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: Malnutrition in all its forms (undernutrition, micronutrient deficiency, overweight and obesity) is a major global challenge, and improving nutrition is a key priority for global development, as recognized in the UN Decade of Action on Nutrition (2016–2025) and the 2030 Agenda for Sustainable Development. In this context, ensuring availability, physical accessibility and affordability of healthy and nutritious food at territorial level is crucial to ensure the achievement of the Sustainable Development Goals (SDGs). In many developing countries, territorial markets are key retail outlets for fruits and vegetables, but also for animal source foods and staple foods. Besides the relevance, data concerning the availability of the different food groups and characteristics of food retailers and consumers in territorial markets are seldom considered in national data collection systems. This publication presents a structured methodology and a series of guidelines for mapping territorial markets, as developed by the Food and Agriculture Organization of the United Nations (FAO), along with representatives of producer organizations and academics.
Abstract: This report presents the results of a collaboration between FAO and the Oxford Poverty and Human Development Initiative (OPHI), at the University of Oxford. The first part of the report proposes a framework for measuring multidimensional poverty in rural areas and describes the motivation for the Rural Multidimensional Poverty Index (R-MPI) proposal, which departs from the established global Multidimensional Poverty Index (global MPI), first designed in 2010 as an international measure of acute poverty covering over 100 developing countries by adding modifications in the dimensions and embedded indicators. The second part of this report presents an empirical test of the proposed R-MPI, using data from four household surveys conducted in Ethiopia, Malawi, the Niger, and Nigeria which are harmonized within the Rural Livelihoods Information System (RuLIS).
Lead authoring unit/office: Statistics Division (ESS)
Abstract: At the heart of this product is the introduction of Shiny RIMA and its advantages as a tool for the monitoring and measurement of resilience. Particularly, this guidance note focuses on today’s relevance of the resilience index measurement and analysis (RIMA), adopted by FAO in 2008, and how Shiny RIMA facilitates resilience analysis. For policymakers and especially for households in risk-prone environments, evaluating resilience and changes over time is deeply meaningful. This document, therefore, aims at shedding light on the improvements that Shiny RIMA can bring to resilience analysis.
Lead authoring unit/office: FAO
Abstract: While diversified aquaculture could reduce both biological and financial risks, the private sector may lack incentives to diversify the species composition of aquaculture production because developing or adopting new species tends to be costly and risky. Conversely, concentrating on the most efficient species can benefit from economies of scale in both production and marketing. With ever-growing concerns over climate change, disease outbreaks, market fluctuations and other uncertainties, species diversification has become an increasingly prominent strategy for sustainable aquaculture development. Policy and planning on species diversification require a holistic, sector-wide perspective to assess the overall prospect of individually promising species that may not be entirely successful when competing for limited resources and markets. The historical experiences of species diversification in global aquaculture can provide guidance for the assessment. This paper develops a benchmarking system to examine species diversification patterns in around 200 countries for three decades to generate information and insights in support of evidence-based policy and planning in aquaculture development. The system uses “effective number of species” (ENS) as a diversity measure that is essentially equivalent to, yet more intuitive than, the widely used Shannon Index. A statistical model is established to estimate a benchmark ENS for each country and construct a benchmarking species diversification index (BSDI) to compare a country’s species diversification with global experiences. Key results are presented and discussed in the main text; and more comprehensive results are documented in Appendix II. The benchmarking system can be used in foresight analyses to help design or refine future production targets (including species composition) in policy and planning for aquaculture development; an example is provided in Appendix I to help practitioners better understand and utilize the system.
Lead authoring unit/office: Fisheries Division (NFI)
Abstract: Forests and the forest sector are important sources of employment, livelihoods and incomes for millions across the globe, particularly in rural areas. They provide jobs in a wide range of activities related to sustainable forest management, the provision and production of timber and other wood and non-wood forest products, the protection of forest ecosystems and biodiversity, and safeguarding the benefits of forests. Despite the relevance of forests for employment and income generation, limited quantitative information is currently available on the subject. This lack of data makes it challenging to quantify the number of people employed in the forest sector, and their contribution to global employment. Notwithstanding, estimating forest-related employment involves methodological challenges such as the standardization and comparability of data collected, as well as the availability of reliable and detailed employment statistics. This study employs a new method to fill the gaps of missing data points in order to provide sound total employment estimates in the forest sector on a global scale.
Lead authoring unit/office: Forestry Division (NFO)
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: 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: Remote sensing offers a scalable and low cost solution for the production of large-scale crop maps, which can be used to extract relevant crop statistics. However, despite considerable advances in the new generation of satellite sensors and the advent of cloud computing, the use of remote sensing for the production of accurate crop maps and statistics remain dependant on the availability of ground truth data. Such data are necessary for the training of supervised classification algorithms and for the validation of the results. Unfortunately, in-situ data of adequate quality for producing crop statistics are seldom available in many countries. In this paper we compare the performance of two supervised classifiers, the Random Forest (RF) and the Dynamic Time Warping (DTW), the former being a data intensive algorithm and the latter a more data frugal one, in extracting accurate crop type maps from EO and in-situ data. The two classifiers are trained several times using datasets which contain in turn an increasing number in-situ samples gathered in the Kashkadarya region of Uzbekistan in 2018. We finally compare the accuracy of the maps produced by the RF and the DTW classifiers with respect to the different number of training data used. Results show that when using only 5 and 10 training samples per each crop class, the DTW reaches a higher Overall Accuracy than the RF. Only when using five times more training samples, the RF starts to perform slightly better that the DTW. We conclude that the DTW can be used to map crop types using EO data in countries where limited in/situ data are available. We also highlight the critical importance in the choice of the location of the in-situ data and its thematic reliability for the accuracy of the final map, especially when using the DTW.
Lead authoring unit/office: Office of Chief Statistician (OCS)
Abstract: Malnutrition is pervasive in both low- and middle-income countries. Yet, there is a scarcity of food intake data collected at the individual level to describe diets, determine the prevalence of inadequate nutrient consumption in populations, and shed light on how diets contribute to the malnutrition burden. In the absence of nationally representative individual-level food intake surveys, particularly in low- and middle-income countries, dietary data collected in household consumption and expenditure surveys (HCES) are being used as a second-best option to make inferences on the food and nutrient consumption of populations. This paper proposes an innovative approach to estimate variability in nutrient intake that uses food data collected in HCES to estimate the prevalence of nutrient inadequacy in a country. This method builds on the approach developed by FAO to estimate the indicator of inequality used in the Prevalence of Undernourishment used in the global monitoring of food insecurity.
Lead authoring unit/office: Statistics Division (ESS)
