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E-grāmata: Spatial Econometric Methods in Agricultural Economics Using R

(University of Chieti-Pescara, Italy)
  • Formāts: 286 pages
  • Izdošanas datums: 22-Dec-2021
  • Izdevniecība: CRC Press Inc
  • ISBN-13: 9781498766838
  • Formāts - PDF+DRM
  • Cena: 52,59 €*
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  • Formāts: 286 pages
  • Izdošanas datums: 22-Dec-2021
  • Izdevniecība: CRC Press Inc
  • ISBN-13: 9781498766838

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Modern tools, such as GIS and remote sensing, are increasingly used in the monitoring of agricultural resources. The developments in GIS technology offer growing opportunities to agricultural economics analysts dealing with large and detailed spatial databases, allowing them to combine spatial information from different sources and to produce different models. The availability of these valuable sources of information makes the advanced models suggested in the spatial statistic and econometric literature applicable to agricultural economics.

This book aims at supporting stakeholders to design spatial surveys for agricultural data and/or to analyse the geographically collected data.

This book attempts to describe the main typology of agricultural data and the most appropriate methods for the analysis, together with a detailed description of the available data sources and their collection methods. Topics such as spatial interpolation, point patterns, spatial autocorrelation, survey data analysis, small area estimation, regional data modelling, and spatial econometrics techniques are covered jointly with issues arising from the integration of several data types.

The theory of spatial methods is complemented by real and/or simulated examples implemented through the open-source software R.
Preface iii
1 Basic Concepts
1(10)
Paolo Postiglione
Roberto Benedetti
Federica Piersimoni
2 Spatial Sampling Designs
11(24)
Francesco Pantalone
Roberto Benedetti
3 Including Spatial Information in Estimation from Complex Survey Data
35(29)
Francesco Pantalone
Maria Giovanna Ranalli
4 Yield Prediction in Agriculture: A Comparison Between Regression Kriging and Random Forest
64(24)
Eugenia Nissi
Annalina Sarra
5 Land Cover/Use Analysis and Modelling
88(20)
Elisabetta Carfagna
Gianrico Di Fonzo
6 Statistical Systems in Agriculture
108(14)
Cecilia Manzi
Federica Piersimoni
7 Exploring Spatial Point Patterns in Agriculture
122(18)
M. Simona Andreano
Andrea Mazzitelli
8 Spatial Analysis of Farm Data
140(20)
Alfredo Cartone
Domenica Panzera
9 Spatial Econometric Modelling of Farm Data
160(25)
Anna Gloria Bille
Cristina Salvioni
Francesco Vidoli
10 Areal Interpolation Methods: The Bayesian Interpolation Method
185(17)
Domenica Panzera
11 Small Area Estimation of Agricultural Data
202(32)
Gaia Bertarelli
Francesco Schirripa Spagnolo
Nicola Salvati
Monica Pratesi
12 Cross-sectional Spatial Regression Models for Measuring Agricultural β-convergence
234(20)
Alfredo Cartone
Paolo Postiglione
13 Spatial Panel Regression Models in Agriculture
254(21)
Paolo Postiglione
Index 275(4)
About the Editors 279
Paolo Postiglione is Professor in Economic Statistics at University of Chieti-Pescara (Italy). He received a Ph.D. in Statistics from the University of Chieti-Pescara in 1998. His research interests mainly concern regional quantitative analysis, spatial statistics and econometrics, spatial concentration, regional economic convergence, agricultural statistics, and spatial sampling.

Roberto Benedetti is Professor in Economic Statistics at University of Chieti-Pescara (Italy). He obtained a Ph.D. in Methodological Statistics in 1994 from La Sapienza University of Rome (Italy). His current research interests focus on agricultural statistics, sample design, small area estimation, and spatial data analysis.

Federica Piersimoni is Senior Researcher at Processes Design and Frames Service in the Methodological Department of the Italian National Statistical Institute, since 1996. Her main research interests concern disclosure control and sample design.