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.
1. Basic Concepts
Paolo Postiglione, Roberto Benedetti and Federica Piersimoni
2. Spatial Sampling Designs
Francesco Pantalone and Roberto Benedetti
3. Including Spatial Information in Estimation from Complex Survey Data
Francesco Pantalone and Maria Giovanna Ranalli
4. Yield Prediction in Agriculture: A Comparison Between Regression Kriging and Random Forest
Eugenia Nissi and Annalina Sarra
5. Land Cover/Use Analysis and Modelling
Elisabetta Carfagna and Gianrico Di Fonzo
6. Statistical Systems in Agriculture
Cecilia Manzi and Federica Piersimoni
7. Exploring Spatial Point Patterns in Agriculture
M Simona Andreano and Andrea Mazzitelli
8. Spatial Analysis of Farm Data
Alfredo Cartone and Domenica Panzera
9. Spatial Econometric Modelling of Farm Data
Anna Gloria Billè, Cristina Salvioni and Francesco Vidoli
10. Areal Interpolation Methods: The Bayesian Interpolation Method
11. Small Area Estimation of Agricultural Data
Gaia Bertarelli, Francesco Schirripa Spagnolo, Nicola Salvati and Monica Pratesi
12. Cross-sectional Spatial Regression Models for Measuring Agricultural β-convergence
Alfredo Cartone and Paolo Postiglione
13. Spatial Panel Regression Models in Agriculture