# 4. Factors associated with the adoption of inputs

It is well established that sustained agricultural growth, paired by an increase in the productivity of the sector, is a necessary condition for a successful structural transformation and for the reduction of poverty. Augmented agricultural productivity is closely linked to the increase in the adoption of improved or modern agricultural inputs, as was the case of the Indian Green Revolution, and to a lesser extent, in countries of Latin America (McCarther and McCord 2017). While in other countries of Sub‐Saharan Africa the use of fertilizer and other agro‐chemicals is higher than often acknowledged, this is not the case in Uganda (Sheahan and Barrett, 2017).

In this section, we shed some light on other factors, besides financial returns, that influence agricultural input use by smallholder farmers in Uganda. We first briefly review the literature on agricultural input use, drawing lessons for the Uganda context. Then, we use the UNPS panel data set, described in Section 2 and spanning 8 years, to assess what factors are correlated with input use. More specifically, we look at the degree to which access to markets, weather conditions and prices affect input use, and also assess how usage rates correlate with extension services and certain household/farm characteristics.

## _4.1 What does the literature on adoption tell us about what constrains adoption?_

The economics literature analyzing the constraints to agricultural technology adoption in a smallholder setting has evolved from studying a farmer’s production function ‐primarily in the US‐ to understanding the limitations that farmers in less developed countries face.<sup>13</sup> Foster and Rosenzweig (2010) provide a comprehensive overview.<sup>14</sup> Initially, studies on input adoption identified labor availability, price uncertainty and household preferences as main constraints to adoption. The first studies on technology adoption, developed within the agricultural economics literature, analyzed the production functions in the context of US agriculture. Farm size (Just and Zilberman 1983, Hennessy 1997), complementarities in adopting multiple technologies (Dorfman 1996), output price uncertainty and variability (Kim et al. 1992), and the substitution between on and off farm labor (Dorfman 1996, Diiro and Sam 2015) were the major predictors of adoption among 13 Here technology refers to both agricultural techniques, such as soil and water management, and improved agricultural inputs: seeds, fertilizer, and pesticides.

14 Other broad reviews include: Sunding and Zilberman (2010), who consider institutional as well as technological innovations in promoting adoption; Sanders and Shapiro and Ramaswamy (1996) provide an overview of technology development for improved agricultural growth in semi‐arid Sub‐Saharan Africa over the previous 3 decades, recommending adoption of intensive technologies in conjunction with water retention techniques and fertilizer application; Rogers (1995) provides an overview of network characteristics aiding diffusion, and Hall (2005) reviews the complementary skills and capital goods needed for adoption to stick; Mullaly et al (2013) review empirical randomized trials regarding technology adoption; Jack (2011) studies specific market inefficiencies (land, labor, credit, risk, input and output markets) preventing adoption and Maertens and Barrett (2012) provide an overview of social learning studies and technology adoption; and De Janvry et al (2016) provide an overview of field experiments studying technology adoption within each of these topics.

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