随着对草原生产系统牛奶国际标准的呼声不断提高,围绕这一主题的监测和评估政策也是如此。各国和牛奶生产商在自己的草食标签下销售牛奶的个别规定包括每年强制放牧天数,从某些标签的120d到其他标签的180d,饮食中指定数量的牧草或在农场饲养和生产的规定饮食比例的草地牧草(GBF)。由于这些多种多样的政策和标签要求在农场进行监控既费力又昂贵,快速经济的代理将有利于验证奶牛在最终产品中消耗的GBF的比例。考虑到这一点,我们利用来自常规牛奶对照的容易获得的中红外光谱数据(n=1132光谱)来开发来自主要以饲料为基础的饮食的4个主要饲料组的二元分类模型:总GBF(≥50%n=955,≥75%n=599,≥85%n=356),牧场(≥20%n=451,≥50%n=284,≥70%n=152),新鲜牧草(牧场+新鲜牧草室内饲喂,≥20%n=517,≥50%n=325,≥70%n=182)和整株玉米(新鲜+保守)(≥10%n=646,≥30%n=187),后者作为阴性对照。我们比较了4种机器学习方法,以评估哪种统计模型在区分这些类别方面表现最好。这些模型中的三个尚未进行群水平饮食比例分类的测试,并且所有四个模型都遵循完全不同的方法:最小绝对收缩和选择操作员(LASSO),偏最小二乘判别分析(PLS-DA),随机森林(RF)和支持向量机(SVM)。季节性一直是以前的饮食牧草比例分类模型中缺少的元素。由于放牧和新鲜牧草室内饲养高度依赖于季节,我们开发了一个指标,将季节性纳入一个一致的,不偏不倚的方式进入我们的模型。我们还测试了3组协变量。第一组仅包括中红外光谱得出的数据,第二个包括中红外光谱得出的数据加上季节性指数,第三个包括中红外光谱得出的数据,季节性指数和其他群体特定信息(DIM,品种和平价)。在群体水平GBF比例二元分类测试的4种机器学习算法中,根据评估指标,LASSO和PLS-DA表现最好;然而,在每个饲料类别中,RF和SVM模型与表现最好的模型评估指标相差不远.我们表现最好的模型,包含季节性指数和群体特定信息的LASSO模型,分类总GBF≥50%,准确率为78.6%,精度85.1%,灵敏度为90.6%,特异性14.1%,F1评分(精度和灵敏度的调和平均值)为87.7%,这与PLS-DA模型非常相似。我们的结果表明,一般来说,LASSO和PLS-DA机器学习算法在饮食GBF分类方面比RF或SVM算法表现更好。
As the call for an international standard for milk from grassland-based production systems continues to grow, so too do the monitoring and evaluation policies surrounding this topic. Individual stipulations by countries and milk producers to market their milk under their own grass-fed labels include a compulsory number of grazing days per year, ranging from 120 d for certain labels to 180 d for others, a specified amount of herbage in the diet or a prescribed dietary proportion of grassland-based forages (GBF) fed and produced on farm. As these multifarious policy and label requirements are laborious and costly to monitor on farm, fast economical proxies would be advantageous to verify the proportion of GBF consumed by the cows in the final product. With this in mind, we employed readily available mid-infrared spectral data (n = 1132 spectra) from routine milk controls to develop binary classification models for 4 main feed groups from a primarily forage-based diet: Total GBF (≥50% n = 955, ≥ 75% n = 599, ≥ 85% n = 356), pasture (≥20% n = 451, ≥ 50% n = 284, ≥ 70% n = 152), fresh herbage (pasture + fresh herbage indoor feeding, ≥ 20% n = 517, ≥ 50% n = 325, ≥ 70% n = 182) and whole plant corn (fresh + conserved) (≥10% n = 646, ≥ 30% n = 187), the latter as a negative control. We compared 4 machine learning methods to assess which statistical model performs best at discriminating these classes. Three of these models have not yet been tested for herd-level dietary proportion classification and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF) and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. As grazing and fresh herbage indoor feeding are highly dependent on the season, we developed an indicator to incorporate seasonality in a consistent, unbiased manner into our models. We also tested 3 sets of covariates. The first set included only mid-infrared spectra derived data, the second included mid-infrared spectra derived data plus seasonality indices and the third included mid-infrared spectra derived data, seasonality indices and additional herd specific information (DIM, breed and parity). Of the 4 machine learning algorithms tested for the binary classification of GBF proportion at herd level, LASSO and PLS-DA performed best according to evaluation metrics; however, the RF and SVM models were not far behind the best performing model evaluation metrics in each feed category. Our best performing model, the LASSO model containing seasonality indices and herd specific information, classified total GBF ≥50% with an accuracy of 78.6%, precision of 85.1%, sensitivity of 90.6%, specificity 14.1% and F1 score (harmonic mean of precision and sensitivity) of 87.7%, this was very similar to the PLS-DA model. Our results suggest that in general LASSO and PLS-DA machine learning algorithms perform better for dietary GBF classification than RF or SVM algorithms.