2021
DOI: 10.1016/j.tfp.2021.100125
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Suitability for agroforestry implementation around Itombwe Natural Reserve (RNI), eastern DR Congo: Application of the Analytical Hierarchy Process (AHP) approach in geographic information system tool

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Cited by 18 publications
(9 citation statements)
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“…The application of agroforestry systems can have a large effect, especially in terms of conservation. Besides being beneficial in conservation, the application of agroforestry can prevent land erosion and degradation, reduce the impact of climate change and increase food productivity [20]. The second: mixed cropping patterns, minimum tillage, planting of ground cover, management of organic matter, permanent vegetation and application of agroforestry covered 38.79%.…”
Section: Resultsmentioning
confidence: 99%
“…The application of agroforestry systems can have a large effect, especially in terms of conservation. Besides being beneficial in conservation, the application of agroforestry can prevent land erosion and degradation, reduce the impact of climate change and increase food productivity [20]. The second: mixed cropping patterns, minimum tillage, planting of ground cover, management of organic matter, permanent vegetation and application of agroforestry covered 38.79%.…”
Section: Resultsmentioning
confidence: 99%
“…Seed access depended largely on seed exchanges among farmers or purchased from informal markets. Since yam diversity is high in DRC (Magwé-Tindo et al, 2018), farmers were also acquiring seeds from forest environments, a practice referred to as ennoblement elsewhere (Scarcelli et al, 2006;Chikwendu and Okezie, 2014;Agre et al, 2021;Adejumobi et al, 2023). In addition to poor farming practices and informal seed system, farmers lacked sufficient knowledge of tuber storage techniques and value addition, leading to high post-harvest losses.…”
Section: Discussionmentioning
confidence: 99%
“…These principal components are linear combinations of the original variables and are ordered by the amount of variance they explain. Mathematically, PCA seeks to find the eigenvalues and eigenvectors of the covariance matrix of the original data, or equivalently, of the correlation matrix when the data are standardized [14][15][16] .…”
Section: Key Indicator Extraction Via Principal Component Analysismentioning
confidence: 99%