Progress and opportunities through use of genomics in animal production.

The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.

The development and validation of a milk feeding behavior alert from automated feeder data to classify calves at risk for a diarrhea bout: A diagnostic accuracy study.

The objective of this diagnostic accuracy study was to develop and validate an alert to identify calves at risk for a diarrhea bout using milk feeding behavior data (behavior) from automated milk feeders (AMF). We enrolled Holstein calves (n = 259) as a convenience sample size from 2 facilities that were health scored daily preweaning and offered either 10 or 15 L/d of milk replacer. For alert development, 132 calves were enrolled and the ability of milk intake, drinking speed, and rewarded visits collected from AMF to identify calves at risk for diarrhea was tested. Alerts that had high diagnostic accuracy in the alert development phase were validated using a holdout validation strategy of 127 different calves from the same facilities (all offered 15 L/d) for -3 to 1 d relative to diarrhea diagnosis. We enrolled calves that were either healthy or had a first diarrheal bout (loose feces ≥2 d or watery feces ≥1 d). Relative change and rolling dividends for each milk feeding behavior were calculated for each calf from the previous 2 d. Logistic regression models and receiver operator curves (ROC) were used to assess the diagnostic ability for relative change and rolling dividends behavior relative to alert d) to classify calves at risk for a diarrhea bout from -2 to 0 d relative to diagnosis. To maximize sensitivity (Se), alert thresholds were based on ROC optimal classification cutoff. Diagnostic accuracy was met when the alert had a moderate area under the ROC curve (≥0.70), high accuracy (Acc; ≥0.80), high Se (≥0.80), and very high precision (Pre; ≥0.85). For alert development, deviations in rolling dividend milk intake with drinking speed had the best performance (10 L/d: ROC area under the curve [AUC] = 0.79, threshold ≤0.70; 15 L/d: ROC AUC = 0.82, threshold ≤0.60). Our diagnostic criteria were only met in calves offered 15 L/d (10 L/d: Se 75%, Acc 72%, Pre 92%, specificity [Sp] 55% vs. 15 L/d: Se 91%, Acc 91%, Pre 89%, Sp 73%). For holdout validation, rolling dividend milk intake with drinking speed met diagnostic criteria for one facility (threshold ≤0.60, Se 86%, Acc 82%, Pre 94%, Sp 50%). However, no milk feeding behavior alerts met diagnostic criteria for the second facility due to poor Se (relative change milk intake -0.36 threshold, Se 71%, Acc 70%, and Pre 97%). We suggest that changes in milk feeding behavior may indicate diarrhea bouts in dairy calves. Future research should validate this alert in commercial settings; furthermore, software updates, support, and new analytics might be required for on-farm application to implement these types of alerts.

Graduate Student Literature Review: The use of integrated sensor data for the detection of hyperketonemia in pasture-based dairy systems during the transition period.

This review evaluates research regarding the use of sensors to predict and manage hyperketonemia (HYK) in dairy cows during the transition period, with a focus on pasture-based systems. By doing so, we assessed the accuracy of HYK detection models, noting that no studies thus far have produced models with sufficient accuracy for practical use. Sensors have been validated for their use in dairy farming, proving they produce reliable and useful information. Research is beginning to focus on the analysis of multiple sensors together as a sensor system, discovering the potential for these technologies to be a valuable aid in decision making and farm management. Of the studies that use sensors to predict and manage disease in dairy cows, few studies use data integration (the process of combining data from multiple sensors which in turn improves model accuracy), highlighting a gap in the literature. Recently published research has focused on the detection of mastitis and lameness in pasture-based systems, with less focus toward the detection of metabolic disease. This is reflected in the lack of studies that report the prevalence of metabolic diseases, such as HYK, in pasture-based systems, especially in Australia and New Zealand. It is suggested that further research focuses on (1) determining the prevalence and impact of HYK in pasture-based systems; (2) exploring the use of sensors for HYK detection in pasture-based systems; (3) improving model accuracy with data integration; and (4) confirming the economic benefit of sensors to justify the cost of investing in sensor systems.

The effectiveness of a virtual fencing technology to allocate pasture and herd cows to the milking shed.

Virtual fencing technology provides an opportunity to rethink the management of intensive grazing systems in general, yet most studies have used products developed and applied to more extensive livestock systems. This research aimed to assess the application of a virtual fencing technology developed for the intensive pastoral dairy industry. The Halter system uses 2 primary cues (sound and vibration) and one aversive secondary cue (a low-energy electrical pulse) to confine cows to a pasture allocation and remotely herd cows. We studied 2 groups of 40 mid-lactation multiparous dairy cows (Bos taurus, predominantly Friesian and Friesian × Jersey, parity 1-8). Cows were milked twice per day and provided 9 kg of pasture DM/d in a 24-h allocation, supplemented with 7 kg of silage and 6 kg of grain DM/d. Training to the Halter system occurred over 10 d, after which cows were managed with the technology for a further 28 d. The type and time of cues delivered were recorded by each collar and communicated via a base station to cloud data storage. Cows took less than a day to start responding to the sound cues delivered while held on a pasture allocation and were moving to the milking parlor without human intervention by d 4 of training. On training d 1, at least 60% of sound cues resulted in an electrical pulse. Across training d 2 to 10, 6.4% of sound cues resulted in a pulse. After the 10-d training period, 2.6% of sound cues resulted in a pulse. During the management period, 90% of cows spent ≤1.7 min/d beyond the virtual fence, received ≤0.71 pulses/d in the paddock and received ≤1 pulse/d during virtual herding to the parlor. By the final week of the management period, 50% of cows received 0 pulses/week in the paddock and 35% received 0 pulses/week during virtual herding. The number of pulses delivered per day and the pulse/sound cue ratio was lower in this study than that previously reported using other virtual fencing technologies. We conclude that the Halter technology is successful at containing lactating dairy cows in an intensive grazing system as well as at remotely herding animals to the milking parlor.

Descriptive evaluation of a camera-based dairy cattle lameness detection technology.

Lameness in dairy cattle is a clinical sign of impaired locomotion, mainly caused by painful foot lesions, compromising the US dairy industry's economic, environmental, and social sustainability goals. Combining technology and on farm data may be a more precise and less labor-intensive lameness detection tool, particularly for early detection. The objective of this observational study was to describe the association between average weekly autonomous camera-based (AUTO) locomotion scores and hoof trimming (HT) data. The AUTO data were collected from 3 farms from April 2022 to March 2023. Historical farm HT data were collected from March 2016 to March 2023 and used to determine cow lesion history and date of HT event. The HT events were categorized as a regular HT (TRIM; n = 2290) or a HT with a lesion recorded (LESION; n = 670). Events with LESION were sub-categorized based on lesion category: digital dermatitis (DD; n = 276), sole ulcer (SU; n = 79), white line disease (WLD; n = 141), and other (n = 174). The data also contained the leg of the LESION, classified as front left (FL; n = 54), front right (FR; n = 146), rear left (RL; n = 281), or rear right (RR; n = 183) leg with 6 events missing the leg. Cows' HT histories were classified as follows: cows with no previous recorded instance of any lesion were classified as TRIM0 (n = 1554). The first instance of any hoof lesion was classified as LESION1 (n = 238). This classification was retained until a subsequent TRIM occurred - recorded as TRIM1 (n = 632). The next unique instance of any lesion following a TRIM1 was classified as LESION2 (n = 86). Any LESION events occurring after LESION1 or LESION2 without a subsequent TRIM were considered a hoof lesion recurrence and classified as LESIONRE1 (n = 164) and LESIONRE2 (n = 22), respectively. TRIM events after LESION2 or LESION2RE (n = 104) or LESION events after LESIONRE1 or LESIONRE2 were classified as LESION_OTHER (n = 160). The AUTO scores from -28 to -1 days prior to the HT event were summarized into weekly scores and included if cows had at least 1 observation per week in the 4 weeks before the event. For all weeks, LESION cows had a higher median AUTO score than TRIM cows. Cows with TRIM0 had the lowest and most consistent median weekly score compared to LESION and other TRIM classifications. Before HT cows with TRIM0 and TRIM1, both had median score increases of 1 across the 4 weeks, while the LESION categories had an increase of 4 to 8. Scores increased with each subsequent LESION event compared to the previous LESION event. Cows with SU lesions had the highest median score across the 4 weeks, WLD had the largest score increase, and DD had the lowest median score and score increase. When grouping a LESION event by leg the hoof lesion was found on, the AUTO scores for four groups displayed comparable median values. Due to the difference between TRIM and LESION events, this technology shows potential for the early detection of hoof lesions.

Comparison of 3 mathematical models to estimate lactation performance in dairy cows.

Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow one to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post-peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large dataset, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

New insight into social relationships in dairy cows and how time of birth, parity, and relatedness affect spatial interactions later in life.

Social interactions between cows play a fundamental role in the daily activities of dairy cattle. Real-time location systems provide on a continuous and automated basis information about the position of individual cows inside barns, offering a valuable opportunity to monitor dyadic social contacts. Understanding dyadic social interactions could be applied to enhance the stability of the social structure promoting animal welfare and to model disease transmission in dairy cattle. This study aimed to identify the effect of different cow characteristics on the likelihood of the formation and persistence of social contacts in dairy cattle. The individual position of the lactating cows was automatically collected once per second for 2 wk, using an ultra-wideband system on a Swedish commercial farm consisting of almost 200 dairy cows inside a freestall barn. Social networks were constructed using the position data of 149 cows with available information on all characteristics during the study period. Social contacts were considered as a binary variable indicating whether a cow pair was within 2.5 m of each other for at least 10 min per day. The role of cow characteristics in social networks was studied by applying separable temporal exponential random graph models. Our results revealed that cows of the same parity interacted more consistently, as well as those born within 7 d of each other or closely related by pedigree. The repeatability of the topological parameters indicated a consistent short-term stability of the individual animal roles within the social network structure. Additional research is required to elucidate the underlying mechanisms governing the long-term evolution of social contacts among dairy cattle and to investigate the relationship between these networks and the transmission of diseases in the dairy cattle population.

Adjusting the timing of inseminations to the time lag on luteolysis alerts results in higher conception in dairy cattle.

Dairy cow fertility is a complex trait that depends on the cow's physiological status, the farm's environmental and management conditions, and their interactions. Already the slightest improvement in fertility can positively impact a farm's profitability and sustainability. In research, milk progesterone (P4) has often been used as an accurate and feasible way to identify a dairy cow's reproduction status. Moreover, in Europe and Canada, it has been used to improve fertility management on commercial farms as it allows to accurately identify reproduction issues, pregnancy and the optimal insemination window. An on-farm P4 device (OPD) automatically samples, measures and monitors the milk P4 concentration of individual cows. To this end, the P4 data is smoothed to be robust for measurement errors and outliers, and fixed thresholds are used to estimate the time of luteolysis preceding ovulation, thereby generating a luteolysis alert (LA). By smoothing the P4 data, the OPD introduces a time lag on the LA. Variation in this time lag is not considered in the estimation of the optimal insemination window that is advised to the farmer. Ignoring this variation might decrease the accuracy of the optimal insemination window and, therefore, decreases the likelihood of conception. We hypothesize that considering the length of the time lag and adapting the advice accordingly improves the conception rate. This observational retrospective study uses an extensive data set from 17 commercial dairy farms that are equipped with an OPD. We estimated the time lag on the alerts and evaluated their relationship with the interval from LA to insemination for successful (n = 3721) and unsuccessful inseminations (n = 3896) separately. Results showed that the probability of conception increases when a longer LA time lag is compensated with a shorter interval from LA to insemination and vice versa. In addition, for successful inseminations, we found a clear negative relation between the time lag and the interval from LA to insemination and the interval was significantly shorter when the time lag of the LA was longer. This negative relation between time lag and interval from LA to insemination was less pronounced for unsuccessful inseminations. Additionally, we evaluated the conception rates for inseminations that are performed too early, in time or too late with respect to the optimal insemination window advised by the OPD, in function of their associated time lags. We found that, for inseminations that were preceded by a short time lag (<8 h), the conception rate was 17.5 percentage points higher when cows were inseminated later than advised. Likewise, when inseminations were preceded by a long time lag (≥24 h), we found that the conception rate was 13 percentage points higher when cows were inseminated earlier than advised. Our results suggest that farmers using an OPD could potentially increase their conception success by compensating the variable time lag on the LA by adapting the interval from alert to insemination accordingly. This could be used to develop reproductive management strategies to improve reproductive performance on those farms, which can positively impact their sustainability.

Improving Lameness Detection in Cows: A Machine Learning Algorithm Application.

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

Learning end-to-end respiratory rate prediction of dairy cows from RGB videos.

Respiratory rate (RR) is an important indicator of the health and welfare status of dairy cows. In recent years, progress has been made in monitoring the RR of dairy cows using video data and learning methods. However, existing approaches often involve multiple processing modules, such as region of interest (ROI) detection and tracking, which can introduce errors that propagate through successive steps. The objective of this study was to develop an end-to-end computer vision method to predict RR of dairy cows continuously and automatically. The method leverages the capabilities of a state-of-the-art Transformer model, VideoMAE, which divides video frames into patches as input tokens, enabling the automated selection and featurization of relevant regions, such as a cow's abdomen, for predicting RR. The original encoder of VideoMAE was retained, and a classification head was added on top of it. Further, the weights of the first 11 layers of the pre-trained model were kept, while the weights of the final layer and classifier were fine-tuned using video data collected in a tie-stall barn from 6 dairy cows. Respiratory rates measured using a respiratory belt for individual cows were serving as the ground truth (GT). The evaluation of the developed model was conducted using multiple metrics, including mean absolute error (MAE) of 2.58 breaths per minute (bpm), root mean squared error (RMSE) of 3.52 bpm, root mean squared prediction error (RMSPE; as a proportion of observed mean) of 15.03%, and Pearson correlation (r) of 0.86. Compared with a conventional method involving multiple processing modules, the end-to-end approach performed better in terms of MAE, RMSE and RMSPE. These results suggest the potential to implement the developed computer vision method for an end-to-end solution, for monitoring RR of dairy cows automatically in a tie-stall setting. Future research on integrating this method with other behavioral detection and animal identification algorithms for animal monitoring in a free-stall dairy barn can be beneficial for a broader application.

A dynamic individual method for yak heifer live body weight estimation using the YOLOv8 network and body parameter detection algorithm.

Live body weight (LBW) is one of the most important parameters for supervising the growth and development of livestock. The yak (Bos grunniens) is a special species of cattle that lives on the Qinghai-Tibetan Plateau. Yaks are more untamed than regular cattle breeds, so it is more challenging to measure their LBW. In this study, YOLOv8 yak detection and LBW estimation models were used to automatically estimate yak LBW in real time. First, the proper posture (normal posture) and individual yak identification was confirmed and then the YOLOv8 detection model was used for LBW estimation from 2-dimensional images. Yak LBW was estimated through yak body parameter extraction and a simple linear regression between the estimated yak LBW and the actual measured yak LBW. The results showed that the overall detection performance for normal yak posture was described by precision, recall, and mean average precision 50 (mAP50) indicators, reaching 81.8%, 86.0%, and 90.6%, respectively. The best yak identification results were represented by precision, recall, and mAP50 values of 97.8%, 96.4%, and 99.0%, respectively. The yak LBW estimation model achieved better results for the 12-mo-old yaks with shorter hair, with values for R2, root mean square error, mean absolute percentage error, and multiple R of 0.96, 2.43 kg, 1.69%, and 0.98, respectively. The results demonstrate that yak LBW can be estimated and monitored in real time using this approach. This study has the potential to be used for daily yak LBW monitoring in an unstressed manner and to save considerable labor resources for large-scale livestock farms. In the future, to reduce the limitations caused by the impacts of yak hair and light condition, datasets of dairy cows and yaks of different ages will be used to improve and generalize the model.

A Living Lab approach to understanding dairy farmers' technology and data needs to improve herd health: Focus groups from 6 European countries.

For successful development and adoption of technology on dairy farms, farmers need to be included in the innovation process. However, the design of agricultural technologies usually takes a top-down approach with little involvement of end-users at the early stages. Living Labs offer a methodology that involve end-users throughout the development process and emphasize the importance of understanding users' needs. Currently, exploration of dairy farmers' technology needs has been limited to specific types of technology (e.g., smartphone apps) and adult cattle. The aim of this study was to use a Living Lab approach to identify dairy farmers' data and technology needs to improve herd health and inform innovation development. We conducted 18 focus groups with a total of 80 dairy farmers from Belgium, Ireland, the Netherlands, Norway, Sweden, and the United Kingdom. Data were analyzed using Template Analysis, and 6 themes were generated representing the fundamental needs of autonomy, comfort, competence, community and relatedness, purpose, and security. Farmers favored technologies that provided them with convenience, facilitated their knowledge and understanding of problems on farm, and allowed them to be self-reliant. Issues with data sharing and accessibility and usability of software were barriers to technology use. Furthermore, farmers were facing problems around recruitment and management of labor and needed ways to reduce stress. Controlling aspects of the barn environment, such as air quality, hygiene, and stocking density, were particular concerns in relation to youngstock management. Overall, the findings suggest that developers of farm technologies may want to include farmers in the design process to ensure a positive user experience and improve accessibility. The needs identified in this study can be used as a framework when designing farm technologies to strengthen need satisfaction and reduce any potential harm toward needs.

Investigation of Climate Effects on the Physiological Parameters of Dairy Livestock (Cow vs. Buffalo).

Nowadays climate change is affecting the planet's biodiversity, and livestock practices must adapt themselves to improve production without affecting animal welfare. This work investigates the influence that some climatic parameters such as Environment Temperature, Relative Humidity, Thermal excursion and Temperature-Humidity Index (THI), can have on milk quantity and quality in two different dairy species (buffaloes and cows) raised on the same farm. A further aim was to understand if THI threshold used for cows could also be used for buffaloes. The climatic parameters were recorded daily through a meteorological station located inside the farm. Milk quantity (converted into ECM) and quality (Fat Percentage-FP; Protein Percentage-PP; Somatic Cell Count-SCC) were measured. Data were analyzed with Spearman's correlation index, separately for buffaloes and cows. The results indicate a greater sensitivity of cows to heat stress and a strong negative correlation of the ECM with meteorological data (p < 0.01). The results of this study may stimulate the use of integrated technologies (sensors, software) in the dairy sector, since the IoT (sensors, software) helps to enhance animal well-being and to optimize process costs, with a precision livestock farming approach.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

Activity behaviors and relative changes in activity patterns recorded by precision technology were associated with diarrhea status in individually housed calves.

The objective of this case-control study was to quantify any association of daily activity behaviors and relative changes in activity patterns (lying time, lying bouts, step count, activity index) with diarrhea status in preweaning dairy calves. Individually housed calves sourced from auction were health-scored daily for signs of diarrhea (fecal consistency loose or watery for 2 consecutive days) for the 28 d after arrival. Calves with diarrhea were pair-matched with healthy controls (n = 13, matched by arrival date, arrival weight, and diagnosis days to diarrheic calves). Mixed linear regression models were used to evaluate the association of diarrhea status, and the diarrhea status by day interaction with activity behaviors (d -3 to d 4) and relative changes in activity patterns (d -3 to d 4) relative to diagnosis of a diarrhea bout. The serum Brix percentage at arrival and daily temperature-humidity index from the calf barn were explored as quantitative covariates, with day as a repeated measure. The baseline for relative changes in activity patterns was set at 100% on d 0. Diarrheic calves were less active; they averaged fewer steps (119.1 ± 18.81 steps/d) than healthy calves (227.4 ± 18.81 steps/d, LSM ± SEM). Diarrheic calves also averaged lower activity indices (827.34 ± 93.092 daily index) than healthy calves (1,396.32 ± 93.092 daily index). We also found also a diarrhea status by day interaction for lying time on d -3, with diarrheic calves spending more time lying (20.80 ± 0.300 h/d) than healthy calves (19.25 ± 0.300 h/d). For relative changes in activity patterns, a diarrhea status by day interaction was detectable on d -2, where diarrheic calves had greater relative changes in step counts (diarrhea 634.85 ± 87.581% vs. healthy 216.51 ± 87.581%) and activity index (diarrhea 316.83 ± 35.692% vs. healthy 150.68 ± 35.692%). Lying bouts were not associated with diarrhea status. These results show that diarrheic calves were more lethargic, and they had relative changes in activity patterns 2 d before clinical signs of diarrhea. Future research should explore the potential of an activity alert that positively indicates an individually housed calf at risk for a diarrhea bout using deviations from relative changes in individual calf activity patterns.

Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems.

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.

Effects of cow reproductive status, parity and lactation stage on behaviour and heavy breathing indications of a commercial accelerometer during hot weather conditions.

Heat stress presents one of the most urgent challenges to modern dairy farming, having major detrimental impacts on cow welfare, health, and production. Understanding the effect of cow factors (reproductive status, parity, and lactation stage) on the physiological and behavioural response to hot weather conditions is essential for the accurate detection and practical application of heat mitigation strategies. To study this, collars with commercial accelerometer-based sensors were fitted on 48 lactation dairy cows to record behaviour and heavy breathing from late spring to late summer. The temperature-humidity index (THI) was calculated from measurements of 8 barn sensors. We found that, above a THI of 84, cows in advanced pregnancy (>90 days) spent more time breathing heavily and less time eating and in low activity than other cows, while cows in early pregnancy (≤90 days) spent less time breathing heavily, more time eating and in low activity. Cows with 3+ lactations showed less time breathing heavily and in high activity and more time ruminating and in low activity than cows with fewer lactations. Although lactation stage interacted significantly with THI on time spent breathing heavily, ruminating, eating, and in low activity, there was no clear indication at which lactation stage cows were more sensitive to heat. These findings show that cow factors affect the cow's physiological and behavioural response to heat, which could be used to provide group-specific heat abatement strategies, thereby improving heat stress management.

Decision support system to classify the vulnerability of broiler production system to heat stress based on fuzzy logic.

In this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to heat conditions using a fuzzy model approach. The model was designed with a multiple-input and a single-output (MISO) approach (input: physical environment and broilers age; output: degree of vulnerability of broilers system). For the validation of the fuzzy model, two approaches were used: (1) records from the scientific literature and (2) meteorological forecasts. First, we validated the model fuzzy with data from the scientific literature; second, we validate the model with data from meteorological forecasts. Both validation approaches were performed in different scenarios of the thermal environment (comfort, discomfort, and discomfort + low heat exchange), broilers' age (21-35 days, 25-39 days, and 28-42 days), and relative cooling efficiency (0% inefficient; and 80% efficient). Then, we applied the model to predict the degree of vulnerability of the broiler system with the help of weather forecasts. The recall and precision of the fuzzy model were high (> 0.9) for the thermal comfort and thermal discomfort + low heat exchange scenarios. In contrast, the fuzzy model was moderate agreement (recall 0.45; precision 0.64) for the thermal discomfort scenario compared to the scientific literature. The application of the model with the weather forecast showed the interaction between the physical and biological systems when submitted to a thermal environment challenge. Regardless of the broilers' age, a high degree of vulnerability was observed in facilities with inefficient cooling system. The fuzzy model developed in this study was efficient to predict the vulnerability of the broiler production system to heat conditions, further, to identify the uncertain conditions associated with broilers' age, relative humidity, and the relative cooling efficiency of the facilities.

Sensor-based behavioral patterns can identify heat-sensitive lactating dairy cows.

Heat stress impairs the health and performance of dairy cows, yet only a few studies have investigated the diversity of cattle behavioral responses to heat waves. This research was conducted on an Italian Holstein dairy farm equipped with precision livestock farming sensors to assess potential different behavioral patterns of the animals. Three heat waves, defined as at least five consecutive days with mean daily temperature-humidity index higher than 72, were recorded in the farm area during the summer of 2021. Individual daily milk yield data of 102 cows were used to identify "heat-sensitive" animals, meaning the cows that, under a given heat wave, experienced a milk yield drop that was not linked with other health events (e.g., mastitis). Milk yield drops were detected as perturbations of the lactation curve estimated by iteratively using Wood's equation. Individual daily minutes of lying, chewing, and activity were retrieved from ear-tag-based accelerometer sensors. Semi-parametric generalized estimating equations models were used to assess behavioral deviations of heat-sensitive cows from the herd means under heat stress conditions. Heat waves were associated with an overall increase in the herd's chewing and activity times, along with an overall decrease of lying time. Heat-sensitive cows spent approximately 15 min/days more chewing and performing activities (p < 0.05). The findings of this research suggest that the information provided by high-frequency sensor data could assist farmers in identifying cows for which personalized interventions to alleviate heat stress are needed.

Digital Livestock Technologies as boundary objects: Investigating impacts on farm management and animal welfare.

Digital Livestock Technologies (DLTs) can assist farmer decision-making and promise benefits to animal health and welfare. However, the extent to which they can help improve animal welfare is unclear. This study explores how DLTs may impact farm management and animal welfare by promoting learning, using the concept of boundary objects. Boundary objects may be interpreted differently by different social worlds but are robust enough to share a common identity across them. They facilitate communication around a common issue, allowing stakeholders to collaborate and co-learn. The type of learning generated may impact management and welfare differently. For example, it may help improve existing strategies (single-loop learning), or initiate reflection on how these strategies were framed initially (double-loop learning). This study focuses on two case studies, during which two DLTs were developed and tested on farms. In-depth, semi-structured interviews were conducted with stakeholders involved in the case studies (n = 31), and the results of a separate survey were used to complement our findings. Findings support the important potential of DLTs to help enhance animal welfare, although the impacts vary between technologies. In both case studies, DLTs facilitated discussions between stakeholders, and whilst both promoted improved management strategies, one also promoted deeper reflection on the importance of animal emotional well-being and on providing opportunities for positive animal welfare. If DLTs are to make significant improvements to animal welfare, greater priority should be given to DLTs that promote a greater understanding of the dimensions of animal welfare and a reframing of values and beliefs with respect to the importance of animals' well-being.