A resource allocation model describing consequences of artificial selection under metabolic stress

Waaij, E.H. van der; Janss, L.L.G.; Bijma, P.

doi:10.1093/ansci/82.4.973

Cited by 22 publications

(31 citation statements)

References 22 publications

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“…Moreover, genotype by environment interactions between seasons make it difficult to select animals that have high performance in all seasons. For example, several studies have shown that selection for the best animals in good years can increase the sensitivity of animals to varying environments (Falconer, 1990;van der Waaij, 2004). This environmental sensitivity can reduce performance in poor years, which can have economic and welfare consequences.…”

Section: Discussionmentioning

confidence: 99%

Breeding objectives for sheep should be customised depending on variation in pasture growth across years

Rose

Mulder

Thompson

et al. 2015

Animal

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Breeding programmes for livestock require economic weights for traits that reflect the most profitable animal in a given production system, which affect the response in each trait after selection. The profitability of sheep production systems is affected by changes in pasture growth as well as grain, meat and wool prices between seasons and across years. Annual pasture growth varies between regions within Australia's Mediterranean climate zone from low growth with long periods of drought to high growth with shorter periods of drought. Therefore, the objective of this study was to assess whether breeding objectives need to be adapted for regions, depending on how reliable the pasture growth is across years. We modelled farms with Merino sheep bred for wool and meat in 10 regions in Western Australia. Across these 10 regions, mean annual pasture growth decreased, and the CV of annual pasture growth increased as pasture growth for regions became less reliable. We calculated economic values for nine traits, optimising management across 11 years, including variation for pasture growth and wool, meat and grain prices between and within years from 2002 to 2012. These economic values were used to calculate responses to selection for each trait for the 10 regions. We identified two potential breeding objectives, one for regions with low or high reliability and the other for regions with medium reliability of pasture growth. Breeding objectives for high or low pasture growth reliability had more emphasis on live weight traits and number of lambs weaned. Breeding objectives for medium reliability of pasture growth had more emphasis on decreasing fibre diameter. Relative economic weights for fleece weight did not change across the regions. Regions with low or high pasture reliability had similar breeding objectives and response to selection, because the relationship between the economic values and CV of pasture growth were not linear for live weight traits and the number of lambs weaned. This non-linearity was caused by differences in distribution of pasture growth between regions, particularly during summer and autumn, when ewes were pregnant, with increases in energy requirements affecting the value of lambs weaned. In addition, increasing live weight increased the intake capacity of sheep, which meant that more poor quality pasture could be consumed during summer and autumn, which had more value in regions with low and high pasture reliability. We concluded that breeding values for sheep production systems should be customised depending on the reliability of pasture growth between years.

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Section: Discussionmentioning

confidence: 99%

Breeding objectives for sheep should be customised depending on variation in pasture growth across years

Rose

Mulder

Thompson

et al. 2015

Animal

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“…different pathogen challenge, nutrient availability or diet composition) affect nutrient intake and allocation and consequently also the observable phenotypes related to health, reproduction and production (e.g. van der Waaij et al, 2000;van der Waaij, 2004;Vagenas et al, 2007aVagenas et al, , 2007bVagenas et al, and 2008Doeschl-Wilson et al, 2008 and2009a). In particular, the models aim to shed light on the ongoing debate about how genetic selection influences the relationship between disease resistance and (re-) production ( Knap and Bishop, 2000;Houdijk and Bü nger, 2006;DoeschlWilson et al, 2009b).…”

Section: Category 2: Models That Consider the Impact Of Infection On mentioning

confidence: 99%

“…As described by van der Waaij et al (2000) and van der Waaij (2004), it is possible to assess the consequences of genetic selection on the animal's ability to cope with infectious challenge with models that do not include explicit expressions for host-pathogen interactions. Representing host-pathogen interactions simply by the extent to which genotypes for production and fitness can be realised (with high infectious pressure corresponding to low realised production), van der Waaij et al (2000) showed that selection for observed production could result in increased disease resistance when animals are exposed to constant infection pressure.…”

Section: Category 2: Models That Consider the Impact Of Infection On mentioning

confidence: 99%

“…In the simplest case, host-pathogen interaction is only indirectly described by the impact of pathogen challenge on the expression of the host genotype (e.g. van der Waaij et al, 2000;van der Waaij, 2004) or by the probability of death due to disease (Houston et al, 2007). Others describe host-pathogen interaction by the impact of pathogen challenge on the immune response alone, ignoring how the immune response affects the within-host pathogen load (e.g.…”

Section: Category 2: Models That Consider the Impact Of Infection On mentioning

confidence: 99%

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The role of mathematical models of host–pathogen interactions for livestock health and production – a review

Doeschl‐Wilson

2011

Animal

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Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host-pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host-pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host-pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host-pathogen interactions in livestock. This has led to relatively simplistic representations of host-pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host-pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.

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“…The allocation of nutrients towards maintenance, growth and immune processes is often thought to be one of the key driving forces that determines the relationship between production performance and resistance e.g. [8,15,29], as it may lead to a trade-off between growth and immunity. A previous model assumed an allocation of available nutrients to immunity and performance traits in proportion to their requirements [26].…”

Section: Introductionmentioning

confidence: 99%

Exploring the assumptions underlying genetic variation in host nematode resistance(Open Access publication)

et al. 2008

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-The wide range of genetic parameter estimates for production traits and nematode resistance in sheep obtained from field studies gives rise to much speculation. Using a mathematical model describing host -parasite interactions in a genetically heterogeneous lamb population, we investigated the consequence of: (i) genetic relationships between underlying growth and immunological traits on estimated genetic parameters for performance and nematode resistance, and (ii) alterations in resource allocation on these parameter estimates. Altering genetic correlations between underlying growth and immunological traits had large impacts on estimated genetic parameters for production and resistance traits. Extreme parameter values observed from field studies could only be reproduced by assuming genetic relationships between the underlying input traits. Altering preferences in the resource allocation had less pronounced effects on the genetic parameters for the same traits. Effects were stronger when allocation shifted towards growth, in which case worm burden and faecal egg counts increased and genetic correlations between these resistance traits and body weight became stronger. Our study has implications for the biological interpretation of field data, and for the prediction of selection response from breeding for nematode resistance. It demonstrates the profound impact that moderate levels of pleiotropy and linkage may have on observed genetic parameters, and hence on outcomes of selection for nematode resistance. gastro-intestinal parasites

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A resource allocation model describing consequences of artificial selection under metabolic stress

Cited by 22 publications

References 22 publications

Breeding objectives for sheep should be customised depending on variation in pasture growth across years

Breeding objectives for sheep should be customised depending on variation in pasture growth across years

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

Exploring the assumptions underlying genetic variation in host nematode resistance(Open Access publication)

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