Stage 1: Finding common ground across cases.

A two-day in-person workshop (May 2018) for all case teams to crystalise the shared research problem and associated research questions. It also involved examining questions, methods and emerging findings from each case and co-generating insights through assessing commonalities and differences. Key points from each case were synthesized and available to all team members as the foundation for joint analysis. Written or virtual contributions were provided by PDFs unable to be present.

ii) Stage 2: Conceptual model (analytical framework) development and testing.

Based on Stage 1 insights and literature review, a subset of the study team drafted a conceptual model (Figs 1 and 2), for potential use as the study’s analytical framework. The team shared this for validation and solicited feedback from larger peer group of researchers from the CGIAR Gender Platform Community of practice on gender and breeding as well as with the PDF members of the study team. This was at the CGIAR Gender Platform ‘Seeds of Change’ Annual Scientific Conference (April 2019).

iii) 3: Deeper dive into breeding program perspective.

The study team undertook a deep dive from the breeding program perspective through in-person and virtual assessments with the geneticists and breeders involved in the studies (May–July 2019, roughly one year after field data gathering). This was done to crosscheck against the original interest of the programs in their own cases (to generate information to inform breeding programs). Two participating breeders gave their views in a live panel discussion, responding to particularly ‘knotty’ problems relating to priority setting and interdisciplinary breeding, as identified by study participants. Further, five participating breeders were interviewed individually as key informants on Skype using a checklist. Breeders’ observations were manually analyzed and categorized into positives and negatives.

iv) Stage 4: Consolidating.

Throughout 2020–2021, the case-based insights from Stage 1 (including additional written case material and associated literature reviews), were assessed in relation to the Stage 2 analytical framework and integrated with Stage 3 insights to generate responses to the identified shared questions. Using virtual and written dialogue, the study team synthesized the insights into this paper.

In terms of scope and limitations, we note multiple issues of potential interest relating to the cases that are important, but beyond the scope. These include detailed commodity preferences, relation to and variation across contexts, in-depth explanations of gendered preferences in each case, and nuances of integration into programs. Similarly, while we present the summary of case’ methodologies, in-depth analysis of methodologies or the relationship of these and results are beyond scope. Finally, we note that the breeding programs associated with these cases are at different stages of gender-responsiveness. Some are already field-testing new products with women and men. Others have used gendered preferences to identify usable traits, while still others are at earlier stages of considering preference information. These, and differences in commodities and contexts, mean that we do not aim to directly compare study results in terms of traits. Moreover, since breeding is a slow process, evaluation of impact of the cases on their programs would be premature.

Novelty.

Based on PDF literature reviews and breeders’ expert knowledge, two cases (3,6) were identified as first to investigate gendered trait preferences for that commodity and location (Table 3). For the remaining six, the information available was limited. For example, the PDF literature review on banana in Sub-saharan Africa found that of 44 trait preference studies, only four gender-disaggregated [26]. Likewise, the fish PDF review surfaced significant data gaps [24]. In these cases, information was novel in terms of filling gaps. For example, while there were gender-disaggregated studies on fish size-related preferences, Case 1 this was the first to investigate preferences in terms of tilapia morphology. A further key aspect of novelty was new information that enabled understanding of preferences, including nuanced meanings, and information on related decision-making or roles (see below).

Scope.

Just over half the cases generated data on actors from a single value chain node: cases 3, 4, 6, 7, 8 elicited preference insights from rural farmers as producer-consumers (or in the case of fodder, ‘producers’), who may use as well as sell at market (Table 3). The other three (1,2,5) spanned multiple value chain nodes: one expanded from producers-consumers to a more explicit focus on processors (2); a second, included expanded further to also include some assessment of traders’ preference (5); and finally, one changed chain contexts and nodes altogether, by assessing low-income urban consumers (who were not producers)(1).

Type of information generated.

All cases generated information not only on identified preferences, but also the relative importance of the identified preferences (usually 10 traits or more). This was measured by qualitative or quantitative ranking (by the respondents themselves) or by assessment of frequency of mention in qualitative data (measured by researcher). In addition to preference information, all cases generated qualitative contextual information about the gender dynamics, including division of labour in relation to the commodity (summarized briefly in Table 4).

Download:

• PPT
  PowerPoint slide
• PNG
  larger image
• TIFF
  original image

Table 4. Gender division of labour regarding the commodity for each case.

https://doi.org/10.1371/journal.pstr.0000025.t004

Immediate utility for breeding program.

At the time of interviews with breeders (roughly one year after data collection), three of the cases (2,4,8) had advanced to reporting prospective usable traits. In the cassava case, women who made cassava products required varieties that stayed white/bright after processing. The program had previously believed that the colour of processed products was determined only by processing methods; the case surfaced that this may also be genetically-controlled. In sorghum, the preference for hard grains can be met by selecting for plants with corneous endosperm (endosperm is the largest part of the sorghum grain). Finally, in the case of sheep and goats, the productivity traits of growth rate and milk yield were identified.

Additionally, the rohu fish case confirmed priority traits already being bred for (larger size and faster growth rates) as preferences to continue. The other identified characteristics of potential interest for the program (flesh content, boniness), require a further round of data gathering to identify specifically heritable traits that could be addressed by the breeding program. Additionally, three of the cases (1,2,3) generated information that informed understanding of the value chains, such as regarding the form of fish consumption (1) or the adoption of improved varieties of cassava or fodder grass (2,3).

Novelty, quality, and utility.

The results indicate that even established public breeding programs have the potential—through interdisciplinary gender trait preference studies such as these—to generate novel information. Whether gap filling or fuller information (needed for interpreting preferences), this is significant in that it represents the potential for programs to enrich their evidence-based prioritization. Broader literature suggests that the role of such studies in gender data gap-filling may be particularly important for noncereal commodities [11].

The results also suggest that breeding programs can use this type of interdisciplinary, gender trait preference studies to add to the quality of their information, in terms of accuracy enabling effective client-targeting and responsiveness. This may be perceived differently by different disciplines. Breeder’s reflections (Table 7) indicated quality in relation to depth and nuance generated by qualitative methods. Social scientists additionally emphasized breadth, in terms of socio-economic information for a new target group or outcome (e.g., case 1’s data linking low-income consumers, nutrition to preferences). Additionally, the cases signal the feasibility of enhancing quality in the sense of sufficient sex-disaggregated and gender-balanced data, which has been a persistent weakness in innovation systems [9]. While further work is needed, the cases’ headway in the much called for—but underdeveloped—area of intersectionality flags feasibility of this dimension as well. Together these reflect a sharpening and refining of programs’ understanding of client groups’ interests. As such, the study reinforces [7,27] in signalling that targeted, mixed methods studies can enable a more refined grasp of client segments, elucidating priorities that reflect the multiple dimensions of clients’ lived experiences.

In terms of immediate utility from the breeding program perspective, results indicate that interdisciplinary gender trait preference studies can generate information about new, potentially actionable traits (e.g., 4 of 8 cases in this study)—but it should not be assumed that they will always or immediately do so. Notably, when they did, new preferences appeared synergistic with current breeding priorities: i.e., none contradicted ‘must have’ breeding priorities (pest and disease resistance, growth). Rather they flagged traits that may increase adoption (e.g., quality traits such as colour for processed cassava or ease-of-use traits such as non-scratchy fodder leaves). Similarly, by expanding to generate data on clients in roles other than ‘producer’, the value-chain lens illustrated by the study (especially cases 1 and 2) may contribute to addressing the challenge evidenced by Thiele et al. [28]: that insufficient priority on consumer’s preferred traits contributes to limited uptake of modern varieties.

Challenges: Saying versus meaning, precision and expectations.

In relation to limitations, study teams identified a key challenge to generating useable and reliable information: accurate interpretation. This aligns with the problem of distinguishing between what is said, heard, and meant [29]. The study found that preferences, in particular around quality-related traits, may be expressed in terms that seem (to the interviewer) ambiguous, or one preference may conceal another. For example, women in Kenya expressed a preference for fodder plants with ‘wide’ leaves. Once clarified, the meaning was that they disliked Napier grass which has narrow leaves and sharp edges that cuts them when harvesting. The preference for ‘sticky’ eba, a doughy food from cassava, meant not that it stuck to the hands, but rather that it could be easily formed into lumps that held together well when eating without sticking to hands. Seemingly vague (to the interviewer) preferences such as these, or ‘tasty’ and ‘small’ bananas, are not directly actionable in breeding programs [30]. More detail is needed on the specifics associated with a preference. As such, quality preferences like these require closer investigation, i.e., the ‘thick description’ favoured by social anthropologists [31] and rely on qualitative skills to avoid intended meanings being ‘lost in translation’. Teams also reflected that social scientists’ awareness of the breeders need for precision, and what that meant (through breeder’s’ eyes), was critical. The foundation for getting the degree of nuance and clarity right—or wrong—thus relies not only on disciplinary skills, but also on the degree and quality of collaboration between disciplines, similar to other complex research challenges [32].

A related challenge emerged around goals and expectations. Overall, the cases set out to “identify trait preferences”. Yet to have practical value for breeding, a preference had to be linked to a useable trait—i.e., specific, quantifiable, hereditable characteristics that were technically feasible for breeding. Some preferences identified, however, were relevant to clients, but not actionable by breeding programs (for example, “price”). Moreover, for breeders, establishing the preference is only the first step in a longer process of justifying trait inclusion, which may include weighing economic values and cost-effectiveness. Even if multiple preferences are identified, only a limited number of traits can be practically included (given that the influence of one trait is reduced as more are added). In relation to this, a disciplinary difference emerged: the objective of social scientists focused on identifying client preferences; the end goal of breeders was to identify usable and economic, high-priority traits. This difference was not fully appreciated at the design stage of the cases. Sharper and earlier joint (interdisciplinary) understanding of objectives, the nature of informational requirements, and of hereditable and economic filters, would likely have helped generate greater usable information in a shorter timeframe, as well as temper expectations about how easily first order information about gendered preferences can translate to usable traits.

Elucidating commonalities and differences through the analytical framework.

The presence of identified gender differences across all cases, despite varying commodities, contexts and methodologies, indicates that the homogeneous view of preferences (model 1, Fig 1) is inadequate. This echoes the presence of gendered trait preference differences in the (limited) literature (e.g., [24,33]). Yet, results did not support a binary mental model either (model 2, Fig 1): only three cases (3,7,8) found traits not mentioned by the opposite gender (Table 5). Rather, the results indicate some gender differences—and considerable overlap—in preferences (i.e., overlaps were present in all cases). This suggests that the model of overlapping trait preferences is best fit here (model 3, Fig 1). This aligns (and refines) Tufan et al.’s [7,p.139] review that signalled “the resulting picture is not always one of clearly divergent preferences”. With only eight cases, it is too early to predict if this pattern would be found more broadly; given the diversity of contexts and commodities, teams and methodologies, it would be a valuable working hypothesis for future testing.

To unpack the overlap further, we turn to insights from the sub-framework addressing four types of difference in gendered preferences. How useful was this lens? We highlight four points:

1. The sub-framework revealed that all four types of difference were present. Within a given case, there was usually more than one type of difference. This confirms that differences in gendered preferences are more complex than previously thought. In other words, a ‘difference’ is not simply the presence or absence of a preference. Difference may take other forms: as per the framework, it may also represent the relative importance attached to the preference (ranking, intensity), the specificity of the preference (granularity), or if one gender’s preferences are a subset of the other gender’s (encapsulation). While there may be other or more variations on types of difference, the sub-framework’s four types enabled greater clarity and precision in thinking about and understanding gendered preferences.
2. The sub-framework elucidated that some types of difference were more common than others. The most common types of difference related to importance ascribed to shared preferences (ranking and intensity); in contrast, only two cases found differences where women’s preferences were not shared by men. While this needs further testing, the sub-framework here suggests that gendered preference differences exist—yet are less likely to be absolute and more likely to relate to differences of importance. This lens thus offers a useful corrective to overly binary assumptions about gender preferences.
3. It appears that identifying the different types of difference (through the sub-framework) may serve varying functions in informing breeding priorities. Identifying differences in ranking and intensity can support programs in addressing preferences that are most important to both women and men (which can help inform assessment of trade-offs). Looking for encapsulation can reveal, for example, that men’s preferences may be a subset of women’s preferences. In this case, if a breeding program focused only on shared preferences, and did not assess encapsulation, it would overlook other preferences (those that were exclusive to women) and potentially miss important traits. Identifying differences in granularity is useful particularly for identifying specialist knowledge to inform new traits or refine existing ones in order to tailor to client preference. The sub-framework on its own, however, does not enable analysis of how differences would translate into risks or benefits if preferences are met or not met (and associated trade-offs)—more needs to be done in this area to inform prioritization.
4. The development of the framework and sub-framework proved valuable in enabling cross-discipline dialogue. This suggests its use and adaptation may serve a programmatic function. In mapping gendered preferences, the framework(s) can facilitate dialogue between social scientists and breeders, supporting interdisciplinarity. This may be particularly useful in visualizing commonalities and differences in a way that supports evidence-based and transparent navigation of trade-offs in the prioritization process (see below, Strategic Implications).

Explaining gendered preference differences.

Gender differences in trait preferences are not biological in nature, but rather socially-constructed [34]. As Ashby [34,p.14] explains “The golden rule for making sense out of gender-differentiated trait preferences is to look for the explanation of how these preferences reflect underlying gender differences in assets, markets, information, and risk, and the ways institutions and policies condition these.” Here we unpack the socially-constructed (and thus dynamic) nature of preferences through examining a factor that emerges across cases: gender division of labour. This refers to the socially-constructed patterns of paid and unpaid work by women and men in households and along value chains (correlating to Ashby’s [34] components of institutions and markets). While the whole picture is more complex, it is an appropriate entry point here in that gender division of labour has been flagged as shaping trait preferences (e.g., [8,11,33]) and because it reflects and shapes power relations, agency, and resources, which in turn shape seed and technology adoption and innovation experiences [8,35].

The pattern of preferences in the results suggest that women and men gave higher priority to traits that are important for the work in which they currently engage, and which is gendered and context-dependent—in other words, it reflects the gender division of labour (Table 4). For example, women (who sell milk) gave a higher priority to milk yield than men (who sell goats) and gave a higher priority to meat in case 7. Conversely, it was men–who predominate in banana transportation–that identified traits associated with robustness during transport to market in case 5 (see also [30]). Women identified preferences for firm meat (making fish easier to cook) and hard sorghum grains (which produce more flour when pounded)—and were also primarily responsible for food preparation (cases 6,4). This association complements Christinck et al. [36] and Galiè et al. [37]’s findings that differing gender work explain why women focused more on production and use-related traits, while men focused more on production and marketing-related traits.

The above illustrates that, in many ways, the cases reflect gendered preferences that align with ‘traditional’ gender division of labour in low-income countries. In these contexts, as a result of constraining norms, interacting with factors such as gendered access to education, control over resources, and more, women are largely responsible for reproductive roles (cooking, feeding, care) and the productive roles socially-associated with these (harvesting, processing), and are more bound to homestead spaces (e.g.,[19,38,39]). Conversely, men–who embody socially-ascribed decision-maker and ‘breadwinner’ roles—tend to be associated with commercial production and higher-return work, public spaces, and emerging technologies (e.g.,[19,38,39]).

However, gender division of labour is neither universal nor immutable—it changes both by context and over time [40,41]. Preferences are thus not only gendered, intersectional, and context-specific—but also changing and influenced by evolving gender and socio-economic contexts. The fact that preferences will evolve with labour, and with progress in women’s empowerment and gender equality, signals that “it can be misleading to assume that trait preferences necessarily follow a ‘traditional’ division of labour” [11,p.259; e.g.,42]. Breeding programs will thus need to be pro-active in understanding changing preferences [11]. Moreover, noting these complex relationships opens the door for programs to consider influence in both directions, i.e., the question of if breeding programs may influence women’s empowerment through trait choices (see Strategic implications).

Practical implications: Data gathering, clients, frameworks and teams.

On a practical level, the study signals four implications for gender-responsive breeding. Given the study substantiating that preferences differ by gender, the first practical implication is basic yet important: in order to be client-responsive, breeding programs need to overcome previous sampling imbalances in AR4D by gathering information from (sufficient samples of) both women and men. While this may seem obvious, it is worth underscoring given the dearth of information available on gendered preferences and the tendencies for oversampling of men in data for innovation [8,9]. Extending this, and aligning with Orr et al. [43], systematizing the integration of socio-economic characteristics such as age, wealth or other along with gender into data collection and analysis will sharpen understanding of client segments [7].

Second, the study experiences suggest that breeding programs would benefit from expanding from a focus on producers’ to other value chain actors’ preferences, in particular, processors and consumers. The study complements the literature (e.g.,[44,45]) in evincing women’s significant post-harvest roles in agricultural and fisheries value chains. As such, widening the lens to post-harvest actors creates a pathway for greater gender-inclusivity. As per the study, this expansion may draw breeding program attention to women as (previously underrecognized) clients and surface potentially important traits.

Third, the study underscores that effective interpretation of (gendered) preferences requires fit-for-purpose data and innovative frameworks. Qualitative, explanatory data—as valued by breeders—are needed for their interpretive and nuanced insights and are an important complement to the quantitative data more commonly used in technical AR4D. This aligns with the private sector-inspired approach to customer segmentation and gender-integrated client profiles [27,43], as well as bringing into the breeding sphere the wider AR4D calls for qualitative methods (e.g.,[46]) and analysis of gender dynamics (e.g., from adoption research [35]). Conceptualization and analysis with enough depth and refinement to interpret gendered preferences implies the need for frameworks, such as the ones used in this study. As demonstrated, considering differences through such frameworks can elucidate critical information about types of differences, including importance (through ranking and/or frequency assessment), granularity and encapsulation, which in turn supports interpretation and use.

Finally, the fourth practical implication from the case experiences is that being more effectively client-responsive and gender-responsive requires building interdisciplinary commitment and ‘muscles’ between breeding teams and social science, including gender, teams. Thoughtful, routinized exchanges, mutual understanding and collaboration emerged as necessary investments. As an implication, this aligns with the call for more balanced respect between disciplines in science [47] and the notion that interdisciplinarity is essential to addressing complex problems in sustainable development [32]. The case experiences suggest, however, that this may take time, capacity development, and purposeful effort and strategies (see also [32]. We note that while this study focused on social science and breeding interdisciplinarity, teams may benefit from nutrition, food science, or other disciplinary expertise. For example, nutritional needs and potential trait responses to them, may not be known to clients and thus not identified, but could be brought into consideration by nutritionists within a multidisciplinary team.

Strategic implications: From preference data to navigating priority setting for gender-responsive breeding in the context of development objectives.

Designs and data presenting a sharp understanding of distinct plus overlapping preferences (model 3), whose preferences are whose, the extent and nature of difference (e.g., importance versus encapsulation), and why, create an evidence-based foundation for the next steps in gender-responsiveness: transparent, equitable and ethical decisions about which preferences to prioritize and thus whose needs and preferences will be met (and whose will not be) (see [7,43]). While details of gender-responsiveness in breeding cycles are beyond scope, study insights raise two important gendered data-to-decision making implications.

First, the study illuminates that a range of prioritization (and synergy or trade-off) scenarios—and thus considerations for gender-responsiveness—may emerge. When gendered preferences overlap or are complementary, breeding programs may be able to devise win-win solutions where an improved variety or species can meet the preferences of both women and men (for example, cassava that is both easier to peel and gives higher yields). Yet in some cases, differences in gendered preferences are such that win-win solutions are not viable in the same product. Using as an example the sheep case (7): men preferred rapid growth to enable quick sale of lambs, whereas women preferred slower growth so that lambs stayed longer with mothers, enabling more milk for women to sell. In instances when win-wins are not viable in the same product, as economically feasible and fit for clients, programs might develop multiple (different) targeted products to meet specific role and gender preferences. If that is not viable, programs may need to choose between priorities of different groups. In doing so, programs will orient to their larger development goals—yet these may also have trade-offs and complex feedback loops. In the sheep example, it is not only rapid income (sale of lambs) versus slower income (sale of milk); this scenario represents choices between nutritional security for children (via milk used for the household) and enhanced household wellbeing (via income), which also depends on who controls the income (as women’s spending is reported to lead to greater household benefits than men’s [48]). Given the need to navigate such complex scenarios, one strategy for gender-responsiveness is for programs to build mechanisms into decision-making (trade-off) processes that keep women as a priority client group at the fore. While it may not entail always generating women-targeted products, such mechanisms could serve to offset historical male-bias in technical innovation processes [9]. Additionally, as a foundation, priority-setting processes in these complex navigations will need to be explicit about why one client group’s preferences are selected over another. To enable transparency, data on who is being bred for—and who is not—could usefully be analyzed over time as part of monitoring and evaluation by teams, (potential) clients and funding agencies.

Second, the study signals important strategic choices breeding programs face in trait prioritization decision-making in relation to gender-related outcomes. In the context of gender-unequal food systems [49], programs are often embedded within larger AR4D institutions and funded by agencies with explicit gender-related goals, such as benefiting or empowering women or addressing persistent gender gaps and barriers. Common approaches to operationalizing these in breeding have involved aiming to meet needs in women’s current circumstances, and thus working on traits for marginal agricultural conditions. For example, prioritizing a variety of sorghum that tolerates low soil fertility, as women often farm in marginal lands. As this aims to respond to and benefit women, it is a step forward from gender-blind prioritization. While valuable, this option has a downside: as it ‘accommodates’ (works around) gender inequalities, it also reinforces (and does not challenge) norms and dynamics that position women in marginal aspects of production (and men in more lucrative ‘male domains’)[19,39]. Strategies for empowerment through breeding would include ensuring women have equitable voice in identifying priorities in the first place (such as through the cases), and complementary strategies to enable meaningful choice amongst breeding products (see [8]). This may have spin-off effects: studies explicitly engaging both genders as valuable ‘clients’ role model a recognition of equality that is potentially (constructively) disruptive in the gender-unequal contexts in which breeding programs operate. Economic empowerment outcomes through breeding may require prioritizing women’s preferences associated with their paid value chain roles (and wider program strategies to ensure women are not displaced). To contribute more deeply and sustainably to addressing persistent gender inequalities, breeding programs might take an even more ‘constructively disruptive’ approach [8], such as prioritizing innovations designed to support women shifting into higher return roles (instead of supporting in marginal roles). For example, a variety of cassava suitable for machine peeling (rather than current hand-processing) might support women in transitioning to machine processing, in combination with complementary AR4D strategies to address underlying structural factors (such as engaging men in addressing norms limiting women’s use of machines and preventing male-takeover). All approaches above are gender-responsive, because they consider women’s needs or preferences and are transparent about whose preferences are (or are not) being met. In deciding which approach(es) to follow, as well as client input and aligning to gender goals, AR4D’s ethical principles—the most important of which is “do no harm”—can help breeding programs navigate these choices [8,50,51].