[Investigations of a controlled, decision tree based procedure for Selective Dry Cow Treatment in Bavarian dairy farms]. / Untersuchungen zu einem kontrollierten, entscheidungsbaumbasierten Verfahren des Selektiven Trockenstellens in Bayerischen Milchviehbetrieben.

OBJECTIVE: Four parameters of a decision tree for Selective Dry Cow Treatment (SDCT), examined in a previous study, were analyzed regarding their efficacy in detecting cows for dry cow treatment (DCT, use of intramammary antimicrobials). This study set out to review wether all parameters (somatic cell count [SCC≥ 200 000 SC/ml 3 months' milk yield recordings prior dry off (DO)], clinical mastitis history during lactation [≥1 CM], culturing [14d prior DO, detection of major pathogens] and California-Mastitis-Test [CMT, > rate 1/+ at DO]) are necessary for accurate decision making, whether there are possible alternatives to replace culturing, and whether a simplified model could replace the decision tree. MATERIAL AND METHODS: Records of 18 Bavarian dairy farms from June 2015 to August 2017 were processed. Data analysis was carried out by means of descriptive statistics, as well as employing a binary cost sensitive classification tree and logit-models. For statistical analyses the outcomes of the full 4-parameter decision tree were taken as ground truth. RESULTS: 848 drying off procedures in 739 dairy cows (CDO) were included. SCC and CMT selected 88.1%, in combination with CM 95.6% of the cows that received DCT (n=494). Without culturing, 22 (4.4%) with major pathogens (8x Staphylococcus [S.] aureus) infected CDO would have been misclassified as not needing DCT. The average of geometric mean SCC (within 100 d prior DO) for CDO with negative results in culturing was<100 000 SC/ml milk, 100 000-150 000 SC/ml for CDO infected with minor pathogens, and ≥ 150 000 SC/ml for CDO infected with major pathogens (excluding S.aureus). Using SCC during lactation (at least 1x > 200 000 SC/ml) and positive CMT to select CDO for DCT, contrary to the decision tree, 37 CDO (4.4%) would have been treated "incorrectly without" and 43 CDO (5.1%) "unnecessarily with" DCT. Modifications were identified, such as SCC<131 000 SC/ml within 100 d prior to DO for detecting CDO with no growth or minor pathogens in culturing. The best model for grading CDO for or against DCT (CDO without CM and SCC<200 000 SC/ml [last 3 months prior DO]) had metrics of AUC=0.74, Accuracy=0.778, balanced Accuracy=0.63, Sensitivity=0.92 and Specificity=0.33. CONCLUSIONS: Combining the decision tree's parameters SCC, CMT and CM renders suitable selection criteria under the conditions of this study. When omitting culturing, lower thresholds for SCC should be considered for each farm individually to select CDO for DCT. Nonetheless, the most accurate model could not replace the full decision tree.

Dairy goat performance in two grazing regimes: silvopastoral rangeland or abandoned agricultural land, and two supplementation treatments in central Mexico.

Twenty Saanen third parturition dairy goats were used in an on-farm 2 × 2 factorial arrangement that ran for 12 weeks, with two grazing regimes and two concentrate types. The grazing regimes evaluated were an extensive silvopastoral native rangeland (SPR) and grazing in an abandoned agricultural land (AAL). Grazing happened between 9:00 and 17:00 h. The two types of concentrate supplement were a high protein concentrate (HP = 180 g CP/kg DM and 13 MJ ME/kg DM) or high energy concentrate (HE = 110  g CP/kg DM and 14.3 MJ ME/kg DM). Goats were milked once a day, providing 250 g of concentrate supplement per goat and day. Animal variables were fat and protein corrected milk yield recorded every day, and milk composition determined for two consecutive days at the end of each experimental week. Flora in the experimental paddocks was characerised and sampled, including grasses, shrubs, trees, legumes and cacti. The data was analysed with the R software using a mixed model with day nested in period as random effect and goat as repeated measure. The SPR had greater (P = 0.002) fat and protein corrected milk yield than AAL, with no differences between concentrate type and no interaction (P > 0.05). There was an interaction (P < 0.01) between grazing regime and concentrate type for fat content in milk, where a reduction in fat content was notorious in the SPR regime. Protein content of milk was greater (P < 0.01) in SPR with no significant effects of concentrate type or the interaction. The number of plant species in SPR was greater. The native silvopastoral system supplemented with the high energy concentrate was the strategy with higher milk yield, and protein and milk fat content, although the interaction between grazing regime and supplement was significant only for milk fat content.

Microwave Dielectric Response of Bovine Milk as Pregnancy Detection Tool in Dairy Cows.

The most reliable methods for pregnancy diagnosis in dairy herds include rectal palpation, ultrasound examination, and evaluation of plasma progesterone concentrations. However, these methods are expensive, labor-intensive, and invasive. Thus, there is a need to develop a practical, non-invasive, cost-effective method that can be implemented on the farm to detect pregnancy. This study suggests employing microwave dielectric spectroscopy (MDS, 0.5-40 GHz) as a method to evaluate reproduction events in dairy cows. The approach involves the integration of MDS data with information on milk solids to detect pregnancy and identify early embryonic loss in dairy cows. To test the ability to predict pregnancy according to these measurements, milk samples were collected from (i) pregnant and non-pregnant randomly selected cows, (ii) weekly from selected cows (n = 12) before insemination until a positive pregnancy test, and (iii) daily from selected cows (n = 10) prior to insemination until a positive pregnancy test. The results indicated that the dielectric strength of Δε and the relaxation time, τ, exhibited reduced variability in the case of a positive pregnancy diagnosis. Using principal component analysis (PCA), a clear distinction between pregnancy and nonpregnancy status was observed, with improved differentiation upon a higher sampling frequency. Additionally, a neural network machine learning technique was employed to develop a prediction algorithm with an accuracy of 73%. These findings demonstrate that MDS can be used to detect changes in milk upon pregnancy. The developed machine learning provides a broad classification that could be further enhanced with additional data.

A multi-methods, multi-actor exploration of the benefits and barriers to milk recording on Irish farms using the COM-B model.

Milk recording is a critical tool in dairy farming, providing individual cow information. When used effectively, this data contributes to on-farm productivity, herd health management decisions and supports prudent veterinary prescribing of antimicrobials. Although an industry and government priority, uptake has been relatively slow in Ireland. This multi-methods, three-part study aimed to gain a comprehensive understanding of the benefits to farm performance, and factors driving uptake of milk recording on Irish dairy farms. It involved an economic analysis of N=516 farms from 2008-2019, a workshop with N=26 stakeholders and an online survey of N=197 non-milk-recording farmers. Quantitative and qualitative data were analysed using econometric models and thematic analysis respectively. Results were synthesised using the COM-B model to gain a deeper understanding of what drives the target behaviour. The study revealed that agricultural education, farm location, farm specialisation in dairy and membership of a farmer discussion group were the main factors influencing uptake of milk recording. Milk recording was associated with a €39.04/cow increase in gross margin, a 177.58 litres/cow increase in milk yield and a reduction of 13,450 cells/ml in bulk milk tank somatic cell count readings. Infrastructural constraints, cost, lack of benefits and workload were the most reported perceived barriers to milk recording by farmers. The Behaviour Change Wheel illustrates how to utilise findings and systematically develop future interventions to increase milk recording uptake. This study highlights the importance of a multi-methods approach to agricultural technology adoption and the need for evidence-based methodology when developing behaviour change interventions.

Evaluation of the environmental impacts of the smallholder milk-production system in Central Mexico.

In order to analyze the environmental performance of Smallholder Dairy Farms (SHDFs) located in the State of Mexico, a Life Cycle Analysis (LCA) was carried out using two methodological approaches (A1 and A2) to estimate and interpret environmental impacts. A1 consisted in obtaining the average inputs and outputs of 15 SHDFs to generate a representative farm life cycle inventory, while A2 included an individual environmental impact analysis per SHDF to obtain average values of the contributions per analyzed midpoint impact category. The feed production subsystem generated the highest contributions to environmental impacts per liter of raw milk produced. Estimated emissions based on A2 approach, resulted in higher environmental impacts compared to results obtained with A1. The estimated values for the midpoint impact categories obtained with A2: Climate change, Fossil depletion, Terrestrial acidification, and Agricultural land occupation, were 8.73%, 30.77%, 100%, and 20.49% higher compared to A1 approach, respectively. While A2 provides more accurate results, it requires more time and resources compared to the integration of a panel of representative dairy farms.

The effect of first-lactation calving season, milk production, and morphology on the survival of Simmental cows.

Longevity in dairy and dual-purpose cattle is a complex trait which depends on many individual and managerial factors. The purpose of the present study was to investigate the survival (SURV) rate of Italian Simmental dual-purpose cows across different parities. Data of this study referred to 2 173 primiparous cows under official milk recording that calved between 2002 and 2020. Only cows linearly classified for type traits, including muscularity (MU) and body condition score (BCS) were kept. Survival analysis was carried out, through the Cox regression model, for different pairwise combinations of classes of milk productivity MU, BCS, and calving season. Herd-year of first calving was also considered in the model. SURV (0 = culled; 1 = survived) at each lactation up to the 6th were the dependent variables, so that, for example, SURV2 equal to 1 was attributed to cows that entered the 2nd lactation. Survival rates were 98, 71, 63, 56, and 53% for 2nd, 3rd, 4th, 5th, and 6th lactation, respectively. Results revealed that SURV2 was not dependent on milk yield, while in subsequent parities, low-producing cows were characterized by higher SURV compared to high-producing ones. Additionally, cows starting the lactation in autumn survived less (47.38%) than those starting in spring (53.49%), suggesting that facing the late gestation phase in summer could increase the culling risk. The present study indicates that SURV in Italian Simmental cows is influenced by various factors in addition to milk productivity. However, it is important to consider that in this study all first-calving cows culled before the linear evaluation - carried out between mid- and late lactation in this breed - were not accounted for. Finding can be transferred to other dual-purpose breeds, where the cows' body conformation and muscle development - i.e. meat-related features - are often considered as important as milk performance by farmers undertaking culling decisions.

A smartphone-based diagnostic analyzer for point-of-care milk somatic cell counting.

BACKGROUND: Mastitis, a pervasive and detrimental disease in dairy farming, poses a significant challenge to the global dairy industry. Monitoring the milk somatic cell count (SCC) is vital for assessing the incidence of mastitis and the quality of raw cow's milk. However, existing SCC detection methods typically require large-scale instruments and specialized operators, limiting their application in resource-constrained settings such as dairy farms and small-scale labs. To address these limitations, this study introduces a novel, smartphone-based, on-site SCC testing method that leverages smartphone capabilities for milk somatic cell identification and enumeration, offering a portable and user-friendly testing platform. RESULTS: The central findings of our study demonstrate the effectiveness of the proposed method for counting milk somatic cells. Its on-site applicability, facilitated by the microfluidic chip, optical system, and smartphone integration, heralds a paradigm shift in point-of-care testing (POCT) for dairy farms and smaller laboratories. This approach bypasses complex processing and presents a user-friendly solution for real-time SCC monitoring in resource-limited settings. This device boasts several unique features: small size, low cost (<$1,000 total manufacturing cost and <$1 per test), and high accuracy. Remarkably, it delivers test results within just 2 min. Actual-sample testing confirmed its consistency with results from the commercial Bentley FTS/FCM cytometer, affirming the reliability of the proposed method. Overall, these results underscore the potential for transformative change in dairy farm management and laboratory testing practices. SIGNIFICANCE: In summary, this study concludes that the proposed smartphone-based method significantly contributes to the accessibility and ease of SCC testing in resource-limited environments. By fostering the use of POCT technology in food safety control, particularly in the dairy industry, this innovative approach has the potential to revolutionize the monitoring and management of mastitis, ultimately benefiting the global dairy sector.

Pregnancy losses in Bos indicus-influenced beef and dairy recipients assigned to a fixed-time embryo transfer protocol.

Pregnancy losses from fixed-time embryo transfer (FTET) to calving were evaluated in Bos indicus-influenced beef and dairy recipients. Data from 4366 FTET events were collected from Nelore × Angus recipient heifers, and from 38538 FTET events in Gir × Holstein recipient heifers and cows. In beef recipients, pregnancy losses were greater (P < 0.01) from FTET (day 7 of the experiment) to day 32 compared with day 32-100 and with day 100 to calving (58.7, 39.5, and 36.7%, respectively), and did not differ (P = 0.56) between these latter periods. Recipients that lost the pregnancy from FTET to day 32 gained less (P < 0.01) body condition score after FTET compared with recipients that maintained the pregnancy. Pregnancy losses from day 32 to calving were greater (P < 0.01) in recipients reared in drylots and moved to pastures on day 32 compared with recipients reared on pasture. In dairy recipients, pregnancy losses from FTET (day 7) to day 32 were greater (P < 0.01) than losses from day 32 to calving (50.4 and 29.4%). Pregnancy losses throughout gestation were greater (P < 0.01) when the FTET event was performed during the warm season, and greater (P < 0.01) in recipients with < 3/8 Gir influence. Recipients with ≥ 3/8 Gir influence experienced a lesser (P ≤ 0.05) increase in pregnancy losses during the warm season compared with recipients with < 3/8 Gir influence. Collectively, this experiment provides novel information about pregnancy losses in B. indicus-influenced herds receiving FTET.

A new approach and insights on modelling the impact of production diseases on dairy cow welfare.

Animal welfare is becoming an important consideration in animal health-related decision-making. Integrating considerations of animal welfare into the decision-making process of farmers involves recognising the significance of health disorder impacts in relation to animal welfare. Yet little research quantifies the impact, making it difficult to include animal welfare in the animal health decision-making process. Quantifying the impact of health disorders on animal welfare is incredibly challenging due to empirical animal-based data collection constraints. An approach to circumvent these constraints is to rely on expert knowledge whereby perceived welfare impairment weights are indicative of the negative welfare effect. In this research, we propose an expertise-based method to quantify the perceived impact of sub-optimal mobility (SOM) on the welfare of dairy cows, because of its welfare importance. We first quantified perceived welfare impairment weights of SOM by eliciting expert knowledge using adaptive conjoint analysis (ACA). Second, using the perceived welfare impairment weights, we derived the perceived welfare disutility (i.e., perceived negative welfare effect) of mobility scores 1-5 (1 = optimal mobility, 5 = severely impaired mobility). Third, using the perceived welfare disutility per mobility score, we quantified the perceived welfare impact at case- and herd-level of SOM for different SOM severity. Results showed that perceived welfare disutility increased with each increase in mobility score. However, the perceived welfare impact of SOM cases with lower mobility scores was higher compared to SOM cases with higher mobility scores. This was because of the longer-lasting duration of the SOM cases with lower mobility scores. Moreover, the perceived herd-level welfare impact was largely due to SOM cases with lower mobility scores because of the longer duration and more frequent incidence compared to the SOM cases with higher mobility scores. These results entail that better welfare of dairy cows with respect to SOM can be achieved if lower mobility scores are detected and treated sooner. Our research demonstrates a novel approach that quantifies the perceived impact of health disorders on animal welfare when empirical evidence is limited.

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities.

Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.

Cohort profile: The FarmMERGE project-Merging human and animal databases to investigate the relationship between farmer and livestock health and welfare. The HUNT Study.

Stockmanship is an important determinant for good animal welfare and health. The goal of the FarmMERGE project is to investigate the associations between farmer health and work environment, and the health, productivity and welfare of their livestock. We merged several livestock industry databases with a major total population-based health study in Norway (The Trøndelag Health Study 2017-2019 (HUNT4)). This paper describes the project's collection and merging of data, and the cohort of farmers and farms that were identified as a result of our registry merge. There were 56,042 participants of HUNT4 (Nord-Trøndelag County participants only, participation rate: 54.0%). We merged a list of HUNT4 participants whose self-reported main occupation was "farmer" (n = 2,407) with agricultural databases containing production and health data from sheep, swine, dairy and beef cattle from 2017-2020. The Central Coordinating Register for Legal Entities was used as an intermediary step to achieve a link between the farmer and farming enterprise data. We identified 816 farmers (89.5% male, mean age 51.3 years) who had roles in 771 farming enterprises with documented animal production. The cohort included 675 unique farmer-farm combinations in cattle production, 139 in sheep, and 125 in swine. We linked at least one HUNT4 participant to approximately 63% of the dairy farms, 53% of the beef cattle farms, 30% of the sheep farms, and 38% of the swine farms in Nord-Trøndelag County in the 2017-2019 period. Using existing databases may be an efficient way of collecting large amounts of data for research, and using total population-based human health surveys may decrease response bias. However, the quality of the resulting research data will depend on the quality of the databases used, and thorough knowledge of the databases is required.

Comparison of Sensor-Based and Audible Detection of Milking Liner Slips during Machine Milking of Dairy Cows.

On-farm milk flow meter technology facilitates real-time assessment of individual cow milking observations and could be used to detect milking liner slips during machine milking of dairy cows. Here, we compared the accuracy of on-farm milk flow meters for detecting milking liner slips with that of audible detection and that of a portable vacuum recording system. Compared to audible detection methods, the on-farm milk flow meter facilitated the detection of milking liner slips with moderate accuracy. Using the vacuum recording system as the gold standard, the milk flow meter system failed to detect most of the liner slips, leading to poor agreement between the two devices. We conclude that the on-farm milk flow meter system tested here compared well with audible detection; however, when vacuum recordings were considered, we found significant levels of under-detection. Taken together, dairy operators may use the on-farm milk flow meter system to inform adjustments of the milking machine settings and monitor milking routine performance. However, the system is not suitable for monitoring short-duration vacuum fluctuations. Future research is warranted to optimize the sensor-based detection of milking liner slips.

Role of infrastructure and operation in disease prevalence in dairy farms: groundwork for disease prevention-based antibiotic stewardship.

Attempts at regulating misuse of antibiotics in the dairy industry have been ineffective, especially in low- and middle-income countries, who also typically have high burden of preventable infectious disease, we propose a disease prevention-based approach to minimize the need and in turn consumption of antibiotics in dairy farms. Since the immediate environment of the animals is key to disease prevalence, we targeted the infrastructure- and operation-related factors in dairy farms and their link with prevalence of most common diseases and symptoms. We conducted four focused group discussions and a cross-sectional survey in 378 dairy farms to investigate disease prevalence and associated infrastructural (housing system, and manger shape), and operational (waste management, feed management, and type of cleaning agent) parameters. The most common diseases (Mastitis and secondary infections related to Foot-and-mouth disease) and symptoms (fever and diarrhoea) in the focus area were linked with the infrastructural and operational factors on the dairy farm with higher disease prevalence reported in dairy farms, where the animals were exposed to variations in diurnal temperatures or were hard to clean. We further used ML classifiers - Neural Network (NN), k-Nearest Neighbour (kNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) - to corroborate the relationship between infrastructure and operations of the dairy farms and disease prevalence- The DT classifier on randomly sampled data could predict the prevalence of the two most common diseases (accuracy = 92%, F1-score = 0.919) Our results open new avenues for cost-effective interventions such as use of curve-edged mangers, use of rubber mats on floors, not reusing leftover feed etc. in dairy farms to prevent the most common diseases and symptoms in dairy farms and reduce the need and consumption of antibiotics.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems.

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

Valuation of livestock population and national feed security to enhance livestock productivity in Ethiopia.

BACKGROUND: Feed is a major input in the livestock industry and covers about 60%-70% of the total cost of producing meat, milk and eggs. Inadequate feed supply in terms of quality and quantity leads to lower production performance in livestock. However, the development of an appropriate livestock production strategy through efficient utilization of existing feed resources could raise the production and per capita consumption of livestock products. Efficiency of feed resource utilization can be measured as the ratio between input to production activities and output (e.g. kg of protein used per unit of meat, milk and eggs produced or hectare of land used per unit of milk produced). METHODOLOGY: This study was designed with the objective of evaluating the livestock population and national feed security to enhance livestock productivity in Ethiopia. To achieve this objective, data were collected from the websites of the Ethiopian Central Statistical Agency from 2007 to 2021, FAO publications and websites, books and journals. The data obtained on different feed resources, livestock population and livestock feed requirement and balance were entered into an MS Excel spread sheet (Excel, 2010) and analysed using the general linear model (PRO GLM) procedure of SAS (2014) and multivariate analysis of covariance. RESULTS: The study results revealed that the livestock population had increased from 58.31 million tropical livestock units (TLU) to 81.10 million tropical livestock units (TLU), and the emission of entericCH4 had increased from 2511.08 Gg/year to 3661.74 Gg/year from 2008 to 2021. The study results also showed that the major available feed resources for ruminants are natural pasture and crop residues, which account for 56.83% (87.56 × 106 ) and 37.37% (57.57 × 106 ) of total feed production in the country, respectively. The contribution of concentrate and improved cultivated pasture and feed from permanent crops used as feed sources is very insignificant (3.05% and 1.96%, respectively). The estimated quantity of these feed resources was sufficient to meet the livestock feed requirement in the country in terms of dry matter (DM), digestible crude protein (DCP) and MEJ, which estimated about 153.31 × 106  t, 4.56 × 106  t and 1203.97 × 109  MJ DM, DCP and MEJ, respectively. The estimated livestock feed requirements were 134.62 × 106 , 4.52 × 106 , and 918.83 × 109 in DM, DCP and MEJ, respectively. The supply covered about 114.33, 100.04 and 131.33% of the DM, DCP and MEJ total annual feed requirements of livestock in the country. Hence, the current feed surplus obtained on feed requirements of ruminants and equines can support the nutrient requirements of 500 × 106 broilers, about 5 × 106 bulls, about 50 × 106 small ruminants or 3 × 106 crossbred lactating dairy cows, yielding 10 L of milk per day. CONCLUSIONS: The findings of study indicated that natural pasture and crop residues cover a major proportion of the annual feed supply in the country. Therefore, proper grazing management, feed conservation practices, improving grazing land vegetation through clearing invasive species, replacing the grazing land with an improved grass and legume mixture, effective collection, conservation and proper utilization of crop residues, and other alternative options such as the use of chemical, physical and biological treatments to improve the nutritive value of fibrous feed should be practiced. More effective extension services and farmer training are also required to increase feed productivity and, hence, human development.

Impacts of preweaning colostrum feeding practices and health measures on dairy cow production, while accounting for genetic potential.

Calf management and health are essential for setting up the foundation of a productive cow. The objectives of this study were to estimate the impact of preweaning practices on milk production parameters while accounting for an animal's genetic potential in New Brunswick, Canada. A retrospective cohort study was performed on 220 heifer calves from eight herds born in 2014-2015. Preweaning practices and health data were recorded by producers and reviewed by the herd veterinarian for each calf. The herd veterinarian also visited the farms to collect serum samples from calves and frozen colostrum samples. The production outcomes assessed were milk, protein and fat yields, standardized to 305 d for the first lactation (L1) and a combined group of lactations two and three (L2 + 3). The genomic potential was determined as genomic parent averages (GPA) for the associated production parameters. Analysis was performed with multivariable linear (L1) and linear mixed (L2 + 3) regression models. In L1, for every 1.0 kg increase in weaning weight, milk, protein, and fat yield increased by 25.5, 0.82, and 1.01 kg, respectively (P < 0.006). Colostrum feeding time (CFT) positively impacted L1 milk and protein production, with feeding between 1-2 h of life producing the greatest estimates of 626 kg of milk and 18.2 kg of protein yield (P < 0.007), compared to earlier or later CFT. Fat yield production was decreased by 80.5 kg (P < 0.006) in L1 when evaluating animals that developed a preweaning disease and were not treated with antibiotics compared to healthy untreated animals. Impacts on L2 + 3 were similar across all production outcomes, with a positive interaction effect of CFT and weaning weight. Compared to CFT < 1 h, the later CFT groups of 1-2 h and > 2 h produced greater yield outcomes of 68.2 to 72.6 kg for milk (P < 0.006), 2.06 to 2.15 kg for protein (P < 0.005), and 1.8 to 1.9 kg for fat (P < 0.045) for every 1 kg increase of weaning weight, respectively. The fit of all models was significantly improved with the inclusion of GPA. These results indicate that colostrum management and preweaning health measures impacted production parameters as adults. The inclusion of GPA significantly improved the accuracy of the models, indicating that this can be an important parameter to include in future studies.

The impact of calf management and health events have been predominately investigated during the preweaning period. However, calfhood events could also impact the animal's health and productivity as an adult. Results from this study indicate that colostrum feeding time and weaning weight were associated with production outcomes (milk, protein, and fat yields) across the first three lactations, and disease and antibiotic treatment can be detrimental to fat yield in the first lactation. By including genetic potential in the assessment of preweaning colostrum practices and health measures on production outcomes, we can more precisely identify areas to optimize calf management.

Biometric identification of dairy cows via real-time facial recognition.

Biometrics methods, which currently identify humans, can potentially identify dairy cows. Given that animal movements cannot be easily controlled, identification accuracy and system robustness are challenging when deploying an animal biometrics recognition system on a real farm. Our proposed method performs multiple-cow face detection and face classification from videos by adjusting recent state-of-the-art deep-learning methods. As part of this study, a system was designed and installed at four meters above a feeding zone at the Volcani Institute's dairy farm. Two datasets were acquired and annotated, one for facial detection and the second for facial classification of 77 cows. We achieved for facial detection a mean average precision (at Intersection over Union of 0.5) of 97.8% using the YOLOv5 algorithm, and facial classification accuracy of 96.3% using a Vision-Transformer model with a unique loss-function borrowed from human facial recognition. Our combined system can process video frames with 10 cows' faces, localize their faces, and correctly classify their identities in less than 20 ms per frame. Thus, up to 50 frames per second video files can be processed with our system in real-time at a dairy farm. Our method efficiently performs real-time facial detection and recognition on multiple cow faces using deep neural networks, achieving a high precision in real-time operation. These qualities can make the proposed system a valuable tool for an automatic biometric cow recognition on farms.

Evaluation of selected risk factors for different stages of digital dermatitis in Dutch dairy cows.

Digital dermatitis (DD) is a painful infectious disease in dairy cattle that causes ulcerative lesions of the skin just above the coronary band, mainly of the hind legs. Estimates for DD prevalence at cow level in the Netherlands range from 20% to 25%. In this study, risk factors for the various stages of DD were identified and quantified. The hind legs of 6766 cows on 88 farms were scored by trained interns, using the M-scoring system (M0-M4.1). Farms in this study were a convenience sample, based on the prevalence of DD as recorded at the latest herd trim, geographical location and willingness of the farmers to participate. A survey with questions about cow environment and herd management was conducted by the intern at the day of scoring. The data were collected between August 2017 and January 2018. DD was found on 38.6% of the scored legs; 49.8% of the cows had DD on at least one leg and M4 was the most frequent stage (20.9%). Not removing manure on a regular basis resulted in lower odds for M2, M4 and M4.1 compared to cleaning by automatic scrapers ten times a day or more (odds ratio [OR]= 0.16, 0.49 and 0.18, respectively). The odds for M2 and M4 lesions were higher in cows aged 3-5 years than in first-calved cows (OR> 1.5 and > 1.7, respectively). Rubber flooring in the passageways resulted in lower odds for both M1 and M2 (OR, 0.06 and 0.32, respectively). Prophylactic use of footbaths treatment with an alternative active compound resulted in significant higher odds for M4 lesions than formalin and a combination of formalin and copper sulphate (OR= 1.69 and 2.04 respectively). The odds for an M4.1 lesion were lower in cows from smaller herds (n = 50-100) compared to large herds (n >100; OR= 0.67).

Heritability and genetic correlations of rumination time with milk-yield and milking traits in Holstein-Friesian cows using an automated milking system.

Knowledge of the values of genetic parameters is a prerequisite for conducting a breeding program. This is especially important for rumination, which is considered an indicator of cow's health. Exploring the genetic relations between rumination time, milk yield, and milking traits could make it a valuable tool in dairy cattle breeding strategies. The objective of the research was to estimate heritability, repeatability, and genetic and phenotypic correlations of rumination time (RT), as well as traits associated with milk yield and milking of dairy cows of the Polish Holstein-Friesian breed kept in herds equipped with an automatic milking system. The research takes into consideration daily results for milking in the first lactation and second lactation, from 1 486 cows of the breed milked between 2013 and 2015 year. Cows were housed in 24 free-stall barns and fed a Partial Mixed Ration feed. The barns had an automated milking system (Astronaut A4 - Lely Industry). The cows received a varied dose of the concentrate, either in the milking robot or the feeding station, depending on the level of their milk yield. Our research has shown that RT was a low heritable trait (0.140 ± 0.039) and had a medium repeatability (0.572 ± 0.007). We detected a positive genetic correlation between RT and milk yield (0.341); however, a statistically significant negative relationship was identified between RT and urea content (-0.418) in milk. Estimations of genetic correlations suggest that selecting for higher RT may correspond to reduced urea content in milk. Investigating the genetics aspect of RT and the relationship with milk yield and milking traits may turn this into one of the useful criterion selections for dairy cattle breeding strategies, but should be used carefully. Further analyses on larger data sets and different populations are necessary.

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows.

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.