Bioeconomic models applied to animal health issues are now commonly observed in literature. This section of literature is very heterogeneous and the underlying methods are very diverse, from very simple methods (partial budgeting) to very complex ones. The objective of the present study is to build a new dynamic stochastic optimisation bioeconomic model applied to the dairy cow sector, that goes beyond some limitations usually found in methods used up to now. First, based on a critical literature review, we highlight four issues of bio-economic stochastic simulation models (BESSMs) applied to dairy cow diseases at the farm level. These models appear as partial (the farm system is not considered as a whole), unbalanced (between the economic and biological parts of the model), closed (to the farm environment) and only partially dynamic. To address these 4 main issues and improve the methodological standards in the microeconomics of dairy cow health management, we secondly develop a new bio-economic sequential optimization model (BESOM), called DairyHealthSim. DairyHealthSim aims to better consider both the context of decision-making and the farming system dynamics to define the best health management strategies in a given context. The biological part of the model simulates the complex dairy production cycle with a holistic approach. It is defined on a cow-week basis, and the weekly probabilities for all cow events, including production, reproduction and diseases, are simulated. The economic part of the model is a mean-variance optimization framework that dynamically represents the farmer's input allocation decision process under constraints. The biological and economic parts are closely integrated and the model is running with back and forth between the 2 parts of the bioeconomic model. Third, an application involving farmers' strategies related to biological risk management, labour willingness and market demand is proposed for dairy production and mastitis management. The results highlight the added value of the farming system-driven system coupled to economic optimization approach. DairyHealthSim identifies the optimal scenario for the entire ten-year simulation period or is based on yearly optimization (sequential modelling). The two different optimal solutions found show the usefulness of considering the dynamics and complexities of the actual field situation. The opportunity cost between the best and alternative solutions demonstrates that some solutions are economic equivalents. In conclusion, compared to approaches where the outcome is reduced to the monetary impact of diseases, DairyHealthSim is far more precise and appropriate for supporting decision-making.

应用于动物健康问题的生物经济模型现在在文献中很常见。这部分文献非常多样化，基础方法也非常多样化，从非常简单的方法（部分预算）到非常复杂的方法。本研究的目的是建立一个适用于奶牛部门的新的动态随机优化生物经济模型，它超越了目前使用的方法中通常存在的一些限制。首先，基于重要的文献综述，我们强调了应用于农场层面奶牛疾病的生物经济随机模拟模型 (BESSM) 的四个问题。这些模型表现为局部的（农场系统不被视为一个整体）、不平衡的（模型的经济和生物部分之间）、封闭的（农场环境）和仅部分动态的。为了解决这 4 个主要问题并提高奶牛健康管理微观经济学的方法标准，我们第二次开发了一种新的生物经济序列优化模型 (BESOM)，称为 DairyHealthSim。DairyHealthSim 旨在更好地考虑决策背景和养殖系统动态，以在特定背景下定义最佳健康管理策略。该模型的生物学部分采用整体方法模拟复杂的乳制品生产周期。它以奶牛周为基础定义，模拟所有奶牛事件（包括生产、繁殖和疾病）的每周概率。该模型的经济部分是一个均值方差优化框架，它动态地表示了农民在约束条件下的投入分配决策过程。生物和经济部分紧密结合，模型在生物经济模型的两个部分之间来回运行。第三，提出了一种涉及农民与生物风险管理、劳动意愿和市场需求相关的策略在乳制品生产和乳腺炎管理中的应用。结果突出了农业系统驱动系统与经济优化方法相结合的附加值。DairyHealthSim 确定整个十年模拟期的最佳方案或基于年度优化（顺序建模）。找到的两个不同的最佳解决方案表明考虑实际现场情况的动态性和复杂性是有用的。最佳解决方案和替代解决方案之间的机会成本表明，某些解决方案在经济上是等价的。总之，与将结果简化为疾病的货币影响的方法相比，DairyHealthSim 在支持决策方面更加精确和合适。