Simulation modelling of dairy cattle performance based on knowledge of genotype, environment and genotype by environment interactions: current status

Bryant, Jeremy; López‐Villalobos, N.; Holmes, C. W.; Pryce, J.E.

doi:10.1016/j.agsy.2004.09.004

Cited by 42 publications

(29 citation statements)

References 59 publications

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“…A particular focus of this aspect in animal breeding is the ability of the animal to achieve its genetic potential for production traits in environments that have greater resource constraints or a larger load of adverse environmental conditions than the environment in which the genetic merit of parents was assessed. This consistency of production performance across environments is usually identified through estimation of genotype by environment interactions and reaction norms (Bryant et al 2005;Knap 2005). The second aspect addresses the biological mechanism through which trade-offs within the animal between production traits and functional traits occur and is generally approached within the framework of resource allocation theory (Beilharz et al 1993).…”

Section: Resilience or Robustness?mentioning

confidence: 99%

Resilience in farm animals: biology, management, breeding and implications for animal welfare

2016

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Abstract.A capacity for the animal to recover quickly from the impact of physical and social stressors and disease challenges is likely to improve evolutionary fitness of wild species and welfare and performance of farm animals. Salience and valence of stimuli sensed through neurosensors, chemosensors and immunosensors are perceived and integrated centrally to generate emotions and engage physiological, behavioural, immune, cognitive and morphological responses that defend against noxious challenges. These responses can be refined through experience to provide anticipatory and learned reactions at lower cost than innate less-specific reactions. Influences of behaviour type, coping style, and affective state and the relationships between immune responsiveness, disease resistance and resilience are reviewed. We define resilience as the capacity of animals to cope with short-term perturbations in their environment and return rapidly to their pre-challenge status. It is manifested in response to episodic, sporadic or situation-specific attributes of the environment and can be optimised via facultative learning by the individual. It is a comparative measure of differences between individuals in the outcomes that follow exposure to potentially adverse situations. In contrast, robustness is the capacity to maintain productivity in a wide range of environments without compromising reproduction, health and wellbeing. Robustness is manifested in response to persistent or cyclical attributes of the environment and is effected via activity of innate regulatory pathways. We suggest that for farm animals, husbandry practices that incorporate physical and social stressors and interactions with humans such as weaning, change of housing, and introduction to the milking parlour can be used to characterise resilience phenotypes. In these settings, resilience is likely to be more readily identified through the rate of return of variables to pre-challenge or normal status rather than through measuring the activity of diverse stress response and adaptation mechanisms. Our strategy for phenotyping resilience of sheep and cattle during weaning is described. Opportunities are examined to increase resilience through genetic selection and through improved management practices that provide emotional and cognitive enrichment and stress inoculation.Additional keywords: affective state, allostasis, animal behaviour, animal temperament, animal welfare, disease resistance, genetic selection, homeostasis, resilience, robustness, stress inoculation.

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Section: Resilience or Robustness?mentioning

confidence: 99%

Resilience in farm animals: biology, management, breeding and implications for animal welfare

2016

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“…This feature is intended to open perspectives of connecting this approach with works focusing on genetic aspects of animal performance and their elaboration from genotypes to animal products (Bryant et al, 2005;DoeschlWilson et al, 2007). These so-called genetic parameters have been conceived as command buttons allowing the use of the model as a simulation tool for differentiated profiles of performance.…”

Section: Scaling Parametersmentioning

confidence: 99%

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

Martin

Sauvant

2010

Animal

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The prediction of the control of nutrient partitioning, particularly energy, is a major issue in modelling dairy cattle performance. The proportions of energy channelled to physiological functions (growth, maintenance, gestation and lactation) change as the animal ages and reproduces, and according to its genotype and nutritional environment. This is the first of two papers describing a teleonomic model of individual performance during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. The conceptual framework is based on the coupling of a regulating sub-model providing teleonomic drives to govern the work of an operating sub-model scaled with genetic parameters. The regulating sub-model describes the dynamic partitioning of a mammal female's priority between life functions targeted to growth (G), ageing (A), balance of body reserves (R) and nutrient supply of the unborn (U), newborn (N) and suckling (S) calf. The so-called GARUNS dynamic pattern defines a trajectory of relative priorities, goal directed towards the survival of the individual for the continuation of the specie. The operating sub-model describes changes in body weight (BW) and composition, foetal growth, milk yield and composition and food intake in dairy cows throughout their lifespan, that is, during growth, over successive reproductive cycles and through ageing. This dynamic pattern of performance defines a reference trajectory of a cow under normal husbandry conditions and feed regimen. Genetic parameters are incorporated in the model to scale individual performance and simulate differences within and between breeds. The model was calibrated for dairy cows with literature data. The model was evaluated by comparison with simulations of previously published empirical equations of BW, body condition score, milk yield and composition and feed intake. This evaluation showed that the model adequately simulates these production variables throughout the lifespan, and across a range of dairy cattle genotypes.

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“…Pasture growth, feed intake, cow liveweight, milk yield (autumn and spring), explore different management and risk scenarios, effect of dairy cow reproduction on farm economic performance. (Moore et al 2007) (Eckard et al 2006;Bryant et al 2007;Cullen et al 2007Cullen et al ,2008Chapman et al 2008;White et al 2008) (Berntsen et al 2005;Hutchings et al 2007) (Ándales et al 2005) (Hill et al 1999;Boschma & Scott 2000;Clark et al 2000Clark et al ,2003Cohen et al 2003;Salmon et al 2004;Bryant et al 2005;Robertson 2006) (Thornley & Verberne 1989;Thornley et al 1995;Cannell & Thornley 1998;Thornley & Cannell 2000;Thornley 2001;Barrettetal. 2004) (Rotz et al 1999b(Rotz et al ,c, 2003Sanderson et al 2001;Corson et al 2006Corson et al ,2007 (Cacho et al , 1999) Wastney et al 2002;Beukes et al 2005aBeukes et al ,b, 2007 was identified, the name of the model and the keyword "simulation" were entered as keywords in web of Knowledge®.…”

Section: Model Identificationmentioning

confidence: 99%

Modelling pastoral farm agro‐ecosystems: A review

Bryant

Snow

2008

New Zealand Journal of Agricultural Research

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A pastoral farm is a complex agro-ecosystem that produces agricultural outputs such as milk, meat and wool. Detailed consideration of the complexities of agro-ecosystem components, and the interactions between these components, may be best approached with simulation modelling. We reviewed nine simulation models (APSIM, EcoMod, FASSET, GRAZPLAN, GPFARM, Hurley Pasture Model, IFSM, LINCFARM, and WFM) used primarily for research of grazing systems. GrassGro (one of the GRAZPLAN suite of decision support tools), LINC-FARM, and WFM had particular strength in their simulation of animal production and farm management. The strengths of APSIM, EcoMod and FAS-SET lie in their simulation of soil nutrient dynam ics, crop or pasture production and spatial variations in soil properties. The Hurley Pasture Model has particular capability to model nutrient cycling in the presence of climate change. The IFSM includes a comprehensive representation of machinery. GP-FARM can be used to predict forage production of different plant functional groups.We suggest that more emphasis could be placed on including the effects of pests and diseases on pasture production and animal performance, more detailed representation of management practices, inclusion of more mechanistic voluntary feed intake and rumen processes and including the effect of specific genes and gene-by-environment interactions on plant quality and yields, nutrient use and animal performance. The Common Modelling Protocol, used in APSIM and the GRAZPLAN suite, may enable the development of a powerful and flexible pastoral agro-ecosystem model which incorporates newly developed modules of the nature mentioned above.

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Simulation modelling of dairy cattle performance based on knowledge of genotype, environment and genotype by environment interactions: current status

Cited by 42 publications

References 59 publications

Resilience in farm animals: biology, management, breeding and implications for animal welfare

Resilience in farm animals: biology, management, breeding and implications for animal welfare

A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

Modelling pastoral farm agro‐ecosystems: A review

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