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Allometric equations for biomass estimation of Enset (Ensete ventricosum) grown in indigenous agroforestry systems in the Rift Valley escarpment of southern-eastern Ethiopia |
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| Authors: | Mesele Negash Mike Starr Markku Kanninen |
| Institution: | 1. Department of Forest Sciences, Viikki Tropical Resources Institute, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, 00014, Helsinki, Finland 3. Wondo Genet College of Forestry and Natural Resources, Hawassa University, P.O. Box 128, Shashmene, Ethiopia 2. Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, 00014, Helsinki, Finland |
| Abstract: | Enset (Ensete ventricosum), commonly known as false banana, is a large thick, single-stemmed, perennial herbaceous banana-like plant growing in the wild of sub-Sahara Africa, Madagascar and parts of Asia. In Ethiopia it has been domesticated and serves as a food plant. While the productivity and management of enset for food (pseudostem and corm) has been studied, little attention has been given to total biomass production and associated carbon sequestration. The objective of this study was to develop and evaluate allometric models for estimating above and belowground biomass and organic matter contents of enset grown in indigenous agroforestry systems in Rift Valley escarpment of south-eastern Ethiopia. Biomass harvesting of 40 plants was carried out at altitudes from 1900 to 2400 m.a.s.l. The mean plant dry weight was 9.4 ± 0.84 kg and organic matter content 94 %. Pseudostem biomass accounted for highest (64 %) of total biomass, followed by corm (24 %) and foliage (12 %). Basal diameter (d 10) was the best predictor variable for total and all biomass components (Spearman r = 0.775–0.980, p < 0.01). The power model using d 10 and height (H) (Y = 0.0007d 10 2.571 H 0.101; R 2 = 0.91) was found to be the best performing model (highest ranking over six good-of-fit statistics) for predicting total biomass. Model performance decreased in the order pseudostem > corm > foliage biomass. The models presented can be used to accurately predict biomass and organic matter of enset in the agroforestry systems of Rift Valley escarpments Ethiopia. |
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