To manage our environment sustainably, professionals must understand the quality and quantity of our natural resources. Statistical analysis provides information that supports management decisions and is universally used across scientific disciplines. Statistics in Natural Resources: Applications with R focuses on the application of statistical analyses in the environmental, agricultural, and natural resources disciplines. This is a book well suited for current or aspiring natural resource professionals who are required to analyze data and perform statistical analyses in their daily work. More seasoned professionals who have previously had a course or two in statistics will also find the content familiar. This text can also serve as a bridge between professionals who understand statistics and want to learn how to perform analyses on natural resources data in R. The primary goal of this book is to learn and apply common statistical methods used in natural resources by using the R programming language. If you dedicate considerable time to this book, you will: Develop analytical and visualization skills for investigating the behavior of agricultural and natural resources data. Become competent in importing, analyzing, and visualizing complex data sets in the R environment. Recode, combine, and restructure data sets for statistical analysis and visualization. Appreciate probability concepts as they apply to environmental problems. Understand common distributions used in statistical applications and inference. Summarize data effectively and efficiently for reporting purposes. Learn the tasks required to perform a variety of statistical hypothesis tests and interpret their results. Understand which modeling frameworks are appropriate for your data and how to interpret predictions. Includes over 130 exercises in R, with solutions available on the book’s website.

This book presents statistical knowledge, and methodology of sampling and data analysis specifically for spatial inventory and monitoring of local natural resources. The text shows how statistical methodology can be embedded in real-life spatial inventory and monitoring projects. The book functions as a design guide for efficient sampling schemes and monitoring systems can be designed, consistent with the aims and constraints of the project.

This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.

Non-market valuation has become a broadly accepted and widely practiced means of measuring the economic values of the environment and natural resources. In this book, the authors provide a guide to the statistical and econometric practices that economists employ in estimating non-market values. The authors develop the econometric models that underlie the basic methods: contingent valuation, travel cost models, random utility models and hedonic models. They analyze the measurement of non-market values as a procedure with two steps: the estimation of parameters of demand and preference functions and the calculation of benefits from the estimated models. Each of the models is carefully developed from the preference function to the behavioral or response function that researchers observe. The models are then illustrated with datasets that characterize the kinds of data researchers typically deal with. The real world data and clarity of writing in this book will appeal to environmental economists, students, researchers and practitioners in multilateral banks and government agencies.

'Natural Resources: Neither Course nor Destiny' brings together a variety of analytical perspectives, ranging from econometric analyses of economic growth to historical studies of successful development experiences in countries with abundant natural resources. The evidence suggests that natural resources are neither a curse nor destiny. Natural resources can actually spur economic development when combined with the accumulation of knowledge for economic innovation. Furthermore, natural resource abundance need not be the only determinant of the structure of trade in developing countries. In fact, the accumulation of knowledge, infrastructure, and the quality of governance all seem to determine not only what countries produce and export, but also how firms and workers produce any good.

Rents to Riches> focuses on the political economy of the detailed decisions that governments make at each step of the natural resource management (NRM) value chain. Many resource-dependent developing countries pursue seemingly shortsighted and suboptimal policies when extracting, taxing, and investing resource rents. The book contextualizes these micro-level outcomes with an emphasis on two central political economy dimensions: the degree to which governments can make credible intertemporal commitments to both resource developers and citizens, and the degree to which governments and inclined to turn resource rents into public goods. Almost 1.5 billion people live in the more than 50 World Bank client countries classified as resource-dependent. A detailed understanding of the way political economy characteristics affect the NRM decisions made in these countries by governments, extractive developers, and society can improve the design of interventions to support welfare-enhancing policy making and governance in the natural resource sectors. Featuring case study work from Africa (Angola, the Democratic Republic of Congo, Ghana, Niger, Nigeria), East Asia and Pacific (the Lao People's Democratic Republic, Mongolia, Timor-Leste), and Latin America and the Caribbean (Bolivia, Chile, Ecuador, Mexico, Trinidad an dTobago_, the book provides guidance for government clients, domestic stakeholders, and development partners committed to transforming natural resource into sustainable development riches.

What are natural resources? And why is it important to prevent natural resources from being wasted? Explore Natural Resources! answers these questions. The 25 projects inspire young readers ages 6-9 to have fun while learning why natural resources are important to all living things and how every child can take care of the earth’s resources through reducing, reusing, and recycling. Kids will read about national parks and early environmentalists, Earth celebrations, and the science behind renewable and nonrenewable resources. With projects and experiments ranging from making a wind-powered car and creating a solar catcher to calculating their water footprint, children will discover that everything comes from the earth. Projects are easy-to-follow, require little adult supervision, and use commonly found household products, many from the recycling box. Through a mixture of fun facts, trivia, jokes, comics, and hands-on activities, kids will Explore Natural Resources! and gain an appreciation of earth’s resources, from its vast oceans to its open skies. Explore Natural Resources! meets common core state standards in language arts for reading informational text and literary nonfiction and is aligned with Next Generation Science Standards. Guided Reading Levels and Lexile measurements indicate grade level and text complexity.

This text provides an advanced introduction to the theory and applications of geostatistics, including tools for description, modeling spatial continuity, spatial prediction, assessment of local uncertainty, and stochastic simulation.

This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference – t-test and U-test; correlation – Spearman’s rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal–Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. – Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel – Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. – Amazon 5-star review It has been very easy to follow and will be perfect for anyone. – Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. – Goodreads, 4-star review

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.