Genetics and Improvement of Forest Trees

Forest tree improvement has mainly been implemented to enhance the productivity of artificial forests. However, given the drastically changing global environment, improvement of various traits related to environmental adaptability is more essential than ever. This book focuses on genetic information, including trait heritability and the physiological mechanisms thereof, which facilitate tree improvement. Nineteen papers are included, reporting genetic approaches to improving various species, including conifers, broad-leaf trees, and bamboo. All of the papers in this book provide cutting-edge genetic information on tree genetics and suggest research directions for future tree improvement

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Genetics and Genomics of Forest Trees

Forest tree genetics and genomics are advancing at an accelerated rate, thanks to recent developments in high-throughput, next-generation sequencing capabilities, and novel biostatistical tools. Population and landscape genetics and genomics have seen the rise of new approaches implemented in large-scale studies that employ the use of genome-wide sampling. Such studies have started to discern the dynamics of neutral and adaptive variation in nature and the processes that underlie spatially explicit patterns of genetic and genomic variation in nature. The continuous development of genetic maps in forest trees and the expansion of QTL and association mapping approaches contribute to the unravelling of the genotype-phenotype relationship and lead to marker-assisted and genome-wide selection. However, major challenges lie ahead. Recent literature suggests that species demography and genetic diversity have been affected both by climatic oscillations and anthropogenically induced stresses in a way calls into question the possibility of future adaptation. Moreover, the pace of contemporary environmental change presents a great challenge to forest tree populations and their ability to adapt, taking into consideration their life history characteristics. Several questions emerge that include, but are not limited to, the interpretation of forest tree genome surveillance and their structural/functional properties, the adaptive and neutral processes that have shaped forest tree genomes, the analysis of phenotypic traits relevant to adaptation (especially adaptation under contemporary climate change), the link between epigenetics/epigenomics and phenotype/genotype, and the use of genetics/genomics as well as genetic monitoring to advance conservation priorities

Forest Pathology and Entomology

The 22 papers that make up this Special Issue deal with pathogen and pest impact on forest health, from the diagnosis to the surveillance of causative agents, from the study of parasites’ biological, epidemiological, and ecological traits to their correct taxonomy and classification, and from disease and pest monitoring to sustainable control strategies

Accurate and efficient clustering algorithms for very large data sets

The ability to mine and extract useful information from large data sets is a common concern for organizations. Data over the internet is rapidly increasing and the importance of development of new approaches to collect, store and mine large amounts of data is significantly increasing. Clustering is one of the main tasks in data mining. Many clustering algorithms have been proposed but there are still clustering problems that have not been addressed in depth especially the clustering problems in large data sets. Clustering in large data sets is important in many applications and such applications include network intrusion detection systems, fraud detection in banking systems, air traffic control, web logs, sensor networks, social networks and bioinformatics. Data sets in these applications contain from hundreds of thousands to hundreds of millions of data points and they may contain hundreds or thousands of attributes. Recent developments in computer hardware allows to store in random access memory and repeatedly read data sets with hundreds of thousands and even millions of data points. This makes possible the use of existing clustering algorithms in such data sets. However, these algorithms require a prohibitively large CPU time and fail to produce an accurate solution. Therefore, it is important to develop clustering algorithms which are accurate and can provide real time clustering in such data sets. This is especially important in a big data era. The aim of this PhD study is to develop accurate and real time algorithms for clustering in very large data sets containing hundreds of thousands and millions of data points. Such algorithms are developed based on the combination of heuristic algorithms with the incremental approach. These algorithms also involve a special procedure to identify dense areas in a data set and compute a subset most informative representative data points in order to decrease the size of a data set. It is the aim of this PhD study to develop the center-based clustering algorithms. The success of these algorithms strongly depends on the choice of starting cluster centers. Different procedures are proposed to generate such centers. Special procedures are designed to identify the most promising starting cluster centers and to restrict their number. New clustering algorithms are evaluated using large data sets available in public domains. Their results will be compared with those obtained using several existing center-based clustering algorithms.Doctor of Philosoph

Integrated nematode management

This book outlines the economic importance of specific plant parasitic nematode problems on the major food and industrial crops and presents the state-of-the-art management strategies that have been developed to reduce specific nematode impacts and outlines their limitations. Case studies to illustrate nematode impact in the field are presented and future changes in nematode disease pressure that might develop as a result of climate change and new cropping systems are discussed.illustrato

Optimizing Plant Water Use Efficiency for a Sustainable Environment

The rising shortage of water resources in crop-producing regions worldwide and the need for irrigation optimisation call for sustainable water savings. The allocation of irrigation water will be an ever-increasing source of pressure because of vast agricultural demands under changing climatic conditions. Consequently, irrigation has to be closely linked with water-use efficiency with the aim of boosting productivity and improving food quality, singularly in those regions where problems of water shortages or collection and delivery are widespread. The present Special Issue (SI) showcases 19 original contributions, addressing water-use efficiency in the context of sustainable irrigation management to meet water scarcity conditions. These papers cover a wide range of subjects including (i) interaction mineral nutrition and irrigation in horticultural crops, (ii) sustainable irrigation in woody fruit crops, (iii) medicinal plants, (iv) industrial crops, and (v) other topics devoted to remote sensing techniques and crop water requirements, genotypes for drought tolerance, and agricultural management. The studies were carried out in both field and laboratory surveys, with modelling studies also being conducted, and a wide range of geographic regions are also covered. The collection of these manuscripts presented in this SI updates on and provides a relevant contribution for efficient saving water resources