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A new standard model for milk yield in dairy cows based on udder physiology at the milking-session level

机译:基于挤奶过程中乳房生理的奶牛产奶量新标准模型

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摘要

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.
机译:奶牛乳房中的牛奶生产是一个复杂而动态的生理过程,迄今为止尚无法解释模型。伍德(Wood)模型是目前的标准模型,本质上是经验模型,代表每日产量,并于1967年发布。在这里,我们开发了一个动态的,综合性的解释模型,用于描述挤奶会议期间的牛奶产量。我们的方法使我们能够正式表示并数学关联已知相关的生物学特征,同时以明确假设的形式考虑随机性和条件元素,然后可以使用现实生活中的数据对其进行测试和验证。使用解释性的数学和生物学模型探索生理过程并查明潜在问题(即“问题发现”),就有可能滤除可以忽略的不重要变量,仅保留那些可能生成最现实模型的变量。这样的建模工作必然是多学科的。这对下游也很有帮助,因为可以通过使用最大似然的参数估计和使用模型残差的统计检验将模型结果与观察到的数据进行比较。整个过程产生了一个连贯的,健壮的,因此可重复的模型。

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