Cassava farming practices and their agricultural and environmental impacts: a systematic map protocol

Abstract Background Cassava (Manihot esculenta) is a staple crop that is important for food security in the tropics. However, cassava farming can have severe environmental impacts, such as habitat destruction and soil degradation, if it is not carefully managed. Therefore, a wide range of agricultural and environmental outcomes should be considered when cassava farming practices are recommended as “good agricultural practices”. We propose a systematic map of research on cassava farming practices and their impacts on yield, quality, profitability, soil, water, wildlife, pathogens, pests, weeds, and other agricultural and environmental outcomes. This map will improve our knowledge of the multifunctionality of cassava farming practices, by answering several questions: Which studies have measured the impacts of cassava farming practices on agricultural and/or environmental outcomes? Which practices and outcomes have been studied, in which countries, and when? Methods We will search for studies of “cassava OR mandioca OR manihot OR manioc OR yuca” in four publication databases (AGRICOLA, AGRIS, Scopus, Web of Science), two repositories of grey literature (including publications from the International Center for Tropical Agriculture and the International Institute of Tropical Agriculture, which have worked extensively on cassava), and the Conservation Evidence database. We will screen the search results using eligibility criteria that are transparently reported and consistently applied. We will not critically appraise the validity of the studies that are included in the map, because we see this map as a subject-wide evidence base that could be used for multiple methods of evidence synthesis, with different criteria for critical appraisal. We have developed a classification of agricultural practices and a classification of agri-environmental outcomes, and we will use these classifications (“taxonomies” or “terminological ontologies”) when coding studies. We have developed a web application (http://www.metadataset.com) with drop-down menus for screening and coding. We will analyse the number of studies by practice, outcome, country, and year, and we will present the results as a searchable database with interactive geographical maps (an “evidence atlas”) that will show knowledge gaps and knowledge clusters