Comparison of forecast models of production of dairy cows combining animal and diet parameters
Comparison of forecast models of production of dairy cows combining animal and diet parameters
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DOI：
10.1016/j.compag.2020.105258
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发表时间：
2020-03-01
影响因子：
8.3
通讯作者：
Sincholle, Vincent
中科院分区：
文献类型：
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作者：
Quoc Thong Nguyen;Fouchereau, Remy;Sincholle, Vincent
本研究以36头法国布列塔尼荷斯坦-弗里西亚奶牛为研究对象，研究了不同营养日粮特征对其产奶性能的影响。对我们的数据集进行了脂肪/蛋白质含量与产奶量的关系分析。随着产奶量的增加，脂肪和蛋白质的产量增加的速度较慢。利用线性模型研究了化学成分对产奶量的影响。数据分析证实了淀粉、粗纤维和蛋白质对产奶量有积极影响的重要性。这一分析也印证了之前关于平价对产量影响的研究。然后，利用线性模型和机器学习方法(支持向量机、随机森林、神经网络)对产奶量进行了预测。我们在个体层面上研究了多元线性回归和基于机器学习的模型在非自回归和自回归两种情况下的性能。考虑到先前观察到的产奶量的自回归模型已被证明显著优于非自回归方法。此外，文中还给出了每种方法的计算代价。而随机森林算法在非自回归和自回归两种方法中都具有最好的性能。支持向量机算法以非常少的计算时间获得了非常接近的性能。支持向量机被证明是精度和计算成本之间的最佳折衷。
We study the effect of nutritional diet characteristics on the lactating Holstein-Friesian dairy cows in Brittany, France from 36 individuals. An analysis of the relations between fat/protein content and milk yield was implemented for our dataset. The fat and protein production increase at a slower rate as milk yield increases. The importance of chemical composition on milk production is studied using the linear model. The data analysis confirms the importance of Starch, crude fiber, and protein which have a positive effect on milk production. This analysis also confirms the previous study on the effect of parity on the production. After that, the milk production forecasting is investigated using both linear models and machine learning approaches (support vector machine, random forest, neural network). We study the performance of multiple linear regression and machine learning-based models in both non-autoregressive and autoregressive cases at the individual level. The autoregressive models, which take into account the previously observed milk yield, have proven to significantly outperform the non-autoregressive approaches. Moreover, the computational cost of each approach is presented in the paper. While the random forest algorithm gives the best performance in both non-autoregressive and autoregressive approaches. The support vector machine algorithm gives a very close performance with a substantial less computing time. The support vector machine is shown to be the best compromise between accuracy and computational cost.