Application of growth models to describe the lactation curves for test-day milk production in Holstein cows

Abstract

In order to describe the lactation curves of milk yield traits, six standard growth models (Brody, logistic, Gompertz, Schumacher, Von Bertalanffy and Morgan) were used. Data were 911,144 test-day records for unadjusted milk yield , 4% fat-corrected milk yield and energy-corrected milk yield from the first lactation of Iranian Holstein cows, which were collected on 834 dairy herds in the period from 2000 to 2011. Each model was fitted to monthly production records of dairy cows using the NLIN and MODEL procedures in SAS. The models were tested for goodness of fit using adjusted coefficient of determination, root means square error, Durbin-Watson statistic, Akaike's information criterion (AIC) and Bayesian information criterion (BIC). The Morgan model provided the best fit of the lactation curves due to the lower values of AIC and BIC than other models, but the Brody model did not fit the production data as well as the other equations. The results showed that the Morgan equation was able to estimate the time to the peak and peak yield more accurately than the other equations. Overall, evaluation of the different growth equations indicated the potential of the nonlinear functions for fitting monthly productive records of Holstein cows.

Introducing a sinusoidal equation to describe lactation curves for cumulative milk yield and composition in Holstein cows

“…For example, Journal of Dairy Research knowing when peak milk yield will occur can assist dairy farmers or managers in planning feeding strategies to maintain peak yield for as long as possible (López et al, 2015). Although several studies have compared non-linear models to fit the lactation curve for milk yield and composition, there are few reports on modeling the lactation curve for cumulative milk yield (Ghavi Hossein-Zadeh, 2014, 2017López et al, 2015) and no reports for cumulative milk composition traits. Therefore, the current study is the first to report on fitting cumulative milk fat and protein yield data, in particular assessing a sinusoidal function as an alternative to conventional models.…”

“…The dataset spanned 2000 through 2011 and is part of the data kept by the Animal Breeding Centre and Promotion of Animal Products of Iran. A detailed description of the data was reported in a previous study (Ghavi Hossein-Zadeh, 2017).…”

Computational technique for simulating variable-order fractional Heston model with application in US stock market

“…There are different performance criteria for measuring goodness of fit typically summarizing discrepancy between observed values and estimated values by the prediction model. We analyze the performance of proposed models, in the viewpoint of root-mean-square error (RMSE) or the residual standard deviation defined as where RSS denotes the square root of residual sum of squares, N and p are the number of observations and parameters in the equation, respectively [47].…”

“…For estimating the Bayesian information criterion (BIC), we apply the following formula [47] Moreover, the relative quality of statistical models for a given set of data can be analyzed in the perspective of the Akaike information criterion (AIC) defined as [48] A lower RMSE , BIC or AIC value indicates a better fit.…”

Heterosis in the components of lactation curves of Girolando cows

“…The use of mathematical models to describe the shape of lactation curves in genetic programmes allows establishing strategies to optimise selection of more efficient genotypes for the farmer in several production systems (Oliveira et al 2007;Hossein-Zadeh 2017). In the last decade, many mathematical models were developed to describe milk yield along lactation (Wood 1967;Ali and Schaeffer 1987;Wilmink 1987).…”