Dairy Farm Management Research Articles

Prevalence of pathogens and antimicrobial resistance of isolated Staphylococcus spp. in bovine mastitis milk in South Korea, 2018-2022.

Staphylococcus spp. are one of the most predominant isolates in milk samples of dairy cows with mastitis worldwide. The aims of this study were to investigate the prevalence of bacterial pathogens in bovine mastitis milk samples in South Korea and the antimicrobial resistance profiles of staphylococcal isolates. In total, 1,245 strains were isolated from 1,260 mastitis quarter milk samples (with somatic cell counts ≥200,000 cells/mL) from 66 dairy farms between 2018 and 2022. The bacterial genus with the highest prevalence in bovine mastitis milk samples was Staphylococcus spp. (33.9%), followed by Streptococcus spp. (11.5%). S. aureus and non-aureus staphylococci (NAS) accounted for 11.0% and 89.0% of staphylococcal isolates, respectively. S. chromogenes was the most prevalent species among the 22 NAS species detected. S. aureus showed the highest resistance rates to penicillin (25.0%) and ampicillin (20.8%), whereas NAS showed the highest resistance rates to penicillin (18.3%), tetracycline (11.4%) and erythromycin (10.1%). Sixteen multidrug-resistant (MDR) isolates were only isolated from NAS, and the most commonly detected antimicrobial resistance gene in the 16 MDR isolates was mecA (75.0%), followed by tetK (62.5%), blaZ (50.0%), ermC (50.0%), and lnuA (43.8%). In conclusion, NAS were the most common isolates from mastitis milk in South Korea and MDR isolates carried a variety of antibiotic resistance genes. Our study suggests that continuous monitoring of the distribution and antimicrobial resistance in Staphylococcus spp., particularly NAS, is needed to improve the effectiveness of management and treatment strategies in dairy farms.

Dairy farm management factors associated with auction sale price of young dairy calves sold at auction markets in Québec, Canada. A cross-sectional study

Each year around 150,000 surplus calves are sold at auction markets in Québec, Canada. Surplus calves (male or female not kept in the herd of origin) are sold at a young age, but these animals are at risk of receiving lower quality neonatal care than replacement heifers. Knowledge of factors associated with a higher selling price could help convince farmers to spend more resources in the care surplus calves. Our objective was to explore the associations between farm management practices and the median percentile of surplus calf selling price per farm at auction markets. The price (CAD/kg of body weight) and the individual identification number of surplus calves sold in 2 auction markets in Québec during 4 sales days in the summer 2019 and in the winter 2020 were recorded. The recorded price of each surplus calf was transformed as percentile for each breed and day of sale. Farmers managing the surplus calves were contacted and interviewed on farm management practices. The data from farmer's interviews were analyzed as potential variables associated with the median percentile of calves' selling price per farm (farm level dependent variable) in a multivariable linear regression model. A total of 509 farmers were contacted, of which 433 farmers agreed to participate, and 409 interviews were retained for statistical analysis. The farms enrolled in the study had sold a median of 2 calves (range 1-19 calves) during the sale days considered. The main breed of surplus calves sold were Holstein (82%) and Angus crossbred calves (9%). The results from the multivariable model showed that median percentile of calves' selling price was positively associated with farms with an average milk production per cow superior to 11,000 L/years (β 0.13, 95% confidence interval (95% CI): 0.045, 0.221) and farms with 3 or more workers available to take care of surplus calves (β 0.08, 95% CI: 0.005, 0.167). Those results indicate that farms having an average milk production per cow superior to 11,000 L/year increase the calves' selling price by 13 percentiles (i.e., from 50th to 63rd) and that farms having at least 3 caretakers increase their median percentile calves' selling price by 8 percentiles (i.e., from 50th to 58th). Median percentile of calves' selling price was negatively associated with farms that vaccinated cows for neonatal calf diarrhea (β -0.06, 95% CI:-0.127, -0.011), that do not disinfect the navel of newborn calves (β -0.07, 95% CI:-0.133, -0.012), that allow transporters to enter the farm's building (β -0.07, 95% CI:-0.130, -0.015) and that used wood shaving as bedding for surplus calves (β -0.08, 95% CI:-0.156, -0.021). Sensitivity analyses performed on farms that have sold 2 or more surplus calves did not show significant changes in the associations found. Despite the fact that the study was based on self-reported questionnaire answers and a small number of calves per farm, it provides insight on farm management practices associated with median percentile of surplus calf selling price at the auction markets. Taking these results in consideration, farmers could potentially improve the market value of their animals.

A novel and convenient lying cow identification method based on YOLOX and CowbodyNet: A study with applications in a barn

The lying time of cows is a key indicator of their health and comfort. The ability to automatically recognize the lying posture of cows while simultaneously realizing individual cow identification can play an important role in improving cow welfare, increasing milk yield, detecting cow diseases in a timely manner and enabling precision dairy farming management. In this paper, a method of individual identification for lying dairy cows in a barn based on YOLOX and a feature extraction network named CowbodyNet is proposed. When new cows join the herd, there is no need to collect a large number of images to retrain the model. It is very convenient to collect several images of newly added cows lying and store them in the database. First, the low-light images collected at night are enhanced by the multiscale retinex with chromaticity preservation (MSRCP) algorithm to improve the image quality. Then, the YOLOX target detection algorithm is applied to detect and segment cows in the lying posture. Following this, the segmented images of lying cows are input into CowbodyNet to generate feature vectors, which are used to construct a feature vector database. Subsequently, the Euclidean distances between the feature vector of a cow to be identified and the feature vectors in the database are calculated to determine the identification result. The proposed method achieves 94.43% lying cow identification accuracy on a data set containing top-view images of 72 cows. Finally, the individual cow detection and identification model is successfully deployed on the Jetson Xavier NX embedded platform. The results demonstrate the effectiveness and practicability of the proposed cow identification method. This study provides effective technical support for the application of identifying individual lying cows.

Risk factors for dairy cows mastitis in Algeria, antibiotic resistance and molecular typing of the causative Staphylococcus aureus.

Adoption of a rational management in dairy farms would improve the milk quality and farmers' income. In the current study, we aimed to describe bovine mastitis in 32 dairy herds, identify the main cow- and herd-associated risk factors, and analyze both epidemiological along with molecular characteristics of Staphylococcus aureus infecting udders. Based on Californian Mastitis Test and clinical examination, the prevalence of mastitis in cows was 52.25% (116/222), of which 6.3% was clinical mastitis and 45.94% was subclinical mastitis. Overall, 218 (24.54%) quarters suffered from mastitis, whose 29.81% (65/218) infected with S. aureus. Mastitis was lowest in mid-lactation with OR = 0.371 with 95% confidence interval (CI) of 0.141-0.976, and in cows separated from their calves (OR = 0.164, 95% CI 0.056-0.477) than suckler cows. Similar results were obtained from S. aureus related mastitis. To assess the genetic lineages of S. aureus isolates, we determined clonal complexes (CC) using DNA microarray hybridization profiles and performed spa typing. The strains were assigned to nine clonal complexes, and 19 spa types; with CC97 (44.77%), and CC22 (40.29%) were the most predominant lineages and t223 (40.29%), t7136 (10.44%), t359 (8.95%) and t267 (5.97%) were the most common spa types. A total of 88.05% (n = 59) isolates were resistant to at least one tested antibiotic while only 4.47% were multi-drug resistant strains. Higher rates of resistance were observed for penicillin (86.5%) and tetracycline (14.9%) respectively. Our results show the need for adoption of feasible mastitis program with special emphasis on sub-clinical mastitis and associated risk factors.

Estimation of economic equations as a management tool in feedlot dairy farming

The aim of this study was to evaluate Pearson's linear correlations between productive and economic indicators as well as to estimate economic equations as tools in dairy farming. We used the database of the Research Group, registered with CNPq under the title ‘Animal and Forage Production in Piaui’, which consisted of two experiments with feedlot lactating cows. Economic and productive indicators were employed. Correlations were determined by Pearson’s linear correlation analysis, using the ‘t’ test. Equations were estimated via regression, using Saeg software (Statistical and Genetic Analysis System), considering α = 0.05. Based on concepts related to Pearson's linear correlation and dairy farming management, we were able to find correlations that allowed us to estimate simple, yet very informative equations, which facilitated the interpretation of the management of a dairy farm. Among the studied variables, net present value, internal rate of return, the cost per liter of milk produced and net margin were those which most contributed to the estimation of equations.

Physico-chemical characteristics of goat, cow and buffalo milk

The present investigation entitled “Physico-chemical characteristics of goat, cow and buffalo milk” was carried out during March to May 2023 at the Dairy Farm Rajola Livestock Production and Management (Unit), Department of Natural resource management, Faculty of Agriculture, Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot – Satna (Madhya Pradesh), to the study physico-chemical qualities of raw milk of goat, cow and buffalo. The results of the investigation regarding the chemical qualities of milk of goat, cow and buffalo have been presented in this chapter with the help of tables and graphs. The study has been divided into the following sub- headings. Protein (%), specific gravity (cc), fat (%), lactose (%), ash (%), total solid (%), water (%) and solid not fat (SNF) (%). In view of the findings and results presented above, it may be concluded that the physico-chemical quality of milk of buffalo was superior to cow and goat milk, due to higher protein. Specific gravity, fat content, lactose, total solid and SNF and lower ash and water content in buffalo milk.

Development of a real-time cattle lameness detection system using a single side-view camera

Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracking, and classification. This research investigates the application of deep learning for cattle lameness detection in dairy farming. Our study employs image processing techniques and deep learning methods for cattle detection, tracking, and lameness classification. We utilize two powerful object detection algorithms: Mask-RCNN from Detectron2 and the popular YOLOv8. Their performance is compared to identify the most effective approach for this application. Bounding boxes are drawn around detected cattle to assign unique local IDs, enabling individual tracking and isolation throughout the video sequence. Additionally, mask regions generated by the chosen detection algorithm provide valuable data for feature extraction, which is crucial for subsequent lameness classification. The extracted cattle mask region values serve as the basis for feature extraction, capturing relevant information indicative of lameness. These features, combined with the local IDs assigned during tracking, are used to compute a lameness score for each cattle. We explore the efficacy of various established machine learning algorithms, such as Support Vector Machines (SVM), AdaBoost and so on, in analyzing the extracted lameness features. Evaluation of the proposed system was conducted across three key domains: detection, tracking, and lameness classification. Notably, the detection module employing Detectron2 achieved an impressive accuracy of 98.98%. Similarly, the tracking module attained a high accuracy of 99.50%. In lameness classification, AdaBoost emerged as the most effective algorithm, yielding the highest overall average accuracy (77.9%). Other established machine learning algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Random Forests, also demonstrated promising performance (DT: 75.32%, SVM: 75.20%, Random Forest: 74.9%). The presented approach demonstrates the successful implementation for cattle lameness detection. The proposed system has the potential to revolutionize dairy farm management by enabling early lameness detection and facilitating effective monitoring of cattle health. Our findings contribute valuable insights into the application of advanced computer vision methods for livestock health management.

Standardization method, testing scenario, and accuracy of the infrared prediction model affect the standardization accuracy of milk mid-infrared spectra

The widespread use of milk mid-infrared (MIR) spectroscopy for phenotype prediction has urged the application of prediction models across regions and countries. Spectra standardization is the most effective way to reduce the variability in the spectral signal provided by different instruments and labs. This study aimed to develop different standardization models for MIR spectra collected by multiple instruments, across 2 provinces of China, and investigate whether the standardization method (piecewise direct standardization, PDS, and direct standardization, DS), testing scenario (standardization of spectra collected on the same day or after 7 mo), infrared prediction model accuracy (high or low), and instrument (6 instruments from 2 brands) affect the performance of the standardization model. The results showed that the determination coefficient (R2) between absorbance values at each wavenumber provided by the primary and the secondary instruments increased from less than 0.90 to nearly 1.00 after standardization. Both PDS and DS successfully reduced spectra variation among instruments, and performed significantly better than non-standardization (P < 0.05). However, DS was more prone to overfitting than PDS. Standardization accuracy was higher when tested using spectra collected on the same time compared with those collected 7 mo after (P < 0.05), but great improvement in model transferability was obtained for both scenarios compared with the non-standardized spectra. The less accurate infrared prediction model (for C8:0 and C10:0 content) benefited the most (P < 0.05) from spectra standardization compared with the more accurate model (for total fat and protein content). For spectra collected after 7 mo from standardization, after PDS the RMSE between predictions obtained by different machines decreased on average by 86 and 94% compared with the values before standardization, for C8:0 and C10:0 respectively. The secondary instrument had no significant effect on the R2 between predictions (P > 0.05). The variation in the spectral signal provided by different instruments was successfully reduced by standardization across 2 provinces in China. This study lays the foundations for developing a national MIR spectra database to provide consistent predictions across provinces to be used in dairy farm management and breeding programs in China. Besides, this provides opportunities for data exchange and cooperation at international levels.

Electronic tracking and identification of animals in agriculture for monitoring herd development and health

Agricultural business methods are no longer possible without computer technology, which helps to identify animals, record productivity and monitor their health. Therefore, the study aims to conduct a comparative analysis of the functionality of the main dairy farm management software for their objective assessment from the point of view of milk producers. For this purpose, information from the official websites of software products was analysed and scientific articles analysing this software were collected. As a result, all software products were divided into 3 categories: software for controlling dairy productivity and managing processes on a dairy farm from dairy equipment manufacturers; such software products include DairyPlan C21 (GEA), DATAFLOW II (Milkline), AfiFarm (Afimilk), DelPro (DeLaval) and FARMSOFT (Farmtec a. s.); dairy farm management software DairyComp 305 and UNIFORM-Agri, as well as farm software from post-Soviet developers – MilkCentre (CIAB Expert). Programmes from dairy equipment manufacturers were almost at the same level and had virtually all the necessary blocks for identifying, controlling and managing animals on a dairy farm. The software developed for dairy farm management is more effective in tandem with one of the milking parlour programmes. This class of software expands the possibilities for managing reproductive and veterinary work on the farm, as well as reduces the workload on staff by generating automatic reports for regulatory authorities and management. The third class of programs is the least developed compared to the above, but it compensates for this shortcoming by constant improvement due to the continuous contact of developers with the manufacturer. Thus, there is a significant number of software products on the software market that can provide dairy management for farms of various sizes

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

BackgroundMastitis, 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. ResultsThe 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. SignificanceIn 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.

Effects of dairy farming management practices on carbon balances in New Zealand’s grazed grasslands: Synthesis from 68 site-years

Globally, the soils underlying managed grasslands are an important reservoir of carbon (C), with the management of the land strongly influencing whether the soils gain, lose or maintain C. Further, improved grassland management, such as rotational grazing and increased sward diversity, are widely promoted to increase soil C sequestration despite limited evidence. Using data from New Zealand’s grazed dairy grasslands, we examined the effect of management practices on C stocks through measurement of the net ecosystem C balance (NECB) across 68 site-years. Management practices tested included year-round rotational grazing, differing pasture sward composition, irrigation, periodic supplemental feed cropping and pasture renewal. Excluding years where supplemental feed cropping occurred, the mean (±95% confidence interval) NECB was −12 ± 30 g C m−2 y−1 (65 site-years; the negative value represents C loss), indicating the soil C stocks of these ecosystems were likely near steady-state. The processes of pasture renewal and the transition between pasture and crops resulted in reduced C inputs from photosynthesis of between −232 and −94 g C m−2. This reduced C input was partially overcome with the application of effluent or manure at the time of cultivation where applied. There was no difference in NECB between irrigated (−26 ± 114 g C m−2 y−1) and non-irrigated (−19 ± 198 g C m−2 y−1) pastures. In general, conventional ryegrass/white clover pastures were more beneficial for C stocks than alternative pasture species and mixes. Periodic cropping for supplemental feed resulted in C losses, with the magnitude depending on crop type and management, although data were limited to three site-years.

Prevalence of Lameness in Dairy Cows and Associated Risk Factors at Hawassa Town Dairy Farms, Ethiopia.

Lameness is one of the greatest constraints on the productivity, health, and welfare of dairy cattle. A cross-sectional study was carried out from March 2021 to September 2021 in Hawassa town with the aim of assessing the prevalence and identifying the associated risk factors of lameness in dairy farms. The study was conducted on 440 animals belonging to 19 randomly selected intensive dairy farms. Data regarding lameness and its possible risk factors were collected both at animal and farm level using a questionnaire. The results showed that the overall prevalence of lameness was 10.2% (n = 45/440). The association of lameness prevalence with various risk factors including milking status, exercise, age, parity, milk yield, and lactation stage was statistically tested using logistic regression model. There was a significant variation in the prevalence of lameness (P < 0.05) between cattle with different milking status, age, parity, milk yield, and stage of lactation by the univariable analysis result. According to the multivariable analysis, only milk yield and lactation stage were statistically associated with the occurrence of lameness. Milking animals (8%) had higher prevalence of lameness than nonmilking (2.2%). The occurrence of lameness increased with milk yield. The highest prevalence of lameness was recorded in the early stage of lactation. Lameness was more frequent in hind limbs (6.6%) than in forelimbs (3.6%). The main causes of lameness observed in this study were both claw overgrowth 10 (2.3%), unequal claw size 10 (2.3%), solar ulcer 8 (1.8%), interdigital necrobacillosis 2 (0.5%), interdigital hyperplasia 2 (0.5%), and digital dermatitis 1 (0.2%). There was no means of early lameness diagnosis in 94.7% of farms. Lameness was found to be an important disease in dairy cows at Hawassa town. Prevention and early diagnosis leading to prompt treatment of lameness in cows should be part of dairy farm management practice.

Isolation, Identification and Antimicrobial Resistance Profile of Pathogenic Staphylococcus Aureus from Intensive Dairy Farms in Modjo and Adama, Central Ethiopia

Staphylococcus aureus plays its most significant animal pathogenic role as cause of intra mammary infections in cattle and small ruminants leading to considerable economic losses in cattle farming. A cross sectional study was conducted from February to June 2019 in Adama and Modjo town, to isolate and identify S. aureus and their susceptible to different antimicrobials. Out of a total of 160 samples examined,20.6 % (33) were positive for S. aureus. Of this, 13/86 (15.1%), 20/74(27%) were positive for S. aureus atModjo and Adama, respectively. All 33 isolates were tested for antimicrobial resistance with 12 selected antibiotics. The isolates were highly susceptible to gentamycin (100%), streptomycin 24(72.7%), ciprofloxacin21 (63.6)), and kanamycine20 (60.6%), however, they were highly resistant to methicillin 31(93.9%), penicillin G 30(90.9%), tetracycline 21 (63.6%) and erythromycin (60.6%) respectively. Out of the resistance S. aureus isolates, 31 (93.9%) were developed multidrug resistant against 8 antibiotics. The S. aureus isolates from the dairy farms in the study area were multi drug resistant. Hence, public awareness about good management in dairy farms and milk handling practices, rational use of drugs and periodic assessment of drug sensitivity test should be done to prevent the current global issue which is antimicrobial resistance.

Study of Existing Barn and Animal Hygiene Practices in Milch Cows and Buffaloes in Sabarkantha District

This study aimed to investigate the dairy farming practices and management in Sabarkantha district. A total of 90 livestock owners were randomly selected from six villages representing the Himatnagar, Idar, and Prantij talukas, encompassing three farm size categories based on the number of milch animals: Category A (&lt;10 Milch animals), Category B (10-20 Milch animals), and Category C (&gt;20 Milch animals). The herd composition analysis revealed that cattle were the primary milch animals in the studied farms, with an average total herd strength of approximately 20 cattle and 5 buffaloes. The respondents' personal characteristics indicated a relatively good level of education, with 32% having attained a graduate level of education. The majority of farmers fell into the 50-65 years age group, and most had more than 10 years of experience in dairy farming. Analysis of milking and healthcare practices revealed that a significant number of farmers employed the knuckling method of milking, which is not recommended. Dry cow therapy and milking parlour usage were less prevalent, but proper drying periods and vaccination and deworming practices were generally followed. Housing practices indicated that most farmers used pacca (concrete) floors and provided adequate light and ventilation. Single-line housing was the most popular system, and drainage facilities were generally adequate. However, milking hygiene practices were found to be suboptimal, with limited use of teat dips or antiseptic udder washing. Overall, the study highlights the need for improvement in milking hygiene practices, along with targeted interventions to enhance dairy management in Sabarkantha district.

DairyCoPilot-Automated data compilation and analysis tools for DairyComp data assets.

Modern dairy farm management requires meaningful data and careful analysis to maximize profitability, cow health, and welfare. Current data platforms, such as DairyComp, lack robust integrated data analysis tools. Producers and consultants need dedicated tools to turn collected data sets into assets for informed decision-making processes. The DairyCoPilot app allows users to rapidly extract health and production data from DairyComp, then compile and analyze the data using a menu-driven point-and-click approach. Prospects for training consultants in applied data analysis skills make DairyCoPilot a tool to identify farm management bottlenecks with less time spent for data analysis, improving cow health, and dairy production. The DairyCoPilot Dashboard R Shiny application is published using RStudio Connect: https://connect.doit.wisc.edu/dairy-copilot/.

EVALUASI MANAJEMEN BUDIDAYA DAN KESEHATAN SAPI PERAH DI BEBERAPA KABUPATEN DI PROVINSI JAWA TIMUR

The demand for cow milk in Indonesia increases every year, but optimal production is not met. This study aimed to assess dairy cattle production management and health in East Java Province to construct improvement strategies and increase milk production. The research method was conducted through interviews with 63 dairy farmers from Sidoarjo, Mojoketo, Malang, Pasuruan, Probolinggo, Jember, and Banyuwangi regencies. This study indicates that 81% of farmers joined to livestock groups and sold fresh milk products to cooperatives or KUD (Koperasi Unit Desa). The average livestock ownership is 6.7 heads, with an average number of lactating cows of 4.5. The average price of milk sold to cooperatives or KUD was 5810 IDR/L and if it sold independently the price was 9769 IDR/L. Breeders provide forage feed as much as 34.67 kg/head/day and concentrate as much as 4.8 kg/head/day. The forage and concentrate feeding frequencies were 2.7 and 1 times a day. As many as 44% of farmers did not experience any problems or obstacles in the cultivation process carried out. A total of 66% faced obstacles that were dominated by feed factors (19.05%) and disease (17.46%). Diseases that infect (besides mastitis) are bloat (29%), hypocalsemia (18%), and diarrhea (7%). As an effort to prevent and increase production, farmers carry out preventive actions through the application of good farming practices (40%), maintaining the cleanliness of housing and livestock (24%), not making any effort (18%), applying feed management (10%), giving supplements and feed additives (8%). In conclusion, dairy farming management in East Java Province needs to be improved, and good husbandry practices need to be carried out by all farmers to increase cow’s milk production.

A TEST TO MEASURE KNOWLEDGE OF DAIRY FRAMERS ABOUT IMPROVED DAIRY FARMING PRACTICES

Dairy farming is practised mainly by resource poor farmers who have limited access to knowledge, skill, training and credit. Extension interventions would help these farmers to access knowledge and technologies that can enhance their standard of living. This would facilitate them to obtain good economic returns. The major objective of study was to evaluate dairy farmers’ knowledge about improved dairy farming practices. The knowledge test of resource poor dairy farmers on four major areas of dairy farming (breeding, feeding, healthcare and management practices) was conducted. A tentative list of 35 items was drafted keeping in view the application of statement suited to the area of study. After getting jury opinion on the items of test, Item difficulty index, Discrimination index and items of validity were worked out. Finally, 28 statements were selected in the final format to measure knowledge about improved dairy farming practices. The reliability coefficient (rtt=0.77) obtained indicated that the internal consistency of the knowledge test developed for the study was very high.

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring.

Dairy is an economically significant industry that caters to the huge demand for food products in people's lives. To remain profitable, farmers need to manage their farms and the health of the dairy cows in their herds. There are, however, many risks to cow health that can lead to significant challenges to dairy farm management and have the potential to lead to significant losses. Such risks include cow udder infections (i.e., mastitis) and cow lameness. As automation and data recording become more common in the agricultural sector, dairy farms are generating increasing amounts of data. Recently, these data are being used to generate insights into farm and cow health, where the objective is to help farmers manage the health and welfare of dairy cows and reduce losses from cow health issues. Despite the level of data generation on dairy farms, this information is often difficult to access due to a lack of a single, central organization to collect data from individual farms. The prospect of such an organization, however, raises questions about data ownership, with some farmers reluctant to share their farm data for privacy reasons. In this study, we describe a new data mesh architecture designed for the dairy industry that focuses on facilitating access to data from farms in a decentralized fashion. This has the benefit of keeping the ownership of data with dairy farmers while bringing data together by providing a common and uniform set of protocols. Furthermore, this architecture will allow secure access to the data by research groups and product development groups, who can plug in new projects and applications built across the data. No similar framework currently exists in the dairy industry, and such a data mesh can help industry stakeholders by bringing the dairy farms of a country together in a decentralized fashion. This not only helps farmers, dairy researchers, and product builders but also facilitates an overview of all dairy farms which can help governments to decide on regulations to improve the dairy industry at a national level.

Dairy farmer and farm staff attitudes and perceptions regarding daily milk allowance to calves

The benefits of feeding calves more milk are increasingly being recognized by dairy farmers. However, most producers have still not implemented higher feeding plans. The aim of the present study was to gain a deeper understanding of farmer and farm staff attitudes, and the perceptions and factors considered in their decision-making regarding daily milk allowances. We collected data through focus group interviews with dairy farmers, farm managers, and calf-care workers who were selected using purposive and snowball sampling. In total, 40 persons (24 women and 16 men) joined a focus group interview (6 in all, each with 5-8 participants). Interviews were recorded, and recordings were transcribed and analyzed thematically. Participants had contrasting opinions about the minimum, maximum, and recommended daily milk allowances to their calves. Their suggested lowest daily milk allowance to sustain animal welfare ranged from 4 to 8-10 L and the maximum allowance from 6 to 15 L. We found that farmers' and farm staff's choices and recommendations of milk-feeding protocols were influenced by a large number of factors that could be grouped into 4 themes: (1) life beyond work, (2) farm facilities and equipment, (3) care of the calves, and (4) profitability and production. Participants' considerations were similar and aimed to maximize daily milk allowance based on farm conditions. However, the allowances they described as optimal for their calves often differed from what they considered practically feasible. We found that the care of the calves and the well-being of the owners and the staff was central in the participants' decision-making, but that this care perspective was challenged by the social and economic sustainability of the farm. Most participants fed their calves twice daily and did not think that increasing that number would be practically feasible. Our results indicate that the participants' viewpoints regarding calves were important for their decision-making about milk allowances. We suggest that a more holistic perspective should be used when advising farmers about milk allowances, putting particular emphasis on the caring and social sustainability aspects of the individual farm.

How do work challenges interact with health management in dairy farms?

Herd health management is a critical issue for the future of dairy systems. The right combination of preventive and curative practices will depend on management system, level of work productivity, and self-sufficiency objectives, and will entail specific skills and work organizations. However, the combination of work dimensions and animal health management has rarely been explored in the literature on a livestock farming system scale. The Grand Ouest region of France spans a diverse array of livestock farming systems that can serve to design herd health management indicators, farming objectives and work arrangements, and explore their linkages. Here we ran semi-structured interviews on 10 dairy farms, analyzed the farmers’ discourses, and built 7 variables and 25 modalities that, for the first time, cover three components, namely herd health, farming objectives and work arrangements, and we tested various associations between these variables. Our interview data confirms that consultants and veterinarians have a key role to play in building a pool of skills adapted to various types of health management system. Data suggests linkages between prevention measures, alternative or conventional curative interventions, and work-related parameters.