By the year 2050, the world's population is expected to reach nine billion. To feed and sustain this projected population, world food production must increase by at least 50 percent on much of the same land that we farm today. To meet this staggering challenge, scientists must develop the technology required to achieve an "evergreen" revolution -- one that increases crop productivity without degrading natural resources. With 30 percent new material, the updated and revised third edition of Growth and Mineral Nutrition of Field Crops covers all aspects of crop growth and mineral nutrition that contribute to sustainable, high-yield agriculture. Bringing together international scientific knowledge of crop production and the impacts of agriculture on the environment, this book: * Includes two new chapters on remediation of heavy-metal contaminated soils and cover crops * Covers theoretical and practical aspects of mineral nutrition of field crops * Provides recommendations for judicious use of fertilizers, which will reduce cost of crop production and enhance high crop yields without risking environmental pollution * Provides growth patterns for annual crops and forages * Includes a handful of color pictures of nutrient deficiencies for easy diagnostic purposes To make the book as practical as possible, each chapter is supported by experimental results and extensive references. A large number of figures and tables are also included to save readers time when researching. The overall emphasis of this reference is on the soil's ability to sustain high crop yields and a healthy human population.

With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery. Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on partitional, cluster sampling, and hierarchical algorithms, the book provides readers with enough detail to obtain a basic understanding of cluster analysis for bioinformatics and drug discovery. The remaining chapters cover more advanced methods, such as hybrid and parallel algorithms, as well as details related to specific types of data, including asymmetry, ambiguity, validation measures, and visualization. This book explores the application of cluster analysis in the areas of bioinformatics and cheminformatics as they relate to drug discovery. Clarifying the use and misuse of clustering methods, it helps readers understand the relative merits of these methods and evaluate results so that useful hypotheses can be developed and tested.