Search Results (28)

Agriculture is responsible for nearly one-quarter of global greenhouse gas (GHG) emissions, with livestock and poultry systems contributing significantly through methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂). Achieving net-zero agriculture demands tools that not only quantify emissions but also guide management decisions and foster behavioral change. The Cool Farm Tool (CFT)—a science-based calculator for farm-level carbon footprints, water use, and biodiversity—has been widely adopted across Europe and parts of the United States. Yet, despite its proven potential, no Canadian studies have tested or adapted CFT, leaving a major gap in the country’s progress toward climate-smart farming. This paper addresses that gap by presenting the first surveys of poultry and dairy producers in Atlantic Canada as a foundation for tailoring and localizing CFT. Our mixed-methods surveys examined farm practices, feed, manure, energy use, waste management, sustainability perceptions, and openness to digital tools. Results on 23 responses (20 for poultry, 3 for dairy) revealed limited awareness but moderate interest in emission tracking: dairy farmers, already accustomed to digital systems such as robotic milking and herd software, were receptive and confident about adopting CFT. Poultry farmers, by contrast, voiced greater concerns over cost, complexity, and uncertain benefits, signaling higher adoption barriers in this sector. These findings highlight both the opportunity and the challenge: while dairy farms appear ready for rapid uptake, poultry requires stronger incentives, clearer value demonstration, and sector-specific customization. We conclude that adapting CFT with regionally relevant data, AI-driven decision support, and supportive policy frameworks could make it a cornerstone for achieving net-zero agriculture in Atlantic Canada. Full article

Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective optimisation of milk yield and nitrogen-related emissions. Using the CowNflow dataset, which integrates individual-level nitrogen balance, feeding, and production data collected under controlled experimental conditions, the framework combines data analytics, feature selection, predictive modelling, and SHAP-based explainability to support decision-making in dairy production. The stacking ensemble model achieved the best predictive performance ($R^2$ = 0.85 for milk yield and $R^2$ = 0.794 for milk urea), providing reliable surrogates for downstream optimisation. Predicted milk urea values were further transformed using empirical equations to estimate urinary urea nitrogen (UUN) and ammonia (NH₃) emissions, offering an indirect yet practical approach to assess environmental sustainability. Furthermore, the predictive models are integrated into a digital twin platform that provides a dynamic, real-time simulation environment for scenario testing, continuous optimisation, and data-driven decision support, effectively bridging data analytics with sustainable dairy system management. This research demonstrates how explainable AI, machine learning, and digital twin engineering can jointly drive sustainable dairy production, offering actionable insights for improving productivity while minimising environmental impact. Full article

Paratuberculosis or Johne’s disease is caused by Mycobacterium avium subsp. paratuberculosis (MAP). The disease is characterized by a chronic and incurable enteritis in ruminants and it is responsible for significant economic losses, also raising concerns about food safety and animal welfare. Effective control is hindered by diagnostic limitations, long incubation periods, and the environmental resistance of the pathogen. This study aimed to reduce the apparent prevalence of paratuberculosis in a single intensive dairy herd through an integrated approach that combines diagnostics and management strategies. All cows over 24 months of age were tested using both fecal PCR and ELISA serology. Digital PCR (dPCR) was used to quantify MAP shedding in fecal-positive animals, enabling prioritization for removal based on environmental contamination risk. Integrating diagnostic tools allowed the precise identification and quantification of high-risk animals. Meanwhile, structural improvements and biosecurity measures were implemented on the farm. Preliminary outcomes suggest a marked reduction in herd-level MAP prevalence, lowering the seroprevalence from 7.6% to 4.5% and the fecal PCR prevalence from 6.5% to 2.8%. This case highlights the effectiveness of combining laboratory testing (serology and molecular diagnostics) and targeted changes in farm management to control paratuberculosis in high-density dairy systems. Full article

Food safety management has evolved with the Hazard Analysis and Critical Control Point (HACCP) system serving as a global benchmark. However, conventional HACCP does not explicitly address environmental sustainability, leading to challenges such as excessive water use, chemical discharge, and energy inefficiency. Green HACCP extends traditional HACCP by integrating Environmental Respect Practices (ERPs) to fill this critical gap between food safety and sustainability. This study is presented as a conceptual paper based on a structured literature review combined with illustrative industry applications. It analyzes the principles, implementation challenges, and economic viability of Green HACCP, contrasting it with conventional systems. Evidence from recent reports and industry examples shows measurable benefits: water consumption reductions of 20–25%, energy savings of 10–15%, and improved compliance readiness through digital monitoring technologies. It explores how digital technologies—IoT for real-time monitoring, AI for predictive optimization, and blockchain for traceability—enhance efficiency and sustainability. By aligning HACCP with sustainability goals and the United Nations Sustainable Development Goals (SDGs), this paper provides academic contributions including a clarified conceptual framework, quantified advantages, and policy recommendations to support the integration of Green HACCP into global food safety systems. Industry applications from dairy, seafood, and bakery sectors illustrate practical benefits, including waste reduction and improved compliance. This study concludes with policy recommendations to integrate Green HACCP into global food safety frameworks, supporting broader sustainability goals. Overall, Green HACCP demonstrates a cost-effective, scalable, and environmentally responsible model for future food production. Full article

Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010–2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework—spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity—to provide a coherent comparative lens across diverse DT implementations. Hybrid edge–cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15–20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management—an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming. Full article

Food loss and waste from the European Union’s dairy supply chain, particularly in the management of fresh milk, imposes significant environmental burdens. This study demonstrates that implementing Radio Frequency Identification (RFID)-enabled digital decision-support tools can substantially reduce these impacts across the region. A cradle-to-grave life cycle assessment (LCA) was used to quantify both the additional environmental burdens from RFID (tag production, usage, and disposal) and the avoided burdens due to reduced milk losses in the farm, processing, and distribution stages. Within the EU’s fresh milk supply chain, the implementation of digital tools could result in annual net reductions of up to 80,000 tonnes of CO₂-equivalent greenhouse gas emissions, 81,083 tonnes of PM_2.5-equivalent particulate matter, 84,326 tonnes of land use–related carbon deficit, and 80,000 cubic meters of freshwater-equivalent consumption. Spatial analysis indicates that regions with historically high spoilage rates, particularly in Southern and Eastern Europe, see the greatest benefits from RFID enabled digital-decision support tools. These environmental savings are most pronounced during the peak months of milk production. Overall, the study demonstrates that despite the environmental footprint of RFID systems, their integration into the EU’S dairy supply chain enhances transparency, reduces waste, and improves resource efficiency—supporting their strategic value. Full article

Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article

Wearable collar technologies have become integral to the advancement of precision livestock farming, revolutionizing how dairy cattle are monitored in terms of their behaviour, health status, and productivity. These devices leverage cutting-edge sensors, including accelerometers, RFID tags, GPS receivers, microphones, gyroscopes, and magnetometers, to provide non-invasive, real-time insights that enhance animal welfare, optimize resource use, and support decision-making processes in livestock management. This systematized review focuses on analyzing the sensors integrated into collar-based systems, detailing their functionalities and applications. However, significant challenges remain, including the high energy consumption of some sensors, the need for frequent recharging, and limited parameter coverage by individual devices. Future developments must focus on integrating multiple sensor types into unified systems to provide comprehensive data on animal behaviour, health, and environmental interactions. Additionally, advancements in energy-efficient designs, longer battery life, and cost-reduction strategies are essential to enhance the practicality and accessibility of these technologies. By addressing these challenges, wearable collar systems can play a pivotal role in promoting sustainable, efficient, and responsible livestock farming, aligning with global goals for environmental and economic sustainability. This paper underscores the transformative potential of wearable collar technologies in reshaping the livestock industry and driving the adoption of innovative farming practices worldwide. Full article

Efficient cattle healthcare management is vital for ensuring productivity and welfare in dairy production, yet traditional record-keeping methods often lack transparency, security, and efficiency, leading to challenges in livestock product quality and healthcare. This study introduces a novel framework leveraging Zero Knowledge (ZK)-Rollups-enhanced Layer 2 blockchain and Non-Fungible Tokens (NFTs) to address these issues. NFTs serve as secure digital certificates for individual cattle health records, ensuring transparency and traceability. ZK-Rollups on the Layer 2 blockchain enhance scalability, privacy, and cost-efficiency, while smart contracts automate key processes such as veterinary scheduling, medication delivery, and insurance claims, minimizing administrative overhead. Performance evaluations reveal significant advancements, with transaction delays of 4.1 ms, throughput of 249.8 TPS, gas costs reduced to 26,499.76 Gwei, and a time-to-finality of 1.1 ms, achieved through ZK-SNARKs (ZK-Succinct Non-Interactive Arguments of Knowledge) integration. These results demonstrate the system’s potential to revolutionize cattle healthcare management by combining transparency, security, and operational efficiency. Full article

The aim of this research was to evaluate the effects of postpartum day and parity season on the lactoferrin (LF), immunoglobulin G (IgG), and chemical composition of Murciano–Granadina goat colostrum during the first 96 h after kidding, and the use of the Brix refractometer to estimate IgG content. A herd of 3500 intensively managed Murciano–Granadina dairy goats (45–50 kg body weight) was used. Colostrum samples were collected from days 1 to 4 postpartum in the winter, spring, summer, and autumn. The colostrum composition was assessed using an automated infrared method; the LF and IgG concentrations were measured using an ELISA, and for the Brix percentage, we used a digital refractometer. Colostrum taken on the first postpartum day showed the highest concentrations of LF, IgG, proteins and non-fat solids (NFSs). As the postpartum days progressed, a rapid decrease in the LF, IgG, protein, and NFS contents and the Brix value was observed. In contrast, the lactose content increased steadily until the fourth postpartum day (p < 0.001). The season influenced milk yield, LF, IgG, protein, fat, and somatic cell content (p < 0.05). LF contents were significantly higher in the spring season, IgG contents were higher in autumn colostrum, and fat components were higher in the winter season. The colostrum Brix value showed a positive correlation with the ELISA colostrum LF (r = 0.716, p < 0.001) and IgG (r = 0.894, p < 0.001) determination; a 20 mg IgG/mL colostrum concentration corresponded to 18 °Brix. Our results corroborate the importance of feeding colostrum to newborns on the first day after birth, not only because of its high level of IgG but also because of its greater presence of the other bioactive protein compounds such as lactoferrin. Full article

The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are “Computers and Electronics in Agriculture” and “Journal of Dairy Science”. It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production. Full article

This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts. Full article

The first aim of this study was to calculate the prevalence of painful lesions of the digits (“alarm” lesions; ALs) in Swiss dairy herds and cow–calf operations over a three-year study period. The following ALs were included in the calculation: the M2 stage of digital dermatitis (DD M2), ulcers (U), white line fissures (WLF) of moderate and high severity, white line abscesses (WLA), interdigital phlegmon (IP) and swelling of the coronet and/or bulb (SW). Between February 2020 and February 2023, digit disorders were electronically recorded during routine trimmings by 40 specially trained hoof trimmers on Swiss cattle farms participating in the national claw health programme. The data set used consisted of over 35,000 observations from almost 25,000 cows from 702 herds. While at the herd-level, the predominant AL documented in 2022 was U with 50.3% followed by WLF with 38.1%, at the cow-level, in 2022, it was DD M2 with 5.4% followed by U with 3.7%. During the study period, within-herd prevalences of ALs ranged from 0.0% to a maximum of 66.1% in 2020. The second aim of this study was to determine herd- and cow-level risk factors associated with digital dermatitis (DD), U and white line disease (WL) in dairy cows using data from 2022. While for DD, analysed herd-level factors appeared to have a greater effect on the probability of its occurrence, the presence of U and WL was mainly associated with the analysed cow-level factors. The risk for DD increased with a higher herd trimming frequency. Herds kept in tie stalls had a lower risk for DD and WL and a higher risk for U compared to herds kept in loose housing systems. Herds with predominantly Holstein Friesian cows as well as Holstein Friesian cows had a higher risk for the occurrence of DD compared to herds and cows of other breeds. With increasing parity, cows had a higher risk of developing U and WL, whereas for DD, parity was negatively associated with prevalence. Cows trimmed during the grazing period had a higher risk of U and WL than cows trimmed during the housing period. These findings may contribute to improve management measures affecting the health of the digits in farms with structures similar to those evaluated in the current study, such as small herds with frequent access to pasture. Further research is warranted to demonstrate how measures addressing the current results combined with those of individual herd risk assessments might contribute to an improvement in the health of the digits in the respective dairy herds. Full article

Pasture management is an important topic for dairy farms with grazing systems. Herbage mass (HM) is a key measure, and estimations of HM content in pastures allow for informed decisions in pasture management. A common method of estimating the HM content in pastures requires manually collected grass samples, which are subjected to laboratory analysis to determine the dry matter (DM) content. However, in recent years, new methods have emerged that generate digital data and aim to expedite, facilitate and improve the measurement of HM. This study aimed to evaluate the accuracy of a rising plate meter (RPM) tool in a practical setting to estimate HM in Austrian pastures. With this study, we also attempted to answer whether the tool is ready for use by farmers with its default settings. This study was conducted on the teaching and research farm of the University of Veterinary Medicine in Vienna, Austria. Data were collected from May to October 2021 in five different pastures. To evaluate the accuracy of the RPM tool, grass samples were collected and dried in an oven to extract their DM and calculate the HM. The HM obtained from the grass samples was used as the gold standard for this study. In total, 3796 RPM measurements and 203 grass samples yielding 49 measurement points were used for the evaluation of the RPM tool. Despite the differences in pasture composition, the averaged HM from the RPM tool showed a strong correlation with the gold standard (R² = 0.73, r_p = 0.86, RMSE = 517.86, CV = 33.67%). However, the results may not be good enough to justify the use of the tool, because simulations in economic studies suggest that the error of prediction should be lower than 15%. Furthermore, in some pastures, the RPM obtained poor results, indicating an additional need for pasture-specific calibrations, which complicates the use of the RPM tool. Full article

This technical note critically evaluates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the swiftly evolving dairy livestock export industry. We focus on the novel application of the Internet of Things (IoT) in long-distance livestock transportation, particularly in livestock enumeration and identification for precise traceability. Technological advancements in identifying behavioral patterns in ‘shy feeder’ cows and real-time weight monitoring enhance the accuracy of long-haul livestock transportation. These innovations offer benefits such as improved animal welfare standards, reduced supply chain inaccuracies, and increased operational productivity, expanding market access and enhancing global competitiveness. However, these technologies present challenges, including individual animal customization, economic analysis, data security, privacy, technological adaptability, training, stakeholder engagement, and sustainability concerns. These challenges intertwine with broader ethical considerations around animal treatment, data misuse, and the environmental impacts. By providing a strategic framework for successful technology integration, we emphasize the importance of continuous adaptation and learning. This note underscores the potential of AI, IoT, and sensor technologies to shape the future of the dairy livestock export industry, contributing to a more sustainable and efficient global dairy sector. Full article