| Sumario: | This report presents a dataset of the e-registration of actors in the agricultural production and
value chains in Democratic Republic of the Congo for assessing the adoption of innovations
and the diffusion of new technologies. Data was collected 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
the laststep, 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 3,550 observations 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. Six
categories of actors were identified: seed producers (1,565), crops producers (1,539), parboilers
(2), millers (39), traders (606) and service providers (98). On average, a farmer grows two
crops. The main crops of farmers are beans (1,017) followed by maize (919), potato (359),
cassava (325), rice (284) and soybean (203).
The dataset is valuable for the diffusion of a large scale of improved technologies and 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 studies of rice value chain actors
and others crops such as maize, cassava, soybeans, sweet potato, banana and beans. Because of
the large number of observations (3,550 actors), data can be used as sampling frame for further
experiments 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.
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