Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-20T00:08:47.088Z Has data issue: false hasContentIssue false

Development of equations, based on milk intake, to predict starter feed intake of preweaned dairy calves

Published online by Cambridge University Press:  16 April 2018

A. L. Silva
Affiliation:
Department of Animal Science, Universidade Federal de Viçosa, 36570.000, Viçosa, Minas Gerais, Brazil
T. J. DeVries
Affiliation:
Department of Animal Biosciences, University of Guelph, Guelph, Ontario, CanadaN1G 2W1
L. O. Tedeschi
Affiliation:
Department of Animal Science, Texas A&M University, College Station, TX 77843-2471, USA
M. I. Marcondes*
Affiliation:
Department of Animal Science, Universidade Federal de Viçosa, 36570.000, Viçosa, Minas Gerais, Brazil
*
Get access

Abstract

There is a lack of studies that provide models or equations capable of predicting starter feed intake (SFI) for milk-fed dairy calves. Therefore, a multi-study analysis was conducted to identify variables that influence SFI, and to develop equations to predict SFI in milk-fed dairy calves up to 64 days of age. The database was composed of individual data of 176 calves from eight experiments, totaling 6426 daily observations of intake. The information collected from the studies were: birth BW (kg), SFI (kg/day), fluid milk or milk replacer intake (MI; l/day), sex (male or female), breed (Holstein or Holstein×Gyr crossbred) and age (days). Correlations between SFI and the quantitative variables MI, birth BW, metabolic birth BW, fat intake, CP intake, metabolizable energy intake, and age were calculated. Subsequently, data were graphed, and based on a visual appraisal of the pattern of the data, an exponential function was chosen. Data were evaluated using a meta-analysis approach to estimate fixed and random effects of the experiments using nonlinear mixed coefficient statistical models. A negative correlation between SFI and MI was observed (r=−0.39), but age was positively correlated with SFI (r=0.66). No effect of liquid feed source (milk or milk replacer) was observed in developing the equation. Two equations, significantly different for all parameters, were fit to predict SFI for calves that consume less than 5 (SFI<5) or more than 5 (SFI>5) l/day of milk or milk replacer: ${\rm SFI}_{{\,\lt\,5}} {\equals}0.1839_{{\,\pm\,0.0581}} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0333_{{\,\pm\,0.0021 }} {\minus}0.0040_{{\,\pm\,0.0011}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}{\rm }\left( {0.8302_{{\,\pm\,0.5092}} {\plus}6.0332_{{\,\pm\,0.3583}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ ; ${\rm SFI}_{{\,\gt\,5}} {\equals}0.1225_{{\,\pm\,0.0005 }} {\times}{\rm MI}{\times}{\rm exp}^{{\left( {\left( {0.0217_{{\,\pm\,0.0006 }} {\minus}0.0015_{{\,\pm\,0.0001}} {\times}{\rm MI}} \right){\times}\left( {{\rm A}{\minus}\left( {3.5382_{{\,\pm\,1.3140 }} {\plus}1.9508_{{\,\pm\,0.1710}} {\times}{\rm MI}} \right)} \right)} \right)}} {\minus}\left( {0.12{\times}{\rm MI}} \right)$ where MI is the milk or milk replacer intake (l/day) and A the age (days). Cross-validation and bootstrap analyses demonstrated that these equations had high accuracy and moderate precision. In conclusion, the use of milk or milk replacer as liquid feed did not affect SFI, or development of SFI over time, which increased exponentially with calf age. Because SFI of calves receiving more than 5 l/day of milk/milk replacer had a different pattern over time than those receiving <5 l/day, separate prediction equations are recommended.

Type
Research Article
Copyright
© The Animal Consortium 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Davison, AC and Hinkley, DV 1997. Bootstrap methods and their application, 1st edition. Cambridge University Press, Cambridge, UK.Google Scholar
Davison, AC, Hinkley, DV and Young, GA 2003. Recent developments in bootstrap methodology. Statistical Science 18, 141157.Google Scholar
Dias, J, Marcondes, MI, Noronha, MF, Resende, RT, Machado, FS, Mantovani, HC, Dill-McFarland, KA and Suen, G 2017. Effect of pre-weaning diet on the ruminal archaeal, bacterial, and fungal communities of dairy calves. Frontiers in Microbiology 8, 117.Google Scholar
Efron, B and Tibshirani, RJ 1998. An introduction to the bootstrap. Chapman & Hall/CRC, Boca Raton, FL, USA.Google Scholar
Gelsinger, SL, Heinrichs, AJ and Jones, CM 2016. A meta-analysis of the effects of preweaned calf nutrition and growth on first-lactation performance. Journal of Dairy Science 99, 62066214.Google Scholar
Hayirli, A, Grummer, RR, Nordheim, E V and Crump, PM 2003. Models for predicting dry matter intake of Holsteins during the prefresh transition period. Journal of Dairy Science 86, 17711779.Google Scholar
Hill, SR, Knowlton, KF, Daniels, KM, James, RE, Pearson, RE, Capuco, AV and Akers, RM 2008. Effects of milk replacer composition on growth, body composition, and nutrient excretion in preweaned Holstein heifers. Journal of Dairy Science 91, 31453155.Google Scholar
Hill, TM, Bateman, HG, Aldrich, JM, Quigley, JD and Schlotterbeck, RL 2013. Evaluation of ad libitum acidified milk replacer programs for dairy calves. Journal of Dairy Science 96, 31533162.Google Scholar
Hill, TM, Bateman, HG, Aldrich, JM and Schlotterbeck, RL 2010. Effect of milk replacer program on digestion of nutrients in dairy calves. Journal of Dairy Science 93, 11051115.Google Scholar
Hoffman, PC, Weigel, KA and Wernberg, RM 2008. Evaluation of equations to predict dry matter intake of dairy heifers. Journal of Dairy Science 91, 36993709.Google Scholar
Jasper, J and Weary, DM 2002. Effects of ad libitum milk intake on dairy calves. Journal of Dairy Science 85, 30543058.Google Scholar
Jolomba, MR 2015. Energy and protein requirements of Holstein-Gir crossbred calves fed with milk added of milk replacer containing increasing levels of dry matter. MS thesis, Universidade Federal de Viçosa, Viçosa, Brazil.Google Scholar
Kertz, AF and Loften, JR 2013. Review: a historical perspective feeding programs in the United States and effects on eventual performance of Holstein dairy calves. The Professional Animal Scientist 29, 321332.Google Scholar
Kertz, AF, Prewitt, LR and Everett, JP 1979. An early weaning calf program: summarization and review. Journal of Dairy Science 62, 18351843.Google Scholar
Khan, M, Lee, HJ, Lee, WS, Kim, HS, Kim, SB, Ki, KS, Ha, JK, Lee, H and Choi, YJ 2007. Pre- and postweaning performance of Holstein female calves fed milk through step-down and conventional methods. Journal of Dairy Science 90, 876885.Google Scholar
Khan, MA, Weary, DM and von Keyserlingk, MAG 2011. Invited review: effects of milk ration on solid feed intake, weaning, and performance in dairy heifers. Journal of Dairy Science 94, 10711081.Google Scholar
Krizsan, SJ, Sairanen, A, Höjer, A and Huhtanen, P 2014. Evaluation of different feed intake models for dairy cows. Journal of Dairy Science 97, 23872397.Google Scholar
Kuehn, CS, Otterby, DE, Linn, JG, Olson, WG, Chester-Jones, H, Marx, GD and Barmore, JA 1994. The effect of dietary energy concentration on calf performance. Journal of Dairy Science 77, 26212629.Google Scholar
Lin, LI 1989. A concordance correlation-coefficient to evaluate reproducibility. Biometrics 45, 255268.Google Scholar
Marcondes, MI, Pereira, TR, Chagas, JCC, Filgueiras, EA, Castro, MMD, Costa, GP, Sguizzato, ALL and Sainz, RD 2016. Performance and health of Holstein calves fed different levels of milk fortified with symbiotic complex containing pre- and probiotics. Tropical Animal Health and Production 48, 15551560.Google Scholar
Marcondes, MI, Tedeschi, LO, Valadares Filho, SC and Chizzotti, ML 2012. Prediction of physical and chemical body compositions of purebred and crossbred Nellore cattle using the composition of a rib section. Journal of Animal Science 90, 12801290.Google Scholar
Miller-Cushon, EK, Bergeron, R, Leslie, KE and DeVries, TJ 2013a. Effect of milk feeding level on development of feeding behavior in dairy calves. Journal of Dairy Science 96, 551564.Google Scholar
Miller-Cushon, EK, Montoro, C, Bach, A and DeVries, TJ 2013b. Effect of early exposure to mixed rations differing in forage particle size on feed sorting of dairy calves. Journal of Dairy Science 96, 32573264.Google Scholar
Nutrient Requirements Council (NRC) 2001. Nutrient requirements of dairy cattle, 7th revised edition. National Academy Press, Washington, DC, USA.Google Scholar
Oliveira, AS and Ferreira, VB 2016. Prediction of intake in growing dairy heifers under tropical conditions. Journal of Dairy Science 99, 11031110.Google Scholar
Overvest, MA, Bergeron, R, Haley, DB and DeVries, TJ 2016. Effect of feed type and method of presentation on feeding behavior, intake, and growth of dairy calves fed a high level of milk. Journal of Dairy Science 99, 317327.Google Scholar
Pell, RJ 2000. Multiple outlier detection for multivariate calibration using robust statistical techniques. Chemometrics and Intelligent Laboratory Systems 52, 87104.Google Scholar
R Development Core Team 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Rodrigues, JPP, Lima, JCM, Castro, MMD, Valadares Filho, SC, Campos, MM, Chizzotti, ML and Marcondes, MI 2016. Energy and protein requirements of young Holstein calves in tropical condition. Tropical Animal Health and Production 48, 13871394.Google Scholar
Roseler, DK, Fox, DG, Chase, LE, Pell, AN and Stone, WC 1997. Development and evaluation of equations for prediction of intake for lactating Holstein dairy cows. Journal of Dairy Science 80, 878893.Google Scholar
SAS Institute Inc 2008. SAS/STAT(r) 9.2 user’s guide. SAS Institute Inc., Cary, NC, USA.Google Scholar
Silper, BF, Lana, AMQ, Carvalho, AU, Ferreira, CS, Franzoni, APS, Lima, JAM, Saturnino, HM, Reis, RB and Coelho, SG 2014. Effects of milk replacer feeding strategies on performance, ruminal development, and metabolism of dairy calves. Journal of Dairy Science 97, 10161025.Google Scholar
Silva, AL 2017. Prediction of starter feed intake of preweaned dairy calves and effects of rumen undegradable protein on performance and digestive characteristics of dairy Holstein heifers. PhD thesis, Federal University of Viçosa, Viçosa, MG, Brazil.Google Scholar
Silva, AL, Marcondes, MI, Detmann, E, Campos, MM, Machado, FS, Valadares Filho, SC, Castro, MMD and Dijkstra, J 2017. Determination of energy and protein requirements for crossbred Holstein×Gyr preweaned dairy calves. Journal of Dairy Science 100, 11701178.Google Scholar
Silva, AL, Marcondes, MI, Detmann, E, Machado, FS, Valadares Filho, SC, Trece, AS and Dijkstra, J 2015. Effects of raw milk and starter feed on intake and body composition of Holstein×Gyr male calves up to 64 days of age. Journal of Dairy Science 98, 26412649.Google Scholar
Souza, GS 1998. Introdução aos modelos de regressão linear e não-linear, 1st edition. EMBRAPA-SPI, Brasília, Brazil.Google Scholar
Souza, MC, Oliveira, AS, Araújo, CV, Brito, AF, Teixeira, RMA, Moares, EHBK and Moura, DC 2014. Short communication: prediction of intake in dairy cows under tropical conditions. Journal of Dairy Science 97, 38453854.Google Scholar
St-Pierre, NR 2001. Invited review: integrating quantitative findings from multiple studies using mixed model methodology. Journal of Dairy Science 84, 741755.Google Scholar
Stamey, JA, Janovick, NA, Kertz, AF and Drackley, JK 2012. Influence of starter protein content on growth of dairy calves in an enhanced early nutrition program. Journal of Dairy Science 95, 33273336.Google Scholar
Sweeney, BC, Rushen, J, Weary, DM and de Passillé, AM 2010. Duration of weaning, starter intake, and weight gain of dairy calves fed large amounts of milk. Journal of Dairy Science 93, 148152.Google Scholar
Tedeschi, LO 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225247.Google Scholar
Tedeschi, LO and Fox, DG 2009. Predicting milk and forage intake of nursing calves. Journal of Animal Science 87, 33803391.Google Scholar
Tedeschi, LO and Fox, DG 2018. The ruminant nutrition system: an applied model for predicting nutrient requirements and feed utilization in ruminants, 2nd edition. XanEdu, Acton, MA.Google Scholar
Tedeschi, LO, Fox, DG and Kononoff, PJ 2013. A dynamic model to predict fat and protein fluxes and dry matter intake associated with body reserve changes in cattle. Journal of Dairy Science 96, 24482463.Google Scholar
Vieira, PAS, Pereira, LGR, Azevêdo, JAG, Neves, ALA, Chizzotti, ML, Santos, RD, Araújo, GGL, Mistura, C and Chaves, AV 2013. Development of mathematical models to predict dry matter intake in feedlot Santa Ines rams. Small Ruminant Research 112, 7884.Google Scholar
Vyas, D and Erdman, RA 2009. Meta-analysis of milk protein yield responses to lysine and methionine supplementation. Journal of Dairy Science 92, 50115018.Google Scholar
Yavuz, E, Todorov, NA, Ganchev, G and Nedelkov, K 2015. Effect of physical form of starter feed on intake, growth rate, behavior and health status of female dairy calves. Bulgarian Journal of Agricultural Science 21, 893900.Google Scholar
Supplementary material: File

Silva et al. supplementary material

Table S1

Download Silva et al. supplementary material(File)
File 15.6 KB
Supplementary material: File

Silva et al. supplementary material

Silva et al. supplementary material

Download Silva et al. supplementary material(File)
File 13.2 KB