Abstract Farms are increasingly expected to contribute to greenhouse gas (GHG) mitigation actions to help governments to achieve GHG reduction commitments. In order to identify key mechanisms for GHG mitigation on farms, many studies use mass flow simulation or optimization models. However, by assuming “best practice” and not accounting for “real farm practices”, these models cannot predict variability between farms. In contrast, studies that include variability between farms can identify determinants that are important factors to reduce GHG emissions. From a farmer's perspective, it is often crucial that these mechanisms also increase farm profitability. The objectives of this article are (1) to explore factors that jointly affect GHG emissions and profitability of dairy farms and, (2) to assess if these factors cause synergies or trade-offs to simultaneously reduce GHG emissions and increase profitability. To assess variability between farms, we utilize detailed site- or farm-specific input variables for a large number of farms. To this end, we combined a detailed high quality dataset of 92 farms for the year 2013 in Bavaria, Germany. In relation to GHG emissions, we collected emission factors from national and international life cycle analysis databases, and applied national and site-specific GHG emission models. Our global sensitivity analysis identified five factors affecting GHG emissions per kg of fat and protein corrected milk in the following order of relative importance (i.e. proportion of farm variability explained): feed use efficiency (26%), weighted nitrogen balance (23%), site specific nitrogen emission factor (15%), milk yield (13%), and replacement rate (8%). Of these five factors, feed use efficiency and milk yield were also relatively important factors for profitability. However, milk yield is strongly interlinked with beef output, an important by-product of our sample dairy farms, and thus needs special attention when defining effective GHG reduction targets. Site-specific nitrogen emission factors cannot be influenced directly by farmers. This leaves three main determinants for farm variability between farms of GHG emissions i.e. on field nitrogen use efficiency, feed use efficiency and replacement rate. Since feed use efficiency was also identified as an important factor increasing profitability, this could be addressed by advisory services assessing synergies between profitability and GHG emissions. On field nitrogen use efficiency and replacement rate were not identified as an important factor affecting profitability and thus may be addressed by additional incentives for farmers, advisory service, or stricter regulations.

中文翻译：

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有联合杠杆吗？确定和排名决定德国巴伐利亚奶牛场温室气体排放和盈利能力的因素

摘要 人们越来越期待农场为温室气体 (GHG) 减排行动做出贡献，以帮助政府实现温室气体减排承诺。为了确定农场温室气体减排的关键机制，许多研究使用质量流量模拟或优化模型。然而，通过假设“最佳实践”而不考虑“实际农场实践”，这些模型无法预测农场之间的差异。相比之下，包括农场之间差异的研究可以确定是减少温室气体排放的重要因素的决定因素。从农民的角度来看，这些机制也增加了农场的盈利能力通常是至关重要的。本文的目的是 (1) 探索共同影响奶牛场温室气体排放和盈利能力的因素，以及，(2) 评估这些因素是否会产生协同效应或权衡，以同时减少温室气体排放和提高盈利能力。为了评估农场之间的可变性，我们为大量农场使用了详细的现场或农场特定的输入变量。为此，我们结合了 2013 年德国巴伐利亚州 92 个农场的详细高质量数据集。关于温室气体排放，我们从国家和国际生命周期分析数据库中收集排放因子，并应用国家和特定地点的温室气体排放模型。我们的全球敏感性分析确定了影响每公斤脂肪和蛋白质校正牛奶温室气体排放量的五个因素，其相对重要性顺序如下（即解释的农场可变性比例）：饲料利用效率（26%）、加权氮平衡（23%）、特定地点的氮排放因子 (15%)、产奶量 (13%) 和替代率 (8%)。在这五个因素中，饲料利用效率和产奶量也是相对重要的盈利因素。然而，牛奶产量与牛肉产量密切相关，牛肉是我们样本奶牛场的重要副产品，因此在确定有效的温室气体减排目标时需要特别注意。特定地点的氮排放因子不能直接受到农民的影响。这留下了农场之间温室气体排放变化的三个主要决定因素，即田间氮利用效率、饲料利用效率和替代率。由于饲料利用效率也被确定为提高盈利能力的一个重要因素，这可以通过评估盈利能力和温室气体排放之间协同作用的咨询服务来解决。