Here we examine the determinants of technology adoption in maize systems to understand the social dimension of technological change (e.g., “who is left behind?”) (Sub-section 4.1), and to assess the scope and limitations of the current approach in capturing the social dimension (Sub-section 4.2). Furthermore, the potential to employ qualitative methods to study the adoption process is explored (Sub-section 4.3). However, as a preamble to this synthesis, a brief description of the nature of the reviewed adoption studies is provided. Four prominent patterns are found interesting, which are depicted in Fig. 3a–d.
One, a drastic increase in the number of adoption studies.
Before 2010, less than five adoption studies were published annually on technological change in maize. After 2015, however, the minimum number of papers published in any year was 15 (Fig. 3a). We will assess in the subsequent sections to see whether this proliferation enabled a better understanding of the technological change in the maize systems of the Global South.
Two, oversimplification of the adoption process.
More than 50% of the studies assessed the binary nature of the adoption process, either in isolation (e.g., with logit model) or jointly with other managerial decisions (e.g., with multivariate logit) (Fig. 3b). One of the reasons for the high use of binary adoption variables is that a significant share of these studies (34%) estimated adoption as a necessary precursor of impact evaluation. The problematic approach of treating adoption as a simple dichotomous process was raised by Feder et al. (1985), and the critique has persisted ever since (Bunclark et al. 2018; Glover et al. 2019).
Three, a high regional concentration of adoption studies.
Most of the reviewed studies were conducted in Africa, particularly in Kenya and Ethiopia (2–3 studies published per year per country) (Fig. 3c). The high regional focus could be due to the targeted R&D investment that prompts research projects to focus on areas where maize production has high livelihood implications, and the research environment is congenial (Ouma et al. 2014; Mathenge et al. 2015). There has been a perceivable gap in understanding the constraints Asian and Latin American farmers face in maize production and marketing.
Four, an expansion of sample size over time.
During 2007–2008, about 300–400 observations were included per adoption analysis. During 2017–2018, however, the average sample size increased to more than 1000 observations (Fig. 3d). The increased sample size is expected to have significant implications on both analytical rigor and the generalizability of the observed patterns. An increase in both number of publications per year and the sample size indicates increasing investment in socioeconomic research in maize in Africa. The effects of this phenomenon on policy formulation, however, remain unclear.
We found that in the adoption literature, five individual technologies have been popularly studied (Fig. 4a). Varietal improvement is the most popular technology, and this was studied in more than 60 papers. The adoption of chemical fertilizers and soil and water conservation technologies was the topic of empirical investigation in 44 studies and 40 studies, respectively. The other major technologies were Conservation Agriculture and organic manure application. The change in the prominence of the technologies in adoption studies over the years is shown in Fig. 4b. While there has been a marked decline in the prominence of chemical fertilizers and no big changes in that of improved varieties in the adoption literature, the natural resource conservation technologies have been growing in academic popularity. The possible reasons behind this pattern are provided in the Stripe Review of Social Sciences in the CGIAR (CGIAR Science Council 2009).
We further examined whether the type of technological interventions determines the econometric approach used in the adoption studies. The technologies were classified into seed-based, managerial or agronomic, and others. The researchers used one of the three groups of econometric approaches in adoption analysis—single/independent binary dependent variable models, models with multiple interrelated binary dependent variables (MIBDV), or single/independent non-binary dependent variable models. From the 137 reviewed studies, we obtained 357 models for examination (i.e., 2.6 models per study). The Pearson χ2 test showed that the type of econometric models used in a study is strongly associated with the type of interventions addressed (Table 1). Technologies under the managerial or agronomic category were evaluated more frequently than seed-based and other technologies in all three model categories. However, the MIBDV models were particularly popular to study the adoption of managerial interventions (Table 2). One of the possible reasons is the composite nature of these interventions, comprising several individual components, of which a large share of farmers adopts only a few. A typical example is Conservation Agriculture, which comprised three components—permanent soil cover by crop residues or cover crops, direct planting of crops with minimum soil disturbance (no-till), and crop rotation (Pittelkow et al. 2015). Farmers often adopt one or more of these components due to various reasons, including the constraints to accessing complementary inputs (Giller et al. 2015). The adoption analyses of Conservation Agriculture use models to account for the adoption of technology components. For example, Tambo and Mockshell (2018) examined farmer adoption of Conservation Agriculture in maize in SSA using the multinomial logit regression framework, as only a minority adopted all three components in combination.
Table 2 also shows that, over the years, the use of models with MIBDV gained prominence in adoption literature. On the other hand, the use of models with single non-binary dependent variables was declining. Technically, these models can also be used to analyze adoption intensity. However, the advantage of MIBDV is that one can trace out the reasons for the non-adoption of a given technology component, and this is not possible with the single-equation models, such as Poisson regression models.
The reviewed studies that estimated adoption models as the first stage of impact assessment were mostly dependent on (single or multiple) binary dependent variable regression models (Table 2). This pattern is unsurprising as there is an array of econometric models, such as treatment-effect models, which require technology adoption to be estimated as a binary variable (Wooldridge 2003; Diagne and Demont 2007; Loevinsohn et al. 2013). Finally, we also found that the selection of the econometric framework varies based on the study country (e.g., studies conducted in Ethiopia use MIBDV models). Socioeconomic researchers working on maize technology adoption form a small, interconnected group who repeatedly use certain modeling approaches.
Determinants of SIA adoption: who is included and who is left behind?
The social dimension of sustainable intensification has multiple aspects (such as labor rights, animal welfare, social inclusion, and equity), but they are rarely examined, as indicated by Garnett and Godfray (2012). Through this adoption review, we examine the social inclusiveness of SIA R&D interventions. Often, the adoption studies, not only those concerning maize, attribute non-adoption or dis-adoption of technology solely to farm households’ characteristics. A farmer’s lack of education and awareness, the small size of the farm, and the sex and ethnicity of the household head are shown as preventing them from adopting the otherwise promising technology. These studies rarely discuss how the characteristics of the technology and the dissemination approaches should have been modified to cater to the heterogeneous demands from farmer groups, such as marginal and small farmers, women, and illiterates. Against this background, we analyzed the adoption literature on the SIA of maize systems by deviating from the conventional line of interrogation of “who is adopting the technology” to “who is excluded from the technology interventions.” Such a change in perspective has high significance; several farmer households do not voluntarily become non-adopters and dis-adopters, but their socioeconomic environment does not facilitate adoption.
The most important determinants of adoption frequency and intensity of SIA technologies are shown in a Word Cloud (Fig. 5a). Further variable details are given in Supplementary Tables S1 and S2. Access to information about farming was one of the most frequently used variables, appearing statistically significant in 64% of the studies, followed closely by landholding size (55%), farmer’s age (53%), and education (51%). Figure 5 b and c show the relative importance of variables as determinants of adoption probability (e.g., farmer adopted/did not adopt the technology on-farm, as a binary variable) and adoption intensity (e.g., area share under the technology as a continuous variable), respectively. The most prominent determinants of adoption probability are literacy (62% of models included it and showed having statistical significance), training obtained by farmers (60%), and farmers’ living conditions or their material wealth (59%). The most prominent adoption intensity variables are literacy and government support; both are statistically significant in about 60% of the adoption models.
We can interpret the absence of these factors as the constraints in the diffusion process. For different sets of SIA technologies, the major constraints are listed in Table 3. The average adoption of technologies varied from 23 (soil and water management practices) to 48% (organic fertilizers and manures). However, these figures should be interpreted with caution; in several studies, the districts and villages with project implementation were selected purposively, and hence the findings cannot be considered representative of the region. The major constraints of adoption, as per the reviewed empirical studies, were (a) limited access to quality information, (b) small landholding size, (c) inferior soil quality/fertility, and (d) farmer characteristics (younger age, lack of education, etc.). Some of these key factors are examined below, to be considered while developing the technologies and inclusive dissemination strategies.
Limited spread of useful information.
In adoption studies, lack of information access has multiple meanings: farmers’ inability to access quality extension services (Husen et al. 2017), lack of social networks as the source of information (Jaleta et al. 2016), or sometimes even the lack of a mobile phone or radio in the household (Smale et al. 2014; Kathage et al. 2016). In several of the studies reviewed, for example, Adegbola and Gardebroek (2007), Makate and Makate (2019), and de Groote et al. (2016), information access was focused as being a major limiting factor of adoption. The prominence is understandable; access to information is one of the few variables used in the adoption models that can be modified through external interventions in the short run. Despite this, many studies have not assessed this variable adequately in their analysis. Information access is a highly endogenous variable. Observed farmer characteristics, such as age and education, and unobserved characteristics, such as motivation, may affect both information access and technology adoption, resulting in biased estimations in conventional regression analysis. How the relative significance of information access would change with the endogeneity correction is difficult to answer. Nonetheless, the existing studies have indicated that access to information is systematically low among farmers belonging to socially and economically marginalized sections of society (Krishna et al. 2019a). We could deduct from the crucial role of information access in the adoption models that there is a persistent lack of social and economic inclusion of the SIA programs in the South.
Reduced access to cultivable land.
Landholding size or cultivable area owned by the farm household reflects the household’s economies of scale, wealth, and ability to bear the risk. Farmers with more land can afford to experiment with innovations in one of their many parcels of land, and farm income would not reduce significantly even if the technology fails. In the literature, the effect of landholding on adoption is highly heterogeneous: the variable influences adoption positively in 32% of cases and negatively in 17%. The constraint posed by the small landholding size for SIA diffusion appears to be highly context- and technology-specific. When we take individual technologies (Table 3), a small landholding size appears to be a major adoption constraint for Conservation Agriculture, possibly because of the relatively indivisible nature of the technology. Landholding size is also linked to information access. As famously noted by Feder and Slade (1986), extension agents in rural areas have been concentrating “on the well-to-do farmers, because their efforts were more likely to produce an immediate and visible impact and because wealthier farmers could offer them personal benefits (meals, accommodation, produce)” (p. 145).
Excluding illiterates and those with limited schooling.
Literacy—a farmer’s ability to read and write—is a binary variable that assesses a household’s human capital. A similar effect is captured by education, often a continuous variable measuring the number of years of formal schooling or the respondent’s highest grade. Education is more widely used in adoption studies than literacy, although literacy more frequently had a significant effect. About 62% of the time when literacy was included in the estimation resulted in positive and significant coefficients, whereas education had only a 30% significance rate (Table S3). The general assumption is that the greater the human capital possessed by a farmer (gained mainly through schooling), the more likely they seek innovation and adopt technologies (Murage et al. 2011). There are not many studies that showed education as having a negative effect on technology adoption. Education (and other human capital variables) could often be positively correlated with family wealth, credit access, and thereby the ability to obtain working capital. Literacy and level of schooling are low in smallholder farming communities of developing countries due to the labor required for housework and work for the family business (Webbink et al. 2012). Women and other marginalized sections of the farming community generally have smaller landholdings, and they tend to complete fewer years in school and have relatively lower levels of literacy (Boissiere 2004). The endogeneity bias and correlations with material wealth may need to be rectified to get the precise impact of education and literacy on adoption. Moreover, specifying literacy as a binary variable is often inadequate for representing the human capital as it does not indicate how well the respondent can read, write, or communicate.
Mostly, farmers who are well educated and have a sizeable plot of land, and belong to socially non-marginalized groups, adopt first and thereby reap a greater proportion of the economic rent from innovation adoption (also known as Schumpeterian rents, which are temporarily available to early adopters of technology). This phenomenon might result in increased economic inequality in rural areas and prevent achieving the stated goals of R&D interventions such as poverty reduction. How can we ensure that diffusion of the technology does not exclude the socially and economically marginalized? Targeting can be a powerful tool to help researchers and extension agents avoid the accumulation of Schumpeterian rents through framing effective and inclusive scaling strategies. Several studies (e.g., Carter et al. 2019, Koppmair et al. 2017, Ricker-Gilbert and Jones 2015) have shown that a temporary subsidy for technologies aimed at agricultural production and storage could create a lasting impact on adoption. Purposive selection of farmers from the marginalized sections of agrarian society for providing technical training also is an effective instrument for targeted development (Van den Berg and Jiggins 2007). Government subsidies also ensure high rates of adoption through nudging and removing the constraint of working capital (Fisher and Kandiwa 2014). These factors reflect the relevance of trusted institutional backing in fostering agrarian development. To contribute to the literature on inclusive development, adoption studies need to incorporate the social dimension of technological change explicitly, strive to address the unique problems faced by the marginalized, and devise and test novel targeting strategies.
The dimension of social and economic inclusion in the technology diffusion process has been addressed only rarely in the adoption literature, despite its high relevance in the R&D initiatives. The notion of inclusive development is becoming increasingly popular in both academic and policy circles, especially with Agenda 21 and the Sustainable Development Goals (SDGs) (United Nations 2017). The small set of studies includes Ghimire and Huang (2015), which examined the effect of household wealth on the intensity of adoption and the use of improved maize varieties in Nepal. Similarly, Langyintuo and Mungoma (2008) estimated adoption models for improved, high yielding maize varieties after stratifying households into poor- and well-endowed groups based on their access to productive assets. With respect to gender analysis, adoption decisions were examined against gendered roles and responsibilities by Theriault et al. (2017), Fisher and Carr (2015), Murage et al. (2015), and Ndiritu et al. (2014) in SSA. Most of these studies, however, did not question the implicit assumption taken up by the R&D projects that SIA technologies are beneficial to all farm households, irrespective of their social and economic status. However, the yield-enhancing technologies in agriculture need not always be instrumental in accomplishing poverty reduction. According to Lowder et al. (2016), most of the 570 million farms in the world are small, and the average farm size in most low-income countries decreased during 1960–2000. Gassner et al. (2019) argued that the small size of land available to several of these households limits the amount of increase in the per capita agricultural income through technology adoption, in order to allow people to move out of poverty. According to Harris and Orr (2014), several of these technologies are only advantageous to farm households with an adequate financial and natural resource base. Dorward (2009) criticized the productivity-focused R&D strategy of agricultural projects as highly ineffective in accomplishing rural development. Mausch et al. (2018) argued that a greater understanding of poor smallholders’ aspirations is required for inclusive development. However, targeting the poor is not confined to technology development/dissemination, and policies and programs also need to implement a stronger emphasis on providing an enabling environment for smallholders to change. More theoretical and methodological research is needed in this connection.
Another limitation of the current body of literature on adoption estimation is that it relies excessively on quantitative models and is largely incapable of facilitating a clear understanding of the complex social context against which technology dissemination takes place. This aspect is further explored in the next sub-section.
The scope and limitations of the current approach to studying SIA adoption
Feder et al. (1985) outlined the conventional methodology of adoption studies, which consists of the representation of adoption decision with a binary variable, selecting a number of potential explanatory variables, and testing the statistical relevance of each of these variables with logistic or probit regression models. Even three decades after the publication of this seminal review paper, the approach of adoption studies in the agricultural field has not changed significantly. Technological changes are still depicted by relatively simple farmer choice that can be represented by a dichotomous variable, often overlooking the dynamic learning process and preferences of farmers that are shaped by their sociocultural context (Krishna et al. 2019b). Many adoption studies inherently assume technological change to be the replacement of old, inferior practices with new, superior ones and are for this reason inherently incapable of addressing the processes of adaptation, creolization, hybridization, and incorporation (Douthwaite et al. 2001, 2003; Glover et al. 2016). The notion that adoption is a continuous process governing the utilization of innovations (Sunding and Zilberman 2001) is crucial for developing a comprehensive analytical framework. Assessment of adoption as a continuous process requires data over a longer period, and currently, most adoption studies conducted in maize systems use cross-sectional datasets, done mostly in the context of ongoing R&D projects. Despite the proliferation of quantitative adoption studies in agriculture and a general increase in sample size over time, this situation has remained unchanged, possibly due to the persistent desire of donors to immediately document and report the effective use of funds back to their constituencies (Krishna et al. 2019b). This has resulted in adoption studies that provide limited insights for policymakers and are inadequate to capture adoption dynamics for in-depth analysis and further improvisation of dissemination strategies in the Global South.
A large share of the empirical studies included in this review carried out adoption analysis merely as a prerequisite for impact assessment (Krishna et al. 2019b). A typical example of this genre is Lunduka et al. (2019), in which the authors examined farmer adoption of drought-tolerant maize varieties in Zimbabwe as a dichotomous variable and used this as the initial step to estimating technology impacts. While this approach has merit, these studies often are unable to provide intricate details on farmers’ decision-making processes and end up overlooking the complexities of adoption decisions. Many other studies have analyzed varietal adoption relatively deeply, looking at farmer awareness and preferences as key determinants. For example, Kassie et al. (2017) recognized the importance of farmers’ perceptions in determining the adoption of drought-tolerant maize varieties and estimated the average implicit price that farmers were willing to pay for drought tolerance as a varietal trait. Nevertheless, not many studies were conducted on the role of perceptions in the adoption of non-varietal technologies in maize systems. A notable exception is a study by Murage et al. (2015), which evaluated gender-specific perceptions and the extent of adoption of a climate-smart technology in SSA.
Recently, the tradeoffs and complementarities between different sustainable technologies have received significant attention. These studies captured farmer adoption of several technologies and modeled them using multivariate probit or multinomial logit models. Abay et al. (2018) and Koppmair et al. (2017) are two representative studies, which have proven the existence of strong complementarities among technologies; the policies that affect the adoption of one technology may influence (favorably or adversely) the adoption of another. Such analyses have highlighted the importance of disseminating the improved inputs and practices together as a “package” instead of promoting them in isolation.
Socioeconomic researchers may put a greater emphasis on the lesser-studied but socially relevant aspects of technology diffusion, such as differential access to production resources and resulting economic inequalities as well as changes in sociocultural values (Albizua et al. 2019). Contrary to the widely held belief, diffusion of disembodied innovations may not always be inclusive. Furthermore, the adoption of some of the disembodied innovations requires coupling with embodied innovations (e.g., pre-emergent herbicides needed for the adoption of Conservation Agriculture). The adoption of the latter is determined by multiple binding constraints on the part of farm households and factor market imperfections (Shiferaw et al. 2015). The existing resource inequalities could affect the diffusion of agricultural innovations (Zeng et al. 2015), and more importantly, even an effective technology diffusion process could worsen the existing inequalities if not targeted.
There has been little effort made to investigate the relevance of technology attributes themselves in determining scaling-out patterns. Most of the existing adoption models focus on farmer attributes and omit the technology traits altogether (Krishna et al. 2019b). Technology fitness—the degree to which the attributes of technology favor its adoption and use—gains greater importance as technology and production systems become more complex (Douthwaite et al. 2001). Similarly, understanding the adoption problem from a system perspective is highly warranted. As observed by Glover et al. (2016), oversimplification of research problems in adoption studies to make them amenable for econometric analysis often provides an inaccurate and misleading picture for the policymakers and fellow researchers. The criticism of Conservation Agriculture studies in southern Africa by Andersson and D’Souza (2014) is valid in this connection: “Current [Conservation Agriculture] adoption studies are methodologically weak, as they are biased by the promotional project context in which they are carried out and build on farm-scale analyses of standard household surveys. [...] As contextual factors appear key influences on smallholders’ farming practices, studies focusing on the wider market, institutional and policy context are also needed” (p.116).
The potential of qualitative methodology in assessing technology change
Most of the empirical research on the adoption of farming technologies has taken place within the disciplinary boundaries of agricultural economics, which generally relies on a quantitative toolset (Debertin and Pagoulatos 1992; Swinton 2018). The persistent scarcity of qualitative adoption studies over the last several decades has been attributed to an intense focus on validating the relationship between variables in agricultural economics (Swinton 2018). The scarcity with respect to the number of qualitative studies, however, does not correspond to their potential to assess the technological change in agrarian communities. For instance, narratives and case studies are found to have great value while assessing the success of the R&D programs (Douthwaite et al. 2003), inclusivity in accessing technology or resource (Panta and Resurrección 2014), the outlier problem (Peiffer and Armytage 2019), etc. Through identifying the underlying reasons that are not amenable for measurement (e.g., feelings, norms), the qualitative studies help researchers obtain greater insights on observing (or, rather, not observing) the expected patterns in adoption.
This review covered the quantitative adoption studies primarily for two reasons. Firstly, several of the qualitative studies on technology change are not crop-specific; they either assess some of the system attributes (e.g., the R&D system associated with a given technology) or invest in studying a dominant issue or theme in the agrarian society (e.g., gender inequality). Secondly, when we searched for qualitative studies conducted in cropping systems with maize as a component that had eluded our previous search, only a few new studies were found (n = 14; listed in Supplementary Table S4). They covered a wide array of topics and followed a diverse set of investigative approaches. For example, Place et al. (2007) focused on the role of poverty in determining the technology change, whereas Gouse et al. (2016) examined the gendered preferences behind technology adoption. As compared to most quantitative studies in maize, the qualitative ones provided a deeper understanding of the adoption process, such as partial adoption (Grabowski and Kerr 2014) and institutional factors shaping adoption (Andersson and D’Souza 2014). Most of these studies have depended on quantitative analysis, in addition to the qualitative tools, to derive insights on the adoption process, which shows that these studies did not altogether devote to a different epistemology for investigation. Nevertheless, the scarcity of qualitative studies on technology change, alongside diversity in their approaches, makes their selection a challenging assignment, which is beyond the scope of the current review. Besides, due to the small number of observations (median of 40 observations/study) and non-random selection of respondents, the obtained patterns cannot be generalized for the entire farming system or study region, unlike in the case of most quantitative studies.
Many of the reviewed qualitative studies generated valuable insights into the technology change in maize systems in their respective study areas. However, they fell short of a clear definition of technology and the adoption process, which is fundamental for enabling a comparison between the studies. Our review might have omitted some qualitative evaluations that did not explicitly state maize as a major component in the cropping system while detailing the technology. Qualitative studies could be made more accessible and comparable by providing greater insights on the nature of the technology and the adoption process. Systematic and rigorous use of mixed methods, which present an opportunity to gain from strengths of both qualitative and quantitative methodologies, would be highly advantageous (Johnson and Onwuegbuzie 2004). For example, there is a high potential to use qualitative comparative analysis (QCA) to study technology change in agriculture, especially at the meso-level (e.g., village-level) as the number of observations at this level is generally too low to establish meaningful associations quantitatively. Although the strategic relevance and practical implications of the mixed approaches are discussed widely in the literature (Bryman 2006; Creswell and Clark 2017), they are yet to gain popularity to assess technology dissemination in agriculture.