Measures of weight distribution of dairy cows to detect lameness and the presence of hoof lesions

There is increasing interest in automated methods of detecting lame cows. Hoof lesion data and measures of weight distribution from 61 lactating cows were examined in this study. Lame cows were identified with different numerical rating scores (NRS) used as thresholds (NRS \u3e3 and NRS ≥3.5) for lameness. The ratio of weight applied to a pair of legs (LWR) when the cow was standing was calculated using a special weigh scale, and the cows were gait scored using a 1 to 5 NRS. Hoof lesions were scored and the cows placed into 1 of 4 mutually exclusive categories of hoof lesion: a) no lesions, b) moderate or severe hemorrhages, c) digital dermatitis, and d) sole ulcers. Regression analysis and receiver operating characteristic (ROC) curves were used to analyze the relation between hoof lesions and LWR. A clear relationship was found between NRS and LWR for the cows with sole ulcers (R2 = 0.79). The LWR could differentiate cows with sole ulcers from sound cows with no hoof lesions [area under the curve (AUC) = 0.87] and lame cows from nonlame cows with lameness thresholds NRS \u3e3 (AUC = 0.71) and NRS ≥3.5 (AUC = 0.88). There was no relationship between LWR and NRS for cows with digital dermatitis. Measurement of how cows distribute their weight when standing holds promise as a method of automated detection of lameness

Measures of weight distribution of dairy cows to detect lameness and the presence of hoof lesions

There is increasing interest in automated methods of detecting lame cows. Hoof lesion data and measures of weight distribution from 61 lactating cows were examined in this study. Lame cows were identified with different numerical rating scores (NRS) used as thresholds (NRS \u3e3 and NRS ≥3.5) for lameness. The ratio of weight applied to a pair of legs (LWR) when the cow was standing was calculated using a special weigh scale, and the cows were gait scored using a 1 to 5 NRS. Hoof lesions were scored and the cows placed into 1 of 4 mutually exclusive categories of hoof lesion: a) no lesions, b) moderate or severe hemorrhages, c) digital dermatitis, and d) sole ulcers. Regression analysis and receiver operating characteristic (ROC) curves were used to analyze the relation between hoof lesions and LWR. A clear relationship was found between NRS and LWR for the cows with sole ulcers (R2 = 0.79). The LWR could differentiate cows with sole ulcers from sound cows with no hoof lesions [area under the curve (AUC) = 0.87] and lame cows from nonlame cows with lameness thresholds NRS \u3e3 (AUC = 0.71) and NRS ≥3.5 (AUC = 0.88). There was no relationship between LWR and NRS for cows with digital dermatitis. Measurement of how cows distribute their weight when standing holds promise as a method of automated detection of lameness

Dairy producer attitudes to pain in cattle in relation to disbudding calves

Pain is an important indicator of poor welfare of livestock. Despite this, pain has largely gone unrecognized in farm animals due to attitudes of producers and veterinarians, although they play a key role in monitoring and managing the perception of animal pain. Producer attitudes toward animal welfare influence livestock management and production. The aim was to quantify dairy producer attitudes to the painfulness of various cattle diseases and disbudding, a painful routine procedure performed on farm to ensure safer handling of cattle. A questionnaire on disbudding-related opinions and practices was sent to 1,000 Finnish dairy producers (response rate: 45%). Attitudes toward disbudding were gauged using a 5-point Likert scale and attitudes to cattle pain scored on an 11-point numerical rating scale. Principal components analysis was used to assess the loadings, which were further tested for differences between producer gender and housing systems with Mann-Whitney U-tests, and between herd milk yield, herd size, and age and work experience of producers with a Kruskal-Wallis test. Four main factors were identified: factor I (“taking disbudding pain seriously”), factor II (“sensitivity to pain caused by cattle diseases”), factor III (“ready to medicate calves myself”), and factor IV (“pro horns”). Female producers took disbudding pain more seriously, were more sensitive to pain caused to cattle by diseases, and were more ready to medicate disbudded calves than male producers. Producers with tie-stalls favored horns over producers with freestalls. Male producers with tie-stalls were sensitive to cattle pain and preferred horns over male producers with freestalls. Female producers with freestalls were more ready to medicate calves, but did not prefer horns more than female producers with tie-stalls. Taking disbudding seriously correlated with sensitivity to pain caused by cattle diseases. Producers with low-milk-yielding herds were less willing to medicate calves and more willing to keep cattle with horns than producers with higher-yielding herds. Older producers were more sensitive to cattle pain than middle-aged and younger producers. No effect was established for taking disbudding pain seriously: the pro-horn factor was associated with work experience, age, and herd size. Women rated pain higher and were more positive toward pain medication for animals than men. Maintaining horns are more important for producers with tie-stalls than for those with freestalls.Peer reviewe

Short communication : Lameness impairs feeding behavior of dairy cows

Peer reviewe

Measuring dairy cow welfare with real-time sensor-based data and farm records: a concept study

Welfare assessment of dairy cows by in-person farm visits provides only a snapshot of welfare and is time-consuming and costly. Possible solutions to reduce the need for in-person assessments would be to exploit sensor data and other routinely collected on-farm records. The aim of this study was to develop an algorithm to classify dairy cow welfare based on sensors (accelerometer and/or milk meter) and farm records (e.g. days in milk, lactation number). In total, 318 cows from six commercial farms located in Finland, Italy and Spain (two farms each) were enrolled for a pilot study lasting 135 days. During this time, cows were routinely scored using 14 animal-based measures of good feeding, health and housing based on the Welfare Quality® (WQ®) protocol. WQ® measures were evaluated daily or approximately every 45 days, using disease treatments from farm records and on-farm visits, respectively. WQ® measures were supplemented with daily temperature-humidity index to account for heat stress. The severity and duration of each welfare measure were evaluated, and the final welfare index was obtained by summing up the values for each cow on each pilot study day, and stratifying the result into three classes: good, moderate and poor welfare. For model building, a machine-learning (ML) algorithm based on gradient-boosted trees (XGBoost) was applied. Two model versions were tested: (1) a global model tested on unseen herd, and (2) a herd-specific model tested on unseen part of the data from the same herd. The version (1) served as an example on the model performance on a herd not previsited by the evaluator, while version (2) resembled a custom-made solution requiring in-person welfare evaluation for model training. Our results indicated that the global model had a low performance with average sensitivity and specificity of 0.44 and 0.68, respectively. For the herd-specific version, the model performance was higher reaching an average of 0.64 sensitivity and 0.80 specificity. The highest classification performance was obtained for cows in poor welfare, followed by cows in good and moderate welfare (balanced accuracy of 0.77, 0.71 and 0.68, respectively). Since the global model had low classification accuracy, the use of the developed model as a stand-alone system based solely on sensor data is infeasible, and a combination of in-person and sensor-based welfare evaluation would be preferable for a reliable welfare assessment. ML-based solutions, even with fair discriminative abilities, have the potential to enhance dairy welfare monitoring

Visual event-related potentials of dogs: a non-invasive electroencephalography study

Previously, social and cognitive abilities of dogs have been studied within behavioral experiments, but the neural processing underlying the cognitive events remains to be clarified. Here, we employed completely non-invasive scalp-electroencephalography in studying the neural correlates of the visual cognition of dogs. We measured visual event-related potentials (ERPs) of eight dogs while they observed images of dog and human faces presented on a computer screen. The dogs were trained to lie still with positive operant conditioning, and they were neither mechanically restrained nor sedated during the measurements. The ERPs corresponding to early visual processing of dogs were detectable at 75–100 ms from the stimulus onset in individual dogs, and the group-level data of the 8 dogs differed significantly from zero bilaterally at around 75 ms at the most posterior sensors. Additionally, we detected differences between the responses to human and dog faces in the posterior sensors at 75–100 ms and in the anterior sensors at 350–400 ms. To our knowledge, this is the first illustration of completely non-invasively mea- sured visual brain responses both in individual dogs and within a group-level study, using ecologically valid visual stimuli. The results of the present study validate the fea- sibility of non-invasive ERP measurements in studies with dogs, and the study is expected to pave the way for further neurocognitive studies in dogs.Previously, social and cognitive abilities of dogs have been studied within behavioral experiments, but the neural processing underlying the cognitive events remains to be clarified. Here, we employed completely non-invasive scalp-electroencephalography in studying the neural correlates of the visual cognition of dogs. We measured visual event-related potentials (ERPs) of eight dogs while they observed images of dog and human faces presented on a computer screen. The dogs were trained to lie still with positive operant conditioning, and they were neither mechanically restrained nor sedated during the measurements. The ERPs corresponding to early visual processing of dogs were detectable at 75–100 ms from the stimulus onset in individual dogs, and the group-level data of the 8 dogs differed significantly from zero bilaterally at around 75 ms at the most posterior sensors. Additionally, we detected differences between the responses to human and dog faces in the posterior sensors at 75–100 ms and in the anterior sensors at 350–400 ms. To our knowledge, this is the first illustration of completely non-invasively mea- sured visual brain responses both in individual dogs and within a group-level study, using ecologically valid visual stimuli. The results of the present study validate the fea- sibility of non-invasive ERP measurements in studies with dogs, and the study is expected to pave the way for further neurocognitive studies in dogs.Peer reviewe

Sensor data classification for the indication of lameness in sheep

Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows.

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow's gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5