Resultados de búsqueda - "learning"

With trust and a little help from our friends: How the Nicaragua learning alliance scaled-up training in agribusiness por Landmann, Dirk H., Cadilhon, Joseph J.

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Instructional design and blended learning to extend the reach of a research product for impact at scale: Reflections from the FEAST tool por Dror, Iddo, Heidkamp, E.

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Systems approaches to innovation in pest management: reflections and lessons learned from an integrated research program on parasitic weeds in rice por Rodenburg, J., Schut, Marc, Demont, M., Klerkx, Laurens, Gbèhounou, G., Oude Lansink, Alfons G., Mourits, M., Rotteveel, T., Kayeke, J., Ast, A. van, Akanvou, L., Cissoko, M., Kamanda, J., Bastiaans, L.

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Community-driven multiple use water services: Lessons learned by the Rural Village Water Resources Management Project in Nepal por Rautanen, S.L., van Koppen, Barbara, Wagle, N.

“…This study explores the first-hand lessons learned in the RVWRMP in Nepal since 2006. This project is embedded within the local government. …”
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Setting up agricultural water management interventions - learning from successful case studies in the Volta and Limpopo river basins por Bruin, A. de, Pateman, R., Barron, Jennie, Balima, M., Ouédraogo, I., Dapola, E.D., Fosu, M., Annor, F.O., Magombeyi, Manuel Simba, Onema, J.M.K.

“…However, sustained and wider uptake of AWM technologies and approaches has not been as successful. We need to learn from successful AWM interventions, those interventions that have led to a sustained or increased uptake of AWM technologies or approaches, and which have led to improved well-being of farmers and livestock keepers in the rural development context of sub-Sahara Africa. …”
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Learning to select and apply qualitative and participatory methods in natural resource management research: self-critical assessment of research in Cameroon por Nchanji, Y.K., Levang, P., Jalonen, R.

“…We present a meta-analysis of a researcher’s experience when applying qualitative and participatory research methods for the first time, and reflect on the challenges and lessons learned that could help other aspiring researchers in conducting research with such methods. …”
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Web-based project monitoring and data management: What did we learn from Africa RISING phase 1? por Haile, Beliyou

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Report on the SAIRLA-Ethiopia National Learning Alliance (NLA) Launch and Outcome Mapping Workshop, March 27-28, 2017 por Getnet, M., Hailegiorgis, T., Eshetu, S., Yasabu, Simret, Zelalem, Y.

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Learning about sustainable cocoa production: a guide for participatory farmer training; 1: integrated crop and pest management por David, S.

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When neglected species gain global interest: Lessons learned from quinoa's boom and bust for teff and minor millet por Andreotti, Federico, Bazile, Didier, Biaggi, Maria Cristina, Callo-Concha, Daniel, Jacquet, Julie, Jemal, Omarsherif M., King, Oliver I., Mbosso, C., Padulosi, Stefano, Speelman, Erika N., van Noordwijk, Meine

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Associative learning of visual cues in the fruit fly parasitoid Diachasmimorpha longicaudata : temporal dynamics and potential consequences on females' fitness por Nussenbaum, Ana Laura, Devescovi, Francisco, Bachmann, Guillermo Enrique, Pacelli, Franco, Garbalena, Micaela Soledad, Segura, Diego Fernando

Materias: “…Learning…”
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Advancing social insect research through the development of an automated yellowjacket nest activity monitoring station using deep learning por Martinez Von Ellrich, Andres, Dreidemie, Carola, Inchaurza, Fernan, Cucurull, Agustin, Basti, Marian, Masciocchi, Maite

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Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms por Arwatchananukul, Sujitra, Chaiwong, Saowapa, Aunsri, Nattapol, Kittiwachana, Sila, Luengwilai, Kietsuda, Trongsatitkul, Tatiya, Mahajan, Pramod, Blasco, José, Rattapon, Saengrayap

“…All the physical, chemical, and acoustic properties were used to classify for maturity and defects using the factor analysis (FA) technique and machine learning (ML). Results showed that maturity was correctly classified at 84.0 % by all parameters, while elected non-destructive parameters (color, specific gravity, and stiffness coefficients) showed lower results for distinguishing pineapples. …”
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Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production por Chaali, Nesrine, Ramírez Gomez, Carlos Manuel, Jaramillo Barrios, Camilo Ignacio, Garr´e, Sarah, Barrero, Oscar, Ouazaa, Sofiane, Calderon Carvajal, John Edinson

“…This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. …”
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South and Southeast Asia Workshop for Nagoya Protocol and Plant Treaty National Focal Points, 27-30 March 2017, International Rice Research Institute Los Baños, the Philippines por Gullotta, Gaia, Halewood, Michael, Sackville-Hamilton, N. Ruaraidh, Drews, Andreas, Garforth, Kathryn, Nnadozie, Kent

Materias: “…experiential learning…”
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AgriLAC Resiliente: Planned Impact Assessment por Lopera, Diana Carolina, Reyes, Byron A., Quintero, Katherine

Materias: “…learning…”
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Artificial intelligence-based biomonitoring of water quality por Pattinson, N. B., Kuen, R.

Materias: “…machine learning…”
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A new framework for predicting and understanding flowering time for crop breeding por Deva, Chetan, Dixon, Laura, Urban, Milan Oldřich, Ramirez‐Villegas, Julian, Droutsas, Ioannis, Challinor, Andrew J.

Materias: “…machine learning…”
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Unleashing the potential of underutilized datasets to improve agricultural decision-making through comprehensive data analysis: An example of rice crop manager (RCM) dataset por Gakhar, Shalini, Sharma, Sheetal

Materias: “…machine learning…”
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Effectiveness of the lead farmer approach in agricultural extension service provision: Nationally representative panel data analysis in Malawi por Ragasa, Catherine

Materias: “…social learning…”
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