Socioeconomic sampling

Béla Teeken, Jill Cairns, Mainassara Abdou Zaman-Allah

Expertise and social inclusiveness. A guide to choose participants for on-farm testing with the tricot approach.

Assuring experienced participants

A common weakness in standard participatory variety selections is that farmers are chosen without eye for their experience and the specific work they are doing and to which local social category they belong. Where this is considered usually very broad general categories are used such as age and sex., occupation, leve of education, farm size. Furthermore, when gender is brought in focus, the practice is mainly on having both men and women farmers in equal numbers evaluating the trials, disregarding their specific expertise or experience in farming. Another problem is that often farmers get chosen who feel comfortable talking and interacting within the sphere of a scientific evaluation, which emphasizes experience in reasoning and talking. This often excludes very skilled persons that however are not able or are normally not allowed to communicate these skills and knowledge through language. But even if the respondent is good at talking it still does not include the tacit knowledge, the embodied skill and knowledge that people have. Breeders are however interested in detailed concrete hands-on information if they want to align with a demand led breeding approach such as the stage gate breeding approach that is now introduced in the CGIAR public sector breeding. Within the current reform to a stage gate breeding approach it is also crucial to get feedback from not only farmers but also processors/prepares and marketers who turn the RTB crop into an edible quality food product.

To overcome these issues while choosing the tricot participants, we therefore work with a so-called purposive sampling using a task group approach with an explicit gender dimension. This gender dimension is not only important within the light of gender equity but is also very practical and concrete if we want to know the expertise and experience people have with regards to work related to the RTB corps because often tasks related to the RTB crop are gendered: certain tasks are often carried out be a specific sex. Important to note here is that a task group is not necessarily a group that works together but a category of people of the same social segment that carry out similar tasks related to the crop.

A task group approach is also in line with a much more performative way of participation instead of only a deliberative one (Richards, 2007) . In such a performative approach people who are verbally not strong or are not allowed to speak up are included and approached more self-evidently and tacitly, because they have been identified as a specific task group within a locally defined and thus relevant social group or intersection of different groups.

From a demand led perspective breeders are interested in good information on the suitability of improved varieties within the livelihoods of the users and therefore want to know the user preferred crop characteristics. To get good information it is therefore important to get this from experienced people that are skilled in farming but also with regards to processing of the crop into the food products and their quality as well as the marketing of the crop. Importantly storability of food products as well as fresh roots tubers and bananas are important in the last instance. E.g if men are are hardly involved into processing cassava into gari , a breeder will not be interested much in men’s preferences with relation to processing because they will not always be able to give correct hands on experience-based knowledge because they do not possess the skill. Men in this case might have indirect knowledge about it (e.g. through their spouses), but certainly not the embodied skills.

A bad example of how participants got chosen is a farce case where someone wanted to work with mango farmers and ended up with only male participants of which none had a mango tree field. When later confronting these participants with this, the village chief and his friends all claimed to have a mango tree in their backyard. It is obvious that a tricot with such participants will not yield the best information.

A task group approach and gender

Sex-disaggregated data collection protocols on variety preferences are problematic as they put upfront sex and gender differences as an explanatory factor. From a political and ethical perspective this approach is also guilty of up-front discrimination based on sex difference. This type of data also renders invisible how gender roles are shaped by the intersections of locally defined identities, such as occupational tasks, immigrant or ‘local’, ethnic/language group, age group and economic status. So instead of initially segregating by sex groups we propose an identification of task groups. Simply: Who does what within each of the locally defined social categories.

This task group approach can capture the intersectionality of local identities by focusing on who does what along the value chain and allows for a closer integration with practices related to participatory trials, post-harvest processing evaluation, food processing and marketing as these connect to specific tasks and thus specific crop characteristics/traits. Identifying who does what in each of the social groups in the community can accurately and concretely tap into the knowledge, skills and working conditions of these groups without referring to gender as a subject, but with the work that different often highly gendered task groups do, as subject.

Taking the task and the work as central entry points avoids the highly political aspects that come into view when explicitly addressing sex and gender. By continuously referring to the work and working conditions related to a specific crop or set of crops, one can much more neutrally gain knowledge about how gendered certain task are, and learn about the constraints and opportunities and preferences of the different task groups. And this is something breeders are interested in if they want to see an increase of adoption of their varieties. In addition, by valuing and showing interest in the work done by different groups one triggers senses of proud and belonging and it facilitates more politically neutral and realistic conversations and cooperation based on the, organization and technicalities of the work and the working conditions and how this work can be done better in the current context. Knorr-Cetina (1999) would call this; building epistemic cultures based on fascination and content of the work. This method communicates focused skilled practice instead of emotion and values as abstract themes.

Therefore, task group based PVS trials and participatory processing evaluation exercises as well as participatory breeding events are perfect social science tools to achieve such a more performative and tacit cooperation based on technological practices and their organization. Such PVS exercises can be done with merchants as a group, the growers as a group as well as processors. Consulting people who are involved in more than one of these activities are an opportunity to catch more task groups at once. And it is this opportunity that we will have to use because choosing someone as a tricot participant, assumes that such a person is farming the crop. In many cases however the person does a lot of more tasks related to the crop. People can simultaneously belong to different task groups and the extent to which they belong to one and not another and the extent to which certain tasks are done by women and men are informative about gender roles and norms and current possibilities and developments within farming, processing and selling.

How to practically use a task group approach?

After applying a representative sampling frame for your region: having a certain amount of communities per state or district in the entire area of focus (there where the crop is commonly grown) and after having consulted e.g. government bodies at different geographical levels to identify active regions where the crop is cultivated, it now comes down to identifying participants within communities on ground.

For this a task group approach distinguishes the following practical stages to follow at village community level:

1. Identify which crop and which tasks around that crop exist: whose experience and skills and work are of interest to us? e.g. Are people who have experience in cultivation as well as processing relevant to the study? This is useful if you want to practically and directly relate the relative importance of agronomic, processing and food quality characteristics. Usually a breeder should be interested in skills/expertise in all the work related to the crop because it can reveal crucial traits within the value chain that are needed to assure adoption of a variety. This will help the breeder in determining the key traits necessary.

2. Identify how a community itself defines its different social groups. This can be done through meetings with village heads and discussions with key informants, transect walks and interviews with several people carrying out the work around the crop under study. This also involves making the tricot’s scientific inquiry explicit among the leading elite and key informants in the community so that they clearly understand that you want to include respondents/participants from all social groups, also the disadvantaged and the less better off, and explicitly try to avoid leadership effect (e.g. Humphreys et al 2006) and state that the village leading elite is not to determine only who participates: cultivate a democratic and equity focused discussion and debate on studying experienced based crop related expertise that exist among the different social groups. Table 1 can help here in mapping the community.

3. Within each locally defined and thus relevant social category, identify ‘Who does what’ in relation to the production, processing and sale of the crop and its food products and identify respondents/participants representing the task or combinations of tasks that you are interested in (crop dependent) without using sex as initial criteria of selection. This should be done by verification that the respondent/participant indeed has detailed knowledge on the tasks that she or he has indicated to master: probe with work related jargon and discuss detailed practices related to each skill. N.B.: Make sure you have detailed knowledge of these practices on board in your field team to make good verification possible. The field team should therefore include breeder, anthropological, socio-economic and food science expertise. Food science is particularly important for the verification of the participants skills and obviously because quality food products and their relation to different varieties can be crucial for adoption.

4. Include participants from each social group if applicable (e.g if Fulani herdsmen do not cultivate or process the RTB crop it would not make sense to include them as participant).

5. Determine how you organize participants. For cassava in Nigeria we identified one lead farmer per 10 participants and 10 participants per community for whom the data collections is managed by the lead farmer that has or does not have a tricot trial her/himself. The 10 participants were representative of the community and included all important local social groups. It was important to build on local authority and respect: the group of 10 farmers chose their own representative! This was found very important to assure providing ownership of the activity to the participants. One could also choose an agricultural extension officer to lead the farmer group, but this can only be done and will only give good data if these officers are respected and almost part of the community, which is often not the case (and it was not the case in most the communities visited in Nigeria). Important to note here is that we chose for a group dynamic (of 10-11 persons in each community, depending on if the lead farmer also was a tricot participant or not) to stimulate the sense of belonging to the project as a group member. One could choose individual farmers even far from each other that will each independently communicate the information to you. Theoretically, from a positivistic sampling position, this might seem more appropriate, but we argue that the performative aspect of sharing the same project with a group creates a spirit of dedication essential in a crowdsourcing approach. It also facilitates better reporting back of information to the farmers: they become a community of colleagues rather than mere sole participants.

N.B. Remember that trial visits and research station staff costs should be reduced to a minimum, so it is very important to invest in choosing and building the right reliable local management unit and this works best using local respected persons and authorities.

6. Determine an appropriate mode of compensation to the participants. We compensated through the lead farmer and then followed up with the participants if they received their payment from the lead farmer. Payment was done through bank transfer. In case a lead farmer with bank accounts cannot be found another system of payment has to be thought of, like paying out during the lead farmer training. A modest sum for the work of the lead farmer moving between participants fields was arranged. The amounts should be low as to assure participants motivation for the project and not just to hang in for the monetary benefits, but it should however be that substantial to motivate participants and make them feel that they are not doing it all for the sake of the research project. These amounts have all to be locally determined based on the context. The agreement should be clearly stated in the agreement made with the lead farmer and signed to avoid disputes.

Table 1. Example of local social groups based on interaction with a village community in the Southwest of Nigeria focusing on cassava. Apparently ‘ethnic group’ was found important to distinguish different people. The groups highlighted in grey are the groups included among the tricot participants as the others are not involved in cassava work.

Locally Relevant Social Group*	Share of Local Population (oral or record share) [B1.4a]	Language (record language) [B1.4b]	Associated Livelihood(s) or Crops [B1.4c]	Tasks Related to Cassava (Women)	Tasks Related to Cassava (Men)	Better Off Group(s)? (Yes=1, No=2) [B1.4d]	Politically Active & Influential Group(s)? (Yes=1, No=2) [B1.4e]
i. Ilaje (originally from Ondo state)	1000	Yoruba dialect	Fishing, selling of fish			2	2
ii. Agatu (Immigrants from Benue state)	500	Agatu	Farming (subsistence and cash crops), farm labourer. Includes cassava	Farming (weeding, harvesting), processing, marketing	Farming (weeding, planting, harvesting), marketing of fresh roots	2	2
iii. Markurdi (Immigrants from Benue state)	100	Tiv/Igede	Farming (subsistence and cash crops), farm labourer. Includes cassava	Farming (weeding), processing, marketing	Farming, marketing of fresh roots	2	2
iv. Hausa	10	Hausa	Trading, fishing			2	2
v. Cotonou (Immigrants from Benin republic)	50	Fon/Ewe	Farming (subsistence and cash crops), particularly cultivate vegetables, tomatoes, and peppers. Includes cassava	Farming (planting, weeding), processing, marketing	Farming and occasional processing	2	2
vii. Fulani	150	Fulfulde	Cattle rearing			2	2
viii. Yoruba	18145	Yoruba	Farming (food and cash crops), trading. Includes cassava	More of processing, some farming (weeding), firm marketing	Farming, marketing of fresh roots	1	1

Having included participants of different task groups will allow us later to disaggregate the data per task group and explore specific preferences of these groups and also intersections of certain task groups with gender: E.g, do women immigrants who are processing mention different characteristics and/or prefer different varieties? The aim here is not to create a product profile for each region, task group and gender as well as for all the intersections between them, but to have all the diversity mapped which will then allow us to identify some crosscutting traits that are e.g. important for women processors or women marketers. This will allow us to create a socially inclusive composite of necessary traits. Here is where the gender equity and social inclusion comes in. Given the data we can now decide which traits are crucial for certain disadvantaged groups. We can now also disaggregate by gender and see if there are important varieties and or traits associated with women processors in general. Here the sex disaggregation is thus possible at the end and not upfront. This makes a lot of difference because by using a task group approach you know that most of the participants in the tricot are skilled persons and you know for each of them in which she or he is skilled. This allows us to more concretely identify traits and varieties relevant to the skills and working conditions of different social groups. This will also allow for defining crosscutting varieties and characteristics while still including the different important equity aspects related to the social and ecological conditions in different regions. This is most important for breeders who are, giving their limited resources, not able to breed for many different segments.

Note: A critique on the proposed approach of purposive selection is that it can be seen as too cumbersome and will take too much time and money. However, we argue that because we are expertise focused these inquiries do not have to take very long, we do not have to emerge ourselves into the communities for days as our focus is very practical and focused. By communicating with the local village authorities as well as with the skilled craftswomen and -men we can arrive at a more contextual expertise-based approach that might not be perfect but is much more realistic and will generate more useful quality data.

At the end of the community visit you should have a list with chosen tricot participants that include (lead) farmers, farmer-processors, farmer marketers, farmer-processors-marketers from each of the local social identified groups. If processing is common in the village and only done by women, this means that the people with this profile can only be women. So, it has to be estimated more or less which group is the largest and then represent them among the participants accordingly

The table 2 below shows and example of 10 chosen participants in a community in Southwest Nigeria based on the information in table 1

Table 2 of chosen participants in 1 community to participate in the tricot evaluation based on a task group approach (using information in table 1)

insert table 2 (can't remember how to convert it)

Participant Local social group skill sex age Name 1 Yoruba Farming, selling fresh m 30 Name 2 Yoruba Farming, selling fresh m 24 Name 3 Yoruba Farming, processing, selling f 45 Name 4 Yoruba Farming, processing, selling f 50 Name 5 Agatu Farming, processing f 35 Name 6 Agatu Farming, m 50 Name 7 Makurdi Farming, selling fresh m 25 Name 8 Makurdi Farming, processing, selling f 55 Name 9 Cotonou Farming, processing, selling m 28 Name 10 Cotonou Farming, processing f 22

Remember this is purposive selection based on interaction with community leaders and key informants choosing representatives of each locally relevant social group and balanced in relation to the population of each social group. This will be done timely enough before tricot package distribution and the planting of the trials in farmers’ fields. This is purposive sampling based on tasks and skills. This is more important than focusing on age categories, although we should try to have a range of different ages as not to exclude a certain category. But here again: if there are e.g. hardly any young women involved in processing then it is not needed to include them. N.B. The exception is of course if specific age groups are mentioned as important local social groups or task, in that case we must properly represent those groups. Quite some time should be invested in assuring the participants commitment and reliability of the lead farmer, as most of the information will pass through telephones coordinated by the lead farmer. So, some time, say a month or two months (depending on the dynamic of the RTB crop) before packages of vines, stems, suckers are brought to the farmers, a good preparation of the team of tricot participants in each community is necessary and good firm telephone communication between the moment of participant selection and actual delivery of the packages has to be maintained. If at the very moment you bring the stems some participants might have dropped (e.g Boef et al 2007) make sure to replace such a participant with another one representing a similar social group and task group. However, if good attention is given to building the team in each community, letting the participants choose their own lead farmer and explaining in a comprehensive format the research, we have seen that hardly any people drop out.

Lead farmer trainings

In the Nigerian cassava setting we organized lead farmer trainings to explain to them how to do the evaluation of the tricot plots with the tricot participants. This training is best be done after the installation of the tricot fields in farmers’ fields, farmers then already have a good picture of the setup of the research, e.g. a week after planting of the trials. If necessary, because of logistic issues the training could also be held before the installation of the trials in farmers fields. Lead farmer training can be organized by bringing all the lead farmers together in one location. In Nigeria this was not an optimal solution as it would mean bringing 32 lead farmers from 4 states together. Instead it was found more practical to bring lead farmers in each state together and organize 4 trainings, one per state. Trainings were held in one of the communities where tricot participants were chosen. This allowed for in the field practicing after a training in a school building or any other publicly available place. Trainings were one day or half day trainings. The format of the training is provided in Annex 1

Important is that the parameters to evaluate (apart from the general observation of the 3 plots) should be based on farmer expressed characteristics and not breeders-based terms. Based on interviews we had earlier with farmers a farmer guide was written up showing the 4 to 5 parameters to evaluate the tricot trials on. (N.B. be selective here the exercise should be kept tacit and simple.) For each parameter respondents are to fill the best and the worst variety. Important here is how you phrase a characteristic so that ‘best’ and ‘worst’ in relation to that characteristic is clear. Annex 2 shows the developed farmer guide in the Nigerian context of cassava. Apart from best or worst for each parameter and the overall evaluation there is also space for farmers to indicate what characteristics have made her/him decide which plant is overall the best: these can be characteristics among those evaluated but also any other. We experienced that it worked best to ask farmers about the best and worst for each of the characteristics (parameters) and to conclude with the overall evaluation. Like that participants have already “interacted” with the trial and observed the plants several times before now given a final verdict.

The trainings are essential to assure that each of the lead farmers understands exactly the meaning of each characteristic. It is therefore crucial that characteristics translated to the local language in the training to arrive at a good understanding of each characteristic. As you see this exercise is also included in the training format in Annex 1. This is also an additional reason why separate trainings in different regions is more practical as languages can largely differ.

As you have seen the lead farmer guide part 1 only includes evaluation up until 9 months. This has three reasons:

1. We wanted to include the most important user characteristics mentioned by farmers and processors with regards to the yield and also with regards to the food products that will be made from the cassava roots, so we had to have time to analyse data on this that were gathered during Nextgen cassava and RTB foods project activities.
2. Most important reason is to give a separate training with regard to the most important harvesting and food processing stages of the tricot quite close to the moment of harvesting as to assure a good spot on evaluation by the leadfarmers together with the tricot participants. This means it is very much advisable to provide two trainings during the whole tricot growing cycle.
3. A second meeting provides a good contact point for the feedback of result and evaluation of the process with the participants, this reinforces connection to the project and ownership.

Administering a RHoMIS core social questionnaire

For the further sake of social differentiation and allowing to segment the dataset later a RHoMIS core social questionnaire will be administered with each of the chosen participants. This might be done when stem cuttings/ vines/suckers are taken to the farmers but might also be done later on halfway the growing cycle if a visit of all the fields is planned which is certainly recommended in the first year of the tricot. This will allow for a good check and good focus on the questionnaire rather than the planting of the trials. It also assures talking to all participants that have really established a trial in good order. It is important to connect the identified skills and tasks performed by each participant to the RHoMIS core questionnaire. This can possibly be combined with the moment of the second training of the lead farmers on the yield and processing stages.

Making tricot inclusive: Gender and social heterogenteity (RTB)

External validity of tricot trials has an important social science aspect. As has been indicated above, tricot trials imply sampling a representative range of use contexts, which are characterized not only by environmental variation, but also by gender and social heterogeneity, which will have an effect on variety preferences through various proximate causal factors. Firstly, crop management tends to reflect cultural and socio-economic conditions and identities (Adekambi et al., 2020). For example, the ability to purchase fertilizers or spend sufficient labor on weeding will influence how the trial plots are managed and will influence perceptions of variety performance. Another example is that farmers and processors might favor a particular variety because of its suitability for preparing a food product that is locally important or consumed by a particular social segment of the population. For example, farmers’ orientation towards market production and household consumption can influence how they perceive traits related to marketability, cooking or taste (Adekambi et al., 2020). Thirdly, the degree to which farmers that participate in tricot trials have adequate knowledge of a different aspect of variety performance will depend on their involvement in different agronomic, processing and culinary activities (Teeken et al., 2020).

Gender is important in all three of these aspects (Weltzien et al., 2019). There may be differences in socio-economic status between men and women, as well as gender-based labor division for crop-related tasks. In the past, many trials have therefore addressed issues of gender by including sex of the participant as an important ovariate. However, so far no tricot data analyses have shown that there are statistically detectable differences between men and women. This contrasts with the finding that trait prioritization exercises often end up with different traits mentioned by men and women, reflecting their tasks and final use of the product (Weltzien et al., 2019). This contrast may have different explanations.

First of all, tricot data and analysis did not include other social identities that can strongly intersect with gender or gender-related constraints on access to resources, knowledge and opportunities. Statistical interactions between these other social variables and gender could be revealed in aggregate datasets. This will only be possible when such data becomes available (see below). Gendered norms and roles do often not follow generalized stereotypes and can change over time, for example when outmigration of men leads to a feminization of agriculture (Abidin, 2004). Certain tasks are executed by both men and women. Gender and social heterogeneity in study areas may lead to aggregate tricot results in which general variation overwhelms any differences between men and women.

On the other hand, existing studies prior to tricot may have some limitations as well. Few studies ask participants to rank the importance of traits directly (Weltzien et al., 2019). Most studies rely on free-listing exercises, in which participants mention all the traits that occur to them. Free-listing has methodological limitations if it is used as a comparative approach. If free-listing is done in focus group discussions, they may be influenced by leadership effects (which make more senior members more influential in the results) (Richards, 2005). Also, free-listing exercises measure perceptual saliency and importance in local discourse, which may not always translate to relative importance in a realistic decision-making context in which tacit knowledge comes into play. Relative weights are often difficult to elicit through deliberation. Another possible factor is the loss of information in translation during data interpretation (for example, overzealous lumping of local concepts into more general categories) and translation from local languages.

Specific elicitation exercises to put weights on traits and segment user groups have become more prevalent recently as a result of methodological simplification, providing viable alternatives to the usual approaches from economics (conjoint analysis) which were somewhat burdensome (Byrne et al., 2012; Steinke and van Etten, 2017). This could provide important opportunities to avoid the limitations of free-listing. These new approaches use pairwise comparisons and are therefore methodologically very similar to the ranking approach used in the tricot approach. Our comments on the specific limitations of free-listing should not be interpreted as a diatribe against free-listing per se or qualitative methods in general, just as a caveat against the possible overinterpretation of qualitative results in uncontrolled and unrepeated comparisons. We advocate for judicious combinations of different qualitative and quantitative methods.

Sex disaggregation used in isolation will tend to overlook other issues that may correlate but also intersect with gender, such as income, occupation, marital status, ethnicity, age, or social status. Sex disaggregation alone as a basis for gender analysis will therefore not capture the high heterogeneity within the two resulting segments and give limited insights in causal relationships. This means we need to move to more subtle approaches that address intersectionality. This will require innovating on methods of analysis to analyze social differences and how they come to bear on trait and varietal choices. Innovation in two directions is ongoing.

The first innovation direction involves the use of RHoMIS (Hammond et al., 2017; van Wijk et al., 2020). This is a standardized household survey method that includes questions about the gendered execution and control of activities and control over the income derived from them. Also, the survey covers questions about household composition, farming system, nutrition, poverty and other indicators. For tricot, a selection of questions and indicators has been made to reduce the length of the questionnaire to the bare minimum to reduce respondent fatigue. The resulting data will be used to analyze the farmer-generated tricot data to determine how gender and socio-economic factors affect trial management, variety performance, and farmer variety preferences. A publication of this “layering” of RHoMIS onto tricot trial data for cassava is forthcoming. The promise of RHoMIS is that it could combine with tricot to a standardized approach that will enable comparisons across studies in variety evaluation. This does not preclude that the precise RHoMIS format as applied in combination with tricot may still need further methodological evolution.

The second innovation direction is to get a better grip on participant recruitment. Again, often fairly simplistic methods are used to address social/gender inclusion, generally quota recruitment to arrive at balanced numbers of men and women as participants. This was done in tricot trials in India, for example (van Etten et al., 2019). In a way, this puts a small set of variables upfront as explanatory factors, ignoring the importance of intersectionality or the possibility that non-identified variables may be more important to differentiate locally important social segments. For example, differences between people who are long-term residents and recent immigrants in the village may be more important than overall gender differences and can constitute important gender differences, for example, where women immigrants are in a very different position than autochthonous women (Forsythe et al., 2016). This would be impossible to capture through sex-based quota sampling, which may miss out migrants entirely. Also, during recruitment, there may be a bias towards more outspoken, talkative individuals who may not always have the best observation and judgement skills for variety evaluation. Random recruitment from the membership base of collaborating organizations has been used. This can suffice if the resulting participants represent the target population and a widely grown crop is targeted, but often local social segments remain invisible and can therefore be under or over-represented Also, in the analysis a reweighting can be done if recruitment is not representative, however excluding participants reduces statistical power and increases the relative costs of studies. However, for RTB crops generally the volume of planting materials is an important limitation. Also, not all farmers may grow relevant quantities of the target crop. Both these cases call for a better-informed sampling strategy.

IITA has implemented a purposive sampling strategy with a gender dimension for cassava trials. This sampling strategy starts with qualitative work in communities to define locally relevant social groups. Participants are then selected making sure that each local social group, and gender within them, in which cassava growing and processing expertise is present is proportionally represented. To achieve this potential participants (cassava farmer/processors) in each group are randomly interviewed and evaluated on thorough experience in cassava farming and processing (using enumerators equally having experience in this domain to assure a good check) to also capture feedback from processors that are important additional stakeholders in addition to farmers and are often also marketers and very much informed by market demand and related traits (see determination of stakeholder/value chain actors section below). This approach then makes it possible to perform a better-informed gender analysis by comparing men and women’s preferences with regard to the same expertise and across different relevant social identities. This is even better facilitated as all participants are interviewed using a RHoMIS questionnaire assuring the availability of standard demographic information next to the locally determined social grouping based on the qualitative research in the communities. This approach therefore focuses on the participation of task groups/segments (Maat, 2018; Richards, 2000; McFeat, 1974). These are segments/groups that are organized around a task (for example, processing cassava into gari) and are internally relatively homogeneous in their work culture (but groups doing the same task may have other differences between them). Task groups develop a focused skilled practice which tends to generate shared language and thinking. Tapping into the expertise of these task groups is therefore an appropriate way to organize participation in order to assure that each participant is skilled and experienced which is an important condition if we want to know about crop related user preferences. It mobilizes participants around a skill set and professional identity in which they tend to take pride. A focus on task groups may also help to avoid micropolitical considerations, make the process transparent, and be more inclusive to less outspoken professionals. Task groups can be identified by tracing who does what task in the crop value chain trajectory from seed to stomach. This is done by considering local identities, including gender, but also other potential factors (e.g., age). If gender is the overriding factor in the constitution of task groups, it would accentuate the need for a nuanced gender analysis that takes into account intersectional identities beyond only sex-disaggregation of data. By using ethnographic observation methods (interviews, transect walks, market visits, etc.) the information to identify these groups can be gathered. IITA has prepared a draft guide to implement this approach (Teeken et al., in preparation).

Ethics, privacy and rights on traditional knowledge (RTB)

Tricot involves human subjects and must therefore observe certain research ethics standards. In general terms, the application of tricot must minimize the possible risks, discomfort, nuisances and costs for participants while maximizing the benefits that they and other farmers may obtain (directly or indirectly) from the trial data obtained through tricot.
tricot is also subject to privacy issues, and data management needs to conform to General Data Protection Regulation as the Alliance of Bioversity International and CIAT is headquartered in the European Union (Italy).

In general, this will mean the following for tricot trials:

• Research ethics clearing is obtained from the relevant Institutional Review Board (IRB).

• Research ethics clearance may be also necessary from a national organization. For this purpose, tricot users must take national laws and guidelines into account.

• Prior informed consent is obtained from all participants, which would allow for data publication after anonymization.

• Participants are given the right to withdraw from the study while it is executed.

• Participants are given the right to withdraw their data from the study while it is in the course of being executed.

• Participants can indicate if they want to be recognized with their name in the publications based on the data. This does not compromise privacy (names cannot be linked to personal identifiable information such as addresses, telephone numbers or coordinates).

In practice, this means the following for the further development of the tricot approach and the ClimMob platform:

• ClimMob should provide features to make it easy for trial designers to follow the principles and procedures indicated above:

• Automatically generated document to request IRB clearance;

• Standardized, short prior informed consent forms and practical ways to implement paper-based signature + photograph of the document, electronic signature, or spoken approval (audio);

• Names of participants that want to be named in the research publication exported by the platform.

• Anonymization of data before exporting. This can be automatized through automatic detection of potential personal identifiable information (see https://dataverse.scio.systems:9443/).

• Throughout the design of an experiment, ClimMob should provide cues to prompt users to consider research ethics, privacy and traditional knowledge rights in the design of tricot trials;

• ClimMob needs to be GDPR-compliant to users (cookie policy, explicit notice about usage of data). The version available at the moment of writing already has this implemented.

A more complex topic that deserves separate discussion is that tricot may be affected by national laws on the access to genetic resources and associated traditional knowledge and the sharing of benefits arising from their use (ABS, for short). There are two aspects in which tricot is affected by ABS, via the use of traditional varieties and via the use of traditional knowledge held by participants. We consider both aspects.

Firstly, tricot may need to observe ABS rules when using traditional varieties. tricot is usually applied to test the performance of new, improved varieties. However, in some cases, genetic materials of traditional varieties are to make comparisons. Although the utilization of the check varieties does not fall within the activities that are usually subject to ABS requirements in most countries, whether or not ABS obligations apply will depend on the definition of utilization adopted by the country of provenance of the variety (i.e., the country where the research is implemented). Therefore, tricot users will need to analyze the applicable access rules in the country where they are operating, obtain the access permits and negotiate mutually agreed terms when necessary. If the country where the traditional varieties come from is a party to the International Treaty on Plant Genetic Resources for Food and Agriculture (Plant Treaty), the acquisition of the traditional varieties for their use in tricot may be subject to the terms and conditions of the Plant Treaty’s multilateral system of access and benefit-sharing. In this case, access to the samples would be facilitated by the Standard Material Transfer Agreement. Since the purpose is not to breed the traditional varieties or incorporate them in new, improved lines, the multilateral system’s mandatory monetary benefit-sharing conditions would not apply, and thus the tricot users would not have any benefit-sharing obligation. However, they would have the obligation to transfer the varieties they have obtained with the SMTA under the same terms and conditions as those of the multilateral system, whenever the recipients of such material are going to use it for conservation, research, training and breeding.

Secondly, tricot may be exposed to ABS laws when using traditional knowledge. Farmers’ ability to perceive crop characteristics is often considered to be part of traditional knowledge related to genetic resources (Mancini et al., 2017). In tricot trials, farmers use their skills to produce new knowledge, which would usually not fall under national ABS laws, but whose use may be anyway subject to rules and protocols related to the interaction with indigenous and local communities, the access to their knowledge and their natural resources. Even if the country has not yet enacted ABS legislation in relation to genetic resources and/or traditional knowledge, or even if the existing laws and regulation do not apply to tricot trials in a particular context, it is wise to observe, the CBD and the Nagoya Protocol principles in the management of farmers’ varieties and knowledge in tricot trials, as ‘best practice’, as recommended by the Guidelines on the Nagoya Protocol for CGIAR Research Centers. This means, among other things, sharing non-monetary benefits back with the participants, in the form of informational results, best performing varieties and other types of technologies.