Holstein; management; random regression; test‐day yield; Food Animals; Animal Science and Zoology
Abstract :
[en] Regular monitoring of body condition score (BCS) changes during lactation is a crucial management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The aim of this study was to investigate the ability of random regression test-day models (RR-TDM) to predict BCS for the entire lactation in dairy cows even if the actual scoring is limited to one BCS record. The data consisted of test-day records of milk yield (MY), fat percentage (FP), protein percentage (PP) and BCS (based on a 9-point scale with unit increments; 1-9) collected from 2014 to 2022 in 128 herds in the Walloon Region of Belgium. In total, 20,698 test-day records on 2166 first-parity Holstein cows (2-12 with an average of 9.42 test-day records per cow) were available for MY, FP and PP; and 7985 records on the same animals (2-12 with an average of 3.68 records per cow) were available for BCS. To estimate the solutions, only one randomly selected BCS record per animal along with all her MY, FP and PP records were used, which were then used to predict BCS data (calibration set). The remaining BCS (1-11 with an average 2.86 BCS records per animal) were used to evaluate the goodness of the predictions (validation set). Multiple-trait RR-TDM was used to estimate (co)variance components through the average information restricted maximum likelihood (AI-REML) algorithm. Predicted BCS were grouped into nine classes as the original observed BCS used for comparison. Pearson correlation between the predicted and observed BCS, prediction error (PE), absolute prediction error (APE) and root mean squared prediction error (RMSE) were calculated. Mean (standard deviation; SD) BCS was 4.97 (1.01), 4.95 (1.07) and 4.98 (1.00) BCS units in the full, calibration and validation datasets, respectively. Pearson correlation between the observed and predicted BCS was 0.71, mean (SD) PE was 0.04 (0.52) BCS units, mean (SD) APE was 0.48 (0.53) BCS units and RMSE was 0.72 BCS units. These findings demonstrate the ability of RR-TDM to predict BCS for the entire lactation using a single BCS record along with available test-day records of milk yield and composition in Holstein dairy cows.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Atashi, Hadi ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS) ; Department of Animal Science, Shiraz University, Shiraz, Iran
Chen, Yansen ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS)
Chelotti, J ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
Lemal, Pauline ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS)
Gengler, Nicolas ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)
Language :
English
Title :
Prediction of body condition score throughout lactation by random regression test-day models.
Gouvernement Wallon SPW DG03-DGARNE - Service Public de Wallonie. Direction Générale Opérationnelle Agriculture, Ressources naturelles et Environnement
Funding text :
Hadi Atashi acknowledges the support of the Walloon Government (Service Public de Wallonie\u2013Direction G\u00E9n\u00E9rale Op\u00E9rationnelle Agriculture, Ressources Naturelles et Environnement, SPW\u2010DGARNE, Namur, Belgium) for its financial support facilitating his stay in Belgium through the ScorWelCow and the WALLeSmart Projects (D65\u20101420 and D65\u20101435). The authors acknowledge the INTERREG NWE projects of HappyMoo (grant agreement NWE730) and HoliCow (grant agreement NWE0100132), co\u2010financed by the Walloon Government (Service Public de Wallonie, Namur, Belgium) and especially the SPW\u2010DGARNE (Direction G\u00E9n\u00E9rale Op\u00E9rationnelle Agriculture, Ressources Naturelles et Environnement) unit for its continued financial support through previous and ongoing projects. Pauline Lemal acknowledges her special scholarship of the University of Li\u00E8ge\u2013Gembloux Agro\u2010Bio Tech (Gembloux, Belgium). Nicolas Gengler, as a former Senior Research Associate of the Fund for Scientific Research\u2013FNRS (Brussels, Belgium), acknowledges his support, also through grant no. T.W005.23 (WEAVE\u2010DFG \u2018HTwoTHI\u2019). The authors also acknowledge the technical support by the Walloon Breeders Association (aw\u00E9 groupe\u2013Elev\u00E9o). The University of Li\u00E8ge\u2013Gembloux Agro\u2010Bio Tech (Gembloux, Belgium) supported computations through the technical platform Calcul et Mod\u00E9lisation Informatique (CAMI) of the TERRA Teaching and Research Centre, partly supported by the Fund for Scientific Research\u2013FNRS under grant no. T.0095.19 (PDR \u2018DEEPSELECT\u2019).
Bastin, C., Laloux, L., Gillon, A., Bertozzi, C., Vanderick, S., & Gengler, N. (2007). First results of body condition score modeling for Walloon Holstein cows. Interbull Bulletin, 37, 170–174.
Berry, D., Lee, J., Macdonald, K., & Roche, J. (2007). Body condition score and body weight effects on dystocia and stillbirths and consequent effects on postcalving performance. Journal of Dairy Science, 90(9), 4201–4211. https://doi.org/10.3168/jds.2007-0023
Berry, D. P., Buckley, F., Dillon, P., Evans, R., Rath, M., & Veerkamp, R. (2003). Genetic relationships among body condition score, body weight, milk yield, and fertility in dairy cows. Journal of Dairy Science, 86(6), 2193–2204. https://doi.org/10.3168/jds.S0022-0302(03)73809-0
Bewley, J., & Schutz, M. (2008). An interdisciplinary review of body condition scoring for dairy cattle. The Professional Animal Scientist, 24(6), 507–529. https://doi.org/10.15232/S1080-7446(15)30901-3
De Vries, A., Barbosa, L., Du, F., Gay, K., Kaniyamattam, K., & Maltz, E. (2013). Prediction of body condition scores in dairy cattle from daily measurements of body weights and milk composition. Paper presented at the 6th European Conference on Precision Livestock Farming, Leuven; Belgium.
Dechow, C., Rogers, G., Sander-Nielsen, U., Klei, L., Lawlor, T., Clay, J., Freeman, A., Abdel-Azim, G., Kuck, A., & Schnell, S. (2004). Correlations among body condition scores from various sources, dairy form, and cow health from the United States and Denmark. Journal of Dairy Science, 87(10), 3526–3533. https://doi.org/10.3168/jds.S0022-0302(04)73489-X
Earle, D. F. (1976). A guide to scoring dairy cow condition. Journal of Agriculture, Victoria, 74, 228–231.
Edmonson, A., Lean, I., Weaver, L., Farver, T., & Webster, G. (1989). A body condition scoring chart for Holstein dairy cows. Journal of Dairy Science, 72(1), 68–78. https://doi.org/10.3168/jds.S0022-0302(89)79081-0
Frizzarin, M., Gormley, I., Berry, D., & McParland, S. (2023). Estimation of body condition score change in dairy cows in a seasonal calving pasture-based system using routinely available milk mid-infrared spectra and machine learning techniques. Journal of Dairy Science, 106(6), 4232–4244. https://doi.org/10.3168/jds.2022-22394
Garnsworthy, P. C. (2006). Body condition score in dairy cows: Targets for production and fertility. Recent Advances in Animal Nutrition, 2006, 61–86.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. https://doi.org/10.2307/2529310
Macdonald, K., & Roche, J. (2004). Condition scoring made easy. In Condition scoring dairy herds (1st ed.). Dexcel Ltd.
Martins, B., Mendes, A., Silva, L., Moreira, T., Costa, J., Rotta, P., Chizzotti, M. L., & Marcondes, M. (2020). Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livestock Science, 236, 104054. https://doi.org/10.1016/j.livsci.2020.104054
Mayeres, P., Stoll, J., Bormann, J., Reents, R., & Gengler, N. (2004). Prediction of daily milk, fat, and protein production by a random regression test-day model. Journal of Dairy Science, 87(6), 1925–1933. https://doi.org/10.3168/jds.S0022-0302(04)73351-2
Mayeres, P., Stoll, J., Reents, R., & Gengler, N. (2002). Alternative modeling of fixed effects in test day models to increase their usefulness for management decisions. Interbull Bulletin, 29, 128.
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276–282.
Mee, J., & Boyle, L. (2020). Assessing whether dairy cow welfare is “better” in pasture-based than in confinement-based management systems. New Zealand Veterinary Journal, 68(3), 168–177. https://doi.org/10.1080/00480169.2020.1721034
Misztal, I., Tsuruta, S., Lourenco, D., Masuda, Y., Aguilar, I., Legarra, A., & Vitezica, Z. (2018). Manual for BLUPF90 family of programs. University of Georgia.
Mulvany, P. (1977). Dairy cow condition scoring. Handout No. 4468 (Vol. 4, pp. 349–353). National Institute for Research in Dairying.
Ptak, E., & Schaeffer, L. (1993). Use of test day yields for genetic evaluation of dairy sires and cows. Livestock Production Science, 34(1–2), 23–34. https://doi.org/10.1016/0301-6226(93)90033-E
Roche, J., Lee, J., Macdonald, K., & Berry, D. (2007). Relationships among body condition score, body weight, and milk production variables in pasture-based dairy cows. Journal of Dairy Science, 90(8), 3802–3815. https://doi.org/10.3168/jds.2006-740
Roche, J. R., Friggens, N. C., Kay, J. K., Fisher, M. W., Stafford, K. J., & Berry, D. P. (2009). Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. Journal of Dairy Science, 92(12), 5769–5801. https://doi.org/10.3168/jds.2009-2431
Roche, J. R., Kay, J. K., Friggens, N. C., Loor, J. J., & Berry, D. P. (2013). Assessing and managing body condition score for the prevention of metabolic disease in dairy cows. Veterinary Clinics: Food Animal Practice, 29(2), 323–336. https://doi.org/10.1016/j.cvfa.2013.03.003
Souissi, W., & Bouraoui, R. (2019). Relationship between body condition score, milk yield, reproduction, and biochemical parameters in dairy cows. In N. M'Hamdi (Ed.), Lactation in farm animals-biology, physiological basis, nutritional requirements, and Modelization. Intechopen. https://doi.org/10.5772/intechopen.85343
Swalve, H. (1995). The effect of test day models on the estimation of genetic parameters and breeding values for dairy yield traits. Journal of Dairy Science, 78(4), 929–938. https://doi.org/10.3168/jds.S0022-0302(95)76708-X
Van Vleck, L. D. (1987). Contemporary groups for genetic evaluations. Journal of Dairy Science, 70(11), 2456–2464. https://doi.org/10.3168/jds.S0022-0302(87)80309-0
Waltner, S., McNamara, J., & Hillers, J. (1993). Relationships of body condition score to production variables in high producing Holstein dairy cattle. Journal of Dairy Science, 76(11), 3410–3419. https://doi.org/10.3168/jds.S0022-0302(93)77679-1
Wildman, E., Jones, G., Wagner, P., Boman, R., Troutt, H., Jr., & Lesch, T. (1982). A dairy cow body condition scoring system and its relationship to selected production characteristics. Journal of Dairy Science, 65(3), 495–501. https://doi.org/10.3168/jds.S0022-0302(82)82223-6
Zink, V., Stipkova, M., & Lassen, J. (2011). Genetic parameters for female fertility, locomotion, body condition score, and linear type traits in Czech Holstein cattle. Journal of Dairy Science, 94(10), 5176–5182. https://doi.org/10.3168/jds.2010-3644
