Effect of Biolep®, Permethrin and Hexaflumuron on mortality of cotton bollworm, Helicoverpa armigera (Noctuidae: Lepidoptera)

Cotton bollworm, Helicoverpa armigera is a major pest in cotton and one of the most polyphagous and cosmopolitan pest species of several crops such as cotton, pulses and vegetables in Asia. Lethal effects of Biolep®, Permethrin and Hexaflumuron belong to three different groups of insecticides were compared on larval stages of H. armigera. The trial was laid out in Randomized Complete Block Design (RCBD) with four treatments including a control and replicated thrice. Our results shown three insecticides, Biolep®, Permethrin and Hexaflumuron had significant difference in larval population mortality of H. armigera. After 3rd day Biolep® caused maximum mortality that was 39 larvae. Permethrin and Hexaflumuron caused 29 and 31 larval mortality after 3rd day, respectively. Generally, the number of mortality decreased and the maximum rate of mortality in 12th day was 7 larvae that obtained by using Hexaflumuron. Our results showed that the Hexaflumuron was persistent in comparison with other insecticides. Biolep® registered above 75% (average 77) reduction in number of larvae on the basis of post-spray data, followed by 68% and 70% each by Permethrin and Hexaflumuron, respectively

Detecting bark beetle infestation using plants canopy chlorophyll content retrieved from remote sensing data

The European bark beetle (Ips typographus, L.) is a potentially severe invasive species in the UK and North America. It is resulting in a high degree of fragmentation, forest productivity, and phenology. Understanding its biology, as well as developing early detection based on its behavior, is an important aspect of its successful management and eradication. Bark beetle infestation causes changes biochemical and biophysical characteristics such as chlorophyll water and nitrogen content. This study showcases the potential of the Canopy Chlorophyll Content (CCC) product derived from remote sensing datasets to detect early bark beetle infestation in Bavarian forest national park. We generated time series CCC maps from RapidEye and Sentinel-2 images of the study area through Radiative transfer model inversion. The CCC products were then classified into infested and healthy using CCC mean and variance collected in 2015 and 2016 from infested and healthy Norway spruce trees in the Park. Reference data obtained from processing and interpretation of high resolution (0.1m) color aerial photographs were used to validate the accuracy of the infestation maps. Our results demonstrated that CCC products as derived from remote sensing data were a rigorous proxy to early detect bark beetle infestation. Validation of the infestation maps revealed > 70% classification accuracy throughout the time-space. Hence, CCC products play a significant role to understand the dynamics of the infestation and improve the management of bark beetle outbreaks in forest ecosystem. Despite these promising results, other plant traits such as dry matter content and Nitrogen content will need to be investigated as additional predictors, which may considerably improve the accuracy of early detection of bark beetle infestation using remote sensing derived products

Laboratory for Essential Biodiversity Variables (EBV) Concepts – The “Data Pool Initiative for the Bohemian Forest Ecosystem”

Forest ecosystems respond very sensitively to climate and atmospheric changes. Feedback mechanisms can be measured via changes in albedo, energy balance and carbon storage. The Bavarian Forest National Park is a unique forest ecosystem with large non-intervention zones, which promote a large scale re-wilding process with low human interference. It provides important ecosystem services of clear water, carbon sequestration and recreation, and has fragile habitats with endangered forest species. The national park is therefore a very suitable field of research to study natural and near natural ecosystem processes. Under the leadership of the national park authority, experts from various European research institutions have joined forces to systematically establish a remote sensing data pool on the Bavarian Forest as a resource for their research. This collaborative effort provides an opportunity to combine various methodological approaches and data and to optimize products by sharing knowledge and expertise. The first objective of the data pool is to develop methods for the establishment of Essential Biodiversity Variables (EBV) based on a very sound and comprehensive data base. The recent advances in tighter collaboration of remote sensing and biodiversity science, especially with regard to the newly established EBV and RS-EBV concepts will help to improve the interdisciplinary research. However, such concepts and especially the underlying remote sensing data need to be developed, adapted and validated against biodiversity patterns. Such process needs an extensive set of in-situ and remotely sensed data in order to allow a thorough analysis. The Bavarian data pool fits these requirements through the commitment of all members and hence provides a variety of remote sensing data sets such as hyperspectral, Lidar as well as CIR and multispectral data, as well as a wealth of in-situ data of zoological and botanical transects. This combination allows setting sensor-specific, as well as species-specific analysis on different aspects, i.e. different processes between managed and natural forest, impact of climate change or species distribution mapping. The second objective is to develop concepts for EBV using Sentinel mission data combined with data from future contributing hyperspectral missions such as EnMAP. Spaceborne hyperspectral data has been identified by the remote sensing related biodiversity community as an important data source. However, the acquisition of airborne data is very expensive for regular coverage of forest stands and the entire forest ecosystem. This drawback will be overcome by the launch of the space-borne imaging spectroscopy mission EnMAP. It is a contributing mission to the Copernicus program and will be launched in 2018. EnMAP is expected to provide high quality imaging spectroscopy data on an operational basis and will be suitable for the retrieval of high resolution plant traits at local scales. First studies within the data pool have been focused on e.g. derivation of plant traits like chlorophyll, LAI and nitrogen and tree species classification with a special focus on rare species within the national park, just to name a few. Objective, purpose and content of the data pool will be shown as well as first selective developments

COVID-19: Is There Evidence for the Use of Herbal Medicines as Adjuvant Symptomatic Therapy?

Background: Current recommendations for the self-management of SARS-Cov-2 disease (COVID-19) include self-isolation, rest, hydration, and the use of NSAID in case of high fever only. It is expected that many patients will add other symptomatic/adjuvant treatments, such as herbal medicines. Aims: To provide a benefits/risks assessment of selected herbal medicines traditionally indicated for “respiratory diseases” within the current frame of the COVID-19 pandemic as an adjuvant treatment. Method: The plant selection was primarily based on species listed by the WHO and EMA, but some other herbal remedies were considered due to their widespread use in respiratory conditions. Preclinical and clinical data on their efficacy and safety were collected from authoritative sources. The target population were adults with early and mild flu symptoms without underlying conditions. These were evaluated according to a modified PrOACT-URL method with paracetamol, ibuprofen, and codeine as reference drugs. The benefits/risks balance of the treatments was classified as positive, promising, negative, and unknown. Results: A total of 39 herbal medicines were identified as very likely to appeal to the COVID-19 patient. According to our method, the benefits/risks assessment of the herbal medicines was found to be positive in 5 cases (Althaea officinalis, Commiphora molmol, Glycyrrhiza glabra, Hedera helix, and Sambucus nigra), promising in 12 cases (Allium sativum, Andrographis paniculata, Echinacea angustifolia, Echinacea purpurea, Eucalyptus globulus essential oil, Justicia pectoralis, Magnolia officinalis, Mikania glomerata, Pelargonium sidoides, Pimpinella anisum, Salix sp, Zingiber officinale), and unknown for the rest. On the same grounds, only ibuprofen resulted promising, but we could not find compelling evidence to endorse the use of paracetamol and/or codeine. Conclusions: Our work suggests that several herbal medicines have safety margins superior to those of reference drugs and enough levels of evidence to start a clinical discussion about their potential use as adjuvants in the treatment of early/mild common flu in otherwise healthy adults within the context of COVID-19. While these herbal medicines will not cure or prevent the flu, they may both improve general patient well-being and offer them an opportunity to personalize the therapeutic approaches

Table 2: Example applications of the use of remote sensing technologies to detect change in vegetation.

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Genetic parameters of live body weight, body measurements, greasy fleece weight, and reproduction traits in Makuie sheep breed

Genetic parameters of production and reproduction traits were estimated using 39,926 records from 5,860 individual progenies of 163 sires and 1,558 dams. The data were collected at Makuie Sheep Breeding and Raising Station (Maku, Iran) from 1989 through 2013. Nineteen traits were classified in four main groups: a) live body weight traits, b) body measurement traits, c) greasy fleece weight traits, and d) reproduction traits. Year of birth, lamb sex, age of dam, and birth type were considered as fixed effects in the animal model. Four different animal models that are differentiated by including or excluding maternal effects were fitted for each trait. The Akaike information criterion was used to determine the most appropriate model for each trait. Parameters were overestimated substantially when maternal effects, either genetic or environmental, were ignored from the models. By ignoring the maternal effects, the traits could be classified into three main groups: body live weight traits with high heritability (0.34-0.46), body measurement and greasy fleece weight traits with medium heritability (0.11-0.27) and reproduction traits with low heritability (0.03-0.20). The genetic correlations among the traits ranged from-0.41 to 0.99. The estimated genetic parameters may be used to set up short/long term breeding program for the selection purpose of Makuie sheep breed