E-registration and spatial referencing and tracking of farmers in three countries: Case study of Ghana, Nigeria and Cote d’Ivoire

This report presents a dataset of the e-registration of farmers in Ghana, Nigeria and Cote d’Ivoire for assessing the adoption of innovations and the diffusion of new technologies. Data were collected from actors after a census conducted in three steps. In the first step, main crops production regions and value chain actors were identified. In the second step, we updated the list of actors based on membership of actors’ associations. In third step, we did the census of all individual actors and geo-localized all farmers’ fields and villages using GPS device. Data were collected for the 2022 growing seasons and the dataset contains 15,285 observations (5,041 in Cote d’Ivoire; 5,039 in Ghana and 5,205 in Nigeria) with 159 variables divided into six sections: (i) preliminary information on the respondents; (ii) socio-economic characteristics; (iii) information on the rice plots; (iv) knowledge, use and access to rice varieties; (v) knowledge, use and access to agricultural equipment and methods; and (vi) information on post harvest activities. Seven categories of actors were identified: seed producers (542), crops producers (13,145), parboilers (289), millers (173), traders (1,323) and service providers (442). On average, a farmers grow two crops. The main crops of farmers are rice (4,087) following by maize (1,782), cassava (1,595), cocoa (1,256) and beans (1,240). The dataset is valuable for the diffusion at large scale of improved technologies and an effective monitoring of the dissemination. Data can be used by scientists to have better understanding of crops value chains, production systems, the level of knowledge, accessibility and adoption of improved rice varieties and agricultural technologies, for further research regarding rice value chain development, technologies testing and socioeconomics study of rice value chain actors and others crops such as maize, cassava, cocoa, et beans. Because of the large number of observations (15,285 actors), data can be used as sampling frame for further experiment or surveys based on random samples. Moreover, the dataset has the potential of generating descriptive statistics at the most disaggregated level of administrative units or villages for different equipment, methods and varieties adopted by gender and country.