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.

Phloem parenchyma cells are involved in local and distant defense responses to fungal inoculation or bark-beetle attack in Norway spruce (Pinaceae).

Abstract: The anatomical response of Norway spruce bark polyphenolic parenchyma cells (PP cells) to inoculation with the phytopathogenic fungus Ceratocystis polonica and attack by its bark-beetle vector Ips typographus was examined. Fungal inoculation on the periderm surface had no effect, while inoculation just below the periderm or halfway into the phloem (mid-phloem) generated detectable responses within 3 wk. The responses included increase in PP cell size and in periodic acid-Schiff's staining of PP cell phenolics, wound periderm initiation from PP cells, and cambial zone traumatic resin duct formation. Fungi were not seen in samples 3 wk after subperiderm or mid-phloem inoculation, but were found in some samples 6 and 9 wk after mid-phloem inoculation. In contrast, inoculations into the cambium resulted in partial (3 wk) or complete (6 and 9 wk) fungal colonization and death of tissue in the infected area. This indicates that PP cells have defenses capable of inhibiting fungal growth. Samples taken near bark-beetle galleries had similar anatomical responses as inoculated samples, validating the inoculation approach to studying defense responses in spruce. These results show that PP cells represent not only a constitutive defense system, but are also involved in local and remote inducible defenses against fungal and beetle attack.

Interspecific hybridization of Eucalyptus: key issues for breeders and geneticists

Abstract: Eucalypt hybrids are significant in forestry, particularly in sub-tropic and tropical regions, where cost efficient, clonal propagation is the key to their exploitation. However, the outstanding success of selected hybrid clones has given a biased impression of the vigor of eucalypt hybrids and the strength of reproductive barriers in the genus. When full account is made of losses through the life cycle, a picture of high incompatibility and inviability often emerges. Hybrid inviability tends to increase with increasing taxonomic distance between parents, but there are exceptions. Hybrids also seem more susceptible to pests than their pure species. Intense selection may still result in elite hybrid clones, but such inviability and susceptibility adds a significant cost to their development. Breeders must carefully evaluate the costs of hybrid development and deployment compared with pure species options. A key to hybrid selection is the rapid development and testing of large populations and application of high selection intensities. However, eventually this approach must be linked with more formal breeding strategies. As most traits are intermediate in F1 hybrids there is increasing interest in advanced generation hybrids to provide desirable trait combinations. In such cases, there is a clear role for marker assisted selection to speed introgression.

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

Abstract: With the increasing threat of recurring landslides, susceptibility maps are expected to play a bigger role in promoting our understanding of future landslides and their magnitude. This study describes the development and validation of a spatially explicit deep learning (DL) neural network model for the prediction of landslide susceptibility. A geospatial database was generated based on 217 landslide events from the Muong Lay district (Vietnam), for which a suite of nine landslide conditioning factors was derived. The Relief-F feature selection method was employed to quantify the utility of the conditioning factors for developing the landslide predictive model. Several performance metrics demonstrated that the DL model performed well both in terms of the goodness-of-fit with the training dataset (AUC = 0.90; accuracy = 82%; RMSE = 0.36) and the ability to predict future landslides (AUC = 0.89; accuracy = 82%; RMSE = 0.38). The efficiency of the model was compared to the quadratic discriminant analysis, Fisher's linear discriminant analysis, and multi-layer perceptron neural network. A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over these other models. The insights provided from this study will be valuable for further development of landslide predictive models and spatially explicit assessment of landslide-prone regions around the world.

Organosilicone surfactants as adjuvants for agrochemicals

Abstract: The literature has been reviewed and 160 references are presented to provide an overview of organosilicones as adjuvants for agrochemicals. Early studies (1973-1981) elucidated the special properties of these compounds, notably their extreme spreading and, by virtue of the exceptionally low surface tensions of their aqueous solutions, the ability to induce infiltration of spray formulations into foliage via stomata. Subsequent research at the New Zealand Forest Research Institute explored these and other properties in-depth, and demonstrated that they enabled the herbicide glyphosate to be used as a cost-effective alternative to 2,4,5-T for the control of forest scrubweeds. As a result, in 198S, «Silwet» L-77 was the first organosilicone adjuvant to be commercialised, and various other products have subsequently been introduced