Abstract Economic results of pig-fattening systems vary greatly and depend mainly on prices of pork and feeds, and pig growth performance (e.g. feed efficiency, 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, we developed a new approach to improve the profitability of pig farms by estimating the best compromise among feeding costs, animal performance, and shipping constraints. We used an individual-based bioeconomic model that simulates the growth of each pig according to its biological traits (e.g. feed intake and protein deposition potential) as a function of different feeding and shipping strategies. The optimization problem is solved using an evolutionary algorithm (CMA-ES, covariance matrix adaptation evolution strategy) that manages the objective function, which is discontinuous, non-convex, nonlinear, and multimodal. Various case studies were constructed to investigate the behavior of the optimization procedure. Effects of pork price on optimal strategies were investigated using three different price scenarios: low (1.173 €/kg), medium (1.314 €/kg), and high (1.662 €/kg) pork prices. Optimizing only feeding strategies improved the gross margin per pig by 5.0% while optimizing shipping strategies improved the mean gross margin per pig by 4.7%. Optimizing both feeding and shipping strategies improved the gross margin per fattened pig by 10% (2.88 €/pig, with medium pork price (1.314€/kg)) compared to the common practice on farms in France. Pork price had a limited effect on feeding decisions when optimized alone, but a strong impact on shipping decisions. Economic optimization of pig fattening inconsistently affected the environmental impacts. However, increasing pork price improved the optimized mean gross margin per pig but increased all environmental impacts. To our knowledge, this is the first tool able to optimize both feeding and shipping strategies while considering effects of variability in growth potential among a batch of pigs. These features allow consideration of the interaction effect of feeding and shipping strategies on the economic outputs of the batch, and investigation of the trade-off between production cost and technical performance. This tool should interest the pig sector since it can identify the best feeding and shipping strategies depending on the economic context. Further work should consider multiobjective optimization with both economic and environmental objectives.

中文翻译：

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基于个体模型的猪育肥饲养和运输策略的经济优化

摘要 生猪育肥系统的经济效益差异很大，主要取决于猪肉和饲料的价格，以及猪的生长性能（例如饲料效率、屠宰重量、瘦肉率）。先前的研究表明，饲养和运输策略是养猪生产经济产出的关键因素。然而，他们未能同时考虑两种策略和猪生长性能的可变性。因此，我们开发了一种新方法，通过估算饲养成本、动物性能和运输限制之间的最佳折衷来提高养猪场的盈利能力。我们使用了一个基于个体的生物经济模型，该模型根据每头猪的生物学特性（例如采食量和蛋白质沉积潜力）作为不同饲养和运输策略的函数来模拟每头猪的生长。优化问题是使用进化算法（CMA-ES，协方差矩阵自适应进化策略）来解决的，该算法管理目标函数是不连续的、非凸的、非线性的和多模态的。构建了各种案例研究来调查优化过程的行为。使用三种不同的价格情景研究了猪肉价格对最优策略的影响：低（1.173 欧元/公斤）、中（1.314 欧元/公斤）和高（1.662 欧元/公斤）猪肉价格。仅优化饲养策略将每头猪的毛利率提高了 5.0%，而优化运输策略将每头猪的平均毛利率提高了 4.7%。与法国农场的常见做法相比，优化饲养和运输策略使每头肥猪的毛利率提高了 10%（2.88 欧元/猪，中等猪肉价格（1.314 欧元/公斤））。单独优化时，猪肉价格对饲养决策的影响有限，但对运输决策的影响很大。猪育肥的经济优化不一致地影响环境影响。然而，猪肉价格上涨提高了每头猪的优化平均毛利率，但增加了所有环境影响。据我们所知，这是第一个能够在考虑一批猪的生长潜力变异性影响的同时优化饲养和运输策略的工具。这些特征允许考虑进料和运输策略对批次经济产出的交互影响，以及研究生产成本和技术性能之间的权衡。该工具应该引起养猪业的兴趣，因为它可以根据经济环境确定最佳饲养和运输策略。进一步的工作应该考虑具有经济和环境目标的多目标优化。