Validation of an acoustic rumination sensor for dairy cows

Transcription

1 Ref: C0682 Validation of an acoustic rumination sensor for dairy cows Kathrin Hendriksen and Wolfgang Büscher, University of Bonn, Institute of Agricultural Engineering, Bonn, Germany Sebastian Hoppe and Christoph Hoffmanns, Federal State Research Farm Haus Riswick, Chamber of Agriculture North Rhine-Westphalia, Kleve, Germany Abstract In the last sixty years different techniques were developed to measure the eating and rumination activity of sheep, goats and cows (Balch, 1958; Hodgson, 1977; Chambers, Hodgson, Milne, 1981). Because of the correlation between feeding parameters and rumination activity there is still interest to develop a useful tool for the quantification of the rumination behaviour for on farm and scientific use. In the last 5 to 10 years two systems were developed with interesting possibilities. One of these sensors, based on acoustic measurement, was validated in different works and is already commercial available for farmers. The aim of the study was to use this rumination sensor to attend a feeding trial concerning different urea supplements. Preliminary validation of the sensor was done comparing data provided by the sensor with data collected by direct observation. Direct observation was done at 19 cows. One 2-h-block was recorded for each of the 19 cows. The average rumination time per two hours was / min and / min if recorded by observer and rumination sensor, respectively. The correlation between direct observation and data collected by the system was r = The correlation for the cows with an average daily rumination time over 500 min was r = 0.82 (p < 0.05). Due to the validation results the feeding trial could not be interpreted. Keywords: dairy cow, sensor technique, validation, animal monitoring, rumination 1. Introduction Rumination is an important part of the digestion process of cattle. Main influencing factors on rumination activity can be divided into feed dependent, animal dependent and environment dependent parameters. Especially the feed composition concerning its structure and components has a high effect on the chewing behaviour of cows. Therefore many researchers already tried to develop a tool for quantification of the rumination behaviour. Since approximately 10 years a rumination sensor is available also for farm use. This rumination sensor includes a microphone that detects the rumination sounds of the cow, a motion sensor, a microprocessor, a storage unit and a battery. The sensor is fixed on a neck collar and placed on the left side of the cow s neck. To guarantee the correct position of the tag a counter weight is placed on the bottom of the collar (Schirmann, von Keyserlingk, Weary, Veira, Heuwieser, 2009; Burfeind et al., 2011). The data are sent to a PC or a stand-alone unit via antenna. A software analyses the rumination time as minutes of 2 hours with a resolution of 2 minutes (Schirmann et al., 2009), and calculates the rumination time of the last 24 hours. The rumination sensor can be used for the early detection of health disturbances and the monitoring of feed consumption, feed ration and feed quality. Furthermore rumination activity can be used as welfare parameter. Proceedings International Conference of Agricultural Engineering, Zurich, /5

2 Schirmann et al. (2009) validated the sensor by comparing the measured values of the sensor with the counted values of two observers. In trial 1 the correlation between the two observers and the system was r = 0.96 (n = 15, p < 0.001) and in trial 2 the correlation was r = 0.92 (n = 36, p < 0.001). For one single case the maximum variation between estimates was 96.3%. The rumination time in this case was 7 minutes and 20 minutes if recorded by observer and rumination sensor, respectively. Pahl, Haeussermann, Mahlkow-Nerge, Grothmann, and Hartung (2012) compared two different types of rumination sensors with each other and with direct observation. The correlation between the direct observation and the data from the acoustic sensor for two different cows was 0.88 (n = 18, p < 0.01). Between the two different sensor types the mean differences per cow ranged from -1.9 min/2h to 24.4 min/2h. The correlation between both systems was r = 0.58 (n = 527, p < 0.01). 2. Materials and Methods The experiment was carried out at the Federal State Research Farm Haus Riswick (Chamber of Agriculture North Rhine-Westphalia) where in total 255 German Holstein dairy cows are kept with an average milk yield of 10,027 kg, with 3.89% fat and 3.29% protein. The experimental barn is designed as an open free stall barn and is divided into 6 different groups. Each group has 24 cubicles, 12 computer-controlled feeding troughs, 2 computercontrolled water troughs and is planned for 24 cows. TMR based on corn silage and grass silage is offered ad libitum via the feeding troughs. For the actual feed trial 48 cows were equipped with an acoustic rumination sensor (SCR Heatime HR System, SCR Engineers Ltd., Israel). Direct observation was done at 19 cows. One 2-h-block was recorded for each of the 19 cows. Each cow was single observed and the position of the sensor was steadily controlled. For the validation of the sensor cows were chosen related to their average daily rumination time measured by the system and divided into 3 classes. Seven cows were chosen with a sensor measured rumination time higher than 500 min/day, six cows had a rumination time between 300 and 500 min/day and six cows had a rumination time lower than 300 min/day. 3. Results and Discussion The average feed consumption over 4 month was /- 3.9 kg DM/day. The rumination time for the 48 cows was measured by the sensor over 4 month. The average rumination time for two hours was / min/2h, ranging from 0 to 113 min/2h. For each cow the monthly average and the 4-month average daily rumination time was calculated (Figure 1). The 4-month average for the 48 cows was 452 +/- 105 min/day, ranging from 230 to 607 min/day measured by the sensor. The wide range concerning the daily rumination time is visible in Figure 1. Schirmann et al. (2009) measured a daily rumination time of 20 cows over three days of /- 5.3 min. Direct observation was done at 19 cows over one 2-h-block. In these observation periods the average rumination time for two hours was / min and / min if recorded by observer and rumination sensor, respectively. During 51 observations Schirmann et al. (2009) measured a rumination time of /- 3.2 min/2h and 34.7 min +/ min/2h if recorded by observer and rumination sensor, respectively. In this investigation there was a higher variation between both estimates. To express the agreement between the observed rumination time and the rumination time measured by the sensor the Bland and Altman (1986) method was used (Figure 2). It showed the mean of the paired measurements assessed by visual observation and rumination sensor (x-axis) versus the difference of both estimates (y-axis). The 95% confidence interval around the mean of the differences was calculated and shown on the plot. The mean Proceedings International Conference of Agricultural Engineering, Zurich, /5

3 difference was min/2h. In general the sensor underestimated the rumination time independent of the mean of both estimates (cf. Pahl et al., 2012). In this investigation the correlation between direct observation and data collected by the system was r = The correlation for cows with an average daily rumination time over 500 min was r = 0.82 (p < 0.05). In Figure 3 the visual and sensor measured rumination time is shown in relation to the rumination time at the observation day. This may indicate that the data of cows with a sensor measured rumination time over 500 min/day is correct. In this examination there was no correlation between the validation results and feed consumption, water consumption and body weight on the observation day. Furthermore there was no correlation between the validation results and the chewing rhythm. All these cow individual parameters had no influence on the validation results. This leads to the hypotheses that the cow individual chewing sound influences the sensor measured rumination time. Burfeind et al. (2011) suggested that the frequency of the sound might be the reason why the accuracy is lower when the sensor is used for calves younger than 9 month. Pahl et al. (2012) showed that the time of the day and the individual cow had an influence on the absolute difference in rumination time between the methods. 4. Conclusions Results of these preselected cows show a low correlation between the rumination time measured by direct observation and the rumination sensor. Further work will be done to detect the differences between the cows in the diverse classes and to find a parameter that correlates to their daily rumination time. Due to the validation results the feeding trial could not be interpreted. Perhaps the new improved sensor generation is able to compensate this weakness. Because dairy farmers work with the deviation of the moving average of the rumination activity the rumination sensor might be useful for farm use. 5. References Balch, C. C. (1958). Observations on the act of eating in cattle. British Journal of Nutrition, 12 (3), Bland, J. M. & Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurements. The Lancet, 338 (8476), Burfeind, O., Schirmann, K., von Keyserlingk, M. A. G., Veira, D. M., Weary, D. M., & Heuwieser, W. (2011). Technical note: Evaluation of a system for monitoring rumination in heifers and calves. Journal of Dairy Science, 94 (1), Chambers, A. R. M., Hodgson, J., & Milne, J. A. (1981). The development and use of equipment for the automatic recording of ingestive behaviour in sheep and cattle. Grass and Forage Science, 36 (2), Hodgson, J. (1977). Factors limiting herbage intake by the grazing animal. In: B. Gilsenan (Ed.), Proceedings: International Meeting on Animal Production from Temperate Grasslands, June, 1977, Dublin, Ireland. Pahl, C., Haeussermann, A., Mahlkow-Nerge, K., Grothmann, A., & Hartung, E. (2012). Comparison of rumination activity records of pressure sensors and acoustic sensors. In: Proceedings. International Conference of Agricultural Engineering CIGR AgEng 2012, July 8-12, 2012, Valencia, Spain. Proceedings International Conference of Agricultural Engineering, Zurich, /5

4 Schirmann, K., von Keyserlingk, M. A. G., Weary, Y. D. M., Veira, D. M. & Heuwieser, M. (2009). Technical note: Validation of a system for monitoring rumination in dairy cows. Journal of Dairy Science, 92 (12), Figure 1: Average daily rumination time per cow and month (n = 48). Figure 2: Differences between rumination times assessed by the visual observation and by the rumination sensor versus the mean of both estimates. (Mean difference = min/2h, 95% confidence interval: to 57.4). Proceedings International Conference of Agricultural Engineering, Zurich, /5

5 Figure 3: Relationship between visual and sensor measured rumination time versus the rumination time at the observation day. Proceedings International Conference of Agricultural Engineering, Zurich, /5