A review of on-farm recording tools for smallholder dairy farming in developing countries.

The dairy industry has been expanding significantly recently, which has prompted the improvement and adoption of increasingly digital dairy recording tools with cutting-edge technology. The study aimed to identify smallholder dairy farmers' recording tools in developing countries. The study presents the results of an extensive literature review conducted using electronic journal databases. The review involved applying a combination of search terms and Boolean operators. The search found a total of 412 research publications. However, only 21 articles with 24 recording tools were deemed appropriate and were subsequently included in the study. Dairy recording entails gathering and managing data on animal information, traceability, health, and productivity that can be conducted using various methods, from manual record-keeping to digitization. The results show that most studies have endeavoured to develop digital recording tools that focus on production performance (PR), mainly milk production, using the Internet of Things (IoT) and mobile phone applications. Moreover, various technologies, such as networks, desktops, and web apps, have also been invented. Given the widespread ownership of mobile phones among the general population, the use of mobile phones continues to be an appealing choice for recording tools. To enhance the advancement of these tools, it is necessary to address technological obstacles, particularly those associated with access and connectivity. In addition, it is also important to consider the continuity of data input and feedback obtained to the farmers, thus helping them evaluate their farms periodically.

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.

[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.

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask.

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method's effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

Effect of low milking frequency on the concentration of antimicrobial proteins in goat milk.

This study examined the effects of low frequency milking on the concentrations of antimicrobial components in goat milk. Sixteen goats were divided into two groups of eight each: milking once every 2 d three times (for six days, three times group) or five times (for 10 days, five times group). On other days, milking was performed once daily. Milk was collected, and milk yield, somatic cell count (SCC), and the concentrations of some antimicrobial proteins such as lactoferrin (LF), S100A7, IgA, and sodium ions (Na+) in milk were measured. Milk yield significantly decreased in both the groups during the low-milking frequency period, followed by an increase above the low frequency milking period in both groups. In contrast, SCC and LF concentrations in milk increased in both groups during the low frequency milking period. The concentration of S100A7 in milk temporarily decreased after the low frequency milking period, followed by a significant increase. The S100A7 concentration during this period was higher in the five times group than in the three times group. These results indicated that low frequency milking induced a gradual decrease in milk yield and a concomitant increase in antimicrobial components, such as LF and S100A7, in milk. This increase in the antimicrobial components may be useful in preventing mastitis.

Early prediction of oestrus for herd fertility management in cattle and buffaloes - a review.

Oestrus is defined as a period when a female animal exhibits characteristic sexual behaviour in the presence of a mature male. Oestrous manifestation in dairy animals is due to the oestrogen (E2) effect on the central nervous system (CNS). It is a critical issue to be considered on a priority basis. Inefficient oestrous detection reduces the fertility status of the herd. The primary and most reliable indicator of oestrus is standing to be mounted by a bull or another female herd mate, signalling receptivity and the pre-ovulatory state in dairy cattle. Oestrous detection is primarily a management challenge requiring skill and vigilance. To improve the efficiency of oestrous detection in dairy cattle, visual observation is one of the best methods if done three times a day; however, heat detection aids, if combined, give better results. However, techniques like using teaser bulls, tail painting, chin ball markers, ultrasound (USG) examination, hormonal analysis and examination of cervicovaginal mucus (CVM) improve oestrous detection efficiency. Moreover, the changes in production systems have reduced the expression of oestrous behaviour among cows, due to higher oestrogen (E2) metabolism. Therefore, automated systems, such as pedometers, accelerometers and acoustic sensors like infrared thermography (IRT) and image processing, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules. From this review, we would conclude that oestrous detection alone contributes considerably to the reproductive status of the herd; therefore, applying different methods of oestrous detection reduces the incidence of missed oestrus and improves the fertility status of the herd.

Effect of a targeted reproductive management program based on automated detection of estrus during the voluntary waiting period on reproductive performance of lactating dairy cows.

The objective of this experiment was to evaluate the effect on reproductive performance of a targeted reproductive management (TRM) program for first postpartum insemination (AI) that prioritized AI at detected estrus (AIE) by providing different intervals for estrus detection based on records of automated estrus alerts (AEA) during the voluntary waiting period (VWP). A secondary objective was to evaluate the association between occurrence of AEA during the VWP and reproductive performance. Lactating Holstein cows (n = 1,260) fitted with neck behavior monitoring sensors for detection of estrus were randomly assigned to a program that used all-timed AI (TAI) for first service (ALL-TAI; n = 632) or a TRM program that prioritized AIE and used TAI only for cows not detected in estrus (TP-AIE; n = 628). Cows in the ALL-TAI treatment received TAI at 76 ± 3 days in milk (DIM) after a Double-Ovsynch protocol. Cows in the TP-AIE treatment were eligible for AIE for 30 ± 3 or 16 ± 3 d after a 49 d VWP if at least one (n = 346) or no (n = 233) AEA were recorded from 15 to 49 DIM. Cows not AIE received TAI after an Ovsynch protocol with progesterone supplementation at 90 ± 3 or 76 ± 3 DIM if the cow had or did not have AEA during the VWP, respectively. Data were analyzed by logistic and Cox's proportional hazard regression. In the TP-AIE treatment, 69.3 % of cows received AIE and more cows with (83.3 %) than without (45.0 %) AEA during the VWP received AIE. Cows in the TP-AIE (69.0 ± 0.7 d) treatment had fewer days from calving to first AI than cows in the ALL-TAI (75.7 ± 0.8 d) treatment. The proportion of cows pregnant by 150 DIM (ALL-TAI = 59.1 % and TP-AIE = 56.0 %) and the hazard ratio (HR) for time to pregnancy (1.0 [95 % confidence interval: 0.9, 1.2]) did not differ between treatments and median days to pregnancy were 102 and 107 for the ALL-TAI and TP-AIE treatments, respectively. Overall, the ALL-TAI (42.3 %) treatment had more first service pregnancies per AI (P/AI) than the TP-AIE (29.0 %) treatment. Cows with AEA during the VWP had greater P/AI (42.5 % vs. 28.9 %), proportion of cows pregnant by 150 DIM (67.4 % vs. 47.0 %), and HR for time to pregnancy (1.6 [1.4, 1.9]) than cows without AEA during the VWP. We conclude that a TRM program that prioritized AIE based on AEA during the VWP led to a similar pregnancy rate and proportion of cows pregnant by mid-lactation than a program that used all-TAI with extended VWP despite fewer P/AI to first service. Also, expression of estrus during the VWP was associated with improved reproductive performance. Thus, AEA during the VWP could be used as a predictor of reproductive potential for TRM of lactating dairy cows.

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.

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.

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.

Effects of pellet starch levels in automatic milking systems on rumen fermentation, plasma metabolites, and milk production in mid-lactation cows.

The aim of this study was to evaluate whether the starch levels in pellets fed to cows in automatic milking systems (AMS) affect subacute ruminal acidosis (SARA) occurrence and metabolite parameters. Twenty-four lactating cows (124.4 ± 49.9 days in milk) were studied in a crossover design with two periods of 21 days each and two treatment groups-a control group fed AMS pellets containing 30.0% of starch dry matter (DM) and an experimental group fed AMS pellets containing 23.5% of starch DM. All cows received the same partial mixed ration (PMR). The 1-hr mean ruminal pH in both groups decreased over 4 hr after feeding on PMR but recovered by the next morning. The ruminal pH was unaffected by either treatment, and both groups developed SARA. The groups had no significant differences in the concentrations of ruminal volatile fatty acids, lipopolysaccharides, plasma acute-phase proteins, other metabolites, and hormones. The milk yield and composition were not different in both groups. Feeding low-starch pellets in the AMS did not contribute to the risk of SARA occurrence in cows and had no additive effects on rumen fermentation, plasma metabolites, or milk production.

Longitudinal modeling of residual carbon dioxide and residual feed intake in the Nordic Red dairy cattle.

Feed utilization efficiency is an important trait in dairy production playing a significant role in reducing feed costs and lowering methane emission. One of the metrics used to measure feed efficiency in dairy cows is residual feed intake (RFI). This metric requires routine measurement of feed intake. Since there is a positive high correlation between heat production and carbon dioxide (CO2) production on the one hand and heat production and efficiency on the other hand, residual carbon dioxide (RCO2) might be a useful metric to improve feed efficiency. The objectives of this study were to model the trajectories of RCO2 and RFI as well as to estimate their repeatabilities and correlations at different stages of lactation. Daily CO2 output and feed intake were recorded from 46 primiparous Nordic Red dairy cows using two Greenfeed Emissions Monitoring™ systems from 2 to 305 days in milk (DIM). Edited data comprised 5 995 daily averages. To calculate predicted values of CO2 and DM intake (DMI), prediction models were developed by fitting multiple regression models to observations. Subsequently, RCO2 and RFI were calculated by subtracting predicted values of CO2 and DMI from their corresponding actual observations. A random regression bivariate model was fitted to estimate repeatabilities and animal correlations within lactation at different DIMs between RCO2 and RFI traits. The model fitted included fixed effects of year-month of recording, lactation month, fixed regressions as well as random regressions for the animal effect. The residual variance was considered to be heterogeneous. Repeatabilities and animal correlations of RCO2 and RFI between selected DIM (for every 30 DIM i.e., 6, 36,…, 246 and 276) were calculated. Repeatability of RCO2 was high at the beginning of lactation (0.72 at DIM 6) and decreased around the peak of milk production (0.27 at DIM 96) and again increased gradually toward the end of lactation. Similarly, RFI also had high repeatability at the beginning (0.86 at DIM 6); however, it decreased in mid-lactation (0.37 at DIM 156) and then increased toward the end of lactation. Animal correlations between RCO2 and RFI were moderate to high on the same DIM and ranged from 0.37 to 0.88. Overall, we found that animals with higher CO2 production than expected also consume more DMI than expected, but the moderate correlation between RCO2 and RFI found in this study calls for more research to assess the potential of RCO2 to become a new feed efficiency metric.

Detection of risk areas in dairy powder processes: The development of thermophilic spore forming bacteria taking into account their growth limits.

Anoxybacillus flavithermus, Geobacillus stearothermophilus and Bacillus licheniformis are the main contaminants found in dairy powders. These spore-forming thermophilic bacteria, rarely detected in raw milk, persist, and grow during the milk powder manufacturing process. Moreover, in the form of spores, these species resist and concentrate in the powders during the processes. The aim of this study was to determine the stages of the dairy powder manufacturing processes that are favorable to the growth of such contaminants. A total of 5 strains were selected for each species as a natural contaminant of dairy pipelines in order to determine the minimum and maximum growth enabling values for temperature, pH, and aw and their optimum growth rates in milk. These growth limits were combined with the environmental conditions of temperature, pH and aw encountered at each step of the manufacture of whole milk, skim milk and milk protein concentrate powders to estimate growth capacities using cardinal models and the Gamma concept. These simulations were used to theoretically calculate the population sizes reached for the different strains studied at each stage in between two successive cleaning in place procedures. This approach highlights the stages at which risk occurs for the development of spore-forming thermophilic bacterial species. During the first stages of production, i.e. pre-treatment, pasteurization, standardization and pre-heating before concentration, physico-chemical conditions encountered are suitable for the development and growth of A. flavithermus, G. stearothermophilus and B. licheniformis. During the pre-heating stage and during the first effects in the evaporators, the temperature conditions appear to be the most favorable for the growth of G. stearothermophilus. The temperatures in the evaporator during the last evaporator effects are favorable for the growth of B. licheniformis. In the evaporation stage, low water activity severely limits the development of A. flavithermus.

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.

Relationship among thermal environment, stage of lactation, body characteristics, yield and milk constituents of dairy Gyr cows managed on pasture.

Our aims were to evaluate changes in body characteristics, milk yield and milk constituents as well as to determine the relationship between the thermal environment and production characteristics during the first lactation of dairy Gyr cows managed on pasture. Between 2013 and 2015, forty-five primiparous dairy Gyr cows were evaluated from prepartum to 10 months of lactation in Southeast of Brazil. Body weight, body condition score (BCS), subcutaneous fat thickness (SFT), milk yield (305 d), and milk constituents were collected monthly and progesterone was collected weekly. Additionally, we determined the temperature humidity index (THI) based on microclimate data. Overall, the cows lost body weight until six months of lactation and there was a progressive decrease in BCS, SFT, milk yield and milk lactose as the months in lactation progressed. In contrast, there was an increase in milk fat, milk protein and milk solids. The thermal environment did not pose a consistent heat challenge, nevertheless, we found a positive correlation between the average THI two days before milk collection with milk yield, fat and lactose contents, but in contrast a negative correlation was found with total solids and protein. In conclusion, the THI and months of lactation affected the yield and constituents of milk. However, more studies are necessary to understand the impacts of body characteristics and thermal environment on yield and milk constituents throughout the productive life of Gyr dairy cows.

Implementation of hazard analysis and critical control point (HACCP) in yogurt production.

This study aimed to review hazard analysis and critical control points (HACCP) in the dairy industry for the production of yogurt. The food safety management system (FSMS) was implemented over the last several decades with several amendments. The need for practical and proactive procedures in the dairy industry was identified so that HACCP implementation could ensure that consumers would always have safe food. The concept of HACCP is a systemic and science-based method that can result in safe dairy products such as yogurt based on the complete analysis of manufacturing processes, recognition of hazards potentially present at all stages of production, and risk prevention. In yogurt production, raw milk receipt, pasteurization, packaging, and storage are the steps most susceptible to contamination and were considered critical control points. Further steps also need to be implemented to achieve other related control measures, and these will be discussed.

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.