Resultados de búsqueda - "learning"

Påverkar hästens kön och människans val av disciplin, vilka träningsmetoder som används? por Persson, Myriam

“…Horses that are to be ridden and trained need to learn how to respond quickly to some human signals, while learning to not react to others as well as possible stimuli in their environment. …”
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Manual de arboricultura en espacios públicos urbanos del Municipio de Managua por Gutiérrez Rodríguez, Nelda de los Ángeles

“…En el año 2008 académicos y estudiantes de la Facultad de Recursos Naturales y del Ambiente (FARENA) de la Universidad Nacional Agraria de Nicaragua (UNA), participaron en la Iniciativa MyCOE (My Community, Our Earth): Geographical Learning for Sustainable Development de la Asociación Americana de Geógrafos (AAA), a través del Proyecto Estudio de la diversidad arbórea urbana pública en las vías principales de los Distritos II, IV y VI del Municipio de Managua, Nicaragua, a partir del cual fueron realizadas una serie de investigaciones desde el 2008 hasta el 2017, cuyos hallazgos y recomendaciones constituyen la base del presente Manual. …”
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Genetic background of temperament traits in standardbred trotters por Berglund, Paulina

“…The second factor named tractability, described cooperative horses that easily learned the task of competing and had a high will to win. …”
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Våga satsa, våga misslyckas : en undersökning av hur lantbrukare påverkas av ett misslyckande por Jutell, Carolina, Malmberg, Malin

Guidelines for community-led multiple use water services: evidence from rural South Africa por van Koppen, Barbara, Molose, V., Phasha, K., Bophela, T., Modiba, I., White, M., Magombeyi, Manuel Simba, Jacobs-Mata, Inga

“…This working paper synthesizes the lessons learned about the six steps of the community-led MUS process in all six communities. …”
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Process and benefits of community-led multiple use water services: comparing two communities in South Africa por van Koppen, Barbara, Magombeyi, Manuel Simba, Jacobs-Mata, Inga, Molose, V., Phasha, K., Bophela, T., Modiba, I., White, M.

“…Based on IWMI’s evidence, tools and manuals, the project team organized learning alliances and policy dialogues from municipal to national level on the replication of community-led MUS by water services authorities; government departments of water, agriculture, and others; employment generation programs; climate and disaster management; and corporate social responsibility initiatives. …”
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Innovation intermediation in a digital age: broadening extension service delivery in Ghana por Munthali, N.

“…Further, by observing and interviewing actors on more informal social media messaging platforms it was established that these platforms can support the coordination of extension activities, timely pest and disease monitoring and knowledge sharing among extension staff and subject matter specialists to enable individual-centred learning and problem solving. Despite this potential, the study also shows that new ICTs’ inherent technical features do not determine their application, but social factors (human abilities and preferences, identity management, sociopolitical influences and the wider institutional environment) shape their use. …”
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RTB Meeting Report: Piloting the G+ Tools por CGIAR Gender and Breeding Initiative

“…Most notably, the project got a no-cost extension until the end of December 2020 and the planned “Evaluation and Learning” workshop was replaced by eight digital meetings taking place from September to December 2020. …”
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Estimating spatially distributed SOC sequestration potentials of sustainable land management practices in Ethiopia por Abera, Wuletawu, Tamene, Lulseged D., Abegaz, Assefa, Hailu, Habtamu, Piikki, Kristin, Soderstrom, Mats, Girvetz, Evan Hartunian, Sommer, Rolf

“…We used simple statistics to assess the SOC change between the two periods, and machine learning models to predict SOC stock spatially. The study showed that statistically significant variation (P < 0.05) of SOC was observed between the two years in two watersheds (Gafera and Adi Tsegora) whereas the differences were not significant in the other two watersheds (Yesir and Azugashuba). …”
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Macroecological patterns of forest structure and allometric scaling in mangrove forests por Rovai, A.S., Twilley, R.R., Castañeda Moya, E., Midway, S.R., Friess, D.A., Trettin, C.C., Bukoski, J.J., Stovall, A.E.L., Pagliosa, P.R., Fonseca, A.L., Mackenzie, R.A., Aslan, A., Sasmito, S.D., Sillanpää, M., Cole, T.G., Purbopuspito, J., Warren, M.W., Murdiyarso, D., Mofu, W., Sharma, S., Tinh, P.H., Riul, P.

“…We used frequentist inference statistics and machine learning models to determine environmental drivers that control biomass allocation within and across mangrove communities globally. …”
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Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation por Bourgeau-Chavez, L.L., Grelik, S.L., Battaglia, M.J., Leisman, D.J., Chimner, R.A., Hribljan, J.A., Lilleskov, E.A., Draper, F.C., Zutta, B.R., Hergoualc'h, Kristell, Bhomia, R.K., Lähteenoja, O.

“…Here we review methods for developing high accuracy peatland maps for the PMFB using a combination of multi-temporal synthetic aperture radar (SAR) and optical remote sensing in a machine learning classifier. The new map produced has 95% overall accuracy with low errors of commission (1–6%) and errors of omission (0–15%) for individual peatland classes. …”
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Evolution of soil fertility research and development in Ethiopia: From reconnaissance to data-mining approaches por Erkossa, Teklu, Laekemariam, Fanuel, Abera, Wuletawu, Tamene, Lulseged D.

“…It is believed that the recent development in data mining and machine-learning approaches creates the opportunities to use the data sets in conjunction with other covariates in order to generate evidence that helps to make better decisions both at strategic and operational levels. …”
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A data-mining approach for developing site-specific fertilizer response functions across the wheat-growing environments in Ethiopia por Abera, Wuletawu, Tamene, Lulseged D., Tesfaye, Kindie, Jiménez, Daniel, Dorado, Hugo, Erkossa, Teklu, Kihara, Job Maguta, Ahmed, Jemal Seid, Amede, Tilahun, Ramírez Villegas, Julián Armando

“…The approach used a machine-learning model (random forest) to capture the relationship between nutrients – nitrogen (N), phosphorous (P), potassium (K), and sulfur (S) – and wheat yield. …”
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A decision makers’ guide to equitable sustainable agricultural intensification por Grabowski, Philip, Fischer, Gundula, Djenontin, I.N.S., Zulu, L., Kamoto, J., Kampanje-Phiri, J., Egyir, I., Darkwah, A.

“…This guide focused on non-survey data collection tools, many of which originate from participatory learning and action, for two reasons: participatory tools encourage reflection by participants to increase stakeholder equity, and they are often better matched to the resource requirements and time constraints of those involved. …”
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Science of scaling research agenda for the CGIAR Regional Integrated Initiative for Diversification in East and Southern Africa (Ukama Ustawi) por Schut, Marc, Mugambi, Samuel

“…In addition, there is a need for investment in capacity development for scaling (internally and with partners) and learning (e.g. about strengths and weaknesses of different approaches). …”
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Mapping Above Ground Carbon Storage and Sequestration in Thoria Watershed, India: A Spatially Explicit Ecosystem Service Assessment Using InVEST Model por Guo, Zhe, Sharma, Himani, Jadav, Mahesh, Zhang, Wei

“…The LCL U map is developed by using a machine learning algorithm with Landsat 7 imageries and ground truth points collected by the local collaborators. …”
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Towards a Replicable Innovative Tool for Adaptive Climate Monitoring and Weather Forecasting Using Traditional Indigenous and Local Indicators to Strengthen AgroWeather Resilience... por Osumba, Joab, Radeny, Maren A.O., Recha, John W.M., Oroma, George W., Nzoka, Oscar, Mbingo, Joyce, Warinda, Enock, Mwale, Simon

“…The study adopted a transdisciplinary, participatory learning and action research (PLAR) model to identify and confirm emerging local weather indicators and what they mean for local rainfall forecasting, and to drive self-organization processes to bring indigenous climate knowledge into practical use in each community. …”
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Opportunities to close wheat yield gaps in Nepal’s Terai: Insights from field surveys, on-farm experiments, and simulation modeling por Devkota Wasti, Mina Kumari, Devkota, Krishna, Paudel, Gokul Prasad, Krupnik, Timothy, James McDonald, Andrew

“…The potential yield gap (difference between simulated potential yield and surveyed population mean) estimated was 4.63 t ha−1, suggesting ample room for growth even for the highest-yielding fields. Machine learning diagnostics of survey data, and on-farm trials identified nitrogen rate, irrigation management, terminal heat stress, use of improved varieties, seeding date, seeding method, and seeding rate as the principal agronomic drivers of wheat yield. …”
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Digital Agri Co-Lab: Fostering research, collaboration & skills for enabling digital innovation in agri-food systems por Nagaraji, Satish, Gardeazabal Monsalve, Andrea, Gopalan, Padmavati

“…Co-Lab’s vision is embodied in its structure, featuring four foundational pillars: a virtual collaborative space that facilitates robust discussions and the sharing of ideas; an innovation repository acting as a comprehensive directory of the latest digital agricultural technologies; a Digital Innovation Navigation Assistant (DINA), an advanced tool powered by Generative AI, that guides stakeholders through the complex terrain of agricultural technologies; and a Learning Network dedicated to capacity development through tailored training courses offered both online and via WhatsApp Bots. …”
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Anticipating social differentiation and unintended consequences in scaling initiatives using GenderUp, a method to support responsible scaling por McGuire, E., Leeuwis, C., Rietveld, A.M., Teeken, B.

“…Through a series of five stages, a GenderUp facilitator guides teams through discussions, learning activities, and practical integration to develop a socially responsible scaling strategy. …”
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