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

Economic 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, 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. This dataset provides code and data used in the article submitted to Agricultural Systems journal on March 31st, 2020

肥育猪养殖系统的经济收益差异显著，其在一定程度上取决于猪肉与饲料价格，以及猪只生长性能（例如屠宰体重、瘦肉率）。既往研究表明，饲喂与出栏策略是生猪生产经济产出的关键影响因素，但此前的研究未能同时兼顾这两类策略与猪只生长性能的变异性。为此，本研究提出了一种新方法，通过在饲喂成本、生猪性能与出栏约束之间寻求最优折中方案，以提升猪场的盈利能力。本研究采用基于个体的生物经济模型，依据不同饲喂与出栏策略对应的生猪生物学特性（例如采食量与蛋白质沉积潜力），模拟每头猪的生长过程。该优化问题使用进化算法（CMA-ES，协方差矩阵自适应进化策略，covariance matrix adaptation evolution strategy）求解，该算法可处理不连续、非凸、非线性且多模态的目标函数。本研究构建了多个案例以探究优化流程的运行特性。本数据集提供了2020年3月31日提交至《Agricultural Systems》期刊的论文中所使用的代码与数据。