Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives

This Technical Report on Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives was prepared within the framework of the Global Strategy to Improve Agricultural and Rural Statistics. The Global Strategy is an initiative endorsed in 2010 by the United Nations Statistical Commission, to provide a framework and a blueprint to meet current and emerging data requirements and the needs of policymakers and other data users. Its goal is to contribute to greater food security, reduced food price volatility, higher incomes and greater well-being for rural populations, through evidence-based policies. The Global Strategy is centred upon 3 pillars: (1) establishing a minimum set of core data (2) integrating agriculture into National Statistical Systems (NSSs) and (3) fostering the sustainability of the statistical system through governance and statistical capacity building. The Action Plan to Implement the Global Strategy includes an important research programme, to address methodological issues for improving the quality of agricultural and rural statistics. The outcome of the research programme is to produce scientifically sound and cost-effective methods that will be used as inputs to prepare practical guidelines for use by country statisticians, training institutions, consultants, etc. To enable countries and partners to benefit at an early stage from research activity results that are already available, it has been decided to establish a Technical Reports Series, to widely disseminate available technical reports and advanced draft guidelines and handbooks. This will also provide an opportunity for countries to give feedback on the papers. Technical reports and draft guidelines and handbooks published in this Technical Report Series have been prepared by senior consultants and experts and reviewed by the Scientific Advisory Committee (SAC)1 of the Global Strategy, the Research Coordinator at the Global Office and other independent senior experts. For some of the research topics, field tests will be organized before final results are included in guidelines and handbooks. The aim of this report on Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives is to enhance disaggregation methods for adaptation to various agricultural situations and datasets. Part 1 reviews the literature on this subject under two topics: i) mapping techniques and ii) small-area estimators. With regard to mapping techniques, the main areal interpolation methods based on regression techniques are presented. SAE methods are classified as: i) model-assisted methods – for example the generalized regression estimator; and ii) model-based methods, which are considered as unit-level and area-level specifications – the empirical best linear unbiased predictors estimator, M-quantile estimator and Fay and Herriot estimator – with spatial specifications where available. Assumptions are explained and the information needed for each method is given, with illustrations from applications to rural and agricultural statistics or to socio-economic statistics. Part 2 examines the reliability of the methods in non-standard situations that commonly arise in agricultural surveys. The main topics are sensitivity to spatial model specification, the modifiable area unit problem, robustness of predictors, complexity of sample design, missing data in spatial datasets and excess of zeros in survey data. This part analyses the methods presented in Part 1, presents the main contributions to the topics and proposes methodological and operational solutions. Part 3 summarizes the issues in the review of mapping techniques and small-area estimators. It also offers remarks and recommendations based on the analysis of the reliability of the methods and draft guidelines for applying them in field tests.