Methods and Standards

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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].

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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].

Guidelines on the measurement of harvest and post-harvest losses. Estimation of crop harvest and post-harvest losses in Malawi. Maize, rice and groundnuts. Field test report

Abstract: A study was conducted in two Agriculture Development District (ADDs) of Malawi, Salima and Lilongwe, to pilot a new methodology for estimating on-farm harvest and post-harvest losses. The study was carried-out with technical support from the Global strategy to improve agricultural and rural statistics (GSARS) of the Food and Agricultural Organization of the United Nations (FAO). This pilot exercise principally aimed at strengthening the capacity of Malawi in generating reliable estimates on post-harvest losses. The data collection was carried out using a household questionnaire which was specifically developed for this exercise. The analysis of the results showed that a significant amount of farm produce is lost during harvesting, followed by threshing. The study also highlighted that on-time harvesting and use of chemicals are considered by farmers as the most effective strategies for preventing on-farm losses, even though farmers are not always in a position to implement these strategies. The authors recommend that a solid baseline on harvest and post-harvest losses be established by replicating on a larger scale this pilot survey for three consecutive years, to account for weather variation and other exogenous factors which may affect losses. The survey would benefit from the integration with existing country-wide data collection systems such as the Agricultural production estimates survey (APES) to ensure low operational costs and sustainability. It is also recommended that Computer assisted personal interviewing (CAPI) should be introduced for future exercises to improve on data quality and timeliness

Guidelines on the measurement of harvest and post-harvest losses. Estimation of maize harvest and post-harvest losses in Zimbabwe. Field test report

Abstract: In the framework of the Global strategy to improve agriculture and rural statistics (GSARS), FAO provided technical assistance to Zimbabwe on the measurement of harvest and post-harvest losses through sample surveys. The technical assistance was provided in the form of a pilot study on estimating harvest and post-harvest losses for major crops in the Makonde district in the communal and A1 farming sectors. The survey focused on maize and sorghum and included the measurement of on-farm losses. The survey captured losses through interviews of farmers as well as through physical measurements. The number of usable data points for sorghum were too few to provide reliable production and loss estimates, hence the results presented in this report mostly refer to maize. The results show that 5.2 percent of grain is lost at harvest and 3.8 percent lost at drying. The comparison of the loss estimates according to the measurement method used shows mixed results; in A1 farming sectors, farmers’ own loss estimates tend to be lower than physical measurement, while the opposite is evidenced in the communal sector (except for drying). Timely harvesting was used by most farmers to limit losses, followed by stooking when harvesting and the use of chemicals to protect crops from pest infestations during storage. Keywords: Post-harvest losses, Zimbabwe, Makonde, Grains

Lead authoring unit/office: Statistics Division (ESS)

Training course on Agricultural Integrated Survey (AGRIS) (Module 0/3) - Training material

Abstract: Prepared by the Global Strategy to improve agricultural and rural statistics (GSARS), this AGRIS training aims to provide enumerators with the skills and knowledge necessary to successfully collect high quality AGRIS data. This training plan covers the training essentials (timing, the training team, physical and technical requirements and ways to keep enumerators engaged), the components of training (elements common to all training sessions and those that vary depending on the AGRIS module(s) being presented), and the training schedule. 

Lead authoring unit/office: Statistics Division (ESS)

Training course on Agricultural Integrated Survey (AGRIS) (Module 1/3) - Introduction

Abstract: Prepared by the Global Strategy to improve agricultural and rural statistics (GSARS), this AGRIS training aims to provide enumerators with the skills and knowledge necessary to successfully collect high quality AGRIS data. At the end of this training unit, you will have: an introduction to your fellow enumerators and supervisor; the beginning of a network of resource people to support you during data collection; and an understanding of the importance of your role as an enumerator in ensuring the quality of the data collected.

Lead authoring unit/office: Statistics Division (ESS)

Training course on the Agricultural Integrated Survey (AGRIS) (Module 2/3) - Economy module

Abstract: Prepared by the Global Strategy to improve agricultural and rural statistics (GSARS), this AGRIS training aims to provide enumerators with the skills and knowledge necessary to successfully collect high quality AGRIS data. At the end of this training unit, you will have: knowledge of the Economy (ECO) Module questionnaire’s subject matter; practice in the administration of the ECO Module questionnaire; comprehension of the ECO Module survey methodology. You will also: reinforce your hands-on familiarity with the survey Computer assisted personal interview (CAPI) application for AGRIS; test the usability of the CAPI application for the Core module.

Lead authoring unit/office: Statistics Division (ESS)

Training course on the Agricultural Integrated Survey (AGRIS) (Module 3/3) - Core module

Abstract: Prepared by the Global Strategy to improve agricultural and rural statistics (GSARS), this AGRIS training aims to provide enumerators with the skills and knowledge necessary to successfully collect high quality AGRIS data. At the end of this training unit, you will have: knowledge of the Core Module questionnaire’s subject matter; practice in the administration of the Core Module questionnaire; comprehension of the Core Module survey methodology. You will also: reinforce your hands-on familiarity with the survey Computer assisted personal interview (CAPI) application for AGRIS; test the usability of the CAPI application for the Core module.  

Lead authoring unit/office: Statistics Division (ESS)

Guidelines on improving and using administrative data in agricultural statistics

Abstract: Developed by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), the ultimate goal of this document is to provide operational guidance to developing countries on how to set up an effective Administrative Data System for Agricultural Statistics (ADSAS), as well as on the improvement, use and integration of administrative data in the national statistical system. The concept of ADSAS refers to the set of all administrative institutions producing administrative agricultural data that may be used for the purposes of agricultural statistics and providing them to the national institution in charge of agricultural statistics for official use and publication.

Lead authoring unit/office: Statistics Division (ESS)

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AGRIS Handbook on the Agricultural Integrated Survey

Abstract: Developed by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), the AGRIS handbook presents the rationale of the system, focusing on the new needs and challenges in surveying farms in the 21st century. In this handbook, the link with SDGs is acknowledged, as the proposed AGRIS Generic Questionnaires will generate basic data for monitoring directly four SDG indicators and provide essential information for another 15 SDG indicators.

Lead authoring unit/office: Statistics Division (ESS)

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Guidelines on data collection for national statistics on forest products

Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), these Guidelines on data collection for national statistics on forest products aim to present and discuss best practices with respect to the collection, compilation and dissemination of statistics on forest products statistics.

Lead authoring unit/office: Statistics Division (ESS)

Guidelines for the incorporation of a woodfuel supplementary module into existing household surveys in developing countries

Abstract: Prepared by the Global Strategy to Improve Agricultural and Rural Statistics (GSARS), the purpose of these Guidelines is to develop a tool named the Woodfuel Supplementary Module (WSM), to be incorporated in existing national household surveys. The ultimate goal is to enable developing countries to gather accurate data on woodfuel production and consumption, the sustainability of its production, and related socio-economic and health outcomes.

Lead authoring unit/office: Statistics Division (ESS)

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