Lexical analysis improves the identification of contextual drivers and farm typologies in the assessment of transitions to agroecology through TAPE – A case study from rural Nicaragua

Abstract

CONTEXTThe Tool for Agroecological Performance Evaluation (TAPE) has been developed to assess the degree of transition to agroecology (Step 1) and the agroecological performance (Step 2) of family farms. However, while methods and indicators for Steps 1 and 2 are well established, the phases of system characterization (Step 0) and the delineation of typologies (Step 1-bis) are not prescribed nor standardised. This results in highly heterogeneous, unstructured and narrative information, subject to the background of the field enumerator. Analytical methods are needed to deal with these, in order to arrive at relevant domains of recommendations for policy and development. OBJECTIVEWe assessed whether lexical analysis improves the ability of TAPE to elucidate: i) how household characteristics and contextual factors influence such agroecological transitions, ii) whether a high degree of transition to agroecology (Step 1) translates into better agroecological performances (Step 2), and iii) whether data from Step 0 could improve the delineation of transition typologies. METHODSWe considered 29 rural households located in separate localities across three regions of Nicaragua and analysed the results of applying TAPE to assess their agroecological transition and performance, combining descriptive and lexical analysis. Iramuteq software was used for lexical analysis, including descendent hierarchical classification by Reinert's method and co-occurrence networks. A corpus text based on information provided by desk review and unstructured interviews during Step 0 was used for system description and identification of context variables; while a matrix of categorical variables based on data from Steps 1, 2 and 0 was used to identify agroecological transition typologies. RESULTS AND CONCLUSIONSLexical analysis allowed to summarize data, describe farm systems and identify context drivers (Step 0) and patterns from highly heterogeneous, subjective information. The assessment of agroecological transition (Step 1) and performance (Step 2) indicates that the farms studied were at least in transition to agroecology. Farm-level indicators of agroecological transition were more advanced than community-level indicators. Using Reinert's method we identified two main groups, that cluster seven classes of farms, representing different policy and development intervention targets. The differences detected between farms managed by women or men highlight the relevance of including gender perspective on TAPE analyses. SIGNIFICANCEWe show that lexical analysis was useful to deal with unstructured, narrative data describing systems and contexts, and for identifying agroecology recommendation domains based on local actors's perspectives.