This book is an attempt to bring together the competences of academics and of experts from national statistical offices to increase the dissemination of the most recent survey methods in the agricultural sector. With this ambition in mind, the authors were asked to extend and update their research and the project was completed by some chapters on specialized topics.

Although the present book can serve as a supplementary text in graduate seminars in survey methodology, the primary audience is constituted by researchers having at least some prior training in sampling methods. Since it contains a number of review chapters on several specific themes in survey research, it will be useful to  researchers actively engaged in organizing, managing and conducting agricultural surveys who are looking for an introduction to advanced techniques from both a practical and a methodological perspective.

Another aim of this book is to stimulate research in this field and, for this reason, we are aware that it cannot be considered as a comprehensive and definitive reference on the methods that can be used in agricultural surveys, since many topics were intentionally omitted. However, it reflects, to the best of our judgement, the state of the art on several crucial issues.

The volume contains 22 chapters of which the first one can be considered as an introductory chapter reviewing the current status of agricultural statistics, and the remaining 21 are divided into five parts:

     I. Census, frames, registers and administrative data (Chapters 2–5). These chapters provide an overview of the basic tools used in agricultural surveys, including some practical and theoretical considerations regarding the definitions of the statistical units. Attention is then focused on the use of administrative data that in the last few years have evolved from a simple backup source to the main element in ensuring the coverage of a list of units. The opportunity to reduce census and survey costs implies growing interest, among statistical agencies, in the use of administrative registers for statistical purposes. However it requires attitudes, methods and terms that are not yet typical in the statistical tradition. The keyword is the harmonization of the registers in such a way that information from different sources and observed data should be consistent and coherent. In particular, the expensive agricultural census activities conducted periodically in every country of the world should benefit from such a radical innovation.

    II. Sample design, weighting and estimation (Chapters 6–9). These chapters review advanced methods and techniques recently developed in the sampling literature as applied to agricultural units, in the attempt to address the distinctive features (c)–(e) described above. Some interesting proposals arise from the field of small area estimation, which has received a lot of attention in recent years due to the growing demand for reliable small-area statistics needed for formulating policies and programmes. An appraisal is provided of indirect estimates, both traditional and model-based, that are used because direct area-specific estimates may not be reliable due to small-area-specific sample sizes.

    III. GIS and remote sensing (Chapters 10–13). These chapters describe the use of the Geographic Information System technology as a tool to manage area- and point-based surveys. These devices are applied to carry out a wide range of operations on spatial information retrieved from many kinds of mixed sources. They provide a detailed description of the procedures currently used in the European Union and United States to develop and sample area frames for agricultural surveys. Additionally, the usefulness of remotely sensed data as the main auxiliary variables for geographically coded units is assessed through empirical evidence, and some techniques to increase the performance of their use are proposed.

    IV. Data editing and quality assurance (Chapters 14–17). These chapters deal with the classical problem of error handling, localization and correction. This is strictly connected with the issue of guaranteeing data quality, which obviously plays a central role within any statistical institute, both in strategic decisions and in daily operations. In this framework, it is evident that quality is not as much concerned with the individual data sets as with the whole set of procedures used. Some approaches to ensure data quality in collecting, compiling, analysing and disseminating agriculture data are described.                                                

    V. Data dissemination and survey data analysis (Chapters 18–22). These chapters examine some experiences in the use of statistical methods to analyse agricultural survey data. In particular, regression analysis (or some of its generalizations) is quite often applied to survey microdata to estimate, validate or forecast models formalized within agricultural economics theory. The purpose is to take into account the nature of the data analysed, as observed through complex sampling designs, and to consider how, when and if statistical methods may be formulated and used appropriately to model agricultural survey data. Web tools and techniques to assist the users to access statistical figures online are then described, for complete, safe and adequate remote statistical analyses and dissemination.