WageningenUR Livestock Research

Wageningen, Netherlands

WageningenUR Livestock Research

Wageningen, Netherlands
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Van Hertem T.,Israel Agricultural Research Organization | Van Hertem T.,Catholic University of Leuven | Parmet Y.,Ben - Gurion University of the Negev | Steensels M.,Israel Agricultural Research Organization | And 10 more authors.
Journal of Dairy Science | Year: 2014

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. © 2014 American Dairy Science Association.


PubMed | Catholic University of Leuven, Ben - Gurion University of the Negev, Israel Agricultural Research Organization, Institute of Agricultural Engineering Agricultural Research Organization the Volcani Center and WageningenUR Livestock Research
Type: Journal Article | Journal: Journal of dairy science | Year: 2014

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 1wk before hoof trimming until 1wk 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 2wk before the trimming. The neck activity level was significantly reduced 1d after trimming (3806 bits/d) compared with before trimming (3896 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.


Estelles F.,Polytechnic University of Valencia | Calvet S.,Polytechnic University of Valencia | Melse R.W.,WageningenUR Livestock Research | Ogink N.W.M.,WageningenUR Livestock Research
Environmental Engineering Science | Year: 2012

Biological scrubbers aim at reducing gaseous ammonia emissions by transferring it to a water phase followed by conversion to nitrite and nitrate. A small part of the removed nitrogen may be emitted as N 2 and N 2O produced as a result of denitrification processes. Due to the large greenhouse warming potential of N 2O, even a small emission could be a point of concern. Determining these N losses in form of N 2 and N 2O via nitrogen balance is an alternative, but little is known about the uncertainty associated to this method. The main aim of this work was to develop an uncertainty model that evaluated N-balances in biological scrubbers in terms of result uncertainty. Secondary objectives were to provide a methodology to determine individual uncertainties involved, and to conduct a sensitivity analysis to identify the main contributors to the final uncertainty. For a defined scenario (biotrickling scrubber, 70% NH 3 removal; 5% of inlet N-NH 3 lost as N 2 and N 2O), the standard uncertainty expressed in relative terms of the average was 132% (released N in form of N 2 and N 2O). Main contributors to the final uncertainty were airflow rate and water volume in the scrubber basin. Uncertainty of the measurements of gaseous NH 3 concentrations and N compounds in water had a reduced effect on the final uncertainty. Based on these results, N balances are not recommended to evaluate N 2 and N 2O formation in biological scrubbers, at least for the conditions considered in this work. © Copyright 2012, Mary Ann Liebert, Inc.


Van Hertem T.,Israel Agricultural Research Organization | Van Hertem T.,Catholic University of Leuven | Maltz E.,Israel Agricultural Research Organization | Antler A.,Israel Agricultural Research Organization | And 7 more authors.
Journal of Dairy Science | Year: 2013

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. 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, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY = 0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 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. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well. © 2013 American Dairy Science Association.


Viazzi S.,Catholic University of Leuven | Van Hertem T.,Catholic University of Leuven | Van Hertem T.,Israel Agricultural Research Organization | Romanini C.E.B.,Catholic University of Leuven | And 6 more authors.
Precision Livestock Farming 2013 - Papers Presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013 | Year: 2013

This study tested and evaluated a computer vision technique to automatically detect lameness in dairy cows. A three-dimensional camera system was used to extract the back posture of the animals automatically from a top view perspective. Four parameters to describe the curvature of the cow's back were used in a decision tree to classify cows as lame or not lame. The experiment was conducted at a commercial Israeli dairy farm and a dataset of 273 cows was recorded by the three-dimensional camera. The classification performance of the 3D algorithm was evaluated against the visual locomotion scores awarded by an expert veterinarian. The analysis resulted in a sensitivity of 75.0% and a specificity of 98% on a 2-point level scale (lame or not lame). These results show that it is possible to use a 3D camera in dairy farming in order to develop a fully automatic lameness monitoring tool for dairy farming.


Van Hertem T.,Israel Agricultural Research Organization | Van Hertem T.,Catholic University of Leuven | Maltz E.,Israel Agricultural Research Organization | Antler A.,Israel Agricultural Research Organization | And 8 more authors.
Precision Livestock Farming 2013 - Papers Presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013 | Year: 2013

Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to quantify the classification performance of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions in an Israeli dairy farm with 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 1436 cows with automatic lameness scores and live locomotion scores was used for calculating classification performance. The analysis of the automatic scores as independent observations led to a correct classification rate of 50.4% on a 5-point level scale. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 87.6% was obtained. The obtained tolerant binary correct classification rate was 88.6%. The automated lameness detection system obtained a tolerant correct classification rate of 88.6%.


Van Hertem T.,Israel Agricultural Research Organization | Van Hertem T.,Catholic University of Leuven | Viazzi S.,Catholic University of Leuven | Steensels M.,Israel Agricultural Research Organization | And 10 more authors.
Biosystems Engineering | Year: 2014

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. © 2014 IAgrE.

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