Sylvia Kalemera

International Institute of Tropical Agriculture

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Papias Binagwa

Tanzania Wildlife Research Institute

Eric Nduwarugira

Institut des Sciences Agronomiques du Burundi

Amos Miningou

Institut de l'Environnement et Recherches Agricoles

Stanley Tamusange

National Agricultural Research Institute

Bruce Mutari

Alliance for a Green Revolution in Africa

Micheal Kilango

Agrárközgazdasági Intézet

Muthoni R Andriatsitohaina

International Center for Tropical Agriculture

Manuel Amane

Sidi Mohamed Ben Abdellah University

Augustine Musoni

Rwanda Agriculture Board

Deidré Fourie

Agricultural Research Council of South Africa

Cooperative Institutions

International Center for Tropical Agriculture

Bioversity International

National Agricultural Research Institute

Makerere University

International Crops Research Institute for the Semi-Arid Tropics

International Institute of Tropical Agriculture

Mikocheni Agricultural Research Institute

International Potato Center

National Agricultural Research Organisation

International Institute of Tropical Agriculture

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Training beneficiaries in the PABRA Project

Harvard Dataverse (2018)

Muthoni R Andriatsitohaina Resty Nagadya Innocent Obilil Augustine Musoni Lesole Sefume

Pan African Bean Research Alliance (PABRA) specifically invests finance, human resource and time in ensuring that the continents bean researchers and staff are up to date and relevant with skills they require. The data sets presented here have been assembled from multiple sources to provide and indicative position of skill and knowledge building initiatives by PABRA and its various partners. The data sets show the number of people trained between the year 2003 t0 2016. Though the data sets provide the numbers, discussion on capacity building as a whole is available in the capacity building section of the PABRA website.

10.7910/dvn/uxlaib

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05 Student Trainings by Theme.tab

Harvard Dataverse (2018)

Muthoni R Andriatsitohaina Resty Nagadya Innocent Obilil Augustine Musoni Lesole Sefume

The data set presents a summary of students according to study themes. Data presented is only for those students having their themes defined. Refer to codebook for variable definitions

Theme (computing)

10.7910/dvn/uxlaib/g2fee1

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Market‐led options to scale up legume seeds in developing countries: Experiences from the Tropical Legumes Project

Plant Breeding (2019)

Jean‐Claude Rubyogo Essègbémon Akpo Lucky O. Omoigui Gaur Pooran S. K. Chaturvedi

Abstract There are several hurdles to ensure sustainable seed production and consistent flow of improved legume varieties in sub‐Saharan Africa (SSA) and South Asia (SA). The unreliable demand, autogamous nature of most of the grain legumes, and slow variety replacement rate by smallholder farmers do not provide strong incentive for private seed companies to invest in legume seed business. Unless a well thought‐out and comprehensive approach to legume seed delivery is developed, current seed shortages will continue, eroding emerging market opportunities. The experiences reported here are collated through a 10‐year partnership project, the Tropical Legumes in SSA and SA. It fostered innovative public–private partnerships in joint testing of innovative market‐led seed systems, skills and knowledge enhancement, de‐risking private sector initiatives that introduced in new approaches and previously overlooked entities in technology delivery. As new public and private seed companies, individual seed entrepreneurs and farmer organizations emerged, the existing ones enhanced their capacities. This resulted in significant rise in production, availability and accessibility of various seed grades of newly improved and farmer demanded legume varieties in the target countries.

Economic shortage

10.1111/pbr.12732

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Citations (17)

01 Codebook.xls

Harvard Dataverse (2018)

Muthoni R Andriatsitohaina Resty Nagadya Innocent Obilil Augustine Musoni Lesole Sefume

Document detailing the traits used

10.7910/dvn/uxlaib/qpz1g3

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Training beneficiaries in the PABRA Project 03 Gender Categorized Training.tab

Harvard Dataverse (2018)

Muthoni R Andriatsitohaina Resty Nagadya Innocent Obilil Augustine Musoni Lesole Sefume

This data set shows the categories of Training of Trainers (ToT), Extensionist/Farmers and Students by men and women. It applies to data where such data was available and hence does not apply to all training beneficiaries. The value presented as Not Available (N/A) means that in that there was no data collected in that particular year. The values presented shows the percentage of Male-Female trained in each of the categories

10.7910/dvn/uxlaib/s5bs6b

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The tricot approach: an agile framework for decentralized on-farm testing supported by citizen science. A retrospective

Agronomy for Sustainable Development (2024)

Kauê de Sousa Jacob van Etten Rhys Manners Erna Abidin Rekiya O. Abdulmalik

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.

10.1007/s13593-023-00937-1

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Planteome/CO_335-common-bean-traits: 1st release

Zenodo (CERN European Organization for Nuclear Research) (2023)

Alberto Fabio Guerrero Julian Pietragalla Marie‐Angélique Laporte M. V. Rao Gloria Mosquera

This is the first release of the Common Bean repository

10.5281/zenodo.10171459

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06 Students by Academic levels.tab

Harvard Dataverse (2018)

Muthoni R Andriatsitohaina Resty Nagadya Innocent Obilil Augustine Musoni Lesole Sefume

This data set presents a summary of number of students trained by the 3 major academic levels of Masters (MSC), PHP and Bachelors (BSC). It is applicable to data where disaggregation by academic level was provided Refer to codebook for variable definitions

10.7910/dvn/uxlaib/9fhwbo

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Planteome/CO_335-common-bean-traits: 1st release

Zenodo (CERN European Organization for Nuclear Research) (2023)

Alberto Fabio Guerrero Julian Pietragalla Marie‐Angélique Laporte M. V. Rao Gloria Mosquera

This is the first release of the Common Bean repository

10.5281/zenodo.10171460

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07 Students trained by gender.tab

Harvard Dataverse (2018)

Muthoni R Andriatsitohaina Resty Nagadya Innocent Obilil Augustine Musoni Lesole Sefume

The dataset shows the student breakdown by gender between 2003 and 2016. It is applicable to data where disaggregation by gender was provided Refer to codebook for variable definitions

10.7910/dvn/uxlaib/603gwv

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