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.
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Abstract: This document reflects the latest reference metadata information available on SDG-indicator 15.4.2 "Mountain Green Cover Index". Last updated: 14 February 2021
Lead authoring unit/office: FAO
Abstract: The overarching principle of the 2030 Agenda for Sustainable Development – “leave no one behind” – calls for more granular and disaggregated data than are currently available in most countries, in order to inform the Sustainable Development Goal (SDG) monitoring process. Recognizing the fundamental role played by disaggregated data and information, the United Nations Statistical Commission (UNSC), at its Forty-seventh Session, requested the IAEG-SDG to form a working group on data disaggregation, with the objective of strengthening national capacities and developing the necessary statistical standards and tools to produce disaggregated data. As a member of the working group on data disaggregation, the Food and Agriculture Organization of the United Nations (FAO) has taken numerous steps towards supporting Member Countries in the production of disaggregated estimates. Within this framework, these Guidelines offer methodological and practical guidance for the production of direct and indirect disaggregated estimates of SDG indicators having surveys as their main or preferred data source. Furthermore, the publication provides tools to assess the accuracy of these estimates and presents strategies for the improvement of output quality, including Small Area Estimation methods.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: This technical note describes how the survey tools of the 50x2030 Initiative satisfy the data requirements of SDG Indicator 5.a.1. It provides guidance on the calculation of the indicator and advises on the potential for detailed analysis beyond the Indicator.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Data collected through surveys, and administrative systems form the foundation of official statistics, and are an invaluable source for research. They are aggregated to generate national estimates by official statisticians, and analyzed by researchers and policy analysts to gain scientific insights which can be translated into policy. These data are commonly referred to as microdata defined as to unitlevel information on individual people or entities (such as individuals, households, business enterprises, farms, or even geographic areas). The power of microdata stems from its granularity. Because microdata contain individual level information, they allow an analyst to investigate the unique ways a certain phenomenon may effect sub-populations. For example, a particular agricultural policy may effect male and female agricultural holders differently. Likewise, a social protection scheme may benefit a particular demographic and disadvantage another. This type of analysis is impossible without highly granular datasets which allow for the analyst to stratify a dataset by a one or more variables. Itisimportantto note here thatthis protocol incorporatesthe FAO Personal Data Protection Principles (AC No. 2021/01) and it is in accordance with the principles.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: Statisticians have long acknowledged the importance of securing this information in order to maintain the trust of the populations they serve. In this regard, the 6th principle of the Fundamental Principles of Official Statistics states “Individual data collected by statistical agencies for statistical compilation, whether they refer to natural or legal persons, are to be strictly confidential and used exclusively for statistical purposes.” Furthermore, FAO’s Statistical Quality Assurance defines Principle 10 as “All data subject to national confidentiality policies (e.g. concerning people and legal entities, or small aggregates) are kept strictly confidential, and are used exclusively for statistical purposes, or for purposes mandated by legislation.” However, while acknowledging the importance of securing individual data, the United Nations also advocates for the free dissemination of microdata. Disseminating microdata allows users to engage in research, increases the transparency and accountability of nationalstatistical institutions, and generate quality improvements through feedback from users (UNSD 2014). The competing principles of data security and microdata dissemination are arbitrated through a domain ofstatistics called Statistical Disclosure Control (SDC). SDC methods allow for protecting a dataset through the application of statistical tools, allowing the institution to safely disseminate the micro dataset. It is important to note here that this protocol incorporates the FAO Personal Data Protection Principles (AC No. 2021/01) and it is in accordance with the principles.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: At the end of each census round, FAO reviews and assesses national census practices, methodologies and results, and summarizes the findings in methodological publications, under the Statistical Development Series (SDS). For the WCA 2010 round (2006–2015), these assessments have been presented in two separate publications. The first one, titled “Main results and metadata by country” (SDS 17), published in 2019, presented a compendium of census metadata and main results for a record number of 127 countries and territories. The SDS 18, i.e. this publication, presents in its first part (Chapters 1 to 12) a methodological review of the national censuses. In its second part (Chapter 13), it illustrates global comparable data on key variables characterizing the structure of agriculture. The global review of census results includes key structural variables that are not available elsewhere. Some examples are number and area of holdings, land tenure and holder gender. Other variables are land size classes, average holding sizes, legal status of holders, household sizes, source of farm labour, land use and operated land.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This e-learning course is part of a series which introduce the FAO Damage and Loss (D&L) methodology, developed to support countries to generate precise and holistic data for the agricultural sector, in response to climate- and weather-related disasters. This course introduces the formulae which are used to compute damage and loss in the agricultural sectors (crops, livestock, forestry, aquaculture and fisheries). It then considers the data requirements and possible sources, and how the data can be used both to report on Sendai and SDG targets, and at national level. (Released in: December 2020; 2 h of learning)
Lead authoring unit/office: FAO
Abstract: Forest products conversion factors provides ratios of raw material input to the output of wood-based forest products for 37 countries of the world. Analysts, policymakers, forest practitioners and forest-based manufacturers often have a need for this information for understanding the drivers of efficiency, feasibility and economics of the sector. In addition, conversion factors are often needed to convert from one unit of measure to another. The publication also includes explanations on the units of measure, the drivers of the ratios, as well as information on physical properties of wood-based forest products. Finally, where reported factors were unavailable, factors from other sources are given.
Lead authoring unit/office: FAO
Abstract: This document presents the results of a validation of the version-2 of the WaPOR database, produced by the FRAME consortium partners, eLEAF and VITO. The report summarises the work done by the validation partner (ITC-UTwente) to assess the quality of the new V2 core data components, currently used to estimate and derive agricultural water productivity for Africa and the Near East. WaPOR represents a comprehensive open access data portal that provides information on biomass productivity (with focus on food and agriculture production) and evapotranspiration (evaporative losses and water use) for Africa and the Near East in near real time covering the period from 1 January 2009 to date. WaPOR offers continuous data on a 10-day average basis across Africa and the Near East at three spatial resolutions. The continental level-1 data (250m) cover entire Africa and the Near East (L1). The national level-2 (100m) data cover 21 countries and four river basins (L2). The third level-3 data (30m) cover eight irrigation areas (L3). The quality assessment focused on the core data of the WaPOR database i.e., the evaporative loss components: plant transpiration (T), soil evaporation (E) and interception (I) combined in ETI, the net primary productivity – NPP, the total (TBP) and above ground biomass productivity (AGBP) and reference evapotranspiration – RET.
Lead authoring unit/office: Land and Water Division (NSL)
Abstract: The CWP Handbook covers the concepts, definitions, classifications and data exchange protocols – and not least the codes as applied to fishery statistics globally. Many of these concepts and definitions are applied in a wider context, but the user is advised to check the validity of such applications. The handbook indicates the principles applied by the international agencies and no attempt has been made to include details of national systems, many of which, having been developed for specific national purposes, may differ from those employed internationally.
Lead authoring unit/office: Fisheries Division (NFI)
