Supporting the development and adoption of automatic lameness detection systems in dairy cattle : effect of system cost and performance on potential market shares

Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was (sic)2.57 per % less missed lame cows, (sic)1.65 per % less false alerts, and (sic)12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system's potential adoption rate

MALT1-deficient mice develop atopic-like dermatitis upon aging

MALT1 plays an important role in innate and adaptive immune signaling by acting as a scaffold protein that mediates NF-kappa B signaling. In addition, MALT1 is a cysteine protease that further fine tunes proinflammatory signaling by cleaving specific substrates. Deregulated MALT1 activity has been associated with immunodeficiency, autoimmunity, and cancer in mice and humans. Genetically engineered mice expressing catalytically inactive MALT1, still exerting its scaffold function, were previously shown to spontaneously develop autoimmunity due to a decrease in Tregs associated with increased effector T cell activation. In contrast, complete absence of MALT1 does not lead to autoimmunity, which has been explained by the impaired effector T cell activation due to the absence of MALT1-mediated signaling. However, here we report that MALT1-deficient mice develop atopic-like dermatitis upon aging, which is preceded by Th2 skewing, an increase in serum IgE, and a decrease in Treg frequency and surface expression of the Treg functionality marker CTLA-4

On the use of on-cow accelerometers for the classification of behaviours in dairy barns

Analysing behaviours can provide insight into the health and overall well-being of dairy cows. Automatic monitoring systems using e.g., accelerometers are becoming increasingly important to accurately quantify cows' behaviours as the herd size increases. The aim of this study is to automatically classify cows' behaviours by comparing leg- and neck-mounted accelerometers, and to study the effect of the sampling rate and the number of accelerometer axes logged on the classification performances. Lying, standing, and feeding behaviours of 16 different lactating dairy cows were logged for 6 h with 3D-accelerometers. The behaviours were simultaneously recorded using visual observation and video recordings as a reference. Different features were extracted from the raw data and machine learning algorithms were used for the classification. The classification models using combined data of the neck- and the leg-mounted accelerometers have classified the three behaviours with high precision (80-99%) and sensitivity (87-99%). For the leg-mounted accelerometer, lying behaviour was classified with high precision (99%) and sensitivity (98%). Feeding was classified more accurately by the neck-mounted versus the leg-mounted accelerometer (precision 92% versus 80%; sensitivity 97% versus 88%). Standing was the most difficult behaviour to classify when only one accelerometer was used. In addition, the classification performances were not highly influenced when only X, X and Z, or Z and Y axes were used for the classification instead of three axes, especially for the neck-mounted accelerometer. Moreover, the accuracy of the models decreased with about 20% when the sampling rate was decreased from 1 Hz to 0.05 Hz

Spatial behaviour of dairy cows is affected by lameness

Lameness is one of the major welfare problems on modern dairy farms, and additionally, it is difficult to control. Lameness is associated with changes in cow behaviour, and efforts have been made to automatically detect these behavioural changes. However, systems relying on a single behavioural variable are likely to fail. Indoor positioning could provide means to measure multiple behavioural variables with a single system. Our aim was to investigate how lameness affects the spatial behaviour of cows, measured with an indoor positioning system. In total, 71 lactating dairy cows were followed during a 7-month study period, with 48 cows in the study simultaneously. Cows were locomotion scored fortnightly with a 10-tier scale, and their daily time spent in the different functional areas of the barn, walking distance, and home range were calculated from the positioning data. Each locomotion score was merged with the 5-day average of the behaviour variables leading up to the scoring day, resulting in 376 observations in the final data. Linear mixed models were fitted with backwards stepwise elimination to test the associations between positioning-based daily behavioral variables and predictor variables comprising locomotion score, parity, lactation stage, breed and the proportion of missing positioning data. Increasing locomotion score was associated with increased time spent in the lying stalls (P = 0.0037) and decreased time spent in the alley (P < 0.0001). Positioning-based feeding time was confounded by parity (P = 0.011) as the model used to estimate the feeding time from the position data was less sensitive in classifying primiparous cows correctly as feeding or not feeding. Severe lameness was also associated with a shorter daily walking distance (P = 0.0447) and smaller core home range (P = 0.005). Proportion of missing positioning data affected only daily walking distance (P < 0.0001) and full home range (P = 0.0059), and distance-based variables seemed more sensitive to data quality compared to spatiotemporal variables. Our results show that indoor positioning of dairy cows has a potential to contribute to development of automatic lameness detection. However, reliability of positioning systems should be improved, and the amount of missing data should be minimised to improve the calculation of distance-based variables

Development of an automated detection system for lameness in cattle: The GAITWISE system

As a result of the growth in dairy production, dairy farms have intensified with more cattle on fewer farms and higher productivity per animal and per caretaker. Consequently, farmers have less time to observe and monitor cows and technology is being used to support the farmers in their management, especially by monitoring the cows health.Besides reduced fertility and mastitis, lameness is one of the top most costly health problems in dairy cows. Unfortunately, not only its effect on farm profitability (caused by drug treatment, veterinary costs, reduced milk production, reduced reproductive performance and shorter life expectancy) is underestimated, but also its detrimental effect on cow health and welfare. With a lameness prevalence of up to 72 %, the levels in European dairy herds are unacceptably high and hence, minimizing the occurrence is one of the greatest challenges the dairy industry is currently facing. To apply proper treatment farmers must be able to detect their lame cows in the herd in an early stage. Therefore, the general aim of this PhD is to develop an automatic system for lameness detection to help farmers in better detecting lame cows in their herd. To achieve this, a walk-over device - called Gaitwise - with an integrated pressure sensitive mat and specific software was developed. Gaitwise measures spatial (e.g. step length), temporal (e.g. stance time) and force related gait variables of claw-floor interactions of cows walking over the measurement zone. Assuming that these gait variables change when a cow develops lameness, Gaitwise could serve as a lameness detection system that alerts the farmer of cows that show abnormal changes in these variables that are related to lameness. In order for this system to be used in daily practice, measurements were fully automated and gait variables are available in real time. In a next step, two groups of gait variables are calculated: the basic gait variables describing the basic gait of cows and ten more specific gait variables that are often used for locomotion scoring in lameness research ( Stride length , stride time , stance time , step overlap , abduction and variables covering asymmetry between left and right limbs in step width, step length, step time, stance time and force). Relevant associations between these measured specific gait variables and corresponding lameness attributes scored by an observer were found. This suggests that the measured variables are functionally relevant for lameness research and detection. Also, a test-retest study revealed that the measurements of the cows walking on the measurement zone are highly reliable. This makes the Gaitwise system a good instrument for gait analysis in the context of lameness detection, because the changes in gait variables due to lameness are likely to be attributed to lameness and not to cow variability. In order to select the specific variables that were most suited for a detection model, the specific gait variables were compared to a reference method for lameness detection usingobserver locomotion scoring of the cows. All the tested variables were different between groups of non-lame, mildly lame and severely lame cows. Variables of asymmetry in step length , asymmetry in stance time , asymmetry in step time and stance time , step overlap and abduction seemed to have the highest potential for the detection of lameness in cattle. The lameness detection model that was built based on these results, showed promising overall sensitivity and specificity, especially in detecting severely lame cows. However, the results of this validation of the detection model revealed that detecting mildly lame cows is the most challenging as the differences with non-lame cows are much smaller. The added value of lameness detection systems would increase considerably when - besides severely lame cows which are more easily spotted by the farmers - also the mildly lame cows could be detected. To improve the detection of mildly lame cases, two approaches were investigated: (1) the potential of other gait variables for detecting lameness in such early stages was investigated and (2) the normal variation of the specific gait variables caused by cow (age, lactation stage, etc.) or environmental (dark environment, wet flooring)) factors was evaluated, because this might cause misclassification of cows and hence hamper the success rate of the detection system. The first approach is based on the fact that in human gait research, increased stride-to-stride fluctuations (i.e. gait inconsistency) were found to be more closely related to early health problems compared to average gait variables. Therefore, the potential of gait inconsistency variables was investigated for early lameness detection in cows. In other words, will a cow that develops lameness first alter its gait by occasionally taking a shorter stride before altering its gait to shorter strides in general? Using two case-control studies, both the basic gait variables and the new gait inconsistency variables were compared between non-lame and severely lame cows and non-lame and mildly lame cows). The inconsistency gait variables were able to show significant differences between non-lame and mildly lame cows, where the more basic gait variables could not. In addition, this data set was used to build a lameness detection model using solely the basic variables and a second model using both the basic and the inconsistency variabels. The second model using the inconsistency variables outperformed by far the model based on only basic gait variables. It was therefore concluded that these new variables of inconsistent gait are promising for assessing lameness at an early stage. In addition, they might even aid in detecting the location of the lameness problem, i.e. which leg is lame. Whether these inconsistency variables are more promising in detecting lameness at an early stage compared to basic or specific gait variables should be investigated in future research using individual detection models. Finally, as cow (e.g. age, gestation stage) or environmental (e.g. wet flooring) factors can also influence cow gait, such changes in gait that are not related to lameness can cause misclassification of cows and hence hamper the success rate of the detection system. Therefore, in the second approach, this normal variation of specific gait variables was investigated in a pilot study. The results show significant influences of wet surfaces, age, lactation and gestation stage on the gait variables. Hence, this normal variation within cows together with the specific way of walking of individual cows should be taken into account when developing a lameness detection algorithm. An individual cow model might therefore outperform the models using group thresholds resulting in better detection of mildly lame cows. This special emphasis on detection of the mildly lame cows will provide more added value for the farmers compared to detecting the severely lame cows.Based on the results obtained in this PhD research, some short-term future work to further improve the Gaitwise system as a lameness detection tool has been suggested. First, future work should focus on pointing out the benefits (less financial losses) to the farmers and decrease the system s production costs. Therefore, the cost-benefit of a lameness detection system should be analysed, the farmers expectations of such lameness detection system should be gathered and more awareness for the effect of lameness on their farm profit and on the health and welfare of their herd should be created. Also possible ways to downscale Gaitwise in order to make it less expensive should be investigated. In addition, several approaches should be tested to further improve the detection level of Gaitwise. The inconsistency variables, adjusted specific variables or other new variables could be tested for their accuracy to detect mildly lame cows. The detection algorithm could be improved by setting individual thresholds instead of group thresholds, by taking the normal variation of gait variables during gestation or lactation into account, and by combining Gaitwise data with data already available on farm and data of other sensortechnologies (accelerometers, StepMetrixTM, etc.).nrpages: 209status: publishe

Review: Quantifying animal feeding behaviour with a focus on pigs (vol 138, pg 37, 2015)

status: publishe