Kauê de Sousa, Jacob van Etten, Rhys Manners, Erna Abidin, Rekiya O. Abdulmalik, Bello Abolore, Kwabena Acheremu, Stephen Angudubo, Amilcar Aguilar, Elizabeth Arnaud, Adventina Babu, Mirna Barrios, Grecia Benavente, Ousmane Boukar, Jill E. Cairns, Edward Carey, Happy Daudi, Maryam Dawud, Gospel Edughaen, James Ellison, Williams Esuma, Sanusi Gaya Mohammed, Jeske van de Gevel, Marvin Gomez, Joost van Heerwaarden, Paula Iragaba, Edith Kadege, Teshale M. Assefa, Sylvia Kalemera, Fadhili Salum Kasubiri, Robert Kawuki, Yosef Gebrehawaryat Kidane, Michael Kilango, Heneriko Kulembeka, Adofo Kwadwo, Brandon Madriz, Ester Masumba, Julius Mbiu, Thiago Mendes, Anna Müller, Mukani Moyo, Kiddo Mtunda, Tawanda Muzhingi, Dean Muungani, Emmanuel T. Mwenda, Ganga Rao V. P. R. Nadigatla, Ann Ritah Nanyonjo, Sognigbé N’Danikou, Athanase Nduwumuremyi, Jean Claude Nshimiyimana, Ephraim Nuwamanya, Hyacinthe Nyirahabimana, Martina Occelli, Olamide Olaosebikan, Patrick Obia Ongom, Berta Ortiz-Crespo, Richard Oteng-Fripong, Alfred Ozimati, Durodola Owoade, Carlos F. Quiros, Juan Carlos Rosas, Placide Rukundo, Pieter Rutsaert, Milindi Sibomana, Neeraj Sharma, Nestory Shida, Jonathan Steinke, Reuben Ssali, Jose Gabriel Suchini, Béla Teeken, Theophilus Kwabla Tengey, Hale Ann Tufan, Silver Tumwegamire, Elyse Tuyishime, Jacob Ulzen, Muhammad Lawan Umar, Samuel Onwuka, Tessy Ugo Madu, Rachel C. Voss, Mary Yeye, Mainassara Zaman-Allah
{"title":"The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective","authors":"Kauê de Sousa, Jacob van Etten, Rhys Manners, Erna Abidin, Rekiya O. Abdulmalik, Bello Abolore, Kwabena Acheremu, Stephen Angudubo, Amilcar Aguilar, Elizabeth Arnaud, Adventina Babu, Mirna Barrios, Grecia Benavente, Ousmane Boukar, Jill E. Cairns, Edward Carey, Happy Daudi, Maryam Dawud, Gospel Edughaen, James Ellison, Williams Esuma, Sanusi Gaya Mohammed, Jeske van de Gevel, Marvin Gomez, Joost van Heerwaarden, Paula Iragaba, Edith Kadege, Teshale M. Assefa, Sylvia Kalemera, Fadhili Salum Kasubiri, Robert Kawuki, Yosef Gebrehawaryat Kidane, Michael Kilango, Heneriko Kulembeka, Adofo Kwadwo, Brandon Madriz, Ester Masumba, Julius Mbiu, Thiago Mendes, Anna Müller, Mukani Moyo, Kiddo Mtunda, Tawanda Muzhingi, Dean Muungani, Emmanuel T. Mwenda, Ganga Rao V. P. R. Nadigatla, Ann Ritah Nanyonjo, Sognigbé N’Danikou, Athanase Nduwumuremyi, Jean Claude Nshimiyimana, Ephraim Nuwamanya, Hyacinthe Nyirahabimana, Martina Occelli, Olamide Olaosebikan, Patrick Obia Ongom, Berta Ortiz-Crespo, Richard Oteng-Fripong, Alfred Ozimati, Durodola Owoade, Carlos F. Quiros, Juan Carlos Rosas, Placide Rukundo, Pieter Rutsaert, Milindi Sibomana, Neeraj Sharma, Nestory Shida, Jonathan Steinke, Reuben Ssali, Jose Gabriel Suchini, Béla Teeken, Theophilus Kwabla Tengey, Hale Ann Tufan, Silver Tumwegamire, Elyse Tuyishime, Jacob Ulzen, Muhammad Lawan Umar, Samuel Onwuka, Tessy Ugo Madu, Rachel C. Voss, Mary Yeye, Mainassara Zaman-Allah","doi":"10.1007/s13593-023-00937-1","DOIUrl":null,"url":null,"abstract":"<div><p>Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers’ priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called <i>triadic comparison of technology options</i> (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot’s recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers’ preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.</p></div>","PeriodicalId":7721,"journal":{"name":"Agronomy for Sustainable Development","volume":"44 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13593-023-00937-1.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agronomy for Sustainable Development","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s13593-023-00937-1","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
Matching crop varieties to their target use context and user preferences is a challenge faced by many plant breeding programs serving smallholder agriculture. Numerous participatory approaches proposed by CGIAR and other research teams over the last four decades have attempted to capture farmers’ priorities/preferences and crop variety field performance in representative growing environments through experimental trials with higher external validity. Yet none have overcome the challenges of scalability, data validity and reliability, and difficulties in capturing socio-economic and environmental heterogeneity. Building on the strengths of these attempts, we developed a new data-generation approach, called triadic comparison of technology options (tricot). Tricot is a decentralized experimental approach supported by crowdsourced citizen science. In this article, we review the development, validation, and evolution of the tricot approach, through our own research results and reviewing the literature in which tricot approaches have been successfully applied. The first results indicated that tricot-aggregated farmer-led assessments contained information with adequate validity and that reliability could be achieved with a large sample. Costs were lower than current participatory approaches. Scaling the tricot approach into a large on-farm testing network successfully registered specific climatic effects of crop variety performance in representative growing environments. Tricot’s recent application in plant breeding networks in relation to decision-making has (i) advanced plant breeding lines recognizing socio-economic heterogeneity, and (ii) identified consumers’ preferences and market demands, generating alternative breeding design priorities. We review lessons learned from tricot applications that have enabled a large scaling effort, which should lead to stronger decision-making in crop improvement and increased use of improved varieties in smallholder agriculture.
期刊介绍:
Agronomy for Sustainable Development (ASD) is a peer-reviewed scientific journal of international scope, dedicated to publishing original research articles, review articles, and meta-analyses aimed at improving sustainability in agricultural and food systems. The journal serves as a bridge between agronomy, cropping, and farming system research and various other disciplines including ecology, genetics, economics, and social sciences.
ASD encourages studies in agroecology, participatory research, and interdisciplinary approaches, with a focus on systems thinking applied at different scales from field to global levels.
Research articles published in ASD should present significant scientific advancements compared to existing knowledge, within an international context. Review articles should critically evaluate emerging topics, and opinion papers may also be submitted as reviews. Meta-analysis articles should provide clear contributions to resolving widely debated scientific questions.