Yam Bahadur Kc

Wattle rust disease, caused by Uromycladium acaciae, has emerged as a critical challenge to Acacia mearnsii in the Awi Zone of northwestern Ethiopia. This study examined the effect of tree spacing and land use history on disease incidence and severity, and explored local perceptions of management practices. A randomised complete block design (RCBD) with three spacing treatments (0.5 × 0.5, 1 × 1, 1.5 × 1.5 m) was implemented across three districts—Fagita Lekoma, Ankasha Guagusa, and Banja Shekudad in 2021. Additionally, disease occurrence in three‐year‐old plantations (common spacing: ~0.5 × 0.5 m) on cropland, grazing land, and plantation land was assessed. Community knowledge and preferences for disease management were gathered through structured surveys and focus group discussions with 150 respondents, including both farmers and forest administrators. The findings revealed that wider spacing (1.5 × 1.5 m) significantly reduced disease incidence by up to 54.87% and severity by 45.74% compared to narrow spacing (0.5 × 0.5 m). Trees planted on cropland exhibited significantly lower disease levels compared to those on grazing or plantation land. Local farmers favoured integrated practices such as increased spacing, sanitation pruning, tilling, weeding, and intercropping with cereals, which align with scientific evidence on disease suppression through microclimate modification and improved soil health. Adopting a 1.5 × 1.5 m planting spacing and prioritising post‐cropland areas are recommended for new plantations in disease‐prone districts. Further research should explore disease‐resistant varieties, species diversification strategies, and closer spacing (< 0.5 × 0.5 m), which are currently practised by some smallholders, to further enhance disease management and inform extension programmes.

Above-ground biomass (AGB) is affected by numerous factors, including topography, climate, land use, or tree/forest attributes. Investigating the distribution and driving factors of AGB within the managed forests in Nepal is crucial for developing effective strategies for climate change mitigation, and sustainable forest management and conservation. A total of 110 field plots (circular 0.02 ha plots with a 9 m radius), and airborne laser scanning (ALS)-light detection and ranging (LiDAR) data were collected in 2021. The random forest (RF) model was employed to predict the AGB at a 30 m × 30 m resolution based on 32 LiDAR metrics derived from ALS returns. The study assessed the relationships between the AGB distribution and nine independent variables using statistical techniques like the random forest model and partial dependence plots. Results showed that the mean value of the estimated AGB was 120 tons/ha, ranging from 0 to 446.42 tons/ha. AGB showed higher values in the northeast and southeast regions, gradually decreasing towards the northwest. Land use land cover, mean annual temperature, and mean annual precipitation were identified as the primary factors influencing the variability in AGB distribution, accounting for 64% of the variability. Elevation, slope, and distance from rivers were positively correlated with AGB, while proximity to roads had a negative correlation. The increase in precipitation and temperature contributed to the initial rise in AGB, but beyond a certain lag, these variables led to a decline in AGB. This study showed the efficiency of the random forest model and partial dependence plots in examining the relationship between the AGB and its driving factors within managed forests. The study highlights the importance of understanding the AGB driving factors and utilizing LiDAR data for informed decisions regarding the region’s sustainable forest management and climate change mitigation efforts.

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R² metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.