When knowledgeable neighbors also share seedlings: diffusion of banana cultivation in Tanzania

Abstract

Networks have been found to increase adoption of technology by providing information about the new technology (e.g., Conley and Udry, 2010). However, little is known about provision of necessary inputs for adoption through networks. Using data from an intervention in Tanzania, I discuss how a farmer's network can also affect the adoption of improved banana cultivation by providing seedlings. A solidarity chain principle obliged project farmers to pass on improved seedlings to other farmers free of charge. I provide a theoretical framework to guide intuition for the empirical results and suggest an empirical distinction between information and input provision through networks. Empirically, I find support for network effects through provision of both information and inputs, jointly boosting the network effect on adoption: a farmer is 39 percentage points more likely to adopt banana cultivation if there is at least one banana grower in the farmer's network compared to none. In this setting, it is not possible to fully disentangle the input and information channel. While the findings suggest that a solidarity chain principle could be an interesting avenue to boost local diffusion of new technologies, more research is needed to unpack the causal impact and the interplay with existing input markets.JEL classifications: D83, O13, O33, Q16

Factors affecting the adoption of high technology in vegetable production in Hanoi, Vietnam

“…In developed nations, for example, Kassie et al (2015) indicated that factors like capital, social networking, and access to information are positively related to HTV adoption. Larsen (2018) also found that HTV practices adoption is positively related to education level, gender, land tenure and farm size.…”

“…Lambrecht et al (2014) added social network factors to the factors affecting the application of technology. Although there are many ways to categorize factors to determine the application of technology, the classification depends on the current technology being studied, the location, and the researcher's interest in choosing the suitable study (Larsen, 2018). In this study, we combine factors from the TPB and other personal, social and economic factors of households identified in previous studies to analyze determinants of high-tech vegetable production in Hanoi.…”

Demand and supply side factors for accelerating varietal turnover: An evidence from soybean in India

“…The difficulty, however, is in the grossly inadequate supply of seed to farmers; an estimate shows that only 50 percent of the total requirement of 70,000 tons is met from all the sources, and out of this only two-fifths is supplied by government sources (Goyal 2014). This lack of seed availability obviously has the direct effect of discouraging a shift to new improved varieties; several studies show that non-availability of required seed stands in the way of adoption of newer varieties (Simtowe et al 2011;Shiferaw, Kebede, You 2008;Shiferaw 2015;Larsen 2019). The conspicuous lack of private sector varieties in the list of farmer-used varieties shows that there is practically no role for the sector in soybean breeding efforts; several studies show that this lack can hamper farmer choice in varietal turnover (Simtowe et al 2011;Kosarek, Garcia, Morris 2001).…”

“…Existing literature does not address impediments to the adoption of new technologies in soybean production despite soybeans being crucial to the augmentation of protein in the diets of vegetarians around the world; this is the case even though large number of studies examine adoption patterns related to various other crops, such as rice (Awotide, Karimov, Diagne 2016;Ghimire, Wen-chi, Shrestha 2015), wheat (Shiferaw et al 2011;Krishna, Spielman, Veettil 2016;Nazli and Smale 2016), maize (Griliches, 1957(Griliches, , 1960Nkonya, Schroeder, Norman 1997;Hintze, Renkow, Sain 2003;Becerril and Abdulai 2010;Ouma and De Groote 2011;Suri 2011;Khonje et al 2015), sorghum (Timu et al 2014), pearl millet (Matuschke and Qaim 2008), cassava (Ojiako, Udensi, Tarawali 2015), pigeonpeas (Otieno et al 2011;, cowpeas (Manda et al 2019), chickpeas (Shiyani et al 2002;Ojiako , Manyong, Ikpi 2007;Asfaw et al 2011;Michler et al 2019), common beans (Katungi et al 2011;Abebe and Bekele 2015), potatoes (Mpogole and Kadigi 2012;Abebe et al 2013), and bananas (Larsen 2019). We found only one study that analyzed drivers of adoption of new varieties of soybean, that by Ojiako, Manyong, and Ikpi (2007) in Nigeria.…”