Resultados de búsqueda - "learning"

I went to Wuhan to investigate the origins of Covid: Here’s what we can learn from Huanan market por Hung Nguyen-Viet

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Diversity for Nutrition and Enhanced Resilience (DiNER) Fairs and Voucher Programming: Evaluation and learning in the Southern Africa Region por Raboanarielina, Cara, Templer, Noel, Davis, Valerie, Wilcox, Michael, Sperling, Louise

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Understanding capacities to scale innovations for sustainable development: a learning journey of scaling partnerships in three parts of Africa por Wigboldus, Seerp, McEwan, M.A., Schagen, Boudy van, Okike, Iheanacho, Mourik, Tom A. van, Rietveld, Anne M., Amole, Tunde A., Asfaw, F., Hundayehu, M.C., Iradukunda, Francois, Kulakow, Peter A., Namanda, S., Suleman, I.

Materias: “…learning capacity…”
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Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches por Prasetyo, L.B., Setiawan, Y., Condro, A.A., Kustiyo, K., Putra, E.I., Hayati, N., Wijayanto, A.K., Ramadhi, A., Murdiyarso, D.

“…We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). …”
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e-resilient-lowland: an e-learning toolkit to improve water control in West African rice producing countries por CGIAR Research Program on Rice

Materias: “…learning…”
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Local adaptation of planting basins through options by context (OxC) co-learning and gender-sensitive methods por CGIAR Research Program on Forests, Trees and Agroforestry

Materias: “…learning…”
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Annual CGIAR gender research virtual conference to showcase research, receive feedback, and promote mutual learning and collaboration por CGIAR GENDER Platform

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Understand low level of adoption of new durum wheat cultivars in Morocco (learning generated about adoption dynamics) por CGIAR Research Program on Wheat

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Monitoring, evaluation and learning within the CGIAR Initiative Sustainable Animal Productivity for Livelihoods, Nutrition and Gender Inclusion (SAPLING) por Marshall, Karen, Poole, Elizabeth J., Njehu, Alice, Baltenweck, Isabelle

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Monitoring, evaluation, learning and impact assessment and scaling preparedness and action (MELIA&SPA): a process-based framework por Minh, Thai Thi

Materias: “…learning…”
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An agent-based model for collaborative learning to combat antimicrobial resistance: Proof of concept based on broiler production in Senegal por Ndekou, P.P., Drake, A., Lomax, J., Dione, Michel M., Faye, A., Nsangou, M.D.N., Korir, Luke, Sklar, E.

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Integrating spatial analysis and machine learning approaches into the protocol for assessing spittlebug (Hemiptera: Cercopidae) resistance in Urochloa sp. por Hernández, Luis Miguel, Espitia-Buitrago, Paula, Cardoso Arango, Juan Andrés, Jauregui, Rosa

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Predictive modeling of nutritional quality in Urochloa pastures from multispectral sensors and images using machine learning approaches por Camelo-Munevar, Rodrigo Andrés, Hernández, Luis Miguel, Jauregui, Rosa, Cardoso Arango, Juan Andrés

Materias: “…machine learning…”
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Predicting High-Magnitiude, Low-Frequency Crop Losses Using Machine Learning: An Application to Cereal Crops in Ethiopia por Warner, James

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An evolving paradigm of agricultural mechanization development: How much can Africa learn from Asia? Discussion on book por Daum, Thomas

“…BOOK LAUNCH An evolving paradigm of agricultural mechanization development: How much can Africa learn from Asia? FEB 9, 2021 - 09:30 AM TO 10:30 AM EST…”
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‘Low- Carbon Future Village’: The story of Qingshan Village, China, and a new journey of international co-learning por Zhang, Wei, Wang, Xinxin, Kumar, Praveen, Chen, Kevin Z.

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A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI) por Saha, Shree, Narayanan, Sudha

Materias: “…machine learning…”
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Sharing tips for rice, chicken and vegetable production: Do voice messages and social learning complement extension services? por Almanzar, Miguel, de Brauw, Alan, Nakasone, Eduardo

“…We evaluate whether farmers received the information, learned it, shared it with non-recipients, and used it, and how the effects of the information campaign on these outcomes changes with being part of an existing farmer group and the proportion of the village population receiving information. …”
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Predicting climate-driven changes in reservoir inflows and hydropower in Côte d'Ivoire using machine learning modeling por Obahoundje, Salomon, Diedhiou, A., Akpoti, Komlavi, Kouassi, K. L., Ofosu, E. A., Kouame, D. G. M.

Materias: “…machine learning…”
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Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia por Mann, Michael L., Malik, Arun S., Warner, James

Materias: “…machine learning…”
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