Modelling nutrient requirements for pigs to optimize feed efficiency

Brossard, Ludovic; Dourmad, Jean-Yves; Garcia-Launay, Florence; Milgen, Jaap van

doi:10.19103/as.2017.0013.10

Cited by 10 publications

(13 citation statements)

References 47 publications

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“…More generally, the development of precision feeding ( PF ) gives new opportunities to better take into account, in real time, the factors influencing nutritional needs ( Brossard et al, 2016 ; Buis et al, 2016 ). In this context, models and decision support systems ( DSS ) have been developed to be integrated into automatic feeders for growing pigs ( Hauschild et al, 2012 ; Cloutier et al, 2015 ; Brossard et al, 2017 ) and more recently for sows ( Gaillard et al, 2019 ; Gauthier et al, 2019 ). These DSS are based on nutritional models that predict daily individual nutrient requirements, considering animals’ characteristics, physiological stage, and housing conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a decision support system for precision feeding of gestating sows

Gaillard

Quiniou

Gauthier

et al. 2020

Journal of Animal Science

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Precision feeding (PF) with the daily mixing of two diets with different lysine content (high (H) or low (L)) was previously reported for growing pigs to reduce protein intake and N excretion compared to a conventional feeding (CF) based on a single diet (C). Using a simulation approach based on farm data, the objective of the present paper was to describe and evaluate a decision support system (DSS) for the PF of gestating sows allowing the daily distribution of a tailored ration to each sow. Two datasets, one of 2511 gestations (farm A) and one of 2528 gestations (farm B), reporting sows’ characteristics at insemination and objectives at farrowing were used as inputs for a Python model. This model, mainly based on InraPorc, calculates the nutrient requirements of each sow over gestation and simulates the impact of PF in comparison to CF. Simulated diets L, H and C contained 3.0, 6.5, and 4.8 g/kg of standardized ileal digestible lysine (SID Lys) and 2.0, 3.3, and 2.5 g/kg of standardized total tract digestible phosphorus (STTD-P), respectively. The influence of farm, parity, gestation week, and their interactions, on calculated SID Lys and STTD-P requirements was analysed applying a mixed model. The calculated SID Lys and STTD-P requirements increased markedly in the last third of gestation (P < 0.01) and were higher for primiparous than for multiparous sows, unless after week 14 for STTD-P requirement. The calculated SID AA and mineral requirements were lower for farm B than farm A (respectively 2.94 vs. 3.08 g/kg for SID Lys and 1.30 vs. 1.35 g/kg for STTD-P, P < 0.01). On average, feed L represented 86% and 92% of the feed projected to be delivered by the PF strategy in farms A and B, respectively. Compared to CF, average calculated dietary SID Lys content was lowered by 27% and 32% with PF, for farms A and B, respectively, while average calculated dietary phosphorus content was lowered by 13% and 16%. The simulated proportions of sows in excess and deficient in SID Lys were reduced with PF. Compared to CF, the PF strategy allowed for a 3.6% reduction in simulated feed cost per sow during gestation, and reduced nitrogen and phosphorus intake (by 11.0% and 13.8%, respectively) and excretion (by 16.7% and 15.4%, respectively). To conclude, these simulations indicate that PF of gestating sow appears to be relevant to meet amino acid requirement while reducing feed cost, and supplies and excretion of nitrogen and phosphorus.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a decision support system for precision feeding of gestating sows

Gaillard

Quiniou

Gauthier

et al. 2020

Journal of Animal Science

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“…In the context of pig production data, population and individual level trait estimation is typically carried out within a framework based on hierarchical regression models ( 19 , 73 ). Under this framework, the overall quality of inferences could be negatively impacted by having to directly estimate multiple variance-covariance parameters ( 27 ), which can be challenging due to data limitations ( 9 ). The technical difficulties associated with this estimation procedure are the main reason why several studies make various working assumptions that neglect trait correlations ( 20 , 54 , 61 ).…”

Section: Discussionmentioning

confidence: 99%

“…Precision feeding strategies aim to accurately match nutrient supply to the demand of animals by formulating feeds that account for the dynamic changes in nutrient requirements, preferably at the individual level (7,8). This is in contrast to standard feeding practises, which typically neglect variation in nutrient requirements among individuals, as they involve formulating feeds that satisfy the estimated nutrient requirements of a nominal average pig in a population, at a given static reference point specified by bodyweight (BW) or age (9). Initial evaluations of precision feeding strategies against standard population level feeding regimes in growing-finishing pigs have been encouraging based on reports of considerable reduction in excretion of nitrogen (N) and phosphorus (P), without any apparent loss in growth performance (10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the issue of the distributions and correlations of the unobserved traits of individuals, a typical approach relies on an explicit specification of their multivariate distribution (20)(21)(22)(23)(24)(25)(26). Within this setting, it is necessary to either assume or estimate multiple mean and variance-covariance parameters from data, which carries uncertainty (27) and can be challenging in practise (9). To avoid these challenges, a potential alternative approach to model trait variation, based on separately inferring individual level distributions within a Bayesian framework, has been recently suggested by Filipe and Kyriazakis (27).…”

Section: Introductionmentioning

confidence: 99%

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A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods

2021

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Recent technological advances make it possible to deliver feeding strategies that can be tailored to the needs of individual pigs in order to optimise the allocation of nutrient resources and contribute toward reducing excess nutrient excretion. However, these efforts are currently hampered by the challenges associated with: (1) estimation of unobserved traits from the available data on bodyweight and feed consumption; and (2) characterisation of the distributions and correlations of these unobserved traits to generate accurate estimates of individual level variation among pigs. Here, alternative quantitative approaches to these challenges, based on the principles of inverse modelling and separately inferring individual level distributions within a Bayesian context were developed and incorporated in a proposed precision feeding modelling framework. The objectives were to: (i) determine the average and distribution of individual traits characterising growth potential and body composition in an empirical population of growing-finishing barrows and gilts; (ii) simulate the growth and excretion of nitrogen and phosphorus of the average pig offered either a commercial two-phase feeding plan, or a precision feeding plan with daily adjustments; and (iii) simulate the growth and excretion of nitrogen and phosphorus across the pig population under two scenarios: a two-phase feeding plan formulated to meet the nutrient requirements of the average pig or a precision feeding plan with daily adjustments for each and every animal in the population. The distributions of mature bodyweight and ratio of lipid to protein weights at maturity had median (IQR) values of 203 (47.8) kg and 2.23 (0.814) kg/kg, respectively; these estimates were obtained without any prior assumptions concerning correlations between the traits. Overall, it was found that a proposed precision feeding strategy could result in considerable reductions in excretion of nitrogen and phosphorus (average pig: 8.07 and 9.17% reduction, respectively; heterogenous pig population: 22.5 and 22.9% reduction, respectively) during the growing-finishing period from 35 to 120 kg bodyweight. This precision feeding modelling framework is anticipated to be a starting point toward more accurate estimation of individual level nutrient requirements, with the general aim of improving the economic and environmental sustainability of future pig production systems.

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“…feed intake, body weight, analytes), data processing and computational methods that estimate the nutrient requirements based on these inputs, and feeding systems that are capable of providing (individually) the adequate amount and precise diet formulation that maximize the desired production trajectory . For example, by modeling the individual nutrient requirements of sows and growing pigs, feeding strategies can be formulated on a per animal basis, thus optimizing efficiency and performance . Because population feed requirements in commercial pig farming are often tailored to the most demanding pigs in order to maximize the desired population response, precision feeding will prevent pigs from receiving more nutrients than they need.…”

Section: High Input–high Output Production Systemsmentioning

confidence: 99%

Prospects for sustainability of pig production in relation to climate change and novel feed resources

Rauw¹,

Rydhmer

Kyriazakis

et al. 2020

J Sci Food Agric

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Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems. The first scenario is a high input–high output system based on sustainable intensification, maximizing animal protein production efficiency on a limited land surface at the same time as minimizing environmental impacts. The second scenario is a reduced input–reduced output system based on selecting animals that are more robust to climate change and are better adapted to transform low quality feed (local feeds, feedstuff co‐products, food waste) into meat. However, in contrast to the first scenario, the latter scenario results in reduced predicted yields, reduced production efficiency and possibly increased costs to the consumer. National evaluation of the availability of local feed and feedstuff co‐product alternatives, determination of limits to feed sourced from international markets, available land for crop and livestock production, desired production levels, and a willingness to politically enforce policies through subsidies and/or penalties are some of the considerations to combine these two scenarios. Given future novel sustainable alternatives to livestock animal protein, it may become reasonable to move towards an added general premium price on ‘protein from livestock animals’ to the benefit of promoting higher incomes to farmers at the same time as covering the extra costs of, politically enforced, welfare of livestock animals in sustainable production systems. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Modelling nutrient requirements for pigs to optimize feed efficiency

Cited by 10 publications

References 47 publications

Evaluation of a decision support system for precision feeding of gestating sows

Evaluation of a decision support system for precision feeding of gestating sows

A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods

Prospects for sustainability of pig production in relation to climate change and novel feed resources

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