Mathematical models in broiler raising

Zoons, Johan; Buyse, Johan; Decuypere, Eddy

doi:10.1079/wps19910020

Cited by 19 publications

(9 citation statements)

References 24 publications

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“…Animal models have been developed for a variety of species and applications: pig growth (Whittemore & Fawcett, 1976;Black et al 1986;Moughan et al 1987;Pomar et al 1991a;Technisch Model Varkensvoeding, 1991); reproducing sows (Pomar et al 1991b;Pettigrew et al 1992); poultry production (Zoons et al 1991;Hruby et al 1994); growing sheep (Gill et al 1984); growing fish (Machiels & Henken, 1986;; growing and reproducing beef cattle (Buchanan-Smith & Fox, 2000); and dairy cattle (Baldwin et al 1987). Some of these models are based exclusively on empirical observations, such as direct relationships between daily lysine and energy intake, and average daily gain and backfat thickness in growing pigs, established using multiple linear regression (Carr et al 1979).…”

mentioning

confidence: 99%

Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals

Birkett

Lange

2001

Br J Nutr

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Conventional models of energy utilization by animals, based on partitioning metabolizable energy (ME) intake or net energy (NE), are reviewed. The limitations of these methods are discussed, including various experimental, analytical and conceptual problems. Variation in the marginal efficiency of utilizing energy can be attributed to various factors: diet nutrient composition; animal effects on diet ME content; diet and animal effects on ME for maintenance (MEm); experimental methodology; and important statistical issues. ME partitioning can account for some of the variation due to animal factors, but not that related to nutrient source. In addition to many of the problems associated with ME, problems with NE pertain to: estimation of NE for maintenance (NEm); experimental and analytical methodology; and an inability to reflect variation in the metabolic use of NE. A conceptual framework is described for a new model of energy utilization by animals, based on representing explicit flows of the main nutrients and the important biochemical and biological transformations associated with their utilization. Differences in energetic efficiency from either dietary or animal factors can be predicted with this model. Modelling: Energy utilization: Nutrient flow representationMathematical models can integrate theories and observations into a coherent framework that can be useful for both conceptual and computational purposes. Animal models have been developed for a variety of species and applications: pig growth (Whittemore & Fawcett, 1976;Black et al. 1986; Moughan et al. 1987;Pomar et al. 1991a; Technisch Model Varkensvoeding, 1991); reproducing sows (Pomar et al. 1991b;Pettigrew et al. 1992); poultry production (Zoons et al. 1991;Hruby et al. 1994); growing sheep (Gill et al. 1984); growing fish (Machiels & Henken, 1986;; growing and reproducing beef cattle (Buchanan-Smith & Fox, 2000); and dairy cattle (Baldwin et al. 1987). Some of these models are based exclusively on empirical observations, such as direct relationships between daily lysine and energy intake, and average daily gain and backfat thickness in growing pigs, established using multiple linear regression (Carr et al. 1979). Application of such empirical models is limited to animal, environmental, and management conditions similar to those used in the trials on which they are based. Furthermore, this approach to representing animal production offers little insight into the mechanistic biological principles of which the measured performance is a consequence.In contrast to the empirical approach, highly complex mechanistic biochemical models have been developed to simulate nutrient metabolism at the level of individual tissues, using differential equations to represent (noncausally) relationships between the various metabolite flow rates. These mechanistic models are most useful for demonstrating biological and biochemical principles, especially at the cellular and inter-cellular levels. Wholeanimal models of this type have been developed, for example in mon...

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confidence: 99%

Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals

Birkett

Lange

2001

Br J Nutr

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“…Para interpretar os diferentes efeitos que os níveis de aminoácidos têm durante todo o ciclo de crescimento das aves, talvez seja necessário separar os efeitos de cada período de fornecimento desses nutrientes, de acordo com cada programa alimentar (dieta inicial, crescimento e final), nas curvas de crescimento (Zoons et al, 1991;Gous, 1998). Curnow (1973) M o d e l a g e m C o m p u t a c i o n a l p a r a P r o d u ç ã o e Pesquisa em Avicultura produto e o desvio padrão dos parâmetros resposta dos frangos.…”

Section: Modelagem Na Determinação De Exigências Nutricionaisunclassified

Modelagem Computacional para Produção e Pesquisa em Avicultura

Rondón

Murakami

Sakaguti

2002

Rev. Bras. Cienc. Avic.

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RESUMOA modelagem computacional foi apresentada como uma ferramenta na tomada de decisões para solucionar problemas complexos e cotidianos na produção e pesquisa em avicultura. Aspectos teóricos relacionados à construção e implementação dos diferentes tipos de modelos matemáticos utilizados em avicultura foram descritos de maneira elementar, tentando deixar o artigo acessível às pessoas que não necessariamente possuem um forte conhecimento de estatística experimental. Uma ênfase especial foi dada à discussão das vantagens da modelagem para resolver o problema complexo de determinar exigências nutricionais das aves em diferentes condições. Uma revisão cronológica das publicações relacionadas aos pacotes computacionais desenvolvidos para auxiliar na formulação de estratégias nutricionais em aves mostrou que eles estão baseados na modelagem do crescimento associada aos modelos de estimação de exigências e de custo mínimo de rações. A evolução e sofisticação desses programas têm acompanhado os rápidos avanços computacionais. Contudo, a eficiência de uma simulação depende dos conhecimentos sobre situações reais. Esses conhecimentos são gerados pelas pesquisas tradicionais e muitas vezes é de fundamental importância a conexão de informações provenientes de diferentes disciplinas, direta ou indiretamente, relacionadas à avicultura. ABSTRACTComputational modeling was presented as a tool to make decisions for solving the daily complex problems in poultry production and research. Theoretical aspects related to construction and implementation of the different types of models used in poultry production and research were elementarily described. We tried to make this paper accessible to people that do not necessarily have a strong background in experimental statistic. Special emphasis was given to discuss the advantages of modeling to solve the complex problem of nutrient requirement estimation for poultry under different conditions. A chronological review of publications related to software which was developed to help the establishment of poultry nutritional strategies showed that they are based on growth modeling associated with models of nutritional requirement and minimum cost diets. The evolution and sophistication of those programs are coming with the advance of information technology. However, the efficiency of a simulation depends on the real situation knowledge that is provided by the traditional research. In many cases, it is fundamentally important to connect information of different disciplines that are direct or indirectly related with poultry production. M o d e l a g e m C o m p u t a c i o n a l p a r a P r o d u ç ã o e Pesquisa em Avicultura

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“…The energy used for growth and maintenance is now calculated. The total metabolizable energy, E (MJ day −1 ) is the sum of the components (Zoons et al . 1991),…”

Section: Component Modelsmentioning

confidence: 99%

Integrated models of livestock systems for climate change studies. 2. Intensive systems

Turnpenny¹,

Parsons²,

Armstrong³

et al. 2001

Global Change Biology

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The potential impact of climate change by the year 2050 on intensive livestock systems in Britain is assessed through the use of simulation models of farming systems. The submodels comprise livestock feeding, livestock thermal balance and the thermal balance of controlled environment buildings and a stochastic weather generator. These are integrated to form system models for growing pigs and broiler chickens. They are applied to scenarios typical of SE England, which is the warmest region of the country and represents the worst case. For both species the frequency of severe heat stress is substantially increased, with a consequent risk of mortality. To offset this, it would be necessary to reduce stocking densities considerably, or to invest in improved ventilation or cooling equipment. Other effects on production are likely to be small

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Mathematical models in broiler raising

Cited by 19 publications

References 24 publications

Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals

Limitations of conventional models and a conceptual framework for a nutrient flow representation of energy utilization by animals

Modelagem Computacional para Produção e Pesquisa em Avicultura

Integrated models of livestock systems for climate change studies. 2. Intensive systems

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