Search Results (6,149)

Effective nitrogen management in dairy cow diets is essential for optimising milk production and minimising environmental nitrogen emissions. This study develops a simplified model to estimate nitrogen excretion in dairy farms, distinguishing excretion by animal category (lactating cows, heifers, calves) and organic matrix (faeces, urine), with nitrogen intake as a key input. A comprehensive literature review guided the selection of equations for estimating nitrogen excretion based on dietary nitrogen content, dry matter intake and milk yield. The model was specifically calibrated for Holstein dairy herd in the Po Valley (Italy) context using data collected from ten Lombardy dairy farms over 30 months, focusing on diet composition and nitrogen excretion via faeces, urine, and milk. Validation against established the literature and the Nitrates Directive (91/676/EEC) excretion factors demonstrated the model’s alignment in estimating nitrogen excretion. Within this context, the proposed framework may support nitrogen management at farm level by providing a practical, descriptive tool to explore nitrogen flows and to identify potential areas for improving nutrient efficiency and reducing environmental impacts. Full article

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

Smallholder farmers in semi-arid regions worldwide face persistent water scarcity, declining soil fertility, and increasing climate variability, which constrain food production. This study investigated soil and water management practices and their effects on soil health, crop productivity, and adoption among smallholder vegetable farmers in a semi-arid area in Kenya. A mixed-methods approach was employed, combining survey data from 397 farmers with a randomized field experiment. Results showed that hand watering (88.7%) and manure application (95.5%) were prevalent, while only 5.7% of farmers used drip irrigation. Compost and mulch treatments significantly improved soil organic carbon (p = 0.03), available water capacity (p = 0.01), and gravimetric moisture content (p = 0.02), with soil moisture conservation practices strongly correlated with higher yields in leafy green vegetables (R = 0.62). Despite these benefits, adoption was hindered by high water costs (42.6%) and unreliable sources (25.7%). Encouragingly, 96.2% of respondents expressed willingness to pay for improved water systems if affordable and dependable. The findings stress the need for integrated water–soil strategies supported by inclusive policy, infrastructure investment, and gender-responsive training to enhance resilience and productivity in smallholder farming under water-scarce conditions across sub-Saharan Africa and other regions globally, contributing to global sustainability targets such as SDG 6, 12 and 15. Full article

Most animal welfare (AW) assessment protocols have been developed for intensive production systems and European cattle, raising concerns about their applicability in the tropics. To compare the results obtained by using the welfare quality (WQ) assessment for fattening cattle in the dry tropics, relevant modifications were implemented in 20 cattle production units (PUs) during the dry (DS) and rainy (RS) seasons. Regarding the principle of good feeding, only during the RS, between 20% and 25% of the farms maintained their animals in the acceptable and good categories, compared to the DS, where all PUs were classified as unacceptable (p < 0.04). Under the “Appropriate Behavior” principle, only 15% and 60% of the PUs maintained their animals at good and acceptable levels, respectively, in the RS, but not in the DS (p < 0.001). Conversely, during the DS, better scores were obtained for the measures and criteria in the Good housing group, with 45%, 50%, and 5% of PU classified as acceptable, good, and excellent, respectively, while for the RS, only 15%, 30%, and 5% reached these levels (p < 0.01). Meanwhile, under the “Good Health” principle, better animal health scores were observed during the RS, with 20%, 30%, and 50% of farms classified as acceptable, good, and excellent, compared to the DS, where only 70% and 10% of farms maintained their animals at good and excellent levels (p < 0.01). It is concluded that better animal welfare (AW) indicators were recorded during the RS, and the adjustments we applied to the conventional WQ protocol comprised a modification for the criterion that included the prolonged absence of thirst as well as adding six new indicators (measures) to the principles of housing, health, and behavior, which are considered essential for evaluating AW in cattle that are managed under extensive conditions by season. Full article

This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers. The study followed a descriptive-analytical approach structured in consecutive methodological phases. The systematic review, conducted following the Kitchenham protocol, identified theoretical factors associated with credit risk, while fieldwork with 300 producers provided the socioeconomic and productive contexts of coffee-growing households. Producer income, cost of living, and farm management expenses were modeled using regression, statistical, and machine learning methods. Subsequently, these variables were integrated to construct a financial risk index, which was normalized using expert scoring. The index was validated using data from 100 additional producers, for whom annual repayment capacity and maximum loan amounts were estimated according to their risk level. The results indicated that incorporating municipal-level economic variables, such as estimated average prices, income, and expenses, enhanced predictive accuracy and improved the rational allocation of loan amounts. The study concludes that credit risk analysis based on variables related to human, productive, and economic capital constitutes an effective strategy for improving access to finance in rural areas. Full article

Pear cultivation in Greece, with an annual production of approximately 81,000 tonnes, constitutes a significant segment of the national fruit industry, particularly in Northern regions such as Macedonia and Thessaly. Despite ranking 8th in the EU in terms of pear production, Greece’s cultivated area is slightly declining, and adoption of smart agriculture technologies (SAT) remains limited. In this context, the present study investigates the preferences, patterns, and barriers of SAT adoption within the Greek pear sector, aiming to lay the groundwork for more effective digital transformation in the agri-food domain. Using a structured interview-based survey, data were collected from 30 pear growers, revealing critical insights into the technological landscape of the sector. A central challenge that emerged was the insufficient internet connectivity in rural farming areas, highlighting the urgent need for improved digital infrastructure to support SAT deployment. Furthermore, the study emphasizes the importance of targeted education and awareness programs to bridge the digital knowledge gap among pear farmers. An especially notable finding concerns the role of the chosen tree training system in influencing SAT uptake: more than 50% of adopters utilize the palmette training system, suggesting a strong correlation between orchard design and technological readiness. Among the SAT categories, Data Analytics and Farm Management Software were the most widely adopted, a trend partly driven by attractive governmental subsidies of €30 per hectare. Importantly, all respondents who had implemented SAT (100%) reported a measurable increase in farm income, reinforcing the technologies’ impact on productivity and profitability. Foremost among the challenges encountered is the deficit in technical knowledge and training. In conclusion, this study offers a comprehensive overview of Greek pear producers’ perceptions, challenges, and emerging opportunities related to smart agriculture. Full article

Climate change and land-use intensification are speeding up the loss of ecosystem services that support human health, food security, and environmental stability. Vegetative interventions—such as urban forests in cities and cover crops in farming systems—are increasingly seen as nature-based solutions for climate adaptation. However, their benefits are often viewed separately. This review combines 20 years of research to explore how these strategies, together, improve provisioning, regulating, supporting, and cultural ecosystem services across various landscapes. Urban forests help reduce urban heat islands, improve air quality, manage stormwater, and offer cultural and health benefits. Cover crops increase soil fertility, regulate water, support nutrient cycling, and enhance crop yields, with potential for carbon sequestration and biofuel production. We identify opportunities and challenges, highlight barriers to adopting these strategies, and suggest integrated frameworks—including spatial decision-support tools, incentive programs, and education—to encourage broader use. By connecting urban and rural systems, this review underscores vegetation as a versatile tool for resilience, essential for reaching global sustainability goals. Full article

Pasture-based ruminant systems link nitrogen (N) nutrition with ecosystem N cycling. Grazing ruminants convert fibrous forages into milk and meat but excrete 65 to 80% of ingested N, creating excreta hotspots that drive ammonia volatilization, nitrate leaching, and nitrous oxide (N₂O) emissions. This review synthesizes ecological and ruminant nutrition evidence on N flows, emphasizing microbial processes, biological N₂ fixation, plant diversity, and urine patch biogeochemistry, and evaluates strategies to improve N use efficiency (NUE). We examine rumen N metabolism in relation to microbial protein synthesis, urea recycling, and dietary factors including crude protein concentration, energy supply, forage composition, and plant secondary compounds that modulate protein degradability and microbial N capture, thereby influencing N partitioning among animal products, urine, and feces, as reflected in milk and blood urea N. We also examine how grazing patterns and excreta distribution, assessed with sensor technologies, modify N flows. Evidence indicates that integrated management combining dietary manipulation, forage diversity, targeted grazing, and decision tools can increase farm-gate NUE from 20–25% to over 30% while sustaining performance. Framing these processes within the global N cycle positions pasture-based ruminant systems as critical leverage points for aligning ruminant production with environmental and climate sustainability goals. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

Multi-use of sea space is increasingly seen as a tool for efficient marine resource management, renewable energy utilisation, and sustainable food production. Multi-use Offshore Installations combine two or more production technologies on a single platform at sea. However, achieving commercial viability faces several challenges: social, technical, environmental, and economic. This research focuses on the social aspect, investigating community perceptions of a multi-use offshore installations over three years from 2019 to 2021. Our research was conducted in Reggio Calabria, Italy, where a prototype was deployed in 2021, and Islay, Scotland, suitable for a full-scale multi-use offshore installation but with no deployment, using community surveys. We used the theories of Social License to Operate and Institutional Analysis and Development to frame our analysis. Our findings indicate that coastal communities prefer wind turbines over fish farming, have low trust in public officials to regulate environmental impacts of a multi-use offshore installation, and that short-term deployment of a prototype does not significantly change opinions. We reflect on the challenges of understanding societal opinions of a multi-use offshore installation, given complex boundary conditions, and that multi-use offshore installations combine familiar technologies into a new and unknown form. We suggest that future research should explore the scale of deployment needed to crystallise community opinions, and the role of regulators in developing social license to operate for multi-use offshore installations. Full article

Campylobacter is a leading foodborne bacterial pathogen, and poultry production is a major reservoir contributing to human exposure. Reducing Campylobacter at farm level is therefore critical to limit downstream contamination. This systematic review and meta-analysis aimed to identify and quantitively summarise the current interventions and control measures applied in poultry farms to control the contamination and bird colonisation by Campylobacter. The Scopus electronic database was accessed to collect primary research articles that focused on observational studies and in vivo experiments, reporting results on Campylobacter concentrations or prevalence in both non-intervened and intervened groups. A total of 4080 studies were reviewed, from which 112 were selected and included in the meta-analysis according to predefined criteria, yielding 1467 observations. Meta-regression models were adjusted to the full data set and by intervention strategy based on the type of outcome measure (i.e., concentration and prevalence). In general terms, the results reveal that the effectiveness to reduce Campylobacter colonisation vary among interventions. A highly significant effect (p < 0.001) was observed in interventions such as organic acids, bacteriophages, plant extracts, probiotics, and organic iron complexes added to feed or drinking water; although drinking water was proven to be a more effective means of administration than feed for extracts and organic acids. In contrast, interventions such as chemical treatments, routine cleaning and disinfection, and vaccination showed both lower and more heterogeneous effects on Campylobacter loads. Vaccination effects were demonstrated to be driven by route and schedule, with intramuscular administration, longer vaccination periods and sufficient time before slaughter linked to greater reduction in Campylobacter colonisation. Probiotics, plant extracts and routine cleaning and disinfection were associated with lower Campylobacter prevalence in flocks. Meta-regression models consistently showed that the interventions were proven more effective when the sample analysed was caecal contents in comparison to faeces (p < 0.001). Overall, the findings of this meta-analysis study emphasise the application of a multi-barrier approach that combines targeted interventions with robust biosecurity and hygiene measures in order to reduce Campylobacter levels in poultry farms. Full article

Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R² of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R² of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R² across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application. Full article

The agricultural sector is predominantly male, with approximately 30% of farms in the EU operated by women. The European Union Rural Pact, the Agri-Food Pact for Skills, and the Common Agricultural Policy have catalysed an increase in agricultural 4.0 research, with the role of women emerging as a subfield of sustainable agriculture. The primary objective of this paper is to evaluate the current literature on women’s roles in smart agriculture, examining the advantages of their participation as a digitally competent workforce that could catalyse improvements in productivity and resilience in rural areas and promote women’s empowerment. A bibliometric study was conducted utilising the Scopus database to fulfil the research objective. This led to the incorporation of 253 articles into the sample. The records were examined using performance analysis and bibliographic coupling (science mapping), facilitated by Biblioshiny 5.0 and VOSviewer 1.6.20 software. The primary findings elucidate essential concepts, predominant study themes, and the temporal progression of the research domain. The identification of numerous women’s role and socio-economic constraints affecting women, which are overlooked in the creation and implementations of technology advancements. Additionally, a research agenda was developed, alongside practical implications for managers and policymakers, to aid the formulation of inclusive agriculture 4.0 projects. Full article

Background/Objectives: Bovine mastitis, a significant global concern in dairy farming, results in substantial economic losses and poses considerable risks to both animal and human health. With the increasing prevalence of antimicrobial resistance (AMR) in mastitis pathogens, the potential for resistant infections to spread from livestock to humans and the environment is becoming a critical public health issue. This narrative review summarizes the current evidence on antimicrobial resistance in pathogens causing bovine mastitis and examines it from a One Health perspective, encompassing animal, human, and environmental interfaces. Results: By examining the complex interplay among animal, human, and environmental health, we highlight key factors that drive resistance, including the overuse of antimicrobials, poor farm management, and environmental contamination. We also discuss innovative strategies, such as enhanced surveillance, pathogen-specific diagnostics, alternatives to antimicrobials, and sustainable farm practices, that can mitigate the emergence of resistance. Key knowledge gaps include a limited understanding of antimicrobial residues, resistant pathogens, and gene transmission pathways and inconsistent implementation of antimicrobial stewardship practices. Conclusions: This review emphasizes the need for a coordinated, multidisciplinary effort to reduce the burden of AMR in bovine mastitis pathogens, ensuring the continued efficacy of antimicrobials and safeguarding public health through responsible management and policy interventions. Full article

Neonatal diarrhea is a major health concern in the livestock industry, and Cryptosporidium parvum is a key pathogen responsible for this condition in calves. Milk management and gut microbiome regulation may play important roles in preventing cryptosporidiosis symptoms. This study analyzed the gut microbiota of neonatal calves fed raw milk (BM) or milk replacer (MR) using a total of 58 fecal samples collected on the same farm in 2022 and 2024. In milk replacer-fed calves, alpha diversity was significantly higher in C. parvum-positive (P) calves without diarrhea (N) (PN, n = 5) than in C. parvum-positive calves with diarrhea(D) (PD, n = 18) (Shannon p = 0.0358; Chao1 p = 0.0598). Beta diversity also differed between PN and PD (PERMANOVA, R² = 0.1763, p = 0.0092). Predicted microbial taxa such as Faecalibacterium (ALDEx2, effect size = 2.31, p = 0.00003) and Butyricicoccus (effect size = 1.31, p = 0.0041) were enriched in PN calves in MR. Comparison between milk types (BM vs. MR) further showed higher species richness in PN calves in MR than in those (n = 5) in BM(Chao1, p = 0.0088), along with significant differences in beta diversity (R² = 0.4112, p = 0.0069). These findings suggest that microbial diversity and the presence of specific taxa may be associated with reduced diarrheal symptoms. Predicted metabolic pathway profiling using a computational functional profiling approach showed the distinct metabolic pathways, including amino acid, carbohydrate, lipid, and vitamin biosynthesis, were enriched in healthier calves in both groups. These results suggest certain functional features of the microbiome could be associated with anti-inflammatory activity and short-chain fatty acid production, potentially mitigating diarrheal symptoms. Full article