Is there a joint lever? Identifying and ranking factors that determine GHG emissions and profitability on dairy farms in Bavaria, Germany

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.