Search Results (202)

Livestock production occupies a paradoxical position with respect to climate change: farm animals are highly vulnerable to heat stress, feed and water scarcity, and climate-sensitive disease, while the sector contributes an estimated 14.5% of anthropogenic greenhouse gas emissions, most of which is biogenic methane from enteric fermentation. This review evaluates the evidence for precision livestock farming (PLF)—continuous, automated, real-time monitoring of individual animals’ health, welfare, production and environmental impact—across dairy and beef cattle, small ruminants, pigs, and poultry. For mitigation, precision feeding and additive-dosing strategies have been associated with enteric methane reductions of approximately 10–25%; for adaptation, wearable and non-invasive sensors have been reported to detect heat-stress-related behavioural changes before productivity losses become apparent, and smart climate-control systems have been associated with housing energy-use reductions of roughly 5–10%. Much of this evidence derives from single-farm, small-sample or short-duration studies and should be read as indicative rather than generalisable. Adoption remains constrained by high investment costs, limited interoperability, insufficient technical support, and uneven applicability to extensive and smallholder systems. We conclude that PLF is a valuable enabling technology that, combined with genetic, nutritional, and management strategies, can strengthen the resilience and environmental sustainability of livestock systems under a changing climate. Full article

Several global challenges are converging: rising organic solid waste generation, growing food demand, and increasingly unfavorable conditions for food production, including more frequent and severe droughts, water scarcity, and limited agricultural land for expansion. Bioconverting organic waste into alternative protein sources has emerged as a promising strategy to address waste management and feed production challenges simultaneously. This study presents a comprehensive systematic literature review on protein production through the bioconversion of residual biomass using the black soldier fly (BSF, Hermetia illucens). The BSF is a highly voracious organism during its larval stage and can substantially reduce organic waste volumes while converting them into biomass rich in proteins and lipids with high nutritional value for livestock and aquaculture feed formulations. The review examines Waste-to-Protein systems from three perspectives: technical, economic, and environmental. The technical perspective focuses on production system operations and substrate properties. The economic perspective addresses profitability indicators, capital and operating costs, economies of scale, and the economic performance of incorporating insect-derived protein into animal production systems. From an environmental perspective, Life Cycle Assessment (LCA) is the predominant method for evaluating WtP-BSF systems. Among the systems assessed using LCA, 63% rely on crop-derived substrates for larval feeding. These substrates have intrinsic commercial value, and together with the environmental burdens associated with energy consumption during BSFL rearing, they may constrain the overall sustainability and profitability of WtP-BSF systems. By evaluating factors such as feed dosage, larval density, actual organic waste, and eco-efficiency metrics for livestock feed, opportunities for a circular economy could be developed in developing countries, helping to decrease dependence on landfills. Full article

Livestock manure is both an environmental burden and potential feedstock for the circular bioeconomy and sustainable biorefinery systems. This study estimated the theoretical potential of livestock manure valorisation using a balanced 50-country panel from 2000 to 2023, with illustrative scenario projections to 2050. Livestock stock data and population data were combined with species-specific coefficients to estimate manure production, standardised theoretical resource CH₄ potential, standardised theoretical resource gross methane energy potential, standardised theoretical resource nitrogen and phosphorus potential, and the recoverable-CH₄ CO₂e value. Country rankings, species contribution analysis, k-means clustering, principal component analysis and ARIMA-based scenario analysis were used to compare manure-resource indicators. Between 2000 and 2023, total estimated manure production increased from 29.25 to 33.18 Gt, while standardised theoretical resource gross methane energy potential increased from 3396.6 to 3977.8 TWh. Standardised theoretical resource nitrogen, standardised theoretical resource phosphorus and recoverable-CH₄ CO₂e value also increased, whereas mean standardised theoretical resource gross methane energy potential per capita declined. In 2023, India, Brazil, China, the USA and Pakistan had the greatest total standardised theoretical resource gross methane energy potential, while Uruguay, New Zealand, Paraguay, Ireland and Argentina had the highest per capita gross methane energy potential. Cattle dominated the estimated manure resource, contributing 76.1% of total manure production. The 2050 scenario values were derived from an exploratory ARIMA trajectory based on 24 annual observations and should be interpreted as model-dependent sensitivity outputs, not as strong long-term forecasts. Under low, medium and high illustrative adoption assumptions, scenario-adjusted 2050 values were 8.04, 5.81, and 2.28 Gt CO₂e, respectively. The study provides standardised theoretical manure-resource indicators for comparative screening, rather than country-specific feasibility estimates or implementation forecasts. Full article

Forage conservation is a strategic component of Mediterranean livestock systems, where seasonal drought, irregular rainfall, and high summer temperatures limit the availability of fresh forage. In these environments, hay and haylage are widely used as preservation strategies, yet their environmental performance remains insufficiently quantified under semi-arid conditions. It is hypothesized that the higher material and energy inputs required for haylage could be compensated for by the combined effects of biomass preservation efficiency and forage productivity, resulting in comparable or lower impacts per unit of conserved forage under Mediterranean farm conditions. To test this hypothesis, a life cycle assessment was conducted to compare hay and haylage production in a forage farm located in Sicily, Italy, considering the 2024–2025 season. The analysis adopted a cradle-to-farm-gate system boundary, and two complementary impact assessment methods, namely the CML-IA baseline method and the ReCiPe 2016 method, were applied. Environmental burdens were calculated per hectare of cultivated land and per ton of dry matter produced to capture both land-based and product-based performance. The choice of functional unit strongly influenced the interpretation of the environmental impact results. Per hectare, hay and haylage displayed comparable overall burdens, with haylage showing higher impacts in impact categories linked to plastic use and wrapping operations. Per ton of dry matter, however, haylage showed consistently lower impacts, attributed to greater biomass recovery and higher dry matter output, reflecting the combined effect of forage composition, crop productivity, and conservation efficiency. Hay systems showed greater burdens per unit of product under the evaluated farm conditions due to prolonged drying periods, repeated field operations, and higher biomass losses during harvesting and storage. The results suggest that, within the specific Mediterranean farm context analyzed, the combined effects of biomass preservation efficiency and crop productivity were important factors influencing environmental performance when impacts were expressed on an output basis. This case study contributes to current knowledge by identifying biomass recovery, rather than input minimization alone, as a key driver of sustainable forage conservation. Full article

Wearable sensors provide significant potential for continuous animal monitoring. However, the practical implementation of such technologies on livestock farms for animal monitoring raises significant challenges related to power efficiency, robustness, and reliability. In this research paper, we propose the design, implementation, and field testing of a low-energy, low-cost, multi-sensor wearable collar specifically designed for continuous monitoring of ruminants using LoRa. The proposed collar is based on a modular hardware platform that incorporates inertial sensing, temperature sensing, and wireless communication, with special attention to sensor choice, location on the body, casing, and mounting mechanism. Reduced weight, environmental protection, and long-term wearability without affecting animal behavior are the primary focus in the hardware design. Power optimization management strategies, including sleep mode and duty cycling functionality, are implemented to maximize autonomy and battery lifetime and are evaluated under realistic operating scenarios. Field deployment was conducted in a ruminant farm, where the wearable devices operated flawlessly for a long time period. Characteristic sensor data are collected, including accelerometer readings induced by animal movement, variability in received signal strength (RSSI), and animal temperature. The system demonstrates stable operation, satisfactory data completeness and consistency on multi-day basis. The knowledge acquired brings into focus the practical challenges and design issues associated with the use of wearable sensors in livestock and offers insights into designing efficient sensor collars for precision livestock farming. 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

The agricultural and livestock sectors of Latin America produce a large number of residues that could be converted into energy through bioenergy production processes. However, the bioenergy sector still faces several limitations across the region, including fragmented logistics systems, weak coordination among institutions, and limited integration of environmental, digital, and compliance-related performance indicators. This review systematically analyses residue-based bioenergy value chains in Latin America between 2015 and 2025 using the PRISMA methodology to evaluate selected peer-reviewed studies and regional reports indexed in Scopus, ScienceDirect, SpringerLink, and IEEE Xplore, and institutional repositories. The final synthesis included 37 studies and institutional contributions, which were further disaggregated into 208 country–residue observations for the regional and feedstock distribution analysis. The review identified three main research gap categories: the limited integration of collection and logistics systems, the insufficient treatment of uncertainty, circularity, and traceability within optimization models, and the weak incorporation of governance and institutional coordination into bioenergy value-chain design. The analysis includes biogas, biomethane and related residue-based systems, with attention to supply-chain optimization, policy alignment, methane mitigation metrics, and traceability requirements. Results indicate that although technologies such as biomass pretreatment, process intensification, and upgrading processes continue to improve conversion performance, most studies still focus mainly on technical feasibility and biomass potential. Less attention is given to governance constraints, uncertainty analysis, and monitoring systems capable of supporting regulatory compliance. This research introduced the Sustainable Bioenergy Chain Management Framework (SBCMF) to respond to these limitations and bring together different aspects of bioenergy management within one analytical structure. The framework combines supply-chain optimization under spatial and temporal constraints, circular economy valorisation, methane-related climate performance, and digital traceability, while also linking techno-economic system design with governance and monitoring requirements. In this way, it can help support the development of more transparent and low-carbon bioenergy systems across Latin America. Full article

One of the most important factors affecting broiler production efficiency is providing appropriate indoor microclimatic conditions, which are significantly influenced by the heating system used. This study presents the results of long-term experimental investigations conducted in two buildings equipped with different heating systems. One of the buildings was equipped with a wall heating system supported by air heaters, while the other was equipped with an underfloor heating system During the winter production cycle, the indoor air temperature in the initial period ranged from 27.2 to 33.2 °C with the underfloor heating system and from 31.9 to 40.0 °C with the conventional radiator-based system. Even greater differences were observed in floor surface temperature, which ranged from 28.0 to 31.4 °C and from 11.0 to 22.0 °C, respectively. In the building with the underfloor heating system, the relative air humidity fluctuated from 43.1% to 66.8%, while in the building without an underfloor heating system, it ranged from 20.3% to 58.6%. During the first week of the winter production cycle, the use of underfloor heating reduced broiler mortality by 77.3%. These findings provide a basis for further research on heat and moisture transfer processes and can support the development and modernization of energy-efficient heating systems in livestock buildings. The results contribute to current efforts toward the sustainable development of poultry production by improving environmental control and enhancing energy efficiency. Full article

Highland pastures in Montenegro, known as katuns, are seasonal settlements without grid power or network coverage and which are located where conventional monitoring is unfeasible. This study presents a solar-powered, off-grid system for livestock and environmental monitoring. It integrates, into a single portable unit, a solar power station, an edge-AI computer, a camera, environmental sensors, a LoRaWAN gateway, and a cellular router for backhaul. All parts are pre-wired in a modular enclosure, deployable by one operator in under 30 min. Data are fed to the agroNET farm-management platform and a purpose-built mobile web application; livestock detection runs on-device using a model from our earlier work. The system was evaluated at three sites, including a highland katun near Žabljak (~1450 m), under a two-phase energy-measurement protocol. During field logging it drew ~75 W on average against ~125 W solar input—a measured surplus that is used to recharge the battery—with a daily monitoring load of ~1560 Wh. The four-panel array’s nameplate potential in summer is an estimated ~3700 Wh/day, indicating substantial headroom relative to the measured load. At 80% depth of discharge the battery gives ~20 h autonomy, and the detection pipeline ran continuously, processing ~10,000 frames at under 3 s latency. The results demonstrate the feasibility of off-grid precision livestock farming, reaching TRL 6. Full article

High-altitude environments, characterized by hypoxia, intense ultraviolet radiation, and low temperatures, pose major challenges to livestock survival. In recent years, researchers have gradually uncovered adaptive mechanisms in livestock across different altitudes using whole-genome resequencing. Previous studies of goat altitude adaptation have been limited by small breed numbers and low sequencing depth, hindering comprehensive exploration of adaptive mechanisms across different altitudes. This study analyzed whole-genome resequencing data from 151 individuals across 17 goat breeds representing three distinct altitude gradients (high, middle, and low). Using both SNPs and structural variations (SVs), we characterized population relationships, gene flow, and the SV landscape, including QTL–SV associations and transposable element interactions. Selective sweep analyses using F_ST, θπ ratio, XP-CLR, XP-EHH, and LFMM identified several candidate genes associated with altitude adaptation, including ABCC4, RPS6, DSG4, and LY9, which were significantly enriched in pathways related to hypoxia response, oxidative stress, energy metabolism, angiogenesis, and nervous system regulation. Notably, ABCC4 showed ABCC4 showed recurrent candidate selection signals in both SNP and SV analyses, suggesting its potential involvement in altitude adaptation. These findings provide multi-level genomic evidence for goat adaptation to high-altitude stress and provide important insights into the adaptive evolution of goats. Full article

This exploratory pilot study analyzes the organizational and environmental determinants associated with stated intention to adopt biogas-solar microgrids within a circular bioeconomy framework. A quantitative, applied, cross-sectional design was used with 71 valid individual responses from participants linked to productive, agro-industrial, livestock, energy, and waste management organizations or projects, selected through nonprobabilistic convenience sampling. The analysis does not measure actual investment, implementation, or use; therefore, the results refer only to declared adoption intention and should not be generalized beyond the sample. The questionnaire measured perceived benefits, barriers, institutional conditions, financial feasibility, environmental value, organizational capabilities, and adoption intention. Content validity was supported by expert judgment, and psychometric reliability was assessed using Cronbach’s alpha and McDonald’s omega. Predictive modeling compared supervised classification, regression, and unsupervised segmentation techniques using train-test validation, cross-validation, and interpretability analyses. ExtraTrees achieved the best exploratory classification performance, with a test ROC-AUC of 0.889, while RandomForestRegressor showed the best regression performance; however, these values should be interpreted as sample-specific evidence rather than as a validated predictive tool. Organizational capabilities and environmental criteria emerged as the most influential predictors, and K-Means suggested two tentative readiness profiles with weak separation. The findings suggest that stated adoption intention is associated with a systemic configuration of organizational maturity, environmental legitimacy, financial feasibility, and institutional support, providing preliminary evidence for future larger sample validation and for decision-support discussions in sustainable energy transitions. Full article

DC series arc faults (SAFs) are a significant safety hazard on the DC side of photovoltaic (PV) systems, with current signals characterized by strong randomness, obvious non-stationarity, and concealed fault features, posing challenges for rapid and accurate detection. With the development of application models such as agricultural PV integration, photovoltaic greenhouses, solar-powered irrigation, and livestock energy supply, the demand for the safe operation of photovoltaic systems in agricultural production scenarios is becoming increasingly prominent. To address the difficulty in fully characterizing the multi-scale dynamic features and local amplitude disturbances of DC SAF signals, this paper proposes a SAF detection method based on multi-scale generalized amplitude-aware permutation entropy (MS-GAAPE). The method extracts MS-GAAPE from arc current signals at various scales using sliding window-based generalized coarse-graining, which preserves temporal sequence information while improving the characterization of local amplitude variations. Particle swarm optimization (PSO) is applied to optimize these multi-scale features, strengthening fault-related information and reducing interference. The optimized features are then processed by a support vector machine (SVM) for SAF detection. The dataset used contains 50,000 samples covering transient conditions such as voltage fluctuations and is divided into a training set and an independent test set in a 70% to 30% ratio. The training set is utilized for feature parameter determination, feature weight optimization, and classification model construction, while the independent test set is reserved solely for final performance evaluation. Experimental results demonstrate that the proposed method achieves excellent detection performance under various operating conditions and load levels, with an accuracy of 99.32% and a total detection time of 103.62 ms, meeting the requirements of the UL1699B standard, thus showcasing strong real-time detection capability and potential for embedded implementation. Full article

Understanding how economic growth (GDP), livestock production (LPI), agriculture, forestry and fishing (AFF), renewable energy consumption (REN), and urbanization (URB) influence carbon emissions is essential for designing effective climate policies in rapidly developing economies such as India. This study examines the long-run and short-run effects of these factors on CO₂ emissions in India during 1990–2024 using the Dual Adjustment Approach (DAA) and the Autoregressive Distributed Lag (ARDL) model. The DAA framework decomposes variables into permanent (trend) and transitory (cyclical) components, allowing a simultaneous assessment of long-run equilibrium and short-run dynamics. Both DAA and ARDL models indicate that GDP and LPI increase CO₂ emissions in the long run, while REN reduces them. AFF exerts a weak effect on emissions compared with the other determinants. URB is associated with lower long-run emissions, supporting the urban efficiency hypothesis, but this depends on sustained infrastructure investment and policy support, rather than automatic results of current urbanization levels. The transitory component analysis shows that short-run fluctuations in GDP increase emissions, while the effects of the remaining variables are driven by long-run structural changes. The findings highlight the importance of expanding renewable energy deployment, improving environmental efficiency in agricultural and livestock production systems, and promoting sustainable urban development to reduce carbon emissions in the case of India. Full article

This paper presents industry-specific time series for GHG emissions, land use, and complete protein production in Canada from 1990 to 2023. This analysis relies on an updated version of the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES). Whereas ULICEES was developed to compare Canada’s livestock industries based on 2001 and 2006 Agricultural Census data, ULICEES-T relies mainly on national agricultural Greenhouse Gas (GHG) emissions reported by Environment and Climate Change Canada (ECCC) to compare all Canadian agronomic land uses within the farm gate. The national CH₄ and N₂O emissions from all livestock are re-aggregated into livestock-specific crop complexes. Fossil CO₂ emissions are simulated using the Farm Fieldwork and Fossil Fuel Energy and Emissions (F4E2) model. Between 2005 and 2020, crop areas that supported livestock decreased from 14 to 10 Mha, whereas in Western Canada, the areas growing non-livestock feed crops increased from 20 to 25 Mha. Over the same 15-year interval, GHG emissions from crop areas not supporting livestock increased from 18 to 27 MtCO₂e, while GHG emissions from livestock decreased from 51 MtCO₂e in 2005 to 42 MtCO₂e in 2020, a drop of 18%. Meanwhile, protein from all Canadian livestock decreased by only 12% over that interval. Reducing N₂O emissions associated with N fertilizer and reduced beef consumption are the two best options for achieving a lower agricultural carbon footprint in Canada. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article