Search Results (3)

We address two main challenges facing the implementation of sustainable forest management (SFM) in the Hyrcanian forest of Iran: inconsistent forest policy and the use of outdated science and techniques. We propose the Sustainable Hyrcanian Forest Management Model (SHFMM) as the best currently available solution to achieve improved management of the northern forests of Iran. The management of the Hyrcanian forests suffers from a lack of scientific knowledge and state-of-the-art technologies. There is a pronounced difference in the mindsets of older and new-school forestry scholars regarding how to approach these deficiencies: the old-school mentality prefers conventional forestry despite its limitations, whereas more recently trained scholars believe that the adoption of 21st-century technological advances would lead to improved management. The lack of trust between policymakers and local communities is another significant challenge and has resulted in conflicts over management practices in the Hyrcanian forests. We suggest that the Hyrcanian Sustainable Forest management model (SHFMM) would provide a hierarchical framework for making decisions. Using this model, each sector—whether state or private—is empowered to make decisions. Further, it encourages all sectors to work together in its holistic implementation. The SHFMM is based on the outcomes of several independent studies of forest management in the Hyrcanian forest. Despite its site specificity, many lessons learned during its development could be applied elsewhere. Full article

Climate change has significant effects on forest ecosystems around the world. Since tree diameter increment determines forest volume increment and ultimately forest production, an accurate estimate of this variable under future climate change is of great importance for sustainable forest management. In this study, we modeled tree diameter increment under the effects of current and expected future climate change, using multilayer perceptron (MLP) artificial neural networks and linear mixed-effect model in two sites of the Hyrcanian Forest, northern Iran. Using 573 monitoring fixed-area (0.1 ha) plots, we measured and calculated biotic and abiotic factors (i.e., diameter at breast height (DBH), basal area in the largest trees (BAL), basal area (BA), elevation, aspect, slope, precipitation, and temperature). We investigated the effect of climate change in the year 2070 under two reference scenarios; RCP 4.5 (an intermediate scenario) and RCP 8.5 (an extreme scenario) due to the uncertainty caused by the general circulation models. According to the scenarios of climate change, the amount of annual precipitation and temperature during the study period will increase by 12.18 mm and 1.77 °C, respectively. Further, the results showed that the impact of predicted climate change was not very noticeable and the growth at the end of the period decreased by only about 7% annually. The effect of precipitation and temperature on the growth rate, in fact, neutralize each other, and therefore, the growth rate does not change significantly at the end of the period compared to the beginning. Based on the models’ predictions, the MLP model performed better compared to the linear mixed-effect model in predicting tree diameter increment. Full article

The Hyrcanian forests of Iran are mainly managed with the single-selection silvicultural technique. Despite significant ecological benefits associated with selection cutting, this type of forest management leads towards more challenging situations where it is difficult to maintain and practice successful forestry than in even-aged systems. Therefore, this study provides relevant management tools in the form of models to estimate low growth levels in Hyrcanian forests. In the present study, estimation of the population growth rate and then the allowable cut rate of these forests using a matrix model have been calculated in the Gorazbon district. For this purpose, the data of 256 permanent sample plots measured during the years between 2003 and 2012, as well as the data recorded about the trees harvested according to the forestry plan, have been used. As a first step, the most frequently occurring tree species were divided into four groups (beech, hornbeam, chestnut-leaved oak, and other species). Compartments of the district were divided into two groups of logged and unlogged compartments. The purpose of this division was to estimate the allowable cut and compare its volume with the volumes of observed and predicted allowable cuts obtained from forestry plans. The results showed that the total operated allowable cut (OAC) in logged compartments was more than the estimated allowable cut (EAC). In unlogged compartments, the total predicted allowable cut (PAC) was more than EAC. A comparison of EAC and OAC showed that hornbeam has been harvested more than its potential. However, chestnut-leaved oak and other species group have depicted opposite trends. Our models provide important advancements for estimating allowable cut that can enhance the goal of practicing sustainable forestry. Full article