Data collection and analysis tools for food security and nutrition
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et al.,
2020). Cloud computing Cloud computing centralizes resources and services remotely and facilitates their use by multiple users without the need for the users to store the resources or install the services on their individual hard drives. GLOSSARY [ 125 GLOSSARY Committee on World Food Security (CFS) The Committee on World Food Security (CFS) is the foremost inclusive international and intergovernmental platform for all stakeholders to work together to ensure food security and nutrition for all. The Committee reports to the UN General Assembly through the Economic and Social Council (ECOSOC) and to the FAO Conference (FAO, n.db). Crowdsensing (or community sensing) Paradigm in which a community leverages devices with sensing and computing capabilities to collectively share data and extract information to measure and map phenomena of common interest (Kraft et al., 2020). Crowdsensing differs from the paradigm of personal sensing, where, in the latter, the phenomena that are monitored belong to an individual user. Crowdsensing is considered to apply to scenarios where the phenomena of interest cannot be easily measured by a single user or device (Ganti, Ye and Lei, 2011). Crowdsourcing Practice of engaging a group of people (i.e., a "crowd"), usually via the internet, to assist in collecting information, ideas, opinions, or other resource for a common goal, such as problem solving, innovation, etc. Data Any set of codified symbols representing units of information regarding specific aspects of the world that can be captured or generated, recorded, stored and transmitted in analogue or digital form. Data analysis tool A set of formal rules used to guide the processing of available data, aimed at obtaining analytic results for a specific purpose or research question. Data curation Active and ongoing management of data to provide an increased number of data sources, to facilitate data discovery and maintain quality for reutilization over time. Data ecosystem An environment in which several actors and entities interact to provide, produce, exchange and consume data. Data ecosystems offer a setting to facilitate the creation, management and sustainability of data sharing initiatives, among others. Data governance Cross-functional framework for managing data as a strategic enterprise asset. In doing so, data governance specifies decision rights and accountabilities for an organization’s decision-making about its data. Furthermore, data governance formalizes data policies, standards and procedures and monitors compliance. Data sovereignty Notion to describe data management that considers the local laws, practices and customs in which the data is based. Decision-support system (DSS) Software-based system that gathers and analyses data from a variety of sources in order to facilitate the decision-making process for management, operations, planning, or optimal solution path recommendation. Digital twin Virtual representation that serves as the real-time digital counterpart of a physical object or system and that helps in decision-making. DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION 126 ] Food Insecurity Experience Scale (FIES) The food insecurity measurement system used as the basis to compute SDG Indicator 2.1.2, The prevalence of moderate or severe food insecurity in the population, based on the Food Insecurity Experience Scale (FIES). FIES is an innovative, experience-based tool aiming to measure access to food at the level of individuals or households. It focuses on self-reported, food- related behaviours and experiences associated with increasing difficulties in accessing food due to resource constraints (FAO, n.dc). Food security “Food security exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (FAO, 2001). Food supply chain An important component of food systems, including all the stages and actors (including private sector businesses), from production, to trade and processing, to retail and consumption, including waste disposal (HLPE, 2017; HLPE, 2020). Food systems All the elements (environment, people, inputs, processes, infrastructures, institutions, etc.) and activities that relate to the production, processing, distribution, preparation and consumption of food, and the output of these activities, including socio-economic and environmental outcomes” (HLPE, 2014). The three constituent elements of food systems are: food supply chains, food environments and consumer behaviour (HLPE, 2017). Geographic Information System (GIS) System with software tools for capturing, storing, analysing and visualizing location-relevant data. Information visualization Process of transforming otherwise abstract data into an interactive, visual form that enables or triggers users to use their mental and visual capabilities, thereby gaining insight and understanding of that data. Interactive Voice Response (IVR) Technology that allows humans to interact with a computer-operated phone system using voice and a dual-tone multi-frequency (DTMF) user interface, allowing them to provide and access information. Internet of Things (IoT) Network of physical objects, which have sensors, software and other technologies to connect and exchange data with other devices and systems over the internet. IoT is often used together with other technologies such as machine learning, analytics, computer vision and robotics. Machine learning Type of artificial intelligence in which computer automation is used to study complex problems through automating solutions. Metadata Data that provides information about other data, intended to help users find relevant information and discover resources. To be effective, metadata should be compiled and published according to appropriate metadata standards, which exist for different disciplines. Microdata Data on the characteristics of members of a population, such as individuals, households or establishments, collected by a census, survey or experiment. [ 127 GLOSSARY Online social media User-generated information, opinions, video, audio and multimedia that are shared and discussed over digital networks. Open data (open-access data) Data that can be freely used, modified and shared by anyone for any purpose. It requires that data fulfil the following four characteristics (Open Definition, n.d.). • Open license or status: The data must be in the public domain or provided under an open license; • Access: The data must be provided as a whole and at no more than a reasonable one-time reproduction cost and should be downloadable via the internet without charge. • Machine readability: The data must be provided in a form readily processable by a computer and where the individual elements of the work can be easily accessed and modified. • Open format: The data must be provided in an open format. An open format is one which places no restrictions, monetary or otherwise, upon its use and can be fully processed with at least one free/libre/open-source software tool. Primary data Data that is collected firsthand; through research, experiments, self- administered surveys, interviews, field observations, etc. Right to food The right of every individual, alone or in community with others, to have physical and economic access at all times to sufficient, adequate and culturally acceptable food that is produced and consumed sustainably, preserving access to food for future generations (de Schutter, 2014). Semantic web Semantic web technologies enable the creation of web-based data stores, the construction of vocabularies and ontologies, and the writing of rules to process the data. At the top of the Semantic web stack is inference, which is reasoning about data-use rules. Sensors A sensor is a device that measures a physical or chemical feature. Sensors include but are not limited to: standard sensors (such as for soil moisture or for tracking animals), weather stations and remote sensing (e.g., via satellite technology). Digital images or video (RGB or hyperspectral) are increasingly used to capture reality. These sensors can be fixed or mobile (on tractors, robots, drones, etc). The development of nano-computers (e.g. Raspberry) and microcontrollers (e.g. Arduino) has facilitated and popularised the use of these sensors, making them accessible to a wide population. Sensors are commonly used in IoT applications. Social gradient A phenomenon that describes a link between health and socioeconomic status in which health outcomes decline as socioeconomic status declines (WHO, 2013). Whereby individuals in lower socioeconomic positions have worse health, and often a lower life expectancy, compared to those in higher socioeconomic positions (WHO, 2013). Stability (as a dimension of food security) Having the ability to ensure food security in the event of sudden shocks (e.g. an economic, health, conflict or climate crisis) or cyclical events (e.g. seasonal food insecurity) (FAO, 2006). DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION 128 ] Sustainability (as a dimension of food security) Food system practices that contribute to long-term regeneration of natural, social and economic systems, ensuring the food needs of the present generations are met without compromising the food needs of future generations (FAO, 2018). System integration and aggregation Different systems can be brought together so that they connect or link to each other, share and exchange data or information (for instance, through Application Programming Interfaces, or APIs). Consequently, it is possible that systems can gather data from other systems (i.e., other data sources) and perform various operations on these data from multiple data sources, such as data fusion, analysis, summarizing, etc. Ubiquitous computing Concept where computing is made to appear or occur anytime and everywhere. Ubiquitous computing has become widespread, especially through mobile computing, where end-users carry their devices (such as mobile phones, including smartphones) and use it them in everyday activities and contexts. Mobile computing applications can be based on SMS, USSD (Unstructured Supplementary Service Data), chatbots, Computer-Assisted Telephone Interviewing (CATI), and other forms of applications (for instance ODK-based technologies such as CommCare, TaroWorks, etc). Utilization (as a dimension of food security) Having an adequate diet, clean water, sanitation and health care to reach a state of nutritional well-being where all physiological needs are met (FAO, 2006). Virtual reality and augmented reality Computer-generated simulated environment with objects and scenes that seem real, making the user feel immersed in their surroundings. Augmented reality (AR) is an interactive experience of a real-world environment where the objects in the real world are enhanced by computer-generated information and features. [ 129 ANNEXES ANNEX TABLE 1. EXAMPLES OF EXISTING FSN DATA-RELATED INITIATIVES (INCLUDING DATABASES, REPOSITORIES, DATA SYSTEMS AND ANALYSIS TOOLS), ORGANIZED BY DIMENSION OF FOOD SECURITY AND NUTRITION Level in the conceptual framework Dimensions of food security and nutrition Availability Stability Sustainability Access Utilization Agency Macro Natural resource base ( FAOSTAT – Land use and land cover ; FAOSTAT – Soil ; FAOSTAT - Pesticides ; FAOSTAT - Fertilizers ; also here ; AQUASTAT ; FISHSTAT ) Earth Observation Google Earth SEPAL International food commodity stocks and trade (FAOSTAT – Trade) Global/ regional food commodity stocks and reserves (e.g., AMIS) Weather and other risk trends and predictions ( Global Climate Risk Index ; Temperature changes ( FAOSTAT – climate ) Greenhouse gas emissions ( FAOSTAT – Emissions ; also here and here ) International food commodity prices ( FAO Food Price Index , AMIS ;) Food composition data ( INFOODS ) Food safety data ( CODEX ) Meso Domestic food availability FAOSTAT – FBS/ SUA FAOSTAT – Food & Diets FAOSTAT - Trade FAOSTAT - Production National food stocks and reserves ( FAOSTAT - FBS ) National food price indices ( ILOSTAT , Premise ) Water and sanitation ( UNICEF- WASH ) Data on market concentration (for agricultural inputs, retail, etc.) at national and global levels Micro Local food systems (Agricultural censuses and surveys 50x2030 , AGRISurvey 50x2030 , LSMS-ISA Early warning information systems ( FAO – GIEWS ; FEWSNET ) Integrated food security phase classification analyses ( IPC Analyses ) Local food prices ( WFP Data Viz , FPMA ) Household incomes and consumption patterns ( HIES , LSMS ) ( FIES ) Food insecurity experience scale (FIES) Household living conditions ( LSMS , MICS , DHS ) Household water access Food insecurity assessment surveys ( FIES, CFSVA, etc. ) Women’s Index in Agriculture (WEAI) (CGIAR), and other women’s empowerment indices; Rural Livelihoods Information Systems (RuLIS) Individual (Outcomes) Dietary intake/diet quality; malnutrition prevalence and related health outcomes (MICS; DHS ; National health and nutrition surveys, etc.) Abbreviations: MICS=Multiple Indicator Cluster Survey; DHS= Demographic and Health Surveys; AMIS= Agricultural Market Information System: HIES=Household Income and Expenditure Surveys N.A.=Not Applicable DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION 130 ] ANNEX TABLE 2. SUMMAR Y OF RISK S, A SSOCIA TED DIGIT AL TECHNOL OGIES, KEY S TAKEHOLDERS AND RISK MITIG ATION MEA SURES Risk Description of risk Digit al technol ogies as sociat ed with the risk Ke y s tak ehol der s (Aff ect ed and act or s) Dat a cy cl e st age(s) Risk mitigation measur e(s) Ethic al, dat a pr ot ection, trus t, jus tic e, identity theft and other violation of priv acy is sues Inc onsider at e digit alization ma y cr eat e c onflict with human right s and jus tic e in F SN AI, r obotic s, etc User s of digit al aut omation solutions for F SN Farmer s, F SN cus tomer s, F SN consumer s aff ect ed by the digit al aut omation (whether the y ar e user s of the digit al aut omation solutions or not FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide digit al aut omation) Go vernment and policy mak er s (e.g., appr opriat e r egulation) Civil society or ganisations Special int er es t gr oup as sociations (e.g., f armer s’ as sociations, consumer s’ as sociations) All st ages Formulation and enactment of appr opriat e laws, r egulations and policies (e.g., ethic s, c onsent, priv acy , dat a pr ot ection, o wner ship, fair c ompetition, and c opyright) Inclusion of the s tak ehol der s in the needs anal ysis, design, pil oting and impl ement ation of digit al aut omation Adoption of digit al solutions that ar e tr anspar ent and giv e user s fr eedom of choic e. F or machine l earning applic ations, al gorithm de vel oper s, model buil der s and domain e xpert s c an pr ovide explanations (f or the applic ation’ s decisions) so that the y c an be included in the applic ation’ s kno wl edge base and output Buil ding the c apacity of user s. F or ins tanc e: pr oviding user s with inf ormation; educ ating user s about their digit al right s and responsibilities; ensuring that user s ar e tr ained or support ed t o handl e r el ev ant t echnol ogies; cr eating an enabling envir onment for user s t o ac ces s the r equir ed digit al infr as tructur e and digit al resour ces; et c Po we r as ymmetry , inequit abl e ac ces s t o dat a, negativ e e xclusiv e int ell ectual pr operty r egimes, unethic al tr acking and t ar geting, and mark et dominanc e attribut abl e to F SN dat a “o wner ship” , dat a priv acy and contr ol Big dat a, AI, cl oud computing, etc User s of digit al applic ations that coll ect or pr oc es s dat a f or F SN Farmer s, F SN cus tomer s, F SN consumer s fr om or about whom dat a ar e c oll ect ed or pr oc es sed (whether the y ar e user s of the digit al applic ations or not) FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide digit al applic ations f or dat a c oll ection or pr oc es sing, big dat a, cl oud computing, et c.) Go vernment and policy mak er s (e.g., appr opriat e r egulation) Civil or ganizations Special int er es t gr oup as sociations (e.g., f armer s’ as sociations, consumer s’ as sociations) All st ages Formulation and enactment of appr opriat e laws, r egulations and policies (e.g., ethic s, c onsent, priv acy , dat a pr ot ection, o wner ship, fair c ompetition, and c opyright) Adopting r esponsibl e appr oaches t o r esear ch and inno vation Pr ot ection of pot entiall y vulner abl e segment s of F SN st ak ehol der s in the society Inclusion of the s tak ehol der s in the needs anal ysis, design, pil oting and impl ement ation of digit al t echnol ogies Considering a policy -driv en s tr at egic o vervie w of the needs and priorities of F SN Anticipating and addr es sing the c onc erns and needs as sociat ed with F SN dat a “o wner ship” , dat a priv acy and c ontr ol Taking int o ac count indir ect and l ong-t erm eff ect s of the digit al technol ogies Cr eating spac es f or F SN s tak ehol der s t o r efl ect on ho w digit alization will aff ect e xis ting F SN inno vation s ys tems [ 131 ANNEXES Risk Description of risk Digit al technol ogies as sociat ed with the risk Ke y s tak ehol der s (Aff ect ed and act or s) Dat a cy cl e st age(s) Risk mitigation measur e(s) Quality of dat a Subjectivity during dat a c oll ection Online so - cial media, cr ow dsour c- ing, mobil e computing, etc FSN dat a anal ys ts and r esear cher s FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide FSN applic ations based on online social media, cr ow dsour cing, mobil e computing, et c.) User s of F SN applic ations based on online social media, cr ow dsour cing, mobil e c omputing, et c Farmer s, F SN cus tomer s, F SN consumer s fr om or about whom dat a ar e c oll ect ed or pr oc es sed (whether the y ar e user s of F SN applic ations based on online social media, cr ow dsour cing, mobil e c omputing, et c. or not) Coll ect, retrie ve and manage dat a Compl ementing with other digit al t echnol ogies or methods that ar e mor e objectiv e Real-w orl d setting chall enges (dis tr action, w eather , et c.) IoT , sensor s, robotic s, cr ow dsour c- ing, mobil e computing, etc FSN dat a anal ys ts and r esear cher s FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide F SN applic ations based on IoT , sensor s, robot s, cr ow dsour cing, mobil e computing, et c.) User s of F SN applic ations based on IoT , sensor s, r obot s, cr ow dsour cing, mobil e c omputing, et c Farmer s, F SN cus tomer s, F SN consumer s fr om or about whom dat a ar e c oll ect ed or pr oc es sed (whether the y ar e user s of F SN applic ations based on IoT , sensor s, r obot s, cr ow dsour cing, mobil e c omputing, et c. or not) Coll ect, retrie ve and manage dat a Cons tant monit oring, t es ting, c alibr ation and enhanc ement of digit al t echnol ogies depl oy ed in r eal-w orl d settings Additionall y using other digit al t echnol ogies or methods t o compl ement F SN dat a obt ained fr om, or t ask s undert ak en by digit al t echnol ogies depl oy ed in r eal-w orl d settings DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION 132 ] Risk Description of risk Digit al technol ogies as sociat ed with the risk Ke y s tak ehol der s (Aff ect ed and act or s) Dat a cy cl e st age(s) Risk mitigation measur e(s) Quality of dat a Ov er -r elianc e on digit al technol ogies that coll ect or pr oc es s onl y numeric dat a ma y do wnpla y import ant nuanc es that c an be gl eaned fr om qualit ativ e dat a Some mobil e phone- based dat a coll ection applic ations FSN dat a anal ys ts and r esear cher s FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide digit al t echnol ogies f or F SN that coll ect or pr oc es s onl y numeric dat a) User s of digit al t echnol ogies f or F SN that c oll ect or pr oc es s onl y numeric dat a Farmer s, F SN cus tomer s, F SN consumer s fr om or about whom dat a ar e c oll ect ed or pr oc es sed (whether the y ar e user s of digit al t echnol ogies for F SN that c oll ect/pr oc es s onl y numeric dat a or not) All st ages Additionall y using c ompl ement ary digit al t echnol ogies or methods that c an c aptur e or pr oc es s qualit ativ e dat a Poor (and in some in - st anc es lack of) int er op - er ability of dispar at e set s of f ood security and nutrition dat a Big dat a, cl oud computing, IoT FSN dat a anal ys ts, r esear cher s (and user s of F SN applic ations that c oll ect, st or e, cur at e or pr oc es s dat a) FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide FSN applic ations that c oll ect, s tor e, cur at e or pr oc es s dat a) Go vernment and policy mak er s Communities of pr actic e Civil society or ganisations All st ages Supporting eff ort s on s tandar ds and int er oper ability (such as thr ough the use of ont ol ogies) [ 133 ANNEXES Risk Description of risk Digit al technol ogies as sociat ed with the risk Ke y s tak ehol der s (Aff ect ed and act or s) Dat a cy cl e st age(s) Risk mitigation measur e(s) Capacity , equity , sc al - ability and sus tainabili - ty is sues Digit al technol ogies inv ol ve relativ el y high infr as tructur al and human capacity c os ts All ne w and emer ging digit al applic ations for F SN Pot ential and activ e user s of ne w and emer ging digit al applic ations f or F SN FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide ne w and emer ging digit al applic ations f or FSN) FSN dat a anal ys ts and r esear cher s Go vernment and policy mak er s Funding or ganizations Special int er es t gr oup as sociations (e.g., f armer s’ as sociations, consumer s’ as sociations) and communities of pr actic e Civil society or ganisations All st ages Tapping int o c ollabor ations Supporting eff ort s f or : impr oving ac ces s t o and aff or dability of technol ogy ; ensuring int er oper ability of dat a and s ys tems; and de vel oping and impl ementing open sour ce t ools Buil ding and enhancing human c apacity . F or ins tanc e: tr aining in c or e dat a c ompet encies (e.g., dat a anal ysis, inf ormation visualization, int erpr et ation and decision making); educ ating user s t o support the dat a cy cl e pr oc es s; et c Educ at e dat a o wner s and dat a pr oduc er s about priv acy , c onsent, dat a usage, dat a o wner ship and the right s the y hav e Responsibl e digit alisation Sc alability and sus tainability is sues All ne w and emer ging digit al applic ations for F SN Pot ential and activ e user s of ne w and emer ging digit al applic ations f or F SN FSN servic e pr ovider s and busines ses (that design, impl ement or pr ovide ne w and emer ging digit al applic ations f or FSN) FSN dat a anal ys ts and r esear cher s Go vernment and policy mak er s Funding or ganizations Special int er es t gr oup as sociations (e.g., f armer s’ as sociations, consumer s’ as sociations) and communities of pr actic e Civil society or ganisations All st ages Continuall y pr oviding demons tr ations of the benefit s or positiv e result s of using the digit al t echnol ogies Adoption of int er disciplinary appr oaches and int er connect ednes s. Rec ognizing the need f or l earning, f eedback, partner ships, and joint action in multi-s tak ehol der settings DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION 134 ] ANNEX TABLE 3: LIST OF COUNTRIES GROUPED BY DATE OF LAST AGRICULTURAL CENSUS ON RECORD No agricultural census conducted in the last 10 years (2012-2022) No agricultural census conducted in the last 20 years (2002-2022) No agricultural census on record Afghanistan Algeria Andorra Algeria Andorra Cuba Andorra Bahamas Faroe Islands Antigua and Barbuda Bahrain Monaco Bahamas Barbados San Marino Bahrain Bosnia and Herzegovina South Sudan Barbados Brunei Darussalam Tokelau Bosnia and Herzegovina Burundi Turkmenistan Brunei Darussalam Cameroon Ukraine Burundi Central African Republic Maldives Cameroon Chad Central African Republic Cuba Chad Democratic People's Republic of Korea Cuba Democratic Republic of the Congo Democratic People's Republic of Korea Djibouti Democratic Republic of the Congo Dominica Djibouti Dominican Republic Dominica Ecuador Dominican Republic Eritrea Ecuador Faroe Islands El Salvador Guyana Eritrea Honduras Ethiopia Iraq Faroe Islands Kenya Guatemala Kuwait Guyana Liberia Haiti Libya Honduras Mauritania Iraq Monaco Jamaica Nigeria Kazakhstan Papua New Guinea Kenya Rwanda [ 135 ANNEXES Kuwait Saint Vincent and the Grenadines Kyrgyzstan San Marino Lebanon Sao Tome and Principe Liberia Sierra Leone Libya Singapore Malawi Solomon Islands Malaysia Somalia Mali South Sudan Mauritania Sudan Monaco Tokelau Mongolia Türkiye Montenegro Turkmenistan Mozambique Ukraine Myanmar Uzbekistan Nicaragua Zambia Niger Zimbabwe Nigeria Maldives North Macedonia Angola* Pakistan Benin* Panama Guinea-Bissau* Papua New Guinea Marshall Islands# Paraguay Qatar* Republic of Moldova Saint Kitts and Nevis* Rwanda Saint Lucia Saint Vincent and the Grenadines San Marino Sao Tome and Principe Seychelles Sierra Leone Singapore Solomon Islands Somalia South Sudan Sudan DATA COLLECTION AND ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION 136 ] Suriname Syrian Arab Republic Tokelau Trinidad and Tobago Türkiye Turkmenistan Uganda Ukraine United Arab Emirates Uruguay Uzbekistan Venezuela (Bolivarian Republic of) Yemen Zambia Zimbabwe Maldives Angola* Benin* Burkina Faso* Comoros* Guinea-Bissau* Madagascar* Marshall Islands# Qatar* Saint Kitts and Nevis* 92 55 10 *Countries with no census during the last 10 or 20 years but with ongoing censuses during the current WCA 2020 round # Agricultural module in Population and Housing Census (AM in PHC) ongoing Source: FAO ESS, Agricultural Census Team [ 137 ANNEXES ANNEX TABLE 4: CARE PRINCIPLES FOR INDIGENOUS DATA GOVERNANCE Collective benefits C1 For inclusive development and innovation C2 For improved governance and citizen engagement C3 For equitable outcomes Authority to control A1 Recognizing rights and interests A2 Data for governance A3 Governance of data Responsibility R1 For positive relationships R2 For expanding capability and capacity R3 For Indigenous languages and worldviews Ethics E1 For minimizing harm and maximizing benefit E2 For justice E3 For future use Source: Research Data Alliance International Indigenous Data Sovereignty Interest Group, 2019 https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab221ce848fb2/1571419335217/CARE+Principles_ One+Pagers+FINAL_Oct_17_2019.pdf SÉCURITÉ ALIMENTAIRE ET NUTRITION: ÉNONCÉ D’UNE VISION GLOBALE À L’HORIZON 2030 i ] Food is a fundamental human right, yet too many people in the world do not have secure access to the food they need. High-quality data and their accurate analysis are essential to design, monitor and evaluate effective food security and nutrition (FSN) policies. Data are also fundamental to ensure accountability of government policies and to monitor their implementation and impact. The data revolution, driven by new technologies, is increasing exponentially the volume and types of data available. This provides great opportunities for informing and transforming food systems, but also presents new challenges which, if not properly tackled, can deepen inequalities. This report presents the inherent complexity and multiple dimensions of FSN data collection, analysis and use – including economic, social, institutional, political, legal and technical dimensions; the types of users involved and the numerous and diverse purposes for which data may be used in food security and nutrition efforts, as well as the extant challenges. The report also advances actionable recommendations to enhance the contribution that data can make to ensuring food security and nutrition for all. CC1865EN Yüklə 5,02 Kb. Dostları ilə paylaş:
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