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

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.


Structure and control of the allocation sub-model. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; fprioG2S, priority flow from growth to survival; fprioS2Pf, priority flow from survival to future progeny; fprioS2Pc, priority flow from survival to current progeny; fprioPc2S, priority flow from current progeny to survival; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status; G2SGEN, genetic-scaling parameter driving allocation to growth by controlling priority transfer from growing to survival; Pc2SGEN, genetic-scaling parameter driving allocation to lactation by controlling priority transfer from current progeny to survival
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Structure and control of the allocation sub-model. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; fprioG2S, priority flow from growth to survival; fprioS2Pf, priority flow from survival to future progeny; fprioS2Pc, priority flow from survival to current progeny; fprioPc2S, priority flow from current progeny to survival; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status; G2SGEN, genetic-scaling parameter driving allocation to growth by controlling priority transfer from growing to survival; Pc2SGEN, genetic-scaling parameter driving allocation to lactation by controlling priority transfer from current progeny to survival

Mentions: The allocation sub-model is made up of four compartments that reflect the priorities for four life functions: growth, future progeny, current progeny and survival. Dynamics of these compartments are based on mass action laws to represent the progressive transfers of priority among functions across various physiological states. The structure of the allocation sub-model is in Fig. 4, where the amounts of priority for the different life functions are given by AllocG for growth (priority for growing), AllocS for somatic functions (priority for survival), AllocPf for gestation (priority to future offspring), and AllocPc for lactation (priority to current offspring).Fig. 4


Disentangling the relative roles of resource acquisition and allocation on animal feed efficiency: insights from a dairy cow model
Structure and control of the allocation sub-model. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; fprioG2S, priority flow from growth to survival; fprioS2Pf, priority flow from survival to future progeny; fprioS2Pc, priority flow from survival to current progeny; fprioPc2S, priority flow from current progeny to survival; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status; G2SGEN, genetic-scaling parameter driving allocation to growth by controlling priority transfer from growing to survival; Pc2SGEN, genetic-scaling parameter driving allocation to lactation by controlling priority transfer from current progeny to survival
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Structure and control of the allocation sub-model. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; fprioG2S, priority flow from growth to survival; fprioS2Pf, priority flow from survival to future progeny; fprioS2Pc, priority flow from survival to current progeny; fprioPc2S, priority flow from current progeny to survival; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status; G2SGEN, genetic-scaling parameter driving allocation to growth by controlling priority transfer from growing to survival; Pc2SGEN, genetic-scaling parameter driving allocation to lactation by controlling priority transfer from current progeny to survival
Mentions: The allocation sub-model is made up of four compartments that reflect the priorities for four life functions: growth, future progeny, current progeny and survival. Dynamics of these compartments are based on mass action laws to represent the progressive transfers of priority among functions across various physiological states. The structure of the allocation sub-model is in Fig. 4, where the amounts of priority for the different life functions are given by AllocG for growth (priority for growing), AllocS for somatic functions (priority for survival), AllocPf for gestation (priority to future offspring), and AllocPc for lactation (priority to current offspring).Fig. 4

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.