https://github.com/agrdatasci/documentation

https://agrdatasci.github.io/documentation/

https://github.com/agrdatasci/documentation

Science Score: 26.0%

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Last synced: 11 months ago · JSON representation

Repository

https://agrdatasci.github.io/documentation/

Basic Info
  • Host: GitHub
  • Owner: AgrDataSci
  • License: cc-by-4.0
  • Language: JavaScript
  • Default Branch: main
  • Homepage:
  • Size: 63.4 MB
Statistics
  • Stars: 1
  • Watchers: 4
  • Forks: 0
  • Open Issues: 3
  • Releases: 0
Created over 2 years ago · Last pushed 11 months ago
Metadata Files
Readme License

README.md

The on-line guide for product use testing in agriculture

The tricot approach (triadic comparison of technology options) is a participatory method designed for product use testing in agriculture. It has been applied in on-farm trials, consumer testing, concept evaluation, and iterative product development. The approach leverages citizen science to generate robust, scalable insights across diverse environments and user contexts. Here's how it integrates into different aspects of product use testing:

  1. On-Farm Testing

Farmers receive three randomly assigned technology options (e.g., seed varieties, fertilizers) and independently evaluate their performance under local conditions. No direct supervision is required, making it cost-effective and scalable, especially in remote areas. Data collection focuses on farmer-reported outcomes such as yield, resilience, and preference, linked to environmental metadata (e.g., soil, climate), socio-economic metadata (e.g., market preferences, household dynamics, management practices) and DNA metadata.

  1. Consumer Testing

Tricot integrates consumer preferences for end-use products (e.g., taste, cooking quality, shelf life). Farmers and end-users assess outputs from tested options (e.g., crops, processed goods) to ensure alignment with market demands. The approach helps bridge the gap between agricultural production and consumer needs by combining field performance with end-user satisfaction.

  1. Concept Testing

Tricot can be used to evaluate broader concepts, such as innovative farming practices, new varieties and agroforestry designs. Participants compare three alternatives in usability, practicality, or benefits, ensuring the development of context-specific solutions. This iterative testing phase supports refining ideas before large-scale implementation.

  1. Farmer-Centric Data and Decision Support

By empowering farmers as co-researchers, tricot generates farmer-driven data, enriching breeding programs and product development pipelines. Insights into environmental interactions and user preferences guide demand-driven breeding and agricultural innovation. The ClimMob Platform enable real-time data collection, analysis, and visualization to inform decision-making.

  1. Scaling and Adaptation

Tricot's simplicity allows broad implementation across geographies, crops, and technologies. The model is adaptable to low-resource settings, supporting smallholders while enabling private sector product testing. It also fosters inclusivity, involving women, youth, and marginalized groups in the innovation process.

  1. Outcomes and Impact

Enhances crop diversity and resilience by tailoring recommendations to local needs. Increases adoption rates by aligning product characteristics with farmer and consumer preferences. Supports sustainable and climate-adaptive agriculture by integrating real-world testing with robust scientific analysis. In summary, the tricot approach is a dynamic, end-to-end solution for product use testing in agriculture, integrating farmer trials, consumer insights, and conceptual testing. It drives innovation by prioritizing user needs, ensuring product relevance, and enabling resilient and inclusive agricultural systems.

Online Reading

You can read this book on-line at the following link:

https://agrdatasci.github.io/documentation/

License

See LICENSE for details.

Owner

  • Name: AgrDataSci
  • Login: AgrDataSci
  • Kind: organization
  • Email: k.desousa@cgiar.org
  • Location: France

We develop methods and tools to support sustainable food systems, rural development and digital inclusion

GitHub Events

Total
  • Issues event: 1
  • Watch event: 2
  • Delete event: 1
  • Issue comment event: 3
  • Push event: 71
  • Pull request event: 53
  • Create event: 4
Last Year
  • Issues event: 1
  • Watch event: 2
  • Delete event: 1
  • Issue comment event: 3
  • Push event: 71
  • Pull request event: 53
  • Create event: 4

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 47
  • Total Committers: 3
  • Avg Commits per committer: 15.667
  • Development Distribution Score (DDS): 0.574
Past Year
  • Commits: 47
  • Committers: 3
  • Avg Commits per committer: 15.667
  • Development Distribution Score (DDS): 0.574
Top Committers
Name Email Commits
Marie-Angélique Laporte m****e@g****m 20
kauedesousa k****a@i****o 16
Rachel Chase r****e@c****g 11
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 1
  • Total pull requests: 43
  • Average time to close issues: N/A
  • Average time to close pull requests: about 3 hours
  • Total issue authors: 1
  • Total pull request authors: 3
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.12
  • Merged pull requests: 41
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 43
  • Average time to close issues: N/A
  • Average time to close pull requests: about 3 hours
  • Issue authors: 1
  • Pull request authors: 3
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.12
  • Merged pull requests: 41
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • kauedesousa (1)
Pull Request Authors
  • kauedesousa (24)
  • rachel-chase (14)
  • marieALaporte (5)
Top Labels
Issue Labels
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Dependencies

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package-lock.json npm
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package.json npm
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