Auvergne - Rhône-Alpes Center, Animal Health Division, National Institute for Agricultural Research, Animal Epidemiology Research Unit, EPIA, INRA, VetAgro Sup, 63122, Saint Genès Champanelle, France.
Nancy-Lorraine Center, Science for Action and Development Division, National Institute for Agricultural Research, Mirecourt Farm, Domaine du Joly BP 35, 88501, Mirecourt, France.
Sci Rep. 2017 Aug 21;7(1):8897. doi: 10.1038/s41598-017-09322-x.
Milk production in dairy cow udders is a complex and dynamic physiological process that has resisted explanatory modelling thus far. The current standard model, Wood's model, is empirical in nature, represents yield in daily terms, and was published in 1967. Here, we have developed a dynamic and integrated explanatory model that describes milk yield at the scale of the milking session. Our approach allowed us to formally represent and mathematically relate biological features of known relevance while accounting for stochasticity and conditional elements in the form of explicit hypotheses, which could then be tested and validated using real-life data. Using an explanatory mathematical and biological model to explore a physiological process and pinpoint potential problems (i.e., "problem finding"), it is possible to filter out unimportant variables that can be ignored, retaining only those essential to generating the most realistic model possible. Such modelling efforts are multidisciplinary by necessity. It is also helpful downstream because model results can be compared with observed data, via parameter estimation using maximum likelihood and statistical testing using model residuals. The process in its entirety yields a coherent, robust, and thus repeatable, model.
奶牛乳房的产奶过程是一个复杂而动态的生理过程,迄今为止一直难以用解释性模型来描述。目前的标准模型——伍兹模型(Wood's model)本质上是经验性的,以日产量来表示,发表于 1967 年。在这里,我们开发了一个动态的、综合性的解释性模型,用于描述挤奶过程中的产奶量。我们的方法允许我们正式表示和数学关联已知相关的生物学特征,同时考虑随机性和条件元素,形式为明确的假设,然后可以使用实际数据进行测试和验证。使用解释性的数学和生物学模型来探索生理过程并指出潜在问题(即“发现问题”),可以过滤掉不重要的变量,只保留那些对生成最真实模型至关重要的变量。这种建模工作必然是多学科的。它在下游也很有帮助,因为可以通过最大似然法进行参数估计,并使用模型残差进行统计检验,将模型结果与观测数据进行比较。整个过程产生了一个连贯、稳健的、因此也是可重复的模型。