Spatial econometric methods in agricultural economics using R

The collection of spatial agricultural data and the spatial analysis of agriculture represent two issues of primary relevance for a large number of people. This book aims at supporting stakeholders to design spatial surveys for agricultural data and/or to analyse the geographically collected data. Hence, the book represents a comprehensive guide in methodological and empirical advanced techniques for practitioners. This volume can also be considered as a primary tool for users from less developed countries, where agriculture is still the prevalent economic sector. Therefore, different contributions may guide one through the application of spatial survey methods, technologies developed in the past decades, such as remote sensing and GIS, and appropriate methods to analyse spatial agricultural data. Applied spatial analysts might also benefit from this work. In particular, a part of the book is devoted to the integration techniques used to merge agricultural data from different sources. Finally, both people from Academic institutions and National Statistical Offices may appreciate the occasion of deepening their knowledge of spatial techniques for agriculture. Although the book could also represent a valued support on spatial methodologies in agriculture for graduate classes, the primary audience is mainly composed by researchers with some prior background in econometrics and spatial statistics. The main objective of this book is to introduce agricultural economists to statistical approaches for the analysis of spatial data. The aim is to illustrate, for the main typologies of agricultural data, the most appropriate methods for the analysis, together with a description of available data sources and collection methods. Spatial econometrics methods for different types of data are described and adopted with reference to typical analyses of agricultural economics. 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. Besides, the different phases of agricultural data collection, analysis, and integration are described in a simple way. The joint use of statistical methods, new technologies, and economic theory is treated considering the peculiarities of spatial data for a proper and efficient analysis of agricultural data. Theoretical aspects of each model are described and complemented by examples on real data that are developed by using the open-source R software. The codes are available in the text, explained with details and in an intuitive way so that the readers can replicate these analyses on their own data. Moreover, any prior knowledge of the R programming environment is not assumed throughout the book. The volume is organized in a number of review chapters on several specific themes. In particular, this book contains 13 Chapters, of which the first one can be considered as an introductory chapter, reviewing the main underlying concepts and presenting each contribution. We would like to thank Alfredo Cartone for reading some parts of this book and for his support in the implementation of some R codes. Thanks also to Vijay Primlani of Science Publishers, CRC Press, for his continuous encouragement to complete this book. Finally, we are grateful to the individual chapter authors for their diligence in writing the documents. We are confident that their work will lead to new insights in the application of spatial econometric methods to agricultural data.