On the analysis of Canadian Holstein dairy cow lactation curves using standard growth functions

López, Secundino; Odongo, N. E.; McBride, R. A.; Kebreab, E.; AlZahal, Ousama; McBride, B.W.; Dijkstra, J.

doi:10.3168/jds.2014-8132

Cited by 36 publications

(48 citation statements)

References 40 publications

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“…The current results indicate that representative non-linear growth functions, especially the sinusoidal and the Morgan equation, provide suitable fits to cumulative lactation curves not only for milk yield but also for fat and protein yields. Consistent with the results of this study, López et al (2015) fitted six classical growth functions (monomolecular, Schumacher, Gompertz, logistic, Richards, and Morgan) to cumulative milk production curves of Canadian Holstein dairy cows and concluded that the fitted classical growth functions could be an alternative to conventional models when analyzing lactation data.…”

Section: Discussionsupporting

confidence: 83%

“…Modelers seek to find parametric descriptors of the shape of the lactation curve to predict characteristics including peak milk yield, time to peak, and persistency. 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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Hossein-Zadeh

Kuhi

López

2020

Journal of Dairy Research

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The aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Hossein-Zadeh

Kuhi

López

2020

Journal of Dairy Research

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“…As a herd-level metric, 55 to 60 DIM is lower than would be intuitive to define peak production (Wasike et al, 2011;López et al, 2015). This is due largely to the inclusion of animals that were culled or died before 100 DIM and to the natural skew toward lower values that is inherent in test-day data.…”

mentioning

confidence: 95%

Exploring relationships between Dairy Herd Improvement monitors of performance and the Transition Cow Index in Wisconsin dairy herds

Schultz

Bennett

Nordlund

et al. 2016

Journal of Dairy Science

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Transition cow management has been tracked via the Transition Cow Index (TCI; AgSource Cooperative Services, Verona, WI) since 2006. Transition Cow Index was developed to measure the difference between actual and predicted milk yield at first test day to evaluate the relative success of the transition period program. This project aimed to assess TCI in relation to all commonly used Dairy Herd Improvement (DHI) metrics available through AgSource Cooperative Services. Regression analysis was used to isolate variables that were relevant to TCI, and then principal components analysis and network analysis were used to determine the relative strength and relatedness among variables. Finally, cluster analysis was used to segregate herds based on similarity of relevant variables. The DHI data were obtained from 2,131 Wisconsin dairy herds with test-day mean ≥30 cows, which were tested ≥10 times throughout the 2014 calendar year. The original list of 940 DHI variables was reduced through expert-driven selection and regression analysis to 23 variables. The K-means cluster analysis produced 5 distinct clusters. Descriptive statistics were calculated for the 23 variables per cluster grouping. Using principal components analysis, cluster analysis, and network analysis, 4 parameters were isolated as most relevant to TCI; these were energy-corrected milk, 3 measures of intramammary infection (dry cow cure rate, linear somatic cell count score in primiparous cows, and new infection rate), peak ratio, and days in milk at peak milk production. These variables together with cow and newborn calf survival measures form a group of metrics that can be used to assist in the evaluation of overall transition period performance.

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“…It is the practice to estimate quantitative characteristics, which include milk-producing ability and fat and protein content in milk, by the phenotype and by manifestation of these characteristics in the conditions where the body grows and develops (1)(2)(3)(4)(5)(6)(7). Today, the dairy cow population in farms in the Russian Federation has 19 breeds and 23 types of stud cattle and inbreeding.…”

Section: Introductionmentioning

confidence: 99%

Productivity and adaptation ability of Holstein cattle of different genetic selections

Gorlov¹,

Bozhkova²,

Shakhbazova³

et al. 2016

Turk J Vet Anim Sci

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On the analysis of Canadian Holstein dairy cow lactation curves using standard growth functions

Cited by 36 publications

References 40 publications

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

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

Exploring relationships between Dairy Herd Improvement monitors of performance and the Transition Cow Index in Wisconsin dairy herds

Productivity and adaptation ability of Holstein cattle of different genetic selections

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