InraPorc: A model and decision support tool for the nutrition of growing pigs

Milgen, Jaap van; Valancogne, Alain; Dubois, Serge; Dourmad, Jean-Yves; Sève, Bernard; Noblet, Jean

doi:10.1016/j.anifeedsci.2007.05.020

Cited by 193 publications

(232 citation statements)

References 23 publications

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“…The efficiencies of using dietary AA for milk protein (k AA ) were calculated using the van Milgen et al (2008) approach. ME = metabolizable energy; MEm = ME for maintenance; MY = milk yield; MCP = milk crude protein; CP diet = crude protein content in the diet (%); ML = milk lactose; MF = milk fat; NE L = net energy in milk; ME L = ME for milk; EBW = empty body weight; BF = back fat.…”

Section: Methodsmentioning

confidence: 99%

Determination of protein and amino acid requirements of lactating sows using a population-based factorial approach

Strathe

Theil

Hansen

et al. 2015

Animal

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Determination of appropriate nutritional requirements is essential to optimize the productivity and longevity of lactating sows. The current recommendations for requirements do not consider the large variation between animals. Therefore, the aim of this study was to determine the amino acid recommendations for lactating sows using a stochastic modeling approach that integrates population variation and uncertainty of key parameters into establishing nutritional recommendations for lactating sows. The requirement for individual sows was calculated using a factorial approach by adding the requirement for maintenance and milk. The energy balance of the sows was either negative or zero depending on feed intake being a limiting factor. Some parameters in the model were sow-specific and others were population-specific, depending on state of knowledge. Each simulation was for 1000 sows repeated 100 times using Monte Carlo simulation techniques. BW, back fat thickness of the sow, litter size (LS), average litter gain (LG), dietary energy density and feed intake were inputs to the model. The model was tested using results from the literature, and the values were all within ±1 s.d. of the estimated requirements. Simulations were made for a group of low-(LS = 10 (s.d. = 1), LG = 2 kg/day (s.d. = 0.6)), medium-(LS = 12 (s.d. = 1), LG = 2.5 kg/day (s.d. = 0.6)) and high-producing (LS = 14 (s.d. = 1), LG = 3.5 kg/day (s.d. = 0.6)) sows, where the average requirement was the result. In another simulation, the requirements were estimated for each week of lactation. The results were given as the median and s.d. The average daily standardized ileal digestible (SID) protein and lysine requirements for low-, medium-and high-producing sows were 623 (CV = 2.5%) and 45.1 (CV = 4.8%); 765 (CV = 4.9%) and 54.7 (CV = 7.0%); and 996 (CV = 8.5%) and 70.8 g/day (CV = 9.6%), respectively. The SID protein and lysine requirements were lowest at week 1, intermediate at week 2 and 4 and the highest at week 3 of lactation. The model is a valuable tool to develop new feeding strategies by taking into account the variable requirement between groups of sows and changes during lactation. The inclusion of between-sow variation gives information on safety margins when developing new dietary recommendations of amino acids and protein for lactating sows.

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Section: Methodsmentioning

confidence: 99%

Determination of protein and amino acid requirements of lactating sows using a population-based factorial approach

Strathe

Theil

Hansen

et al. 2015

Animal

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“…In the InraPorc model (van Milgen et al, 2008), several model parameters are used as inputs to define an animal profile (for ad libitum feed intake and growth (2006) differed from the observed performance, the meanPD was adjusted manually so that the predicted ADFI and ADG corresponded as close as possible to the observed values. Through this operation, a set of model parameters was obtained for each pig, and the InraPorc model was then used to predict the daily standardised ileal digestible Lys (SID Lys) requirement for each pig.…”

Section: Simulation Methodsmentioning

confidence: 99%

“…Through this operation, a set of model parameters was obtained for each pig, and the InraPorc model was then used to predict the daily standardised ileal digestible Lys (SID Lys) requirement for each pig. The SID Lys requirement in InraPorc (2006) is calculated using a factorial approach and accounting for requirements for maintenance and protein deposition (van Milgen et al, 2008). Requirements for maintenance include the basal endogenous losses (0.313 g/kg dry matter intake; Noblet et al, 2002) and losses due to integuments (4.5 mg/kg BW 0.75 per day) and basal turnover of protein (23.9 mg/kg BW 0.75 per day) (Moughan, 1998).…”

Section: Simulation Methodsmentioning

confidence: 99%

“…In InraPorc (2006), protein deposition is described using a Gompertz function (van Milgen et al, 2008). Because of the close relationship between protein deposition and daily gain, we used the Gompertz function to describe the change in BW as a function of age.…”

Section: Individual Characterisation Of Pigs In a Populationmentioning

confidence: 99%

“…Consequently, the mean performance of the group will be lower than expected. Pig growth models such as those developed by Whittemore and Fawcett (1976), Moughan et al (1987), Knap (2000) or van Milgen et al (2008) have been designed to simulate the response of an individual animal. Due to the importance of considering between-animal variation, stochasticity has been included in modelling approaches to study the impact of between-animal variation on performance (Ferguson et al, 1997;Knap, 2000;Pomar et al, 2003;Schinckel et al, 2003;Wellock et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy

Brossard

Dourmad

Rivest

et al. 2009

Animal

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Considerable progress has been made in the nutritional modelling of growth. Most models typically predict (or analyse) the response of a single animal. However, the response to nutrients of a single, representative animal is likely to be different from the response of the herd. To address the variation in response between animals, a stochastic approach towards nutritional modelling is required. In the present study, an analysis method is presented to describe growth and feed intake curves of individual pigs within a population of 192 pigs. This method was developed to allow end-users of InraPorc (a nutritional model predicting and analysing growth in pigs) to easily characterise their animals based on observed data and then use the model to test different scenarios. First, growth and intake data were curve-fitted to characterise individual pigs in terms of BW (Gompertz function of age) and feed intake (power function of BW) by a set of five parameters, having a biological or technico-economical meaning. This information was then used to create a population of virtual pigs in InraPorc, having the same feed intake and growth characteristics as those observed in the population. After determination of the mean lysine (Lys) requirement curve of the population, simulations were carried out for each virtual pig using different feeding strategies (i.e. 1, 2, 3 or 10 diets) and Lys supply (ranging from 70% to 130% of the mean requirement of the population). Because of the phenotypic variation between pigs and the common feeding strategies that were applied to the population, the Lys requirement of each individual pig was not always met. The percentage of pigs for which the Lys requirement was met increased concomitantly with increasing Lys supply, but decreased with increasing number of diets used. Simulated daily gain increased and feed conversion ratio decreased with increasing Lys supply ( P , 0.001) according to a curvilinear-plateau relationship. Simulated performance was close to maximum when the Lys supply was 110% of the mean population requirement and did not depend on the number of diets used. At this level of Lys supply, the coefficient of variation of simulated daily gain was minimal and close to 10%, which appears to be a phenotypic characteristic of this population. At lower Lys supplies, simulated performance decreased and variability of daily gain increased with an increasing number of diets ( P , 0.001). Knowledge of nutrient requirements becomes more critical when a greater number of diets are used. This study shows the limitations of using a deterministic model to estimate the nutrient requirements of a population of pigs. A stochastic approach can be used provided that relationships between the most relevant model parameters are known.

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Energy and Energy Metabolism in Swine

Noblet

Milgen

2012

Sustainable Swine Nutrition

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InraPorc: A model and decision support tool for the nutrition of growing pigs

Cited by 193 publications

References 23 publications

Determination of protein and amino acid requirements of lactating sows using a population-based factorial approach

Determination of protein and amino acid requirements of lactating sows using a population-based factorial approach

Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy

Energy and Energy Metabolism in Swine

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