Geospatial characterization of climate-smart agroforestry in two contrasting physiographic zones of Rwanda
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Efficient use of rich natural resources notably land, is one of the most important indicators of economic progress.
The unmatched population growth with production has triggered increased demand for food. Nations have
prioritized sustainable agriculture as a coping strategy. Climate-smart agroforestry (CSAF) can be one of the
options to increase productivity, income, and food security, and stabilize the environment. CSAF denotes a
practiced farming system of combining trees with crops or animals (AF) evolved in practices to enhance productivity
and feed the food-insecure people while coping with the adverse effects of climate change. This study
investigates the land suitability for CSAF in the Bugesera and Rulindo regions of Rwanda. Nine variables were
considered for investigation in the study viz. elevation, slope, soil type, rainfall, temperature, LU/LC, distance
from roads and trade centers, and landslide risks. The analysis used two commonly known techniques (AHP and
GIS) integrated to classify and sort out the suitable land for CSAF practices and development. Results identified
three CSAF suitability zones, ranging from 1,662.82 ha (1.60 %) as highly suitable and 90,123.78 ha (86.62 %) as
moderately suitable to 12,262.50 ha (11.78 %) less suitable zones in Bugesera. In Rulindo, suitability zones range
from 709.92 ha (9.69 %) as highly suitable and 6,514.56 ha (88.92 %) as moderately suitable to 102.24 ha (1.39
%) less suitable land for CSAF. Results further showed that the available means suitable land for CSAF are
34,683.03 ha in Bugesera (34,683.03 ± 48,304.71) and 2,442.24 ha in Rulindo (2,442.24 ± 3,539.79). Land
suitability scores for CSAF largely varied across sites (F = 1.33, p = 0.31). Cross-validation using ground-truthing
information (field visit and collection of GPS-based ground coordinates of random locations of actual CSAF)
mostly supported the generated CSAF suitability maps (nearly 91 % of ground-based locations supported the
model output). This study integrates GIS with AHP to plan CSAF farming and scaling up. In sites such as Bugesera
and Rulindo where investigations on CSAF are scanty, these results reveal the extent of CSAF farming in the
targeted areas. They can provide direction for future land use modifications, better land stewardship, and costeffective
solutions in study areas, and other agroclimatic zones. Moreover, this study will pave the way for
further studies on the potential CSAF and possibly required interventions for the assessed areas.
Description
Journal Article
Keywords
Climate-smart agroforestry, Multicriteria analysis techniques, Modelling, Suitability analysis, Rwanda