Resultados de búsqueda - "learning"

Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability por El Bilali, A., Brouziyne, Youssef, Attar, O., Lamane, H., Hadri, A., Taleb, A.

Materias: “…machine learning…”
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Writing about nutrition in Indian newspapers: Lessons learned from the OneWorld POSHAN Media Fellowship por Kumar, Rahul, Knebel, Elisa

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Coupling Process-Based Models and Machine Learning Algorithms for Predicting Yield and Evapotranspiration of Maize in Arid Environments por Attia, Ahmed, Govind, Ajit, Qureshi, Asad Sarwar, Feike, Til, Rizk, Mosa Sayed, Shabana, Mahmoud Mohamed Abd ElHay, Kheir, Ahmed M.S.

“…Crop models (CMs) are powerful tools for predicting yield and water use, but they still have some limitations and uncertainties; therefore, combining them with machine learning algorithms (MLs) could improve predictions and reduce uncertainty. …”
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Dynamic networks of interactive learning and agricultural research for development: three critical roles for agricultural advisory services por Maatman, Arno, Wongtschowski, Mariana, Heemskerk, Willem, Sellamna, Nour, Davis, Kristin E.

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A simple algorithm outperforms a machine learning approach for quantifying spittlebug damage in tropical grasses por Ruiz-Hurtado, Andres Felipe, Espitia, Paula, Cardoso, Juan Andres, Jauregui, Rosa Noemi

“…Considering the large data volumes in breeding trials, where five replicates of ~150 genotypes are assessed to spittlebug damage, often with limited availability of ground truth data, unsupervised learning approaches like clustering are preferred for damage segmentation. …”
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Growth, distribution and poverty in Madagascar: learning from a microsimulation model in a general equilibrium framework por Cogneau, Denis, Robilliard, Anne-Sophie

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Rural Finance Policies 8 - Informal markets: what lessons can we learn from them? por Sharma, Manohar, Zeller, Manfred

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Learn from the experts: how we move beyond emergency relief in fragile and conflict-affected settings por Nohayi, Ngowenani

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WOCAT SLM Approach: “Lessons learned from the "Mind the Gap" project: Improving Dissemination Strategies [Tunisia]” por Verbist, Joren, Rudiger, Udo, Najjar, Dina, Dhehibi, Boubaker, Derbel, Sondos

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Report on learning in the Lower Kafue Multistakeholder Platform in Zambia: addressing gender, social inclusion and power dynamics por Ng’ambi, F., Mapedza, Everisto D., Buisson, Marie-Charlotte, Siangulube, F. S., Kasoma-Pele, Winnie

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D4N2024 Resources – Learning Lab 2b: Unlocking opportunities for policy wins por Wangdi, Chime P., Youssefi, Farbod

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How one farmer is learning and leading the way in improved millet and ground nut seed production In Uganda por Aluoch, Marion, Gichuru, Lilian, Muricho, Geoffrey, Ojiewo, Christopher Ochieng

Materias: “…learning…”
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Growth charts only marginally improved maternal learning from nutrition education and growth monitoring in Lesotho por Ruel, Marie T., Habicht, Jean-Pierre

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Mature products & assets: learning toolkit for mDSR-Based low-carbon rice production advisories por Excellence in Agronomy

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Machine learning based gridded/ digital soil mapping for Kapiti Research Station and Wildlife Conservancy, Kenya por Cherotich, Fredah, Leitner, Sonja, Pearce, F., Rufino, Mariana C., Quinton, J., Dhulipala, Ram, Salavati, M., Gluecks, Ilona V., Whitbread, Anthony M., Paliwal, Ambica

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A literature review of citizen science for hydrological monitoring - with specific focus on lessons learned in developing countries por Kwakye, E., Barron, J., Adusei-Gyamfi, J., Atampugre, Gerald, Tilahun, Seifu A.

“…Notably, this meta-review found that most of the ‘lessons learned’ in scientific review papers focused on data collection. …”
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Mature products & assets: learning toolkit for mDSR-based low-carbon rice production advisories por Excellence in Agronomy

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Creating safe spaces for decision-making in conservation agriculture: using the Gender Action Learning System methodology por Enokenwa Baa, Ojongetakah, Chinyopiro, A., Nortje, Karen

“…It helps all people involved to generate lessons that guide future planning, monitoring, evaluation, and learning (PM&E). The flexibility in adapting the GALS methodology tool allows for use across age groups, genders, ethnicities, educational levels, and other social classification criteria. …”
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Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms por Cem Akcapınar, M., Cakmak, B.

“…Using various machine learning algorithms, 21 yield prediction models for Bayraktar-2000, 12 for Kızıltan-91 and 8 for Bezostaya-1 were presented as alternatives, with model performances (coefficient of determination, R2) ranging between 0.74 and 0.97, 0.73 and 0.96, and 0.69 and 0.87, respectively.…”
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How consistent are Earth Observation-based machine learning models for predicting poverty in sub-Saharan Africa? por Mlambo, Reason

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