Abstract Background Reducing greenhouse gas emissions is a vital step in limiting climate change and meeting the goals outlined in the COP 21 Paris Agreement of 2015. Studies have suggested that agriculture accounts for around 11% of total greenhouse gas emissions and the industry has a significant role in meeting international and national climate change reduction objectives. However, there is currently little consensus on the mechanisms that regulate the production and assimilation of greenhouse gases in arable land and the practical factors that affect the process. Practical advice for farmers is often overly general, and models based on the amount of nitrogen fertiliser applied, for example, are used despite a lack of knowledge of how local conditions affect the process, such as the importance of humus content and soil types. Here, we propose a systematic map of the evidence relating to the impact on greenhouse gas flux from the agricultural management of arable land in temperate regions. Methods Using established methods for systematic mapping in environmental sciences we will search for, collate and catalogue research studies relating to the impacts of farming in temperate systems on greenhouse gas emissions. We will search 6 bibliographic databases using a tested search string, and will hand search a web-based search engine and a list of organisational web sites. Furthermore, evidence will be sought from key stakeholders. Search results will then be screened for relevance at title, abstract and full text levels according to a predefined set of eligibility criteria. Consistency checking will be employed to ensure the criteria are being applied accurately and consistently. Relevant studies will then be subjected to coding and meta-data extraction, which will be used to populate a systematic map database describing each relevant study’s settings, methods and measured outcomes. The mapping process will help to identify knowledge gaps (subjects lacking in evidence warranting further primary research) and knowledge clusters (subjects with sufficient studies to allow a useful full systematic review), and will highlight best and suboptimal research methods.