The Precision Approach of the Lactation Curve in Sirohi Goats Using Non-Linear Models
الموضوعات :
L. Gautam
1
(Department of Animal Genetics and Breeding, College of Veterinary and Animal Science, Rajasthan University of Veterinary and Animal Sciences, Bikaner, 334001, India)
H. Ashraf Waiz
2
(Department of Livestock Production Management, College of veterinary and Animal Science, Rajasthan University of Veterinary and Animal Science, Bikaner, 313601, India)
الکلمات المفتاحية: Exponential, gamma, inverse quadratic polynomial, mixed log, polynomial re-gression,
ملخص المقالة :
Lactation knowledge enables total milk yield prediction from single and multiple lactation test days. The objective of this study was to compare different non-linear lactation curve models and to select the best fit model for evaluation of the Sirohi goat's milk production curve. Data retrieved fortnightly test day milk yield (FTDMY) in the various days (15, 30, 45, 60, 75, 90, 105, 120, 135 and 150) at 22.630 fortnightly test day milk yield of 2,263 Sirohi does in different lactations at All India Coordinated Research project area period from 2004 to 2016. Gamma, inverse quadratic polynomial, exponential, mixed log, and polynomial regression were evaluated to describe the lactation curve. The mean FTDMY increased from 0.811 ± 0.004 kg on Td1 (15th day of lactation) to 1.025 ± 0.005 kg on Td3 (45th day of lactation) and then decreased to 0.379 ± 0.001 kg on Td10 (150th day of lactation), with a coefficient of variation ranging from 20.40% to 28.68%. The polynomial regression function had the best adjusted R2 value of 99.4% and the smallest root mean square error of 0.003 kg., with expected peak yield, persistency, and total milk yield were 1.03 kg, 60.8%, and 115.73%, respectively. Out of the five lactation curve models examined, the polynomial regression function produced an outstanding model for predicting fortnightly test day milk output in Sirohi goats, with a relatively strong R2 and a low root mean square error (RMSE).
Akpa G.N., Asiribo E.O., Oni O.O. and Alawa P. (2001). The influence of non-genetic factors on the shape of lactation curves in Red Sokoto goats. J. Anim. Sci. 72, 233-239.
Ali T.E. and Schaeffer L.R. (1987). Accounting for covariance among test days milk yields in dairy cows. Canadian J. Anim. Sci. 67, 637-644.
Bilgin O.C., Esenbuga N. and Davis M. (2010). Comparison of models for describing the lactation curve of Awassi, Morkaraman and Tushin sheep. Arch. Tierzucht. 53, 447-456.
Brody S. (1964). Bioenergetics and Growth. Reinhold Publishing Corporation, New York.
Catillo G., Macciotta N.P.P., Caretta A. and Cappio-Borlino A. (2002). Effect of age and calving season on lactation curves of milk production traits in Italian water buffaloes. J. Dairy Sci. 85, 1298-1306.
FAOSTAT. (2020). Database of the Food and Agricultural Organization (FAO) of the United Nations. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy.
Fernandez C., Sanchez A. and Garces C. (2002). Modeling the lactation curve for test-day milk yield in Muciano-Granadina goats. Small Rumin. Res. 46, 29-41.
Gipson T.A. and Grossman M. (1989). Diphasicanalysis of lactation curves in dairy goats. J. Dairy Sci. 72, 1035-1044.
Guo Z. and Swalve H.H. (1995). Modeling of the lactation curves as a sub-model in the evaluation of test day records. Interbull Bulletin. 11, 4-7.
Kapadiya D.B., Prajapati D.B., Jain A.K., Mehta B.M., Darji V.B. and Aparnathi K.D. (2016). Comparison of Surti goat milk with cow and buffalo milk for gross composition, nitrogen distribution, and selected minerals content. Vet. World. 9, 710-179.
Keskin I. and Dag B. (2006). Comparison of different mathematical models for describing the complete lactation of Akkramanewes in Turkey. Asian-Australasian J. Anim. Sci. 19, 1551-1556.
Leon-Velarde C.U., McMillan I., Gentry R.D., Wilton J.W. (1995). Models for estimating typical lactation curves in dairy cattle. J. Anim. Breed Genet. 112, 333-340.
Macciotta Nicolo P.P., Dimauro C., Salvatore, Rassu P.G., Steri R. and Pulina G. (2011). The mathematical description of lactation curves in dairy cattle. Italian J. Anim. Sci. 10, 212-223.
Nazli T. (2017). The nutritional value and health benefits of goat milk components. MS Thesis. Ankara Univ., Ankara, Turkey.
NBAGR. (2018). National Bureau of Animal Genetic Resources, India. Available at: http://www.nbagr.res.in.
Nelder J.A. (1966). Inverse polynomials, a useful group of multifactor response functions. Biometrics. 22, 1128-1141.
Ruiz R., Oregui L.M. and Hohenboken W.D. (2000). Comparison of models for describing the lactation curve of Latxa sheep and an analysis of factors affecting milk yield. J. Dairy Sci. 83, 2709-2719.
Sahoo S.K., Singh A., Gupta A.K., Chakravarty A.K., Amnhore G.S. and Dash S.K. (2015). Comparative evaluation of different Lactation curve in function for prediction of Bi-Monthly Test day milk yield in Murrak buffaloes. Anim. Sci. Report. 9, 89-94.
Waheed A. and Khan M.S. (2013). Lactation curve of Beetal goats in Pakistan. Arch. Tierzucht. 56, 892-898.
White I. M.S., Thompson R. and Brotherstone S. (1999). Genetic and environmental smoothing of lactation curves with cubic splines. J. Dairy Sci. 82, 632-638.
Wilmink J.B.M. (1987). Adjustment of test day milk, fat and protein yield for age, season and stage of lactation. Livest. Prod. Sci. 16, 335-348.
Wood P.D.P. (1967). Algebraic model of the lactation curve in cattle. Nature. 216, 164-165.
