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Greenhouse gas (GHG) emissions from livestock production must be urgently tackled to substantially reduce their contribution to global warming. Simply reducing livestock numbers to this end risks impacting negatively on food security, rural livelihoods and climate change adaptation. We argue that significant mitigation of livestock emissions can be delivered immediately by improving animal health and hence production efficiency, but this route is not prioritized because its benefits, although intuitive, are poorly quantified. Rigorous methodology must be developed to estimate emissions from animal disease and hence achievable benefits from improved health through interventions. If, as expected, climate change is to affect the distribution and severity of health conditions, such quantification becomes of even greater importance. We have therefore developed a framework and identified data sources for robust quantification of the relationship between animal health and greenhouse gas emissions, which could be applied to drive and account for positive action. This will not only help mitigate climate change but at the same time promote cost-effective food production and enhanced animal welfare, a rare win-win in the search for a sustainable planetary future.

Respiratory disease is an ongoing challenge for calves in the dairy sector with a relatively high prevalence and impact on welfare and economics. Applying scoring protocols for detecting respiratory disease requires that they are easily implemented, consistent between observers and fast to use in daily management. This study was conducted in one Danish dairy farm from September 2020 through January 2021. The study included 126 heifer calves enrolled in the age of 17 to 24 d. All calves were observed every second day for a period of 46 d. At each visit all calves were scored with a new Visual Analog Scale (VAS) and the Wisconsin Calf Health Scoring Chart (WCHSC). We calculated agreement between the 2 scoring systems based on conditional probability to score higher or lower than a cut-off in the VAS compared with a specified cut-off in WCHSC used as reference test. A generalized mixed effects regression model was performed to develop a model that could estimate the prevalence of respiratory disease and the overall agreement between the 2 scoring systems. The overall agreement between the VAS and WCHSC was 89.6%. The second part of the study assessed inter-observer reliability between 2 experienced observers and between an experienced observer and veterinary students. The inter-observer reliability was calculated by intra-class correlation coefficient and was 0.58 between experienced observers and was 0.34 between an experienced observer and veterinary students indicating a moderate to poor reliability between the observers. It was possible to use VAS as an alternative clinical scoring method, which primarily focuses on the general condition of the individual calf rather than specific categories of clinical signs. Our study set up lacked a comparison to other diagnostic tools i.e., thoracic ultrasound to confirm the findings which should be considered in future studies when exploring VAS as a screening tool for detection of respiratory disease in dairy calves.

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 imm

Non-perforating abomasal lesions occur with a high prevalence in slaughtered dairy cattle. Ante mortem diagnosis is a challenge, but the presence of occult blood in feces is suggested as a diagnostic criterion. The lower detection limit of Hemo-Fec® (Med-Kjemi, Asker, Norway) and Hemoccult II® SENSA® (Beckman Coulter, Brea, California, USA) for fecal occult blood were estimated. The Hemo-Fec® and Hemoccult II® SENSA® could detect 1-2 mL and 2-4.5 mL of blood in 1000 g of feces, respectively. Therefore, the Hemo-Fec® test was selected to access hemoglobin degradation in the rumen to establish if blood from outside the gastrointestinal tract could result in false-positive tests and an observational study to estimate the diagnostic sensitivity and specificity. Rumen microbiota did not degrade hemoglobin in a 1% blood concentration in vitro during 48 h of fermentation. The Hemo-Fec® test was only able to detect cattle with ≥4 acute lesions (diagnostic sensitivity: 0.40 [95% confidence interval (95% CI): 0.32-0.48] and ≥4 chronic lesions (sensitivity: 0.44 [95% CI: 0.35-0.52]). The Hemo-Fec® test had no diagnostic potential to detect superficial erosions or scar tissue in abomasa. Furthermore, the specificity was 0.71 [95% CI: 0.68-0.75%], and a positive test is thus not equivalent with abomasal lesions in cattle.

Dairy farming is a complex production system involving biological, technological and human inputs. Therefore, ‘general knowledge of cause and effect’ often seems inadequate to identify and implement optimal management procedures. To solve herd-specific problems, this paper explores the potential of planned experiments for internal use at the farm level to take advantage of local causal relationships. The shift towards larger dairy herds with access to automatic data recordings of a large number of relevant inputs and performance indicators supports the development of management tools that are able to estimate the effect of changes made in daily management on individual farms. The concept of EVolutionary OPeration (EVOP) implies making small systematic changes in production factors or procedures while running the production and continuously evaluating the results. The aim of this study was to develop and evaluate the feasibility of implementing EVOP in commercial dairy herds as an integral part of herd management. The concept of EVOP-Dairy is based on five principles: (1) farmer-driven identification of areas for improvement; (2) herd-specific goals for the interventions to be evaluated in EVOP trials; (3) a short EVOP trial period; (4) simple, but statistically sound, EVOP designs including data access and (5) regular estimation of intervention effects and frequent reporting to the farmer. The project involved three activities: first, visiting a number of dairy farms with the aim of identifying areas for management improvements and to define potential EVOP interventions and relevant designs of EVOP trials; second, conducting a series of EVOP trials to develop data registration, statistical models, analysis and reporting; third, interviewing the farmers to obtain their opinion of the conceptual idea and the process. These activities were documented for the twelve project farms, and five different EVOP trials are described in detail to illustrate the concept. In co

Automatic flushing of milking clusters between milking events is a control measure aimed at reducing transmission of mastitis pathogens from infectious milk to a subsequently milked cow. We evaluated the effect of flushing with cold water and flushing with water containing peracetic acid (PAA) on the concentration of Staphylococcus aureus in teat cup liners. Thirty-two clusters in a swing-over milking parlor (Dairymaster, Causeway, Ireland) were subjected to a simulated milking with S. aureus–contaminated milk. Sixteen clusters were not flushed (controls), whereas 8 clusters were flushed with cold water (966 ± 32 mL) and 8 clusters were flushed with water containing PAA (200 mL/mL). A random teat cup in each cluster was sampled by rinsing with a phosphate buffer. Teat cup samples were cultured on the day following collection on Baird-Parker plates to determine the concentration of S. aureus. In teat cup samples from control clusters, the mean concentration of S. aureus was 2.8 × 105 cfu/mL. The concentration of S. aureus was zero in teat cup samples from clusters flushed with cold water. In teat cup samples from clusters flushed with water containing PAA, the concentration of S. aureus was in general reduced compared with control clusters, but S. aureus was not removed completely. However, the automatic cluster flushing did not function properly when clusters were flushed with water containing PAA; thus, results reflected the effect of inadequate function rather than the effect of adding disinfectant to the flushing water. Before the main study, we conducted a pilot study to evaluate whether teat cup sampling with swabs and sample analysis with quantitative PCR were appropriate methods for the main study. Specifically, we evaluated the effect of swab sample mass on detection of S. aureus by quantitative PCR in the laboratory, Further, we compared PCR and bacterial culture on detection of S. aureus in a suspension following disinfection of the suspensio

Behandling af yverbetændelse er ansvarlig for størstedelen af antibiotikaforbruget i økologisk mælkeproduktion. Ofte fravælges antibiotikabehandling af mild yverbetændelse. Ubehandlede køer udgør imidlertid en fortsat smitterisiko for de øvrige køer i besætningen. Afgoldning af en enkeltkirtel er ophørt malkning af én kirtel i laktationen, mens koens øvrige kirtler fortsat malkes. Hvis en mild yverbetændelse kan lokaliseres til en enkelt kirtel hos koen, kan afgoldning af denne kirtel medvirke til at mindske smitten til andre køer. Virkningen af afgoldning af enkeltkirtler på mælkeproduktionen og koens kliniske udvikling blev undersøgt ved et eksperimentielt studie i fem økologiske malkekvægsbesætninger med malkerobotter. Halvfjerds køer med forhøjet celletal blev tilfældigt fordelt på to forsøgsbehandlinger; kirtel-afgoldning eller fortsat malkning. Køerne blev løbende undersøgt klinisk og ydelsesdata blev udtrukket fra malkerobotterne. I de første par uger efter kirtel-afgoldning sås kirtelhævelse og mælkeløb. Derefter aftog hævelsen og blev løbende erstattet af kirtelsvind i månederne efter kirtelafgoldningen. En del af køerne udviklede alvorlige kliniske tegn som forhøjet temperature (13 pct.) og tegn på smerte (29 pct.). Der var et gennemsnitligt tab på ca. 4 kg mælk/dag ved kirtel-afgoldning. En lavere kirtel-ydelse forud for afgoldning var forbundet med et mindre produktionstab, men jo tidligere i laktationen kirtlen blev afgoldet, desto bedre kompenserede de øvrige kirtler for ydelsestabet.

Piglet mortality is a major problem in organic pig production affecting both farm economy and animal welfare. Knowledge is scarce on the risk factors of piglet mortality in Danish commercial organic pig production. The objectives of this study were to evaluate season, litter size, parity, sow body condition and stillborn littermates as risk factors for early piglet mortality and crushing of liveborn piglets from parturition until castration at day 3 to 5 postpartum (pp). The study was conducted over a 1-year period in nine commercial Danish organic pig herds practicing outdoor farrowing all year round. Data included recordings on 3393 farrowings with 50 284 liveborn piglets of which 14.8% died before castration. A subset of the dead piglets were collected and necropsied to identify crushed piglets. The average number of liveborn piglets per litter was 14.8 (SD=3.7) and the average time from parturition until castration was 4.1 (SD=1.7) days. A negative binomial regression analysis was used to model the effect of the predictive variables on the early piglet mortality accounting for different time periods from parturition to castration. An increase in maternal body condition score (BCS) and parity significantly increased the risk of dying between parturition and castration. Early mortality was found to be lowest during spring (March to May) and highest during summer (June to August). Being born into a litter with one or more stillborn littermates increased the risk of early mortality. The risk factors for crushing of piglets were evaluated using a logistic analysis. A significant effect of parity and litter size was found where the odds of at least one piglet in a litter with mortality was diagnosed as crushed increased with increasing parity and litter size. In conclusion, being born during summer (June to August), high parity and maternal BCS and stillborn littermates were found to be risk factors for piglet mortality between parturition and castration. In addition

In Danish organic pig production, one-third af total bom piglets die befare weaning, and stillbirth has previously crudely been estimated to account for 2 7% af the total preweaning mortality. The objective af this study was to evaluate season, litter size, parity and body condition af the sow as risk factors for stillbirth in nine commercial Danish organic pig herds. The study was conducted over a 1-year period, and the data included registrations an 5170 farrowings with 82 906 total bom piglets. The average number af total bom piglets per titter was 16. 0, and the number af stil/bom piglets per titter was 1.1. A significant effect af season was seen with an odds ratio for stillbirth af 1. 15 during summer (May to August) compared with the remaining part af the year. A non-linear effect af titter size was seen where an increase in litter size from 11 to 16 resulted in an odds ratio af stillbirth af 1.11. An increase in titter size from 16 to 21 resulted in an odds ratio af stillbirth af 1 .45. A significant interaction between body condition and parity was present. In first parity sows, an increase in body condition score from 2 (thin) to 3 (moderate) and from 3 to 4 (fat) increased the probability af stillbirth with an odds ratio af 1.23 and 1.36, respectively. In sows with parity above 4, an increase in body condition score from 2 to 3 and from 3 to 4 decreases the probability af stillbirth with an odds ratio af 0.68 and 0. 79, respectively. In conclusion, increasing litter size and being bom during the summer months af May to August were found to be risk factors for stillbirth. Furthermore, an interaction between body condition and parity showed that thin sows with parity above 4 had a substantially increased risk of stillbirth compared with normal and fat sows with parity above 4. In contrast, for parity 1 sows risk af stillbirth was increased in fat sows.

Modern dairy herds resemble factories. Cows, organized in production units, are manufacturingmilk from many components (e.g. concentrates, silage). However, both production units andcomponents can greatly differ between each other. Therefore, production optimization based ongeneral recommendations might be inefficient. Instead, as in manufacturing industry, decisionsupport could be based on systematic experimentation within ongoing production system. Thisconcept, known as Evolutionary Operations (EVOP), is based on small changes to the productionsystem. However, a challenge here is lack of a tool which would allow a farmer to assess how smallchanges, for example in feeding, influence productivity. The objective of this study was to constructa multivariate dynamic linear model (DLM) to assess the intervention effect on milk production.The DLM was built to account for intervention at individual and herd level. It consisted of anobservation and a system equation. The observation equation links the observations to parametersdescribing the herd (lactation curve), individual cows and an intervention effect. The systemequation expresses how the parameters may change over time. The lactation curve was modeledby two linear expressions and was parameterized using: milk yield 60 days after calving, slope overthe first 60 days in milk and slope after 60 days in milk. The variance components of the DLM wereestimated using a maximum likelihood method. The application of the model was demonstratedon a field experiment in a commercial herd with 4 automatic milking systems (AMS). The herdwas split into 2 groups based on the AMS. The experiment relied on two steps. The first step wasto reduce the feed energy given to cows in the AMS and instead supply the feed energy to the cowsat the feed bunk. The second step was to reduce the feed energy given in two of the four AMS. TheDLM presented here was successful in providing estimates of the effects on milk yield of change infeed energy gi, Modern dairy herds resemble factories. Cows, organized in production units, are manufacturing milk from many components (e.g. concentrates, silage). However, both production units and components can greatly differ between each other. Therefore, production optimization based on general recommendations might be inefficient. Instead, as in manufacturing industry, decision support could be based on systematic experimentation within ongoing production system. This concept, known as Evolutionary Operations (EVOP), is based on small changes to the production system. However, a challenge here is lack of a tool which would allow a farmer to assess how small changes, for example in feeding, influence productivity. The objective of this study was to construct a multivariate dynamic linear model (DLM) to assess the intervention effect on milk production. The DLM was built to account for intervention at individual and herd level. It consisted of an observation and a system equation. The observation equation links the observations to parameters describing the herd (lactation curve), individual cows and an intervention effect. The system equation expresses how the parameters may change over time. The lactation curve was modeled by two linear expressions and was parameterized using: milk yield 60 days after calving, slope over the first 60 days in milk and slope after 60 days in milk. The variance components of the DLM were estimated using a maximum likelihood method. The application of the model was demonstrated on a field experiment in a commercial herd with 4 automatic milking systems (AMS). The herd was split into 2 groups based on the AMS. The experiment relied on two steps. The first step was to reduce the feed energy given to cows in the AMS and instead supply the feed energy to the cows at the feed bunk. The second step was to reduce the feed energy given in two of the four AMS. The DLM presented here was successful in providing estimates of the effects on milk yield of cha