Resultados de búsqueda - Open learning.

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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Yield prediction models for some wheat varieties with satellite-based drought indices and machine learning algorithms por Cem Akcapınar, M., Cakmak, B.

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High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning por Constenla-Villoslada, Susana, Liu, Yanyan, McBride, Linden, Ouma, Clinton, Mutanda, Nelson, Barrett, Christopher B.

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Facilitating forests of learning: Enabling an adaptive collaborative approach in community forest user groups: a guidebook por McDougall, C., Pandit, B.H., Banjade, M.R., Paudel, Krishna Prasad, Ojha, H., Maharjan,M., Rana, S., Bhattarai, T., Dangol, S.

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Making rangelands more secure in Cameroon: Lessons learned and recommendations for policy makers, development actors and pastoralists por Azuhnwi, B., Gidado, M.J., Nsoh, M.F., Ndamba, M., Flintan, Fiona E.

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Integrated landscape approaches to managing social and environmental issues in the tropics: learning from the past to guide the future por Reed, J.D., Vianen, J. van, Deakin, E.L., Barlow, J., Sunderland, Terry C.H.

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On the linkage between hydrology and society—learning from history about two-way interactions for sustainable development por Pande, S., Uhlenbrook, Stefan

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COVID-19 impacts on food systems, poverty, and diets: Lessons learned from country-level analyses por Pauw, Karl, Thurlow, James

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Community-based conservation of freshwater resources: Learning from a critical review of the literature and case studies por Zhang, Wei, ElDidi, Hagar, Masuda, Yuta J., Meinzen-Dick, Ruth S., Swallow, Kimberly A., Ringler, Claudia, DeMello, Nicole, Aldous, Allison

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Leveraging agriculture for nutrition in South Asia: What do we know, and what have we learned? por Gillespie, Stuart, Poole, Nigel, van den Bold, Mara, Bhavani, R. V., Dangour, Alan D., Shetty, Prakash

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Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids por He, Kunhui, Yu, Tingxi, Gao, Shang, Chen, Shoukun, Li, Liang, Zhang, Xuecai, Huang, Changling, Xu, Yunbi, Wang, Jiankang, Boddupalli, Prasanna, Hearne, Sarah, Li, Xinhai, Li, Huihui

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Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit por Lorente, Delia, Escandell-Montero, P., Cubero, Sergio, Gómez-Sanchís, Juan, Blasco, José

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Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa por Muthoni, Francis K., Thierfelder, Christian L., Mudereri, B.T., Manda, J., Bekunda, Mateete A., Hoeschle-Zeledon, Irmgard

“…Integration of machine learning (ML) and free remotely sensed big data have opened huge opportunities for data-driven insights into complex problems in agriculture. …”
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Supporting enterprises in capturing waste value: lessons learned from the CapVal sanitation project in Ghana por Mansour, G., Darteh, B., Jabagi, E., Nikiema, Josiane, Cofie, Olufunke O.

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Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report por Ileperuma, Kaveesha, Jampani, Mahesh, Sellahewa, Uvindu, Panjwani, Shweta, Amarnath, Giriraj

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Food for thought? Experimental evidence on the learning impacts of a large-scale school feeding program in Ghana por Aurino, Elisabetta, Gelli, Aulo, Adamba, Clement, Osei-Akoto, Isaac, Alderman, Harold

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Ethiopian Agroclimate Advisory Platform (Edacap-V2): Forecast module co-learning with national partners por Ahmed, Jemal, Degefie, Tibebe, Desta, Lulseged, Teshome, Asaminew, Grossi, Amanda, Dessalegn, Olika, Ghosh, Aniruddha

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EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models por Yu, Tingxi, Zhang, Hao, Chen, Shoukun, Gao, Shang, Liu, Ze, Wang, Jiankang, Crossa, Jose, Montesinos-Lopez, Osval Antonio, Hearne, Sarah, Li, Huihui

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Transformative bottom‐up change in highly dynamic food environments: Learning from living labs in Africa por Vermue, Ardjan, Renting, Henk, Termote, Celine, Musita, Consolata, Segreto, Claudia, Wertheim‐Heck, Sigrid

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Exploratory spatial analysis of Lyme disease in Texas—What can we learn from the reported cases? por Szonyi, Barbara, Srinath, I., Esteve-Gassent, M., Lupiani, B., Ivanek, R.

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