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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.


Conceptual diagram of the model illustrating the connections among sub-models. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; AcqB, basal acquisition; AcqL, lactation acquisition; ME Acquired, metabolizable energy acquired; GERes, resource gross energy density; NDFRes, resource fiber content; CORes, proportion of concentrate feedstuff in resource; PSURV, probability of survival; PCONC, probability of conception; AliveStat, Boolean for living status; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status
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Fig1: Conceptual diagram of the model illustrating the connections among sub-models. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; AcqB, basal acquisition; AcqL, lactation acquisition; ME Acquired, metabolizable energy acquired; GERes, resource gross energy density; NDFRes, resource fiber content; CORes, proportion of concentrate feedstuff in resource; PSURV, probability of survival; PCONC, probability of conception; AliveStat, Boolean for living status; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status

Mentions: The model structure is made up of four sub-models: acquisition, allocation, utilization and physiological status (Fig. 1). Acquisition and allocation sub-models are core modules that integrate the genetic determinants and lifetime dynamic changes. Utilization and physiological status are supporting modules that are based on simple principles and existing approaches. They are not the focus of the modelling effort since our aim is not to study the mechanisms that are associated with energy utilization and reproduction.Fig. 1


Disentangling the relative roles of resource acquisition and allocation on animal feed efficiency: insights from a dairy cow model
Conceptual diagram of the model illustrating the connections among sub-models. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; AcqB, basal acquisition; AcqL, lactation acquisition; ME Acquired, metabolizable energy acquired; GERes, resource gross energy density; NDFRes, resource fiber content; CORes, proportion of concentrate feedstuff in resource; PSURV, probability of survival; PCONC, probability of conception; AliveStat, Boolean for living status; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Conceptual diagram of the model illustrating the connections among sub-models. AllocG, allocation to growth; AllocS, allocation to survival; AllocPf, allocation to future progeny; AllocPc, allocation to current progeny; AcqB, basal acquisition; AcqL, lactation acquisition; ME Acquired, metabolizable energy acquired; GERes, resource gross energy density; NDFRes, resource fiber content; CORes, proportion of concentrate feedstuff in resource; PSURV, probability of survival; PCONC, probability of conception; AliveStat, Boolean for living status; GestStat, Boolean for gestating status; LacStat, Boolean for lactating status
Mentions: The model structure is made up of four sub-models: acquisition, allocation, utilization and physiological status (Fig. 1). Acquisition and allocation sub-models are core modules that integrate the genetic determinants and lifetime dynamic changes. Utilization and physiological status are supporting modules that are based on simple principles and existing approaches. They are not the focus of the modelling effort since our aim is not to study the mechanisms that are associated with energy utilization and reproduction.Fig. 1

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.