Sampling Spatial Units for Agricultural Surveys

ISBN-13: 9783662526309 / Angielski / Miękka / 2016 / 325 str.

Roberto Benedetti; Federica Piersimoni; Paolo Postiglione

Opis

The research and its outcomes presented here focus on spatial sampling of agricultural resources. The agricultural data analyzed in this book help policymakers and market stakeholders to monitor the production of agricultural goods and its effects on environment and food safety.

Recenzje

"This monograph presents a contemporary study of sample surveys by geographically distributed data in the agricultural sector. ... Each chapter contains many dozen references up to the most recent sources. The monograph can be very helpful for lecturers, graduate students, and researchers using survey methods in general, and particularly in spatial agricultural studies." (Stan Lipovetsky, Technometrics, Vol. 59 (1), February, 2017)

"This book is a meticulously organized treatise of applying spatial data methods to sample surveys (primarily in agriculture), with the computational engine powered by the R software. ... It is mainly an intermediate-level reference book for graduate students and (agricultural) researchers to get introduced to the nuances of spatial statistics in survey sampling, and quickly move to hands-on computing. ... If you are enamoured with the versatility of R, I highly recommend buying it." (Dipankar Bandyopadhyay, Journal of Statistical Software, Vol. 6, February, 2016)

Spis treści

Preface.- 1 Essential Statistical Concepts, Definitions, and Terminology.- 2 Overview and Brief History.- 3 GIS: The Essentials.- 4 An Introduction to Remotely Sensed Data Analysis.- 5 Setting Up the Frame.- 6 Sampling Designs.- 7 Spatial Sampling Designs.- 8 Sample Size and Sample Allocation.- 9 Survey Data Collection and Processing.- 10 Advances in Sampling Estimation.- 11 Small Area Estimation.- 12 Spatial Survey Data Modeling.

O autorze

Roberto Benedetti is Professor of Economic Statistics at University of Chieti-Pescara (Italy). He was visiting researcher at the National Centre for Geographic Information Analysis of the University of California at Santa Barbara, at Regional Economics Applications Laboratory of University of Illinois at Urbana-Champaign, at Centre for Statistical and Survey Methodology of University of Wollongong, and received a Ph.D. in Methodological Statistics from the University of Rome in 1994. From 1994 to 2001, he held positions at the Italian National Statistical Institute as the head of the Agricultural Statistics Service. His current research interests focus on agricultural statistics, sample design, small area estimation, and spatial data analysis.

Federica Piersimoni is Senior Researcher at Agricultural Statistics Service within the Department of Economic Statistics of Italian National Statistical Institute from 1996. She was visiting researcher at Regional Economics Applications Laboratory of University of Illinois at Urbana-Champaign, and at Centre for Statistical and Survey Methodology of University of Wollongong. In 2014 she received a Ph.D. in Economics and Statistics from the University of Chieti - Pescara. Her main research interests concern disclosure control, sample design, and agricultural statistics.

Paolo Postiglione is Associate Professor of Economic Statistics at University of Chieti-Pescara (Italy). He was visiting researcher at Regional Economics Applications Laboratory of University of Illinois at Urbana-Champaign, at Regional Research Institute of West Virginia University, and received a Ph.D. in Statistics from the University of Chieti - Pescara in 1998. From 1996 to 2001 he was Statistical Executive at Ministry of Transports and Navigation in staff position of the Head of Human Resources. His current research interests focus on spatial statistics and econometrics, spatial sampling, regional economic convergence, models for spatial non-stationary data, and agricultural statistics.

Blurb

The research and its outcomes presented here focus on spatial sampling of agricultural resources. The authors introduce sampling designs and methods for producing accurate estimates of crop production for harvests across different regions and countries. With the help of real and simulated examples performed with the open-source software R, readers will learn about the different phases of spatial data collection. The agricultural data analyzed in this book help policymakers and market stakeholders to monitor the production of agricultural goods and its effects on environment and food safety.