Integrating Remote Sensing and Participatory Scenario Planning to Model Agroforestry Futures in Ethiopia

Abstract ID: 3.11479 | Not reviewed | Requested as: Talk | TBA | TBA

Gebreyohannes Zenebe Teka (1)
Amanuel, Zenebe (1,2); Emiru, Birhane (1,2,3); Atkilt, Girma (1,2); Niguse, Hagazi (4); Robert, Marchant (5); Aster, Gebrekirstos (4)

(1) Institute of Climate and Society (MU-ICS), Mekelle University, Mekelle, Ethiopia
(2) Department of Land Resource Management and Environmental Protection, College of Dryland Agriculture and Natural Resources, Mekelle University, Mekelle, Ethiopia
(3) Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), Ås, Norway
(4) World Agroforestry Centre (ICRAF), United Nations Avenue, P.O. Box 30677-00100, Nairobi, Kenya
(5) Department of Environment and Geography, University of York, York YO10 5DD, United Kingdom

Categories: Adaptation, Biodiversity, Conservation, Remote Sensing, Spatial Planning
Keywords: Biodiversity, Climate Resilience, KESHO, Land Change Modeler

Categories: Adaptation, Biodiversity, Conservation, Remote Sensing, Spatial Planning
Keywords: Biodiversity, Climate Resilience, KESHO, Land Change Modeler

Abstract

Agroforestry plays a crucial role in enhancing climate resilience, restoring degraded landscapes, and improving rural livelihoods in Ethiopia. However, its successful implementation influenced by a complex mix of socio-environmental, political, and cultural factors. This study addresses the need for sustainable land-use planning by integrating remote sensing with the KESHO participatory scenario development approach, a structured framework engaging diverse stakeholders to model agroforestry futures. The study was conducted in Gedeo zone, and Northwestern Tigray, two distinct agro-ecological settings in Ethiopia. On both study sites, community leaders, farmers, experts, planners, and researchers participated in a workshop with the aim to identify historical change timelines, current and future drivers of change, co-development of alternative scenarios, and identification of desired and undesired futures, allowing them to envision and plan for sustainable land use based on their local knowledge and preferences. Four alternative scenarios were developed for each site. Additionally, historical (2000, 2012) and current (2024) land use land cover (LULC) maps were generated from Landsat imagery using Random Forest classifier. In Gedeo, high population density, land scarcity, food insecurity, and the expansion of cash crops like Khat and sugarcane, threatening traditional coffee and enset-based agroforestry systems. In Tigray, agroforestry is promoted for land restoration, but socio-political and environmental challenges, including the Tigray war erupted in 2020, have disrupted agriculture and displaced millions, hindering recovery efforts. The study demonstrates the importance of integrating local knowledge with remote sensing for operational land-use planning. The co-developed scenarios was combined into a spatial modeling framework using Terrset Land Change Modeler to predict potential LULC changes. Insights on past, present and future agroforestry systems from the two distinct agro-ecological zones demonstrate how such tools can support decision-making processes at local and national levels, contributing to sustainable land management and climate change adaptation strategies in Ethiopia.

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