Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period.

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of ß-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.

Digital technology adoption in livestock production with a special focus on ruminant farming.

Digitalisation is an integral part of modern agriculture. Several digital technologies are available for different animal species and form the basis for precision livestock farming. However, there is a lack of clarity as to which digital technologies are currently used in agricultural practice. Thus, this work aims to present for the first time the status quo in Swiss livestock farming as an example of a highly developed, small-scale and diverse structured agriculture. In this context, the article focuses on the adoption of electronic sensors and measuring devices, electronic controls and electronic data-processing options and the usage of robotics in ruminant farming, namely, for dairy cattle, dairy goats, suckler cows, beef cattle and meat-sheep. Furthermore, the use of electronic ear tags for pigs and the smartphone usage for barn monitoring on poultry farms was assessed. To better understand the adoption process, farm and farmer's characteristics associated with the adoption of (1) implemented and (2) new digital technologies in ruminant farming were assessed using regression analyses, which is classified at a 10% adoption hurdle. The results showed clear differences in the adoption rates between different agricultural enterprises, with both types of digital technologies tending to be used the most in dairy farming. Easy-to-use sensors and measuring devices such as those integrated in the milking parlour were more widespread than data processing technologies such as those used for disease detection. The husbandry system further determined the use of digital technologies, with the result that farmers with tie stall barns were less likely to use digital technologies than farmers with loose housing systems. Additional studies of farmers' determinants and prospects of implementation can help identify barriers in the adoption of digital technologies.

Evaluation and application potential of an accelerometer-based collar device for measuring grazing behavior of dairy cows.

The commercially available collar device MooMonitor+ was evaluated with regards to accuracy and application potential for measuring grazing behavior. These automated measurements are crucial as cows feed intake behavior at pasture is an important parameter of animal performance, health and welfare as well as being an indicator of feed availability. Compared to laborious and time-consuming visual observation, the continuous and automated measurement of grazing behavior may support and improve the grazing management of dairy cows on pasture. Therefore, there were two experiments as well as a literature analysis conducted to evaluate the MooMonitor+ under grazing conditions. The first experiment compared the automated measurement of the sensor against visual observation. In a second experiment, the MooMonitor+ was compared to a noseband sensor (RumiWatch), which also allows continuous measurement of grazing behavior. The first experiment on n = 12 cows revealed that the automated sensor MooMonitor+ and visual observation were highly correlated as indicated by the Spearman's rank correlation coefficient (rs) = 0.94 and concordance correlation coefficient (CCC) = 0.97 for grazing time. An rs-value of 0.97 and CCC = 0.98 was observed for rumination time. In a second experiment with n = 12 cows over 24-h periods, a high correlation between the MooMonitor+ and the RumiWatch was observed for grazing time as indicated by an rs-value of 0.91 and a CCC-value of 0.97. Similarly, a high correlation was observed for rumination time with an rs-value of 0.96 and a CCC-value of 0.99. While a higher level of agreement between the MooMonitor+ and both visual observation and RumiWatch was observed for rumination time compared to grazing time, the overall results showed a high level of accuracy of the collar device in measuring grazing and rumination times. Therefore, the collar device can be applied to monitor cow behavior at pasture on farms. With regards to the application potential of the collar device, it may not only be used on commercial farms but can also be applied to research questions when a data resolution of 15 min is sufficient. Thus, at farm level, the farmer can get an accurate and continuous measurement of grazing behavior of each individual cow and may then use those data for decision-making to optimize the animal management.

Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows.

Feeding behaviour is an important parameter of animal performance, health and welfare, as well as reflecting levels and quality of feed available. Previously, sensors were only used for measuring animal feeding behaviour in indoor housing systems. However, sensors such as the RumiWatchSystem can also monitor such behaviour continuously in pasture-based environments. Therefore, the aim of this study was to validate the RumiWatchSystem to record cow activity and feeding behaviour in a pasture-based system. The RumiWatchSystem was evaluated against visual observation across two different experiments. The time duration per hour at grazing, rumination, walking, standing and lying recorded by the RumiWatchSystem was compared to the visual observation data in Experiment 1. Concordance Correlation Coefficient (CCC) values of CCC=0.96 for grazing, CCC=0.99 for rumination, CCC=1.00 for standing and lying and CCC=0.92 for walking were obtained. The number of grazing and rumination bouts within one hour were also analysed resulting in Cohen's Kappa (κ)=0.62 and κ=0.86 for grazing and rumination bouts, respectively. Experiment 2 focused on the validation of grazing bites and rumination chews. The accordance between visual observation and automated measurement by the RumiWatchSystem was high with CCC=0.78 and CCC=0.94 for grazing bites and rumination chews, respectively. These results indicate that the RumiWatchSystem is a reliable sensor technology for observing cow activity and feeding behaviour in a pasture based milk production system, and may be used for research purposes in a grazing environment.

Disrupted seasonal biology impacts health, food security and ecosystems.

The rhythm of life on earth is shaped by seasonal changes in the environment. Plants and animals show profound annual cycles in physiology, health, morphology, behaviour and demography in response to environmental cues. Seasonal biology impacts ecosystems and agriculture, with consequences for humans and biodiversity. Human populations show robust annual rhythms in health and well-being, and the birth month can have lasting effects that persist throughout life. This review emphasizes the need for a better understanding of seasonal biology against the backdrop of its rapidly progressing disruption through climate change, human lifestyles and other anthropogenic impact. Climate change is modifying annual rhythms to which numerous organisms have adapted, with potential consequences for industries relating to health, ecosystems and food security. Disconcertingly, human lifestyles under artificial conditions of eternal summer provide the most extreme example for disconnect from natural seasons, making humans vulnerable to increased morbidity and mortality. In this review, we introduce scenarios of seasonal disruption, highlight key aspects of seasonal biology and summarize from biomedical, anthropological, veterinary, agricultural and environmental perspectives the recent evidence for seasonal desynchronization between environmental factors and internal rhythms. Because annual rhythms are pervasive across biological systems, they provide a common framework for trans-disciplinary research.

Replicating disease spread in empirical cattle networks by adjusting the probability of infection in random networks.

Comparisons between mass-action or "random" network models and empirical networks have produced mixed results. Here we seek to discover whether a simulated disease spread through randomly constructed networks can be coerced to model the spread in empirical networks by altering a single disease parameter - the probability of infection. A stochastic model for disease spread through herds of cattle is utilised to model the passage of an SEIR (susceptible-latent-infected-resistant) through five networks. The first network is an empirical network of recorded contacts, from four datasets available, and the other four networks are constructed from randomly distributed contacts based on increasing amounts of information from the recorded network. A numerical study on adjusting the value of the probability of infection was conducted for the four random network models. We found that relative percentage reductions in the probability of infection, between 5.6% and 39.4% in the random network models, produced results that most closely mirrored the results from the empirical contact networks. In all cases tested, to reduce the differences between the two models, required a reduction in the probability of infection in the random network.

Electronic bolus design impacts on administration.

Electronic identification of animals has become increasingly important worldwide to improve and ensure traceability. In warm and hot climates, such as Brazil, boluses can have advantages over ear tags as the internal devices reduce the risks of ear tag losses, tissue damage, and lesions on the ear. Electronic boluses, however, are often perceived as having negative characteristics, including reported difficulties of administration in small ruminants. This paper describes the factors associated with bolus design that affect the swallowing of a bolus in sheep. Other factors that might influence bolus swallowing time have also been considered. In addition, the effect of bolus design on its performance was evaluated. A total of 56 Suffolk ewes were used to assess the ease of administration and retention of 3 types of electronic ruminal boluses (mini, 11.5 × 58.0 mm and 21.7 g; small, 14.8 × 48.5 mm and 29.5 g; standard, 19.3 × 69.8 mm and 74.4 g) during a whole productive year, including pregnancy and lamb suckling. Ewe age (5.6 ± 2.3 yr) and weight (85.07 ± 8.2 kg BW) were recorded, as well as time for bolus swallowing. The deglutition of the bolus and any resulting blockages in the esophagus were monitored by visual observations. Retention and readability of the boluses were regularly monitored for d 1, wk 1, mo 1, and every mo until 1 yr. Time for bolus swallowing differed substantially with bolus type and was greater (P < 0.05) for the standard bolus (32.8 ± 6.9 s) when compared to small and mini boluses, which did not differ (8.5 ± 2.0 vs. 9.2 ± 2.7 s; P > 0.05). The bolus o.d. and length were positively correlated with swallowing time (P < 0.01). The ewe weight was negatively correlated with swallowing time (P < 0.05). At 6 mo all electronic boluses showed 100% retention rate, and at 12 mo, bolus retention was 100%, 94.5%, and 100% for mini, small, and standard boluses, respectively (P > 0.05). At 12 mo, all boluses showed 100% readability, except for small boluses, which had a readability of 94.5%. In conclusion, bolus design affected swallowing time and bolus readability. A reduction in boluses length and o.d. needs to be carried out to provide ease of administration and for boluses to be used as an effective means of electronic identification. Therefore, this study shows that adequately designed boluses are safe and suitable for identifying adult sheep and can therefore be used in hot climates.

Does diverse grazing behavior of suckler cows have an impact on predicted methane emissions?

A modeling study based on a dataset from a large-scale grazing study was used to identify the potential impact of grazing behavior and performance of diverse cow genotypes on predicted methane (CH4) emissions. Lactating cows grazing extensive seminatural grassland and heath vegetation were monitored with Global Positioning System collars and activity sensors. The diet selected by cows of 3 different genotypes, Aberdeen Angus cross Limousin (AxL), Charolais (CHA), and Luing (LUI), was simulated by matching their locations during active periods with hill vegetation maps. Measured performance and activity were used to predict energy requirements, DMI, and CH4 output. The cumulative effect of actual performance, diet selection, and actual physical activity on potential CH4 output and yield was estimated. Sensitivity analyses were performed for the digestibility of intake, energy cost of activity, proportion of milk consumed by calves, and reproductive efficiency. Although with a better performance (P < 0.05), LUI required less total energy than the other genotypes (P < 0.001) as the other 2 spent more energy for maintenance (P < 0.001) and activity (P < 0.001). By selecting a better quality diet (P < 0.03), estimated CH4 of CHA cow-calf pairs was lower than AxL (P = 0.001) and slightly lower than LUI (P = 0.08). Energy lost as CH4 was 0.17 and 0.58% lower for LUI than AxL and CHA (P < 0.002). This study suggests for the first time that measured activity has a major impact on estimated CH4 outputs. A 15% difference of the cow-calf pair CH4 was estimated when using different coefficients to convert actual activity into energy. Predicted CH4 was highly sensitive to small changes in diet quality, suggesting the relative importance of diet selection on heterogeneous rangelands. Extending these results to a farm systems scale, CH4 outputs were also highly sensitive to reductions in weaning rates, illustrating the impact on CH4 at the farm-system level of using poorly adapted genotypes on habitats where their performances may be compromised. This paper demonstrates that variations in grazing behavior and grazing choice have a potentially large impact on CH4 emissions, illustrating the importance of including these factors in calculating realistic national and global estimates.

The influence of empirical contact networks on modelling diseases in cattle.

We present two stochastic models of the passage of an SEIR (susceptible-latent-infected-resistant) disease through herds of cattle. One model is based on a contact network constructed via continuously recorded interaction data from two herds of cattle, the other, a matching network constructed using the principles of mass-action mixing. The recorded contact data were produced by attaching proximity data loggers to two separate herds of cattle during two separate recording periods. The network constructed using the principles of mass-action mixing uses the same number of contacts as the recorded network but distributes them randomly amongst the animals. The recorded networks had a greater number of repeated contacts, lower closeness and clustering scores and greater average path length than the mass-action networks. A lower proportion of simulations of the recorded network produce any disease spread when compared to those simulations of the mass-action network and, of those that did, fewer infected animals were predicted. For all parameter values tested, within the sensitivity analysis, similar differences were found between the recorded and mass-action network models.