Animal最新文献

Ruminants play an important part in the food supply chain, and manipulating rumen microbiota is important to maximising ruminants’ production. Rumen microbiota through rumen fermentation produces as major end products volatile fatty acids that provide animal’s energy requirements, and microbial CP. Diet is a key factor that can manipulate rumen microbiota, and each variation of the physical and chemical composition creates a specific niche that selects specific microbes. Alteration in the chemical composition of forage, the addition of concentrates in the diet, or the inclusion of plant extract and probiotics, can induce a change in rumen microbiota. High-throughput sequencing technologies are the approaches utilised to investigate the microbial system. Also, the application of omics technologies allows us to understand rumen microbiota composition and these approaches are useful to improve selection programmes. The aim of this review was to summarise the knowledge about rumen microbiota, its role in nutrient metabolism, and how diet can influence its composition.

To properly formulate diets, the ability to accurately estimate feed intake is critical as the amount of feed consumed will influence the amount of nutrients delivered to the animal. Inaccurate intake estimates may lead to under- or over-feeding of nutrients to the animal. Individual differences in equine forage intake are well-known, but predictive equations based on animal and nutritional factors are not comprehensive. The objective of the present study was to consolidate the current body of knowledge in the published literature on voluntary forage DM intake (VFDMI) in equines and conduct a meta-analysis to identify driving factors, sources of heterogeneity, and develop predictive equations. Therefore, a systematic literature search was applied and identified 61 publications which met the inclusion criteria. From each study, the outcomes of interest (e.g., forage intake), diet composition (e.g., forage information, nutrient composition), and animal factors (e.g., sex, age, breed, BW, exercise level) were extracted. Forage intake was analyzed as two different outcome variables: (1) VFDMI in kg/d and (2) VFDMI in g/kg BW. Linear mixed model analysis treating study as a random effect was applied, using a backward−stepping approach to identifying potential driving variables for VFDMI (both units) where all terms have P < 0.1. The best fitting models for VFDMI included similar factors (also across kg/d and g/kg BW) such as forage quality (i.e., neutral detergent fiber or CP content), forage type (i.e., grass, legume, or mixed), the animals’ size category (i.e., horses vs ponies), and some management factors (i.e., pasture access). As anticipated, forage intake increased when higher quality forages were fed (i.e., lower neutral detergent fiber or higher CP), potentially due to improved digestibility. Additionally, VFDMI increased as BW increased but ponies increased their VFDMI more per every kg increase in BW compared to horses. Lastly, pasture access (i.e., grazing) may influence VFDMI such that pastured animals consume less than stalled animals, possibly due to the time it takes to graze forage. In conclusion, equations to predict equine VFDMI with high accuracy and precision (concordance correlation coefficient  = 0.82 – 0.95; root mean squared error RMSE = 0.82–5.49) were developed which could be applied in practice by equine nutritionists or owners and managers. The results of this meta-analysis confirm that animal traits and forage quality have a significant impact on the VFDMI of equines and should be accounted for when formulating diets to ensure nutritional requirements are met.

Lameness is a common issue on dairy farms, with serious implications for economy and animal welfare. Affected animals may be overlooked until their condition becomes severe. Thus, improved lameness detection methods are needed. In this study, we describe kinematic changes in dairy cows with induced, mild to moderate hindlimb lameness in detail using a “whole-body approach”. Thereby, we aimed to identify explicable features to discriminate between lame and non-lame animals for use in future automated surveillance systems. For this purpose, we induced a mild to moderate and fully reversible hindlimb lameness in 16 dairy cows. We obtained 41 straight-line walk measurements (containing > 3 000 stride cycles) using 11 inertial measurement units attached to predefined locations on the cows’ upper body and limbs. One baseline and ≥ 1 induction measurement(s) were obtained from each cow. Thirty-one spatial and temporal parameters related to limb movement and inter-limb coordination, upper body vertical displacement symmetry and range of motion (ROMz), as well as pelvic pitch and roll, were calculated on a stride-by-stride basis. For upper body locations, vertical within-stride movement asymmetry was investigated both by calculating within-stride differences between local extrema, and by a signal decomposition approach. For each parameter, the baseline condition was compared with induction condition in linear mixed−effect models, while accounting for stride duration. Significant difference between baseline and induction condition was seen for 23 out of 31 kinematic parameters. Lameness induction was associated with decreased maximum protraction (−5.8%) and retraction (−3.7%) angles of the distal portion of the induced/non-induced limb respectively. Diagonal and lateral dissociation of foot placement (ratio of stride duration) involving the non-induced limb decreased by 8.8 and 4.4%, while diagonal dissociation involving the induced limb increased by 7.7%. Increased within-stride vertical displacement asymmetry of the poll, neck, withers, thoracolumbar junction (back) and tubera sacrale (TS) were seen. This was most notable for the back and poll, where a 40 and 24% increase of the first harmonic amplitude (asymmetric component) and 27 and 14% decrease of the second harmonic amplitude (symmetric component) of vertical displacement were seen. ROMz increased in all these landmarks except for TS. Changes in pelvic roll main components, but not in the range of motion of either pitch or roll angle per stride, were seen. Thus, we identified several kinematic features which may be used in future surveillance systems. Further studies are needed to determine their usefulness in realistic conditions, and to implement methods on farms.

Selecting and raising dairy animals that are more likely to reach their potential is a strategy to increase milk production efficiency and overall profitability. However, indicators are necessary for the early identification of animals that are less likely to perform well, allowing for their early culling and ensuring that resources are allocated to those with the highest potential. The objective of this study was to analyze the association between early-life animal health and performance with longevity, production, and profitability. After data cleaning, the following early-life measures (i.e., predictors) were available for 363 female calves born between June 2014 and November 2015 in eight dairy herds from New Brunswick, Canada (average: 45 calves/farm; SD: 26.1 calves/farm; median: 42 calves/farm; range: 15–95 calves/farm): birth weight, weaning weight, weaning age, weaning average daily gain (weaning ADG), immunoglobulin G (IgG) serum concentration, the occurrence of navel infection, diarrhea, and pneumonia, and if animals received antibiotic treatment between birth and weaning. Their subsequent length of life (LL), length of productive life (LPL), lifetime cumulative energy-corrected milk (ECM), and lifetime cumulative milk value (i.e., response variables) were provided by the Canadian dairy herd improvement agency. Bayesian Additive Regression Tree models were trained for each response variable using 5-fold cross-validation. Models were evaluated using the RMSE and R². The three most important predictors were identified using permutation, and the relationship between response variables and important predictors was assessed using accumulated local effect plots. The RMSE for LL, LPL, ECM, and milk value were 1.43 years, 1.37 years, 16 314.94 kg, and $CAD 11 525.68, respectively, whereas the R² values were 0.30, 0.25, 0.29, and 0.29, respectively, indicating a moderate relationship between predictors and response variables. Non-linear relationships were found between the response variables and important predictors. Animals born with low or high birth weights were associated with decreased LL, LPL, ECM, and milk value. The highest LL, LPL, and milk value was observed for calves weaned between 1.9 and 2.0 months old, followed by a decline for calves weaned at older ages. The lowest LL and ECM were associated with weaning ADG of 0.786 kg/day, while 0.787 kg/day was associated with the lowest LPL. Lastly, both ECM and milk value were highest when serum IgG values were 1 659 mg/dL. These findings provide valuable insights for optimizing early culling decisions and enhancing the productivity and profitability of dairy farms.

The Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is a very prevalent viral pathogen that can induce reproductive failure in infected sows. PRRSV infection can result in smaller litters, foetal death, late-term abortions and retarded growth of infected piglets. Not all sows respond equally to the infection partly due to genetic factors. In this study, we aimed to characterise the genetic variability of pig resilience to PRRSV infection by using a stability reproductive performance (SRP) index as a proxy of resilience. By comparing reproductive data from 183 sows, we selected 48 sows with extreme SRP values, measured as the difference in piglets lost at farrowings before and during a PRRSV outbreak. Short-read DNA fragments were sequenced from selected sows using an Illumina platform. The analysis of whole-genome sequencing information identified 16 genome regions associated with the SRP classification (cut-off P-value < 10⁻⁶). Functional evaluation of the positional candidates by gene-ontology identifiers and their participation in biological pathways were used to identify genes involved in virus entry and replication (vimentin, RAC1 and OAZ2) but also in immune responses from the host (IRF1, and IL4, IL5 and IL13). Importantly, genes related to chemokines, extracellular proteins and cell-to-cell junction integrity might contribute to placental microseparations, facilitating the trafficking of viral particles from sow to foetus that takes place during the pathogenesis of transplacental PRRSV infection. However, given the small number of animals in the study, these results shall need to be validated in larger populations.

Monitoring animal location and proximity can provide useful information on behaviour and activity, which can act as a health and welfare indicator. However, tools such as global navigation satellite systems (GNSS) can be costly, power−hungry and often heavy, thus not viable for commercial uptake in small ruminant systems. Developments in Bluetooth Low Energy (BLE) could offer another option for animal monitoring, however, BLE signal strength can be variable, and further information is needed to understand the relationship between signal strength and distance in an outdoor environment and assess factors which might affect its interpretation in on-animal scenarios. A calibration of a purpose-built device containing a BLE reader, alongside commercial BLE beacons, was conducted in a field environment to explore how signal strength changed with distance and investigate whether this was affected by device height, and thus animal behaviour. From this calibration, distance prediction equations were developed whereby beacon distance from a reader could be estimated based on signal strength. BLE as a means of localisation was then trialled, firstly using a multilateration approach to locate 16 static beacons within an ∼5 400 m2 section of paddock using 6 BLE readers, followed by an on-sheep validation where two localisation approaches were trialled in the localisation of a weaned lamb within ∼1.4 ha of adjoining paddocks, surrounded by nine BLE readers. Validation was conducted using 1 days’ worth of data from a lamb fitted with both a BLE beacon and separate GNSS device. The calibration showed a decline in signal strength with increasing beacon distance from a reader, with a reduced range and earlier decline in the proportion of beacons reported at lower reader and beacon heights. The distance prediction equations indicated a mean underestimation of 12.13 m within the static study, and mean underestimation of 1.59 m within the on-sheep validation. In the static beacon localisation study, the multilateration method produced a mean localisation error of 22.02 m, whilst in the on-sheep validation, similar mean localisation errors were produced by both methods – 19.00 m using the midpoint and 23.77 m using the multilateration method. Our studies demonstrate the technical feasibility of localising sheep in an outdoor environment using BLE technology; however, potential commercial application of such a system would require improvements in BLE range and accuracy.