Farmer versus Researcher data collection methodologies: Understanding variations and associated trade-offs

Abstract

The number of non-experts (such as farmers) participating in research activities has increased over the years, with the aim of them addressing their heterogeneous conditions. The situation has resulted in them being engaged in data collection through a process called crowdsourcing. The study examined the level of variation between data sets and the conclusions drawn from data collected using researcher (expert) and farmer (non-expert) methodologies, and also determined the associated trade-offs for using either methodology. The results showed a low convergence between individual observations of the methodologies on most variables with coefficients ranging from |0.39| to |0.60|. However, there was stronger convergence in the conclusions drawn when the results were aggregated (r>|0.80|) for all the variables tested in this study. Therefore, expert and non-expert data were equivalent for average results. However, data may not be comparable for understanding variations in technology performance due to lack of precision in the subjective assessments of farmers relative to the objective measurements of the researcher