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: The Food and Agriculture Organization of the United Nations (FAO) is building a land cover monitoring system in Lesotho in support of ReNOKA (‘we are a river’), the national program for integrated catchment management led by the Government of Lesotho. The aim of the system is to deliver land cover products at a national level on an annual basis that can be used for global reporting of official land cover statistics and to inform appropriate land restoration policies. This paper presents an innovative methodology that has allowed the production of five standardized annual land cover maps (2017–2021) using only a single in situ dataset gathered in the field for the reference year, 2021. A total of 10 land cover classes are represented in the maps, including specific features, such as gullies, which are under close monitoring. The mapping approach developed includes the following: (i) the automatic generation of training and validation datasets for each reporting year from a single in situ dataset; (ii) the use of a Random Forest Classifier combined with postprocessing and harmonization steps to produce the five standardized annual land cover maps; (iii) the construction of confusion matrixes to assess the classification accuracy of the estimates and their stability over time to ensure estimates’ consistency. Results show that the error-adjusted overall accuracy of the five maps ranges from 87% (2021) to 83% (2017). The aim of this work is to demonstrate a suitable solution for operational land cover mapping that can cope with the scarcity of in situ data, which is a common challenge in almost every developing country.
Lead authoring unit/office: Office of Chief Statistician (OCS)
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: 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: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), these Guidelines on Farm Typology propose a tool to be used to classify agricultural holdings by multiple dimensions, aiming at enhancing comprehension of the farm structures and production diversity both between and within countries, and at more efficient targeting in agricultural and rural policies and investments.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this training material is designed for managers, statisticians, sampling experts, GIS specialists and professional staff from the National Statistical Offices and statistical units of the Ministries of Agriculture. The objectives of the presentation are to initiate the audience on the use of GIS and remote sensing tools to build an area frame. It will be based on these tools: Qgis (Design of segments and points and execution of some processing tasks); Open foris collect (design of the land use/cover survey form); Collect earth and google earth pro (remote data collection of the land use/cover survey).
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this literature review presents the main findings of bibliographic research on the application of remote sensing to agricultural and rural statistics.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this Technical Report aims to provide guidelines for the application of different remote sensing products in various agricultural landscapes, and for the preparation of an integrated database to be used as a baseline in constructing the Master Sampling Frame.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), this report contains a literature review of classifications and typologies developed in agriculture and a preliminary proposal for classification principles that can feed into the guidelines for establishing an international framework for farm typologies.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: The course introduces spatial planning, identifying its rationale and benefits, its key principles and the main stages in the spatial planning process. It represents a useful reference for all those who want to promote and implement spatial planning in their countries as an instrument to reconcile and harmonize different, often conflicting, public and private interests on land, fisheries and forests.
Lead authoring unit/office: FAO
Abstract: Prepared in the framework of the Global Strategy to improve Agricultural and Rural Statistics (GSARS), this technical paper on identifying the most appropriate sampling frame for specific landscape types is the result of a comprehensive literature review on the subject, followed by a gap analysis and development of innovative methodological proposals for addressing any issues that emerged.
Lead authoring unit/office: Statistics Division (ESS)
