Search Results (1,996)

Climate change poses significant challenges to agricultural production, water availability, and food and nutrition security, particularly in semi-arid regions where rural livelihoods depend heavily on rain-fed agriculture. In Eswatini, increasing temperatures, erratic rainfall, and recurrent droughts have intensified pressures on smallholder farming systems; however, limited empirical research has examined how climate variability simultaneously affects agriculture, water resources, and household food security within an integrated food–water–nutrition nexus framework. Therefore, this study assessed the impacts of climate change on agricultural production, water availability, food security, and access to nutritious food among smallholder households in the Lubombo Region of Eswatini. A concurrent triangulated mixed-methods approach was employed, combining quantitative data from 880 households with qualitative insights from open-ended responses. Descriptive statistics, Spearman’s correlation, binary logistic regression, and thematic analysis were used to analyse the data. The findings reveal that climate change significantly affects both agricultural and water systems, which in turn directly influence household food security outcomes. The majority of households reported declining crop yields (56.5%), widespread food shortages (79.5%), meal skipping (69.0%), and high levels of food-related anxiety (87.6%). Water insecurity is also prevalent, with over 83% of households experiencing water shortages and nearly all respondents indicating that climate change has affected water access. Correlation and regression analyses demonstrate that drought frequency and reduced rainfall are the strongest predictors of both water insecurity and food insecurity, highlighting the central role of climate variability. Water scarcity emerged as a critical pathway linking climate change to food insecurity, with strong associations between water shortages and reduced crop yields, food shortages, and coping strategies such as skipping meals. Qualitative findings further highlighted declining agricultural productivity, loss of traditional foods, reduced dietary diversity, and increased psychological stress associated with food insecurity. The study concludes that food insecurity in the Lubombo region is multidimensional, driven by the interconnected effects of climate variability, water scarcity, and socio-economic constraints. The findings emphasise the need for integrated, climate-resilient strategies that simultaneously address agricultural production, water resource management, and household adaptive capacity to enhance food and nutrition security. Future research should evaluate the effectiveness of nexus-based adaptation strategies and climate-smart interventions in enhancing long-term food, water, and nutrition security in vulnerable smallholder farming systems. Full article

Background: Manure-amended agricultural soil is a critical reservoir of antibiotic resistance genes (ARGs), posing escalating threats to environmental health and food safety. However, the temporal trajectories of ARG prevalence throughout the complete reproductive cycle of cash crops, and their mechanistic linkages with fertilization regimes and microbial community succession, remain inadequately understood. Methods: To bridge this knowledge gap, we conducted an in situ field experiment over the entire growth period of Chinese cabbage at a long-term manure-amended farm in Tianjin, China. Six contrasting fertilization strategies were evaluated: unfertilized control (CK1), unfertilized baseline control (CK2), traditional full-rate combined manure–chemical fertilization (TF), traditional half-rate combined manure–chemical fertilization (T1), half-dose sole manure fertilizer (T2), and half-dose sole chemical fertilizer only (T3). Results: Our results demonstrated that ARG abundance and associated mobile genetic elements (MGEs) exhibited a pronounced transient surge immediately post-fertilization, yet reverted to baseline levels by harvest, revealing a tangible resilience of the soil resistome. Notably, the optimized half-organic fertilization (T2) effectively curtailed the proliferation of manure-derived pathogenic taxa while preserving beneficial keystone phyla (e.g., Acidobacteria and Proteobacteria), indicating a trade-off between nutrient provisioning and ecological filtering. Co-occurrence network analysis further identified MB-A2-108, Saccharimonadales, and Rokubacteriales as pivotal hosts for multidrug-resistant ARGs, underscoring that microbial interspecific interactions—rather than taxonomic richness alone—are the primary drivers of resistome succession. Quantitative risk assessment confirmed that the T2 regimen reduced the composite ARG contamination index (CF_zone) by 25% relative to conventional full fertilization (TF), while maintaining comparable cabbage yields. Conclusions: Collectively, our findings advocate for precision organic fertilization as a nature-based solution that synchronizes nutrient supply with crop demand, curtails ARG propagation, and mitigates long-term agroecological risks. Full article

Lameness remains one of the most significant welfare and economic challenges in modern dairy production. Traditional detection methods, particularly visual locomotion scoring, are limited by subjectivity, inconsistent application, and infrequent monitoring, which often delays the recognition of painful lesions. This review synthesizes recent validation studies of automated lameness detection (ALD) technologies and evaluates their diagnostic performance, validation design, and practical implementation across dairy systems. Studies were screened for relevance by a single reviewer based on title, abstract, and full-text content. Included studies represented sensor-based, pressure-based, vision-based, and multimodal detection platforms, with reported accuracies ranging from approximately 70% to 98% depending on modality and environmental setting. Vision-based systems demonstrated strong performance in controlled conditions, whereas field-validated systems showed more moderate but potentially more generalizable accuracy. Pressure-based platforms reported high diagnostic discrimination via area under the curve (AUC) analysis but face infrastructural limitations in commercial settings. Risk-of-bias assessment indicated that controlled experimental studies without external validation may overestimate deployment performance. Despite technological advances, variability in validation protocols, lesion thresholds, and environmental robustness limits direct comparison across systems. Future research should prioritize multi-farm external validation, standardized benchmarking frameworks, and multimodal integration within precision livestock farming ecosystems to improve reliability and adoption. Full article

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

Agricultural drones have been increasingly adopted to improve production efficiency and address labor shortages in agriculture. However, their broader socioeconomic effects remain unclear, particularly regarding whether drone-assisted farming may increase unemployment pressure or attract excessive capital intervention that threatens farmers’ access to farmland and the sustainable development of agricultural production systems. To address these issues, this study evaluates the development scale and economic effects of agricultural drones in China. A comparative analytical framework is constructed to quantify production costs, return on investment (ROI), and labor demand under traditional and drone-assisted farming modes. Corn and citrus are selected as representative grain and fruit crops for empirical analysis. The results indicate the following: (1) Drone-assisted farming reduces production costs. (2) At the baseline prices, the ROI of drone-assisted corn and citrus production increases to 10.53% and 7.65%, respectively, but remains low, suggesting a limited risk of excessive capital intervention. (3) At the national scale, drone-assisted farming generates estimated cost savings for corn and citrus. (4) In terms of labor effects, agricultural drones mainly help alleviate agricultural labor shortages rather than generate large-scale unemployment, while also improving production efficiency and supporting sustainable agricultural development. Full article

Accurate wind speed forecasting plays a crucial role in the safe and stable operation of wind farms and the efficient integration of renewable energy into modern power systems. However, wind speed exhibits complex spatiotemporal variations affected by diverse meteorological conditions, making high-precision prediction a long-standing bottleneck in wind power scheduling. This paper develops a U-shaped spatiotemporal feature fusion network named U-STNet, which realizes joint modeling of inter-turbine spatial correlations and multi-period long-range temporal dependencies. The model maps raw wind speed series to high-dimensional embeddings and adopts an encoder–decoder U-shaped architecture to complete feature encoding, reconstruction and multi-scale feature extraction, which effectively captures the inherent periodic and seasonal patterns of wind speed. Experiments on the SDWPF wind farm dataset show that U-STNet obtains competitive prediction accuracy across all multi-step forecasting horizons. Compared with traditional statistical models, recurrent neural networks and state-of-the-art Transformer baselines, the proposed method exhibits more stable error accumulation characteristics and superior long-step prediction performance. This verifies the effectiveness of jointly modeling turbine spatial topology and multi-scale temporal dependencies for wind speed forecasting. Full article

In response to the severe challenges posed by extreme sandstorm weather to the operational safety of WTs and grid stability, this paper proposes an MPC-based power optimization control strategy for WFs. Simulation results indicate that, compared with the traditional PD strategy, the proposed MPC strategy significantly reduces the active power fluctuations of individual WTs, smoothly tracks grid dispatch orders with an overall power tracking accuracy improvement, and effectively lowers the operational risk index of turbines across the farm (ranging from 6.90% to 57.14% for the ten evaluated turbines). Furthermore, the proposed strategy substantially mitigates the angular acceleration fluctuation amplitude of the drive train components (e.g., reducing peak angular accelerations of drive-train masses by up to 35%) and reduces the fore-aft and lateral displacement oscillations of the tower top (reducing peak displacement variations by approximately 25% and 40%, respectively), providing comprehensive structural load mitigation while ensuring WF power output stability and grid safety. This study provides a theoretical basis and technical approach for the intelligent operation and risk prevention and control of WFs under extreme meteorological conditions. Full article

Ensuring long-term agricultural sustainability depends heavily on preserving soil health, a process fundamentally governed by humic substances (HSs) and their vital physicochemical and biological functions. However, because intensive farming rapidly degrades natural HSs reserves, external replenishment has become essential, driving the expansion of the humic preparations market. This article constitutes a comprehensive review of the entire technological chain of humic preparations: from the identification of raw materials, through advanced extraction techniques, up to agrochemical mechanisms in the soil–plant system. Both traditional fossil deposits (leonardite, brown coal, peat) and renewable waste sources fitting into the concept of the circular economy were discussed. Classical alkaline extraction was confronted with green methods such as ultrasound-assisted (UAE), microwave-assisted (MAE) or high voltage electrical discharge (HVED) extraction, which allow for shortening the operation time and reducing the consumption of reagents. Strategies of integrating HSs with mineral fertilizers (coating, liquid formulas, organo-mineral products) and their direct impact on improving nutrient use efficiency (NUE), mitigating plant abiotic stress, agricultural performance, and environmental impact were described in detail. Research perspectives were also presented, including, among others, economic aspects of scaling up humic technologies and an assessment of the development potential of innovative nanofertilizers functionalized with HSs. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article

Extensive livestock farming is characteristic of the landscape of the Mediterranean regions of Southern Iberian Peninsula. These production systems based on dryland pastures provide a wide range of services and contribute to maintaining environmental balance when compared with intensive agricultural or other livestock production systems. The Portuguese Montado is a habitat of Community importance and is protected under the European Natura 2000 network. This makes any research aimed at preserving, maintaining, or restoring this ecosystem particularly important to ensure its sustainable management. The main objectives of this study were to evaluate: (i) the impact of dolomitic limestone application on soil pH; (ii) the relation between multiple soil parameters; (iii) the impact of grazing preferences and livestock stocking rates on topsoil compaction; (iv) temporal and spatial sheep grazing patterns and sward productivity, quality and floristic composition throughout the growing vegetative season. This study was carried out during the vegetative cycle of 2023/2024 on a 4-ha pasture field located at Mitra farm (Southern Portugal). The experimental design included four treatments resulting from the combination of limestone application (with and without) and stocking rate (traditional: 7 sheep ha⁻¹; high: 18 sheep ha⁻¹). Sheep grazing preferences, soil compaction and fertility, sward productivity, quality and floristic composition were monitored at 48 sampling areas. The results confirm that improving soil pH through the application of dolomitic limestone is an effective, although slow and gradual process. When combined with grazing management through increased stocking rates, several important outcomes were observed: (i) preferential grazing areas did not exhibit significant differences in soil trampling; (ii) higher stocking rates resulted in less selective grazing; (iii) soil amendment and higher livestock stocking rates contributed to greater pasture crude protein content; and (iv) the influence of pasture quality on grazing preferences depended on the phase of the pasture-grazing cycle. Overall, these findings are promising indicators of sustainability for extensive animal production in Mediterranean dryland silvopastoral systems. However, with regard to the sward, as this study only monitored a single growing season, will need to be validated in future studies. Full article

Smallholder farmers in South Africa face increasing climate variability, yet their agronomic decision-making depends on timely and reliable climate information. Although digital ICT platforms are expanding, limited evidence exists on how their effectiveness compares to human-based advisory systems. The study addresses this gap by examining the determinants of ICT-based climate information adoption using logistic regression and assessing its influence on agronomic decisions through propensity score matching (PSM). A cross-sectional survey and multistage sampling were used to collect data from 217 smallholder crop farmers in the Eastern Cape. The results indicate that 65% of farmers accessed climate information through ICT platforms, while 35% relied on human-based sources. The adoption of ICT-based information is driven by education, digital literacy, mobile network reliability, the perceived value of real-time updates, and smartphone ownership, whereas habitual dependence on traditional channels hinders digital uptake. PSM estimates show that ICT-based climate information is 10–15% less effective than human-based sources in shaping planting decisions, input use, and the timing of farm operations, likely due to digital literacy and infrastructure constraints. The study demonstrates that access to digital tools does not automatically translate into effective use and recommends a hybrid information model integrating digital platforms with trusted human intermediaries to strengthen climate resilience and agronomic decision-making. Full article

This paper considers the problem of early detection of late blight (Phytophthora infestans) in tomatoes based on computer vision and machine learning methods. The main purpose of the study was to develop a representative dataset of images of tomato leaves and an approach to extracting informative features for classifying the stages of disease development. A new dataset was generated using tomato plants grown under greenhouse conditions, with leaf images subsequently captured under controlled laboratory conditions, including five stages of late blight progression with variability in imaging devices, lighting conditions, and temporal disease dynamics. To improve the quality of image analysis, a preprocessing stage was applied, including conversion to grayscale, median filtering, and binarization using the Otsu method. In addition to the traditional textural features, fractal analysis was used to quantify the structural complexity of the affected leaf areas. To verify the information content of the selected features, classification experiments were conducted using Random Forest, XGBoost, and Support Vector Machine models, and the quality was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of textural and fractal features contributes to a more accurate distinction between the stages of disease. The developed dataset and the proposed approach can be used in further research on plant disease diagnosis, agricultural monitoring, and precision farming systems although it should be acknowledged that the dataset is limited to greenhouse settings, and field-scale generalizability requires further validation. Full article

This study quantifies and assesses the gap between the perceptions of 550 farming households on the Caribbean island of Hispaniola and the opinions of 60 regional experts on climate change. This quantification draws on the traditional knowledge of farming households, which is based on environmental observations, including meteorological, biological, and astrological ones, as well as on the experts’ opinions, which are grounded in scientific observations. To measure this gap, we asked 24 identical questions to both the experts and the farming households, using the experts’ responses as a benchmark. The experts’ statements were used as a reference to reflect the reality of climate change, given that the majority of experts’ answers converge. We then quantified the average distance for each farming household relative to these references. Fisher’s exact test and Pearson chi-square (χ²) test were used to assess this distance. A binary regression model was then used to identify the main factors influencing the gap in farming households’ perceptions, as well as to examine whether this gap is associated with greater socioeconomic vulnerability. The results revealed that in Haiti, 70% of farming households had a different perception from experts’one. In the Dominican Republic, this proportion was 47.50%. Vulnerable (OR = 12.94, p < 0.001) and very vulnerable (OR = 4.18, p < 0.05) farming households were more likely to have a different perception of climate change compared to experts. Full article

Biogas plays an increasingly important role in advancing decarbonisation goals, particularly in rural regions where livestock and agricultural waste can be converted into renewable energy. However, predicting annual farm-level biogas electricity output remains challenging due to operational variability, uncertain co-digestion practices, and external constraints such as grid integration and environmental disruptions. Traditional point prediction models often provide limited support for practical decision-making because they do not explicitly represent uncertainty around the estimated output. To address this limitation, this paper proposes a data-driven prediction interval framework for annual farm-level biogas electricity-output estimation. The study does not model future temporal horizons; rather, it estimates annual electricity generation at the farm level and constructs prediction intervals around these estimates. The proposed framework combines Self-Organising Map (SOM)-based clustering with Lower Upper Bound Estimation (LUBE) to account for operational heterogeneity among biogas-producing farms. SOM clustering is performed using pre-prediction operational covariates, specifically cattle population and co-digestion status, while electricity output is used only as the prediction target and for post-hoc interpretation. For each operational cluster, a neural prediction interval model is trained using the LUBE approach. A priority-weighted extension is then incorporated to reflect cluster-level operational or strategic importance in the evaluation of interval performance. Experiments on a real-world dataset from U.S. biogas systems show that the proposed framework can support a trade-off between interval width and coverage performance across standard confidence levels. By combining operational clustering with uncertainty-aware interval estimation, the method improves the interpretability and practical relevance of annual farm-level biogas electricity-output prediction for infrastructure planning. Full article

Recent developments and applications of high-throughput omics technologies have modernised animal biotechnology, offering extraordinary comprehension of animal reproduction. The current review discusses the applications and advancement of high-throughput omics technologies in animal reproductive biology. The data for this review were sourced from old and recently published work in different journals. The internet databases used to access the manuscripts were ResearchGate, Google Scholar, ScienceDirect, Web of Science, PubMed, and the Directory of Open Access Journals. The results showed that omics technologies have been previously used in human biology, and with time, have come to be introduced in animal reproductive biology. Studies using these technologies showed that genomics analysis identified at least 180 CNVs and 49 candidate genes enriched in vital fertility pathways, including retinol metabolism, steroid hormone biosynthesis, and Hippo signalling, while transcriptomics analysis screened the molecular dynamics of reproductive indicators at both the cellular and molecular levels. Pig oocytes matured in vitro or in vivo were subjected to transcriptome analysis. These analyses generate large amounts of ‘Big Data’. As a result, omics can handle large amounts of data generated through the analyses, but it needs bioinformatics experts and advanced computers, making it less affordable at the farm level. However, widely utilised technologies, including assisted reproductive technologies (ARTs), have hit their limit after years of use. To break this plateau of traditional ARTs, future research must give integrative ARTs and high-throughput omics technologies top priority, as they have clearly been shown to improve farm animals’ reproductive success. The synergy of increased reproductive efficiency and a smaller ecological footprint is crucial for improving the accuracy and sustainability of livestock systems, ultimately promoting global food security. Full article