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Disentangling the relative roles of resource acquisition and allocation on animal feed efficiency: insights from a dairy cow model

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ABSTRACT

Background: Feed efficiency of farm animals has greatly improved through genetic selection for production. Today, we are faced with the limits of our ability to predict the effect of selection on feed efficiency, partly because the relative importance of the components of this complex phenotype changes across environments. Thus, we developed a dairy cow model that incorporates the dynamic interplay between life functions and evaluated its behaviour with a global sensitivity analysis on two definitions of feed efficiency. A key model feature is to consider feed efficiency as the result of two processes, acquisition and allocation of resources. Acquisition encapsulates intake and digestion, and allocation encapsulates partitioning rules between physiological functions. The model generates genetically-driven trajectories of energy acquisition and allocation, with four genetic-scaling parameters controlling these processes. Model sensitivity to these parameters was assessed with a complete factorial design.

Results: Acquisition and allocation had contrasting effects on feed efficiency (ratio between energy in milk and energy acquired from the environment). When measured over a lactation period, feed efficiency was increased by increasing allocation to lactation. However, at the lifetime level, efficiency was increased by decreasing allocation to growth and increasing lactation acquisition. While there is a strong linear increase in feed efficiency with more allocation to lactation within a lactation cycle, our results suggest that there is an optimal level of allocation to lactation beyond which increasing allocation to lactation negatively affects lifetime feed efficiency.

Conclusions: We developed a model to predict lactation and lifetime feed efficiency and show that breaking-down feed conversion into acquisition and allocation, and introducing genetically-driven trajectories that control these mechanisms, permitted quantification of their relative roles on feed efficiency. The life stage at which feed efficiency is evaluated appears to be a key aspect for selection. In this model, body reserves are also a key component in the prediction of lifetime feed efficiency since they integrate the feedback of acquisition and allocation on survival and reproduction. This modelling approach provided new insights into the processes that underpin lifetime feed efficiency in dairy cows.

Electronic supplementary material: The online version of this article (doi:10.1186/s12711-016-0251-8) contains supplementary material, which is available to authorized users.

No MeSH data available.


Dynamic changes in dry matter intake in the acquisition sub-model over two reproductive cycles of a dairy cow. Total dry matter intake is made up of a basal component (AcqB, solid line) and a lactation component (AcqL, dotted line)
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Fig3: Dynamic changes in dry matter intake in the acquisition sub-model over two reproductive cycles of a dairy cow. Total dry matter intake is made up of a basal component (AcqB, solid line) and a lactation component (AcqL, dotted line)

Mentions: The acquisition sub-model is the second core sub-model since it simulates dynamic changes in dry matter intake throughout lifetime. Acquisition is made up of a basal acquisition component, AcqB and a lactation acquisition component, AcqL as illustrated on Fig. 3. The basal component describes the maturation of the biological structures linked to resource acquisition as the female matures. The lactation component represents the increase in resource acquisition that is induced by the lactating status. As for allocation, in addition to changes in physiological status, dynamic changes in acquisition are driven by the genetic-scaling parameters, AcqBGEN and AcqLGEN, allowing the scaling of DM intake curves. Different values can be implemented to simulate genetic differences in the acquisition profiles among individuals.Fig. 3


Disentangling the relative roles of resource acquisition and allocation on animal feed efficiency: insights from a dairy cow model
Dynamic changes in dry matter intake in the acquisition sub-model over two reproductive cycles of a dairy cow. Total dry matter intake is made up of a basal component (AcqB, solid line) and a lactation component (AcqL, dotted line)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5037647&req=5

Fig3: Dynamic changes in dry matter intake in the acquisition sub-model over two reproductive cycles of a dairy cow. Total dry matter intake is made up of a basal component (AcqB, solid line) and a lactation component (AcqL, dotted line)
Mentions: The acquisition sub-model is the second core sub-model since it simulates dynamic changes in dry matter intake throughout lifetime. Acquisition is made up of a basal acquisition component, AcqB and a lactation acquisition component, AcqL as illustrated on Fig. 3. The basal component describes the maturation of the biological structures linked to resource acquisition as the female matures. The lactation component represents the increase in resource acquisition that is induced by the lactating status. As for allocation, in addition to changes in physiological status, dynamic changes in acquisition are driven by the genetic-scaling parameters, AcqBGEN and AcqLGEN, allowing the scaling of DM intake curves. Different values can be implemented to simulate genetic differences in the acquisition profiles among individuals.Fig. 3

View Article: PubMed Central - PubMed

ABSTRACT

Background: Feed efficiency of farm animals has greatly improved through genetic selection for production. Today, we are faced with the limits of our ability to predict the effect of selection on feed efficiency, partly because the relative importance of the components of this complex phenotype changes across environments. Thus, we developed a dairy cow model that incorporates the dynamic interplay between life functions and evaluated its behaviour with a global sensitivity analysis on two definitions of feed efficiency. A key model feature is to consider feed efficiency as the result of two processes, acquisition and allocation of resources. Acquisition encapsulates intake and digestion, and allocation encapsulates partitioning rules between physiological functions. The model generates genetically-driven trajectories of energy acquisition and allocation, with four genetic-scaling parameters controlling these processes. Model sensitivity to these parameters was assessed with a complete factorial design.

Results: Acquisition and allocation had contrasting effects on feed efficiency (ratio between energy in milk and energy acquired from the environment). When measured over a lactation period, feed efficiency was increased by increasing allocation to lactation. However, at the lifetime level, efficiency was increased by decreasing allocation to growth and increasing lactation acquisition. While there is a strong linear increase in feed efficiency with more allocation to lactation within a lactation cycle, our results suggest that there is an optimal level of allocation to lactation beyond which increasing allocation to lactation negatively affects lifetime feed efficiency.

Conclusions: We developed a model to predict lactation and lifetime feed efficiency and show that breaking-down feed conversion into acquisition and allocation, and introducing genetically-driven trajectories that control these mechanisms, permitted quantification of their relative roles on feed efficiency. The life stage at which feed efficiency is evaluated appears to be a key aspect for selection. In this model, body reserves are also a key component in the prediction of lifetime feed efficiency since they integrate the feedback of acquisition and allocation on survival and reproduction. This modelling approach provided new insights into the processes that underpin lifetime feed efficiency in dairy cows.

Electronic supplementary material: The online version of this article (doi:10.1186/s12711-016-0251-8) contains supplementary material, which is available to authorized users.

No MeSH data available.