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Climate change is projected to increase in vulnerable areas of the world, and marginalized communities residing in rural areas are more vulnerable to the change. The perceptions of climate change and adaptation strategies made by such communities are important considerations in the design of adaptation strategies by policy-makers. We examined the most marginalized indigenous group "Chepang" communities' perceptions towards this change, variability, and their attitudes to adaptations and adapted coping measures in mid-hills of Nepal. We interviewed 155 individuals from two Chepang communities, namely, Shaktikhor and Siddhi in Chitwan district of Nepal. We also analyzed biophysical data to assess the variability. The findings showed that the Chepang community has experienced significant impacts of climate change and variability. They attributed crop disease, insect infestation, human health problem, and weather-related disaster as the impacts of climate change. Strategies they have adopted in response to the change are the use of intense fertilizers in farmland, hybrid seeds cultivation, crop diversification, etc. Local level and national level adaptation policies need to be designed and implemented as soon as possible to help climate vulnerable communities like Chepangs to cope against the impacts of climate change.

Individual tree growth models are important decision-making tools for forest management. We developed individual tree basal area growth models with Blue pine (Pinus wallichiana) data from Lete and Kunjo areas of Mustang district in Nepal. The sample trees were identified from all applicable ages, sizes, site qualities, and stand conditions and were cut. Diameters and ages were measured on the cut surface of stump (at 30 cm above ground). With the application of the auto-regressive error-structured modelling approach, we fitted Bertalanffy function to the data from 94 stumps by using basal area growth per year as dependent variable and stump age or stump diameter as independent variable. The age-independent individual tree basal area growth model showed better fits (R 2 adj ¼ 0.8324) than its agedependent counterpart (R 2 adj ¼ 0.8174). Because of having better fits and being easier for application, the ageindependent model is recommended for predicting basal area growth per year at an individual tree level for Blue pine across Lete and Kunjo areas of Mustang district.

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