Laporte, M.-A. | Search bioRxiv

bioRxiv2024

Citizen science informs demand-driven breeding of opportunity crops

CONTEXTOpportunity crops, also known as neglected and underutilized species (NUS), offer benefits to diversify food systems with nutritious and climate-resilient foods. A major limitation to incorporate these crops in farming systems is the lack of improved varieties impedes farmers accessing quality planting material of these crops. OBJECTIVESThe study explored how citizen science methods can support demand-driven breeding and seed production of opportunity crops using leafy amaranth - a nutritious and hardy vegetable- as a case study. The study identified farmer preferences and market segments, with particular attention to gender and social differentiation. METHODSWe used the tricot approach to conduct participatory on-farm trials of 14 varieties with 2,063 farmers from Benin, Mali, and Tanzania. We then analyzed farmer trait and varietal preferences in aggregate and among segments of farmers, generated using cluster analysis. RESULTSFarmers overall preferences for amaranth varieties was driven principally by plant survival, yield, leaf size, taste, and marketability. Distinct farmer segments (older women generalists, young women specialists, older men generalists, and young men specialists) preferred different varieties depending on gender, business-orientation. DISCUSSION AND CONCLUSIONThe farmer segments identified here, along with their unique variety preferences provide valuable information for breeders and seed enterprises, and support demand-driven amaranth breeding and seed system development. We specifically noted the need for breeding programs to understand the preferences of young amaranth specialists, both men and women, and to explore organoleptic and market-related properties of amaranth. SIGNIFICANCEOur findings on differentiated producer preferences will support scaling seed supply of amaranth in Africa to diversify farming systems with a climate-resilient and nutritious crop. The methods used and lessons learned from our citizen science exercise can be applied to enhance breeding and seed supply of other opportunity crops that are underutilized in Africa or other continents. HighlightsO_LIFarmer citizen science methods reveal seed market insights for opportunity crops C_LIO_LIAmaranth variety preferences vary widely across gender, age, and countries C_LIO_LIYoung amaranth producers focus on market and sensory traits compared to old producers C_LIO_LILarge-scale farmer feedback guides opportunity crop breeding for diverse segments C_LI

plant biology↗

bioRxiv2019

COPO: a metadata platform for brokering FAIR data in the life sciences

Scientific innovation is increasingly reliant on data and computational resources. Much of todays life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.\n\nCOPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share it with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible. Building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing.\n\nAvailabilityCOPO is entirely open source and freely available on GitHub at https://github.com/collaborative-open-plant-omics. A public instance of the platform for use by the community, as well as more information, can be found at copo-project.org.

bioinformatics↗