Stochastic Modeling In Range Management

Although rangelands are one of the most important ecological-economic systems on our planet, researchers thus far have paid scant attention to the study of range management from a theoretical perspective. As such, this book provides a systematic approach to the theoretical study of range management under uncertainty. In particular, the book demonstrates how the theory of stochastic processes and the techniques of stochastic modelling can be used to construct and analyse theoretical models of jointly determined rangelands and thereby shed light on the task of range management. Because specialised grazing systems involving the rotation of animals between pastures have been a major focus of range managers since the 1950s, this book pays considerable attention to questions that arise when specialised grazing systems such as short duration grazing are used to manage rangelands. Specific issues that are analysed in this book include the optimal number of paddocks in short duration grazing, the steady state distribution of animals in short duration grazing. Also, the effect of potential irreversibilities, the choice between the stocking rate and time, herding in ranch operations, and the role of time in range management, is discussed.

An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial engineering, production planning, and supply chain management. Each contribution is aimed at graduate students working in operations research, probability, and statistics.

Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance. Markov chains, random walks, stochastic differential equations and other stochastic processes are used throughout the book and systematically applied to economic and financial applications. In addition, a dynamic programming framework is used to deal with some basic optimization problems. The book begins by introducing problems of economics, finance and insurance which involve time, uncertainty and risk. A number of cases are treated in detail, spanning risk management, volatility, memory, the time structure of preferences, interest rates and yields, etc. The second and third chapters provide an introduction to stochastic models and their application. Stochastic differential equations and stochastic calculus are presented in an intuitive manner, and numerous applications and exercises are used to facilitate their understanding and their use in Chapter 3. A number of other processes which are increasingly used in finance and insurance are introduced in Chapter 4. In the fifth chapter, ARCH and GARCH models are presented and their application to modeling volatility is emphasized. An outline of decision-making procedures is presented in Chapter 6. Furthermore, we also introduce the essentials of stochastic dynamic programming and control, and provide first steps for the student who seeks to apply these techniques. Finally, in Chapter 7, numerical techniques and approximations to stochastic processes are examined. This book can be used in business, economics, financial engineering and decision sciences schools for second year Master's students, as well as in a number of courses widely given in departments of statistics, systems and decision sciences.

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. Presents both the theory and applications of the different aspects of Markov processes Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.

This book reflects current developments in the economics of agrobiodiversity and focuses its attention on the role agrobiodiversity can have for economic development. As a new and rapidly expanding subfield at the interface of environmental/ecological, agricultural and development economics, the editors and contributors to this volume provide a thorough, structured and authoritative coverage of this field. Topics covered include the economic modelling of agrobiodiversity, policy and governance solutions for the conservation of biodiversity in agricultural landscapes, contracts, markets and valuation. The authors include well-known and respected academics and researchers who have a real policy perspective into the role of agrobiodiversity and economic development. The book provides coherent and up to date coverage of the economics of in-situ agrobiodiversity conservation which is to a large extent currently absent. Though the material in the volume is primarily written for economists, its content and style are highly relevant and accessible to ecologists and conservation biologists, and to academics from other broad disciplines that are located within the areas of economics and ecology.

The papers in this volume were presented at a symposium/workshop on "The Estimation and Analysis of Insect Populations" that was held at the University of Wyoming, Laramie, in January, 1988. The meeting was organized with financial support from the United States - New Zealand Cooperative Science Program and the University of Wyoming. The purpose was to bring together approximately equal numbers of quantitative biologists and biometricians in order to (1) provide a synthesis and evaluation of currently available methods for modeling and estimating parameters of insect population, and to (2) stimulate research into new methods where this is appropriate. The symposium/workshop attracted 46 participants. There were 35 papers presented in four subject areas: analysis of stage-frequency data, modeling of population dynamiCS, analysis of spatial data, and general sampling and estimation methods. New results were presented in all these areas. All except one of the papers is included in the present volume.

This chapter uses a new ecological-economic framework to study the role of time in range management in a dynamic and probabilistic setting. We first construct a theoretical model of a parcel of rangeland in which time restrictions are used to manage the land. We then show how the dynamic and the stochastic attributes of this rangeland can be used to construct two managerial objectives that are ecologically and economically meaningful. Finally, using these two objectives, we discuss an approach to range management in which the manager has two interrelated goals. This manager maximizes the profits from range operations and (s)he also takes steps to move the rangeland away from the least desirable state of existence.

Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines. This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included.