Priscilla Ntuchu Kephe
Dados Biográficos
| ID | 7994036 |
|---|---|
| NOME | Priscilla Ntuchu Kephe |
| PRENOMES | Priscilla Ntuchu |
| SOBRENOME | Kephe |
| ASSINATURA | KEPHE P N |
| AFILIAÇÕES | Leibniz Association |
| ORCID | 0000-0002-8995-8148 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| OBRAS COMO AUTOR | 2 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2024 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
Towards a comprehensive decision support system for agroforestry systems
Agroforestry systems (AFSs) enhance biodiversity, productivity, and climate resilience, yet their inherent complexity challenges traditional modeling approaches. This study presents an integrated framework combining process-based modeling (PBM), machine learning, and life cycle assessment (LCA) into a user-friendly decision support system (DSS) for AFS analysis and optimization. Multi-scenario simulations using Hi-sAFe (16 configurations varying …
The need for evidence-based climate risk and adaptation assessments
The need for evidence-based climate risk and adaptation assessments: lessons learned from the AGRICA project, Murken, Lisa, Tomalka, Julia, Abigaba, David, Amoah, Antwi-Bosiako, Amougou, Joseph Armathé, Anaba, Muriel, Arumugam, Ponraj, Awori, Eres, Léa Baek, Hye-Rin, Below, Till, Cartsburg, Matti, Chemura, Abel, Cronauer, Carla, Didovets, Iulii, Gizaw, Adefires Worku, Gleixner, Stephanie, Gloy, Nele, Grams, Enrico, Habtemariam, Lemlem, Hampf, Ann…
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The need for evidence-based climate risk and adaptation assessments
The need for evidence-based climate risk and adaptation assessments: lessons learned from the AGRICA project, Murken, Lisa, Tomalka, Julia, Abigaba, David, Amoah, Antwi-Bosiako, Amougou, Joseph Armathé, Anaba, Muriel, Arumugam, Ponraj, Awori, Eres, Léa Baek, Hye-Rin, Below, Till, Cartsburg, Matti, Chemura, Abel, Cronauer, Carla, Didovets, Iulii, Gizaw, Adefires Worku, Gleixner, Stephanie, Gloy, Nele, Grams, Enrico, Habtemariam, Lemlem, Hampf, Ann…
Towards a comprehensive decision support system for agroforestry systems
Agroforestry systems (AFSs) enhance biodiversity, productivity, and climate resilience, yet their inherent complexity challenges traditional modeling approaches. This study presents an integrated framework combining process-based modeling (PBM), machine learning, and life cycle assessment (LCA) into a user-friendly decision support system (DSS) for AFS analysis and optimization. Multi-scenario simulations using Hi-sAFe (16 configurations varying …
Climate change (2 obras) · Adaptation (eye (1 obra) · Agriculture Sustainability and Environmental Impact (1 obra) · Agroforestry and silvopastoral systems (1 obra) · Carbon Footprint (1 obra) · Climate change adaptation (1 obra) · Climate change impacts on agriculture (1 obra) · Climatology (1 obra) · Computer Science (1 obra) · Decision support system (1 obra)