In response to a growing demand for high-quality and internationally-comparable statistics, FAO develops, implements and promotes methods and standards to guide national data producers in generating and using sound statistics. In particular, the Organization is committed to provide national statistical systems with internationally recognized definitions, concepts and classifications as well as methodological guidance for the production of high quality statistics related to food and agriculture.
This interface allows you to search for statistical classifications, guidelines and handbooks, technical reports, working papers and methodological documents, and capacity development resources. You can search by SUBJECT (general, agriculture, forestry, fishery and aquaculture, and natural resources) or use the ADVANCED SEARCH to search by keyword, country, language and lead authoring unit/office. Comments, suggestions and inquiries can be addressed to: [email protected].
You can access here all the statistical guidelines and handbooks, technical reports, working papers and methodological documents, and capacity development resources.
Type a keyword in the free text search box or refine your search by keyword, country, language and lead authoring unit/office.
Comments, suggestions and inquiries can be addressed to: [email protected].
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this material for in-classroom training on the use of CAPI for agricultural surveys targets decision makers, survey managers, questionnaire designers, trainers of field staff and data analysts. At the end of this session, the audience will: a) Know the difference between Administrator, HQ, and Supervisor; b) Be familiar with workflow of Survey Solutions and events in the life a case; c) Know how to access HQ and Supervisor; d) HQ primary functions (create Supervisor, Interviewers, Import Template, Create Cases, Assign Cases to Supervisor); e) Supervisor primary functions (assign cases to Interviewers); f) Use HQ and Supervisor to approve/reject completed cases; f) Know about creating field reports with HQ and Supervisor.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this material for in-classroom training on the use of CAPI for agricultural surveys targets decision makers, survey managers, questionnaire designers, trainers of field staff and data analysts.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this material for in-classroom training on the use of CAPI for agricultural surveys targets decision makers, survey managers, questionnaire designers, trainers of field staff and data analysts.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), these guidelines set out a methodology for the collection of data and information on various aspects of the fisheries and aquaculture sector, especially concerning small-scale operators.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: These Guidelines for the compilation of Food Balance Sheets have been developed under the Global Strategy to improve Agricultural and Rural Statistics (GSARS), with the intention to provide countries with a user-friendly handbook that can aid in the construction of country-level Food Balance Sheets (FBS) as a tool for policy analysis.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: The purpose of this handbook on Remote Sensing for Agricultural Statistics is to provide guidelines on the use of remote sensing in the context of agricultural statistics. Since the mid-1970s, remote sensing has been considered a promising technique for improving agricultural statistics. Various applications of remote sensing have taken place on all continents and today, several approaches may be considered mature enough to contribute to the sustainability of agricultural statistics. In the context of the Global Strategy to improve Agricultural and Rural Statistics (hereafter, GSARS or Global Strategy; see World Bank, 2011), remote sensing has been identified as a prime contributor to the localization and geocoding of the sampling units, a point of reference for Master Sampling Frames (MSFs), a methodological improvement in design and estimation terms, as a way to achieve sustainability and as a core data set for indicators linked to land uses and covers.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This literature review and gaps analysis is undertaken in the context of the research line on the measurement of agricultural productivity and efficiency of the Global Strategy to Improve Agricultural and Rural Statistics. It seeks to define the different concepts and present the main measurement methods for agricultural productivity and efficiency.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: The objective of the research topic “SUST-3, Data Collection Methods for Agri-environmental Indicators” is to provide a framework for agricultural sustainability indicators and related statistical definitions, and measurements tools covering the economic, social and environmental dimensions. This document was produced by the Global Strategy to improve Agricultural and Rural Statistics (GSARS).
Lead authoring unit/office: Statistics Division (ESS)
Abstract: In this technical report prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), the literature search is linked to the pilot project by identifying salient issues and gaps in surveys of fisheries and aquaculture. Also discussed in the report are ways in which various methods for conducting surveys of fisheries and aquaculture deal with different aspects of these challenges.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this report introduces the problem of reconciling agricultural census and survey data by exploring methods to recalculate the survey weights, focusing on how to combine information from agricultural censuses.
Lead authoring unit/office: Statistics Division (ESS)
