Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of “Bovine,” exposure of “Statistical Analysis or Machine Learning,” and outcome of “Methane Emissions”. The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

中文翻译：

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预测奶牛甲烷排放的方法：从传统方法到机器学习


使用传统记录技术测量奶牛甲烷 （CH4） 排放既复杂又昂贵。根据代理信息估计 CH4 排放的预测模型提供了一种可用的替代方案。这篇综述涵盖了在预测奶牛 CH4 排放时采用的不同建模方法，并强调了它们各自的优势和局限性。遵循系统评价和荟萃分析首选报告项目 （PRISMA） 制定的指南;Scopus、EBSCO、Web of Science、PubMed 和 PubAg 均被查询标题包含与“牛”群体、“统计分析或机器学习”暴露以及“甲烷排放”结果相关的搜索词的论文。检索于 2022 年 12 月执行，未设置发布日期范围。符合条件的论文是通过统计或机器学习 （ML） 方法研究奶牛 CH4 排放预测的论文，并提供英文版本。初步检索返回了 299 篇论文，其中 55 篇符合纳入讨论的条件。来自 55 篇论文的数据是通过探索的 CH4 发射预测方法综合的，包括机理建模、经验建模和机器学习。发现机理模型非常准确，但它们需要难以获得的输入数据，如果不精确，可能会产生误导性结果。相比之下，经验模型仍然更加通用，但在其原始发展范围之外应用时会受到很大影响。商业奶牛场的 CH4 排放预测可以使用任何方法，但是，他们使用的性状必须在商业农场环境中获得。 牛奶脂肪酸 （MFA） 似乎是正在研究的最受欢迎的商业可及性状，然而，基于 MFA 的模型产生了矛盾的结果，应该在获得稳健的准确性之前进行整合。ML 模型通过各种高级算法为预测奶牛 CH4 排放提供了一种新方法，并且可以通过杂交或堆叠技术促进异构数据类型的组合。除此之外，它们还提供了通过插补策略提高数据集复杂性的能力。这些机会使 ML 模型能够解决传统预测方法面临的限制，并增强对商业农场的预测。