Resultados de búsqueda - Reflective learning.

Governing a collective bad: Social learning in the management of crop diseases por Damtew, Elias, Mierlo, B. van, Lie, R., Struik, P.C., Leeuwis, Cees, Lemaga, B., Smart, C.D.

“…A series of facilitated discussion and reflection sessions about late blight management created the social learning space for potato farmers in Ethiopia. …”
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Collaborative multi-stakeholder spaces to learn about socio-technical bundling por Puskur, Ranjitha, Nchanji, Eileen, Cosmas, Lutomia, Ketema, Dessalegn, Muteti, Thomas, Gartaula, Hom, Mukhopadhyay, Prama, Mapedza, Everisto D., Chadha, Deepali

“…Learning labs to promote spaces for learning and reflection among stakeholders including development agencies, farmers and researchers on the gender-responsive socio-technical innovation bundling approach to enhance resilience of women farmers, empowerment and food security. …”
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Nutrition-sensitive agriculture: What have we learned so far? por Ruel, Marie T., Quisumbing, Agnes R., Balagamwala, Mysbah

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Machine learning and big data techniques for satellite-based rice phenology por Aguilar-Ariza, Andrés

“…The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. …”
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Capacity building for ACM: Lessons learned from training in distinct contexts por Cronkleton, P., Evans, K., Larson, A.M.

“…Training in ACM is not simply about teaching a new methodology, it is about building an ethos of experimentation, learning and collaboration. Our goal in training was to create self-reinforcing cycles in which participants reflected on their learning so that they could consciously facilitate learning with others. …”
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Applying social learning where ‘business-as-usual’ solutions no longer work for complex problems and programs por Le Borgne, Ewen

“…Key messages • Social learning facilitates sharing and learning beyond individuals to networks and systems. …”
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Lessons learned and ways forward on CGIAR capacity development: A discussion paper por Staiger Rivas, Simone, Dror, Iddo, Babu, Suresh Chandra, Rudebjer, P.G., Kosina, Petr, Diop, N.N., Maru, Joyce, Bamba, Zoumana

“…The paper explores the lessons learned from CGIAR’s experience with CD and reflects the findings of a working group that was brought together in late 2012.…”
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Processes in the NBDC: People, platforms, innovation systems, networking, learning, pollicies, and institutions por Tucker, Josephine, Kessagne, A., Swaans, Cornelis P.M., Amede, Tilahun, Adenew, B., Duncan, Alan J.

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Building from gender experiences and learning for phase 2 of the CGIAR research programs por Pyburn, Rhiannon

“…Examples are ways to systematize gender budgeting, ensuring that gender scientists are supported for the the time they allocate, and having their contribution reflected in the authorship of publications. Interview with Rhiannon Pyburn (Royal Tropical Institute) for the book "A different kettle of fish? …”
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The role of learning in technology adoption: Evidence on hybrid rice adoption in Bihar, India por Gars, Jared, Ward, Patrick S.

“…Using specially designed experiments conducted as part of a primary survey in the field, we identify which of four broad learning heuristics most accurately reflects individuals’ information processing strategies. …”
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Mekong River Delta crop mapping using a machine learning approach por Ghosh, Surajit, Wellington, Michael, Holmatov, Bunyod

“…Radar bands from Sentinel-1 and Sentinel-2 reflectance bands were important predictors of crop type, perhaps due to their capacity to differentiate between periodically flooded rice fields and perennial tree cover, which were the predominant classes in the Delta. …”
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Analysis of learning cycles in participatory environment and development projects: lessons from Nepal por Hiyama, C., Keen, M.

“…In this research, learning cycles were defined as a ‘continuous process of situation analysis, collaborative planning, action and critical reflection’ (see Section 3.4), while empowerment was defined as ‘a process through which individuals, as well as local groups and communities, enhance their capacity to initiate action to improve their well-being by gaining decision-making power’ (see Section 2.2). …”
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Foodgravity: understand food flows using classic gravity model and explainable artificial intelligence techniques por Dusabe, B.

“…To untangle this complexity, this research combined concepts of classic gravity model with machine learning techniques, relying on Explainable Artificial Intelligence techniques (xAI) to enhance the transparency of the predictive models. …”
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Behind the trees, an opportunity to learn: the adoption of experience capitalization at the forest development department in Argentina por Tapella, Esteban, Naves, Natalia

“…In order to encourage a critical reflection within such projects, and circulate the lessons learned, the National Department of Forest and Industrial Development at the Argentinean Ministry of Agriculture has held experience capitalization workshops with its staff. …”
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Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties por 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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Solving ‘wicked’ problems: can social learning catalyse adaptive responses to climate change? por Epp, Marissa van, Garside, Ben

“…It encourages stakeholders to work together to implement and test solutions through iterative cycles of learning, action and reflection.~This working paper synthesises evidence from five diverse initiatives employing social learning approaches in response to such problems using the Climate Change and Social Learning initiative’s monitoring and evaluation framework. …”
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Introducing an ecologically-based pest management approach in Cambodia through adaptive learning networks por Flor, Rica Joy, Maat, Harro, Stuart, Alexander, Then, Rathmuny, Choun, Saban, Chhun, Sokunroth, Hadi, Buyung Asmara Ratna

“…We assessed a new method – Adaptive Learning Networks – from reflections of varied stakeholders, farmer diaries and survey data procured in 2016 and 2019. …”
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Machine Learning Approach for Prediction of Area Under Cultivation and Production for Vegetatively Propagated Crops por Ahishakiye, E., Ogero, K., Namada, S., Rajendran, S.

“…After hyperparameter tuning, the Stacked Model performed better, yielding R² values of 0.8260 for area prediction and 0.7883 for production forecasting, outperforming the individual models. The results reflect the potential of the ensemble learning model to improve the accuracy of agricultural forecasts. …”
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Scaling Gem Parboiling And Rice By-Products: Insights And Lessons Learned From 2022 To 2025 por Dossou Yovo, Elliott Ronald, Diabate, Fatoumata, Suh, Neville Ndohnwi

“…Through brainstorming, practical demonstrations, storytelling, video-based learning, and group reflection, participants gained hands-on understanding of parboiling processes, equipment operation, and business opportunities tied to value-added rice products. …”
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Estimation of Water Stress in Potato Plants Using Hyperspectral Imagery and Machine Learning Algorithms por Duarte Carvajalino, Julio Martin, Silva Arero, Elías Alexander, Góez Vinasco, Gerardo Antonio, Torres Delgado, Laura Marcela, Ocampo Paez, Oscar Dubán, Castaño Marín, Angela María

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