Search Results - "Learning Research."

Triple S PLUS is Ready for Scaling: Learning, Research, Impact ...Together by International Potato Center

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Learning to learn: research into adaptive and collaborative management of community forests by McDougall, C.

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Universities are committed to supporting CBBP to synergize learning, research, and community services by Belay, Berhanu, Getachew, Tesfaye, Haile, Aynalem

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Agrolyser for cassava; lessons learned: research from IITA, NRCRI, UI, Lagos ADA and Imo State ADP by Akoroda, M.O., Okechukwu, R.

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2022 Learning and Optimization: Focus Group Discussion on the QA process by CGIAR System Management Office

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2023 Learning and Optimization Process: Inaugural Focus Group Discussion with IDTs by CGIAR System Management Office

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The Gender Equality in Research Scale: A tool for monitoring and encouraging progress on gender integration in research for and in development by Paez Valencia, Ana Maria, Ihalainen, Markus, Elias, Marlène, Sijapati Basnett, Bimbika

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Drone technology in agriculture by Dhulipala, Ram

“…CGIAR Research Initiative on Digital Innovation collaborated with Clearskies Learning & Research Pvt. Ltd to organize a three-day “Awareness Webinar on Drone Technology in Agriculture” for the Initiative's stakeholders in South Africa between November 28-30, 2022. …”
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Learning alliances: An approach for building multistakeholder innovation systems by Lundy, Mark M., Gottret, María Verónica, Ashby, Jacqueline A.

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Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya by Paliwal, Ambica, Cherotich, Fredah, Leitner, Sonja, Pearce, F., Rufino, M., Quinton, J., Dhulipala, Ram, Salavati, M., Gluecks, Ilona V., Whitbread, Anthony M.

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Evaluation, learning and change in research and development organizations: concepts, experiences, and implications for the CGIAR by Horton, Douglas, Galleno, V., Mackay, Ronald

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Evidence for action: A One Health learning platform on interventions to tackle antimicrobial resistance by Wernli, D., Jørgensen, P.S., Parmley, E.J., Troell, M., Majowicz, S., Harbarth, S., Léger, A., Lambraki, I., Graells, T., Henriksson, Patrik J.G., Carson, C., Cousins, M., Ståhlgren, G.S., Mohan, C.V., Simpson, A.J.H., Wieland, Barbara, Pedersen, K., Schneider, A., Chandy, S.J., Wijayathilaka, T.P., Delamare-Deboutteville, Jerome, Vila, J., Lundborg, C.S., Pittet, D.

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Researcher-implementer partnerships for learning and impact: Lessons from IFPRI's experiences with nutrition-sensitive programming by Bliznashka, Lilia, Sproule, Katie

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Culture of impact in agricultural research organisations: What for and how? by Ferré, Marie, Blundo-Canto, Genowefa, Stachetti Rodrigues, Geraldo, Ramírez-Gómez, María-Margarita, Vedovoto, Graciela Luzia, Agudelo-Chocontá, Beatriz-Elena, Marques, Daniela Vieira, Flores, Roberto Manolio Valladão, Rodríguez-Borray, Gonzalo-Alfredo, de Souza, Mirian Oliveira, Goulet, Frédéric, Vásquez-Urriago, Ángela-Rocío, Sánchez-Lozano, Juliana-Ivonne, Pinto, Daniela Maciel, Zambrano-Moreno, Gregorio-Salomón, Londoño-Arias, María-Aidé, Zapata-Tamayo, Cristóbal-Alfonso, de Romémont, Aurelle

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Catalysing learning for development and climate change: an exploration of social learning and social differentiation in CGIAR by Shaw, Alison, Kristjanson, Patricia M.

“…A more explicit recognition of the role and ‘added value’ that social learning research approaches have can enhance its visibility and ultimately the effectiveness of CGIAR’s vast research partnerships.…”
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Learning from Each Other: Experience Sharing Workshop on Implementation of Smart Pack and CBBP among Ethiopian Public Universities by Belay, Berhanu, Getachew, Tesfaye, Haile, Aynalem

“…The key lessons captured in implementing CBBP in the university program includes showcasing other programs to have local data and practices in teaching resource and contribute to the quality and relevance of education, resource mobilization, and demonstrating the synergy of learning, research, and community services, ensuring teamwork to bring impactful community services and a continuous capacity building and provision of training materials which could be reflected in teaching and learning efforts of universities. …”
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Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia by Lamos Díaz, Henry, Puentes Garzón, David Esteban, Zarate Caicedo, Diego Alejandro

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Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties by Delgadillo Durana, Diego A., Vargas García, Cesar A., Varón Ramíreza, Viviana M., Calderón, Francisco C., Montenegroa, Andrea C., Reyes Herreraa, Paula H.

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Culture of impact in agricultural research organisations: What for and how? by Ferré, Marie, Blundo Canto, Genowefa, Rodrigues, Geraldo Stachetti, Ramírez Gomez, María Margarita, Vedovoto, Graciela Luzia, Agudelo Chocontá, Beatriz Elena, Marques, Daniela Vieira, Valladao Flores, Roberto Manolio, Rodríguez Borray, Gonzalo Alfredo, de Souza, Mirian Oliveira, Goulet, Frédéric, Vásquez Urriago, Angela Rocío, Sanchez Lozano, Juliana Ivonne, Pinto, Daniela Maciel, Zambrano Moreno, Gregorio Salomón, Londono Arias, María Aidé, Zapata Tamayo, Cristóbal Alfonso, de Romémont, Aurelle

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CY-Bench: a comprehensive benchmark dataset for sub-national crop yield forecasting (Preprint) by Paudel, D., Kallenberg, M., Ofori-Ampofo, S., Baja, H., van Bree, R., Potze, A., Poudel, P., Saleh, A., Anderson, W., von Bloh, M., Castellano, A., Ennaji, O., Hamed, R., Laudien, R., Lee, D., Luna, I., Meroni, M., Mutuku, J.M., Mkuhlani, S., Richetti, J., Ruane, A.C., Sahajpal, R., Shai, G., Sitokonstantinou, V., de Souza Noia Junior, R., Srivastava, A., Strong, R., Sweet, L., Vojnovic, P., Athanasiadis, I.N.

“…CY-Bench was conceptualized and developed within the Machine Learning team of the Agricultural Model Intercomparison and Improvement Project (AgML) in collaboration with agronomists, climate scientists, and machine learning researchers. It features publicly available sub-national yield statistics and relevant predictors—such as weather data, soil characteristics, and remote sensing indicators—that have been pre-processed, standardized, and harmonized across spatio-temporal scales. …”
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