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
