Sampling
Béla Teeken, Jill Cairns, Mainassara Abdou Zaman-Allah
Introduction
On-farm testing increasingly relies on trial designs that are practical for farmers yet robust enough to generate reliable evidence across diverse environments. The tricot approach meets this need by asking participating farmers to test small, manageable sets of technologies under their own management and local conditions, and to provide simple, preference based rankings for key traits (for example, earliness, or taste). Because tricot uses incomplete blocks distributed across many farms, its statistical power comes from replication over space and seasons rather than from large, researcher-managed plots.
A successful tricot trial therefore begins with a thoughtful socio-economic and geographic sampling strategy that captures relevant differences among households and landscapes, so results reflect who farms where—and under what constraints. Farm selection should aim to represent the target population of environments and management practices, while ensuring sufficient numbers of independent farms to capture variability and support subgroup analyses (for instance, by soil type or rainfall zone).
Socioeconomic sampling
Recruiting experienced participants
Participatory variety selection often recruits farmers using broad demographic categories such as age, sex, education, occupation, or farm size, without considering the specific tasks people perform or their expertise. When gender is considered, the usual practice is simply to include equal numbers of men and women evaluating the trials, regardless of their experience with the crop.
Another limitation is that participants are often those comfortable speaking in research settings. This may exclude highly skilled people whose knowledge is mainly practical or embodied. For demand-led breeding approaches, breeders need detailed information on farming practices, processing, food preparation, and marketing, which can only be provided by people directly involved in these activities.
To address this issue, tricot trials use purposive sampling based on a task group approach with an explicit gender dimension. Task groups refer to categories of people who perform similar crop-related activities—such as cultivation, processing, or marketing—within locally defined social groups.A task group approach is also in line with a much more performative way of participation instead of only a deliberative one (Richards, 2005).
From a demand-led perspective, breeders need information on the suitability of improved varieties within users’ livelihoods and therefore need feedback from skilled farmers, processors, and marketers. These actors provide insights into agronomic performance, processing qualities, food characteristics, and market demand.
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. This approach can obscure how gender roles intersect with other locally relevant identities such as occupation, migration status, ethnicity, age, or economic status. Instead of beginning with gender categories, the task group approach identifies who performs which tasks within the crop value chain.
Focusing on tasks connects directly to practical activities such as cultivation, processing, food preparation, and marketing. This helps researchers access knowledge, skills, and working conditions without treating gender as the primary analytical entry point. By emphasizing work and expertise, discussions about gendered roles often emerge naturally. Knorr-Cetina (Knorr Cetina, 1999) would call this building epistemic cultures based on fascination and content of the work.
Participatory variety selection exercises organized around task groups therefore provide a useful way to capture practical knowledge about crop traits. Individuals may belong to multiple task groups—for example farmer-processors or farmer-marketers—and the degree of their involvement across tasks provides insights into gender roles and value-chain dynamics.
Practical implementation of a task group approach
After defining a regional sampling frame (e.g. selecting communities in areas where the crop is widely grow), the next step is identifying participants within each community. The following stages can guide this process.
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Identify relevant tasks Determine which crop-related activities are relevant to the study, such as cultivation, processing, food preparation, or marketing.
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Identify locally defined social groups This can be done through meetings with community leaders, key informants, transect walks, and interviews with people involved in crop-related work. This includes 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.
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Map tasks within social groups Within each locally relevant group, identify who performs which activities in producing, processing, and marketing the crop. Participants should be selected based on demonstrated knowledge and practical experience.
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Include relevant groups only Groups not involved in the crop value chain should not be included as participants.
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Organize participants locally In Nigerian cassava trials, ten participants per community were organized under one lead farmer responsible for coordinating communication and data collection. Allowing participants to select their own lead farmer helped ensure local legitimacy and ownership of the activity.
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.
- Determine compensation mechanisms Participants should receive modest compensation acknowledging their effort without creating incentives that overshadow intrinsic motivation. Investing time in establishing a reliable local coordination structure reduces the need for frequent visits by research staff and improves the consistency of data collection.
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 |
Participant selection and preparation
Participant recruitment should be purposive and based on interaction with community leaders and key informants to identify representatives of locally relevant social groups. Participants should represent these groups roughly in proportion to their presence in the community.
Selection should focus on tasks and skills rather than demographic categories such as age, although some age diversity should be maintained where possible.
Adequate time should be invested in ensuring the commitment of participants and the reliability of the lead farmer, as communication and coordination often occur through them by telephone. Preparation of participant teams should begin one to two months before distributing planting materials.
If participants withdraw when planting materials arrive (e.g. (de Boef & Thijssen, 2007)) they should be replaced with individuals representing similar social and task groups.
Table 2 of chosen participants in one community in Southwest Nigeria who participated in the tricot evaluation based on a task group approach (using information in Table 1).
| 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 |
Lead farmer trainings
Lead farmer trainings are organized to explain how to evaluate tricot plots and coordinate participants. Ideally these trainings take place after trial plots have been installed so farmers already understand the trial setup, although they can also occur earlier if necessary.
In Nigeria, organizing trainings at the state level rather than nationally allowed more practical field exercises and accommodated regional language differences.
Evaluation parameters should reflect farmer-expressed characteristics rather than breeder terminology. Based on earlier interviews, a simple guide with four to five parameters is prepared, asking participants to identify the best and worst variety for each parameter before providing an overall ranking.
Training ensures that lead farmers clearly understand each parameter, and translation into local languages is essential for consistent interpretation.
Administering a RHoMIS core social questionnaire
For the further sake of social differentiation and allowing to segment the dataset later, a Rural Household Multi-indicator Survey (RHoMIS) core social questionnaire will be administered with each of the chosen participants.
This survey may be conducted during distribution of planting materials or during a mid-season field visit, which also allows researchers to confirm that trials have been properly established. Linking RHoMIS data to participant tasks enables analysis of how socio-economic conditions influence trial management, performance, and preferences.
Making tricot inclusive: Gender and social heterogenteity
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 plays an important role in these dynamics because tasks within crop value chains are often gendered (Weltzien et al., 2019). However, tricot analyses have not consistently detected strong statistical differences between men and women, even though trait prioritization studies often show gender-related differences. One reason may be that gender interacts with other social variables, such as income, occupation, migration status, or ethnicity, which are rarely captured in analyses.
Traditional methods such as free-listing exercises may also have limitations. They reflect salience in discourse rather than relative importance in real decision-making contexts and may be influenced by leadership effects or translation issues (Richards, 2005). Newer approaches based on pairwise comparisons offer alternatives that better capture trade-offs among traits (Byrne et al., 2012; Steinke et al., 2017).
Two methodological innovations address these challenges.
First, the RHoMIS survey provides standardized information on household characteristics, gendered control of activities and income, farming systems, and socio-economic indicators (Hammond et al., 2017; van Wijk et al., 2020). A shortened version is used in tricot trials to reduce respondent fatigue while enabling analysis of how social factors influence trial outcomes.
Second, participant recruitment moves beyond simple gender quotas toward purposive sampling based on locally identified social groups and task expertise. Qualitative research identifies these groups, and participants are selected to ensure representation of relevant expertise across the value chain. Random interviews within these groups verify participants’ experience in cultivation, processing, or marketing.
This approach allows gender analysis to be conducted after data collection, comparing preferences among men and women within the same task groups and across different social identities.
Task groups develop specialized knowledge, language, and practices. Recruiting participants from these groups ensures that tricot trials draw on real expertise and capture the diversity of user preferences across the crop value chain. Task groups can be identified through ethnographic methods such as interviews, transect walks, and market observations.
By combining task-group sampling with standardized socio-economic data, tricot trials can generate socially inclusive insights into varietal preferences while still identifying cross-cutting traits that breeders can target across regions and user groups.
Ethics, privacy and rights on traditional knowledge
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.
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.