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 modelling 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 which investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning 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 synthesised by the CH4 emission prediction approach explored, including mechanistic modelling, empirical modelling, and machine learning (ML). 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 utilise 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 hybridisation 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) 排放量既复杂又昂贵。预测模型根据代理信息估算 CH4 排放量，提供了一种可行的替代方案。本综述涵盖了预测奶牛 CH4 排放量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。遵循系统审查和荟萃分析首选报告项目 (PRISMA) 规定的指南； Scopus、EBSCO、Web of Science、PubMed 和 PubAg 均被查询，以查找标题包含与“牛”种群相关的搜索词、“统计分析或机器学习”曝光以及“甲烷排放”结果的论文。检索于 2022 年 12 月执行，未设定发布日期范围。符合条件的论文是那些通过统计或机器学习方法研究奶牛 CH4 排放预测的论文，并且有英文版。初步检索返回 299 篇论文，其中 55 篇符合纳入讨论的条件。通过探索的 CH4 排放预测方法综合了 55 篇论文的数据，包括机械建模、经验建模和机器学习 (ML)。人们发现机械模型非常准确，但它们需要很难获得输入数据，如果不精确，可能会产生误导性的结果。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，但它们使用的特征必须在商业农场环境中可以获得。 乳脂肪酸 (MFA) 似乎是正在研究中最受欢迎的商业性状，然而，基于 MFA 的模型产生了矛盾的结果，应该在获得可靠的准确性之前进行整合。机器学习模型通过各种先进算法提供了一种预测奶牛 CH4 排放的新颖方法，并且可以通过杂交或叠加技术促进异质数据类型的组合。除此之外，它们还提供通过插补策略提高数据集复杂性的能力。这些机会使机器学习模型能够解决传统预测方法面临的局限性，并增强对商业农场的预测。