A Model to Describe Growth Patterns of the Mammary Gland During Pregnancy and Lactation

Dijkstra, J.; Dhanoa, M. S.; Maas, J.A.; Hanigan, M.D.; Rook, A. J.; Beever, D. E.

doi:10.3168/jds.s0022-0302(97)76185-x

Cited by 138 publications

(210 citation statements)

References 50 publications

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“…This provided the metabolic model Molly with a homeorhetic trajectory for milk production, that is, a demand function that changed with days from calving, independent of nutrient supply. Subsequently, this homeorhetic trajectory for milk production was refined by developing increasingly sophisticated descriptions of mammary cell proliferation and decline (Dijkstra et al, 1997;Pollott, 2000;Vetharaniam et al, 2003b). Although these mammary gland models incorporate progressively more Figure 1 A schematic representation of the main challenge for improving prediction of nutrient partition: that is, to incorporate partition according to genetic drives in models that partition nutrients to maintain physiological balance.…”

Section: Introductionmentioning

confidence: 99%

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Friggens

Brun-Lafleur

Faverdin

et al. 2013

Animal

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In recent years, it has become increasingly clear that understanding nutrient partitioning is central to a much broader range of issues than just being able to predict productive outputs. The extent to which nutrients are partitioned to other functions such as health and reproduction is clearly important, as are the efficiency consequences of nutrient partitioning. Further, with increasing environmental variability, there is a greater need to be able to predict the ability of an animal to respond to the nutritional limitations that arise from the environment in which it is placed. How the animal partitions its nutrients when resources are limited, or imbalanced, is a major component of its ability to cope, that is, its robustness. There is mounting evidence that reliance on body reserves is increased and that robustness of dairy cows is reduced by selection for increased milk production. A key element for predicting the partition of nutrients in this wider context is to incorporate the priorities of the animal, that is, an explicit recognition of the role of both the cow's genotype (genetic make-up), and the expression of this genotype through time on nutrient partitioning. Accordingly, there has been a growing recognition of the need to incorporate in nutritional models these innate driving forces that alter nutrient partitioning according to physiological state, the genetically driven trajectories. This paper summarizes some of the work carried out to extend nutritional models to incorporate these trajectories, the genetic effects on them, as well as how these factors affect the homeostatic capacity of the animal. At present, there are models capable of predicting the partition of nutrients throughout lactation for cows of differing milk production potentials. Information concerning genotype and stage of lactation effects on homeostatic capacity has not yet been explicitly included in metabolic models that predict nutrient partition, although recent results suggest that this is achievable. These developments have greatly extended the generality of nutrient partitioning models with respect to the type of animal and its physiological state. However, these models remain very largely focussed on predicting partition between productive outputs and body reserves and, for the most part, remain research models, although substantial progress has been made towards developing models that can be applied in the field. The challenge of linking prediction of nutrient partitioning to its consequences on health, reproduction and longevity, although widely recognized, is only now beginning to be addressed. This is an important perspective for future work on nutrient partitioning.

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Section: Introductionmentioning

confidence: 99%

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Friggens

Brun-Lafleur

Faverdin

et al. 2013

Animal

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“…On the other hand, the increase of the number of parameters often leads to computational problems. VanRaden et al (2006), suggested a modification of the mechanistic model of Dijkstra et al (1997). The model was effective in modelling the average extended lactations curves of US Holstein.…”

Section: Introductionmentioning

confidence: 99%

Analysis of lactation shapes in extended lactations

Steri

Dimauro

Canavesi³

et al. 2012

animal

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In order to describe the temporal evolution of milk yield (MY) and composition in extended lactations, 21 658 lactations of Italian Holstein cows were analyzed. Six empirical mathematical models currently used to fit 305 standard lactations (Wood, Wilmink, Legendre, Ali and Schaeffer, quadratic and cubic splines) and one function developed specifically for extended lactations (a modification of the Dijkstra model) were tested to identify a suitable function for describing patterns until 1000 days in milk (DIM).Comparison was performed on individual patterns and on average curves grouped according to parity (primiparous and multiparous) and lactation length (standard <305 days, and extended from 600 to 1000 days). For average patterns, polynomial models showed better fitting performances when compared with the three or four parameters models. However, LEG and spline regression, showed poor prediction ability at the extremes of the lactation trajectory. The Ali and Schaeffer polynomial and Dijkstra function were effective in modelling average curves for MY and protein percentage, whereas a reduced fitting ability was observed for fat percentage and somatic cell score. When individual patterns were fitted, polynomial models outperformed nonlinear functions. No detectable differences were observed between standard and extended patterns in the initial phase of lactation, with similar values of peak production and time at peak. A considerable difference in persistency was observed between 200 and 305 DIM. Such a difference resulted in an estimated difference between standard and extended cycle of about 7 and 9 kg/day for daily yield at 305 DIM and of 463 and 677 kg of cumulated milk production at 305 DIM for the first-and second-parity groups, respectively. For first and later lactation animals, peak yield estimates were nearly 31 and 38 kg, respectively, and occurred at around 65 and 40 days. The asymptotic level of production was around 9 kg for multiparous cows, whereas the estimate was negative for first parity.

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“…The first attempt was performed by Neal and Thornley (1983) by way of a theoretical hormone controlling the division rate of basic udder cells to provide active secretory cells in the mammary gland. Further developments were carried out by Dijkstra et al (1997), Vetharaniam et al (2003a and2003b) and Pollott (2004). This principle of modelling was also used to direct adipose tissue anabolism and catabolism (Sauvant and Phocas, 1992;Baldwin, 1995;Martin and Sauvant, 2007) or to provide kinetics of plasma growth hormone and insulin driving exchanges between the tissues and udder (Danfaer, 1990).…”

Section: Teleonomic Argumentsmentioning

confidence: 99%

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

Martin

Sauvant

2010

Animal

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The prediction of the control of nutrient partitioning, particularly energy, is a major issue in modelling dairy cattle performance. The proportions of energy channelled to physiological functions (growth, maintenance, gestation and lactation) change as the animal ages and reproduces, and according to its genotype and nutritional environment. This is the first of two papers describing a teleonomic model of individual performance during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. The conceptual framework is based on the coupling of a regulating sub-model providing teleonomic drives to govern the work of an operating sub-model scaled with genetic parameters. The regulating sub-model describes the dynamic partitioning of a mammal female's priority between life functions targeted to growth (G), ageing (A), balance of body reserves (R) and nutrient supply of the unborn (U), newborn (N) and suckling (S) calf. The so-called GARUNS dynamic pattern defines a trajectory of relative priorities, goal directed towards the survival of the individual for the continuation of the specie. The operating sub-model describes changes in body weight (BW) and composition, foetal growth, milk yield and composition and food intake in dairy cows throughout their lifespan, that is, during growth, over successive reproductive cycles and through ageing. This dynamic pattern of performance defines a reference trajectory of a cow under normal husbandry conditions and feed regimen. Genetic parameters are incorporated in the model to scale individual performance and simulate differences within and between breeds. The model was calibrated for dairy cows with literature data. The model was evaluated by comparison with simulations of previously published empirical equations of BW, body condition score, milk yield and composition and feed intake. This evaluation showed that the model adequately simulates these production variables throughout the lifespan, and across a range of dairy cattle genotypes.

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A Model to Describe Growth Patterns of the Mammary Gland During Pregnancy and Lactation

Cited by 138 publications

References 50 publications

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time

Analysis of lactation shapes in extended lactations

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

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