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

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.

Automated summary

This study presents a method of quantifying preferences and developing typologies in breeding programs by applying an online trait preference survey approach. The results demonstrate the usefulness of this methodology in addressing the expanding needs of plant breeding programs within smallholder contexts. This innovative approach provides insights into trait preferences and helps set breeding priorities that represent diverse interests.