Resultados de búsqueda - DEVELOPMENT AI

  1. Principles for socially inclusive digital tools for smallholder farmers: A guide. Version 2 por Dittmer, Kyle M, Burns, Sessie, Shelton, Sadie, Wollenberg, Eva

    Publicado 2024
    “…The principles support digital developers and managers using digital tools with farmers and help funders, farmers’ organizations, or NGOs hold developers accountable to social inclusion principles. …”
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    Informe técnico
  2. Combining temperature-dependent life table data into Insect Life Cycle Model to forecast fall armyworm Spodoptera frugiperda (JE Smith) distribution in maize agro-ecological zones... por Adan, M., Tonnang, H.E.Z., Kassa, C.E.F., Greve, K., Borgemeister, C., Goergen, G.

    Publicado 2024
    “…The research further projected the Establishment Risk Index (ERI), Activity Index (AI), and Generation Index (GI) for FAW under current and future climates (2050 and 2070) using RCP 2.6 and RCP 8.5 scenarios. …”
    Enlace del recurso
    Journal Article
  3. Free online trainings on soil health monitoring with satellite based remote sensors por Huq, Rafiq, Lesueur, Didier

    Publicado 2024
    “…Each session featured three one-hour lectures as outlined below: 1. « Earth Observation and Soil Health: Where Innovation Meets Practice » - Focus: The origins and development of Earth Observation (EO) technology, its applications innatural sciences and soil health monitoring, as well as its opportunities and limitations. 2. « Satellite-Based Remote Sensing: How Earth Observation Enhances Soil Health Monitoring » - Focus: Various remote sensing (RS) technologies, their application in soil data collection, and the role of RS data in monitoring soil health. 3. « From Data to Decisions: Translating Remote Sensing Insights into Practical Soil Health Solutions » - Focus: AI-based agricultural informatics, the use of machine learning in data analysis and automation, and the importance of baseline data for machine learning algorithms.…”
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  4. What do we know about the future of crop pests and diseases in relation to food systems? por Petsakos, Athanasios, Montes, Carlo, Pequeno, Diego, Schiek, Benjamin, Sonder, Kai

    Publicado 2025
    “…Artificial intelligence (AI) and related methods can assist in the development of robust and adaptable models to capture the impacts of P&D on food systems.…”
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    Capítulo de libro
  5. Prioritizing research efforts to increase onfarm income generation: the case of cassavabased farmers in periurban Southern Cameroon por Duindam, J.W., Hauser, S.

    Publicado 2011
    “…The fallow period is mostly around 2-4 years with natural regrowth typically dominated by Chromolaena odorata (Ngobo et aI., 2004). The urban demand for cassava products is currently higher than the supply which improves cassava income generation potential and justifies the development of more commercially orientated fields. …”
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    Capítulo de libro
  6. Contesting Climate Futures: mapping misinformation narratives and networks on Telegram por Tucci, Giulia, Carneiro, Bia, Bastos, Joao Guilherme

    Publicado 2025
    “…“Contesting Climate Futures: mapping misinformation narratives and networks on Telegram” is a collaborative research project developed during the Digital Methods Initiative (DMI) Summer School 2025, under the theme “Social Media at a Crossroads, and the Sensitivity of AI Platforms.” …”
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    Conjunto de datos
  7. Advancing Farmer-Led Low-Emission Small Ruminants Production: Partners, Beneficiaries & Climate-Smart Innovations in Ethiopia and Beyond por Getachew, Tesfaye, Rischkowsky, Barbara A., Belay, Berhanu, Rekik, Mourad, Haile, Aynalem

    Publicado 2025
    “…Through the SmaRT Pack innovation framework, the program integrates eight complementary innovations, genetic improvement, low-cost Artificial Insemination (AI), ultrasonography for reproductive management, feed development and utilization, digital data systems (DTREO), market linkage models, inclusive youth and women engagement, improved husbandry practices, and enhanced animal-health interventions. …”
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  8. Sample Earth: Machine-Learning–Ready Land-Cover Reference Dataset por Vantalon, Thibaud, Luong, Phuong Thi, Perez Escobar, Jorge Andres, Tello Dagua, Jhon Jairo, Phan, Trong Van, Nguyen, Hang, Hong Nguyen, Hoa Nguyen, Reymondin, Louis

    Publicado 2025
    “…It contains GPS-located land-cover samples that can be used to train and validate AI models that generate detailed, accurate maps, with a focus on coffee and cocoa production systems. …”
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    Conjunto de datos
  9. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies por Trabucco, Antonio, Zomer, Robert J., Bossio, Deborah A., Straaten, Oliver van, Verchot, Louis V.

    Publicado 2008
    “…About 27% (200 Mha) was in the highest impact class, exhibiting an 80-100% decrease in runoff, and prevalent in drier areas (based on Aridity Index (AI)), the semi-arid tropics, and in conversion from grasslands and subsistence agriculture. …”
    Enlace del recurso
    Journal Article

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