Searchable dataset on state-of-the-art agricultural greenhouse gas mitigation measures detailing their potential contribution to emissions abatement and existing gaps in knowledge

This comprehensive dataset presents the quality-controlled (QC) results of a systematic review based on peer-reviewed literature and relevant ‘grey’ reports to address the question ’can the agricultural sector in the UK reduce, or offset, its direct agricultural emissions based on existing evidence?’. We considered the different implications of mitigation measures in terms of food security, energy, environmental degradation, and value for money related to the mitigation measures. To do so, we followed the Collaboration for Environmental Evidence (CEE) guidelines to create our systematic review. The search included different online scientific databases (e.g., Web of Science Core Collection; Scopus) and specialist websites of relevant UK organisations (e.g., Department for Environment and Rural affairs (Defra) (http://defra.gov.uk/); National Farmers' Union (NFU) (https://www.nfuonline.com/)). We used the search terms within three categories (activity (e.g., ‘arable crops’, ‘pasture’), intervention (e.g., ‘practice’, ‘measure’), and outcome (e.g., ‘carbon footprint’, ‘greenhouse gas emissions’), which were combined using the Boolean operator “OR”. However, we combined the three categories into a search string using the Boolean operator “AND”. The temporal boundary of the literature search included relevant data published between 2017 and 2022. The geographic boundary focussed on UK-specific literature; however, studies which covered global scale, including the UK, were also considered and only UK related data was assessed. Article screening was evaluated for relevance based on the eligibility criteria at three levels: title, abstract and full text, using the systematic review software Rayan. Eligible studies were subject to a critical appraisal. We assessed study validity and categorised relevant studies as “validated”, “not validated” and “unclear validity” (the latter could also be considered ‘inconclusive’). Validity criteria included both susceptibility to bias (internal validity: study design, strength of evidence and reliability/replicability) and relevance of the study for our review questions (external validity). A study was categorised to be ‘unclear’ if it did not report sufficient details to judge its validity (e.g., vague methodological description or it is difficult to interpret the efficacy of the mitigation measure). We retained 53 relevant studies covering several agricultural management practices and technologies which can be deployed on far