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: Mapping the spatial relationship between forests, trees and the people that live in and around them is key to understanding human-environment interactions. First, quantifying spatial relationships between humans and forests and trees outside forests can help decision-makers develop spatially explicit conservation and sustainable development indicators and policies to target priority areas. This study combined tree cover and human population density data to map the spatial relationship between forests, trees and people on a global scale providing estimates of the number of forest-proximate people and tree-proximate people for 2019. The methodology relies on spatial overlays that combine global-scale remotely sensed data on tree cover (as a proxy for forest cover) and gridded human population data to identify people that live in or close to forests and trees. Evidence on the number and spatial distribution of people living within or near forests and trees outside forests may, therefore, support decision-makers to 1) target projects in priority areas; 2) prioritize among alternative sites; 3) reduce the cost of achieving environmental or socio-economic objectives; 4) improve the effectiveness of monitoring, including by estimating the numbers of people who will be affected or have been affected as a result of an intervention, or affected by biophysical changes to forests (e.g. deforestation, fire or floods); and/or 5) more effectively and assuredly reaching target populations.
Lead authoring unit/office: Forestry Division (NFO)
Abstract: These guidelines are intended to assist countries in understanding the agronomic parameters involved in the computation of the agricultural component of the Sustainable Development Goal (SDG) indicator 6.4.1 on the change in water use efficiency over time. They provide a detailed explanation of the calculation process for calculation by countries willing to generate a more accurate estimation using their national data. The guidelines provide the minimum standard method using an estimated or default value proposed by FAO, as well as the available methodologies to progressively improve the accuracy through a monitoring ladder for countries that have more comprehensive and accurate data on their main crops areas and productions.
Lead authoring unit/office: Land and Water Division (NSL)
Abstract: Offered by the European Commission, the Degree of Urbanisation is a method to delineate cities, towns, suburbs and rural areas for international statistical comparison. It has been adopted by the 51st Statistical Commission of the United Nations. This training provides the necessary background to apply independently the Degree of Urbanisation method. We offer a multi-level learning experience across the various aspects of the Degree of Urbanisation, to help understanding what it is and how it can be applied to your own data. Applications include Sustainable Development Goals monitoring and breakdown by urbanisation class, disaster risk management, environmental and climate investigations, demography, development and cooperation.
Abstract: The MOOC was jointly developed by the Food and Agriculture Organization of the United Nations (FAO) and the United Nations Framework Convention on Climate Change (UNFCCC) and funded by the Capacity-Building Initiative for Transparency (CBIT) trust fund of the Global Environment Facility (GEF).
Lead authoring unit/office: Forestry Division (NFO)
Abstract: The national forest monitoring system (NFMS) assessment tool has been developed under the project “Building global capacity to increase transparency in the forest sector (CBIT-Forest)” implemented by Food and Agriculture Organization of the United Nations (FAO) and funded by the Capacity-Building Initiative for Transparency (CBIT) trust fund of the Global Environment Facility (GEF). The tool aims to assist countries in carrying out a comprehensive capacity assessment of forest monitoring across three complementary themes – institutional arrangements, measurement and estimation, and reporting and verification.
Lead authoring unit/office: Forestry Division (NFO)
Abstract: A lack of institutional and individual capacity often undermines the long-term impact of otherwise technically sound programmes. To support efforts towards sound and impactful forest monitoring, the Food and Agriculture Organization of the United Nations (FAO) has developed a national forest monitoring system (NFMS) assessment tool to help countries identify capacity gaps and weaknesses in order to address their real needs in a targeted manner.
Lead authoring unit/office: Forestry Division (NFO)
Abstract: “Building global capacity to increase transparency in the forest sector (CBIT-Forest)” is a project led by the Food and Agriculture Organization of the United Nations (FAO) and financed by the Capacity-building Initiative for Transparency (CBIT) trust fund of the Global Environment Facility (GEF) with a lifespan of two and a half years. The global project strengthened the institutional and technical capacities of developing countries to collect, analyze and disseminate forest-related data. It supported countries in meeting the enhanced transparency framework (ETF) requirements of the Paris Agreement and contributed information necessary to track progress related to implementing and achieving their Nationally Determined Contributions (NDCs).
Lead authoring unit/office: Forestry Division (NFO)
Abstract: This technical report presents a case study based on the use of a small area estimation (SAE) approach to produce disaggregated estimates of SDG Indicator 5.a.1 by sex and at granular sub-national level. In particular, after introducing the framework for using SAE techniques, the report discusses a possible model-based technique to integrate a household or agricultural survey measuring the indicator of interest with census microdata, in order to borrow strength from a more comprehensive data source and produce estimates of higher quality. The discussed estimation approach could also be extended or customized for the integration of survey data with alternative data sources, such as administrative records, and/or geospatial information, and for the disaggregation of other (SDG) indicators based on survey microdata.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: In 2021, FAO delivered training aimed at building capacity for the use of Earth Observations data and machine learning to produce annual national land cover maps and to extract land cover statistics.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: In 2021, FAO delivered training aimed at building capacity for the use of Earth Observations data and machine learning to produce annual national land cover maps and to extract land cover statistics.
Lead authoring unit/office: Office of Chief Statistician (OCS)
