Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Implementation of an automatic 3D vision monitor for dairy cow locomotion in a commercial farm
    Hertem, Tom Van; Schlageter Tello, Andrés ; Viazzi, Stefano ; Steensels, Machteld ; Bahr, Claudia ; Romanini, Carlos Eduardo Bites ; Lokhorst, Kees ; Maltz, Ephraim ; Halachmi, Ilan ; Berckmans, Daniel - \ 2018
    Biosystems Engineering 173 (2018). - ISSN 1537-5110 - p. 166 - 175.
    Automated monitoring - Back curvature - Computer vision - Cow traffic - Implementation

    The objective of this study was to evaluate the system performance of a 3D vision system for automatic locomotion monitoring implemented in a commercial dairy farm. Data were gathered during 633 milking sessions on a Belgian commercial dairy farm. After milking, the cows walked in a single-lane alley where the video recording system with a 3D depth camera was installed. The entire monitoring process including video recording, video pre-processing by filtering, cow identification and video analysis was automated. Image processing extracted six feature variables from the recorded videos. Per milking session, 224 ± 10 cows (100%) were identified on average by a radio-frequency identification (RFID) antenna, and 197 ± 16 videos were recorded (88.1 ± 6.6%) by the camera. The cow identification number was merged automatically to a recorded video in 178 ± 14 videos (79.4 ± 5.5%). After video pre-processing and analysis, 110 ± 24 recorded cow-videos (49.3 ± 10.8%) per session resulted in an automatic locomotion score. Daily and cow-individual variations on the merging and analysis rate were due to cow traffic. The minimal cow traffic interval required between consecutive cows was 15 s for optimal merging. System performance was affected by lactation stage, parity of the cows and recording duration. The feature variables curvature angle of back around hip joints (Area Under the Receiver Operating Characteristics Curve (AUC) = 0.719) and back posture measurement (AUC = 0.702) could be considered as fair lameness classifiers. Cow traffic affected the success rate of the video processing. Therefore, automatic monitoring systems need to be adapted to the farm layout.

    Lameness detection in dairy cattle : single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing
    Hertem, T. Van; Bahr, C. ; Schlageter Tello, A. ; Viazzi, S. ; Steensels, M. ; Romanini, C.E.B. ; Lokhorst, C. ; Maltz, E. ; Halachmi, I. ; Berckmans, D. - \ 2016
    Animal 10 (2016)9. - ISSN 1751-7311 - p. 1525 - 1532.
    dairy cow - individual history - lameness detection - multi-sensing - sensor technology

    The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

    Hoof lesion detection with manual and automatic locomotion scores in dairy cattle
    Schlageter Tello, Andres ; Hertem, Tom van; Viazzi, S. ; Bokkers, Eddie ; Groot Koerkamp, P.W.G. ; Steensels, Machteld ; Romanini, C.E.B. ; Bahr, C. ; Halachmi, I. ; Berckmans, D. ; Lokhorst, Kees - \ 2015
    In: Precision livestock farming applications / Halachmi, Ilan, Wageningen Academic Publishers - ISBN 9789086862689 - p. 65 - 70.
    The detection of hoof lesions is an important management practice in dairy farms. Under farm conditions, manual locomotion scoring is often used to detect hoof lesions. Recently, different automatic locomotion scoring systems have been developed. The objective of this study was to determine the capability of a manual (MLS) and an automatic (ALS) locomotion score for hoof lesion detection. The experiment was performed at a dairy farm with 250 milking cows. The presence and severity of hoof lesions were assessed while cows were hoof trimmed. Manual locomotion scoring was performed before hoof trimming. Automatic locomotion scoring was performed with a system based on a 3D camera, positioned in top-down perspective. Both manual and automatic locomotion scoring were performed using a 5-level scale and later transformed into a lame/non-lame classification (lame ≥3). The lame/non-lame classification from MLS and ALS was used to calculate the sensitivity and specificity using as reference hoof lesions and severe hoof lesions. The percentage of cows which had hoof lesions at each level of the 5-level MLS was 72% at level 1, 86% at level 2, 89% at level 3, 96% at level 4, and 50% at level 5. The percentage of cows with severe hoof lesions at each level of the MLS was 34% at level 1, 52% at level 2, 62% at level 3, 82% at level 4 and 50% at level 5. The percentage of cows which had hoof lesions at each level of the 5-level ALS was 89% at level 1, 75% at level 2, 78% at level 3, 82% at level 4 and 100% at level 5. The percentage of cows with severe hoof lesions at each level of the ALS was 37% at level 1, 39% at level 2, 59% at level 3, 45% at level 4 and 100% at level 5. When transformed into a lame/nonlame classification MLS showed a sensitivity of 36% and specificity of 81% when hoof lesions were used as reference and a sensitivity of 43% and specificity of 78% when severe lesions were used as reference. For the ALS, sensitivity for hoof lesions was 47% and for severe hoof lesions was 58%. In conclusion, both manual and automatic locomotion scores demonstrated a poor to moderate capability to detect hoof lesions and severe hoof lesions.
    Use of sensor systems on Dutch dairy farms
    Steeneveld, W. ; Hogeveen, H. - \ 2015
    In: Precision Livestock Farming Applications / Halachmi, Ilan, Wageningen Academic Publishers - ISBN 9789086862689 - p. 77 - 86.
    A survey was developed to investigate the reasons for investing or not in sensor systems on dairy farms, and to investigate how sensor systems are used in daily cow management. This survey was sent to 1,672 Dutch dairy farmers. The final dataset consisted of 512 dairy farms (response rate of 30.6%); 202 farms indicated that they have one or more sensor systems and 310 farms indicated that they do not have any sensor systems. In total, for 95 dairy farms with oestrus detection sensor systems, information about the average calving interval for the years 2003 to 2013 was available. In addition, for 30 dairy farms with oestrus detection sensor systems for young stock, information about the average first calving age was available for the years 2003 to 2013. The most common sensors on farms with an automatic milking system are sensor systems to measure the colour and electrical conductivity of milk. In total, 41% of farms with an automatic milking system had activity meters/pedometers for dairy cows, and 70% of farms with a conventional milking system and sensor systems also had activity meters/pedometers for dairy cows. The main reasons for investing in activity meters/pedometers for dairy cows were to improve detection, improve the profitability of the farm and to gain insight into the fertility level of the farm. The most important reasons for not investing in sensor systems were economic. Having an oestrus detection sensor system was not linked with the average calving interval of the farm. Furthermore, having an oestrus detection sensor system for young stock was not linked with the average first calving age. These results suggest that the farmers use the same rules on when to start inseminating as without oestrus detection sensor systems, and as a result there is no change in first calving age and calving interval.
    Economic Modelling to evaluate the benefits of precision livestock
    Kamphuis, C. ; Steeneveld, W. ; Hogeveen, H. - \ 2015
    In: Precision Livestock Farming Applications / Halachmi, Ilan, Wageningen : Wageningen Academic Publishers - ISBN 9789086862689 - p. 87 - 94.
    ‘Precision Livestock Farming’ (PLF) technology is an emerging research field which develops management tools aimed at continuous automatic monitoring of animal production, including real-time monitoring of growth, health and welfare. The purpose of PLF is to support farmers in making daily management decisions by providing extra ‘senses’, and to make farmers less dependent on human labour. Many PLF concepts have been developed in recent years, but the uptake of most of these technologies on commercial farms has been slow. Reasons for this slow uptake include the fact that these PLF technologies generate substantial amounts of data but this data is not converted into useful information for decision management. Another reason is that the investment in PLF technologies can be significant, whereas the economic benefits of the investment are unknown. Insight into the on-farm economics of PLF is therefore important. The objective of the study was to develop a value creation tool that models the economic impact of PLF technologies on dairy, fattening pig and broiler farms. The tool uses technical parameters, and the economic impact of PLF implementation can be estimated at farm level by estimating the impact of PLF technologies on these technical parameters. Twenty key global suppliers of PLF technologies were approached in order to gain insight into their views on which of these technical parameters are affected by their PLF technology and to what extent. The knowledge acquired will be used to validate the tool and to gain insight into the costs and benefits of PLF technologies. This current paper specifically reports on the value creation tool developed for dairy farms. Automated heat detection (Nedap N.V., Groenlo, the Netherlands) is used to demonstrate how this tool works and to calculate the potential added value of this PLF technology. The value creation tool will assist, ultimately, in the development of PLF technologies that add value to onfarm decision-making processes.
    The potential of using sensor data to predict the moment of calving for dairy cows
    Rutten, C.J. ; Steeneveld, W. ; Kamphuis, C. ; Huijps, K. ; Hogeveen, H. - \ 2015
    In: Precision Livestock Farming Applications / Halachmi, Ilan, Wageningen : Wageningen Academic Publishers - ISBN 9789086862689 - p. 161 - 167.
    On dairy farms, management of calving is important for the health of dairy cows and the survival rate of calves born. Although an expected calving date is known, farmers need to check their cows regularly to estimate the moment when a cow will start calving. A sensor system which predicts the moment of calving could help farmers to check cows effectively for the occurrence of dystocia. In this study, a total of 450 cows on two farms were equipped with Agis SensOor sensors (Agis Automatisering B.V., Harmelen, the Netherlands), which measure rumination activity, activity and temperature hourly. Data were collected over a one-year period. During that period, the exact moment of 417 calvings was recorded using camera images of the calving pen taken every 5 minutes. In total 110 calvings could be linked with sensor data. The moment when calving started was defined as the hour in which the camera images showed the cow having contractions or labour initially started. Two logit models were developed: a reduced model with the expected calving date as the independent variable and a full model which additionally included independent variables based on sensor data. The areas under the Receiver Operating Characteristic curves were 0.682 and 0.878 for the reduced and full model with, at a false positive rate of 10%, sensitivities of 22 and 69%, respectively. Results indicated that the inclusion of sensor data improved prediction of the start of calving and thus that the sensor data used have some potential for predicting the moment of calving.
    Evaluating progesterone profiles to improve automated oestrus detection
    Kamphuis, C. ; Huijps, K. ; Hogeveen, H. - \ 2015
    In: Precision Livestock Farming Applications / Halachmi, Ilan, Wageningen Academic Publishers - ISBN 9789086862689 - p. 279 - 285.
    Adoption of automated heat detection technologies is increasingly popular in the dairy industry. Generally speaking, farmers invest in only one technology on the assumption that this system will find most, if not all, cows in heat. It is, however, known that these technologies do not find all cows in heat. It has been suggested that automated heat detection may improve when sensor data are combined, where this involves combining different sensor measurements, e.g. linking activity with rumination data. So far, the option of combining different technologies has not been studied for the obvious reason that no commercial farms are using technologies from several suppliers. The Smart Dairy Farming (SDF) project, a Dutch initiative, brings together technology providers, knowledge institutions and dairy farms to improve the longevity of dairy cows by developing innovative tools to improve animal health, reproduction and feeding strategies. The SDF project offers a unique opportunity to research whether combining different sensing technologies improves automated heat detection. To do this, progesterone profiles were created by daily measurement of progesterone in milk from 31 cows, over a 24-day period, at two farms participating in the SDF project. One automated heat detection technology is used on both farms, and each farm has a second, different, technology running simultaneously. Heat alerts generated and farmers’ observations were compared with progesterone profiles. The data were used to provide insight into the following issues: do heat detection technologies provide alerts for cows in heat; when do they alert for heat events; how do farmers use the information from the heat detection technologies; and whether the exact timing of true heat may be improved by combining heat alerts. Finally, possible explanations will be studied for those heat events that remain undetected by both oestrus detection systems and farmers’ observations.
    Comparison of locomotion scoring for dairy cows by experienced and inexperienced raters using live or video observation methods.
    Schlageter-Tello, A. ; Bokkers, E.A.M. ; Groot Koerkamp, P.W.G. ; Hertem, T. van; Viazzi, S. ; Romanini, C.E.B. ; Halachmi, I. ; Bahr, C. ; Berckmans, D. ; Lokhorst, K. - \ 2015
    Animal Welfare 24 (2015). - ISSN 0962-7286 - p. 69 - 79.
    ensure high repeatability - training-program - milk-production - weighted kappa - holstein cows - risk-factors - lameness - cattle - agreement - reliability
    Lameness is considered a major problem in dairy production. Lameness is commonly detected with locomotion scores assigned to cows under farm conditions, but raters are often trained and assessed for reliability and agreement by using video recordings. The aim of this study was to evaluate intra- and inter-rater reliability and agreement of experienced and inexperienced raters for locomotion scoring performed live and from video, and to calculate the influence of raters and the method of observation (live or video) on the probability of classifying a cow as lame. Using a five-level locomotion score, cows were scored twice live and twice from video by three experienced and two inexperienced raters for three weeks. Every week different cows were scored. Intra- and inter-rater reliability (expressed as weighted kappa, ¿w)) and agreement (expressed as percentage of agreement, PA) for live/live, live/video and video/video comparisons were determined. A logistic regression was performed to estimate the influence of the rater and method of observation on the probability of classifying a cow as lame in live and video observation. Experienced raters had higher values for intra-rater reliability and agreement for video/video than for live/live and live/video comparison. Inexperienced raters, however, did not differ for intra- and inter-rater reliability and agreement for live/live, live/video and video/video comparisons. The logistic regression indicated that raters were responsible for the main effect and the method of observation (live or from video) had a minor effect on the probability for classifying a cow as lame (locomotion score =3). In conclusion, under the present experimental conditions, experienced raters performed better than unexperienced raters when locomotion scoring was done from video. Since video observation did not show any important influence in the probability of classifying a cow as lame, video observation seems to be an acceptable method for locomotion scoring and lameness assessment in dairy cows.
    Automatic lameness detection by computer vision and behavior and performance sensing
    Hertem, T. van; Viazzi, S. ; Bites Romanini, C. ; Bahr, C. ; Berckmans, D. ; Schlageter-Tello, A. ; Lokhorst, K. ; Rozen, D. ; Maltz, E. ; Halachmi, I. - \ 2014
    Automatic Lameness detechtion by computer vision and behavior and performance sensing
    Hertem, T. van; Steensels, M. ; Viazzi, S. ; Romanini, C.E.B. ; Schlageter Tello, A.A. ; Lokhorst, C. ; Maltz, E. ; Halachmi, I. ; Bahr, C. ; Berckmans, D. ; Hong, S. - \ 2014
    The objective was to compare automatic lameness detection methods based on daily automatic measurements of cow’s back posture and behavioral and performance variables. The experimental setup was located in a commercial Israeli dairy farm of 1,100 Israeli Holstein cows. All cows were housed in open, roofed cowsheds with dried manure bedding and no stalls. All cows were equipped with a commercial neck activity and ruminating time data logger. Milk yield was measured with a milk flow sensor. Cow gait recordings were made during 4 consecutive nighttime milking sessions with a 3D image camera. From the videos, the “inverse radius” of the back posture contour and the “back posture measurement” were extracted. The reference in this study was a daily live locomotion score of the animals. A dataset of 186 cows with 4 video-based lameness scores and 4 live locomotion scores was built. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods and milk session related variables. Data of lame cows – cows recognized and treated for lameness – was compared with data of non-lame cows. A logistic regression model was built with the highest correlated behavioral and performance variables. Model validation was done with 10-fold cross-validation. The analysis of the video-based scores as independent observations leads to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on 4 consecutive “back posture measurement”-scores and “inverse radius”-scores obtained a correct classification rate of 60.8%. Strict binary classification to lame vs. not-lame categories reached 80.7% correct classification rate. In addition, the logistic regression model included 7 model input variables (the daily milk yield; the slope coefficient of the daily milk yield; the nighttime/daytime neck activity ratio; the milk yield week difference ratio; the milk yield week difference; the neck activity level during the daytime; the ruminating time during nighttime). After 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. The combination of image processing and behavioral monitoring is believed to further improve the lameness detection accuracy.
    Manual and automatic locomotion scoring systems in dairy cows: A review
    Schlageter-Tello, A. ; Bokkers, E.A.M. ; Groot Koerkamp, P.W.G. ; Hertem, T. van; Viazzi, S. ; Romanini Bites, E. ; Halachmi, I. ; Bahr, C. ; Berckmans, D. ; Lokhorst, K. - \ 2014
    Preventive Veterinary Medicine 116 (2014)1-2. - ISSN 0167-5877 - p. 12 - 25.
    risk-factors - clinical lameness - lying behavior - holstein cows - reproductive-performance - genetic-parameters - detect lameness - leg health - floor type - interobserver reliability
    The objective of this review was to describe, compare and evaluate agreement, reliability, and validity of manual and automatic locomotion scoring systems (MLSSs and ALSSs, respectively) used in dairy cattle lameness research. There are many different types of MLSSs and ALSSs. Twenty-five MLSSs were found in 244 articles. MLSSs use different types of scale (ordinal or continuous) and different gait and posture traits need to be observed. The most used MLSS (used in 28% of the references) is based on asymmetric gait, reluctance to bear weight, and arched back, and is scored on a five-level scale. Fifteen ALSSs were found that could be categorized according to three approaches: (a) the kinetic approach measures forces involved in locomotion, (b) the kinematic approach measures time and distance of variables associated to limb movement and some specific posture variables, and (c) the indirect approach uses behavioural variables or production variables as indicators for impaired locomotion. Agreement and reliability estimates were scarcely reported in articles related to MLSSs. When reported, inappropriate statistical methods such as PABAK and Pearson and Spearman correlation coefficients were commonly used. Some of the most frequently used MLSSs were poorly evaluated for agreement and reliability. Agreement and reliability estimates for the original four-, five- or nine-level MLSS, expressed in percentage of agreement, kappa and weighted kappa, showed large ranges among and sometimes also within articles. After the transformation into a two-level scale, agreement and reliability estimates showed acceptable estimates (percentage of agreement = 75%; kappa and weighted kappa = 0.6), but still estimates showed a large variation between articles. Agreement and reliability estimates for ALSSs were not reported in any article. Several ALSSs use MLSSs as a reference for model calibration and validation. However, varying agreement and reliability estimates of MLSSs make a clear definition of a lameness case difficult, and thus affect the validity of ALSSs. MLSSs and ALSSs showed limited validity for hoof lesion detection and pain assessment. The utilization of MLSSs and ALSSs should aim to the prevention and efficient management of conditions that induce impaired locomotion. Long-term studies comparing MLSSs and ALSSs while applying various strategies to detect and control unfavourable conditions leading to impaired locomotion are required to determine the usefulness of MLSSs and ALSSs for securing optimal production and animal welfare in practice. Copyright © 2014 Elsevier B.V. All rights reserved.
    Effect of merging levels of locomotion scores for dairy cows on intra- and interrater reliability and agreement
    Schlageter-Tello, A. ; Bokkers, E.A.M. ; Groot Koerkamp, P.W.G. ; Hertem, T. van; Viazzi, S. ; Romanini, C.E.B. ; Halachmi, I. ; Bahr, C. ; Berckmans, D. ; Lokhorst, K. - \ 2014
    Journal of Dairy Science 97 (2014)9. - ISSN 0022-0302 - p. 5533 - 5542.
    ensure high repeatability - lameness scoring system - sampling strategies - training-program - standard errors - lying behavior - weighted kappa - cattle - prevalence - gait
    Locomotion scores are used for lameness detection in dairy cows. In research, locomotion scores with 5 levels are used most often. Analysis of scores, however, is done after transformation of the original 5-level scale into a 4-, 3-, or 2-level scale to improve reliability and agreement. The objective of this study was to evaluate different ways of merging levels to optimize resolution, reliability, and agreement of locomotion scores for dairy cows. Locomotion scoring was done by using a 5-level scale and 10 experienced raters in 2 different scoring sessions from videos from 58 cows. Intra- and interrater reliability and agreement were calculated as weighted kappa coefficient (¿w) and percentage of agreement (PA), respectively. Overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were determined for the 5-level scale and after transformation into 4-, 3-, and 2-level scales by merging different combinations of adjacent levels. Intrarater reliability (¿w) ranged from 0.63 to 0.86, whereas intrarater agreement (PA) ranged from 60.3 to 82.8% for the 5-level scale. Interrater ¿w=0.28 to 0.84 and interrater PA=22.6 to 81.8% for the 5-level scale. The specific intrarater agreement was 76.4% for locomotion level 1, 68.5% for level 2, 65% for level 3, 77.2% for level 4, and 80% for level 5. Specific interrater agreement was 64.7% for locomotion level 1, 57.5% for level 2, 50.8% for level 3, 60% for level 4, and 45.2% for level 5. Specific intra- and interrater agreement suggested that levels 2 and 3 were more difficult to score consistently compared with other levels in the 5-level scale. The acceptance threshold for overall intra- and interrater reliability (¿w and ¿ =0.6) and agreement (PA =75%) and specific intra- and interrater agreement (=75% for all levels within locomotion score) was exceeded only for the 2-level scale when the 5 levels were merged as (12)(345) or (123)(45). In conclusion, when locomotion scoring is performed by experienced raters without further training together, the lowest specific intra- and interrater agreement was obtained in levels 2 and 3 of the 5-level scale. Acceptance thresholds for overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were exceeded only in the 2-level scale.
    The effect of routine hoof trimming on locomotion score, ruminating time, activity and milk yield of dairy cows
    Hertem, T. van; Parmet, Y. ; Steensels, M. ; Maltz, E. ; Antler, A. ; Schlageter Tello, A.A. ; Lokhorst, C. ; Romanini, C.E.B. ; Viazzi, S. ; Bahr, C. ; Berckmans, D. ; Halachmi, I. - \ 2014
    Journal of Dairy Science 97 (2014)8. - ISSN 0022-0302 - p. 4852 - 4863.
    level risk-factors - digital dermatitis - foot lesions - herd-level - reproductive-performance - lameness prevalence - cattle - health - claw - parameters
    The objective of this study was to quantify the effect of hoof trimming on cow behavior (ruminating time, activity, and locomotion score) and performance (milk yield) over time. Data were gathered from a commercial dairy farm in Israel where routine hoof trimming is done by a trained hoof trimmer twice per year on the entire herd. In total, 288 cows spread over 6 groups with varying production levels were used for the analysis. Cow behavior was measured continuously with a commercial neck activity logger and a ruminating time logger (HR-Tag, SCR Engineers Ltd., Netanya, Israel). Milk yield was recorded during each milking session with a commercial milk flow sensor (Free Flow, SCR Engineers Ltd.). A trained observer assigned on the spot 5-point locomotion scores during 19 nighttime milking occasions between 22 October 2012 and 4 February 2013. Behavioral and performance data were gathered from 1 wk before hoof trimming until 1 wk after hoof trimming. A generalized linear mixed model was used to statistically test all main and interactive effects of hoof trimming, parity, lactation stage, and hoof lesion presence on ruminating time, neck activity, milk yield, and locomotion score. The results on locomotion scores show that the proportional distribution of cows in the different locomotion score classes changes significantly after trimming. The proportion of cows with a locomotion score =3 increases from 14% before to 34% directly after the hoof trimming. Two months after the trimming, the number of cows with a locomotion score =3 reduced to 20%, which was still higher than the baseline values 2 wk before the trimming. The neck activity level was significantly reduced 1 d after trimming (380 ± 6 bits/d) compared with before trimming (389 ± 6 bits/d). Each one-unit increase in locomotion score reduced cow activity level by 4.488 bits/d. The effect of hoof trimming on ruminating time was affected by an interaction effect with parity. The effect of hoof trimming on locomotion scores was affected by an interaction effect with lactation stage and tended to be affected by interaction effects with hoof lesion presence, indicating that cows with a lesion reacted different to the trimming than cows without a lesion did. The results show that the routine hoof trimming affected dairy cow behavior and performance in this farm.
    Hoof lesion detection of dairy cows with manual and automtic locomotion scores 1
    Schlageter-Tello, A. ; Hertem, T. van; Viazzi, S. ; Bokkers, E.A.M. ; Groot Koerkamp, P.W.G. ; Steensels, M. ; Bahr, C. ; Halachmi, I. ; Berckmans, D. ; Lokhorst, C. - \ 2014
    - p. 158 - 158.
    Effect of cow traffic on an implemented automatic 3D vision monitor for dairy cow locomotion
    Hertem, T. van; Steensels, M. ; Viazzi, S. ; Bahr, C. ; Romanini, C.E.B. ; Lokhorst, C. ; Schlageter Tello, A.A. ; Maltz, E. ; Halachmi, I. ; Berckmans, D. - \ 2014
    Automatic lameness detection based on consecutive 3D-video recordings
    Hertem, T. van; Viazzi, S. ; Steensels, M. ; Maltz, E. ; Antler, A. ; Alchanatis, V. ; Schlageter-Tello, A. ; Lokhorst, C. ; Romanini, C.E.B. ; Bahr, C. ; Berckmans, D. ; Halachmi, I. - \ 2014
    Biosystems Engineering 119 (2014). - ISSN 1537-5110 - p. 108 - 116.
    dairy-cattle - risk-factors - milk-yield - clinical lameness - gait assessment - scoring system - back posture - cows - locomotion - herds
    Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions on an Israeli dairy farm, using a 3D-camera. A live on-the-spot assessed 5-point locomotion score was the reference for the automatic lameness score evaluation. A dataset of 186 cows with four automatic lameness scores and four live locomotion score repetitions was used for testing three different classification methods. The analysis of the automatic scores as independent observations led to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on four individual consecutive measures obtained a correct classification rate of 60.2%. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 90.9% was obtained. Strict binary classification to Lame vs. Not-Lame categories reached 81.2% correct classification rate. The use of cow individual consecutive measurements improved the correct classification rate of an automatic lameness detection system.
    Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows
    Viazzi, S. ; Bahr, C. ; Hertem, T. van; Schlageter-Tello, A. ; Romanini, C.E.B. ; Halachmi, I. ; Lokhorst, C. ; Berckmans, D. - \ 2014
    Computers and Electronics in Agriculture 100 (2014). - ISSN 0168-1699 - p. 139 - 147.
    reproductive-performance - hoof pathologies - milk-yield - lameness - cattle - gait - locomotion - assessments - impact
    In this study, two different computer vision techniques to automatically measure the back posture in dairy cows were tested and evaluated. A two-dimensional and a three-dimensional camera system were used to extract the back posture from walking cows, which is one measurement used by experts to discriminate between lame and not lame cows. So far, two-dimensional cameras positioned in side view are used to measure back posture. This method, however, is not always applicable in farm conditions since it can be difficult to be installed. Shadows and continuous changes in the background also render image segmentation difficult and often erroneous. In order to overcome these problems, a new method to extract the back posture by using a three-dimensional camera from top view perspective is presented in this paper. The experiment was conducted in a commercial Israeli dairy farm and a dataset of 273 cows was recorded by both the three-dimensional and two-dimensional cameras. The classifications of both the two-dimensional and the three-dimensional algorithms were evaluated against the visual locomotion scores given by an expert veterinary. The two-dimensional algorithm had an accuracy of 91%, while the three-dimensional algorithm had an accuracy of 90% on the evaluation dataset. These results show that the application of a three-dimensional camera leads to an accuracy comparable to the side view approach and that the top view approach can overcome limitations in terms of automation and processing time.
    The effect of hoof trimming on the locomotion score, netc activity and ruminating time of dairy cows.
    Hertem, T. van; Maltz, E. ; Viazzi, S. ; Bites Romanini, C. ; Bahr, C. ; Berckmans, D. ; Lokhorst, K. ; Schlageter-Tello, A. ; Antler, A. ; Halachmi, I. - \ 2013
    In: Book of Abstracts of the 64th Annual Meeting of the European Association for Animal Production. - - p. 152 - 152.
    Within and between observer agreement for specific levels locomotion score for dairy cows
    Schlageter-Tello, A. ; Bokkers, E.A.M. ; Groot Koerkamp, P.W.G. ; Hertem, T. van; Viazzi, S. ; Romanini, C.E.B. ; Halachmi, I. ; Bahr, C. ; Berckmans, D. ; Lokhorst, C. - \ 2013
    In: Proceedings 17th International Symposium and 9th International Conference on Lameness in Ruminants. - - p. 88 - 89.
    Application of image based filtering to improve the performance of an automated lameness detection system for dairy cows
    Romanini, C.E.B. ; Bahr, C. ; Viazzi, S. ; Hertem, T. van; Schlageter-Tello, A. ; Halachmi, I. ; Lokhorst, C. ; Berckmans, D. - \ 2013
    In: ASABE Annual International Meeting. - - p. 4222 - 4227.
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