Data Science In Agriculture And Natural Resource Management

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.

This book assesses recent developments in the analysis of agricultural policy and water resource management, and highlights the utility and theoretical rigor of quantitative methods for modeling agricultural production, market dynamics, and natural resource management. In diverse case studies of the intersection between agriculture, environmental quality and natural resource sustainability, the authors analyze economic behavior - both at aggregate as well as at individual agent-level - in order to highlight the practical implications for decision-markers dealing with environmental and agricultural policy. The volume also addresses the challenges of doing robust analysis with limited data, and discusses the appropriate empirical approaches that can be employed. The studies in this book were inspired by the work of Richard E. Howitt, Emeritus Professor of Agricultural Economics at the University of California at Davis, USA, whose career has focused on the application of robust empirical methods to address concrete policy problems.

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.

Part 1: Introduction Chapter 1: What is Natural Resources Research? Chapter 2: At Least Read This. Chapter 3: Sidetracks Part 2: Planning Chapter 4: Introduction to Research Planning Chapter 5: Concepts Underlying Experiments Chapter 6: Sampling Concepts Chapter 7: Surveys and Studies of Human Subjects Chapter 8: Surveying Land and Natural Populations Chapter 9: Planning Effective Experiments Part 3: Data Management Chapter 10: Data Management Issues and Problems Chapter 11: Use of Spreadsheet Packages Chapter 12: The Role of a Database Package Chapter 13: Developing a Data Management Strategy Chapter 14: Use of Statistical Software Part 4: Analysis Chapter 15: Analysis - Aims and Approaches Chapter 16: The DIY Toolbox - General Ideas 16.1 Opening the Toolbox 221 Chapter 17: Analysis of Survey Data Chapter 18: Analysis of Experimental Data Chapter 19: General Linear Models Chapter 20: The Craftsman's Toolbox Chapter 21: Informative Presentation of Tables, Graphs and Statistics Part 5: Where Next? Chapter 22: Current Trends and their Implications for Good Practice Chapter 23: Resources and Further Reading.

Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.

Part I: Introduction; Part II: Valuation of ecosystem services and biophysical indicators of NRM impacts; Part III: Methodological advances for a comprehensive impact assessment; Part IV: NRM impact assessment in practice.

This book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.