Information acquisition and the adoption of improved crop varieties

Abstract

Inadequate information on the benefits of and knowledge about innovative agricultural technologies continue to be a major constraint to technology adoption among smallholder farmers in developing countries. The low adoption of new technologies is one of the causes of low productivity and high poverty incidence among smallholder farmers, particularly in Africa. In this paper, I briefly review the literature on social networks and technology diffusion, and argue that the diffusion potential of social networks is underexplored. I then present results from two empirical studies on the impact of social networks on the adoption of improved crop varieties in Ghana and Ethiopia. The results reveal that farmers' peer adoption decisions and experiences, as well as information from trained development agents positively and statistically influence their adoption decisions. I also find that network structural characteristics such as lower segmentation within networks, high credibility of the information, and high effectiveness and efficiency of the amount of information flow tend to improve information acquisition and speed up diffusion of improved crop varieties.

Accessing the effect of smartphone agricultural applications on land transfer: evidence from Sichuan province in China

“…, 2021; Che et al. , 2022; Abdulai, 2023), we measure peer effect by the average share of SAAs, using farm households except for the household under consideration ( n −1), in a given community. We compute peer effects as n /( m −1), where n represents the number of a farmer's peers using SAAs in the given community, and m represents the total number of households surveyed in that community.…”

“…According to peer effect theory (Sampson and Perry, 2018), an individual's behavior is affected by their peer's behavior. Following previous studies (Steinh€ ubel et al, 2020;Kolady et al, 2021;Che et al, 2022;Abdulai, 2023), we measure peer effect by the average share of SAAs, using farm households except for the household under consideration (nÀ1), in a given community. We compute peer effects as n/(mÀ1), where n represents the number of a farmer's peers using SAAs in the given community, and m represents the total number of households surveyed in that community.…”

“…A farmers' decision to adopt the SAA is likely to be affected by the SAAs use status of their neighbors. This is due to the tendency for farmers to either mimic their peers or through information sharing, they are influenced to adopt SAA (Abdulai, 2023). The information sharing or mimicry on SAA is less likely to directly influence the farmer's decision to transfer in land or the willingness to make such decisions in the future [1].…”

Information Acquisition Ability and Farmers’ Herd Behavior in Rice–Crayfish Coculture System Adoption

“…This provides them with different information and knowledge to feed their requirements [48][49][50]. Studies have shown that both quantity (number of information sources) and quality of information influence the adoption of new technologies and sustainable agricultural practices [51][52][53].…”

Variability in Morphological, Biochemical, and Proximate Yield Composition among Predominant Amaranthus hybridus Cultivars in South-West Nigeria

“…Among the many factors contributing to food insecurity, malnutrition, and low socio-economic index among rural farmers are inadequate farm inputs, low irrigation, climate change, land degradation, and obsolete planting methods [19]. Additionally, the cultivation of underperforming cultivars due to the lack of access to superior varieties exacerbates these challenges [20,21]. However, an often underestimated yet significant factor limiting yields on A. hybridus farms is the inadvertent mixing of seeds from different varieties, each with different yield capacities.…”