Search Results (41)

Rumination time is considered a sensitive behavioral indicator of physiological and metabolic status in dairy cows, yet its relationships with biochemical and milk quality parameters under commercial robotic milking conditions remain insufficiently described. This study combined precision monitoring technologies, serum biochemical profiling, and in-line milk analysis to evaluate physiological differences among early-lactation Holstein cows according to rumination time. A total of 88 cows were classified into three rumination time categories (>527, 412–527, and <412 min/day). Milk production traits, milk quality indicators, and blood biochemical parameters were compared among groups, and univariable regression analysis was performed to identify variables associated with rumination time. Cows in the low rumination group showed higher milk temperature, electrical conductivity, and somatic cell count, as well as lower milk protein percentage. They also showed higher concentrations of total protein, urea, gamma-glutamyl transferase, and lactate dehydrogenase, while triglyceride concentrations were lower. Regression analysis identified electrical milk conductivity, creatinine, magnesium, potassium, and chloride as variables associated with rumination time. These findings indicate that reduced rumination time is associated with changes in milk quality and biochemical parameters in early-lactation dairy cows, suggesting that rumination monitoring may provide useful information for identifying cows experiencing physiological and metabolic challenges under commercial farming conditions. Full article

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

Automated feeding robots (AFR) are increasingly being used on North American dairy farms to reduce dependency on human labor for feeding. These systems mix, deliver, and push up feed to cows at any frequency or interval desired, allowing for more frequent feed delivery than conventional feeding systems (CFS). This observational study investigated differences in ration consistency, milk components, milk fatty acid profile, and cow behavior between herds using AFR and those using CFS. Sixteen commercial dairies with automated milking systems (AMS) in the upper Midwest United States were paired based on herd size and location into eight blocks each consisting of one CFS and one AFR herd. Feed bunk samples were collected at four equally spaced time points for 3 consecutive d and analyzed for coefficient of variation (CV) of nutrient composition and particle size distribution. Bulk tank milk samples were collected 1 ×/d for 3 d and analyzed for fat, protein, milk urea nitrogen (MUN), lactose, and milk fatty acid (FA) profile. Daily AMS visit intervals, milk yield and composition, and rumination time data were collected from AMS software. A linear mixed model tested fixed effects of feeding system, block, and the random effect of day nested within block. The CV of feed bunk DM, ADF, NDF, and lignin was lower in AFR. Bulk tank milk fat, protein, and MUN were not different between AFR or CFS. AFR had a greater proportion of de novo synthesized FA, but no difference in preformed or mixed FA. Herds with AFR had a shorter AMS visit interval with more AMS refusals per day than CFS. Results imply that AFR may be associated with lower daily variation in fiber concentration at the feed bunk, increased mammary de novo fatty acid synthesis, and increased frequency of cow visits to the AMS compared to conventional PMR feeding. Full article

This study, conducted on a commercial dairy farm using a robotic milking system in Victoria, Australia, examined the effects of Polygain™ (The Product Makers Australia), a polyphenol-rich sugarcane feed material (PRSFM), on CH₄ emissions in grazing dairy cattle using an inverse-dispersion model (IDM) combined with open-path laser techniques. Thirty lactating Holstein Friesian cows (aged 2–5 years with an average body weight of 663 kg and average daily milk production of 28.9 kg) were divided into two dietary treatment groups of fifteen cows each. Before the measurement, the PRSFM (0.25%) was supplemented for 3 weeks as an adaptation period and mixed with pellet feed for the treatment group. Over the 2-week measurement period, CH₄ production (MP) was 495 ± 12 and 377 ± 12 (mean ± standard error) g CH₄/animal/day for the control and treatment groups, respectively. Methane intensity (MI) was 17.04 and 13.01 g CH₄/animal/kg milk/day in the control and treatment groups, respectively. On average, Polygain supplementation reduced MP and MI by 24%. This potential CH₄ reduction extrapolated across Australia contributes to a 2.63% reduction in national agricultural emissions. This study underscores the potential of Polygain for CH₄ mitigation in dairy cattle. Full article

The current Bangladeshi dairy sector faces many problems related to sustainability indicators from economic, social, and environmental perspectives. In this circumstance, they must combine cutting-edge innovation to overcome growing sustainability concerns and technical revolutions to become smart farms. This study analyzes how dairy farmers might use cutting-edge technologies in their dairy sub-processes to determine the benefits of achieving additional productivity and efficiency. This paper examines precision livestock farming, information analytics, and alternative energy sources to reduce environmental hazards and increase resource efficiency. Using cutting-edge technologies like artificial intelligence (AI), machine learning (ML), robotics (RPA), Internet of Things (IoT), data analytics, system dynamics, and simulation modeling can assist the farmers in improving the results. Analyzing developing country case studies and best practices reveals crucial answers for reconciling sustainability stewardship and operational efficiency. The system dynamics method builds a simulation model and finds the projected results before implementing it in real life. The findings provide considerable waste reduction and productivity gains through technological deployments. The simulation model creates two scenarios of ‘current’ and ‘technology-adopted’ processes to examine the transformational benefits of sustainable practices. A case study method was adopted for this technology deployment to organize a comprehensive strategy that blends technology and sustainability. This study ends with recommendations for dairy farmers and policymakers to create a resilient and environmentally friendly dairy operation to secure the dairy sector’s long-term viability in transforming technologies. Future farms can follow the practical, technical, and policy, as well as recommendations to improve their processes, such as smart farm concepts available in academia and dairy-developed countries. Full article

The dairy supply chain encompasses all stages involved in the production, processing, distribution, and delivery of dairy products from farms to end consumers. Artificial intelligence (AI) refers to the use of advanced technologies to optimize processes and make informed decisions. Using the PRISMA methodology, this research analyzes AI technologies applied in the dairy supply chain, their impact on process optimization, the factors facilitating or hindering their adoption, and their potential to enhance sustainability and operational efficiency. The findings show that artificial intelligence (AI) is transforming dairy supply chain management through technologies such as artificial neural networks, deep learning, IoT sensors, and blockchain. These tools enable real-time planning and decision-making optimization, improve product quality and safety, and ensure traceability. The use of machine learning algorithms, such as Tabu Search, ACO, and SARIMA, is highlighted for predicting production, managing inventories, and optimizing logistics. Additionally, AI fosters sustainability by reducing environmental impact through more responsible farming practices and process automation, such as robotic milking. However, its adoption faces barriers such as high costs, lack of infrastructure, and technical training, particularly in small businesses. Despite these challenges, AI drives operational efficiency, strengthens food safety, and supports the transition toward a more sustainable and resilient supply chain. It is important to note that the study has limitations in analyzing long-term impacts, stakeholder resistance, and the lack of comparative studies on the effectiveness of different AI approaches. Full article

The Korean agricultural sector faces increasing challenges such as an aging population, labor shortages, and the liberalization of agricultural markets. To overcome these challenges, the Korean government has striven to enhance the competitiveness of agriculture by introducing AI-based technologies to the agricultural sector, labeling this as smart farming. This study estimates farm-level benefits of adopting smart farming technologies, robotic milking systems, in Korean dairy farms. The benefits are estimated by comparing the productivity (i.e., the savings of labor input, increased calf production, and increased milk production) of adopting and non-adopting farms. Our study uses the propensity score matching method to address potential problems from confounding factors, sample selection bias, and the small number of adopters. Our results show that farms that adopted robotic milking systems produced 0.10 to 0.11 more calves per year than farms that did not adopt the system. The adopters also increased milk production by 2.44 kg to 2.88 kg per head/day, while reducing labor input by 0.15 to 0.30 per head/week. However, the reduced labor input was not statistically significant. When the analysis was extended to regard the farm characteristics, the labor input became significant from small and family-run farms. We also found that the increase in the number of calves produced per head was statically significant from small farms, family-run farms, and farms with successors. The increased milk production per head was statistically significant from large farms, farms employing hired workers, and farms with successors. Our findings suggest that the Korean government continue promoting smart farming technologies such as the robotic milking system to increase the adoption rate. The findings can also provide useful information about target markets of this technology, which can be used to increase the adoption rate and ultimately enhance the sustainability and competitiveness of the Korean dairy industry. Full article

The objective of this study was to evaluate the effects of breed, lactation number, and lameness on lying time, milk yield, milk urea nitrogen concentration (MUN), progesterone concentration (P₄), and the calving-to-conception interval (CCI) of lactating dairy cows in Central Texas. A total of 84 lactating dairy cows (Holsteins, Jerseys, and crossbreeds) from a commercial dairy farm in Central Texas were randomly selected and enrolled in this study from October 2023 to February 2024. Cows (60 ± 7 DIM) were enrolled in cohorts weekly for five weeks and were randomly fitted with an IceQube pedometer (IceRobotics, Edinburgh, UK) to track lying time. Lameness and body condition scores (BCS) were recorded, and blood samples were collected once a week. Parameters of reproductive performance included insemination rate, conception rate, pregnancy rate, and the CCI. Monthly dairy herd improvement association (DHIA) testing included milk yield and MUN concentrations. Breed and lactation number had a significant effect on milk yield, MUN concentration, lying time, BCS, and lameness (p < 0.001). Lactation number had a significant effect on P₄ concentrations (p < 0.001). There was a positive correlation between lameness and milk yield (p = 0.014) and a negative correlation between lameness and MUN concentrations (p = 0.038). Full article

Practical experience demonstrates that the development of agriculture is following the path of automating and robotizing operational processes. The operation of feed pushing in the feeding alley is an integral part of the feeding process and significantly impacts dairy cattle productivity. The aim of this research is to develop an algorithm for automatic positioning and a mobile remote-control system for a wheeled robot on a dairy farm. The kinematic and dynamic motion characteristics of the wheeled robot were obtained using software that allows simulation of physical processes in an artificial environment. The mobile application was developed using Swift tools, with the preliminary visualization of interfaces and graphic design. The system uses technical vision based on RGB cameras and programmed color filters and is responsible for the automatic positioning of the feed-pusher robot. This system made it possible to eliminate the inductive sensors from the system and suspend the labor effort required for assembling the contour wire of the feed alley. By assessing the interaction between the mobile app and the feed pusher via the base station connected to the Internet and located on the farm, the efficiency and accuracy of the feedback was measured. Furthermore, remote changes in the operating regime of the robot (start date) were proven to be achievable, and the productiveness of the food supplement dispenser also became manageable. Full article

Dairy cattle predicted by genomic breeding values to be heat tolerant are known to have less milk production decline and lower core body temperature increases in response to elevated temperatures. In a study conducted at the University of Melbourne’s Dookie Robotic Dairy Farm during summer, we identified the 20 most heat-susceptible and heat-tolerant cows in a herd of 150 Holstein Friesian lactating cows based on their phenotypic responses (changes in respiration rate, surface body temperature, panting score, and milk production). Hair samples were collected from the tip of the cows’ tails following standard genotyping protocols. The results indicated variation in feed saved and HT genomic estimated breeding values (GEBVs) (p ≤ 0.05) across age, indicating a potential for their selection. As expected, the thermotolerant group had higher GEBVs for HT and feed saved but lower values for milk production. In general, younger cows had superior GEBVs for the Balanced Performance Index (BPI) and Australian Selection Index (ASI), whilst older cows were superior in fertility, feed saved (FS), and HT. This study demonstrated highly significant (p ≤ 0.001) negative correlations (−0.28 to −0.74) between HT and GEBVs for current Australian dairy cattle selection indices (BPI, ASI, HWI) and significant (p ≤ 0.05) positive correlations between HT and GEBVs for traits like FS (0.45) and fertility (0.25). Genomic selection for HT will help improve cow efficiency and sustainability of dairy production under hot summer conditions. However, a more extensive study involving more lactating cows across multiple farms is recommended to confirm the associations between the phenotypic predictors of HT and GEBVs. Full article

The global recent development trend in dairy farming emphasizes the automation and robotization of milk production. The rapid development rate of dairy farming requires new technologies to increase the economic efficiency and improve production. The research goal was to increase the milk production efficiency by introducing the technology to automatically assess the fatness of a dairy herd in 0.25-point step on a 5-point scale. Experimental data were collected on the 3D ToF camera O3D 303 installed in a walk-through machine on robotic free-stall farms in the period from August 2020 to November 2022. The authors collected data on 182 animals and processed 546 images. All animals were between 450 and 700 kg in weight. Based on the regression analysis, they developed software to find and identify the main five regions of interest: the spinous processes of the lumbar spine and back; the transverse processes of the lumbar spine and the gluteal fossa area; the malar and sciatic tuberosities; the tail base; and the vulva and anus region. The adequacy of the proposed technology was verified by means of a parallel expert survey. The developed technology was tested on 3 farms with a total of 1810 cows and is helpful for the non-contact evaluation of the fatness of a dairy herd within the herd’s life cycle. The developed method can be used to evaluate the tail base area with 100% accuracy. The hungry hole can be determined with a 98.9% probability; the vulva and anus area—with a 95.10% probability. Protruding vertebrae—namely, spinous processes and transverse processes—were evaluated with a 52.20% and 51.10% probability. The system’s overall accuracy was assessed as 93.4%, which was a positive result. Animals in the condition of 2.5 to 3.5 at 5–6 months were considered healthy. The developed system makes it possible to divide the animals into three groups, confirming their physiological status: normal range body condition, exhaustion, and obesity. By means of a correlation dependence equal to R = 0.849 (Pearson method), the authors revealed that animals of the same breed and in the same lactation range have a linear dependence of weight-to-fatness score. They have developed an algorithm for automated assessment of the fatness of animals with further staging of their physiological state. The economic effect of implementing the proposed system has been demonstrated. The effect of increasing the production efficiency of a dairy farm by introducing the technology of automatic evaluation of the fatness of a dairy herd with a 0.25-point step on a 5-point scale had been achieved. The overall accuracy of the system was estimated at 93.4%. Full article

Automatic milking systems (AMSs) for medium and large dairy farms in China require manual assistance to attach the teat cup, which greatly affects the milking efficiency and labor costs. In this regard, it is necessary to realize the automatic completion of cow teat attachment work. To address this issue, the authors developed a teat cup attachment robot for an AMS based on the theory of the solution of inventive problems (TRIZ). Specifically, we developed an enhanced algorithm for teat detection and designed a six-degree-of-freedom manipulator with integrated drive control. The design parameters were simulated and analyzed to validate their efficacy, while the rationality of the manipulator’s movement during teat cup attachment was verified. The maximum displacement and angle error of the cup was 1.625 mm and 1.216 mm, respectively, as verified by the teat cup attachment error test. A dynamic response test showed that the manipulator could follow the teat of the cow in real time. The attachment time for teat cups was 21 s per cow, with a success rate of 98%. The performance of the teat cup attachment robot was capable of meeting the automatic attachment teat cup needs for medium and large dairy farms during milking. Full article

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock. Full article

The aim of this study was to investigate variability in enteric CH₄ emission rate and emissions per unit of milk across lactations among dairy cows on commercial farms in the UK. A total of 105,701 CH₄ spot measurements were obtained from 2206 mostly Holstein-Friesian cows on 18 dairy farms using robotic milking stations. Eleven farms fed a partial mixed ration (PMR) and 7 farms fed a PMR with grazing. Methane concentrations (ppm) were measured using an infrared CH₄ analyser at 1s intervals in breath samples taken during milking. Signal processing was used to detect CH₄ eructation peaks, with maximum peak amplitude being used to derive CH₄ emission rate (g/min) during each milking. A multiple-experiment meta-analysis model was used to assess effects of farm, week of lactation, parity, diet, and dry matter intake (DMI) on average CH₄ emissions (expressed in g/min and g/kg milk) per individual cow. Estimated mean enteric CH₄ emissions across the 18 farms was 0.38 (s.e. 0.01) g/min, ranging from 0.2 to 0.6 g/min, and 25.6 (s.e. 0.5) g/kg milk, ranging from 15 to 42 g/kg milk. Estimated dry matter intake was positively correlated with emission rate, which was higher in grazing cows, and negatively correlated with emissions per kg milk and was most significant in PMR-fed cows. Mean CH₄ emission rate increased over the first 9 weeks of lactation and then was steady until week 70. Older cows were associated with lower emissions per minute and per kg milk. Rank correlation for CH₄ emissions among weeks of lactation was generally high. We conclude that CH₄ emissions appear to change across and within lactations, but ranking of a herd remains consistent, which is useful for obtaining CH₄ spot measurements. Full article

In recent decades, there have been considerable technological developments in the agriculture sector to automate manual processes for many factors, including increased production demand and in response to labor shortages/costs. We conducted a review of the literature to summarize the key advances from installing emerging technology and studies on robotics and automation to improve agricultural practices. The main objective of this review was to survey the scientific literature to identify the uses of these new technologies in agricultural practices focusing on new or reduced occupational safety risks affecting agriculture workers. We screened 3248 articles with the following criteria: (1) relevance of the title and abstract with occupational safety and health; (2) agriculture technologies/applications that were available in the United States; (3) written in English; and (4) published 2015–2020. We found 624 articles on crops and harvesting and 80 articles on livestock farming related to robotics and automated systems. Within livestock farming, most (78%) articles identified were related to dairy farms, and 56% of the articles indicated these farms were using robotics routinely. However, our review revealed gaps in how the technology has been evaluated to show the benefits or potential hazards to the safety and well-being of livestock owners/operators and workers. Full article