Informational resources used by farmers with ruminants and monogastrics for animal health monitoring: importance of sensory indicators.

Managers of health in livestock systems are asked to shift from a curative approach to a more preventive approach. This change requires sociological and technical reconfiguration and raises the issue of how changes are implemented by farmers and their technical support ecosystem (advisors, trainers, veterinarians). Here, we report work conducted in western France by an Agricultural European Innovation Partnership Operational Group bringing together animal scientists and sociologists to advance knowledge on animal health in a range of livestock sectors, i.e. dairy cattle, beef cattle, small ruminants (sheep, goats), poultry and pigs. In this study, our aim was to answer this question: what are the Informational Resources (I.R.) that farmers use to promote animal health of their herds? First, we used a survey to characterize 129 I.R. used by advisors, then, we used statistical analysis to classify these I.R. into six clusters. Second, we organized eight focus-group sessions that involved a total of 50 farmers from across all livestock sectors to find out how they mobilize the I.R. and what they see as important for animal health monitoring practice. Finally, we performed individual interviews with 42 farmers to expand the data captured in the collective focus groups. Results showed that farmers and advisors have a broad and diverse range of I.R. to help monitor animal health. We identified six clusters of I.R.: regulatory tools, periodic reports, tools for farmer-led monitoring, tools and indicators for national reference datasets, slaughterhouse and laboratory indicators, and training delivered to farmers. During focus group, livestock farmers identified some of their I.R. within these clusters but they also cited other daily routines that help them monitor animal health that were not cited by advisors. We found that farmers mainly use sensory indicators (typically smell, sight, touch) in their daily practice whereas advisors mainly use relatively sophisticated retrospective monitoring tools. Farmers also cited the importance of indicators that can rapidly objectify any change in animal condition, behavior, or health. This work finds a split in the distribution of animal health management roles, with farmers implementing daily checks whereas advisors run periodic health surveillance, thus revealing differentiated roles and needs between farmers and their advisors.

Mitigation of gaseous emissions from dairy livestock: A farm-level method to examine the financial implications.

Feeding the world's population while minimising the contribution of agriculture to climate change is one of the greatest challenges facing modern society. This challenge is particularly pronounced for dairy production where the carbon footprint of products and the mitigation costs are high, relative to other food stuffs. This paper reviews a number of mitigation measures that may be adopted by dairy farmers to reduce greenhouse gas emissions from their farms. A simulation model is developed to assess the cost-benefit of a range of mitigation measures. The model is applied to data from Ireland, a country with a large export-oriented dairy industry, for a range of farms including top, middle and bottom performing farms from a profitability perspective. The mitigation measures modelled included animal productivity, grass production and utilisation, better reproductive performance, early compact calving, reduced crude protein, decreased fertiliser N, protected urea, white clover, slurry tank cover and low emission slurry spreading (LESS). The results show that over half of the greenhouse gas abatement potential and most of the ammonia abatement potential were realised with cost-beneficial measures. Animal and feed-related measures that increased efficiency drove the abatement of GHG emissions. Low-emission slurry spreading was beneficial for the bottom and middle one-third of farms, while protected urea and reducing nitrogen use accounted for most of the ammonia abatement potential for the most profitable farms. Results showed that combining mitigation measures resulted in a decrease of 23%, 19%, and 12% in GHG emissions below 2020 levels for the bottom, middle, and top performing dairy farms, respectively. The findings imply that top dairy farms, that are already managed efficiently and optimally, may struggle to achieve the national and international GHG reduction targets with existing technologies and practices.

Curbing methane emissions from Italian cattle farms. An agroeconomic modelling simulation of alternative policy tools.

Methane (CH4) emissions from cattle farms have been prioritised on the EU agenda, as shown by recent legislative initiatives. This study employs a supply-side agroeconomic model that mimics the behaviour of heterogeneous individual farms to simulate the application of alternative economic policy instruments to curb CH4 emissions from Italian cattle farms, as identified by the 2020 Farm Accountancy Data Network survey. Simulations consider increasing levels of a tax on each tonne of CH4 emitted or of a subsidy paid for each tonne of CH4 curbed with respect to the baseline. Individual marginal abatement costs are also derived. Besides, to consider possible technological options to curb emissions, a mitigation strategy is simulated, with different levels of costs and benefits to appraise the potential impacts on the sector. Relevant reductions in operating income are foreseen, the most substantial in farm types and size classes characterised by lower levels of carbon productivity. The introduction of the mitigation strategy shows that the outcome in terms of mitigation potential, without undermining production level, highly depends on the implementation costs, but can also vary widely due to heterogeneous farms' economic performances. Policy implications are also derived.

Animal welfare and farm economy-exploring the relationship between dairy animal welfare indicators and economic efficiency.

This paper explores the associations between a set of mainly health and productivity related animal welfare indicators in dairy farming calculated based on herd recordings, and short-term economic efficiency. To explore this association, we merged a dataset with animal welfare indicators and a dataset with farm economic data from 459 Norwegian farms. Gross margin including direct payments together with milk quota, farmland and number of animals and were used to calculate economic efficiency. The analysis was performed in two steps. First, Data Envelopment Analysis (DEA) was used to rank the farms according to efficiency. Second, the machine learning method boosting was applied to analyze the associations between economic efficiency and the different welfare indicators. The findings show that for most welfare indicators, at indicator values below the national average a positive association exists between increasing indicator values and economic efficiency. For some indicators such as for example Fertility, Calves and Youngstock, this positive association continues also beyond the national average. However, for other indicators such as for example Claw and Udder health, a curvilinear relationship exists with a turning point from where the efficiency is negatively associated with increasing indicator values. Possible explanations for the associations between economic efficiency and animal welfare indicators are outlined.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms.

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6 ± 5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81 ± 0.47. The PMC caused an average milk loss of 0.77 ± 0.26 kg per lactating cow per day, which corresponded to an average production loss of 2.38 ± 0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Farm management and economic factors associated with bulk tank total bacterial count in Holstein dairy herds in Iran.

The objectives of this research were (1) to study different factors affecting milk total bacterial count (TBC) and (2) to estimate the economic value associated with TBC in Holstein dairy herds in Iran. The relationships between bulk tank TBC and farm management and economic factors were examined on 56 randomly selected intensive dairy farms. Herd management factors associated with bulk tank TBC were determined using mixed linear models. The median bulk tank TBC for the sample herds was 299 (range 81-1185) × 103 cfu/ml. The average economic premium opportunity from bulk tank TBC was US$ 1.32 per ton of milk ranging from US$ 0.02 per ton of milk for herds applying wet tissue procedures as teat cleaning material and washing the water troughs three times per day to US$ 5.20 per ton of milk for herds with dirty barns. Results showed that the following management factors were associated with low TBC and high economic value: frequency of cleaning water troughs, teat cleaning material, the frequency of milk delivery to the processor, bedding material, herd size, education level of workers, udder washing material, material of milking parlor wall, frequency of disinfection of the calving area, presence of veterinarian, water quality control, having a hospital pen and barn hygiene. In conclusion, our findings highlight the need to pay more attention to farm management issues, particularly farm hygiene practices to reduce milk TBC and so reduce the economic burden of TBC in dairy herds in Iran.

Economic considerations of breeding for polledness versus disbudding in beef cattle.

The domestication of animals has rendered horns less necessary for survival. Moreover, the use of polled and disbudded animals is interesting in order to avoid injuries of animals and handlers, among other advantages. We therefore conducted a comparative economic analysis of different traditional disbudding techniques versus selective breeding for polledness in Nelore cattle, the main beef breed of tropical systems in Brazil. The cost to obtain animals without horns was estimated in three different scenarios: disbudding with hot iron, disbudding with caustic paste, and phenotypic selection for polled animals. Price quotations of the materials were obtained in different states of the country and averaged. An initial frequency of horned animals of 92.16% was obtained based on the records of the Brazilian Association of Zebu Breeders. Selective breeding was found to be the best cost-effective scenario. This result differs from intensive production systems of dairy cattle in which traditional disbudding continues to be the best cost-effective scenario. The main explanation is the lack of difference in the price of Nelore semen from polled and horned bulls. Phenotypic selection for polled animals is the best cost-effective method, and it is in accordance with welfare practices. Care should be taken regarding the intensive use of few polled breeding animals in order to avoid inbreeding depression in other traits.

Monitoring bovine dairy calf health and related risk factors in the first three months of rearing.

BACKGROUND: Rearing replacement heifers is pivotal for the dairy industry and is associated with high input costs for the preweaned calves, due to their higher susceptibility to diseases. Ensuring calf health and viability calls for systematic approaches in order to mitigate the costs induced by managing sick calves and to ensure animal welfare. The objective of this study was to develop a systematic and feasible health-monitoring tool for bovine dairy calves based on repeated clinical observations and diagnostic results of calves at three time points; the 1st (T0), the 3rd (T1) and the 12th (T3) week of age. The study included observations from 77 dairy heifer calves in nine Danish commercial dairy herds. Immunisation status was assessed by serum Brix% at T0. Clinical scoring included gastrointestinal disease (GD) and respiratory disease (RD). The average daily weight gain (ADWG) was estimated from heart-girth measurements. Pathogen detection from nasal swabs and faecal samples were analysed for 16 respiratory and enteric pathogens by means of high-throughput real time-PCR. All measures obtained in each herd were visualised in a panel to follow the health status of each calf over time. RESULTS: The individual clinical observations combined with diagnostic information from immunisation and pathogen detection form each enrolled calf are presented in a herd dashboard illustrating the health status over the study period. This monitoring revealed failure of passive transfer (Brix% < 8.1) in 31% of the 77 enrolled calves, signs of severe GD peaked at T0 with 20% affected calves, while signs of severe RD peaked at T2 with 42% affected calves. ADWG over the first eight weeks was estimated to be 760 g (± 190 g). Pathogen profiles varied between herds. CONCLUSIONS: The large variation in both clinical disease and pathogen occurrence across herds emphasizes the need for herd specific monitoring. Combining the results of the present study from measures of immunisation, health and growth from individual calves in one visualisation panel allowed for the detection of patterns across age groups in the specific herds, showing promising potential for early detection and interventions that can lead to enhanced calf health and welfare.

Comparison of the use of life cycle assessment and ecological footprint methods for evaluating environmental performances in dairy production.

One popular methodology for assessing the environmental impact of livestock sector is Life Cycle Assessment (LCA), that quantifies the environmental impact of a product. Ecological Footprint (EF) performs an environmental sustainability assessment, by comparing the demand for natural capital by an economic activity with the offer of such capital within a certain territory. The aim of the study was the comparison between LCA and EF in assessing the environmental performances of milk production, assuming as case study three cattle farms with increasing levels of production intensity. Different metrics and functional units (FU) (i.e., fat and protein corrected milk, FPCM and hectare) were adopted for LCA analysis, considering some of the major impact categories. For greenhouse gases emissions, the Global Warming Potential (GWP) and the Global Temperature Potential (GTP) were considered. Both metrics were calculated assuming or not the distinction between biogenic and fossil methane. Adopting GWP as a metric, the results per kg of FPCM provided by the LCA highlighted a different trade off compared to the EF method: the farm with the highest productive intensity produced the least impactful milk in terms of GWP but had the most negative Ecological Balance (EB). The same occurred for the other impact categories. When GTP was adopted, or the hectare was considered as FU, the least intensive farm, characterized by greater feed self-sufficiency, became the one that produced the least impactful milk and had the least negative EB. The study highlighted the scientific significance of the integration between the two approaches for creating a comprehensive representation of the effects of human activities on the environment. The LCA method evaluates impacts intensity referred to a specific functional unit and its results are strongly influenced by productive efficiency; the EF method evaluates environmental sustainability of productions in relation to the territory that supports them.

Dairy cow longevity and farm economic performance: Evidence from Swedish dairy farms.

The longevity of dairy cows is mainly determined by farmers' subjective culling decisions and can be linked to the environmental impact of dairy production and to the social acceptance of the industry. Still, the economic impacts of dairy cow longevity are not well understood. The aim of this study was to examine how herd average dairy cow longevity is related to the farm economic outcome. We used 3 indicators of economic outcome: technical efficiency, profitability, and average milk yield per cow. We used 2 indicators of dairy cow longevity: average herd length of life and average herd length of productive life. The study was based on a unique and detailed dataset from Swedish dairy agriculture, where herd-management data from the national dairy herd recording scheme were combined with farm-level economic variables from the Swedish Farm Accountancy Survey, for a total of 1,959 observations from 2010 to 2018. The regression results highlight that both measures of average herd dairy cow longevity have an overall positive and significant association with farm-level economic performance. These associations had an inverted U-shape, which implies that the association is first positive and then declines. Descriptive statistics indicate that the point where the maximum economic performance is attained varied across the economic indicators. Our results are relevant for individual dairy farmers and their advisors, who are interested in understanding how herd average longevity relates to economic performance on the farms. Our results are also important from a greater sustainability perspective, because linking them to previous knowledge about the environmental and social sustainability benefits of keeping cows longer highlights longevity-associated trade-offs between those benefits and the farm economic outcomes.

Variation in partial direct costs of treating clinical mastitis among 37 Wisconsin dairy farms.

The objective of this observational study was to describe variations in partial direct costs of clinical mastitis (CM) treatments among 37 dairy herds using data obtained from herd management records. Animal health and drug purchase records were retrospectively collected from 37 Wisconsin dairy herds for a period of 1 yr. Each farm was visited to verify case definitions, recording accuracy, and detection criteria of CM cases. Descriptive statistics were used to summarize cost of drugs and milk discard. Differences in costs among protocols, intramammary (IMM) products, parities, days in milk, and recurrence were analyzed using ANOVA. Of 20,625 cases of CM, 31% did not receive antimicrobial treatment. The average cost of drugs and milk discard (including cases that were not treated) was $192.36 ± 8.90 (mean ± SE) per case and ranged among farms from $118.13 to $337.25. For CM cases treated only with IMM antimicrobials, milk discard accounted for 87% of total costs and was highly influenced by duration of therapy. Differences in costs were observed among parities, recurrence, and stage of lactation at case detection. Eight different treatment protocols were observed, but 64% of cases were treated using only IMM antimicrobials. Treatment costs varied among protocols; however, cases treated using both IMM and injectable antimicrobials as well as supportive therapy had the greatest costs as they were also treated for the longest duration. Ceftiofur was used for 82% of cases that received IMM antimicrobials while ampicillin was used for 51% of cases treated using injectable antimicrobials. With the exception of ceftiofur and pirlimycin IMM products, many IMM products were given for durations that exceeded the maximum labeled duration. For cases treated using only IMM therapy, as compared with observed costs, we estimated that partial direct costs could be reduced by $65.20 per case if the minimum labeled durations were used. Overall, partial direct costs per case varied among herds, cow factors, and treatment protocols and were highly influenced by the duration of therapy.

Assessment of carbon footprint of milk production and identification of its major determinants in smallholder dairy farms in Karnataka, India.

Indian dairy enterprise is dominated by smallholder dairy farms that contribute 72% of the country's total milk production. These smallholder dairy farms are often considered to emit substantial greenhouse gases (GHG) but are poor in productive performances. Therefore, it is crucial to estimate the carbon footprint (CF) of milk production of the smallholder Indian dairy farms. The primary objectives of the study were (1) Assessing the CF of milk production of smallholder dairy farms through life cycle analysis in south-interior Karnataka, India; (2) Identifying the hotspots of GHG emissions and significant factors influencing the CF of milk production in smallholder dairy production system. The study accounted GHG emissions from different sources and considered multiple functions of the smallholder production system. Estimations were made based on primary data collected from 47 farms and associated secondary data. For estimating the CF of milk production, the emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) on a CO2-equivalent (CO2-eq) basis from feed production, enteric fermentation, manure management, transport and energy usage were allocated to fat- and protein-corrected milk (FPCM) based on mass balance, price (crop byproducts and residues) and feed digestibility. Principal component analysis and stepwise linear regression analysis were performed to identify the major factors influencing the CF. The average total GHG emissions (kg CO2-eq yr-1 farm-1) attributable to milk production based on mass, economic, and digestibility allocations were 8,936, 8,641, and 8,759, respectively. The contributions of CH4, N2O, and CO2 to the total farm GHG emission were 70.6%, 20.5%, and 7.69%, respectively. The major emission hotspots were CH4 emission from enteric fermentation (66.8%) and GHG emission from feed production (23.0%). The average CF of cradle-to-dairy cooperative milk production varied from 1.45 to 1.81 kg CO2-eq kg FPCM-1. The CF of milk production was more than 2-fold greater, when milk yield was below 3,500 kg lactating cow-1 yr-1. The FPCM yield 100 kg body weight-1, dry matter intake, and CH4 emission from manure management were the strongest determinants of the CF and explained 83.4% of the observed variation. The study emphasized the importance of considering multiple functions of a mixed crop-livestock-based dairy production system for estimating CF per unit of product. The results suggest that maintaining high-yielding dairy animals and adopting appropriate feeding strategies for better feed utilization are the possible effective interventions for reducing the CF of milk production.

Climate change and socio-economic assessment of PLF in dairy farms: Three case studies.

Precision Livestock Farming (PLF) techniques include sensors and tools to install on livestock farms and/or animals to monitor them and support the decision making process of farmers, finally early detecting alerting conditions and improving the livestock efficiency. Direct consequences of this monitoring include enhanced animal welfare, health and productivity, improved farmer lifestyle, knowledge, and traceability of livestock products. The indirect consequences, instead, include improved Carbon Footprint and socio-economic indicators of livestock products. In this context, the aim of this paper is to develop an indicator applicable to dairy cattle farming that takes into account concurrently these indirect consequences. The indicator was developed combining the three sustainability pillars (with specific criteria): environmental (carbon footprint), social (5 freedoms of animal welfare and antimicrobial use) and economic (cost of technology and manpower use). The indicator was then tested on 3 dairy cattle farms located in Italy, where a baseline traditional scenario (BS) was compared with an alternative scenario (AS) where PLF techniques and improved management solutions were adopted. The results highlighted that the carbon footprint reduced in all AS by 6-9 %, and the socio-economic indicators entailed improvements in animals and workers welfare with some differences based on the tested technique. Investing in PLF techniques determines positive effects on all/almost all the criteria adopted for the sustainability indicator, with case-specific aspects to consider. Being a user-friendly tool that supports the testing of different scenarios, this indicator could be used by stakeholders (policy makers and farmers in particular) to identify the best direction towards investments and incentive policies.

A stochastic animal life cycle simulation model for a whole dairy farm system model: Assessing the value of combined heifer and lactating dairy cow reproductive management programs.

This analysis introduces a stochastic herd simulation model and evaluates the estimated reproductive and economic performance of combinations of reproductive management programs for both heifers and lactating cows. The model simulates the growth, reproductive performance, production, and culling for individual animals and integrates individual animal outcomes to represent herd dynamics daily. The model has an extensible structure, allowing for future modification and expansion, and has been integrated into the Ruminant Farm Systems model, a holistic dairy farm simulation model. The herd simulation model was used to compare outcomes of 10 reproductive management scenarios based on common practices on US farms with combinations of estrous detection (ED) and artificial insemination (AI), synchronized estrous detection (synch-ED) and AI, timed AI (TAI, 5-d CIDR-Synch) programs for heifers; and ED, a combination of ED and TAI (ED-TAI, Presynch-Ovsynch), and TAI (Double-Ovsynch) with or without ED during the reinsemination period for lactating cows. The simulation was run for a 1,000-cow (milking and dry) herd for 7 yr, and we used the outcomes from the final year to evaluate results. The model accounted for incomes from milk, sold calves, and culled heifers and cows, as well as costs from breeding, AI, semen, pregnancy diagnosis, and calf, heifer, and cow feed. We found that the interaction between heifer and lactating dairy cow reproductive management programs influences herd economic performance primarily due to heifer rearing costs and replacement heifer supply. The greatest net return (NR) was achieved when combining heifer TAI and cow TAI without ED during the reinsemination period, whereas the lowest NR was obtained when combining heifer synch-ED with cow ED.

Organic conversion tea farms can have comparable economic benefits and less environmental impacts than conventional ones-A case study in China.

Lack of experience concerning the organic conversion period and its associated challenges have made it difficult for conventional farmers to convert to organic farming. In this study, using a combined life cycle assessment (LCA) with data envelopment analysis (DEA) approach, we investigated farming management strategies, and environmental, economic, and efficiency impacts of organic conversion tea farms (OCTF, N = 15) compared to conventional (CTF, N = 13) and organic (OTF, N = 14) tea farms in Wuyi County, China for a year-round (2019). We found that the OCTF reduced agricultural inputs (environmental impacts) and applied more manual harvesting (increased added value) to pull through the conversion period. According to the LCA results, OCTF showed a similar integrated environmental impact index compared with OTF but significantly (P < 0.05) lower than CTF at both midpoint and endpoint levels. In terms of economic assessment, OTF showed the significantly highest total revenue (18.7 thousand $ ha-1 yr-1) and profit (13.7 thousand $ ha-1 yr-1) (P < 0.05) among the farm types. In contrast, OCTF and CTF did not show significant differences in relation to these economic indicators (P > 0.05). The total cost and cost-profit ratio did not show significant differences among the three farm types. Considering the DEA analysis, there were no significant differences in the technical efficiency of all farm types. However, the eco-efficiency of OCTF and OTF was significantly higher than that of CTF. Therefore, conventional tea farms can survive the conversion period with competitive economic and environmental benefits. In this regard, policies should promote organic tea cultivation and agroecological practices to enhance the sustainable transformation of tea production systems.

Assessment of a joint farmer-veterinarian discussion about biosecurity using novel social interaction analyses.

Veterinarians are trusted by farmers and play an important role in assisting them to implement biosecurity. More research is needed that particularly focuses on the impact of joint farmer-veterinarian discussions to further understand the role of communication in altering biosecurity behaviours. The aim of this study was to analyse joint dairy cattle farmer-veterinarian discussions about the adoption of on-farm biosecurity using novel social interaction methodologies. Farmer and veterinarian stakeholders were invited to take part in a face-to-face meeting. Introductory presentations were given, followed by separate facilitated veterinarian and farmer discussions. All stakeholders were brought together for a final facilitated group discussion which was audio recorded. Corresponding transcripts from the recordings were analysed via thematic and conversation analyses. Conversation analysis assessments such as turn taking, repair, sequence organisation, overlap and asymmetry were employed to investigate the nature of the conversation. Thematic analysis identified the negative repercussions of conflicting information or ineffective communication surrounding biosecurity implementation. The type of, and importance of, the relationship farmers had with veterinarians and other stakeholders was highlighted. The need to provide personalised biosecurity protocols on farms was identified. Four key factors were identified via conversation analyses. These included: 1) how the conversation facilitated agreement, 2) how the conversation allowed the farmer and veterinarian participants to learn from one another in real time, 3) how the discussion enabled participants to expand upon points they were making, and 4) how participants were able to obtain a greater understanding of the other participants' opinions, even without total resolution. Debate around the effective implementation of biosecurity measures on farms, explored using novel techniques, demonstrated the potential for utilising a discussive approach between veterinarians and farmers to lead to solutions not previously considered. Because of the nature of the discussion, conversation analysis resulted in an informative approach to encapsulating the nuanced dialogue between stakeholders, highlighting the potential of this analysis framework.

Drivers and indicators of dairy animal welfare in large-scale dairies (review).

While animal welfare concerns are rising globally, this has not been the case with lower- and middle-income economies in Africa and Asia such as Zimbabwe. These developing countries have their own problems which are not reported in developed countries, such as the harsh economic environment, limited technologies and different political and food security priorities. These factors limit focusing on animal welfare. Meanwhile, studies on animal welfare in these countries have been limited. The task of determining animal welfare is a very complex and can sometimes be very subjective since there is no gold standard protocol to be used in many developing countries. This paper reviews the main factors that are used to assess dairy animal welfare at a farm situation. The factors were categorised and generally discussed as drivers and indicators of dairy animal welfare. Key indicators reviewed in this study include but are not limited to production performance indicators, body condition scores, cleanliness scores, presence of clinical disease signs and physiological and behavioural indicators. Dairy animal welfare drivers discussed in this paper include but not limited to the general design of dairy cattle housing, presence of foot bath and shading facilities, presence and use of maternity paddocks, state of feeder and water troughs.

The costs of chronic mastitis: A simulation study of an automatic milking system farm.

Mastitis is a production disease in dairy farming that causes economic losses. Especially chronic mastitis (i.e., mastitis cases continuing longer than 28 days) can substantially affect the risk of transmission of intramammary infections (IMI) and total milk production losses. Insights into the impact of chronic mastitis on production and farm economics are needed to guide chronic mastitis decision-making. We aimed to estimate the costs of chronic mastitis with a Monte Carlo simulation model in which the costs of chronic mastitis were estimated as part of the total mastitis costs. The model simulated milk yields, IMI dynamics, somatic cell count (SCC), and pregnancy status on an average Dutch dairy farm with 100 cow places over 9 years. The model was parameterized using information from the literature and actual sensor data from automatic milking system (AMS) farms. The daily subclinical milk production losses were modeled using a generalized additive model and sensor data. Transmission of IMI was modeled as well. The model results indicated median total costs of mastitis of € 230 per generic IMI case (i.e., a weighted average of all pathogens). The most substantial cost factors were the extra mastitis cases due to transmission, culling, and milk production losses. Other significant costs originated from dry cow treatments and diverted milk. The model also indicated median total costs due to chronic mastitis of € 118 (51 % of the total mastitis costs). The share of chronic mastitis relative to the total mastitis costs was substantial. Transmission of contagious bacteria had the largest share among the chronic mastitis costs (51 % of the costs of chronic cases). The large share of chronic mastitis costs in the total mastitis costs indicates the economic importance of these mastitis cases. The results of the study point to the need for future research to focus on chronic mastitis and reducing its presence on the AMS dairy farm.

Life cycle assessment of sheep cheese production in a small dairy factory from Romanian rural area.

The paper aims to analyse the life cycle of sheep milk cheese production, to investigate supply chain of cheese and to suggest improvements for minimizing the environmental impacts associated with this agri-food product. The functional unit chosen was 1 kg of sheep milk cheese. Modelling of the production process was performed based on real data obtained from a cheese production company in Romania. Two methods (CML and ReCiPe), included in GaBi software, were used for the environmental impact assessment. The following impacts: abiotic depletion potential elements, acidification potential, eutrophication potential, global warming potential, human toxicity potential (included in CML method) and climate change, fossil depletion, freshwater eutrophication, particulate matter formation, photochemical oxidant formation, terrestrial acidification (included in ReCiPe method), were calculated. Results highlighted that energy production and diesel consumption are the main activities which contribute to the environmental impacts associated with cheese product. Emissions from diesel production and consumption contribute to global warming potential with 11.70% and 82.36%, respectively. Wastewater treatment mainly contributes to freshwater eutrophication with 43.58%, followed by raw milk production (37.52%), diesel production (12.34%) and others. The results of this study showed that the values for most impact categories decrease when the energy is obtained by renewable sources compared to the energy produced by conventional procedures. It was concluded that, in order to reduce the environmental impacts associated with sheep cheese production, the energy used in the process needs to be more environmentally friendly.

Green fodder cultivation improves technical efficiency of dairy farmers in semi-arid tropics of central India: a micro-analysis.

This study assessed the impact of improved green fodder production activities on technical efficiency (TE) of dairy farmers in climate vulnerable landscapes of central India. We estimated stochastic production frontiers, considering potential self-selection bias stemming from both observable and unobservable factors in adoption of fodder interventions at farm level. The empirical results show that TE for treated group ranges from 0.55 to 0.59 and that for control ranges from 0.41 to 0.48, depending on how biases are controlled. Additionally, the efficiency levels of both adopters and non-adopters would be underestimated if the selectivity bias is not appropriately accounted. As the average TE is consistently higher for adopter farmers than the control group, promoting improved fodder cultivation would increase input use efficiency, especially in resource-deprived small holder dairy farmers in the semi-arid tropics.