Multi-stakeholder Framework (MSF)
Grasping the complexity of seed systems is a challenge for those who are working in a new location or crop, whether to understand the existing seed systems or to conduct projects to improve them. The multi-stakeholder framework (MSF) addresses this challenge by providing a snapshot of a seed system in a specific crop, location, and time.
The MSF is an adaptation of the seed system security assessment (SSSA) (Remington et al. 2002; Sperling 2008; FAO 2015) built around concepts derived from food security: access, availability, and utilization (quality). The MSF considers seed regulations and policies, sustainability, and gender as crosscutting themes. The MSF was tested in 13 case studies, finding that gender roles are important in seed systems and that ignoring the differences between women and men can lead to coordination breakdowns that can threaten seed security (Bentley et al. 2018). The MSF has been applied in 17 countries and 7 crops (Table 11.2).
The MSF has been used to:
Understand the seed sourcing behavior of cassava farmers and to identify entry points for decentralized stem multipliers (DSMs) in Nigeria (Pircher et al. 2019).
Identify stakeholders in the potato production system in Georgia (Andersen Onofre et al. 2021).
Review a sweetpotato project promoting a systematic reflection from different stakeholder perspectives in Tanzania (Ogero et al. 2015).
Identify participants and design key informant interviews and focus group discussions to explore regulations for potato and cassava seed in Vietnam, Nigeria, and Kenya (Wossen et al. 2020; Gatto et al. 2021; McEwan et al. 2021c; Spielman et al. 2021).
Identify stakeholders and the main features and bottlenecks of potato seed systems to refine research questions and design a household survey in Ecuador (Navarrete et al. 2019).
Estimate seed security of teff and wheat in Ethiopia (Mulesa et al. 2021).
The MSF can be used as the starting point for a comprehensive analysis of bottlenecks in a seed system, to monitor an intervention (McEwan et al. 2021a), and for cross crop/region comparison among interventions (Bentley et al. 2018). Users of the MSF usually gain an understanding of the complexity in structure and interactions between stakeholders, including tensions which are not obvious prior to using the tool. The MSF is multidisciplinary and transdisciplinary, which may be a challenge during workshops or field visits, but also a benefit, encouraging the users to take a more holistic view of seed systems.
Impact Network Analysis (INA)
Impact network analysis (INA) is a tool for anticipating the outcomes of a seed system project that is underway or in planning. It is a new tool based on modeling a system as a combination of (1) a network of people or institutions who may influence each other and have transactions and (2) a network of the movement between farms of seed, varieties, and potentially of pathogens or pests (Garrett et al. 2018; Garrett 2021a, 2021b). Results from scenario analyses support decision-making by researchers, policymakers, and practitioners.
INA includes an R package that simulates outcomes for scenarios defined by the user (Garrett 2021a, 2021b, updates at garrettlab.com/ina/). It provides scenario analyses in stochastic simulations to evaluate questions such as the following: (1) How likely is a new variety to spread through a seed system, and how could changes in the system make it spread further? (2) What will be the most effective sampling strategy for monitoring disease spread through a seed system? (3) What strategy for managing disease spread is likely to be most effective? (4) Do men and women (and other social groups) receive equitable benefits from the current seed system, or what changes would be necessary?
INA has been applied in combination with the MSF and the ISH approach to help design a new potato seed system in the Republic of Georgia, taking into account risks from diseases such as potato wart (caused by Synchytrium endobioticum), and identifying key locations to monitor the disease to prevent losses (Andersen Onofre et al. 2021). INA is currently being applied with seed tracing to develop strategies for deploying clean seed to slow the spread of cassava mosaic disease in SE Asia (Delaquis et al. 2018; Andersen et al. 2020). The INA framework was applied to understand potato seed systems in Ecuador (Buddenhagen et al. 2017) and sweetpotato seed systems in Uganda (Andersen et al. 2019), where these two studies provided groundwork for applications in new systems. INA is also currently being applied in collaboration with the Kenya Plant Health Inspectorate Service (KEPHIS) to evaluate strategies for managing disease in Kenyan potato seed systems (Gachamba et al. 2022) and for banana and mango disease and pest risk assessment in Haiti and the Caribbean (Dantes et al. 2020; Fayette et al. 2020).
Seed Tracker (ST)
The Seed Tracker (ST) is an ICT tool that digitally links seed value chain actors, tracks seed production, and organizes seed information for stakeholders. The ST’s digital data collection tools are usable on any Internet-enabled device with an Android operating system. It offers secure individual and group accounts and a database with analytics and geographic information system (GIS) tools. The ST covers all stages of the seed value chain and the needs of stakeholders: researchers, extensionists, regulators, seed producers, traders, service providers, and farmers. It supports seed production planning, seed traceability, seed inventory management, and quality assurance. The Seed Tracker allows regulatory authorities to monitor the production of certified seed and allows real-time information exchange between seed producers and regulators. It is also a business tool that helps to link seed producers with customers. It offers real-time information on seed production by seed class, variety, volume, and location. The ST can be customized to fit different crops, national seed regulations, and user-defined needs. ST can potentially map gender-disaggregated information which policymakers and extension services can use to inform seed delivery strategies.
Integrated Seed Health (ISH) Approaches and Models
Seed systems that spread diseases or pests can do more harm than good. Understanding seed degeneration, and how to manage it, is important for supporting better seed systems. The “integrated seed health approach” combines three management components to help farmers decide how to manage seed health: periodic purchase of healthy seed, disease resistance, and on-farm disease management (Thomas-Sharma et al. 2016). A model called “seedHealth” identifies the combinations of these three components most likely to be successful, to support training and decision-making by researchers, policymakers, and practitioners (Thomas-Sharma et al. 2017; using an online dashboard link at garrettlab.com/seedhealth/; Garrett and Xing 2021).
At regional scales, a fourth component of ISH approaches is phytosanitary management to prevent the introduction of new pathogens and pests. The seedHealth model can be used to answer questions such as: (1) How frequently would it benefit farmers to buy certified/quality-declared seed, and/or to access a new variety? (2) What would the effect be of strengthening particular types of on-farm management, e.g., training to support positive selection? (3) Do differences in men’s and women’s access to, and use of, the components of seed health management lead to different levels of success?
ISH approaches and the seedHealth model are being used to study seed health in systems such as sweetpotato in Tanzania (Ogero et al. 2019) and potato in Kenya (Gachamba et al. 2022). The ISH approach has been applied in combination with the MSF and INA to help design a new potato seed system in the Republic of Georgia that protects against the spread of diseases such as potato wart, balancing on-farm management, resistant varieties, and new seed certification standards (Andersen Onofre et al. 2021). Seed health in a potato system in Ecuador was studied to support “management performance mapping,” identifying locations in the Andes and Kenya where support for positive selection of farmer-saved seed is likely to have the greatest benefit (Buddenhagen et al. 2022). The seedHealth model can also be applied to evaluate better phytosanitary standards, to address the trade-off between higher availability of seed and poorer seed health (Choudhury et al. 2017). In banana bunchy top management, the model has been used to visualize and predict the strategies for managing disease spread and seed degeneration and to compare the performance of specific control options under different field conditions (I. Nduwimana, pers. comm.).
Seed Tracing (STg)
An important issue in the seed systems of RT&B is how new varieties diffuse. These systems are mostly informal, so the exchange between farmers is the main avenue for distributing new varieties. Seed tracing can be used to understand the diffusion of seed from formal to informal networks, and within farmer networks, thus providing strategic information for seed interventions and for policymakers.
In Ethiopia, a seed tracing study found that an NGO distributed seed potato of new varieties to wealthier farmers, who shared seed tubers frequently, including with poor farmers who rarely shared seed (Tadesse et al. 2017b). The wealthier farmers were key in variety diffusion, but also potentially in spreading pests and diseases.
Among Rwandan cassava farmers, seed tracing was used to inform the design of commercial seed businesses (Fig. 11.5). As in the Ethiopian case, better-off growers were more likely to obtain a new variety from formal sources, while poor farmers accessed new varieties from fellow farmers. Most (60%) seed transactions were for cash, suggesting that there are market opportunities. Yet, they were all one-time acquisitions. Once they obtained the variety, all farmers multiplied their own material, and 80% shared this with fellow farmers (Kilwinger et al. 2021b). This is common with the introduction of new VPC varieties: after a first spike of demand, the new variety gets absorbed into the informal seed system (Barker et al. 2021), discouraging commercial seed businesses.
A study of legume seed (Almekinders et al. 2020) found that men most often shared with men, and women with women, but men shared with women more often than the other way around. Such patterns of gendered seed flow could have implications for introducing new varieties. Yet, there was little effect on who the seed eventually spread to. The study allowed the project to report to donors that it had reached an estimated 2.5 million farmers in Africa through direct distribution and spontaneous diffusion of seed over the course of the project (Almekinders et al. 2020; Sikkema 2020).
Small N Exploratory Case Study (SN)
A small N exploratory case study collects data on formal and informal seed systems of a crop at the level of the farmers: what varieties do they grow and what are their patterns of seed saving, replacement, and sourcing? This is useful when diagnosing a seed system and identifying the challenges in improving local availability and access to quality seed: a first step that leads to deeper reconnaissance and seed system intervention. Typically, the core of the data collection is a survey with well-targeted questions for 35–50 farmers in a few communities. Because seed use practices, variety preferences, and needs of better-off and poor farmers often differ, it is worthwhile to collect data on both types of farmers and on male and female farmers.
A small team can collect the data relatively quickly. These inexpensive studies can be designed and carried out by staff members of an NGO or an agency that is active in the area and may later support seed activities. In contrast to surveys with many farmers and hired enumerators (i.e., large N surveys of 400 farmers or more), small N surveys can be enlightening for the data collectors, who may later help to implement the seed project. Our survey experience in Nigeria showed that joint analysis and discussion of the data were important learning opportunities for the local staff to arrive at a joint understanding of the cassava seed systems of the farmers they worked with (see Pircher et al. 2019).
This type of study belongs to a family of small N approaches (White and Phillips 2012) and has proven to be publishable, especially when gathering the first information about a seed system. For example, in the RTB case of banana seed systems, there was limited understanding of how management of the mat and suckers influenced variety choice and how it related to the farmer’s age and gender (Kilwinger et al. 2019). In these situations, the additional information was acquired through semiformal interviewing or focus group discussions and use of the four-square method (see below).
Four-Square Method (FSM)
The four-square method (FSM) originally meant identifying a community’s common, unique, and endangered crop varieties for genetic conservation (Grum et al. 2008). It comprises four squares that are drawn on the ground or on a chart. Each of the four squares holds the names of varieties of interest based on their abundance, i.e., if a variety is grown by many or few households, on a large or a small area:
Many households on large area.
Many households on small area.
Few households on large area.
Few households on small area.
The FSM has been adapted to assess crop diversity and popularity within a community and to create discussions around seed systems. The method can generate an inventory of varieties grown in a particular place and discuss their importance with farmers. Such information helps to identify seed interventions needed to conserve crop varieties and to highlight desirable traits in new varieties. The classification can also be a quick way of assessing the penetration of new varieties or changes in the popularity over time in response to seed systems or environmental stressors (Simbare et al. 2020). The FSM is often used in a focus group discussion with men or women to capture gender-related differences in appreciation of varieties and their traits (Mulugo et al. 2021).
The FSM has been used to study:
The changes in varietal diversity of East African highland bananas in banana bunchy top disease outbreak areas of Burundi (Simbare et al. 2020).
Farmers’ production objectives regarding banana diversity in central Uganda (Kilwinger et al. 2019).
Cassava diversity, loss of landraces, and farmers’ preference criteria in southern Benin (Agre et al. 2016).
Yam diversity and production in Southern Ghana (Nyadanu and Opoku-Agyeman 2015).
Varietal diversity and genetic erosion of cultivated yams in Togo (Dansi et al. 2013).
Seed interventions and cultivar diversity in pigeon pea in Eastern Kenya (Audi et al. 2008).
Farmers’ limited uptake of tissue culture banana seed in central Uganda (Mulugo et al. unpublished data).
In all these cases, the FSM was complemented with other methods such as literature review, key informant interviews, Venn diagrams, participatory value chain mapping, participatory rapid market appraisal, household surveys, and other tools of the toolbox, e.g., small N exploratory case study.
The FSM has been used to assess different seed system contexts including (1) before an intervention to understand the existing seed systems and to identify key issues for the project and (2) during interventions to monitor or evaluate them. The FSM creates a versatile overview and has been adapted elsewhere in dietary diversity studies (Aboagye et al. 2015) and gender studies in banana seed systems (Nkengla-Asi et al. 2020). The results can help to identify entry points for further research, for example, identifying varieties to study in greater depth using INA (I. Nduwimana, pers. comm.). Nkengla-Asi et al. (2020) adapted the method to classify household seed decision-making based on the level of responsibility and consultation between men and women, to reveal areas of common understanding and potential conflict. Other uses of the method could be developed.
Means-End Chains (MEC)
Means-end chain (MEC) analysis
is an approach from the field of consumer studies developed in the 1980s (Reynolds and Gutman 1988). Since its development, the method has been applied in diverse fields such as tourism, food quality and preference, and sustainable behavior. Recently, it has also been used to understand how farmers evaluate, and why they value, different agricultural products, practices, and innovations (e.g., Okello et al. 2019; Urrea-Hernandez et al. 2016). The method is based on several psychological theories and takes into consideration differences among individuals’ experiences. The means-end chain analysis identifies such differences as respondents are invited to select and verbalize their own personally relevant attributes to evaluate a product, service, or practice and relate those to their personal values (Walker and Olson 1991).
The method is promising in cross-cultural and exploratory research as it avoids forcing respondents into predetermined categories (Watkins 2010). And the psychological theories on which the method is based are similar to those underlying new approaches to understand adoption (Kilwinger and van Dam 2021a). For example, the framework to understand technological change developed by Glover et al. (2019) is based on the theory of affordances (Gibson 1977), which has considerable overlap with the personal construct theory (Kelly 1955). Also, Tricot trials (van Etten et al. 2019) make use of the principle of asking farmers to differentiate between three choices.
One MEC study with Andean potato farmers found that farmers and experts understand seed quality differently (Urrea-Hernandez et al. 2016), suggesting that understanding farmers’ perceptions of quality seed is important for developing effective seed interventions. A MEC study of farmers’ perceptions of formal and informal sources of banana planting material in Uganda showed that all farmers (large and small, male and female) had similar goals, but considered different variety traits and the benefits derived from them to achieve those goals (Kilwinger et al. 2020). Some of these Ugandan farmers expected to find the planting material of these varieties in nurseries, while others planned to get it from fellow farmers. These farmers care about variety traits, but also about the source of their planting material. It is important to understand which variety traits farmers prefer, as well as how they like seed to be delivered.
Experimental Auctions (EA)
A key challenge in developing VPC seed systems is understanding and predicting demand for different types and quality of planting material. The viability of the seed system depends on whether farmers perceive the seed as a quality planting material and whether they are willing to pay a premium for that quality. To get those insights, various types of experimental auctions can elicit “true willingness to pay.” This tool allows comparing the premium value given to seeds, varieties, or variety traits by different groups, e.g., men and women farmers. This tool can also map out seed market size and segments for various types of customers. Outcomes can support more competitive pricing policies, attract different types of seed producers and customers, and nicely complement other tools (e.g., SEGSBAT described below).
Experimental auctions have recently been conducted among bean farmers in Tanzania and cowpea farmers in Ghana, to evaluate their willingness to pay for certified, quality-declared, and recycled seed, concluding that farmers were willing to pay a slightly higher price for what they perceived as better seed (Maredia et al. 2019).
There has been less use of experimental auction approaches with vegetatively propagated crops. Due to the economic differences between VPC planting material and grain seeds, the method needs to be further evaluated and adapted (several studies to do this are underway led by members of the toolbox group). However, the method has yielded useful preliminary results which are already shaping seed system strategies. In Rwanda, this tool was applied in 29 villages in six leading sweetpotato production areas to estimate willingness to pay for high-quality sweetpotato planting materials and drivers of the demand for these vines (Fig. 11.6). The study also estimated willingness to pay a premium for biofortified varieties (rich in provitamin A) as opposed to the non-biofortified local ones. The preliminary results showed that true willingness to pay a price premium for quality attributes is significantly higher than the current price for the sweetpotato seed.
In Lao PDR, 21 experimental auctions in cassava areas around the country unearthed large differences in stem prices linked to villages’ historical experiences with commercial cassava production (Delaquis et al. unpublished data; Fig. 11.7). In all sites, bids were higher for phytosanitary-tested seed and elite varieties than for farmer seed. The auctions also elicited how many bundles of seed were desired, which varied widely, demonstrating different seed purchase strategies (purchasing a few bundles for testing vs. going all-in and buying enough to replace the farmer’s whole supply). Demand curves generated from the results are also informing early stage, clean seed multiplication initiatives in the country with price points and preferences that can shape areas of intervention and outgrower strategies. This example demonstrates the tool’s use at several stages in the project cycle.
Seed Regulatory Framework Analysis (SRFA)
More than 95% of the seed of RT&B crops flows through informal channels (e.g., farmer saved, purchased from neighbors and local markets). Yet current seed regulatory frameworks do not recognize this and may act as a constraint to improving the quality of vegetatively propagated seed. Most national seed policies and regulations were developed using the experiences from grain seed, especially hybrids. The characteristics of RT&B crops, such as clonal reproduction and specific plant health constraints that contribute to seed degeneration, have not been fully recognized. This means that regulatory processes need to be revised to remain relevant
Multidisciplinary teams of researchers, together with seed regulators, have used the Seed Regulatory Framework Analysis Tool (McEwan et al. 2021b; Spielman et al. 2021) to assess the implications of current seed regulatory frameworks in Kenya, Nigeria, and Vietnam. The teams have asked if implementing regulations increases the availability and access to quality seed potato, for whom, and with what consequences. In Kenya, stakeholders are gathering around two key narratives. The first narrative, “quality at any cost,” ties potato (and other VPCs) to national food security objectives, arguing that yields will only increase within a regulatory framework that provides certified seed at scale, minimizes the risk of pests and diseases, and protects the reputation of seed producers and the hard-earned credibility of the country’s regulator. The second narrative, “local quality assurance,” introduces “clean” (healthy) seed production models that build off the entrepreneurial spirit of smallholder farmers and their organizations and allows for more relaxed quality standards and informal trade (McEwan et al. 2021c).
In Kenya, the increased understanding that VPC seed faces different challenges than grain seed has led to separate regulations for vegetatively propagated crops, perhaps the first instance in sub-Saharan Africa. In Vietnam, despite strict regulations on the production and trade of VPC seed, the rules are weakly enforced. Instead, seed producers and traders signal quality to farmers through trust, reputation, and long-term relationships. This may be effective at a localized scale, but these informal systems are unlikely to accommodate expansion of the cassava and potato sectors and unlikely to effectively manage increases in pest and disease pressures that result from cross border trade or climate change (Gatto et al. 2021). In Nigeria, findings have led to decentralized policy and regulatory approaches to managing the cassava seed system, prioritizing investment in innovative capacity at the community and enterprise levels (Wossen et al. 2020).
Sustainable Early Generation Seed Business Analysis Tool (SEGSBAT)
The transition from breeder seed to pre-basic (i.e., first generation) seed production is a major bottleneck in the smooth functioning of a formal seed system. An early generation seed (EGS) company requires predictable revenues based on competitive and affordable prices for market-preferred varieties. Using the sustainable early generation seed business analysis tool (SEGSBAT)
(Rajendran and McEwan 2021a, 2021b), public and private sector institutions in 11 countries in sub-Saharan Africa analyzed the financial sustainability of their sweetpotato EGS businesses (International Potato Center 2017). Multidisciplinary teams first determined accurate costs of EGS production which were then used to calculate the appropriate price of EGS products and formulate a pricing strategy to attract more customers, increase revenue, and create a positive net cash flow (Fig. 11.8). Partners in six countries improved continuity of funding and met at least 90% of their recurrent seed production costs from season to season. Most institutions reduced the gap between production and sales, which increased marketed surplus. By having a detailed cost structure, users identified and addressed production inefficiencies to reduce the cost of goods sold, e.g., by reducing the number of tissue culture plantlets required, and optimizing screenhouse production (Rajendran et al. 2017). Partners used SEGSBAT to develop business plans to guide sustainable sweetpotato EGS production, a first for RT&B crops in Africa (Gurmu et al. 2019).
Applying this tool revealed the specific challenges of determining the production costs of VPC seed, including (1) varying multiplication rates due to varietal characteristics, changes in temperature, growing conditions, and ratooning practices; (2) multiple stages in seed production, which may take place in different locations, i.e., pathogen tested tissue culture micro-propagation, hardening tissue culture plantlets for screenhouse multiplication before producing commercial seed in open fields; and (3) because seed is alive, wastage can be high and this must be factored into production costs.
Use of this tool highlighted that current methods for estimating seed requirements for production planning are inadequate (International Potato Center 2017). There are clear opportunities to continue working with public and private EGS producers and their networks of seed entrepreneurs to match SEGSBAT and other tools from the toolbox to the different stages in the product life cycle as part of the handover for commercialization from breeding outputs to seed value chain actors.
Discipline-specific jargon can be a big obstacle to reaching a wider audience (Bullock et al. 2019). Seed system initiatives often provide a glossary of terms to help readers to understand key concepts. However, definitions may be generated by the authors themselves, to apply only within the context of their particular initiative. This can lead to confusion as many different interpretations arise and are misused or repeated out of context.
Common definitions are especially important for an emerging research area like seed systems, which brings together concepts from many different disciplines. The toolbox itself contains technical content from economics, behavioral science, network analysis, botany, agronomy, plant pathology, policy analysis, and gender studies, so most readers will encounter unfamiliar terms.
The glossary of RT&B seed systems developed for the toolbox (Delaquis et al. 2020) lends clarity to this issue by compiling definitions cited in literature across disciplines and providing the context of each term, references and links to the original sources, and the date of last modification. Over time, new terms can be added to the interactive glossary on the toolbox website, and existing definitions can be updated. Having definitions in one public place facilitates disambiguation and opens dialogue.
The glossary provides a stand-alone reference, supports the use of all tools in the toolbox by a wide audience, and can track changing definitions as seed systems research evolves and new concepts emerge, serving as a resource for anyone working on seed systems.