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Search Results (1,186)

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Keywords = irrigated farming

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23 pages, 2768 KiB  
Article
Sustainable Cotton Production in Sicily: Yield Optimization Through Varietal Selection, Mycorrhizae, and Efficient Water Management
by Giuseppe Salvatore Vitale, Nicolò Iacuzzi, Noemi Tortorici, Giuseppe Indovino, Loris Franco, Carmelo Mosca, Antonio Giovino, Aurelio Scavo, Sara Lombardo, Teresa Tuttolomondo and Paolo Guarnaccia
Agronomy 2025, 15(8), 1892; https://doi.org/10.3390/agronomy15081892 - 6 Aug 2025
Abstract
This study explores the revival of cotton (Gossypium spp. L.) farming in Italy through sustainable practices, addressing economic and water-related challenges by integrating cultivar selection, arbuscular mycorrhizal fungi (AMF) inoculation, and deficit irrigation under organic farming. Field trials evaluated two widely grown [...] Read more.
This study explores the revival of cotton (Gossypium spp. L.) farming in Italy through sustainable practices, addressing economic and water-related challenges by integrating cultivar selection, arbuscular mycorrhizal fungi (AMF) inoculation, and deficit irrigation under organic farming. Field trials evaluated two widely grown Mediterranean cultivars (Armonia and ST-318) under three irrigation levels (I-100: 100% crop water requirement; I-70: 70%; I-30: 30%) across two Sicilian soil types (sandy loam vs. clay-rich). Under I-100, lint yields reached 0.99 t ha−1, while severe deficit (I-30) yielded only 0.40 t ha−1. However, moderate deficit (I-70) maintained 75–79% of full yields, proving a viable strategy. AMF inoculation significantly enhanced plant height (68.52 cm vs. 65.85 cm), boll number (+22.1%), and seed yield (+12.5%) (p < 0.001). Cultivar responses differed: Armonia performed better under water stress, while ST-318 thrived with full irrigation. Site 1, with higher organic matter, required 31–38% less water and achieved superior irrigation water productivity (1.43 kg m−3). Water stress also shortened phenological stages, allowing earlier harvests—important for avoiding autumn rains. These results highlight the potential of combining adaptive irrigation, resilient cultivars, and AMF to restore sustainable cotton production in the Mediterranean, emphasizing the importance of soil-specific management. Full article
(This article belongs to the Section Farming Sustainability)
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23 pages, 4324 KiB  
Article
Monitoring Nitrogen Uptake and Grain Quality in Ponded and Aerobic Rice with the Squared Simplified Canopy Chlorophyll Content Index
by Gonzalo Carracelas, John Hornbuckle and Carlos Ballester
Remote Sens. 2025, 17(15), 2598; https://doi.org/10.3390/rs17152598 - 25 Jul 2025
Viewed by 444
Abstract
Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs [...] Read more.
Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI2) for N uptake estimates, and (iii) explore the SCCCI2 and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI2 model (RMSE = 20.52, Bias = −6.21 Kg N ha−1, and MAPE = 11.95%) outperformed other models assessed. The SCCCI2, squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha−1) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI2 for N uptake estimates in ponded and non-ponded rice crops. Full article
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21 pages, 16254 KiB  
Article
Prediction of Winter Wheat Yield and Interpretable Accuracy Under Different Water and Nitrogen Treatments Based on CNNResNet-50
by Donglin Wang, Yuhan Cheng, Longfei Shi, Huiqing Yin, Guangguang Yang, Shaobo Liu, Qinge Dong and Jiankun Ge
Agronomy 2025, 15(7), 1755; https://doi.org/10.3390/agronomy15071755 - 21 Jul 2025
Viewed by 427
Abstract
Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a [...] Read more.
Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m3 ha−1 and deficit irrigation (M) at 450 m3 ha−1, along with five fertilization treatments (at a rate of 180 kg N ha−1): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha−1) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article
(This article belongs to the Section Precision and Digital Agriculture)
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20 pages, 2246 KiB  
Article
The Occurrence and Distribution of Herbicides in Soil and Irrigation Canals in a High-Input Farming Region of Serbia
by Dragana Linda Mitić, Mira Pucarević, Mira Milinković, Sanja Lazić, Aleksandra Šušnjar, Slavica Vuković, Jelena Ećimović, Siniša Mitrić and Dragana Šunjka
Environments 2025, 12(7), 246; https://doi.org/10.3390/environments12070246 - 17 Jul 2025
Viewed by 547
Abstract
This study aims to improve the understanding of, and provide insights into, the environmental fate of herbicides currently used in agriculture, which is addressed through the analysis of the quality of canal water used for irrigation and the agricultural soil in the immediate [...] Read more.
This study aims to improve the understanding of, and provide insights into, the environmental fate of herbicides currently used in agriculture, which is addressed through the analysis of the quality of canal water used for irrigation and the agricultural soil in the immediate vicinity. The research was conducted in the main agricultural region of Serbia, characterized by intensive crop production in conventional agriculture. Monitoring was focused on the Danube–Tisza–Danube canal system, specifically the Bogojevo–Bečej section. The presence of 41 currently used herbicides was analyzed in 520 soil samples collected from two depths (0–30 cm and 30–60 cm), as well as in 100 canal water samples. Results showed a high frequency of clopyralid, 2,4-D-methyl ester, terbuthylazine, fenoxaprop-ethyl, and aclonifen, with the highest amountsbeingterbuthylazine and quizalofop-ethyl, which was possibly a consequence of their recent application shortly before sampling. Concentrations of herbicide residues at different depths were closely similar, without the impact of soil mechanical and chemical characteristics on herbicide levels. In canal water characterized as moderately salty and slightly alkaline, herbicide residues were far below the maximum allowable concentrations, suggesting that the canal water is suitable for aquatic life, irrigation, and other uses. The findings suggest that the appropriate use of herbicides in regions under intensive agriculture is important for reducing environmental contamination. Full article
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22 pages, 1797 KiB  
Article
Forcing the SAFY Dynamic Crop Growth Model with Sentinel-2 LAI Estimates and Weather Inputs from AgERA5 Reanalysis and CM SAF SARAH-3 Radiation Data for Estimating Crop Water Requirements and Yield
by Anna Pelosi, Angeloluigi Aprile, Oscar Rosario Belfiore and Giovanni Battista Chirico
Remote Sens. 2025, 17(14), 2464; https://doi.org/10.3390/rs17142464 - 16 Jul 2025
Viewed by 205
Abstract
The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental [...] Read more.
The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental for estimating crop water requirements (CWR) and yield. This study used the latest reanalysis dataset, AgERA5, combined with the up-to-date CM SAF SARAH-3 Satellite-Based Radiation Data as meteorological inputs of the SAFY dynamic crop growth model and a one-step evapotranspiration formula for CWR and yield estimates at the farm scale of tomato crops. The Sentinel-2 (S2) estimates of Leaf Area Index (LAI) were used to force the SAFY model as soon as they became available during the growing stage, according to the satellite passages over the area of interest. The SAFY model was calibrated with ground-based weather observations and S2 LAI data on tomato crops that were collected in several farms in Campania Region (Southern Italy) during the irrigation season, which spans from April to August. To validate the method, the model estimates were compared with field observations of irrigation volumes and harvested yield from a monitored farm in the same region for the year 2021. Results demonstrated that integrating AgERA5 and CM SAF weather datasets with S2 imagery for assimilation into the SAFY model enables accurate estimates of both CWR and yield. Full article
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24 pages, 836 KiB  
Article
Effect of Farming System and Irrigation on Physicochemical and Biological Properties of Soil Under Spring Wheat Crops
by Elżbieta Harasim and Cezary A. Kwiatkowski
Sustainability 2025, 17(14), 6473; https://doi.org/10.3390/su17146473 - 15 Jul 2025
Viewed by 321
Abstract
A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: [...] Read more.
A field experiment in growing spring wheat (Triticum aestivum L.—cv. ‘Monsun’) under organic, integrated and conventional farming systems was conducted over the period of 2020–2022 at the Czesławice Experimental Farm (Lubelskie Voivodeship, Poland). The first experimental factor analyzed was the farming system: A. organic system (control)—without the use of chemical plant protection products and NPK mineral fertilization; B. conventional system—the use of plant protection products and NPK fertilization in the range and doses recommended for spring wheat; C. integrated system—use of plant protection products and NPK fertilization in an “economical” way—doses reduced by 50%. The second experimental factor was irrigation strategy: 1. no irrigation—control; 2. double irrigation; 3. multiple irrigation The aim of the research was to determine the physical, chemical, and enzymatic properties of loess soil under spring wheat crops as influenced by the factors listed above. The highest organic C content of the soil (1.11%) was determined in the integrated system with multiple irrigation of spring wheat, whereas the lowest one (0.77%)—in the conventional system without irrigation. In the conventional system, the highest contents of total N (0.15%), P (131.4 mg kg−1), and K (269.6 mg kg−1) in the soil were determined under conditions of multiple irrigation. In turn, the organic system facilitated the highest contents of Mg, B, Cu, Mn, and Zn in the soil, especially upon multiple irrigation of crops. It also had the most beneficial effect on the evaluated physical parameters of the soil. In each farming system, the multiple irrigation of spring wheat significantly increased moisture content, density, and compaction of the soil and also improved its total sorption capacity (particularly in the integrated system). The highest count of beneficial fungi, the lowest population number of pathogenic fungi, and the highest count of actinobacteria were recorded in the soil from the organic system. Activity of soil enzymes was the highest in the integrated system, followed by the organic system—particularly upon multiple irrigation of crops. Summing up, the present study results demonstrate varied effects of the farming systems on the quality and health of loess soil. From a scientific point of view, the integrated farming system ensures the most stable and balanced physicochemical and biological parameters of the soil due to the sufficient amount of nutrients supplied to the soil and the minimized impact of chemical plant protection products on the soil. The multiple irrigation of crops resulting from indications of soil moisture sensors mounted on plots (indicating the real need for irrigation) contributed to the improvement of almost all analyzed soil quality indices. Multiple irrigation generated high costs, but in combination with fertilization and chemical crop protection (conventional and integrated system), it influenced the high productivity of spring wheat and compensated for the incurred costs (the greatest profit). Full article
(This article belongs to the Special Issue Soil Fertility and Plant Nutrition for Sustainable Cropping Systems)
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20 pages, 2421 KiB  
Article
Mitigation of Water-Deficit Stress in Soybean by Seaweed Extract: The Integrated Approaches of UAV-Based Remote Sensing and a Field Trial
by Md. Raihanul Islam, Hasan Muhammad Abdullah, Md Farhadur Rahman, Mahfuzul Islam, Abdul Kaium Tuhin, Md Ashiquzzaman, Kh Shakibul Islam and Daniel Geisseler
Drones 2025, 9(7), 487; https://doi.org/10.3390/drones9070487 - 10 Jul 2025
Viewed by 423
Abstract
In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which [...] Read more.
In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R2 = 0.76), photosystem II efficiency (R2 = 0.78), maximum fluorescence (R2 = 0.64), and apparent transpiration rate (R2 = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R2 = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R2 = 0.56) and vapor pressure deficit (R2 = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R2 = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article
(This article belongs to the Special Issue Recent Advances in Crop Protection Using UAV and UGV)
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18 pages, 3154 KiB  
Article
Water Saving and Environmental Issues in the Hetao Irrigation District, the Yellow River Basin: Development Perspective Analysis
by Zhuangzhuang Feng, Qingfeng Miao, Haibin Shi, José Manuel Gonçalves and Ruiping Li
Agronomy 2025, 15(7), 1654; https://doi.org/10.3390/agronomy15071654 - 8 Jul 2025
Viewed by 327
Abstract
Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in [...] Read more.
Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article
(This article belongs to the Section Farming Sustainability)
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24 pages, 8603 KiB  
Article
Evaluating the Potential of Improving In-Season Potato Nitrogen Status Diagnosis Using Leaf Fluorescence Sensor as Compared with SPAD Meter
by Seiya Wakahara, Yuxin Miao, Dan Li, Jizong Zhang, Sanjay K. Gupta and Carl Rosen
Remote Sens. 2025, 17(13), 2311; https://doi.org/10.3390/rs17132311 - 5 Jul 2025
Viewed by 379
Abstract
The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common [...] Read more.
The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R2 of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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23 pages, 7766 KiB  
Article
Spatiotemporal Evaluation of Soil Water Resources and Coupling of Crop Water Demand Under Dryland Conditions
by Yaoyu Li, Kaixuan Li, Xifeng Liu, Zhimin Zhang, Zihao Gao, Qiang Wang, Guofang Wang and Wuping Zhang
Agriculture 2025, 15(13), 1442; https://doi.org/10.3390/agriculture15131442 - 4 Jul 2025
Viewed by 237
Abstract
Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation [...] Read more.
Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges. Full article
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15 pages, 1268 KiB  
Article
Knowledge and Awareness of Bovine Fasciolosis Among Dairy Farm Personnel in the Eastern Cape Province, South Africa
by Zuko Mpisana, Mandla Yawa, Mhlangabezi Slayi, Nkululeko Nyangiwe, James Oguttu and Ishmael Festus Jaja
Parasitologia 2025, 5(3), 33; https://doi.org/10.3390/parasitologia5030033 - 4 Jul 2025
Viewed by 295
Abstract
Fascioliasis, a parasitic disease caused by liver flukes of the genus Fasciola, remains a significant threat to livestock productivity globally. Despite its economic and zoonotic importance, the knowledge levels of dairy farm personnel regarding this disease remain insufficiently explored in South Africa. [...] Read more.
Fascioliasis, a parasitic disease caused by liver flukes of the genus Fasciola, remains a significant threat to livestock productivity globally. Despite its economic and zoonotic importance, the knowledge levels of dairy farm personnel regarding this disease remain insufficiently explored in South Africa. This study assessed knowledge and awareness of bovine fasciolosis, including its etiology, risk factors, clinical signs, zoonotic implications, and control measures, among dairy farm personnel in the Eastern Cape Province. A structured questionnaire was randomly administered to 152 dairy farm workers. Descriptive statistics and Chi-square tests were used to examine associations between respondents’ demographic characteristics and their knowledge of fasciolosis. Most respondents were males (65.8%), aged 31–40 years (45.4%), with tertiary education (64%), over six years of experience (65%), and residing in inland regions (65.4%). A high proportion reported implementing pasture management practices such as irrigation (90.8%), pasture resting (69.8%), and rotation (94.7). Significant associations were found between geographic location and knowledge of Fasciola spp. as the causative agent, as well as awareness of swampy areas and water snails as key risk factors (p < 0.01). Educational level was significantly associated with awareness of the zoonotic potential of fasciolosis (p < 0.01), and regional location influenced knowledge on control practices (p < 0.01). These findings highlight persistent gaps in the understanding of bovine fasciolosis among dairy farm personnel, particularly in relation to causative agents, clinical signs, and zoonotic risk. Strengthening extension services, enhancing community awareness, and implementing targeted training programs are essential to address these knowledge gaps and improve disease control strategies in the Eastern Cape Province. Full article
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22 pages, 2196 KiB  
Review
A Review of IoT and Machine Learning for Environmental Optimization in Aeroponics
by Muhammad Amjad, Elanchezhian Arulmozhi, Yeong-Hyeon Shin, Moon-Kyung Kang and Woo-Jae Cho
Agronomy 2025, 15(7), 1627; https://doi.org/10.3390/agronomy15071627 - 3 Jul 2025
Viewed by 853
Abstract
Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing [...] Read more.
Traditional farming practices are becoming increasingly inadequate to meet global food demand due to water scarcity, prolonged production cycles, climate variability, and declining arable land. In contrast, aeroponic, smart, soil-free farming technologies offer a more sustainable alternative by reducing land use and providing efficient water use, given that aeroponics intermittently delivers water in mist form rather than maintaining continuous root zone moisture. However, aeroponics faces critical challenges in irrigation management due to non-standardized structures and limited real-time control. A key limitation is the inability to dynamically respond to temperature (T), relative humidity (RH), light intensity (Li), electrical conductivity (EC), pH, and photosynthesis rate (Pn), resulting in suboptimal crop yields and resource wastage. Despite growing interest, there remains a research gap in integrating internet of things (IoT) and machine learning technologies into aeroponic systems for adaptive control. IoT-enabled sensors provide real-time data on ambient conditions and plant health, while ML models can adaptively optimize misting intervals based on the fluctuations in Pn and environmental inputs. These technologies are particularly well suited to address the dynamic, data-intensive nature of aeroponic environments. This review purposes a novel, standardized IoT–ML framework to control irrigation by emphasizing IoT sensing and ML-based decision making in aeroponics. This integrated approach is essential for minimizing water loss, enhancing resource efficiency, and advancing the sustainability of controlled-environment agriculture. Full article
(This article belongs to the Section Water Use and Irrigation)
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19 pages, 1369 KiB  
Review
Current State of Arsenic, Fluoride, and Nitrate Groundwater Contamination in Northern Mexico: Distribution, Health Impacts, and Emerging Research
by Mélida Gutiérrez, María Teresa Alarcón-Herrera, María Socorro Espino-Valdés and Luz Idalia Valenzuela-García
Water 2025, 17(13), 1990; https://doi.org/10.3390/w17131990 - 2 Jul 2025
Viewed by 505
Abstract
The plateaus of north-central Mexico have an arid to semiarid climate and groundwater naturally contaminated with inorganic arsenic (iAs) and fluoride (F). Like other arid and semiarid areas, this region faces great challenges to maintain a safe supply of drinking and irrigation water. [...] Read more.
The plateaus of north-central Mexico have an arid to semiarid climate and groundwater naturally contaminated with inorganic arsenic (iAs) and fluoride (F). Like other arid and semiarid areas, this region faces great challenges to maintain a safe supply of drinking and irrigation water. Studies conducted in the past few decades on various locations within this region have reported groundwater iAs, F, and nitrate-nitrogen (NO3-N), and either their source, enrichment processes, health risks, and/or potential water treatments. The relevant findings are analyzed and condensed here to provide an overview of the groundwater situation of the region. Studies identify volcanic rocks (rhyolite) and their weathering products (clays) as the main sources of iAs and F and report that these solutes become enriched through evaporation and residence time. In contrast, NO3-N is reported as anthropogenic, with the highest concentrations found in large urban centers and in agricultural and livestock farm areas. Health risks are high since the hot spots of contamination correspond to populated areas. Health problems associated with NO3-N in drinking water may be underestimated. Removal technologies of the contaminants remain at the laboratory or pilot stage, except for the reverse osmosis filtration units fitted to selected wells within the state of Chihuahua. A recent approach to supplying drinking water free of iAs and F to two urban centers consisted of switching from groundwater to surface water. Incipient research currently focuses on the potential repercussions of irrigating crops with As-rich water. The groundwater predicaments concerning contamination, public health impact, and irrigation suitability depicted here can be applied to semiarid areas worldwide. Full article
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28 pages, 1008 KiB  
Article
Assessment of Farm Vulnerability to Climate Change in Southern France
by Abderraouf Zaatra, Mélanie Requier-Desjardins, Hélène Rey-Valette, Thierry Blayac and Hatem Belhouchette
Land 2025, 14(7), 1388; https://doi.org/10.3390/land14071388 - 1 Jul 2025
Viewed by 531
Abstract
Climate change (CC) is a major threat to agriculture, the sector that supports the territorial economy in the Pays Haut Languedoc et Vignoble (PHLV) region (south France). In this region, farms have been facing the negative effects of CC for several decades. The [...] Read more.
Climate change (CC) is a major threat to agriculture, the sector that supports the territorial economy in the Pays Haut Languedoc et Vignoble (PHLV) region (south France). In this region, farms have been facing the negative effects of CC for several decades. The implementation of agriculture adaptation policies requires a coherent and integrated tool that mobilizes approaches for territorial development, vulnerability assessments, and feasibility. The purpose of this research is to provide a multi-criteria assessment of farm vulnerability to CC in the PHLV region. An index of farm vulnerability was developed based on the classic model of vulnerability, which is the product of exposure and sensitivity divided by adaptive capacity. This assessment was conducted at the farm level, by combining biophysical variables (such as soil type and irrigation) and socioeconomic variables (such as agricultural experience and crop insurance), selected based on a literature review and interviews with local stakeholders and local experts. To solve the weighting problem, we differentiated between a “calculated vulnerability”, without any specific weighting of the vulnerability variables, and a “declared vulnerability” that integrates the scores assigned to the importance of each variable for each farmer surveyed, based on their awareness. Afterward, a discriminant analysis was used to identify the factors that determine the vulnerability classes. Our results show that (i) the majority of the surveyed farms have a relatively high vulnerability index, but wine farms and cereal farms are the most vulnerable; (ii) for all farms the “declared vulnerability” is lower than the “calculated vulnerability”, showing that farmers underestimate their vulnerability; (iii) there is an interesting link between the low level of vulnerability and the adaptation efforts already made over the past ten years by certain farms that have changed or introduced crops and improved their agricultural practices. Full article
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13 pages, 3041 KiB  
Article
Changes of Plant Growth and Soil Physicochemical Properties by Cultivating Different Economic Plant Species in Saline-Alkali Soil of Hetao Oasis, Inner Mongolia
by Rong Ma, Fengmei Du, Yongli Qin, Jianping Lv, Guanying Xing, Youjie Xu, Na Fu, Jun Qiao, Guangyu Hong and Shaokun Wang
Agriculture 2025, 15(13), 1421; https://doi.org/10.3390/agriculture15131421 - 30 Jun 2025
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Abstract
Due to prolonged irrigation from the Yellow River, a large area of farmland in the Hetao Oasis has undergone different degrees of salinization and alkalization, leading to reduced crop yields and incapable soil for plant growth. To enhance the productivity of the farmland [...] Read more.
Due to prolonged irrigation from the Yellow River, a large area of farmland in the Hetao Oasis has undergone different degrees of salinization and alkalization, leading to reduced crop yields and incapable soil for plant growth. To enhance the productivity of the farmland with saline-alkali soils, it is important to select salt-tolerant economic plant species that are capable of growing under the local climate and soil conditions in the Hetao Oasis. We conducted the experiment by planting Ziziphus jujuba var. spinose, Elaeagnus angustifolia, Hippophae rhamnoides and Lycium chinense in the Bayan Taohai Farm of the Hetao Oasis. Changes of plant growth (the survival rate, plant height, canopy, basal diameter and new branch length) and soil physicochemical properties (soil organic carbon, total carbon, total nitrogen, pH, electrical conductivity and particle size distribution) were continuously monitored during two growing seasons. Results indicated that, by the end of the first growing season, the survival rate of the Z. jujuba was less than 10%, making it unsuitable for plantation in the saline-alkali soils of the Hetao Oasis. In terms of plant growth, the E. angustifolia exhibited the highest survival rate (94.71%) and the fastest growth rate, indicating that E. angustifolia is adapted in the saline-alkali soils of the Hetao Oasis. The survival rates for L. chinense and H. rhamnoides were 86.46% and 65.64%, respectively, indicating that these species could grow in the saline-alkali soils, but at a slower rate. From the perspective of soil improvement, E. angustifolia, H. rhamnoides and L. chinense could reduce the soil pH, and E. angustifolia could significantly increase soil nutrients. In conclusion, it is not recommended to plant Z. jujuba, while the E. angustifolia is recommended as a proper economic species to be widely planted in the saline-alkali soils of the Hetao Oasis. H. rhamnoides could be selectively planted in areas with better soil conditions, and the L. chinense could be planted following soil improvement measurements. The research enhanced the effective utilization of the saline-alkali farmland and provided proper economic plant species for sustainable agriculture management in the Hetao Oasis of Inner Mongolia. Full article
(This article belongs to the Special Issue Soil Microbial Community and Ecological Function in Agriculture)
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