From traits to typologies: Piloting new approaches to profiling trait preferences along the cassava value chain in Nigeria

Abstract

Breeding programs are increasing efforts towards demand‐led breeding approaches to ensure that cultivars released meet the needs of end users including processors, traders, and consumers, and that they are adopted by farmers. To effectively deploy these approaches, new tools are required to better understand and quantify the degree of preference differences among alternative trait changes competing for measurement and selection effort. The purpose of this study was to present a method of quantifying preferences and developing typologies according to breeding priorities by applying an online trait preference survey approach to cassava (Manihot esculenta Crantz). This paper presents a conjoint analysis based on Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) to help guide breeding programs in understanding trait preferences across value chain roles and social contexts and set breeding priorities that represent diverse interests. Trait preferences were assessed using a comprehensive survey and analysis package incorporating a core adaptive conjoint method (1000minds, 2020). Trait selection was based on a trade‐off of 11 cassava traits carried out with 792 cassava value chain actors in four geopolitical regions in Nigeria. Principal component and cluster analyses revealed three clusters (typologies) of respondents according to their trait preferences. The results demonstrate the usefulness of this methodology that innovates on previous trait preference approaches to address the expanding needs of plant breeding programs within smallholder contexts.

Gender-inclusive consumer studies improve cassava breeding in Nigeria

“…For instance, the 1000minds-Rhomis results reinforce the importance of recognizing social differences among men and women, and how individual and household characteristics interact to influence trait preference variability. This information can inform trait prioritization and guide development of breeding products that have higher social impact, serve the more vulnerable and align with development goals (Teeken et al, 2021 ; see Balogun et al, 2021 ).…”

“…Social segmentation was gender disaggregated (with key informant interviews, FGDs) in terms of livelihoods, wealth categories (Forsythe et al, 2021). RHoMIS-Rural household multi-indicator survey (Hammond et al, 2017), tricot (de Souza et al, 2024, poverty probability index (PPI) and 1,000 minds surveys were conducted between 2020 and 2021 to gain in-depth understanding of preferred traits by geographical location (Balogun et al, 2021) Value Chain Analysis or Mapping (2021) The value chain mapping for gari showed percentage contribution to the different nodes by production, processing, wholesale, retail, consumption (fresh and processed forms), all disaggregated by gender Study of trait preferences Trait preferences disaggregated by gender were elicited in RTBfoods, NextGen, and tricot-Rhomis projects by NRCRI and IITA from 2017 to 2023 for targeted breeding and demand-driven research (Madu et al, 2021a;de Sousa et al, 2023) Use of G+ Tools for consumer or product profile assessments G+ tools were applied for product profiles (gari and fufu). Possible harm and positive benefits were highlighted for traits at the production, processing, and consumption stages for both gari and fufu (Madu et al, 2021a,b) Farmer-managed or small-scale, seed production…”

Editorial: Agile data-oriented research tools to support smallholder farm system transformation

“…Milner et al, Marinus et al, and Caulfield et al each compiled secondary data from previous implementations of the Rural Household Multi Indicator Survey (RHoMIS) (Hammond et al, 2017;van Wijk et al, 2020). Gotor et al combined data from RHoMIS and the tricot approach (van Etten et al, 2019;Brown et al, 2022), while Teeken et al combined data from RHoMIS and an application of the 1000 minds tool (Hansen and Ombler, 2008;Balogun et al, 2022). Another three articles collected novel data using the RHoMIS tool and presented analyses based upon that data (Alary et al; MacLaren et al; Périnelle et al).…”

“Whose demand?” The co-construction of markets, demand and gender in development-oriented crop breeding

“…Among others, several CGIAR breeding programmes, some with financial support from the EiB, have carried out projects in cooperation with the private company AbacusBio (e.g. Ibabao 2019;Teeken et al 2021;Balogun et al 2022). AbacusBio uses a software called 1000Minds, which is based on conjoint analysis that 'forces' surveyed value chain actors to make trait-by-trait trade-offs, where each trait has a quantitative value (Balogun et al 2022).…”

“…Ibabao 2019;Teeken et al 2021;Balogun et al 2022). AbacusBio uses a software called 1000Minds, which is based on conjoint analysis that 'forces' surveyed value chain actors to make trait-by-trait trade-offs, where each trait has a quantitative value (Balogun et al 2022). The output of the survey "provides insights into segmentation of the population (…) [and] also informs derivation of economic values that can be employed by breeders in making selection decisions" (Balogun et al 2022).…”