% and 90% WD. Wind incidence was as influential as speed: at 65.6% WD, increasing wind from 0 to 6 m s⁻¹ reduced UC from 84.4% to 28.6% for φ = 0°, to 52.0% for 45°, and to 76.1% for 90°. Findings support nighttime scheduling, towpaths avoiding wind-parallel operation, and tighter spacings under windy conditions. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Graphical abstract

23 pages, 3434 KB

Open AccessArticle

Spatial Variability in Soil Attributes and Multispectral Indices in a Forage Cactus Field Irrigated with Wastewater in the Brazilian Semiarid Region

by Eric Gabriel Fernandez A. da Silva, Thayná Alice Brito Almeida, Raví Emanoel de Melo, Mariana Caroline Gomes de Lima, Lizandra de Barros de Sousa, Jeferson Antônio dos Santos da Silva, Marcos Vinícius da Silva and Abelardo Antônio de Assunção Montenegro

AgriEngineering 2025, 7(7), 221; https://doi.org/10.3390/agriengineering7070221 - 8 Jul 2025

Viewed by 1925

Abstract

Multispectral images obtained from Unmanned Aerial Vehicles (UAVs) have become strategic tools in precision agriculture, particularly for analyzing spatial variability in soil attributes. This study aimed to evaluate the spatial distribution of soil electrical (EC) and total organic carbon (TOC) in irrigated forage cactus areas in the Brazilian semiarid region, using field measurements and UAV-based multispectral imagery. The study was conducted in a communal agricultural settlement located in the Mimoso Alluvial Valley (MAV), where EC and TOC were measured at 96 points, and seven biophysical indices were derived from UAV multispectral imagery. Geostatistical models, including cokriging with spectral indices (NDVI, EVI, GDVI, SAVI, and NDSI), were applied to map soil attributes at different spatial scales. Cokriging improved the spatial prediction of EC and TOC by reducing uncertainty and increasing mapping accuracy. The standard deviation of EC decreased from 1.39 (kriging) to 0.67 (cokriging with EVI), and for TOC from 15.55 to 8.78 (cokriging with NDVI and NDSI), reflecting a 43.5% reduction in uncertainty. The indices, EVI, NDVI, and NDSI, showed strong potential in representing and enhancing the spatial variability in soil attributes. NDVI and NDSI were particularly effective at finer grid resolutions, supporting more efficient irrigation strategies and sustainable agricultural practices. Full article

(This article belongs to the Special Issue Agrometeorology and Agricultural Water Management: Technology Advances and Applications in Cropping Systems)

► Show Figures

Figure 1

26 pages, 4697 KB

Open AccessArticle

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

Cited by 5 | Viewed by 3487

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: 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)

► Show Figures

Figure 1

26 pages, 3632 KB

Open AccessArticle

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

Cited by 5 | Viewed by 3234

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 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)

► Show Figures

Figure 1

21 pages, 6578 KB

Open AccessArticle

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

Cited by 6 | Viewed by 2264

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 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)

► Show Figures

Graphical abstract

Review

Jump to: Research

21 pages, 3523 KB

Open AccessReview

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

Cited by 85 | Viewed by 32512

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 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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI