Forest Research Institute

About: Forest Research Institute is a facility organization based out in Dehra Dūn, India. It is known for research contribution in the topics: Population & Forest management. The organization has 5320 authors who have published 7625 publications receiving 185876 citations.

An improved micropropagation protocol for teak

Abstract: An improved micropropagation protocol has been developed for teak (Tectona grandis) Nodal explants placed on MS medium supplemented with 222 μM benzylaminopurine and then serially transferred to fresh medium after 12, 24, 48 and 72 h gave maximum culture establishment (768%) Establishment was reduced when explants were retained in the initial culture medium longer than 12 h Explants collected in May showed maximum (768%) response Placement of the explants on MS medium supplemented with 222 μM benzylaminopurine and 057 μM indole-3-acetic acid resulted in the maximum average number of shoots In vitro raised micro shoots were rooted ex vitro by dipping in indole-3-butyric acid (98 mM) for 2 min followed by planting in polyethylene pots containing a soil:vermiculite (1:1 v/v) mixture This treatment resulted in 779% survival of the plantlets They were weaned in a glasshouse and finally moved to an agro-net shade house

Effect of wide spacing on increment and branch properties of young Norway spruce

Abstract: The effects of stand density on increment and branch properties were studied in three spacing experiments of Norway spruce [Picea abies (L) Karst] The stand densities ranged from 350 stems ha−1, regarded as open-grown trees, up to 1,600 stems ha−1, corresponding to the density recommended for forestry practice Properties of all the branches were measured from the stem apex downwards The study material included a total of 5,661 branches from 45 trees Increasing stand density resulted in a decrease in radial increment as well as shorter and narrower crowns, but it had no effect on height increment The average number of spike knots per tree was 087, 027, and 033 in densities of 350, 700 and 1,600 ha−1, respectively Additionally, in the widely spaced stands of 350 stems ha−1, the fraction of trees having spike knots was high (over 50%) At a density of 1,600 ha−1, the sample trees had somewhat less branches in a whorl compared with the more widely spaced plots The most pronounced effect of stand density was the increase in branch diameter with decreasing stand density At a density of 350 ha−1, the maximum branch diameter of all the sample trees exceeded the diameter limit of quality class B in the European quality requirements for round wood The results give some indication that trees subjected to severe competition would produce smaller branches per unit of crown projection area However, the possibilities for reducing branch dimensions relative to stem and crown size through competition appear quite restricted

A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

Abstract: This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards.