Development of Smart Space Architecture for Dairy Farming Management

Community empowerment through the management of dairy cows is one of the flagship programs of the Tulungagung Regency Government which aims to improve the economy of the people of Kampung Susu Geger, who incidentally are dairy farmers so that farmers can spread their wings in marketing the livestock products they have managed through this empowerment after that. Asset-Based Community Development (ABCD) is one approach in The process of community empowerment through the management of dairy farms in Geger Village. Data collection techniques used were: (1) observation, (2) interviews, and (3) documentation. This paper has results showing that: (1) The process of Community Empowerment through the management of dairy farms in Geger Village is generally quite mature in terms of planning by selecting activities based on priority needs of community groups. In general, its implementation can support the success of the Empowerment Program through managing dairy farms and improving the community's economy.

Effective management of a dairy farm has to focus on individual animal apart from group or herd management since 'smallest production unit in the dairy is the individual animal’. In this context, precision dairy farming (PDF) aims to manage the basic production unit in order to exploit its maximal production capacity. PDF is the use of information and technology based farm management system to measure physiological, behavioural and production indicators of individual animals to improve management strategies, profitability and farm performance. PDF applications are finding their way on dairy farms, although there seem to be differences in the uptake of PDF applications between dairy systems. The authors have attempted to identify different PDF tools utilized across the globe and have highlighted the status of adoption in Indian scenario by highlighting about few farms/organizations involved in its utilization and uptake over the years. In this direction, the authors have also focused on several benefits and challenges faced by developing countries including India since the benefits are often not immediately apparent and they require more management expertise along with an investment of time and money to realize. In addition, the adoption rate depends on various factors like farmer education, farm size, perceptions of risk, ownership of a non-farm business etc. Addressing these issues is very essential for the uptake of technologies and hence, an effort has been made to propose strategies for adoption and operationalization of PDF in India and other developing countries where the similar scenario exists. The study also highlights that PDF in many developing countries including India is in its infancy, but there are tremendous opportunities for improvements in individual animal and herd management in dairy farms. The progressive farmers or the farmers’ groups, with guidance from the public and private sectors, and professional associations, can adopt it on a limited scale as the technology shows potential for raising yields and economic returns on fields with significant variability, and for minimizing environmental degradation. Additional research needs to be undertaken to examine the adoption process for not only successful adoption of technology, but also to solve the issues associated with the technology adoption. Further, right extension approaches and advisory services for the farmers interested in PDF needs to be undertaken for its effective application under different socio-economic and ecological conditions.

The aim of this study was to develop a financial and renewable multi-objective optimization (FARMOO) method for dairy farms. Due to increased global milk production and European Union policies concerning renewable energy contributions, the optimization of dairy farms from financial and renewable standpoints is crucial. The FARMOO method found the optimal combination of dairy farm equipment and management practices, based on a trade-off parameter which quantified the relative importance of maximizing farm net profit and maximizing farm renewable contribution. A PV system model was developed and validated to assess the financial performance and renewable contribution of this technology in a dairy farming context. Seven PV system sizes were investigated, ranging from 2 kWp to 11 kWp. Multi-objective optimization using a Genetic Algorithm was implemented to find the optimal combination of equipment and management practices based on the aforementioned trade-off parameter. For a test case of a 195 cow spring calving dairy farm in Ireland, it was found that when the relative importance of farm net profit was high, a PV system was not included in the optimal farm configuration. When net profit and renewable contribution were of equal importance, the optimal farm configuration included an 11 kWp PV system with a scheduled water heating load at 10:00. Multi-objective optimization was carried out for the same test case with the goals of maximizing farm net profit and minimizing farm CO2 emissions. Under this scenario, the optimal farm configuration included an 11 kWp PV system when the relative importance of farm net profit was low. This study included a sensitivity analysis which investigated the use of a 40% grant aid on PV system capital costs. This sensitivity analysis did not significantly improve the financial feasibility of PV systems on dairy farms. Moreover, it was found that load shifting of a farm’s water heating enabled the majority of the PV system’s electricity output to be consumed. Hence the use of batteries with small PV systems on dairy farms may not be necessary. The method described in this study will be used to inform policy and provide decision support relating to PV systems on 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.

A total of 20,823 Holstein Lai Sind crossbred dairy cows from three farms in Central and East-Southern Vietnam were examined to clarify the prevalence of clinical mastitis and evaluate its effects from July to October 2022.The results indicated that clinical mastitis in cows was present at a relatively low rate (7.05%), and the high-density farms had a higher rate of clinical mastitis in cows than in other farms.In a hot, humid month, the incidence of clinical mastitis in cows (7.78%) was higher than in other months in this study, especially in the high-density farm.Cows at the first parity were clinical mastitis (32.58%) higher than at other parities, followed by cows at the second parity.As indicated, farm scale, months, and parity affected the incidence of clinical mastitis in cows in those survey farms.Clinical mastitis caused the fluctuation in milk yield, which was determined at 15.458.67 to 18.440.72kg/cow/day; moreover, the milk yield of clinical mastitis cows in the large-scale farm showed a significant fluctuation.Among examined bacterial pathogens, Streptococcus agalactia (34.65%) was the most detected from milk samples of clinical mastitis cows, followed by Klebsiella (14.47%) and Escherichia coli (10.09%), but Staphylococcus spp. was present at the lowest rate (0.88%).The treatment followed the veterinarian's guidelines and was adequate for clinical mastitis cows in this study within five days (79.15%).Thus, mastitis management in dairy farms is significantly required to prevent disease spreading in farms, especially in high-density herds.

\n The correct identification of mastitis-causing pathogens is a key factor in the successful management of dairy farms. Techniques such as culture medium, qPCR, and 16S rRNA sequencing have been used to detect important microorganisms in raw bovine milk samples. However, due to costs, some challenges remain. Machine learning methods have been shown as an attractive alternative, as they can integrate different sources of data, with a diversity of purposes. New studies focusing on the detection of clinical and subclinical mastitis highlight the potential of applied machine learning methods to the management of mastitis in dairy farms. In this work, we evaluate the performance of three machine learning methods to detect the most abundant mastitis-causing pathogen in individual raw milk bovine samples integrating data from milk composition and 16S rRNA sequencing. We show the potential for the identification of Escherichia coli and Staphylococcus aureus. For abundance greater than 3% in individual samples, an accuracy of 100% and 86% was achieved, respectively. These results show that not only subclinical and clinical mastitis can be detected by machine learning methods, but some mastitis- causing pathogens either. Moreover, to maximize the information obtained from 16S rRNA sequencing, we evaluate in silico genetic diversity for different regions of the 16S rRNAgene and validate the results by Illumina sequencing. We show that for better detection of microorganisms associated with bovine mastitis, the V2-V3 region detects a higher prevalence with more relative abundance. We hope that this work can contribute to better management of dairy farms as well as the development of new tools for the control of bovine mastitis.\n

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.

Knowledge Representation Methods for Dairy Decision Support Systems

Effects of employer management on employee recruitment, satisfaction, engagement, and retention on large US dairy farms

This paper presents a model to quantify total greenhouse gas (GHG) emissions from dairy farms. The model, which is based on a whole farm management approach, accounts for the variability that occurs in GHG emissions among farm production and management practices. The variation is accommodated in six dairy farm management systems (FMS), which broadly include typical dairy production systems in South Africa. These are pasture-based with high or low stocking rates, total mixed ration with high or low stocking rates, and partial mixed ration with high or low stocking rates. Three variations of functional units that were used to evaluate the environmental impacts of various FMS are defined as per animal unit = kg CO 2-eq head -1 yr -1 ; per unit of farm area = kg CO 2-eq ha -1 yr -1 , and per unit of product = kg CO 2-eq kg FPCM -1 , where FPCM is fat and protein corrected milk. The results show a range of GHG emissions in CO 2-eq among the FMS with various methodological approaches because of the large impact from different emission factors, which vary between accounting methods. The more detailed equations were recommended to effectively improve environmental impacts. These more detailed non-linear equations tended to predict more biologically realistic emissions when compared with the linear equations in which over or under-predictions of GHG were observed. The most prominent drivers for GHG emissions across all FMS were from enteric methane (CH 4 ) and nitrous oxide (N 2 O) from soil management. Rankings among FMS varied according to output methodology and functional units. GHG emissions expressed per animal or per unit area differ greatly from those expressed from a given level of product. In conclusion, the accounting methodologies that are described in this paper to predict GHG emissions of animal-related origin performed sufficiently across all FMS, and could be applied to quantify the carbon footprint of dairy production systems in South Africa. Keywords : Carbon dioxide equivalents, dairy production, methane, nitrous oxide

Attitudes, management practices, and herd performance — a study of ontario dairy farm managers. I. Descriptive aspects

BackgroundMastitis is a common driver of antibiotic use in dairy farms and is exacerbated in low-income settings by the lack of diagnostics and treatment strategies. We assessed the decision-making process of animal health providers (AHPs) in managing mastitis in small-holder dairy farms in Kiambu County, Kenya. Data were collected from 114 AHPs and using item response theory, scales were developed to measure attitudes toward udder health, and multivariable linear regression was used to analyse demographic factors associated with these attitudes.ResultsOverall, 90% of AHPs diagnose clinical mastitis based on clinical symptoms such as visible udder signs and milk changes, with little diagnostic testing support. Antibiotic treatment was initiated immediately after clinical examination by all, 80% and 50% of AHPs in severe, moderate, and mild mastitis cases, respectively. β-lactams (namely penicillins) and aminoglycosides which were administered mostly parenterally, were the frequently reported antibiotic classes used in treatment of mastitis irrespective of the severity. AHPs with a larger farmer client base and those who did not treat mild mastitis cases with antibiotics had significantly higher mean attitude scores. Treatment of mastitis is primarily based on clinical judgment, with limited microbiological diagnostic support, and parenteral antibiotics are used empirically as first-line therapy.ConclusionsWe recommend development of mastitis management support tools such as treatment guidelines and advocate for adoption of routine on-farm rapid testing supported by bacterial culture to guide treatment decision making and antibiotic choice.

This study was done to establish relationships between management and sanitary practices on ewe dairy farms and the quality of milk produced. For this purpose, a survey was carried out on 118 farms and a total of 121,117 animals in the Castilla-La Mancha region (Southeast Spain) in which the veterinarians of the Association of Sanitary Defence participated. Flocks varied considerably in size, ranging from 120-7,200 sheep, with an average milking period lasting 3-5 months and milk yields of between 50 and 150 litres per lactation by mechanical systems on 75.0% of farms. Sanitary practices during milking are still not commonplace; 53.3% performed post milking teat disinfection, 37.0% did the California mastitis test, 31.1% had a dry therapy programme and only 18.6% of farms kept records of animals with clinical mastitis. Nonetheless, the results revealed that 42.7% of the farms surveyed showed good quality hygienic milk (bulk milk somatic cell count, BMSCC<600 × 103 cells mL-1). On most farms, the rate of animals with clinical mastitis was less than 5.0%, while that of subclinical mastitis cases was less than 5.0% on 25.6% of farms. Certain factors like mechanical milking, post milking teat disinfection, recording animals with clinical mastitis and controlling the frequency of animals with subclinical mastitis did not exceed 5.0%. Those risk factors for increased BMSCC levels were significant. Therefore, farmers are recommended to bear these factors in mind, and to instil the importance of carrying out these practices when implementing a good dairy farm practices system.

Bovine mastitis could reduce the milk production and the quality of the bulk tank milk (BTM). Antibiotic treatments through intramammary or parenteral methods are being widely used in dairy farms. A cross-sectional study to investigate for general farm management and pre-test the questionnaire was performed in Southwestern Yunnan province, China. A total of 134 dairy farms were included. Milking cows of each farm were determined for the presence of clinical (CM) and sub-clinical (SCM) mastitis using the California Mastitis Test (CMT). Rates of CM and SCM in studied farms ranged from 2-11%, and 24-69%, respectively. The incidence of antibiotic residues in BTM of all farms was very high (32%, 44/134). All antibiotic contaminated samples were from smallholder dairy farms. Factors significantly associated with the presence of antibiotic contamination included farm region, antibiotics usage, persons performing mastitis treatment, and rates of CM. Rates of CM were significantly associated with the farm region, cleanliness of udders before milking, and the number of milking cows. Our results emphasize that the risk factors of dairy farm management should be paid attention, which can reduce mastitis prevalence and antibiotic contamination in BTM in Southwestern China.

PurposeDairy farms, along with livestock and specialty crop farms, face a tight labor supply and increasing labor costs. To overcome the challenging labor market, farm managers can increase labor-use efficiency through both human resource and capital investments. However, little is known about the relationship between such investments and farm profitability. The purpose of this paper is to examine the relationship between dairy farm financial performance and labor-use efficiency, as measured by labor productivity (milk sold per worker equivalent); labor costs (hired labor cost per unit of milk sold and hired labor cost per worker); and investment in labor-saving equipment.Design/methodology/approachCluster analysis is applied to partition dairy farms into three performance categories (high/middle/low), based on farms’ rate of return on equity, asset turnover ratios and net dairy income per hundredweight of milk. Next, the annual financial rank is fitted into both random- and farm-level fixed-effects ordered logit and linear models to estimate the relationship between dairy farms’ financial performance and labor-use efficiency. This study also investigates the implications of using a single financial indicator as a measure of financial performance, which is the dominant approach in literature.FindingsThe study finds that greater labor productivity and cost efficiency (as measured by hired labor cost per unit of milk sold) are associated with better farm financial performance. No statistically significant relationship is found between farm financial performance and both hired labor cost per worker and advance milking systems (a proxy of capital investment in labor-saving technology). Future studies would benefit from better measurements of labor-saving technology. This study also demonstrates inconsistency in regression results when individual financial variables are used as a measure of financial performance. The greater labor-use efficiency on high-performing farms may be a combination of hiring more-skilled workers and managerial strategies of reducing unnecessary labor activities. The results emphasize the importance of managerial strategies that improve overall labor-use efficiency, instead of simply minimizing total labor expenses or labor cost per worker.Originality/valueThis study examines the importance of labor productivity and labor cost efficiency for dairy farm management. It also develops a novel approach which brings a more comprehensive financial performance evaluation into regression models. Furthermore, this study explicitly demonstrates the potential for inconsistent results when using individual financial variable as a measure of financial performance, which is the dominant measurement of financial performance in farm management studies.