Modeling the effects of stressors on the performance of populations of pigs1

Wellock, I. J.; Emmans, G. C.; Kyriazakis, Ι.

doi:10.2527/2004.8282442x

Cited by 43 publications

(52 citation statements)

References 32 publications

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“…This interest is mainly related to the difference that can occur between the mean population response (i.e. the mean of all individuals) and the response of the average individual of the population Wellock et al, 2004). Introduction of stochasticity in models requires knowledge of not only the average values of model parameters and their variation, but also of the correlation between parameters (Ferguson et al, 1997;Kyriazakis, 1999), and the method proposed here allows obtaining these.…”

Section: Discussionmentioning

confidence: 99%

“…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). As the animal is described by a limited number of parameters in most models, inclusion of between-animal variation in modelling studies requires the determination of individual values of these parameters.…”

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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…No environmental stressors were assumed to operate on the pigs (Wellock et al, 2004). The main model inputs were: (1) pig growth traits, including initial state; (2) feed composition; and (3) feeding plan; while the model outputs for an individual pig were: (1) average daily gain (ADG); (2) body composition; (3) feed intake and (4) soluble and insoluble, and hence total P excreted.…”

Section: Methodsmentioning

confidence: 99%

“…Potential ADG was the sum of the potential gains of protein, lipid, ash (including P) and water. A total of 5% of the BW gain was assumed to be gut fill (Wellock et al, 2004).…”

Section: Methodsmentioning

confidence: 99%

“…The protein and lipid growth of a certain pig phenotype can be described by a Gompertz function with the following parameters (representing growth traits): protein content at maturity (Pr m , kg), lipid content at maturity (L m , kg) and the relative growth rate at the inflection point of the growth curve (B, day − 1 ), in accordance with Ferguson et al (1997), Knap (2000), Emmans and Kyriazakis (2001), Pomar et al (2003) and Wellock et al (2004): dPr=dt ¼ Pr B lnðPr m =PrÞ kg=day; and dL=dt ¼ L B lnðL m =LÞkg=day Strategies to decrease P excretion by pigs where Pr and L are the body protein and lipid contents (kg), respectively.…”

Section: Generating Variation In Pig Growthmentioning

confidence: 98%

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Quantifying the consequences of nutritional strategies aimed at decreasing phosphorus excretion from pig populations: a modeling approach

Symeou

Leinonen

Kyriazakis

2016

animal

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There is a global imperative to reduce phosphorous (P) excretion from pig systems. In this study, a previously validated deterministic model was modified to be stochastic, in order to investigate the consequences of different management strategies on P excretion by a group of growing pigs. The model predicts P digestion, retention and excretion from feed composition and growth parameters that describe a specified pig phenotype. Stochasticity was achieved by introducing random variation in the latter. The strategies investigated were: (1) changing feed composition frequently in order to match more closely pig digestible P (digP) requirements to feed composition (phase feeding) and (2) grouping pigs into light and heavy groups and feeding each group according to the requirements of their group average BW (sorting). Phase feeding reduced P excretion as the number of feeding phases increased. The effect was most pronounced as feeding phases increased from 1 to 2, with a 7.5% decrease achieved; the increase in phases from 2 to 3 was associated with a further 2.0% reduction. Similarly, the effect was more pronounced when the feed targeted the population requirements for digP at the average BW of the first third, rather than the average requirements at the mid-point BW of each feeding sequence plan. Increasing the number of feeding phases increased the percentage of pigs that met their digP requirements during the early stages of growth and reduced the percentage of pigs that were supplied <85% of their digP requirements at any stage of their growth; the latter may have welfare implications. Sorting of pigs reduced P excretion to a lesser extent; the reduction was greater as the percentage of pigs in the light group increased from 10% to 30% (from 1.5% to 3.0% reduction, respectively). This resulted from an increase in the P excreted by the light group, accompanied by a decrease in the P excreted by the remaining pigs. Sorting increased the percentage of light pigs that met their dig P requirements, but only slightly decreased the percentage of heavy pigs that met these requirements at any point of their growth. Exactly the converse was the case as far as the percentage of pigs that were supplied <85% of their digP requirements were concerned. The developed model is flexible and can be used to investigate the effectiveness of other management strategies in reducing P excretion from groups of pigs, including precision livestock feeding.

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A method to estimate the environmental impacts from genetic change in pig production systems

Ottosen

Mackenzie

Wallace

et al. 2019

Int J Life Cycle Assess

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Purpose The environmental impacts (EIs) of the global pig production sector are expected to increase with increasing global pork demand. Although the pig breeding industry has made significant progress over the last decades in reducing its EI, previous work has been unable to differentiate between the improvements made through management improvements from those caused by genetic change. Our study investigates the effect of altering genetic components of individual traits on the EI of pig systems. Methods An LCA model, with a functional unit of 1 kg live weight pig, was built simulating an intensive pig production system; inputs of feed and outputs of manure were adjusted according to genetic performance traits. Feed intake was simulated with an animal energy requirement model. A correlation matrix of the genetic variance and correlations of traits was pooled from data on commercial pig populations in the literature. Three sensitivity analyses were applied: one-at-a-time sensitivity analysis (OAT) used the genetic standard deviations, clusters-of-traits sensitivity analysis (COT) used the genetic standard deviations and clustering based on correlations, and the sensitivity index (SI) applied the full correlation matrix. Five EI categories were considered: global warming potential, terrestrial acidification potential, freshwater eutrophication potential, land use, and fossil resource scarcity. Results and discussion The different EI categories showed similar behaviour for each trait in the sensitivity analyses. OAT showed up to 18% change in EI relative to baseline for energy maintenance and around 3% change in EI relative to baseline for most other traits. COT grouped traits into a grower/finisher cluster (up to 17% change relative to baseline), a reproductive cluster (up to 7% change relative to baseline), and a sow robustness cluster (up to 2% change relative to baseline), all clusters including negative correlations between traits. By including genetic correlations, the SI went from being influenced by maintenance, and finisher and gilt growth rate into solely being dominated by maintenancen and protein-to-lipid ratio responsible for above 0.8 and 0.35 of the variance in EI respectively. Conclusions We developed a novel methodology for evaluating EIs of changes in correlated genetic traits in pigs. We found it was essential to include correlations in the sensitivity analysis, since the local and global sensitivity analyses were not affected to the same extend by the same traits. Further, we found that finisher growth rate, body protein-to-lipid ratio, and energy maintenance could be important in reducing EI, but mortalities and sow robustness had little effect.

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Modeling the effects of stressors on the performance of populations of pigs1

Cited by 43 publications

References 32 publications

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

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

Quantifying the consequences of nutritional strategies aimed at decreasing phosphorus excretion from pig populations: a modeling approach

A method to estimate the environmental impacts from genetic change in pig production systems

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