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: Samples used in most surveys are either not large enough to guarantee reliable direct estimates for all relevant sub-populations, or do not cover all possible disaggregation domains. After having described a holistic strategy for producing disaggregated estimates of Sustainable Development Goal (SDG) indicators, this paper discusses alternative sampling and estimation methods that can be applied when sample surveys are the primary data source. In particular, the paper focuses on strategies that can be implemented at different stages of the statistical production process. At the design stage, the paper describes a series of sampling approaches that ensure a “sufficient” sampling size for each disaggregation domain. In this context, the article highlights the main limitations of traditional sampling approaches and shows how ad-hoc techniques could overcome some of their key constraints. At the analysis stage, it discusses an indirect model-assisted estimation approach to integrate data from independent surveys and censuses, eliminating costs deriving from redesigning data collection instruments, and ensuring a greater accuracy of the final disaggregated estimates. A case study applying the abovementioned method on the production of disaggregated estimates of SDG Indicator 2.1.2 (Prevalence of Moderate and Severe Food Insecurity) is then presented along with its main results.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: With the adoption of the 2030 Agenda for Sustainable Development, the production of high quality disaggregated estimates of Sustainable Development Goal (SDG) indicators has taken greater significance. In this context, sample surveys are characterized by samples that are either not large enough to guarantee reliable direct estimates for all relevant sub-populations, or that do not cover all possible disaggregation domains. To address these issues, indirect estimation approaches such as small area estimation (SAE) techniques can be adopted. The literature on the use of SAE in official statistics is broad and in continuous progress, yet the number of case studies on SAE methods applied to SDG indicators can still be expanded. After a brief review of the main SAE approaches available along with their principal fields of application, the present paper aims contributing to fill this gap by presenting a case study on SAE to produce disaggregated estimates of SDG Indicator 2.3.1, measuring average labour productivity of small-scale food producers. The discussed empirical exercise is based on a Fay-Herriot area-level SAE model, integrating survey data with area-level auxiliary information retrieved from multiple trustworthy geospatial information systems. Area-level SAE models have the advantage of being easy to implement and do not require accessing survey microdata and unit-level auxiliary information. These characteristics, jointly with the great potentials offered by modern geospatial information systems, offer the possibility of producing good quality disaggregated estimates of SDG indicators at high frequency and granular disaggregation level.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: This document is part of the FAO Statistics working paper series and part of the series of operational guidelines of the FAO Survey Team providing practical cost effective orientations to countries on agricultural surveys from the conception and implementation to data dissemination. The present document is focused on operational clarifications on the definitions of agricultural holdings and operational guidance for establishing lists of agricultural holdings for agricultural surveys.
Lead authoring unit/office: Statistics Division (ESS)
Abstract: FAO Statistics Working Paper 21/22 is part of the methodological works of the FAO’s Survey Team to provide operational guidance on selected areas of agricultural survey methodology with an overall objective to promote cost effective practices in agricultural surveys implementation.
Lead authoring unit/office: Statistics Division (ESS)
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: 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)
Abstract: This technical report provides findings of field test conducted in identified states / districts / municipalities / study area in Mexico on the basis of sampling methodology for estimation of postharvest losses of horticultural crops (fruits and vegetables) developed by the team led by Dr. Tauqueer Ahmad, Head, Division of Sample Surveys, Indian Agricultural Statistics Research Institute, Institute of Indian Council of Agricultural Research (ICAR-IASRI), New Delhi, India. The Technical Report entitled “Findings from the field test conducted on estimating post-harvest losses of fruits and vegetables in Mexico” contains details of findings of the developed methodology implemented in Mexico, including challenges encountered and lessons learnt. It is expected that this report will help the users from different countries in designing surveys for measurement of post-harvest losses of horticultural crops (fruits and vegetables).
Lead authoring unit/office: Statistics Division (ESS)
Abstract: This technical report provides findings of a field test conducted in identified districts / study area in Zambia on the basis of sampling methodology for estimation of harvest and post-harvest losses of animal products (meat and milk) developed by the team led by Dr. Tauqueer Ahmad, Head, Division of Sample Surveys, Indian Agricultural Statistics Research Institute, Institute of Indian Council of Agricultural Research (ICAR-IASRI), New Delhi, India. The Technical Report entitled “Findings from the field test conducted on estimating harvest and post-harvest losses in Zambia. Meat and milk” contains details of findings of the developed methodology implemented in Zambia including challenges encountered and lessons learnt. It is expected that this report will help the users from different countries in designing surveys for measurement of harvest and post-harvest losses of animal products (meat and milk).
Lead authoring unit/office: Statistics Division (ESS)
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)
