In sub-Saharan African (SSA), smallholder-based agriculture is the main source of livelihood, especially in the rural areas, where most of the population live. Livelihood challenges such as poverty and hunger are prevalent among farming and rural households in the region, and they are largely attributed to low agricultural productivity [1
]. Some local non-governmental organizations (NGOs) and international development agencies are promoting organic farming as a pro-poor strategy for improving agricultural productivity and the livelihood conditions of rural and farming households on the sub-continent [2
]. They argue that organic farming management practices can help address poor soil fertility and severely degraded soils, two major underlying factors for low agricultural productivity in SSA [2
]. They also contend that organic farming can open financially rewarding differentiated domestic and international markets, with livelihood enhancing opportunities for African smallholder farmers [2
]. For excluding the use of synthetic inputs, advocates characterize organic farming as a labor-intensive, low-cost, and low-input technology, and claim that it is well-suited to the socio-economic conditions of smallholder farmers in SSA [2
]. As a result, they expect a widespread adoption of the technology by smallholder farmers in SSA [6
However, apart from Uganda, Tanzania, and Tunisia, the adoption of organic farming in Africa has been generally low, for reasons yet to be well-investigated [4
]. While Tunisia’s organic farming success story is state-driven [4
], Uganda’s and Tanzania’s are driven by strong local organic farming non-governmental organizations (NGOs) and support from international development agencies such as the Export Promotion of Organic Products from Africa (EPOPA) [2
]. Other factors that explain the status of organic farming in the three countries include the creation of domestic organic farming certification bodies, as well as locally adapted and internationally oriented organic farming regulatory frameworks/standards, organic farming export market linkage, and growing domestic markets for organic farming [2
]. The generally low adoption of organic farming in SSA may be connected to the shortage and nutrient limitations of the soil organic materials on smallholder farms in SSA [7
]; the difficulty of keeping weeds under control in organic production; the competing uses of organic matter materials as fodders for animals, as materials for building construction, and as fuel for cooking [8
]; a poor awareness of the technology; and a lack of supportive institutional environments [6
]. Through a Nigerian case study, we attempt to bridge the existing gaps in the understanding of the factors affecting the adoption of organic farming by smallholder farmers in SSA.
Over a decade ago, participatory guaranteed systems (PGS)-certified organic agriculture was introduced to farmers in southwestern Nigeria by some local non-government organizations, which include the National Organic Association of Nigeria (NOAN), the coordinating body for all organic farming stakeholders in the country. The PGS is a locally focused, group-based, and smallholder farmer-oriented quality assurance first-party certification system [10
]. It guarantees and certifies a farm to be organic through a participatory and peer-review process involving a range of stakeholders, which include NOAN, agronomists, and farmers. In January 2014, NOAN worked with stakeholders in the Nigerian organic farming sector to develop the PGS for certifying a farm and its products as organic in Nigeria [11
]. NOAN believes that the inability of most of the farmers in Nigeria to afford synthetic herbicides, pesticides, and fertilizer [12
], and the growing domestic urban market and demand for organic products, especially leafy vegetables, in Ibadan and some parts of southwestern Nigeria, will stimulate adoption [13
However, the adoption of organic farming by smallholder farmers in Nigeria has been limited and low [11
]. Except for a newsletter article [16
] and a “research report” [17
], the reasons for the low adoption status of an OA in Nigeria have not been studied. Existing studies mostly explored the market for organic farm produce [15
] and of farmers’ perceptions of practices, which are related but not unique to OAs [20
]. Thus, using a livelihood framework and a mix of qualitative data collection techniques, we studied the factors driving and constraining the adoption of an OA in Nigeria. By adoption, we mean farmers who have adopted and are still practicing PGS-certified OAs. We delimited our study to southwestern Nigeria and organic leafy vegetable production (OLVP) for the following reasons: Southwestern Nigeria is the part of the country where an OA was first introduced, and it is widely promoted and the most established, yet has low adoption rates [11
]; a high market potential exits for organic leafy vegetables in southwestern Nigeria [14
]; and OLVP is the major organic cropping system in southwestern Nigeria [23
]. We also explored the role of gender and gender-related constraints, such as control over household resources, on the adoption of OLVP.
2. Delineating and Characterizing Adoption Decision-Making
Adoption decision-making is characterized by non-linearity and complexity [24
]. According to Meijer et al. [24
], this is due to the non-linear interactions of extrinsic (e.g., innovation attributes) and intrinsic variables (e.g., knowledge of the innovation), which inform adoption decisions, and the difficulty in teasing out the interdependencies of the mediating variables. To disentangle the complexity, their study proposed a comprehensive framework that captured the interactions between the extrinsic and intrinsic variables and the adoption decisions [24
]. Nazziwa-Nviiri and colleagues attributed the complexity to the interactions of several push and pull factors associated with adoption decision-making [26
]. These include institutional and access-related variables, agroecological factors, and farm household characteristics. To Fisher et al. [27
] and Dinh et al. [28
], the complexity exists partly because the livelihood impacts of a technology cannot be determined a priori, and sometimes not even after its adoption. Framing adoption from a behavioral change viewpoint, Straub argued that it is a complex decision-making process, because it is mediated by cognitive, affective (emotional), and contextual factors, which no one theory can account for [29
]. Others ascribed the complexity to the embedding and intersection of an adoption decision environment with gendered norms and culture, differentiated access to and control over resources, and heterogeneous intrahousehold decision-making dynamics [30
]. Adding new insight, Olabisi et al. [25
] contended that adoption decision contexts are not only complex, but are also inherently dynamic, as farmers’ choices and the decisional criteria informing their choices are not static; they may change from year to year. Unlike Olabisi et al. [25
], van den Broeck et al. [33
] and Pedzisa et al. [34
] used adoption intensity to operationalize their construction of the dynamic nature of the adoption decision context. Agent-based modeling (ABM) was used to explore the dynamic complexity associated with adoption decision-making [25
]. To deal with the complexity, some studies have proposed livelihood-based multidimensional frameworks, which integrated technology attributes, the various facets of rural livelihood systems, and their institutional embedding [28
The above discussion suggests that the adoption decision-making context is inherently dynamic and complex, as it is influenced by multiple factors, which are characterized by interdependencies and non-linear behavior [24
]. This implies a need for tools that can help capture and disentangle the dynamic complexity. From the review, this could be done by using systems modelling tools such as ABM [25
], or multidimensional comprehensive analytical frameworks that account for different factors that intersect with decision-making to inform adoption studies [24
]. Appropriating insights from the foregoing, we construed technology adoption as a dynamic livelihood strategy choice made by farmers within a complex and multi-factorial decision environment, by drawing on their livelihood assets, mediated by their institutional and vulnerability contexts. We also recognized that gender per se [36
], the gendered nature of farmers’ livelihood, and intrahousehold decision-making contexts and technology-specific attributes can mediate an adoption decision [30
]. Therefore, to be able to capture and disentangle the complexity of the decisional phenomenon, akin to Meijer et al. [24
] and Dinh et al. [28
], we developed a multi-dimensionally linked gender-aware framework to inform this study, data collection, and analysis. We also draw on the literature that used adoption intensity to embody the dynamic characterization of the technology adoption process as a non-static discrete choice made by farmers [33
]. So, we defined adoption intensity as the proportion of a farmer’s cultivated land allocated to PGS-certified OLVP.
3. Conceptual Framework
This study was informed by a gender-aware livelihood framework, which is named the Technology Adoption Livelihood Assets Framework (TALAF), and was developed by drawing on existing modifications to the sustainable livelihood framework (SLF) [28
]. The TALAF retains the following constitutive elements of SLF: livelihood assets, vulnerability context, institutional and policy context, livelihood activities, and livelihood outcomes. The TALAF accounts for the intersectionality of the multidimensional the factors that are associated with adoption decision-making [38
]. Akin to the SLF, at the core of the TALAF (Figure 1
) is a pentagon of five capital assets (human, natural, financial, social, and physical capital assets), considered to be imperative to the livelihood pursuits of farming households [38
]. To develop the TALAF, we integrated factors beyond livelihood assets into the original SLF, which can affect and gender technology adoption decision-making. These include factors such as intrahousehold decision-making dynamics, culture, gendered division of labor, access to and control over resources, and technology attributes [31
]. By explicitly including components such as intrahousehold decision-making dynamics, access to and control over resources, and household head gender in TALAF, our goal was to capture gender and the power relation factors that can affect adoption decision, but that were left out of SLF [38
In developing TALAF, it was assumed that farmers’ decisions about whether to adopt a technology as a livelihood strategy choice, or not, will be made by drawing on their livelihood assets base, in anticipation of certain livelihood outcomes [30
]. The livelihood outcomes comprises a complex mix of economic and non-economic outcomes, such as improved soil fertility status, improved household food and nutritional security, expanded marketing opportunities, increased farm income, and enhanced health conditions [3
]. It was also supposed that farmers’ adoption decision-making will be mediated by their vulnerability contexts (e.g., fluctuation in prices and market opportunities and a farm’s declining profitability), the institutional factors of their embedding, and their livelihood activities (farm/off-farm) [35
It was further assumed that factors such as household composition, technology attributes, access to and control over resources, and gendered division of labor and culture will intersect with farmers’ adoption decision-making. Here, insights were appropriated from the claim that the adoption of a technology can be affected by its attributes [41
], and from the literature on the factors that gender the adoption of a technology by men and women [31
]. Following studies such as Adato and Meinzen-Dick [38
], we chose not to frame and integrate culture as a form of livelihood capital in TALAF. This is because, as aptly argued by some studies, the term cultural capital or asset is problematic, given that capital assets are economic-oriented [35
]. Consistent with our definitional delineation of adoption decision-making as a dynamic phenomenon, it was further assumed that the decision to continue using a technology, and to intensify or scale down the extent of use of the technology (adoption intensity), will be affected by the technology livelihood outcomes, including on a farmer’s livelihood assets and vulnerability contexts. This was captured in the TALAF by the double-headed arrows, connecting one component of the framework to the other. The double-headed arrows foreground the role of feedbacks and learning in this study’s conception of technology adoption decision-making. Here, the conception of feedbacks builds on the authors’ of [43
] definition, as including “the information that is obtained about the outcomes, characteristics, and/or consequences of” adopting a technology (p. 109).
4.1. Study Sites
This study was conducted at Ajibode, Akinyele, and Elekuru (Figure 2
), the three areas where PGS-certified OLVP takes place in Ibadan, Oyo State, Nigeria. They experience between 1200 to 1600 mm mean annual rainfall [44
], and a bimodal rainfall pattern (March/early April, and October/mid-November) [45
], with a brief dry spell in August [47
]. At Ajibode, an urban area, PGS-certified OLVP takes place along the bank of River-Ona (henceforth referred to as Ona), on a stretch of land behind the University of Ibadan Botanical Garden. At Akinyele, PGS-certified OLVP occurs in a peri-urban. At Elekuru, a rural setting and the remotest of our study sites, PGS-certified OLVP takes place across different swampy areas.
4.2. Respondent Selection
We collected data from (1) adopters, (2) non-adopters, and (3) NOAN officers and government organic desk officers in our study areas. Adopters are farmers that have adopted and are still practicing PGS-certified OLVP. Non-adopters are farmers who grow their leafy vegetables using synthetic inputs and have never adopted certified OLVP. Familiarization visits were undertaken to our study sites and to NOAN’s office at the University of Ibadan, during which the lists of adopters were collected and later merged. Some adopters were purposefully selected from the merged list for semi-structured interviewing, based on criteria such as gender, age, their year of adoption of organic farming, marital status, and the positions held in their organic farmer groups. The gender criterion was to help understand how the adoption of OLVP may be gendered. The year of adoption was to help ensure that respondents have a rich information on the issues to be discussed. The age consideration was to reflect the heterogeneity of the age group of the adopters’ population, especially in Ajibode, and the possible differences in the value-orientation, which may have informed adoption decisions. The inclusion of the leaders and ordinary members of the organic farmers group in Akinyele and Ajibode was to help capture disconfirming opinions on issues such as the challenges associated with the adoption of OLVP in our study areas. All of the four adopters in Akinyele were interviewed. The non-adopter’s selection was based on snowball sampling. Adopters in Ajibode and Elekuru helped to identify non-adopters, who, in turn, introduced us to other conventional leafy vegetable farmers in their areas. In Akinyele, we were unable to interview any non-adopters, including females, because of time and logistic constraints, as well as the unavailability of respondents. However, from the individual interviews and focus group with adopters in Akinyele, we gathered some information on why some farmers in the area did not, or were yet to adopt organic farming.
4.3. In-Depth Semi-Structured Interviews and Focus Groups
As shown in Table 1
, we conducted a total of 15 in-depth, semi-structured interviews with adopters (n = 10 males; n = 5 females) and 9 with non-adopters (n = 8 male; n = 1 female). Where possible, the female spouses of the male respondents were interviewed. The interviews were structured to reveal the motivations, household-decision making dynamics, institutional and vulnerability factors, and other underlying considerations that shaped respondents’ adoption decisions. The interviews also probed the barriers and gendered issues affecting the adoption of OLVP, with non-adopters asked under what conditions they would re-consider their decisions about OLVP. Care was taken to ensure that adopters spoke to the factors that influenced their decision at the time they adopted OLVP. Five gender-differentiated and two mixed-sex focus groups were also conducted with adopters (n = 38) and non-adopters (n = 11). Of the 49 farmers who participated in the seven focus groups, 22 were males and 27 were females (Table 2
). The focus groups explored how reproduction, production, the sexual division of labor, and the labor-intensive nature of organic farming affected its adoption. The constraints limiting the adoption of OLVP and the interplay of vulnerability and institutional issues with adoption decisions were also discussed.
4.4. Participant Observations, Field Visits, and Group Discussions
To obtain on-farm dynamics information, participant observation visits to adopters and non-adopters’ farms in Ajibode were undertaken. During the visits, farmers’ on-farm challenges were discussed, with attention paid to their on-farm social-capital bonding relational dynamics, and who was doing what farm operations. Detailed notes were taken. Two of the participant observant field visits to the organic farmers morphed into group discussions, enabling us to gather multiple views on pest and weed infestation, and on other difficult issues in OLVP. Thrice, observational visits were undertaken to Elekuru market, during which the marketing aspects of organic and conventional farming were explored. The issues discussed were used to inform the focus groups in Elekuru.
4.5. Expert Interviews and Data Analysis
Five expert interviews were conducted to gain deeper insights into the institutional factors affecting the adoption of organic farming in our study areas, and to explore some of these issues raised by farmers. These included three officers of NOAN, the organic desk officers at the Oyo State Agricultural Development Program, and the Federal Ministry of Agriculture, Oyo State Directorate. The expert interviews and two individual interviews were conducted in English. All of the other interviews and focus groups were conducted in Yoruba, the native language in the study areas. The interviews conducted in Yoruba were translated verbatim to English. All of the interviews and focus groups were recorded, transcribed verbatim, and coded manually and electronically using NVivo (QSR International (Americas) Inc., Burlington Massachusetts, United States), together with the field notes. We drew on thematic analyses and our study’s conceptual framework for our coding and data analysis.
6. Discussion and Conclusions
Our findings revealed that exposing farmers to information about the economic viability of organic farming, the potential health effects of chemical pesticides and herbicides, and to the knowledge of organic pest and soil fertility management can motivate adoption. Our study also suggests that adopters’ existing social capital, such as their ties with non-adopters and religious formations, can serve as a means of disseminating information and knowledge about organic farming, and for aiding institutional linkages that can spur adoption. This highlights one way through which the linkage between social capital and institutional context variable (NOAN) in TALAF can help create the knowledge (human capital) that can motivate adoption. Therefore, encouraging adopters and organic farmers groups to cultivate improved bonding and linkage ties with non-adopters may enhance adoption in our study areas.
The finding that farmers’ subjective experiential knowledge (human capital) of the distinctions in their crops grown with and without synthetic fertilizer influenced adoption aligns with the literature [51
]. This may mean that expert-based information about organic farming may not be adequate to foster adoption. Where possible, it seems imperative to identify and broadly frame issues around explicit farmers’ experiential knowledge when promoting organic farming to farmers. This may enable them to contextualize, relevantize, and trust expert information, which is likely to motivate adoption.
As conceived in TALAF, the concern that organic farming could worsen farmers’ vulnerability conditions was a major barrier to adoption. The finding relates to respondents’ lack of knowledge about the efficacy of organic pest control techniques. According to TALAF, this connotes a lack of human capital, which can be addressed by training farmers on organic pest management. The perceived vulnerability to the yield and financial losses from occasional heavy precipitation during the dry season, which hindered adoption, was associated with the swampiness of the respondents’ farms. From a TALAF perspective, this connotes an obstacle to adoption by respondents’ natural capital asset. The finding is also related to a lack of awareness and knowledge about the suit of practices in organic farming, such as composting and cover cropping, which can help reduce vulnerability to crop and financial losses from weather vagaries and waterlogging [10
]. Therefore, increased attention to human capital building training aimed at reducing vulnerability to yield and financial losses in organic farming, while enhancing and sustaining productivity, can help spur adoption.
Specific capacity building needs identified and stressed by adopters and non-adopters include training and providing farmers with information on organic pest, weed, crop, and soil health management, and the provision of information on how organic farming could fetch farmers higher prices and increase their stock of financial assets through increased productivity and profitability. This becomes imperative given that the lack of knowledge about the economic viability of organic farming and its production techniques was not only evident, but also inhibited adoption in this study. As further indicated by the respondents, particularly non-adopters, the required capacity building should be provided by NOAN and government agricultural agencies, such as the Oyo State Agricultural Development Program (OSADEP).
Farmers whose on-farm livelihood activities were not competing for time for their farm work were more likely to adopt organic farming [39
]. This fairly conforms to our findings on the linkage between adoption and the circumstances of some respondents’ off-farm livelihood activities, which left them with a lot of idle time. The authors of [52
] reported that income from off-farm activities stimulated the adoption of organic farming. Our study suggests otherwise, as lack of reliable income from off-farm activities made many respondents adopt organic farming. In accordance with TALAF, this finding was also because the respondents felt that organic farming offered them the prospect of improved livelihood conditions through increased and profitable farm income (financial capital). This implies that farmers whose livelihood activities make them financially vulnerable may more likely adopt organic farming if it guarantees them a prospect of earning a good and stable income.
Our study also suggests that accentuating only the economic livelihood benefits of organic farming to farmers may not be sufficient to stimulate adoption. The potential non-economic benefits of organic farming mediated adopters’ decision-making, and should be emphasized when introducing the technology to farmers. Incentivizing the non-economic motives may be a way to increase the attractiveness of OLVP for adoption. The finding aligns with studies that reported that adoption decision-making was shaped by multiple intertwined motives, ranging from economic/financial to market, environmental, food-safety, as well as personal, family, and community health considerations [3
The finding that some farmers who were satisfied with the livelihood outcomes of growing their leafy vegetables conventionally on their financial condition were opposed to organic farming is consistent with the literature [3
]. Such farmers may not find organic farming attractive for adoption [3
]. This study further suggests that organic may be less attractive for adoption by farmers who considered it not attributively amenable to relatively big farms. From a TALAF perspective, this implies that farmers’ natural capital asset vis-à-vis their farm size is likely to affect the adoption of organic farming in the study areas. So, targeting organic at farmers who are cultivating relatively large farms may decrease the likelihood of adoption.
That lack of financial support to hire labor or access organic inputs constrained adoption, and was partly related to the dearth of lucrative markets that can make organic farming economically viable and self-sustaining, without any financial support. Nevertheless, the finding suggests that institutional credit access or financing support for organic farming may be necessary in order to enable farmers to overcome financial obstacles to adoption [53
]. These conventional and organic farmers were selling on the same markets, with conventional leafy vegetables attracting better prices discouraging adoption because of the relatively low consumer awareness of organic farming, and because the market for organic farming in Ibadan is still small and underdeveloped, despite its potential for growth [14
]. In Elekuru, the market factors that inhibited adoption pertain to its remoteness, as well as transportation and telecommunication constraints, which made it difficult to reach Elekuru organic farmers to notify them of the demand for their produce in urban locations. The finding was also related to the reliance of Elekuru organic farmers on NOAN to access the lucrative markets for organic farming in urban locations, a role which NOAN lacks the capacity to discharge effectively. The market constraints implicate a need for increased consumer awareness creation about the benefits of patronizing and consuming organic products, the expansion of existing pricier markets for organic in Ibadan and the creation of newer ones in the rural areas, and building the capacity of the organic farmers group in Elekuru to facilitate market linkage with buyers in urban locations.
We found mixed evidence about gender and the adoption of organic farming. Consistent with the authors of [36
], we found more female adopters than males, and that women farmers were more likely than men adopt organic farming, based on the perception that it is a low-cost technology. This is because, compared with their male counterparts, female adopters experienced greater financial difficulties buying chemical inputs to grow their leafy vegetables. Many female conventional leafy vegetable farmers in the area were facing the same problem. As envisioned in TALAF, our study also showed that the domestic gender division of labor and intrahousehold decision making authority of men over women’s time could discourage women from adopting organic farming. The findings were related to women situatedness in male-headed households, patriarchal gender cultural norms, and the attribute of organic farming as a time-consuming technology. This study further revealed that the physically demanding nature of organic farming may discourage women more than men from adopting the technology. This finding is related to the exclusion of synthetic herbicides in organic and women’s domestic role. From a TALAF perspective, this illustrates that women have limited physical capacity for the arduous work in organic farming, and their lack of financial capital to hire labor. The findings provide an empirical justification for TALAF, underlying the proposition that gender-related and technology-specific factors could gender technology adoption decision-making. They also show that TALAF can permit a gendered analysis of the adoption of agricultural technologies.
As anticipated in TALAF, this study showed that the multifaceted interplay of many factors, such as farmers’ livelihood assets, their vulnerability contexts, and livelihood activities and gender-related variables, simultaneously shaped adoption decision-making. The findings reflected most of the factors and the causal linkages that were theorized in TALAF as being responsible for shaping and gendering adoption decision-making. Nonetheless, this study indicated that some respondents’ primary livelihood activities influenced their adoption decision-making, a linkage that is not captured in TALAF. The fact that some respondents adopted organic farming because producing safe foods for public consumption was, to them, indicative of their faithfulness to God, implies a direct linkage between livelihood outcomes and adoption decision-making. This linkage is also missing in TALAF. Aside from the omissions, our findings suggest that TALAF has the conceptual capability to inform a comprehensive understanding of the multiple factors that interact to influence adoption decision-making. More studies in other geographic and/or sociocultural contexts are required in order to affirm this and reveal the limitations of TALAF in relation to adoption decision-making.
As the first empirical research that investigated the factors that affect the adoption of organic farming in Nigeria, our findings have broader implications for the promotion of the technology in the country. Our findings offer useful insights about the factors that can be drawn upon to develop policies and interventions intended to stimulate the adoption of organic farming in Ibadan and Nigeria. Such policies and programs should account for the arrays of motivations and underlying factors, which synchronously and non-linearly interact to inform adoption decision-making. Finally, demonstrating the utility of TALAF, our study shows that the conceptual framework can assist in visualizing and accounting for the different multifaceted and interconnected factors that interact to influence and gender adoption decision-making.