Dataset of the survey on e-registration and geo-referenced of rice value chain actors for the diffusion of technologies: Case of Benin and Côte d'Ivoire. uri icon

abstract

  • The paper presents a dataset of the e-registration of rice value chain actors in Benin and Cote d'Ivoire for assessing the adoption of innovations and the diffusion of new rice technologies. Data were collected from actors after a census conducted in three steps. In the first step, main rice production regions and rice 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 2018 growing seasons. The dataset contains 17,639 observations (9,000 in Benin and 8,639 in Cote d'Ivoire) 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: foundation seed producers (420), certified seed producers (1,212), paddy rice producers (14,230), parboilers (1,735), millers (188) and traders (1,429). The dataset is available online at Mendeley data repository. 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 the rice value chains, rice 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. Because of the large number of observations (17,639), 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. (C) 2020 The Authors. Published by Elsevier Inc.

publication date

  • 2020
  • 2020