Reta Getabalew Teshome, Tolera Motuma, Mokria Mulugeta
Wondo Genet College of Forestry and Natural Resources, Hawassa University, Shashemene, Ethiopia.
Ethiopian Forest Development (EFD), Addis Ababa, Ethiopia.
PLoS One. 2025 May 7;20(5):e0322025. doi: 10.1371/journal.pone.0322025. eCollection 2025.
Dry Afromontane forests in Ethiopia are crucial for carbon sequestration; however, the absence of robust biomass and carbon stock estimation models hinders accurate assessment. This study addresses this limitation by developing and validating site-specific, multispecies biomass estimation models for Wof-Washa plantation and natural forests. Biometric data were collected from 127 harvested trees representing seven dominant species from both plantation and natural forests. Aboveground biomass (AGB) was regressed against diameter at breast height (DBH) as the sole predictor, with stepwise inclusion of total height (H), crown area (CA), and wood density (ρ). Weighted nonlinear least squares regression was performed to fit new models for each forest, their performance was evaluated using the root mean square error (rRMSE), pseudo-R2, and relative mean prediction error (rMPE %). The best-selected model using DBH and H explained 90% and 95% of the variation in the AGB of plantation and natural forests, respectively. This model produced the lowest bias (rMPE = 5.9% for plantation and 2.5% for natural forests) compared to pan-tropical models. Our findings demonstrated that our optimal model provides accurate AGB predictions at plot and landscape levels. This confirms that the models can provide sufficiently reliable estimations of carbon stocks, indicating the potential for national carbon accounting and thereby enhancing decision-making in the study forests. Therefore, the findings of this research contribute directly to enhancing the accuracy of carbon dynamic monitoring and supporting sustainable forest management, a crucial component in global efforts to combat climate change.
埃塞俄比亚干旱的阿夫罗山地森林对碳固存至关重要；然而，缺乏强大的生物量和碳储量估算模型阻碍了准确评估。本研究通过开发和验证沃夫-瓦沙人工林和天然林的特定地点多物种生物量估算模型来解决这一限制。从127棵采伐树木收集生物特征数据，这些树木代表人工林和天然林的七个优势树种。将地上生物量（AGB）与胸径（DBH）进行回归，以DBH作为唯一预测因子，并逐步纳入树高（H）、树冠面积（CA）和木材密度（ρ）。进行加权非线性最小二乘回归以拟合每个森林的新模型，使用均方根误差（rRMSE）、伪R2和相对平均预测误差（rMPE%）评估其性能。使用DBH和H选择的最佳模型分别解释了人工林和天然林AGB变化的90%和95%。与泛热带模型相比，该模型产生的偏差最小（人工林的rMPE = 5.9%，天然林的rMPE = 2.5%）。我们的研究结果表明，我们的最优模型在样地和景观水平上提供了准确的AGB预测。这证实了这些模型能够提供足够可靠的碳储量估计，表明了国家碳核算的潜力，从而加强了对研究森林的决策。因此，本研究结果直接有助于提高碳动态监测的准确性并支持可持续森林管理，这是全球应对气候变化努力的关键组成部分。
