To overcome well-known difficulties in establishing reliable models based on large data sets, the Random Forest Regression (RFR) method is applied to study economical breeding and milk production of dairy cows. As for the features of RFR,there are several positive experiences in various areas of applications supporting that with RFR one can achieve reliable model predictions for industrial production of any product providing a useful base for decisions. In this study, a data set of a period of ten years including about eighty thousand cows was analysed by means of RFR. Ranking of production control parameters is obtained, the most important explanatory variables are found by computing the variances of the target variable on the sets created during the training phases of the RFR. Predictions are made for the milk production and the conception of the calves with high accuracy on given data and simulations are used to investigate prediction accuracy. This paper is primarily concerned with the mathematical aspects of a forthcoming work focused on the agricultural viewpoints. As for future mathematical research plans, the results will be compared with models based on factor analysis and linear regression.

中文翻译：

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荷斯坦黑白花奶牛泌乳和成功授精的随机森林回归模型

为了克服基于大型数据集建立可靠模型的众所周知的困难，将随机森林回归（RFR）方法用于研究奶牛的经济育种和产奶量。至于RFR的功能，在各种应用领域中都有一些积极的经验支持RFR可以为任何产品的工业生产实现可靠的模型预测，从而为决策提供有用的基础。在这项研究中，通过RFR分析了十年期间的数据集，其中包括约八万头奶牛。获得生产控制参数的排名，通过在RFR训练阶段创建的集合上计算目标变量的方差，可以找到最重要的解释变量。对牛奶的产量做出预测，并根据给定的数据对小牛的概念进行高精度预测，并通过模拟研究预测精度。本文主要关注即将针对农业观点开展的工作的数学方面。至于未来的数学研究计划，将把结果与基于因子分析和线性回归的模型进行比较。