Economic optimization of feeding and shipping strategies in pig-fattening units with an individual-based model

International audience The economic results of pig farming systems are highly variable and depend on the price of feeds, pig performance, and pork price.  Shipping strategy affects farm income since over- and under-weight pigs tend to lose in the gross margin.  Therefore, feeding and shipping strategies are major levers for improvement

Multiobjective feed formulation for pig: methodological approach and application

Animal production is responsible for several environmental impacts, to which feed production has usually themajor contribution. However, the traditional least-cost feed formulation (LCF) method minimizes the cost withoutconsideration of its environmental impacts. Multi-objective feed formulation has already been proposed to reduce the environmental impacts of pig feeds. It includes an objective function which is a weighted sum (WS) of the normalised values of feed cost, and four environmental impacts of the feed (climate change, land occupation, phosphorus demand and cumulated energy demand) calculated by Life Cycle Assessment. Normalised values are calculated dividing them by their reference value (REF) obtained with LCF (method Norm1). An additional factor α, ranging from 0 to 1 to explore the space of optimal solutions, is then used to weight the relative influence of feed cost and environmental impacts. The aim of this study was to explore potential improvements of the multi-objective method, with applications to pig feeds. We compared WS method with the ϵ-constraint method, which consists in minimizing a single objective while setting a maximum constraint for another one. In our case, the objective function was the weighted sum of the environmental impacts and the constraint was applied on feed cost. Then, we compared the behaviour of the model with Norm1 and with a new normalization method (Norm2), which subtracts the minimum criterion value to the criterion calculated and divides it by the difference between the REF and the minimum criterion values. WS and ϵ-constraint methods produced some common optimal solutions but only ϵ-constraint method allowed to obtain all the solutions of the Pareto front of the problem. Norm2 allowed defining solutions, which reduce consistently all environmental impacts whereas Norm1 allowed compensations between the impacts, which can potentially lead to increase of some impacts. Multi-objective feed formulation method can be substantially improved by implementing Norm2 to avoid possible compensations between impacts, and by applying the ϵ-constraint method to have access to the whole set of feasible optimal solutions

Bi-level optimisation of feeding and shipping strategies in pig-fattening units

International audienceEconomic results of pig-fattening systems vary greatly and depend in part on prices of pork and feeds, as well as pig growth performance (e.g. slaughter weight, lean percentage). Previous studies revealed that feeding and shipping strategies are critical factors in the economic outputs of pig production. However, they failed to consider both strategies and the variability in pig growth performance simultaneously. Consequently, the objective of this study was to develop a new procedure to improve the profitability of pig farms by estimating the best compromise among feeding costs, pork price, animal performance, and shipping constraints. We considered a bi-level programming problem in which the upper-level represents a bioeconomic model that simulates the growth of each pig according to its biological traits whereas the lower-level represents a linear least-cost feed formulation. Bioeconomic decisions taken at the upper-level are live weight at diet changes, the percentage of mean amino acid requirement to be covered at the start of each phase, and the target weight for slaughter. Maximising the mean gross margin per fattened pig is the objective function at the upper-level. It depends on pork price and feeds cost (the objective function of the lower-level) which results from the proportion of each feed ingredient (the lower-level decision variables) at the lower-level. The optimisation problem at the upper-level is solved using an evolutionary algorithm. We considered three sets of prices: average pork and feed prices, high pork and low feed prices, and low pork and high feed prices. The changes in pork prices had major impacts on shipping decisions while had minor impacts on feeding decisions. Optimising the shipping strategy at the same time modified the optimal feeding strategy. Considering the bi-level optimisation model improved the gross margin by 1.65 €/pig (5.2%) compared to the situation where each model (the bioeconomic model and least-cost feed formulation) was optimised separately, and by 3.59 €/pig (11.2%) compared to the common practice on farms in France

Economic optimization of feeding strategy in pig-fattening units with an individual-based model

International audienceThe economic results of pig farming systems are highly variable and depend on the price of feeds, pig performance, and pork price. Shipping strategy affects farm income since over- and under-weight pigs tend to lose in the gross margin. Therefore, feeding and shipping strategies are major levers for improvement

Optimisation économique en élevage porcin : un modèle pour piloter l'atelier d'engraissement

Les résultats économiques des élevages porcins dépendent des prix du porc et des matières premièresalimentaires, ainsi que des performances techniques des ateliers de production. Les coûts d'alimentation, ainsique le poids d'abattage et le taux de muscle des pièces de chaque porc interviennent dans la construction de lamarge brute. Les stratégies alimentaires et de gestion des abattages des porcs à l'engrais sont donc des facteurs clés des résultats économiques. Différents modèles et outils ont été précédemment développés pour prédire les effets des stratégies alimentaires sur les performances techniques et les résultats économiques (lnraPorc, MOGADOR ... ). Ils ont montré qu'il est nécessaire de prédire la trajectoire de croissance de chaque porc pour estimer correctement les résultats de l'atelier. Ils ne permettent cependant pas d'identifier la stratégied'alimentation optimale dans un contexte économique donné. L'objectif de ce travail était de développer un outilcapable de maximiser la rentabilité de l'atelier d'engraissement dans différents contextes économiques, enproposant le meilleur compromis entre coût d'alimentation et niveau de performance des animaux.L'outil associe un modèle bioéconomique de l'atelier d'engraissement et une procédure d'optimisation de lamarge brute moyenne par porc. Le modèle bioéconomique simule la croissance de chaque porc selon sonpotentiel d'ingestion et de croissance et selon la stratégie d'alimentation. Il calcule la marge brute moyenne parporc engraissé et les impacts environnementaux de la production (par Analyse de Cycle de Vie).La procédure d'optimisation maximise la marge brute moyenne par porc en trouvant le poids cible à l'abattage(PVa), la durée maximale d'engraissement (Dmax) et la meilleure stratégie d'alimentation biphase (BP) en termesde composition de la ration (pourcentages de deux aliments A et B formulés pour couvrir respectivement 110% et90% du besoin en lysine digestible du porc moyen à 30 kg et 120 kg de poids vif) et de poids vif moyen auchangement de phase. Cette procédure utilise un algorithme évolutionnaire adapté à la résolution de problèmesnon linéaires et discontinus (CMA-ES, Covariance Matrix Adaptation Evolution Strategy). Après une phase devalidation des conditions d'utilisation de l'outil, différents scénarios ont été explorés. Dans le contexte économique retenu pour les simulations, l'outil permet d'augmenter la marge brute de 1,33€/porc en cherchant la meilleure BP, en comparaison à un scénario de référence (SR) représenta nt les pratiques habituelles. En ajoutant PVa et Dmax aux variables de décision, l'outil propose une solution qui améliore la marge brute de 2,81 €/porc par rapport à SR. L'outil permet de maximiser le résultat économique de l'atelier d'engraissement en cherchant la meilleure séquence alimentaire biphase et la meilleure gestion des abattages. Les futurs travaux viseront à réaliser une optimisation économique et environnementale conjointe de l'atelier d'engraissement

An economic and environmental optimization model for pig-fattening units using a carbon tax

International audienceEconomic and environmental sustainability is a major concern for pig production systems (PPS). Previous studies showed that formulating low-impact diets using a carbon tax could decrease the climate change (CC) impact of pigfattening units. However, they did not consider the effect of interactions between feed formulas, feeding and shipping strategies (FFSS) on the environmental impacts of pig production. Consequently, the objective of this study was to investigate effects of a carbon tax on economically optimized FFSS and the resulting economic and environmental performances. We used a bi-level optimization model in which the upper level represents a bioeconomic model that simulates the growth of a batch of pigs and optimizes both the amino acid contents in growing and finishing feeds, the level of feed supply, and the shipping strategy. The lower level represents a linear least-cost feed formulation. The model’s behaviour was investigated in four contexts of recent feed and pork prices (low price: L and high price: H; feed: F and pork: P) at different carbon tax level. Optimized FFSS were highly sensitive to both the economic context and the carbon tax level. Without carbon tax, CC impact was lower in LF-LP than in the other economic contexts. With HF, the optimal amino acid contents and feed supply decreased as tax level increased. With LF, the optimal amino acid contents in the finishing diet increased as tax level increased, to improve feed conversion ratio. With increasing tax level, peas and cereal by-products were replaced with cereals and oil meals in pig diets. The highest potential of CC mitigation was obtained with HF-LP context, whereas LF-LP context had the lowest potential of CC mitigation because it resulted in low CC impact, even without application of a carbon tax. Optimizing both levels (FFSS) while applying a carbon tax decreased CC by up to 43% and saved up to 26% of income compared to FFSS optimized without carbon tax. This model provides a valuable tool to investigate the adaptation potential of PPS to the application of a carbon tax

Towards the next generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

The rumen ecosystem harbours a galaxy of microbes working in synthrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation basis approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this review, we discuss computational biology tools to analyse the rumen microbiome and two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis (COBRA) approaches. We will discuss how these methods can be used to produce the next generation models of the rumen microbiome

Towards the next generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

The rumen ecosystem harbours a galaxy of microbes working in synthrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation basis approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this review, we discuss computational biology tools to analyse the rumen microbiome and two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis (COBRA) approaches. We will discuss how these methods can be used to produce the next generation models of the rumen microbiome

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism. [Abstract copyright: Copyright: © 2024 Davoudkhani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Modelling nutrition and management in swine production systems to reduce their environmental impacts: a life cycle perspective

International audienceModelling nutrition and management in swine production systems to reduce their environmental impacts: a life cycle perspectiv