Consequences of genetic change in farm animals on food intake and feeding behaviour

Emmans, G. C.; Kyriazakis, Ι.

doi:10.1079/pns200059

Cited by 66 publications

(51 citation statements)

References 49 publications

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“…Although all these models recognize the need for genetically driven trajectories (either explicitly or implicitly), they do not invoke (physiological state dependent) genetic expression, but instead use time as the driver to generate the homeorhetic trajectories. This may seem like a detail, but studies of growth have shown that relating trajectories to measures of physiological state, for example, degree of maturity, greatly extends the ability of models to accommodate genotype differences (Taylor, 1980;Emmans and Kyriazakis, 2001;Doeschl-Wilson et al, 2007).…”

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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“…where P maint is maintenance P requirements (g/day), p is a coefficient (g/day) expected to be constant across pig genotypes (Emmans and Kyriazakis, 2001), Pr is the pig actual body protein weight (kg) and Pr m is its protein content at somatic maturity (kg).The value of p was estimated to be 0.1293 (g/day) from the data of Jongbloed (1987), by making the following assumptions: (i) the relationship between Pr and body weight (BW) is allometric (Whittemore et al, 1988) and (ii) the Pr m of the crossbred Dutch Landrace × Dutch Yorkshire gilts used was 30 kg (Knap 2000). The advantages of equation (2) over existing estimates of P maint are that it can be applied across pig sizes and genotypes, and account for genetic change.…”

Section: Prediction Of Food Intakementioning

confidence: 99%

Modelling phosphorus intake, digestion, retention and excretion in growing and finishing pigs: model description

Symeou

Leinonen

Kyriazakis

2014

Animal

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Low phosphorus (P) digestibility combined with intensive pig production can increase P diffuse pollution and environmental load. The aim of this paper was to develop a deterministic, dynamic model able to represent P digestion, retention and ultimately excretion in growing and finishing pigs of different genotypes, offered access to diets of different composition. The model represented the limited ability of pig endogenous phytase activity to dephosphorylate phytate as a linear function of dietary calcium (Ca). Phytate dephosphorylation in the stomach by exogenous microbial phytase enzymes was expressed by a first order kinetics relationship. The absorption of non-phytate P from the lumen of the small intestine into the blood stream was set at 0.8 and the dephosphorylated phytate from the large intestine was assumed to be indigestible. The net efficiency of using digested P was set at 0.94 and assumed to be independent of BW, and constant across genotype and sex. P requirements for both maintenance and growth were made simple functions of body protein mass, and hence functions of animal genotype. Undigested P was assumed to be excreted in the feaces in both soluble and insoluble forms. If digestible P exceeded the requirements for P then the excess digestible P was excreted through the urinary flow; thus the model represented both forms of P excretion (soluble and insoluble) into the environment. Using a UK industry standard diet, model behaviour was investigated for its predictions of P digestibility, retention and excretion under different levels of inclusion of microbial phytase and dietary Ca, and different non-phytate P : phytate ratios in the diet, thus covering a broad space of potential diet compositions. Model predictions were consistent with our understanding of P digestion, metabolism and excretion. Uncertainties associated with the underlying assumptions of the model were identified. Their consequences on model predictions, as well as the model evaluation are assessed in a companion paper.Keywords: modelling, phosphorus, phytase enzymes, phytate, pig ImplicationsCurrently there is some disagreement about the phosphorus (P) requirements of pigs of different genotypes and how digestible P contents of pig diets are calculated, especially for diets that include different amounts of phytate and nonphytate P. Achieving a balance between meeting digestible P requirements for optimum growth and health, and avoiding excess P intake would lead to a reduction in diffuse P levels in manure and effluents, and environmental impact from pig systems. A simulation model that predicts P intake, digestion, retention and excretion is the first, necessary step towards achieving this aim. IntroductionPhosphorus (P) is an important mineral for both the metabolism and skeletal development of the growing pig (NRC, 2012).In pig diets, P is the third most expensive nutrient required, after carbohydrates (energy) and protein. The high cost of P is due to the low digestibility of plant dietary P, which results in the need to sup...

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“…The initial state of the pig was described by its initial body weight (BW0), from which the chemical composition of the pig was calculated assuming that the pig had its ideal composition set by its genotype (Emmans and Kyriazakis, 2001). The potential rate of protein retention was determined by pig phenotype and current protein weight only.…”

Section: Methodsmentioning

confidence: 99%

“…The protein and lipid growth of a certain pig phenotype can be described by a Gompertz function with the following parameters (representing growth traits): protein content at maturity (Pr m , kg), lipid content at maturity (L m , kg) and the relative growth rate at the inflection point of the growth curve (B, day − 1 ), in accordance with Ferguson et al (1997), Knap (2000), Emmans and Kyriazakis (2001), Pomar et al (2003) and Wellock et al (2004): dPr=dt ¼ Pr B lnðPr m =PrÞ kg=day; and dL=dt ¼ L B lnðL m =LÞkg=day Strategies to decrease P excretion by pigs where Pr and L are the body protein and lipid contents (kg), respectively.…”

Section: Generating Variation In Pig Growthmentioning

confidence: 99%

Quantifying the consequences of nutritional strategies aimed at decreasing phosphorus excretion from pig populations: a modeling approach

Symeou

Leinonen

Kyriazakis

2016

animal

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There is a global imperative to reduce phosphorous (P) excretion from pig systems. In this study, a previously validated deterministic model was modified to be stochastic, in order to investigate the consequences of different management strategies on P excretion by a group of growing pigs. The model predicts P digestion, retention and excretion from feed composition and growth parameters that describe a specified pig phenotype. Stochasticity was achieved by introducing random variation in the latter. The strategies investigated were: (1) changing feed composition frequently in order to match more closely pig digestible P (digP) requirements to feed composition (phase feeding) and (2) grouping pigs into light and heavy groups and feeding each group according to the requirements of their group average BW (sorting). Phase feeding reduced P excretion as the number of feeding phases increased. The effect was most pronounced as feeding phases increased from 1 to 2, with a 7.5% decrease achieved; the increase in phases from 2 to 3 was associated with a further 2.0% reduction. Similarly, the effect was more pronounced when the feed targeted the population requirements for digP at the average BW of the first third, rather than the average requirements at the mid-point BW of each feeding sequence plan. Increasing the number of feeding phases increased the percentage of pigs that met their digP requirements during the early stages of growth and reduced the percentage of pigs that were supplied <85% of their digP requirements at any stage of their growth; the latter may have welfare implications. Sorting of pigs reduced P excretion to a lesser extent; the reduction was greater as the percentage of pigs in the light group increased from 10% to 30% (from 1.5% to 3.0% reduction, respectively). This resulted from an increase in the P excreted by the light group, accompanied by a decrease in the P excreted by the remaining pigs. Sorting increased the percentage of light pigs that met their dig P requirements, but only slightly decreased the percentage of heavy pigs that met these requirements at any point of their growth. Exactly the converse was the case as far as the percentage of pigs that were supplied <85% of their digP requirements were concerned. The developed model is flexible and can be used to investigate the effectiveness of other management strategies in reducing P excretion from groups of pigs, including precision livestock feeding.

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Consequences of genetic change in farm animals on food intake and feeding behaviour

Cited by 66 publications

References 49 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

Modelling phosphorus intake, digestion, retention and excretion in growing and finishing pigs: model description

Quantifying the consequences of nutritional strategies aimed at decreasing phosphorus excretion from pig populations: a modeling approach

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