Resultados de búsqueda - "learning"

Rift Valley fever (RVF) project on Zooniverse, a citizen science platform that allows registered volunteers to identify animals in landscapes from drone imagery por CGIAR Research Program on Agriculture for Nutrition and Health

Materias: “…learning…”
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Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh por CGIAR Research Program on Climate Change, Agriculture and Food Security

Materias: “…learning…”
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Evaluating agricultural weather and climate services in Africa: A learning agenda on climate information services in sub-Saharan Africa por Vaughan, Catherine, Hansen, James, Roudier, Philippe, Watkiss, Paul, Carr, Edward R.

“…Priority learning areas include: broadening our view of potential users, and uses, of WCS; filling of geographic and demographic gaps; and quantifying the extent to which “good practice” leads to improved outcomes and impacts.…”
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Learning adaptation to climate change from past climate extremes: Evidence from recent climate extremes in Haryana, India por Aryal, Jeetendra Prakash, Jat, Mangi Lal, Sapkota, Tek Bahadur, Rahut, Dil Bahadur, Rai, Munmun, Jat, Hanuman Sahay, Sharma, Parbodh Chander, Stirling, Clare M

“…This study aims to examine whether farmers learn from their past experiences of exposure to climate extremes and use the knowledge to better adapt to future climate extremes. …”
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Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon por Silatsa, F.B., Yemefack, Martin, Tabi, F.O., Heuvelink, G.B., Leenaars, J.G.

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Lessons learned from application of the “Indicators of Resilience in Socio-ecological Production Landscapes and Seascapes (SEPLS)” under the Satoyama initiative por Dunbar, William, Subramanian, Suneetha M., Matsumoto, Ikuko, Natori, Yoji, Dublin, Devon, Bergamini, Nadia, Mijatović, Dunja, González Álvarez, Alejandro, Yiu, Evonne, Ichikawa, Kaoru, Morimoto, Yasuyuki, Halewood, Michael, Maundu, Patrick, Salvemini, Diana, Tschenscher, Tamara, Mock, Gregory

“…In this project, a set of 20 indicators were identified to capture different aspects of resilience in SEPLS, and examples are included from various contexts around the world, with the purpose of identifying lessons learned and good practices for resilience assessment. …”
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Building financial resilience in pastoral communities in Africa: Lessons learned from implementing the Kenya Livestock Insurance Program (KLIP) por Fava, Francesco P., Jensen, Nathaniel D., Sina, J., Mude, Andrew G., Maher, B.

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Opportunities and challenges for mangrove management in Vietnam: Lessons learned from Thanh Hoa, Thai Binh and Quang Ninh provinces por Pham Thu Thuy, Vu, T.P., Pham, D.C., Dao, L.H.T., Nguyen, V.T., Hoang, N.V.H., Hoang, T.L., Dao, T.L.C., Nguyen, D.T.

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Report from Kenya’s Climate Smart Agriculture Multi-Stakeholder Platform (CSA MSP) Livestock Learning Platform meeting por Cramer, Laura K.

“…The Learning Platforms bring stakeholders together quarterly to discuss various topics. …”
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Digital extension Interactive Voice Response (IVR) mLearning: Lessons learnt from Uganda pig value chain por Dione, Michel M., Kang'ethe, Edwin, Poole, Elizabeth J., Ndiwa, Nicholas N., Ouma, Emily A., Dror, Iddo

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Improving and diversifying planted forages through selection, breeding and inclusion in local production systems - Examples of South-South learning por Peters, Michael, Mwendia, Solomon W., Castiblanco, Valheria, Hoek, Rein van der, Burkart, Stefan, Douxchamps, Sabine, Ohmstedt, Uwe, Notenbaert, An Maria Omer

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Policy learning as complex contagion: how social networks shape organizational beliefs in forest-based climate change mitigation por Gronow, A., Brockhaus, Maria, Gregorio, M. di, Karimo, A., Ylä-Anttila, T.

“…Policy learning can alter the perceptions of both the seriousness and the causes of a policy problem, thus also altering the perceived need to do something about the problem. …”
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Mining the gaps: Using machine learning to map 1.2 million agri-food publications from the Global South por Commission on Sustainable Agriculture Intensification

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Climate Change Agriculture and Food Security Learning Platform 5: cross-cutting platform on gender and climate-smart agriculture por Huygens, Sophia, Nyasimi, Mary

Materias: “…learning…”
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Global nutrition leaders have applied learning from the Transforming Nutrition course in their work as program practitioners and policy makers por CGIAR Research Program on Agriculture for Nutrition and Health

Materias: “…learning…”
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Set of solutions using remote sensing and supervised learning to replace autoregressive integrated moving average models to forecast weather por CGIAR Research Program on Wheat

Materias: “…learning…”
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Consolidating models successes routing CSAPs scaling: Learnings from Service providers promoting climate smart technologies in Bihar por CGIAR Research Program on Climate Change, Agriculture and Food Security

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Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia por Masolele, Robert N., Sy, Veronique de, Marcos, Diego, Verbesselt, Jan, Gieseke, Fabian, Mulatu, Kalkidan Ayele, Moges, Yitebitu, Sebrala, Heiru, Martius, Christopher, Herold, Martin

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Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches por Álvarez Mendoza, Cesar I., Guzman, Diego, Casas, Jorge, Bastidas, Mike, Polanco, Jan, Valencia Ortiz, Milton Orlando, Montenegro, Frank, Arango, Jacobo, Ishitani, Manabu, Gómez Selvaraj, Michael

Materias: “…machine learning…”
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The Integrated system for Natural Capital Accounting (INCA) in Europe: twelve lessons learned from empirical ecosystem service accounting por La Notte, A., Vallecillo, S., Grammatikopoulou, L., Polce, C., Rega, C., Zulian, G., Kakoulaki, G., Grizzetti, B., Ferrini, S., Zurbaran-Nucc, M., Bendito, E., Vysna, V., Paracchini, M.L., Maes, J

“…Through the compilation of nine Ecosystem Services (ES) accounts, INCA can make available to any interested ecosystem accountant a number of lessons learned. Amongst the conceptual lessons learned, we can mention: (i) for accounting purposes, ES should be clustered according to the existence (or not) of a sustainability threshold; (ii) the assessment of ES flow results from the interaction of an ES potential and an ES demand; (iii) the ES demand can be spatially identified, but for an overarching environmental target, this is not possible; ES potential and ES demand could mis-match; (iv) because the demand remains unsatisfied; (v) because the ES is used above its sustainability threshold or (vi) because part of the potential flow is missed; (vii) there can be a cause-and-effect relationship between ecosystem condition and ES flow; (viii) ES accounts can complement the SEEA Central Framework accounts without overlapping or double counting. …”
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