Assessing the extent and determinants of adoption of improved cassava varieties in south-western Nigeria

Abstract

This paper investigates the determinants of adoption of improved cassava varieties in south-westernNigeria. The data come from a farm household survey of 841 households selected using a three-stage stratified random sampling procedure. The data collection was conducted in 2011 by the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. Empirical estimates of a Double-Hurdle model revealed that adoption increases with the age of the household head and is influenced by the gender of the household head, hired labour, cultivated land, and access to credit. The results further showed that the intensity of adoption is influenced by hired labour and farm size; access to information about the improved cassava varieties is determined by the age, gender, and level of education of the household head, and by off-farm income.

Perception and determinants of agricultural technology adoption in North Shoa Zone, Amhara Regional State, Ethiopia

“…On the contrary, farmer's age may negatively influence the adoption of agricultural technologies. It may be that older farmers are more risk averse and less willing to access information about new technologies since older farmers may incur higher search costs for the new technologies; hence lack information on the existence of new and improved agricultural technologies (Awotide et al, 2014;Milkias & Abdulahi, 2018;Simtowe et al, 2016).…”

Drivers of adoption and impacts of an eco-friendly agricultural technology in Bangladesh

“…Several factors, such as farmers' characteristics and technological, institutional, and economic factors, may influence adoption decisions (Mendola 2007;Awotide et al 2014)…”

“…. The selection of the explanatory variables for this study was based on the previous literature on agricultural technology adoption and priori expectations(Simtowe et al 2011;Awotide et al 2014;Mmbando and Baiyegunhi 2016;Schreinemachers et al 2016;Ma and Abdulai 2018;Rahman et al 2018;Danso-Abbeam et al 2019;Issahaku and Abdulai 2019;Rahman and Barmon 2019). A description of the explanatory variables used in our models is provided in Table1.…”

Determinants of Small-Scale Mechanization for Potato Farming: A Case from Bangladesh

“…The adoption of new technology is generally influenced by different factors, such as farm and sociodemographic characteristics, economic factors, information transformation mechanisms, and institutional factors (Mendola 2007, Awotide et al 2014. The selection of the explanatory variables for this study was based on the previous literature and prior expectations (Rahman et al 2011, Aryal et al 2019.…”