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: Sustainable Development Goal 12.3 is defined as the goal that “by 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses”. The e-learning course covers the sub-indicator 12.3.1. a Food Loss Index (FLI) which will aid countries in reducing food losses along production and supply chains. The lessons cover the index and its components, along with strategies and guidelines for collecting, integrating and modelling the necessary data from a variety of sources.
Lead authoring unit/office: FAO
Abstract: This paper presents new estimates of the extent of food consumption inequality in mainland China and discusses their implications for the estimated prevalence of undernourishment (PoU). The new food consumption inequality estimates are based on the joint analysis of food consumption and food expenditure data obtained from two separate household surveys, covering the period from 2011 to 2017. The results reveal much less inequality in dietary energy consumption than previously assumed and imply a substantial downward revision of the estimated series of the PoU for China, which becomes more in line with other assessments of food insecurity and with other development indicators. This document is part of FAO Statistics Working Paper Series. Revised 27 July 2020, minor edits made on p. 16
Lead authoring unit/office: Statistics Division (ESS)
Abstract: The overall objective of this virtual training was to provide (government officials responsible for monitoring SDG indicator 2.4.1) capacity development on the methodology, data collection and analysis relevant to sustainable food and agriculture and how to asses data gaps starting from available national and subnational (farm-level) information and associated reporting processes through 3 half-days virtual trainings.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This report provides a summary and highlights from a technical meeting on “Dietary Data Collection, Analysis and Use: Taking Stock of Country Experiences and Promising Practices in Low- and Middle-Income Countries”, jointly convened by the Food and Agriculture Organization of the United Nations and the Intake Center for Dietary Assessment, on December 11–13, 2019 at FAO headquarters in Rome, Italy. The meeting, which brought together experts from 20 LMICs across different regions of the world, aimed overall to promote South–South learning, cross-regional networking, and the sharing of experiences with national (or large-scale), government-led, government-owned, quantitative 24-hour dietary recall surveys in LMICs.
Lead authoring unit/office: FAO
Abstract: The objective of this course is to learn about the Enhanced Transparency Framework (EFT) under the Paris Agreement. It will be useful to those wishing to understand the importance of forest-related data collection, analysis and dissemination in meeting the Enhanced Transparency Framework requirements.
Lead authoring unit/office: Forestry Division (NFO)
Abstract: Prepared by the Asian Development Bank (ADB) and the Food and Agriculture Organization (FAO) of the United Nations, this handbook is designed to help national statistical officers and other interested readers embrace the efficiencies of CAPI-based data collection to supersede the traditional pen and paper interviewing method. We hope that it contributes to the adoption of other innovative tools and technologies that further strengthen national statistical systems.
Lead authoring unit/office: FAO
Abstract: Prepared by the Asian Development Bank (ADB) and the Food and Agriculture Organization (FAO) of the United Nations, this handbook is designed to help national statistical officers and other interested readers embrace the efficiencies of CAPI-based data collection to supersede the traditional pen and paper interviewing method. We hope that it contributes to the adoption of other innovative tools and technologies that further strengthen national statistical systems.
Lead authoring unit/office: FAO
Abstract: Food-based dietary guidelines (also known as dietary guidelines) are intended to establish a basis for public food and nutrition, health and agricultural policies and nutrition education programmes to foster healthy eating habits and lifestyles. They provide advice on foods, food groups and dietary patterns to provide the required nutrients to the general public to promote overall health and prevent chronic diseases.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This course has been developed to support countries in the analysis and reporting for Indicator 2.4.1 of the 2030 Sustainable Development Goals (Proportion of agricultural area under productive and sustainable agriculture), and to facilitate the understanding of the main concepts underpinning the methodology. Audience The course is primarily intended for those who play a role in data collection, analysis and reporting for SDG Indicator 2.4.1, including agronomists, statisticians, enumerators and data analysts, as well as policy makers and people with an interest in the process. You will learn about The concept of sustainable agriculture and the importance of SDG Indicator 2.4.1 The key features of the Indicator, with a focus on its 11 themes that span economic, social and environmental dimensions The use of the farm survey and alternative options for data collection The methodology for analyzing, computing and reporting this SDG Indicator Course structure The course consists of 4 lessons, ranging from approximately 25 to 40 minutes duration each: Lesson 1 – Introduction to SDG Indicator 2.4.1 Lesson 2 – Key features of SDG Indicator 2.4.1 Lesson 3 – Collecting the data Lesson 4 – Analysing and reporting
Lead authoring unit/office: Statistics Division (ESS)
Abstract: These guidelines are intended to assist countries to participate in the assessment of SDG 6.4.2 on water stress by contributing data and information on environmental flows (EF). These data are necessary for calculation of the SDG 6.4.2 (UNSD, 2017) indicator on water stress, for which countries are required to submit information to FAO who is custodian of this SDG indicator. This guideline provides a minimum standard method, principally based on the Global Environmental Flows Information System (GEFIS), which is accessible via http://eflows.iwmi.org, and is the approach that will be used to generate the country EF data that will make up the global 6.4.2 report. Countries that have more comprehensive and accurate EF data will be able to make use of that data when checking the global dataset produced by FAO and also to add additional details to their Voluntary National report on SDG 6.4.2.
Lead authoring unit/office: Land and Water Division (NSL)
