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Predicting methane (CH 4 ) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH 4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R 2 v  = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH 4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH 4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Bovine respiratory diseases (BRDs) have major socioeconomic impacts in the beef sector. Antimicrobials have been traditionally used to prevent the development of BRDs upon arrival in fattening units. Currently, from a "One Health and One Welfare" perspective, alternative solutions are being investigated. Preconditioning programmes that aim at reducing stress and reinforcing immune functions have been proposed to decrease BRDs incidence. In this study, we assessed the effect of a preconditioning protocol set up in nine commercial beef herds on young bulls' behaviour, health, and performance. Preconditioned bulls (PREC) were weaned indoors 50 days before departure, and progressively fed a concentrate diet supplemented with vitamins and trace elements. They also received an anthelmintic treatment, and two doses of a trivalent vaccine against respiratory pathogens at 4-week intervals. In contrast, controls (CTRL) were kept on pasture with their dams with no vaccination or particular diets. All calves were transported together to a single sorting facility to be sorted by experimental groups, origins, and BW, forming new groups before dispatch to four fattening units. At arrival, CTRL were treated with anthelmintic drugs and received one injection of the same BRDs vaccine. No intervention was performed on PREC bulls. BRD-scores were 0.73 in PREC versus 0.07 in CTRL (P = 0.01) during the preconditioning period and 0.96 in PREC versus 0.54 in CTRL (P = 0.41) after 15 days in the fattening units. Morbidity estimates were 18% in PREC versus 1% in CTRL (P < 0.001) during the preconditioning period and 32% in PREC versus 19% (P = 0.07) in CTRL after 15 days in the fattening units. Poor housing conditions during preconditioning and a different aetiology could partly explain these results. At arrival to fattening, the average daily gain (ADG) was 1 605 g for PREC versus 1 140 g (P = 0.012) for CTRL. After fattening for 180 days, differences in BW, ADG, carcass weight and conformation were inconclusive. In Europe, preconditioning programmes including vaccination must be set up coherently with good husbandry practices and with a global adaptation of batch management of the beef sector otherwise, they may be less effective than expected., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the 15 N natural abundance in plasma proteins (plasma δ 15 N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen, were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency, two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ 15 N was significantly correlated with feed efficiency measured through the two criteria traits and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ 15 N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance that influenced the relation between plasma δ 15 N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ 15 N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ 15 N and feed efficiency that varied according to the efficiency index and the diet utilised., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

The nature of the relationship between humans and farm animals has multiple repercussions on the animals and the farmers and varies with farmers attitudes towards their animals. In particular, this relationship influences animal welfare and human working conditions. The present study, part of a larger research project investigating human-animal relationship (HAR) in pig farming and ways to improve it, had two objectives: 1) to investigate the HAR in a diversity of pig farming situations and to evaluate the possible correlation between farmer attitudes, pigs' reactions to humans, husbandry practices, animal health, welfare and productivity and 2) to find a way to rapidly assign a farmer to a profile, in order to better adapt course content during training sessions on HAR. The study focused on 52 farrow-to-finish farms and consisted of a semi-structured interview with the farmer, observations of the farmer in contact with his/her livestock, two human approach tests conducted on sows and growers and productivity data. Finally, a questionnaire was left at the farm to be filled out by all stockpersons on the farm. Interviews, analyzed using a thematic analysis followed by multiple correspondence analysis and ascendant hierarchical clustering, showed that some farmers develop husbandry practices to improve their HARs and identified three farmer profiles that have been named in relation to the place of the HAR in their profession: Profile 1 farmers for whom HAR is secondary in their work with their pigs, Profile 2 for whom the HAR is useful in their work with their pigs and Profile 3 for whom HAR is central in their work with their pigs. Logistic regression models of the relationships between behavioral tests and productivity data showed that confident sows produce and wean more piglets than fearful sows and that sows of Profile 3 farmers trust more humans than those of Profile 1 or 2 farmers. Farmers' responses to the questionnaire did not predict their profile assignment. Our results confirm the main role of the farmer's attitude towards animals on their farming practices, animal welfare and productivity. The farmers who have more confident pigs and better productivity are those for whom the farm animal is central to their profession, express the most pleasure in working with them and convey empathy for them., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Dairy systems are a source of pollutant emissions, such as greenhouse gases (GHG) and NH3 that are associated with impacts on the environment. Gas emissions in barns are related mainly to diet intake and chemical composition, N excretion and manure management. A reduction in dietary N is known to be an effective way to reduce N excretion and the resulting NH3 emissions. However, most studies consider manure in liquid form with frequent removal from the barn. In deep litter systems, several processes can occur during the accumulation of solid manure that result in variable gas emissions. The objective of this experiment was to investigate the influence of the interaction between dietary CP (low or high) and manure management (liquid or solid) on gas emissions (NH3, N2O, CH4) at the barn level. Dietary treatments provided either low (LowN; 12% CP) or high (HighN; 18% CP) degradable protein to modify the amount of total ammonia nitrogen (TAN) excreted. The cows were housed for two 8-week periods in two mechanically ventilated rooms equipped to manage manure either in liquid (LM; slurry) or solid form (SM; deep litter). In the LM treatment, N balance was measured for 4 days. As expected, animals fed the LowN diet ingested 35% less N and excreted 65% less N in their urine, with no reduction in faecal N excretion and N secretion in milk. On the LowN diet, excretion of urea-N and NH3-N emissions were reduced regardless of the manure management. On the HighN diet, urinary urea-N excretion was three times as high, while NH3-N emissions were 3.0 and 4.5 times as high in LM and SM, respectively. Manure management strongly influenced CH4-C emissions, which were 30% higher in SM than in LM, due to the accumulation of litter. Moreover, gas emissions from solid manure increased over the accumulation period, except for NH3 on the LowN diet. Finally, our results suggest that methods used for national inventories would become more accurate by considering the variability in TAN excretion, which is the primary factor that influences NH3 emissions.