A key step toward rational microbiome engineering is sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously capture compositions of host-associated microbiomes and host phenotypes. To that end, we present a generative model based on the mechanistic consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (inferred latent variables), while species' resource preferences remain conserved. Simultaneously, the latent variables are used to model phenotypic states of hosts. microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address three salient questions in host-associated microbial ecologies: (i) which host phenotypes maximally constrain the composition of the host-associated microbiomes? (ii) how context-specific are phenotype/microbiome associations, and (iii) what are plausible microbiome compositions that correspond to desired host phenotypes? Our approach aids the analysis and design of microbial communities associated with host phenotypes of interest.

One of the main challenges in animal breeding systems is determining estradiol (E2) in livestock samples as simple and minimally invasive as possible, Thus, a nonenzymatic biosensor screen-printed electrode (SPE) was developed by modifying nanohybrid palladium nanoparticles (PdNPs), and carbon dots anchored on a nanosilica particle (PdNPs/C.dots/SiO), denominated SPE/PdNPs/C.dots/SiO, and successfully tested for the direct detection of estradiol in livestock samples. PdNPs were directly obtained by a one-step synthesis through carbon dot reduction. Hybrid nanomaterials were characterized by atomic force microscopy, high-resolution transmission electron microscopy, and electrochemical impedance. The combination of PdNPs with C.dots resulted in a nonenzymatic biosensor supported on a screen-printed platform with superior electrocatalytic properties regarding the oxidation of E2 when compared to unmodified sensors. Modifications in the working electrode resulted in high sensitivity toward E2 determination within a linear range from 0.005 to 14.0 μmol L with a limit of detection of 1.0 nmol L. The recovery rate of E2 in bovine serum samples and urine samples ranged from 92 to 106%. Interference studies showed that peak current variation (Δ ) among all interferents evaluated and E2 did not exceed ±2%. The newly developed sensor stands out not only for its high sensitivity but also for its quick and simple way of production while also being disposable after analysis, providing a simple, sensitive, and practical approach for the determination of reproductive hormones in livestock.

Feeding behavior is known to affect the welfare and fattening efficiency of yaks in feedlots. With the advancement of machine vision and sensor technologies, the monitoring of animal behavior is progressively shifting from manual observation towards automated and stress-free methodologies. In this study, a real-time detection model for individual yak feeding and picking behavior was developed using YOLO series model and StrongSORT tracking model. In this study, we used videos collected from 11 yaks raised in two pens to train the yak face classification with YOLO series models and tracked their individual behavior using the StrongSORT tracking model. The yak behavior patterns detected in trough range were defined as feeding and picking, and the overall detection performance of these two behavior patterns was described using indicators such as accuracy, precision, recall, and F1-score. The improved YOLOv8 and Strongsort model achieved the best performance, with detection accuracy, precision, recall, and F1-score of 98.76%, 98.77%, 98.68%, and 98.72%, respectively. Yaks which have similar facial features have a chance of being confused with one another. A few yaks were misidentified because their faces were obscured by another yak's head or staff. The results showed that individual yak feeding behaviors can be accurately detected in real-time using the YOLO series and StrongSORT models, and this approach has the potential to be used for longer-term yak feeding monitoring. In the future, a dataset of yaks in various cultivate environments, group sizes, and lighting conditions will be included. Furthermore, the relationship between feeding time and yak weight gain will be investigated in order to predict livestock weight.

Temperature and precipitation have previously been associated with infections. The association between salmonellosis and precipitation might be explained by antecedent drought conditions; however, few studies have explored this effect.

Genome editing is the latest breeding tool capable of accelerating the rate of genetic improvement for health and well-being traits in food animals. It enables the introduction of beneficial alleles within a single generation, including those that are of low frequency or absent in the population, while effectively bypassing linkage drag. For the dairy industry, genome editing can be used to make rapid genetic improvements that are precise, efficient, and transgene-free for functional traits that are not practically addressed without disrupting conventional breeding goals for overall economic merit based on genomic selection. Herein, various case studies for dairy cattle breeding are presented that demonstrate applications of genome editing for enhancing heat stress tolerance, reduced disease susceptibility, and other qualitative traits absent in some breeds. One case highlights the success of simultaneous editing of multiple loci through recent advancements in embryonic stem cell biology. Multiplexed editing is crucial for addressing the polygenic nature inherent to many economically important traits in livestock. However, maximizing the benefits of genome editing depends on the continued discovery of targets for editing that are commercially important. Commercialization also depends on rapidly evolving regulatory statutes for risk assessment, where some countries already permit the commercialization of cattle with non-GMO genome alterations through existing regulations. New breeding technologies such as genome editing are now poised to have significant impact in equipping elite performance cattle to be more resilient to infectious disease and climate change without the loss of production gains obtained from decades of selection.

The objective of this cross-sectional observational study was to describe the transport conditions of calves at the time of their arrival at the 2 major livestock auction markets in the province of Québec, Canada, and to identify characteristics that affect bedding cleanliness. A particular emphasis was placed on the transport environment of young dairy calves commonly being marketed for veal production. During 4 d per auction site (n = 2 sites), 2 d in summer and 2 d in winter, the descriptive characteristics including type of transports, number of calves per transport, separation from other transported animals, as well as presence of ventilation sources (e.g., open holes allowing natural ventilation), bedding, and bedding cleanliness, were determined. A total of 507 different transports were included, representing a total of 4,054 calves sold during these 8 d. The vast majority of calves (95% [n = 3,845]) were transported by commercially designed trailers (long commercial trailers (n = 358; 70.6% of all transport types), short commercial trailers (n = 62; 12.2%), or multideck trailers (n = 15; 3%). A minority of calves (5%) were either transported by homemade trailers (n = 30; 5.9% of transport) or other types of transports (n = 42; 8.3%). The presence of any ventilation source in the calves' transportation area was observed in 86% of transports and increased in summer versus winter (odds ratio: 2.75 [95% CI: 1.58-4.79]). Bedding was present in 96% of evaluated transports. The majority (68%) of calves' transport flooring area was considered clean, with less than 33% of the calves' area soiled with manure. The dirtiness of calves' transport flooring area was lower in winter than in summer (odds ratio = 0.63 [0.43-0.92]) and in site B than in site A (odds ratio = 0.57 [0.38-0.94]). This study gives interesting insight into transportation and unloading conditions of surplus calves in commercial auction markets.

The capacity of animals to cope with environmental perturbations, hereafter called resilience, is an increasingly important trait. Resilience at the level of the animal is an emergent property of multiple underlying mechanisms (physiological, immunological, behavioral). This means that there is no direct measure of resilience, no easy key traits. Resilience is a latent variable that may be inferred from multivariate measures. Further, the flexibility that resilience provides is evidenced in the rate of response to, and rate of recovery from, the environmental perturbation. Thus, it requires time-series measurements. The increasing availability of on-farm precision livestock technologies, which are capable of providing time-series measures of performance and of various physiological and health biomarkers, offer the opportunity to move toward large-scale phenotyping of resilience. There have been numerous studies putting forward methods to quantify resilience. These methods can be classified as being data driven or concept driven. However, new candidate resilience proxies need to be validated. This is tricky to do because there is no direct measure of resilience, no easy gold standard measure. Per definition, good resilience will benefit the animal. Thus, the accumulated consequences of resilience can be used to evaluate resilience proxies. All other things being equal, it is expected that good resilience will be associated with a longer functional longevity (longevity adjusted for production level), with more reproductive cycles, and with fewer disease events. Recent examples of this approach of evaluating resilience proxies against the accumulated consequences of resilience are discussed. They show clearly that operational resilience proxies that are heritable and have been validated against the consequences of good resilience can be derived from on-farm time-series data. With the aim of deriving more nuanced phenotypes, there are an increasing number of studies that have taken up the challenge of attempting to statistically combine the information coming from multiple time-series measures. These studies show how multivariate time-series statistics can be used to derive more nuanced resilience phenotypes that capture some of the underlying mechanisms of resilience. In conclusion, the recent studies reviewed here have shown that operational and heritable resilience proxies exist, that they can form the basis for selection for resilience, and that more nuanced phenotypes are attainable, which will allow selection for resilience to be tailored according to prevailing environmental challenge types.

Dairy cattle are frequently housed in freestalls with limited space, affecting social interactions between individuals. Social behavior in dairy cattle is gaining recognition as a valuable tool for identifying sick animals, but its application is hampered by the complexities of analyzing social interactions in intensive housing systems. In this context, precision livestock technologies present the opportunity to continuously monitor dyadic spatial associations on dairy farms. The aim of this study is to evaluate the accuracy of predicting social behavior of dairy cows using social network analysis. Daily social networks were built using the position data from 149 cows over 14 consecutive days of the study period. We applied the separable temporal exponential random graph models to estimate the likelihood of formation and persistence of social contacts between dairy cows individually and to predict the social network on the subsequent day. The correlation between the individual degree centrality values, the number of established social contacts per individual, between the predicted and observed networks ranged from 0.22 to 0.49 when the structural information from network triangles was included in the model. This study presents a novel approach for predicting animal social behavior in intensive housing systems using spatial association information obtained from a real-time location system. The results indicate the potential of this approach as a crucial step toward the larger goal of identifying disruptions in dairy cows' expected social behavior.

Interdevice precision and accuracy are not investigated for precision livestock farming (PLF) technologies, but are fundamental for the use of data in populational metrics and to compare cows' data. This study aimed to validate a behavior monitoring collar (BMC; CowMed, Santa Maria, RS, Brazil) and its interdevice reliability. First, we compared observations with the BMC, and second the interdevice precision and accuracy for rumination, feeding activity, and idle time of lactating dairy cows. Holstein cows (n = 23) were housed in a voluntary milk system freestall barn and fitted with 2 devices within the same cow. Observations were made over 2 periods of one day (0700 to 1100 h, 1400 to 1700 h); the 7 h per cow were summarized for each behavior to assess the agreement of observed behavior and BMC data. To assess the interdevice reliability, 26 d of BMC data were summarized by day per cow for both devices. Pearson correlation (r), coefficient of determination (R), Lin's concordance correlation coefficient (ρ), linear regression, and Bland-Altman plots (BAP) were calculated for each period of observation. For the validation, we found high correlations for feeding activity, very high for idle time, but low correlations for rumination. The BAP were deemed acceptable and without bias; BAP mean differences ± SD were 0.83 ± 4.01, -0.48 ± 4.15, and 7.17 ± 3.94 min/h for rumination, feeding activity, and idle time, respectively. The slope of the linear regression did not differ from 1 for any behaviors but idle. For interdevice comparison, we found moderate correlations for feeding activity and idle time, and a low correlation for rumination. The BAP was deemed acceptable and without bias; BAP mean differences were -0.36 ± 2.84, 0.45 ± 3.51, and -0.06 ± 2.81 min/h for rumination, feeding activity, and idle time, respectively. All slopes of the linear regressions differed from 1 except feeding time. Thus, the interdevice comparison did not meet the accuracy criteria. In summary, this study validated the precision of the BMC for recording feeding activity of lactating dairy cows.

Reducing methane (CH) emissions from agriculture, among other sectors, is a key step to reducing global warming. There are many strategies to reduce CH emissions in ruminant animals, including genetic selection, which yields cumulative and permanent genetic gains over generations. A single-step genomic evaluation for methane efficiency (MEF) was officially implemented in April 2023 for the Canadian Holstein breed, aiming to reduce CH emissions without affecting production levels. This evaluation was achieved by using milk mid-infrared (MIR) spectral data to predict individual cow CH production. The genetic evaluation model included milk MIR predicted CH (CH4), along with milk yield (MY), fat yield (FY), and protein yield (PY), as correlated traits. Traits were expressed in kilograms per day (MY, FY, and PY) or grams per day (CH4). The MiX99 software was used to fit the single-step, 4-trait animal model. Genomic breeding values for CH4 were then obtained by re-parameterization, using recursive genetic linear regression coefficients on MY, FY, and PY, giving a measure of MEF that is genetically independent of the production traits. The estimated breeding values were expressed as relative breeding values with a mean of 100 and standard deviation of 5 for the genetic base population, where a higher value indicates the animal produces lower predicted CH. This national genomic evaluation is another tool that will lower the dairy industry's carbon footprint by reducing CH emissions without affecting production traits.