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Background: Increasing awareness of the environmental and public health impacts of expanding and intensifying animal-based food and farming systems creates discord, with the reliance of much of the world's population on animals for livelihoods and essential nutrition. Increasing the efficiency of food production through improved animal health has been identified as a step towards minimising these negative effects without compromising global food security. The Global Burden of Animal Diseases (GBADs) programme aims to provide data and analytical methods to support positive change in animal health across all livestock and aquaculture animal populations. Methods: In this study, we present a metric that begins the process of disease burden estimation by converting the physical consequences of disease on animal performance to farm-level costs of disease, and calculates a metric termed the Animal Health Loss Envelope (AHLE) via comparison between the status quo and a disease-free ideal. An example calculation of the AHLE metric for meat production from broiler chickens is provided. Findings: The AHLE presents the direct financial costs of disease at farm-level for all causes by estimating losses and expenditure in a given farming system. The general specification of the model measures productivity change at farm-level and provides an upper bound on productivity change in the absence of disease. On its own, it gives an indication of the scale of total disease cost at farm-level. Interpretation: The AHLE is an essential stepping stone within the GBADs programme because it connects the physical performance of animals in farming systems under different environmental and management conditions and different health states to farm economics. Moving forward, AHLE results will be an important step in calculating the wider monetary consequences of changes in animal health as part of the GBADs programme. Funding: Bill & Melinda Gates Foundation, the UK Foreign, Commonwealth an

When deciding on the voluntary waiting period of an individual cow, it might be useful to have insight into the persistency for the remainder of that lactation at the moment of the insemination decision, especially for farmers who consider persistency in their reproduction management. Currently, breeding values for persistency are calculated for dairy cows but, to our knowledge, prediction models to accurately predict persistency at different moments of insemination are lacking. This study aimed to predict lactation persistency for DIM 305 at different insemination moments (DIM 50, 75, 100, and 125). Available cow and herd level data from 2005 to 2022 were collected for a total of 20,508 cows from 85 herds located in the Netherlands and Belgium. Lactation curve characteristics were estimated for every daily record using the data up to and including that day. Persistency was defined as the number of days it takes for the milk production to decrease by half during the declining stage of lactation, and calculated from the estimated lactation curve characteristic 'decay'. Four linear regression models for each of the selected insemination moment were built separately to predict decay at DIM 305 (decay-305). Independent variables included the lactation curve characteristics at the selected insemination moment, daily milk yield, age, calving season, parity group and other herd variables. The average decay-305 of primiparous cows was lower than that of multiparous cows (1.55 *10 -3 vs. 2.41*10 -3, equivalent to a persistency of 447 vs. 288 days, respectively). Results showed that our models had limitations in accurately predicting persistency, although predictions improved slightly at later insemination moments, with R 2 values ranging between 0.27 and 0.41. It can thus be concluded that, based only on cow and herd milk production information, accurate prediction of persistency for DIM 305 is not feasible.

Farmers, veterinarians and other animal health managers in the livestock sector are currently missing sufficient information on prevalence and burden of contagious endemic animal diseases. They need adequate tools for risk assessment and prioritization of control measures for these diseases. The DECIDE project develops data-driven decision-support tools, which present (i) robust and early signals of disease emergence and options for diagnostic confirmation; and (ii) options for controlling the disease along with their implications in terms of disease spread, economic burden and animal welfare. DECIDE focuses on respiratory and gastro-intestinal syndromes in the three most important terrestrial livestock species (pigs, poultry, cattle) and on reduced growth and mortality in two of the most important aquaculture species (salmon and trout). For each of these, we (i) identify the stakeholder needs; (ii) determine the burden of disease and costs of control measures; (iii) develop data sharing frameworks based on federated data access and meta-information sharing; (iv) build multivariate and multi-level models for creating early warning systems; and (v) rank interventions based on multiple criteria. Together, all of this forms decision-support tools to be integrated in existing farm management systems wherever possible and to be evaluated in several pilot implementations in farms across Europe. The results of DECIDE lead to improved use of surveillance data and evidence-based decisions on disease control. Improved disease control is essential for a sustainable food chain in Europe with increased animal health and welfare and that protects human health.

Milk production loss due to mastitis in dairy herds is economically important. Before estimating the economic impacts of mastitis, it is crucial to quantify the association between mastitis and milk production. The objective of this study was to estimate the association between somatic cell count (SCC, as an indicator of intramammary infection due to mastitis) and milk production for dairy cows in Lombardy, Italy. The test-day (TD) records data of 3816 dairy herds located in three different geographical areas of Lombardy from January 2016 to December 2018 were used. After data editing, the final dataset comprised 10,445,464 TD records from 2970 farms and 826,831 cows. The analysis was carried out by using a mixed-effects model with six fixed effects (geographical Area, Breed, Days in Milk, Parity, Season and Year) and nested random effects for each cow and herd. The results confirmed that the SCC had a negative association with milk production. On average, this study found that any two-fold increase of SCC resulted in a milk production loss of 0.830 (95% CI: -0.832, -0.828) kg/cow/day in the whole of Lombardy. These results can be used for economic calculations on the costs of mastitis.

Staphylococcus aureus (S. aureus) strains with considerable genetic and phenotypic differences have previously been identified. The economic and epidemiologic impact of S. aureus mastitis has been investigated, but none of these studies took differences between strains into account. Here we aimed to investigate how differences between S. aureus strains affect the economic and epidemiologic outcome of various intervention strategies against clinical and subclinical intramammary infections. Five S. aureus strains were modelled using a stochastic bio-economic model simulating a dairy herd of 200 cows using single-day time steps. The strain characteristics of the five simulated S. aureus strains (general, contagious, spill-over, clinical and persistent) were based on divergent phenotypes as described in literature. Outcomes of the model included incidence (both clinical and subclinical), number of antibiotic treatment days, number of culled cows, and net income. Intervention strategies against clinical and subclinical intramammary infections were based on (variations of) intramammary antibiotic treatment, testing, and culling. Both single and multiple pathogen (intramammary infection caused by S. aureus, Escherichia coli, and non-aureus staphylococci) scenarios were simulated to determine the effect of the five S. aureus strains on the impact of 19 different intervention strategies. The results showed that the incidence (both clinical and subclinical), number of treatment days, number of culled cows, and net income varied considerably for the different S. aureus strains. Comparison of the model outcomes within and between strains showed that for most intervention strategies the relative impact differed per strain. However, the intervention strategy with the best outcome for most variables and strains was the culling of cows with a recovery probability lower than 50%. This shows that the relative economic and epidemiologic impact of most of the modelled intervention s

BHB-concentrations is mostly used for detection hyperketonaemia. Most studies applied BHB-concentrations in blood and a few studies have used BHB-measurements in milk. On-set, duration and maximum BHB-concentrations are different aspects of ketosis dynamics. The aim of this study is to investigate how different parameters of ketosis dynamics correlate with each other and the second objective of this study is to investigate if the severity of keto-sis can be reflected in an area under the curve of the repeated BHB-measurements in milk detected with the DeLaval Herd NavigatorTM. Within the Herd Navigator an inline sampler inside the AMS automatically takes a representative sample of 80 ml milk from each indi-vidual cow during the milking process and measures BHB-concentrations at least daily from 3 to 20 days in milk and between 21 and 60 days in milk at least once every 4 days. The in-vestigated data, provided by Lattec, which included 48 herds working with the Herd Naviga-tor in the Benelux in June 2020. Data of unique calving’s between July 2018 and August 2019 were included. The final dataset included 130,247 BHB-measurements of 4030 unique calving’s. The cur-rent study defined three aspects of ketosis dynamics: the onset of ketosis, the duration of ke-tosis and the maximum BHB-concentration. Descriptive statistics were performed of the dif-ferent aspects of ketosis. Mean onset of ketosis was 18 DIM, 15 DIM and 12 DIM for cows with parity 1, 2 and 3+ respectively. Mean duration of ketosis was 21 days for cows with parity 1, 27 days parity 2 cows and 32 days for cows with parity 3+. The correlation between the onset of ketosis and the duration of ketosis was -0.546 (parity 2 cows), the correlation between the duration and maximum BHB-concentration was 0.460 (parity 2 cows). The area under the curve is calculated as a sum of all BHB-measurements between 3 and 60 DIM. The mean area under the curve was 3,67 for parity 1 cows, 4,14 for parity 2 cows and 4,63 for parity

The associations between reproductive performance, milk yield and health status with the risk of culling, and thus with a cow’s longevity, have been well documented at the individual cow level. Associations at individual cow level may, however, not be valid at herd level due to interrelated herd management aspects and/or policy restrictions. The objective of this study was to explore the association of herd performance indicators with herd-level dairy cow longevity under Dutch production conditions. Longevity was expressed by three different measures, viz. age at culling, lifetime milk production of culled cows and culling rate. The evaluated herd performance indicators included factors on milk production, youngstock rearing, reproduction and health performance as registered on 10 719 Dutch commercial dairy herds during the period 2007–2016. Averaged over herds and the evaluated period, the age of culled milking cows was 2 139 days (5.8 years, SD±298 days), the lifetime milk production of culled cows was 31 238 kg (SD±7,494 kg), and the culling rate was 0.24 (SD ±0.08). A mixed linear regression modelling approach was applied to evaluate the association of each of the three longevity measures with the selected herd performance indicators. The results indicated that only four herd performance indictors (herd size, herd expansion, heifer ratio and the proportion of cows with potential subclinical ketosis) shared significant associations with all three longevity variables. Generally, the strength of the associations between each of the evaluated longevity measures and herd performance indicators was only limited. The absence of strong associations between the longevity measures and herd performance indicators reveal that there is potential of extending cattle longevity without affecting the herd performance in terms of milk production, reproduction and health. Moreover, only part of the observed variance in longevity among the herds over time was explained by the herd

More and more European countries have implemented a bovine viral diarrhea virus (BVDV) control program. The economic effects of such programs have been evaluated in simulations, but empirical studies are lacking, especially in the final stage of the program. We investigated the economic (gross margin) and production effects (milk yield, somatic cell count, and calving interval) of the herds obtaining BVDV-free certification based on longitudinal annual accounting and herd performance data from Dutch dairy herds between 2014 and 2019, the final stages of the Dutch national BVDV-free program. This study was designed as a case-control study: two types of case herds were defined for two analyses. The case herds in the first analysis are herds where the BVDV status changed from “BVDV not free” to “BVDV free” during the study period. The not-free status refers to a herd that participated in the BVDV-free program but had not yet obtained the BVDV-free certification. In the second analysis, the case herds started participating in the Dutch BVDV-free program during the study period and obtained the BVDV-free certification. Control herds in both analyses were BVDV-free during the entire study period. Potential bias between the covariates of the two herd groups was reduced by matching case and control herds using the propensity score matching method. To compare the differences between case and control herds before and after BVDV-free certification, we used the time-varying Difference-in-Differences estimation (DID) methodology. The results indicate that there was no significant change in milk yield, somatic cell count, calving interval, and gross margin upon BVDV-free certification. There are several possible explanations for the non-significant effects observed in our study, such as the final stage of the BVDV control program, not knowing the true BVDV infection situation in case herds and not knowing if control measures were implemented in case herds prior to participating i