Resultados de búsqueda - "learning"

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.

Materias: “…machine learning…”
Enlace del recurso

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

“…This report documents the technical content, highlights of the discussion, lessons learned, and strategic recommendations aimed at ensuring national ownership, deployment, and long-term scalability of EDACaP’s forecast-based advisory services.…”
Enlace del recurso

From theory to practice: lessons from transformative learning through a net-zero design competition por Mehmood, A., Cheema, Abdur Rehman, Salam, M., Ahmed, S. U. D., Moghayedi, A., Vassilev, V., Aburamadan, R.

Materias: “…experiential learning…”
Enlace del recurso

A critical synthesis of remote sensing and machine learning approaches for climate hazard impact on crop yield por Obahoundje, Salomon, Tilahun, Seifu A., Zemadim, Birhanu, Schmitter, Petra

“…This review critically assesses the application of machine learning (ML) algorithms and remote sensing (RS) products in detecting and predicting climate hazards, as well as their impacts on crop yields. …”
Enlace del recurso

Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments por Alimagham, Seyyedmajid, van Loon, Marloes P, Ramirez Villegas, Julian, Berghuijs, Herman N.C., Rosenstock, Todd Stuart, van Ittersum, Martin K.

Materias: “…machine learning…”
Enlace del recurso

Using Stakeholder Cognitive Maps to Inform the Implementation of IRRI’s HER+ West Bengal Livestock Learning Lab por Goswami, R., Singh, K., Mukhopadhyay, P., Chadha, D., Puskur, Ranjitha, Gartaula, H.N.

“…A Theory of Change (ToC) was developed together with partners and key stakeholders to guide the implementation of co-designed STIBs in learning labs (LLs) in West Bengal, India. Learning labs are multisectoral, multistakeholder, and multidisciplinary engagement and learning spaces for collaborative practice and research. …”
Enlace del recurso

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

Materias: “…machine learning…”
Enlace del recurso

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

Materias: “…machine learning…”
Enlace del recurso

Finding solutions where they are: Working with adaptation pioneers to support social learning and scaling of locally-led innovation por Habermann, Birgit, Gichuki, Leah

“…The presentation focused on the work with the adaptation pioneers in Kenya and key achievements in social learning and scaling.…”
Enlace del recurso

SCALING SMART VALLEYS APPROACH FOR LAND AND WATER MANAGEMENT: INSIGHTS AND LESSONS LEARNED FROM 2022 TO 2025 por Suh, Neville Ndohnwi, Djagba, Justin, Dossou Yovo, Elliott Ronald

“…Lively exchanges between the trainer and participants enriched the training, allowing field-based questions to clarify technical principles and contextualize lessons learned from Smart Valleys implementation in Mali between 2022 and 2025. …”
Enlace del recurso

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

Enlace del recurso

Using scenarios to make decisions about the future: anticipatory learning for the adaptive co-management of community forests por Wollenberg, Eva Karoline, Edmunds, D., Buck, L.

Enlace del recurso

Proses pembelajaran (learning lessons) promosi sertifikasi hutan dan pengendalian penebangan liar di Indonesia por Tacconi, L., Obidzinski, K., Agung, F.

Materias: “…learning…”
Enlace del recurso

A weighted decision and tenurial niche approach to analyzing adaptive learning in the social forests of northeastern Zimbabwe por Chahweta, C., Mandondo, A.

Enlace del recurso

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.

Enlace del recurso

Evaluating land use and livelihood impacts of early forest carbon projects: lessons for learning about REDD+ por Caplow, S., Jagger, P., Lawlor, K., Sills, E.

“…The ‘Bali Road Map’ of UNFCCC COP-13 calls for sharing lessons learned from demonstration activities that aim to reduce emissions from deforestation and degradation and enhance forest carbon stocks (now known as ‘REDD+’). …”
Enlace del recurso

Learning alliances for the broad implementation of an integrated approach to multiple sources multiple uses and multiple users of water por Penning de Vries, Frits W.T.

Enlace del recurso

Why documenting research data? is it worth the extra effort? learnings from the Fakara metadata encoding exercise por Traoré, A.

Enlace del recurso

Quality linked to geographical origin and geographical indications: Lessons learned from six case studies in Asia por Lecoent, A., Vandecandelaere, E., Cadilhon, Joseph J.

Enlace del recurso

What you will learn in this course: Training manual for dairy farmers in the traditional sector in India (in Assamese and Hindi) por Sarma, A., Qutub, A.B., Bayan, Baban, Grace, Delia, Deka, D., Saharia, K.K., Bora, K., Islam, M., Joshi, M., Barbaruah, M.I., Bhagabati, N., Deka, Ram Pratim, Talukdar, R.B.

Enlace del recurso