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.

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.

- Analyses real data sets from start to conclusion. - Includes an extensive set of examples of the use of R to construct graphs and maps and to model and analyze spatial data. - Provides background information on exploratory and graphical data analysis and on spatial econometrics methods. - Lists the possible types of spatial data used to analyze and model agriculture economics phenomena (and offers several codes for each example in the R software environment). - Presents the methods of spatial data analysis and of spatial econometric modeling appropriate for each agricultural data type. - Examines how each spatial data type can be used to explore spatial structures and how the spatial effects can be properly added to agricultural economics models. - Outlines methods for model estimation when data is not available for the whole population but for a sample survey. - Illustrates the simplest and more sophisticated methods both to convert data from one type to another and to integrate different spatial data sources.

- Analyses real data sets from start to conclusion. - Includes an extensive set of examples of the use of R to construct graphs and maps and to model and analyze spatial data. - Provides background information on exploratory and graphical data analysis and on spatial econometrics methods. - Lists the possible types of spatial data used to analyze and model agriculture economics phenomena (and offers several codes for each example in the R software environment). - Presents the methods of spatial data analysis and of spatial econometric modeling appropriate for each agricultural data type. - Examines how each spatial data type can be used to explore spatial structures and how the spatial effects can be properly added to agricultural economics models. - Outlines methods for model estimation when data is not available for the whole population but for a sample survey. - Illustrates the simplest and more sophisticated methods both to convert data from one type to another and to integrate different spatial data sources.