A dynamic model of feed intake regulation
in dairy cows. Model evaluation

Petruzzi, H.; Danfær, A.; Friggens, Nicolas

doi:10.22358/jafs/67385/2004

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Accepting the notion of an evolutionary derived, genetically regulated process, body reserve change has been reported to improve nutritional management in practice. Including a compulsory mobilization of energy in early lactation improves prediction of feed intake during this period (Petruzzi et al, 2004;Tedeschi et al, 2006). This has also been used to predict delays in return to estrus cycling (Friggens and Chagunda, 2005;Blanc and Agabriel, 2008) and to identify viable dry cow feeding strategies (Friggens et al, 2004a;Drackley, 2009).…”

Section: Models To Predict and Profile Interlactation Bcs Changementioning

confidence: 99%

Invited review: Body condition score and its association with dairy cow productivity, health, and welfare

Roche

Friggens

Kay

et al. 2009

Journal of Dairy Science

952

829

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The body condition score (BCS) of a dairy cow is an assessment of the proportion of body fat that it possesses, and it is recognized by animal scientists and producers as being an important factor in dairy cattle management. The scale used to measure BCS differs between countries, but low values always reflect emaciation and high values equate to obesity. The intercalving profile of BCS is a mirror image of the milk lactation profile. Cows lose condition for 50 to 100 d postcalving, because of homeorhetic changes that occur in the somatotropic axis and the sensitivity of peripheral tissues to insulin, and the upregulation of lipolytic pathways in adipose tissue. Management and feeding have little effect on early postcalving BCS loss (wk 1 to 4 postcalving) until the natural period of insulin resistance has passed and the somatotropic axis has recoupled. There is evidence, however, that management and diet can influence the timing of recoupling of the somatotropic axis and the sensitivity of peripheral tissues to insulin, and gene expression differences in adipose tissue 30 d in milk confirm an effect of energy intake on lipogenic enzymes. The BCS in which a cow calves, nadir BCS, and the amount of BCS she loses postcalving are associated with milk production, reproduction, and health. Body condition score may also be a valid indicator of animal welfare, but further research is required to determine the effect of BCS and BCS change on how a cow "feels." Although the actual strength of the association may vary, there is relative consistency in the associations among calving and nadir BCS, and BCS change on milk production, postpartum anestrous, the likelihood of a successful pregnancy and days open, the risk of uterine infection, and the risk of metabolic disorders. For many production and health variables, the association with BCS is nonlinear, with an optimum calving BCS of 3.0 to 3.25 (5-point scale); lower calving BCS is associated with reduced production and reproduction, whereas calving BCS >/=3.5 (5-point scale) is associated with a reduction in early lactation dry matter intake and milk production and an increased risk of metabolic disorders. Ongoing research into the automation of body condition scoring suggests that it is a likely candidate to be incorporated into decision support systems in the near future to aid producers in making operational and tactical decisions.

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Section: Models To Predict and Profile Interlactation Bcs Changementioning

confidence: 99%

Invited review: Body condition score and its association with dairy cow productivity, health, and welfare

Roche

Friggens

Kay

et al. 2009

Journal of Dairy Science

952

829

View full text Add to dashboard Cite

show abstract

“…For example, with very few exceptions (e.g. Petruzzi et al, 2004), current methods to predict energy requirements are based solely on estimates of milk production, milk composition and maintenance. They do not explicitly allow for any genetically driven body energy mobilisation.…”

Section: Introductionmentioning

confidence: 99%

The effect of breed and parity on curves of body condition during lactation estimated using a non-linear function

Friggens

Badsberg²

2007

Animal

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The objectives of this study were to see if the body condition score curve during lactation could be described using a model amenable to biological interpretation, a non-linear function assuming exponential rates of change in body condition with time, and to quantify the effect of breed and parity on curves of body condition during lactation. Three breeds were represented: Danish Holstein (n ¼ 112), Danish Red (n ¼ 97) and Jerseys (n ¼ 8). Cows entered the experiment at the start of first lactation and were studied during consecutive lactations (average number of lactations 2, minimum 1, maximum 3). They remained on the same dietary treatment throughout. Body condition was scored to the nearest half unit on the Danish scale (see Kristensen (1986); derived from the Lowman et al. (1976) system) from 1 to 5 on days: 2, 14, 28, 42, 56, 84, 112, 168, 224 after calving. Additionally, condition score was recorded on the day of drying off the cow, 35, 21, and 7 days before expected calving and finally on the day of calving. All condition scores were made by the trained personal on the research farm, where the same person made 92% of the scores. The temporal patterns in condition score were modelled as consisting of two underlying processes, one related to days from calving, referred to as lactation only, the other to days from (subsequent) conception, referred to as pregnancy. Both processes were assumed to be exponential functions of time. Each process was modelled separately using exponential functions, i.e. one model for lactation only and one for pregnancy, and then a combined model for both lactation only and pregnancy was fitted. The data set contained 467 lactation periods and 378 pregnancy periods. The temporal patterns in condition score of cows kept under stable and sufficient nutritional conditions were successfully described using a two component non-linear function. First lactation cows had shallower curves, they had greater condition scores at the nadir of the curve. Danish Holstein and Jersey were thinner at the end of the mobilisation period having lost more body condition than the Danish Red breed. Although the dairy breeds ended up being thinner there were no significant differences in the rate at which they lost body condition.

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Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example

Friggens¹,

Newbold²

2007

Animal

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Prediction of nutrient partitioning is a long-standing problem of animal nutrition that has still not been solved. Another substantial problem for nutritional science is how to incorporate genetic differences into nutritional models. These two problems are linked as their biological basis lies in the relative priorities of different life functions (growth, reproduction, health, etc.) and how they change both through time and in response to genetic selection. This paper presents recent developments in describing this biological basis and evidence in support of the concepts involved as they relate to nutrient partitioning. There is ample evidence that at different stages of the reproductive cycle various metabolic pathways, such as lipolysis and lipogenesis, are up or down regulated. The net result of such changes is that nutrients are channelled to differing extents to different organs, life functions and end-products. This occurs not as a homeostatic function of changing nutritional environment but rather as a homeorhetic function caused by the changing expression of genes for processes such as milk production through time. In other words, the animal has genetic drives and there is an aspect of nutrient partitioning that is genetically driven. Evidence for genetic drives other than milk production is available and is discussed. Genetic drives for other life functions than just milk imply that nutrient partitioning will change through lactation and according to genotype -i.e. it cannot be predicted from feed properties alone. Progress in describing genetic drives and homeorhetic controls is reviewed. There is currently a lack of good genetic measures of physiological parameters. The unprecedented level of detail and amounts of data generated by the advent of microarray biotechnology and the fields of genomics, proteomics, etc. should in the long-term provide the necessary information to make the link between genetic drives and metabolism. However, gene expression, protein synthesis etc, have all been shown to be environmentally sensitive. Thus, a major challenge in realising the potential afforded by this new technology is to be able to be able to distinguish genetically driven and environmentally driven effects on expression. To do this we need a better understanding of the basis for the interactions between genotypes and environments. The biological limitations of traditional evaluation of genotype £ environment interactions and plasticity are discussed and the benefits of considering these in terms of trade-offs between life functions is put forward. Trade-offs place partitioning explicitly at the centre of the resource allocation problem and allow consideration of the effects of management and selection on multiple traits and on nutrient partitioning.Keywords: cattle, genotype environment interaction, nutrient partitioning, plasticity IntroductionIn its broadest sense, the term 'nutrient partitioning' refers to the processes by which available nutrients are channelled, in varying proportions, to different met...

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A dynamic model of feed intake regulation in dairy cows. Model evaluation

Cited by 4 publications

References 23 publications

Invited review: Body condition score and its association with dairy cow productivity, health, and welfare

Invited review: Body condition score and its association with dairy cow productivity, health, and welfare

The effect of breed and parity on curves of body condition during lactation estimated using a non-linear function

Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example

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