Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm² 5-year^-1 and volume: 0.0008 m³ 5-year^-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm² 5-year^-1 and 0.0393 m³ 5-year^-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling.

Growth and yield models are critically important for forest management planning. Biophysical factors such as light, temperature, soil water, and nutrient conditions are known to have major impacts on tree growth. However, it is difficult to incorporate these biophysical variables into growth and yield models due to large variation and complex nonlinear relationships between variables. In this study, artificial intelligence technology was used to develop individual-tree-based basal area (BA) and volume increment models. The models successfully account for the effects of incident solar radiation, growing degree days, and indices of soil water and nutrient availability on BA and volume increments of over 40 species at 5-year intervals. The models were developed using data from over 3000 permanent sample plots across the province of Nova Scotia, Canada. Model validation with independent field data produced model efficiencies of 0.38 and 0.60 for the predictions of BA and volume increments, respectively. The models are applicable to predict tree growth in mixed species, even- or uneven-aged forests in Nova Scotia but can easily be calibrated for other climatic and geographic regions. Artificial neural network models demonstrated better prediction accuracy than conventional regression-based approaches. Artificial intelligence techniques have considerable potential in forest growth and yield modelling.

Forest soils have high carbon densities compared to other land-uses. Soil carbon sequestration is important to reduce CO₂ concentrations in the atmosphere. An effective climate change mitigation strategy involves limiting the emissions of greenhouse gases from soils. Khyber Pakhtunkhwa is the most forested province of Pakistan, hosting about one-third of the country’s 4.5 × 10⁶ ha forest area. Soil organic carbon in the province’s forests was estimated through a field-based study carried out during 2014–17 covering the whole province. Data was collected from 373 sample plots laid out in different forest types using a stratified cluster sampling technique. The total quantity of soil organic carbon was estimated at 59.4 × 10⁶ t with an average of 52.4 ± 5.3 t/ha. About 69% of the total soil carbon is present in temperate forests. Subtropical broad-leaved and subtropical pine forests constitute 11.4% and 8.8% of the soil carbon stock respectively. Similarly, subalpine and oak forests have respective shares of 5.1% and 5.7% in the soil carbon pool. The lowest carbon stock (0.1%) was found in dry-tropical thorn forests. The highest soil carbon density was found in subalpine forests (69.5 ± 7.2 t/ha) followed by moist temperate forests (68.5 ± 6.7 t/ha) and dry temperate forests (60.7 ± 6.5 t/ha). Oak forests have carbon density of 43.4 ± 7.1 t/ha. Subtropical pine, subtropical broad-leaved and dry tropical thorn forests have soil carbon densities of 36.3 ± 3.7, 32.8 ± 6.2 and 31.5 ± 3.5 t/ha, respectively. The forests of the Khyber Pakhtunkhwa province have substantial amounts of soil carbon which must be conserved for climate change mitigation and maintenance of sound forest health.