Triacontanol regulates morphological traits and enzymatic activities of salinity affected hot pepper plants.

Potential role of triacontanol applied as a foliar treatment to ameliorate the adverse effects of salinity on hot pepper plants was evaluated. In this pot experiment, hot pepper plants under 75 mM NaCl stress environment were subjected to foliar application of 25, 50, and 75 µM triacontanol treatments; whereas, untreated plants were taken as control. Salt stress had a significant impact on morphological characteristics, photosynthetic pigments, gas exchange attributes, MDA content, antioxidants activities, electrolytes leakage, vitamin C, soluble protein, and proline contents. All triacontanol treatments significantly mitigated the adversative effects of salinity on hot pepper plants; however, foliar application triacontanol at 75 µM had considerably improved the growth of hot pepper plants in terms of plant height, shoot length, leaf area, plant fresh/dry biomasses by modulating above mentioned physio-biochemical traits. While, improvement in gas exchange properties, chlorophyll, carotenoid contents, increased proline contents coupled with higher SOD and CAT activities were observed in response to 75 µM triacontanol followed by 50 µM triacontanol treatment. MDA and H2O2 contents were decreased significantly in hot pepper plants sprayed with 75 µM triacontanol followed by 50 µM triacontanol foliar treatment. Meanwhile, root and shoot lengths were maximum in 50 µM triacontanol sprayed hot pepper plants along with enhanced APX activity on exposure to salt stress. In crux, exogenous application triacontanol treatments improved hot pepper performance under salinity, however,75 µM triacontanol treatment evidently was more effective in mitigating the lethal impact of saline stress via controlling the ROS generation and increment in antioxidant enzyme activities.

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

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 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 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.