Merge pull request #6 from AgrDataSci/dev

Dev

docs/01-introduction-to-tricot/introduction.md

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-# Tricot Introduction
+# Introduction to the tricot approach 
+
+> Jacob van Etten, Jonathan Steinke, Kauê de Sousa, Rachel Chase
 
 **Speeding up agricultural innovation through large-scale participatory experiments**
 

docs/01-introduction-to-tricot/steps.md

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-# The 10 steps of a Tricot project
+# The 10 steps of a tricot experiment
+
+> Rachel Chase
 
 This guide provides a short overview of each of the 10 steps needed to develop and implement a tricot project.
 

docs/02-planning/introduction.md

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 # Planning a tricot experiment
 
+> Béla Teeken, Gospel Edughaen, Kauê de Sousa, Rachel Chase
+
 ## Problem Identification
 - Defining research questions and objectives.
 - Understanding the target farming system.

docs/03-experimental-design/bean-protocol.md

@@ -1,12 +1,5 @@
 # Bean Trial Protocol
 
-**Authors**
-
-Abel Moges, Kidane Tumsa, Julius Mbiu, OneAcre Fund, Michael Kilango,
-Nestory Shida, Stanley Nkalubo, Wilber Ssekandi, Sylvia Kalemera,
-Fadhili Kasubiri, Teshale Mamo, Clare Mukankusi, Jean Claude Rubyogo,
-Edith Kadege, Kaue de Souza, Rhys Manner
-
 ## Introduction
 
 This document provides a standardised overview of the basic information

docs/03-experimental-design/geographic-sampling.md

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 # Geographic sampling
 
+> Joost van Heerwaarden, Jacob van Etten
+
 Geographic sampling babla

docs/03-experimental-design/socioeconomic-sampling.md

@@ -1,10 +1,11 @@
 # Socioeconomic sampling
 
-> Expertise and social inclusiveness. A guide to choose participants for on-farm testing with the tricot approach.  
+Expertise and social inclusiveness. A guide to choose participants for on-farm testing with the tricot approach.  
 
-> Béla Teeken (IITA)
+> Béla Teeken, Jill Cairns, Mainassara Abdou Zaman-Allah
 
 ## Assuring experienced participants
+
 A common weakness in standard participatory variety selections is that farmers are chosen without eye for their experience and the specific work they are doing and to which local social category they belong. Where this is considered usually very broad general categories are used such as age and sex., occupation, leve of education, farm size. Furthermore, when gender is brought in focus, the practice is mainly on having both men and women farmers in equal numbers evaluating the trials, disregarding their specific expertise or experience in farming. Another problem is that often farmers get chosen who feel comfortable talking and interacting within the sphere of a scientific evaluation, which emphasizes experience in reasoning and talking. This often excludes very skilled persons that however are not able or are normatively not allowed to communicate these skills and knowledge through language. But even if the respondent is good at talking it still does not include the tacit knowledge, the embodied skill and knowledge that people have. Breeders are however interested in detailed concrete hands-on information if they want to align with a demand led breeding approach such as the stage gate breeding approach that is now introduced in the CGIAR public sector breeding. Within the current reform to a stage gate breeding approach it is also crucial to get feedback from not only farmers but also processors/prepares and marketers who turn the RTB crop into an edible quality food product.
 
 To overcome these issues while choosing the TRICOT participants, we therefore work with a so-called purposive sampling using a task group approach with an explicit gender dimension. This gender dimension is not only important within the light of gender equity but is also very practical and concrete if we want to know the expertise and experience people have with regards to work related to the RTB corps because often tasks related to the RTB crop are gendered: certain tasks are often carried out be a specific sex. Important to note here is that a task group is not necessarily a group that works together but a category of people of the same social segment that carry out similar tasks related to the crop.

docs/03-experimental-design/standard-operating-procedures.md

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 # Standard operating procedure
 
+> Marie-Angélique Laporte, Almendra Cremaschi
+
 A crop protocol provides a standardised overview of the basic information that will be collected during a crop tricot trial. The protocol provides an overview of the data collection moments during the trials and the variables and traits collected at each data collection moment. Farmers evaluate varieties in their on-farm trials and provide comparative observations by ranking the varieties based on their performance throughout their growth and post-harvest qualities including taste and consumption preferences.
 

docs/03-experimental-design/tpp.md

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-# Crop variety for advancement
+# Target product profiles
+
+> Ganga Rao Nadigatla, Harish Gandi
 
-Target product profiles

docs/04-climmob-suite/ODK.md

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 # ODK
 
-ODK intro blabla
+ODK intro 

docs/04-climmob-suite/climmob.md

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 # ClimMob platform
 
-ClimMob platform blabla
+> MrBot Software Solutions 
+
+Experimental citizen science offers new ways to organize on-farm testing of crop varieties and other agronomic options. Its implementation at scale requires software that streamlines the process of experimental design, data collection and analysis, so that different organizations can support trials. This article considers ClimMob software developed to facilitate implementing experimental citizen science in agriculture. We describe the software design process, including our initial design choices, the architecture and functionality of ClimMob, and the methodology used for incorporating user feedback. Initial design choices were guided by the need to shape a workflow that is feasible for farmers and relevant for farmers, breeders and other decision-makers. Workflow and software concepts were developed concurrently. The resulting approach supported by ClimMob is triadic comparisons of technology options (tricot), which allows farmers to make simple comparisons between crop varieties or other agricultural technologies tested on farms. The software was built using Component-Based Software Engineering (CBSE), to allow for a flexible, modular design of software that is easy to maintain. Source is open-source and built on existing components that generally have a broad user community, to ensure their continuity in the future. Key components include Open Data Kit, ODK Tools, PyUtilib Component Architecture. The design of experiments and data analysis is done through R packages, which are all available on CRAN. Constant user feedback and short communication lines between the development teams and users was crucial in the development process. Development will continue to further improve user experience, expand data collection methods and media channels, ensure integration with other systems, and to further improve the support for data-driven decision-making.

docs/04-climmob-suite/data-submission.md

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 # Data Submission
 
-Data Submission blabla
+Data Submission 

docs/05-implementation/implementation.md

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+---
+sidebar_position: 1
+---
+
+# Implementing a tricot experiment 
+
+> Kauê de Sousa, Rachel Chase
+
+Setting up experiments. For OFT, best practices in planting and maintaining the plots. For consumer testing, best practices in handling the samples. Preparing the packages for distribution.

docs/06-data-analysis/data-analysis.md

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+---
+sidebar_position: 1
+---
+
+# Analysis of tricot data 
+
+> Kauê de Sousa, Joost van Heerwaarden
+
+Analytical framework. Overview of statistical models (e.g., Plackett-Luce, Bradley-Terry). Tools for analyzing ranking data. Visualization. Integration with other datasets (agroclimatic, soil, and socio-economic data). Case studies."

docs/06-data-analysis/vignette-1-trait-prioritization-and-crop-performance.md

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 ---
+sidebar_position: 2
 title: "Integrating farmer-generated data and agro-climatic data for crop variety selection"
 ---
 

docs/06-data-analysis/vignette-2-gari-eba-consumer-testing.md

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 ---
+sidebar_position: 3
 title: "Consumer preference of cassava gari-eba"
 ---
 

docs/07-feedback-dissemination/feeback.md

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+---
+sidebar_position: 1
+---
+
+# Feedback and dissemination of results to stakeholders
+
+> Anna Müller, Charlotte Schumann, Juan Manuel Londoño
+
+Feedback session to participants. Follow up and reporting.

docs/09-good-bad-ugly/_category_.json → docs/08-good-bad-ugly/_category_.json

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 {
   "label": "Good, Bad and Ugly",
-  "position": 9,
+  "position": 8,
   "link": {
     "type": "generated-index",
     "description": "Case stories on tricot"

docs/08-good-bad-ugly/case-01.md

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+---
+sidebar_position: 1
+---
+
+# Banana on-farm testing Guadalupe
+
+> Charlotte Guichard, Lucile Tonuitti
+
+# Cassava variety release in Nigeria
+
+> Béla Teeken and team
+
+# Cacao on-farm testing Ghana
+
+> Jacob van Etten, Jacob Ulzen
+
+# Potato in Rwanda
+
+> Thiago Mendes, OneAcre Fund
+
+# Seed systems in Rwanda 
+
+> OneAcre Fund
+
+# Seed system in Ethiopia
+
+> Gareth Borman and team 
+
+# Multi-crop network: the case of pigeon pea and cassava in Malawi
+
+> Martina Occelli, Esnart Yohane and team 
+
+# Consumer testing in Cameroon and Nigeria
+
+> Béla Teeken and team
+

docs/09-FAQ/FAQs.md

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+---
+sidebar_position: 1
+---
+
+# Frequently asked questions
+
+https://climmob.net/blog/wiki/?post_type=st_faq
+
+https://climmob.net/blog/wiki/climmob-and-tricot-resources/
+
+## What is the incentive for growers to participate?
+
+Through participation in a tricot experiment, growers are exposed to new technologies: For example, they may try out new crop varieties directly under the conditions of their own farm. This way, participating growers can learn about new options to improve their farming and might discover useful innovation under realistic conditions. Many growers are also motivated by being part of a research project, interacting with researchers and contributing to knowledge generation.
+
+Even when a grower does not immediately identify a suitable option among the three tested technologies, participation can be useful: growers often discuss results with their neighbors, exchange seeds, and subsequently try out options that were successful on other participants’ farms.
+
+## Can I merge data from two different tricot projects?
+
+You can merge the data if both projects are testing the same technology and you have at least one technology option (e.g. the same crop variety) in both projects. The merger can be done, for example, in the R package using ClimMob R Tools and your API key. You find your API key in the ClimMob software, by clicking on your name >>> Profile.
+
+## Does the ODK Collect app work on iOS smartphones?
+
+As of today, there is no ODK Collect app for iOS. You need an Android phone to run ODK Collect.
+
+

docs/09-FAQ/_category_.json

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+{
+  "label": "FAQs and other resources",
+  "position": 9,
+  "link": {
+    "type": "generated-index",
+    "description": "Frequently asked questions"
+  }
+}

docs/09-FAQ/resources.md

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+---
+sidebar_position: 2
+---
+
+https://climmob.net/blog/wiki/climmob-and-tricot-resources/

docs/10-appendices/appendices.md

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+---
+sidebar_position: 1
+---
+
+# Appendices

docs/intro.md

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-# The on-line guide for decentralized testing with the tricot approach
+# Product Use Testing in Agriculture
 
-This can be used by a generic page for introducing the whole set of documentation
+The *tricot* approach (triadic comparison of technology options) is a participatory [@vanetten_tricot], decentralized method where participants test three randomly assigned options on their use context. Using citizen science, it integrates participatory insights and site-specific data to guide breeding and agricultural innovation. Scalable and cost-effective, tricot empowers farmers and enhances crop diversity and resilience.