Merge pull request #7 from AgrDataSci/dev

Dev

Author: kauedesousa

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

@@ -1,9 +1,9 @@
 # Socioeconomic sampling
 
-Expertise and social inclusiveness. A guide to choose participants for on-farm testing with the tricot approach.  
-
 > Béla Teeken, Jill Cairns, Mainassara Abdou Zaman-Allah
 
+Expertise and social inclusiveness. A guide to choose participants for on-farm testing with the tricot approach.  
+
 ## 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.

docs/03-experimental-design/tpp.md

@@ -4,5 +4,5 @@ sidebar_position: 1
 
 # Target product profiles
 
-> Ganga Rao Nadigatla, Harish Gandi
+> Ganga Rao Nadigatla, Harish Gandhi
 

docs/09-FAQ/resources.md

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 sidebar_position: 2
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-https://climmob.net/blog/wiki/climmob-and-tricot-resources/
+https://climmob.net/blog/wiki/climmob-and-tricot-resources/
+
+Guide
+
+Jacob van Etten, Rhys Manners, Jonathan Steinke, Elsa Matthus, Kauê de Sousa. 2020. The tricot approach. Guide for large-scale participatory experiments. Rome (Italy): Alliance of Bioversity International and CIAT. https://hdl.handle.net/10568/109942
+
+This is a short, full-colour guide intended for practitioners who are not yet familiar with tricot. It explains the rationale of tricot and gives an overview of the experimental cycle.
+
+
+Main publications
+
+All publications about the tricot approach are free and open access.
+
+1. Jacob van Etten, Kauê de Sousa, […] Jonathan Steinke. 2019. Crop variety management for climate adaptation supported by citizen science. PNAS 116(10): 4194-4199. https://doi.org/10.1073/pnas.1813720116
+
+This paper describes the application of large tricot trials in Nicaragua, Ethiopia and India. It demonstrates the potential of tricot to generate insights into variety adaptation, recommend adapted varieties, and aid smallholder farmers in responding to climate change. It is the first large-scale application of climate analysis on tricot data.
+
+2. Jonathan Steinke, Jacob van Etten and Pablo Mejía-Zelan. 2017. The accuracy of farmer-generated data in an agricultural citizen science methodology. Agronomy for Sustainable Development 37: 32. https://doi.org/10.1007/s13593-017-0441-y
+
+This paper shows that farmers provide accurate data in tricot trials. Their rankings converge with expert rankings for four traits. The variation between farmers still allows for accurate overall ranking of the varieties.
+
+3. Eskender Beza, Jonathan Steinke, Jacob van Etten et al. 2017. What are the prospects for large-N citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers participating in “tricot” crop research trials. PLoS ONE 12(5): e0175700. https://doi.org/10.1371/journal.pone.0175700
+
+This paper investigates the motivation of farmers who participate in tricot trials across three contrasting contexts: Honduras, Ethiopia and India. Farmers are motivated by a wide range of reasons, including intrinsic and extrinsic factors. They do not see it as a pastime, but also do not expect monetary compensation. They expect technical information and access to seeds as reward of their participation.
+
+4. Jacob van Etten, et al. 2019. First experiences with a novel farmer citizen science approach: Crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies (tricot). Experimental Agriculture, 55(S1). https://doi.org/10.1017/S0014479716000739
+
+This paper provides an explanation of the tricot approach, how it compares to previous approaches, and some first applications. Note that it uses the Bradley-Terry model, which was replaced by the Plackett-Luce model in later publications.
+
+5. Kauê de Sousa, Jacob van Etten, […] Matteo Dell’Acqua. 2021. Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment. Communications Biology 4, 944. https://doi.org/10.1038/s42003-021-02463-w
+
+This paper shows that tricot can be effectively combined with genomic selection for highly accurate selection in challenging production environments. Tested with durum wheat in Ethiopia, 3D-breeding doubled prediction accuracy compared to conventional methods, identifying genotypes with superior local adaptation across seasons to improve breeding decisions.
+
+6. Heather Turner, Jacob van Etten, David Firth, Ioannis Kosmidis. 2020. Modelling rankings in R: the PlackettLuce package. Comput Stat 35, 1027–1057. https://doi.org/10.1007/s00180-020-00959-3
+
+This article explains the Plackett-Luce model and its implementation in R, as used by the ClimMob platform.
+
+7. David Brown, Sytze de Bruin, Kauê de Sousa, […] Jacob van Etten. 2022. Rank-based data synthesis of common bean on-farm trials across four Central American countries. Crop Science. https://doi.org/10.1002/csc2.20817
+
+This article provides an approach to combine data from different tricot trials to obtain insights for regional analysis using on-farm data.
+
+8. Oladeji Emmanuel Alamu, Béla Teeken, et al. 2023. Stablishing the linkage between eba’s instrumental and sensory descriptive profiles and their correlation with consumer preferences: implications for cassava breeding. Journal of the Science of Food and Agriculture. https://doi.org/10.1002/jsfa.12518
+
+This article links tricot data to laboratory instrumental data to understand consumers’ preferences with implications for breeding programs.
+
+9. Kauê de Sousa, David Brown, Jonathan Steinke, Jacob van Etten. 2023. gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation. SoftwareX. https://doi.org/10.1016/j.softx.2023.101402
+
+This paper introduces the gosset package used on ClimMob. It demonstrates the package functionality using the case study of a decentralized on-farm trial of common bean (Phaseolus vulgaris L.) varieties in Nicaragua.
+
+10. Pieter Rutsaert, Jason Donovan, Harriet Mawia, Kauê de Sousa, Jacob van Etten. 2023. Future market segments for hybrid maize in East Africa. Market Intelligence Brief Series 2. Montpellier: CGIAR. https://hdl.handle.net/10883/22467
+
+Introduces a novel approach to assess market demands in seed systems using decentralized testing under the tricot approach.
+
+11. Carlos Quirós, Kauê de Sousa, […] Jacob van Etten. 2023. ClimMob: Software to Support Experimental Citizen Science in Agriculture. SSRN. http://dx.doi.org/10.2139/ssrn.4463406
+
+Introduces the ClimMob software.
+
+12. Jacob van Etten, Kauê de Sousa, […] Hale Ann Tufan. 2023. Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products. PNAS 120 (14). https://doi.org/10.1073/pnas.2205771120
+
+This paper brings a perspective on opportunities and challenges of data-driven approaches for crop diversity management (genebanks and breeding) in the context of agricultural research for sustainable development in the Global South.
+
+ClimMob YouTube channel
+
+All videos about ClimMob and tricot are here.
+https://www.youtube.com/channel/UCmqo4KCZwX8R-H4SNkXfuSA/playlists
+The videos are subdivided in three playing lists, as shown below.
+
+1. YouTube videos – Introduction to tricot
+These videos provide a general introduction to tricot as an on-farm testing approach. It explains the rationale and underlying concepts.
+https://www.youtube.com/watch?v=uCZ9Hw5hubU&list=PLpT37wNlyZlRH2_K-sevTeLh2-bhYkY2h
+
+2. YouTube videos – Introduction to ClimMob software
+These videos provide a step-by-step explanation of how to set up an experiment on ClimMob.
+https://www.youtube.com/watch?v=tkOwXG_Jyy4&list=PLpT37wNlyZlQNIrLdW7G91Xqaz_S3x_z0
+
+3. YouTube videos – data analysis with R
+The ClimMob platform provides trial-level analysis. For advanced analyses, R packages are available. These videos explain how to use them.
+https://www.youtube.com/watch?v=pKYGjtwjagc&list=PLpT37wNlyZlS2QL67Qn-eLI8oETBr5sKm