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The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective.
de Sousa, Kauê; van Etten, Jacob; Manners, Rhys; Abidin, Erna; Abdulmalik, Rekiya O; Abolore, Bello; Acheremu, Kwabena; Angudubo, Stephen; Aguilar, Amilcar; Arnaud, Elizabeth; Babu, Adventina; Barrios, Mirna; Benavente, Grecia; Boukar, Ousmane; Cairns, Jill E; Carey, Edward; Daudi, Happy; Dawud, Maryam; Edughaen, Gospel; Ellison, James; Esuma, Williams; Mohammed, Sanusi Gaya; van de Gevel, Jeske; Gomez, Marvin; van Heerwaarden, Joost; Iragaba, Paula; Kadege, Edith; Assefa, Teshale M; Kalemera, Sylvia; Kasubiri, Fadhili Salum; Kawuki, Robert; Kidane, Yosef Gebrehawaryat; Kilango, Michael; Kulembeka, Heneriko; Kwadwo, Adofo; Madriz, Brandon; Masumba, Ester; Mbiu, Julius; Mendes, Thiago; Müller, Anna; Moyo, Mukani; Mtunda, Kiddo; Muzhingi, Tawanda; Muungani, Dean; Mwenda, Emmanuel T; Nadigatla, Ganga Rao V P R; Nanyonjo, Ann Ritah; N'Danikou, Sognigbé; Nduwumuremyi, Athanase; Nshimiyimana, Jean Claude.
Afiliação
  • de Sousa K; Digital Inclusion, Bioversity International, Montpellier, France.
  • van Etten J; Department of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar, Norway.
  • Manners R; Digital Inclusion, Bioversity International, Montpellier, France.
  • Abidin E; International Institute of Tropical Agriculture (IITA), Kigali, Rwanda.
  • Abdulmalik RO; Reputed Agriculture 4 Development Stichting & Foundation, Kumasi, Ghana.
  • Abolore B; Department of Plant Science, Institute for Agricultural Research, Ahmadu Bello University, Zaria, 810211 Nigeria.
  • Acheremu K; International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria.
  • Angudubo S; Savanna Agricultural Research Institute, Council for Scientific and Industrial Research, Tamale, Ghana.
  • Aguilar A; National Crop Resources Research Institute, Kampala, Uganda.
  • Arnaud E; Centro Agronómico Tropical de Investigación y Enseñanza, Managua, Nicaragua.
  • Babu A; Digital Inclusion, Bioversity International, Montpellier, France.
  • Barrios M; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Benavente G; Centro Agronómico Tropical de Investigación y Enseñanza, Managua, Nicaragua.
  • Boukar O; Digital Inclusion, Bioversity International, Montpellier, France.
  • Cairns JE; International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria.
  • Carey E; International Maize and Wheat Improvement Center (CIMMYT), Harare, Zimbabwe.
  • Daudi H; Reputed Agriculture 4 Development Stichting & Foundation, Kumasi, Ghana.
  • Dawud M; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Edughaen G; Lake Chad Research Institute, Lagos, Nigeria.
  • Ellison J; International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria.
  • Esuma W; One Acre Fund, Kigali, Rwanda.
  • Mohammed SG; National Crop Resources Research Institute, Kampala, Uganda.
  • van de Gevel J; Centre for Dryland Agriculture, Bayero University, Kano, Nigeria.
  • Gomez M; Digital Creativity Lab, University of York, York, UK.
  • van Heerwaarden J; Fundación para la Investigación Participativa con Agricultores de Honduras (FIPAH), La Ceiba, Atlántida Honduras.
  • Iragaba P; Department of Plant Sciences, Wageningen University and Research, Wageningen, The Netherlands.
  • Kadege E; National Crop Resources Research Institute, Kampala, Uganda.
  • Assefa TM; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Kalemera S; School of Life Sciences and Bioengineering, The Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania.
  • Kasubiri FS; Crops for Nutrition and Health, International Center for Tropical Agriculture (CIAT), Arusha, Tanzania.
  • Kawuki R; Crops for Nutrition and Health, International Center for Tropical Agriculture (CIAT), Arusha, Tanzania.
  • Kidane YG; Crops for Nutrition and Health, International Center for Tropical Agriculture (CIAT), Arusha, Tanzania.
  • Kilango M; National Crop Resources Research Institute, Kampala, Uganda.
  • Kulembeka H; Biodiversity for Food and Agriculture, Bioversity International, Addis Ababa, Ethiopia.
  • Kwadwo A; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Madriz B; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Masumba E; Council for Scientific and Industrial Research-Crops Research Institute, Kumasi, Ghana.
  • Mbiu J; MrBot Software Solutions, Cartago, Costa Rica.
  • Mendes T; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Müller A; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Moyo M; International Potato Center (CIP), Nairobi, Kenya.
  • Mtunda K; Digital Inclusion, Bioversity International, Montpellier, France.
  • Muzhingi T; International Potato Center (CIP), Nairobi, Kenya.
  • Muungani D; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • Mwenda ET; Department of Food, Bioprocessing and Nutrition Science, Raleigh, NC USA.
  • Nadigatla GRVPR; International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria.
  • Nanyonjo AR; Tanzanian Agricultural Research Institute, Arusha, Tanzania.
  • N'Danikou S; Dryland Legumes and Cereals, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya.
  • Nduwumuremyi A; National Crop Resources Research Institute, Kampala, Uganda.
  • Nshimiyimana JC; World Vegetable Center (ARVDC), Arusha, Tanzania.
Agron Sustain Dev ; 44(1): 8, 2024.
Article em En | MEDLINE | ID: mdl-38282889
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Agron Sustain Dev Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Agron Sustain Dev Ano de publicação: 2024 Tipo de documento: Article País de afiliação: França