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Consequences of variation in feeding behaviour for the probability of animals starting a meal as estimated from pooled data

Published online by Cambridge University Press:  18 August 2016

M. P. Yeates*
Affiliation:
Animal Nutrition and Health Department, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
B. J. Tolkamp
Affiliation:
Animal Nutrition and Health Department, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
I. Kyriazakis
Affiliation:
Animal Nutrition and Health Department, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG, UK
*
Address for correspondence: Animal Nutrition and Health Department, Scottish Agricultural College, Bush Estate, Penicuik EH26 0PH, UK. E-mail: [email protected]
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Abstract

Better understanding of how animals regulate their intake may be gained by analysis of feeding behaviour. This is often recorded in terms of feeding events, e.g. visits to feeders, which can be clustered into meals. This enables calculation of the probability of animals starting a meal in relation to time since the last meal, which is thought to give insight into food intake regulation. Starting probabilities are often calculated with pooled data but recent work suggests that pooling may strongly affect conclusions.

In this study we analysed feeding behaviour of cows to investigate how previous conclusions about feeding behaviour may have been affected by pooling. Using parameters derived from experimental data, we constructed simulation models to further explore under what circumstances pooling, either across day and night or across individuals, could affect the interpretation of starting probabilities. Data were simulated to explore the consequences of pooling as either the proportion of meals occurring during the day or the individual variation in their mean number of meals per 24 h changed. Simulation allowed us to extend the analysis of the consequences of pooling for the interpretation of starting probabilities.

Analysis of experimental data, collected with 16 dairy cows, showed that they ate a mean of six meals per 24 h. Individual variation resulted in a proportional CV of the individual mean number of meals per 24 h of 0·14. Cows ate a mean proportion of 0·59 of their meals during the day. Analysis of experimental data suggested that pooling, conducted in previous studies, has probably led to a quantitative underestimation of the increase in starting probability with time since the last meal but not a qualitative misinterpretation of the direction of change in the starting probability.

Simulation studies showed that pooling had no serious consequences when the mean number of meals per 24 h, or the variation about this mean, was low. However, as the number of meals per 24 h and variation increased, pooling led to conclusions that may wholly misrepresent both magnitude and direction of the change in starting probabilities calculated separately for the individuals or for day and night. This may explain why the results of some published studies seem not to agree with biological principles of food intake regulation.

Type
Ruminant nutrition, behaviour and production
Copyright
Copyright © British Society of Animal Science 2003

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References

Beauchemin, K. A., Maekawa, M. and Christensen, D. A. 2002. Effect of diet and parity on meal patterns of lactating dairy cows. Canadian Journal of Animal Science 82: 215223.Google Scholar
Berthoud, H. R. 2002. Multiple neural systems controlling food intake and body weight. Neuroscience and Biobehavioural Reviews 26: 393428.Google Scholar
Bigelow, J. A. and Houpt, T. R. 1988. Feeding and drinking patterns in young-pigs. Physiology and Behavior 43: 99109.Google Scholar
Burden, R. L. and Faires, J. D. 1985. Numerical analysis. Prindle, Weber and Schmidt, Boston.Google Scholar
Campling, R. C. and Morgan, C. A. 1981. Eating behaviour of housed dairy cows – a review. Dairy Science Abstracts 43: 5763.Google Scholar
De Castro, J. M. 1975. Meal pattern correlations: facts and artifacts. Physiology and Behavior 15: 1315.Google Scholar
Demaria-Pesce, V. H. and Nicolaidis, S. 1998. Mathematical determination of feeding patterns and its consequence on correlational studies. Physiology and Behavior 65: 157170.Google Scholar
Dürst, B., Senn, M. and Langhans, W. 1993. Eating patterns of lactating dairy-cows of 3 different breeds fed grass ad-lib. Physiology and Behavior 54: 625631.Google Scholar
Elizalde, H. F. and Mayne, C. S. 1993. The effect of degree of competition per feed space on the dry matter intake and eating behaviour of dairy cows offered grass silage. Proceedings of the third research conference, British Grassland Society, Greenmount College, pp. 137138.Google Scholar
Ellis, T. M. R., Philips, I. R. and Lahey, T. M. 1994. Fortran 90 programming. Addison-Wesley, Harrow.Google Scholar
Everitt, B. S. 1998. Dictionary of statistics. Cambridge University Press, Guildford.Google Scholar
Forbes, J. M. 1985. The importance of meals in the regulation of food intake. Proceedings of the Nutrition Society of Australia 10: 1424.Google Scholar
Haer, L. C. M. de and Merks, J. W. M. 1992. Patterns of daily food intake in growing pigs. Animal Production 54: 95104.Google Scholar
Huillet, T. and Raynaud, H.-F. 1999. Rare events in a log-Weibull scenario – application to earthquake magnitude data. European Physical Journal, B 12: 457469.Google Scholar
Johnson, N. L., Kotz, S. and Balakrishnan, N. 1994. Continuous univariate distributions. John Wiley and Sons, New York.Google Scholar
Le Magnen, J. 1985. Hunger. Cambridge University Press, Cambridge..Google Scholar
Le Magnen, J. and Devos, M. 1980. Parameters of the meal pattern in rats: their assessment and physiological significance. Neuroscience and Biobehavioral Reviews 4: 111.Google Scholar
Leger, D. W. and Didrichsons, I. A. 1994. An assessment of data pooling and some alternatives. Animal Behaviour 48: 823832.Google Scholar
Machlis, L., Dodd, P. W. D. and Fentress, J. C. 1985. The pooling fallacy-problems arising when individuals contribute more than one observation to the data set. Zeitschrift für Tierpsychologie 68: 201214.Google Scholar
Martin, P. and Kraemer, H. C. 1987. Individual-differences in behavior and their statistical consequences. Animal Behaviour 35: 13661375.Google Scholar
Metz, J. H. M. 1975. Time patterns of feeding and rumination in domestic cattle. >Communications of the Agricultural University, Wageningen, The Netherlands, 75–12.Google Scholar
Minitab. 1998. Minitab for Windows, release 12. 1. Minitab Inc., USA.Google Scholar
Morgan, C. A., Emmans, G. C., Tolkamp, B. J. and Kyriazakis, I. 2000a. Analysis of the feeding behavior of pigs using different models. Physiology and Behavior 68: 395403.Google Scholar
Morgan, C. A., Tolkamp, B. J., Emmans, G. C. and Kyriazakis, I. 2000b. The way in which the data are combined affects the interpretation of short-term feeding behavior. Physiology and Behavior 70: 391396.Google Scholar
Musial, F., Kowalski, A., Enck, P. and Kalveram, K. T. 2000. A computer-controlled long-term recording system for temporal analysis of ingestive and excretory behaviors in miniature-pigs. International Journal of Psychophysiology 35: 5455.Google Scholar
Numerical Algorithms Group. 1993. Library manual mark 16. Numerical Algorithms Group, Oxford.Google Scholar
Orr, R. J., Penning, P. D., Harvey, A. and Champion, R. A. 1997. Diurnal patterns of intake rate by sheep grazing monocultures of ryegrass or white clover. Applied Animal Behaviour Science 52: 6577.Google Scholar
Panksepp, J. 1973. Reanalysis of feeding patterns in the rat. Journal of Comparative and Physiological Psychology 82: 7894.Google Scholar
Quiniou, N., Dubois, S. and Noblet, J. 2000a. Voluntary feed intake and feeding behaviour of group-housed growing pigs are affected by ambient temperature and body weight. Livestock Production Science 63: 245253.Google Scholar
Quiniou, N., Renaudeau, D., Dubois, S. and Noblet, J. 2000b. Effect of diurnally fluctuating high ambient temperatures on performance and feeding behaviour of multiparous lactating sows. Animal Science 71: 571575.Google Scholar
Redondo, E., Regodon, S., Franco, A., Masot, J., Gazquez, A. and Cardinali, D. P. 2003. Day-night changes in plasma melatonin levels, synaptophysin expression and ultrastructural properties of pinealocytes in developing female sheep under natural long and short photoperiods. Histology and Histopathology 18: 333342.Google Scholar
Savory, C. J. 1981. Correlations between meals and inter-meal intervals in Japanese quail and their significance in the control of feeding. Behavioural Processes 6: 2336.Google Scholar
Schwartzkopf-Genswein, K.S, Atwood, S. and McAllister, T. A. 2002. Relationships between bunk attendance, intake and performance of steers and heifers on varying feeding regimes. Applied Animal Behaviour Science 76: 179188.Google Scholar
Sibly, R. M., Nott, H. M. R. and Fletcher, D. J. 1990. Splitting behavior into bouts. Animal Behaviour 39: 6369.Google Scholar
Simpson, S. and Ludlow, A. 1986. Why locusts start to feed: a comparison of causal factors. Animal Behaviour 34: 480496.Google Scholar
Slater, P. J. B. 1974. The temporal pattern of feeding in the Zebra finch. Animal Behaviour 22: 506515.Google Scholar
Slater, P. J. B. and Lester, N. P. 1982. Minimizing errors in splitting behavior into bouts. Behaviour 79: 153161.Google Scholar
Sprent, P. 1993. Applied nonparametric statistical methods. Chapman and Hall, London.Google Scholar
Stamer, E., Junge, E. and Kalm, E. 1997. [Temporal pattern of feeding of dairy cows kept in groups.] Archiv für Tierzucht 40: 195214.Google Scholar
Tolkamp, B. J., Allcroft, D. J., Austin, E. J., Nielsen, B. L. and Kyriazakis, I. 1998a. Satiety splits feeding behaviour into bouts. Journal of Theoretical Biology 194: 235250.Google Scholar
Tolkamp, B. J., Dewhurst, R. J., Friggens, N. C., Kyriazakis, I., Veerkamp, R. F. and Oldham, J. D. 1998b. Diet choice by dairy cows. 1. Selection of feed protein content during the first half of lactation. Journal of Dairy Science 81: 26572669.Google Scholar
Tolkamp, B. J., Friggens, N. C., Emmans, G. C., Kyriazakis, I. and Oldham, J. D. 2002. Meal patterns of dairy cows consuming mixed foods with a high or a low ratio of concentrate to grass silage. Animal Science 74: 369382.Google Scholar
Tolkamp, B. J. and Kyriazakis, I. 1997. Measuring diet selection in dairy cows: effect of training on choice of dietary protein level. Animal Science 64: 197207.Google Scholar
Tolkamp, B. J. and Kyriazakis, I. 1999a. A comparison of five methods that estimate meal criteria for cattle. Animal Science 69: 501514.Google Scholar
Tolkamp, B. J. and Kyriazakis, I. 1999b. To split behaviour into bouts, log-transform the intervals. Animal Behaviour 57: 807817.Google Scholar
Tolkamp, B. J., Kyriazakis, I., Oldham, J. D., Lewis, M., Dewhurst, R. J. and Newbold, J. R. 1998c. Diet choice by dairy cows. 2. Selection for metabolizable protein or for ruminally degradable protein? Journal of Dairy Science 81: 26702680.Google Scholar
Tolkamp, B. J., Schweitzer, D. P. N. and Kyriazakis, I. 2000. The biologically relevant unit for the analysis of short-term feeding behavior of dairy cows. Journal of Dairy Science 83: 20572068.Google Scholar
Yeates, M. P., Tolkamp, B. J., Allcroft, D. J. and Kyriazakis, I. 2001. The use of mixed distribution models to determine bout criteria for analysis of animal behaviour. Journal of Theoretical Biology 213: 413425.Google Scholar