Global Forum on Food Security and Nutrition (FSN Forum)

Congratulations on a timely and thorough analysis of the challenges, and suggestions of possible solutions, to “Data collection and analysis tools for food security and nutrition “. Below, please find three overarching comments. 

The framework and systems thinking 

Considering the conceptual framework is built on ecological models, it might work well as a communication tool, but in order to move on to decision-making, building systems models that could be dynamic hypothesis of how the determinants at the different levels are interconnected and driving food security and nutrition would be important. Furthermore, such models could be used to simulate different scenarios using the national data which could be particularly useful in step 3 and 4 of the data driven decision making cycle,  

Building the infrastructure and governance of the data 

Based on the overview of existing initiatives on multi-country sources of data (table 1) there seem to be a potential for FAO/WHO/the UN system to take a leading role in building an infrastructure that is accessible to all, ensures the relevance of the data collected with reference to the conceptual framework/systems models and avoids duplication of data collection. 

Setting up infrastructures that enables researchers to use “big data” to address the relationship between the food systems, the consumer choices, nutrition and health across countries, and socio-demographic groups of the population is important but challenging to get support for. A recent attempt to get the proposal of the Food, Nutrition and Health Research Infrastructure on the ESFRI Roadmap in Europe was unsuccessful (https://fnhri.eu/), but might be relevant to draw on. 

Best-practice cases with cost estimates 

The complexity of, and the resources and capacities, required to deliver the data as outlined in the framework might make it look unrealistic to achieve this. Although there is probably no country currently doing this, there might be best practice cases that can be shared as examples and also be used to indicate the cost of collecting, analysing and using the data to provide the output that the report is envisioning. 

Kind regards Professor Nanna Lien,

Public Health Nutrition, Department of Nutrition, University of Oslo, Norway

Madame, Monsieur,

La France remercie les membres de l'équipe du projet et du Comité directeur du HLPE pour cette consultation électronique sur le projet version 0 de rapport.

Veuillez trouver en pièce jointe et dans le cadre ci-dessous, la position de la France sur le projet de version 0 du rapport du HLPE.

Sincères salutations,

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La France remercie le HLPE pour ce projet version zéro du rapport sur les outils de collecte et d’analyse de données pour la sécurité alimentaire et la nutrition (SAN), qui présente une approche équilibrée entre, d’une part, les nombreuses opportunités offertes par les progrès dans la collecte et l’analyse des données pour améliorer l’efficacité des politiques de sécurité alimentaire et de nutrition et, d’autre part, les risques, notamment éthiques, juridiques mais aussi les biais qui peuvent fausser l’information qui sous-tend les programmes et politiques publics.La collecte régulière de données statistiques sur l’agriculture et l’alimentation est importante pour évaluer l’efficacité des politiques et des programmes en faveur de la sécurité alimentaire et la nutrition et pour nourrir les réflexions des décideurs.

Le projet relève des points importants, tels que :

• les contraintes financières pesant sur la majorité des pays à faible revenu et à revenu intermédiaire de la tranche inférieure pour la production et le traitement de données agricoles et alimentaires ;
• le besoin de formation en capital humain pour permettre à tous les acteurs de bénéficier des opportunités offertes par les technologies émergentes ;
• le risque que les avancées technologiques accentuent la fracture sociale, notamment au détriment de ceux qui disposent d’un faible accès et/ou habileté numériques ;
• les problèmes éthiques soulevés en l’absence de structures de gouvernance solides ;
• le besoin d’engager le plus possible les utilisateurs dans l’élaboration des technologies et des systèmes de collecte et traitement des données ;
• la nécessité de vérifier la robustesse des données et l’absence de biais, et expliciter le cas échéant les limites du système de collecte et de traitement utilisé et l’importance de l’interopérabilité des systèmes.

Sur la définition des priorités à l'utilisation des données

Le Document ne fait pas référence aux recommandations politiques du CSA sur l’ « agroécologie et les autres approches innovantes» adoptées en juin 2021 qui proposent déjà des recommandations en matière d’utilisation des données. (cf. paragraphe 3u)

L’état des lieux proposé par le rapport du HLPE est essentiel et sera très utile pour l’ensemble des parties prenantes pour connaitre les principales bases de données existantes et travailler à l’interopérabilité et à la mise en place de synergies tout en limitant les doublons.

Le rapport souligne que la principale difficulté réside dans la collecte de données de qualité, notamment pour ce qui concerne les populations les plus vulnérables, souvent les moins équipées et les plus éloignées de la prise de décision. Le manque de données rend ces populations souvent « invisibles » ce qui ne facilite pas la remontée d’alerte et l’action préventive en matière de SAN.

La France salue l’importance qui sera donnée à la dimension éthique de la collecte, du traitement et de l’utilisation des données, ainsi que l’attention qui sera accordée à l’ensemble des risques relatifs, entre autres, à la protection des données sensibles, au respect de la vie privée et à la prévention contre les utilisations malveillantes des données.

Si le document met en avant des constats, à ce stade, il ne met pas suffisamment en avant les sauvegardes et les mises en garde contre une utilisation néfaste des données (sous-section 4.4.1. Ethical and data security issues) et ne donne pas d’exemple de pratiques et moyens de s’en prévenir.

Initiatives et tendances existants en matière de collecte et d'analyse des données relatives à la SAN.

Un historique du traitement des données des systèmes alimentaires pourrait être proposé pour mieux comprendre la situation actuelle et le progrès qu’il reste à accomplir, ainsi que les opportunités, les avantages mais aussi les risques et inconvénients liés aux récentes innovations technologiques (en lien avec la numérisation notamment) en matière de collecte de données. La collecte de données massive via les outils numériques peut en effet entrainer certains biais, le rapport pourrait utilement se pencher sur les évolutions en termes de traitement statistique et de méthodologie permettant d’éviter les biais statistiques. 

L’agriculture 4.0, qui est censée permettre la collecte automatique, l’intégration et l’analyse de données provenant des champs, de capteurs ou d’autres sources tierces n’est accessible qu’à une faible part de la population agricole (1%) ; ces données pourraient être mieux documentées. La question de la propriété et de l’utilisation de ces données, et de la transparence des algorithmes de traitement  est un sujet central, notamment pour les agriculteurs, qui doivent pouvoir rester les décisionnaires éclairés sur leurs exploitations.

Dans la sous-section 3.1.1 (insufficient resources for data collection), page 14, la France reconnaît le manque de données sur l’agriculture familiale, qui joue pourtant un rôle déterminant pour assurer la sécurité alimentaire et la nutrition, notamment dans les pays en développement. A ce titre, la France souhaiterait attirer l’attention des auteurs sur l’Observatoire de l’Agriculture du Monde (World Agriculture Watch - https://www.fao.org/world-agriculture-watch/fr/), qui devrait être coté parmi les initiatives existantes sur les données pour la sécurité alimentaire et la nutrition. L’OAM vise à mettre en place un cadre méthodologique harmonisé pour fournir des informations appropriées sur la structure et les performances des exploitations familiales.

Cette section indique également, p. 13, que les données concernant la sécurité sanitaire des aliments sont lacunaires. Si la France tient à souligner le rôle central joué, dans ce domaine, par le Codex Alimentarius, il pourrait être précisé que le Codex ne procède pas en propre à la collecte de données mais repose sur des bases de données externes (concernant les régimes alimentaires, l’exposition à des agents chimiques ou naturellement présents dans les aliments) qui lui sont transmises de manière transparente. L’EFSA ouvre régulièrement l’accès à ses propres bases de données (expositions aux contaminants chimiques), de même que d’autres entités ; il est cependant à noter que peu de données sont transmises par les pays moins développés ou à économie de transition, ce qui peut résulter en une représentativité limitée de certaines données à l’échelle mondiale. Enfin, l’assertion – non démontrée -selon laquelle la fixation de limites inférieures aux seuils résultant de l’évaluation des risques sanitaires peut occasionner des perturbations commerciales occulte une très large part des normes du Codex, qui ne correspondent pas à de telles limites.

Il serait également utile de mieux connaitre les initiatives privées qui existent et les bases de données à l’échelle nationale performantes et déterminantes pour la production comme pour la distribution.

Sur les contraintes en matière de capacités

Il est essentiel que les données statistiques soient accessibles de manière équitable. La question des logiciels d’analyse et de traitement pour disposer de tableaux de bords facilement utilisables est également essentielle.

Sur les données qualitatives, la France rappelle que le risque de biais pour ce type d'enquête est encore plus élevé et des stratégies spécifiques doivent être mises en place pour les contrôler.

Enfin, afin d’assurer que le plus grand nombre de parties puisse tirer profit des bénéfices liés à la collecte et au traitement des données, nous suggérerions que le rapport traite de la question de la disponibilité des données et de leur analyse dans différentes langues.

Sur le rôle des technologies nouvelles et émergentes en matière de collecte et d'analyse des données relatives à la SAN.

L’objectif de ces technologies doit être d’offrir un soutien dans le processus de prise de décision pour l’ensemble les acteurs de la chaîne d’approvisionnement. Le rapport n’aborde pas suffisamment la question de la prise en compte des 3 dimensions de la durabilité (environnemental, social et économique) : il est essentiel que les données et les outils d’aide à la décision visent à améliorer la durabilité des systèmes alimentaires pour relever les défis du changement climatique et de la biodiversité (réduction de l’utilisation des pesticides et des engrais, gestion de la biodiversité, des sols…) : il serait utile d’avoir une partie dédiée à cet enjeu.

Par ailleurs, le projet n’aborde pas suffisamment les risques liés à un accès inégal aux nouvelles technologies, qui entrainent une augmentation de l’asymétrie d’information dans les chaines de valeur.

Sur les défis en matière de gouvernance.

La France apprécie le fait qu’une sous-section soit dédiée à l’importance des cadres législatifs, réglementaires et politiques pour prévenir les risques mentionnés précédemment et promouvoir la dimension éthique des données, ainsi qu’au besoin d’informer les utilisateurs sur leurs droits et de renforcer leurs capacités. Nous apprécions également la référence au Règlement général sur la protection des données (RGPD), dont les dispositions les plus pertinentes eu égard au périmètre du rapport pourraient être détaillées. Signalons également dans ce cadre la stratégie du SGNU en matière de données.

La gouvernance des données est clé lorsqu’il est question de la protection des données, des questions de confidentialité,  des données à caractère personnel, des droits de propriété intellectuelle. Ces thématiques doivent être portées par une stratégie globale. Un certain nombre d’acteurs ont adopté des chartes sur la gestion des données qui devraient nourrir le rapport (par exemple la charte du G7 au niveau mondial, et la charte de la Fédération nationale des syndicats d’exploitants agricoles (FNSEA) au niveau national).

Les principes et règles de protection des données doivent être précisés à toutes les différentes étapes du traitement des données, y compris lors de la collecte, de l'utilisation et du partage des données, ainsi que de la mise à disposition de celles-ci. Cela souligne l'importance de disposer d'un cadre de protection juridique des données clair et actualisé.

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Fin de commentaires

There is substantial potential for improving the transparency of the data basis of global estimates of food security. In particular, the Prevalence of Undernourishment-model of FAO is dependent on household survey data to estimate the distribution and inequality of caloric consumption in the population. Yet timely survey data are infrequently available for many countries. As witnessed in the 2020 China revision of estimates, which substantially reduced the number of estimated undernourished in China and globally (Cafiero et al 2020), this lack of survey data can have dire consequences for the exactitude and uncertainty of estimates.

The significant instability that can be observed in historical estimates requires transparency in the data basis for the estimates of the PoU (Iversen in prep). Yet FAO does not publicly list exactly what survey data its estimates are based on. Such opacity breeds mistrust and uncertainty as evidenced by Pogge (2016), and would be easy to remedy by being much more transparent about the data basis of PoU estimates.

This commenter has repeatedly reached out to FAO to get an oversight into the relevant household survey data, with no response forthcoming from the agency.

References:

Pogge (2016) The Hunger Games. Food Ethics 1(1).

Cafiero, Feng and Ishaq (2020) Methodological Note on New Estimates of the Prevalence of Undernourishment in China.

Iversen (in prep) Chronology of Global Hunger Estimation. Manuscript in preparation.

U.S. comments on Data Collection and Analysis Tools for Food Security and Nutrition  

The United States thanks the High-Level Panel of Experts (HLPE) for their work in producing this Zero Draft Report on data collection and analysis tools for food security and nutrition. We appreciate the opportunity to provide feedback early in the process and look forward to continued engagement and consultations as the workstream develops. Our general comments are below, followed by more specific comments, and finally comments that respond to question eight posed in the consultation.

General Comments:

• The United States believes the Introduction should be re-written with a more balanced tone. Statements such as, “food systems have failed us” overlook the complexity of international food security and overlook significant food security goals that have been achieved over the last twenty-five years – despite challenges associated with COVID-19. The Introduction should set the scene, but also seek to identify achievements and opportunities rather than simply disparaging the agricultural sector writ large.

• The United States believes that it is critically important for the HLPE to use internationally recognized and agreed concepts and definitions for issues that underpin the reports it produces. The four pillars of food security are well known and internationally accepted and therefore must provide the basis for this and future reports. We note that the concept of “agency” is vaguely defined and the linkages between this concept and food security remain unclear, and the definition of “sustainability”, as utilized by Clapp et al., fails to sufficiently consider all three pillars: economic, social, and environmental and seems to already be partially captured by the dimension on stability. Further, the evidence base for the use of this new definition is severely limited. The HLPE citing itself and a journal article published in 2022 (several of the authors of which also sit on the HLPE) that simply makes the case for six dimension of food security is insufficient and inappropriate for underpinning a Report that is designed to provide the foundation for multilaterally agreed policy guidance. We strongly urge the HLPE to stick to internationally agreed definitions so as to reduce confusion and ensure the panel’s credibility as a science-policy interface.

• Overall, in the Report, there is not enough focus on available measures. Validated measures are necessary in order to collect data. If the position of the HLPE is that adequate measures are already available, that should be made clear. In reading the report, the assumption seems to be that measures are available, but that high-quality data is not being collected. We are not sure that is the intended message. We suggest adding a chapter to the report between current chapters one and two that focuses on measures or indicators that could or should form the basis of data collection initiatives. This will help to make later discussions more concrete.

• The Report recognizes that a major challenge with many data sources is the timeliness of the data.  However, we did not see explicit recognition of what can be a larger challenge: that most data collected is a lagging indicator – a snapshot of how things were at a moment in time in the past, rather than how things necessarily are now, or how they may become.  Current developments may not be reflected in data, due to sudden developments (e.g., civil strife).  Thus, the value of sets of indicators that are forward looking or predictive, in addition to those that measure at a past moment in time.

• We are glad to see discussions of data quality (4.4.3 and mentioned in 3.3) but believe the report would benefit from further consideration of data integrity. The discussion regarding interpretation in Sec 3.3 and in Sec 4.4.1 on use of the data for political purposes is on target, thought it only looks at the post-collection use of the data and not ensuring unbiased data collection. Sec 5.1 doesn’t fully elaborate on this either.

Specific Comments

• P. 10-11: The discussion on “Conceptual framework for a systemic view of FSN determinants and outcomes,” including Figure 1, does not appear to explicitly recognize the role of war and other armed conflict or civil disturbance as a proximate driver of food insecurity. Yet we know that it is a major driver in some countries / regions (as recognized in the first sentence in Box 2 on Page 20).  But it should perhaps also be reflected explicitly in the -macro and/or -meso level determinants discussion on page 10 and in Figure 1 on page 11.
• P. 10 of 63, Par 6: Why is the focus only on “local food, health, and environment systems”? This should be explained or broadened.
• P. 16 of 63, Example 1:
  • What is “ASF” intended to convey in this table?
  • Column 3, row 2: what is meant by “Relevant policy (e.g., exist, and/or enforce)”? Please explain.
  • Column 2, row 4: Inappropriate focus on “local producers”. In isolation, this undermines the importance of supply chains in addressing shared objectives.  Please broaden.
  • Column 3, row 6: what does “risk to access” mean? Please explain.
• P. 17 of 63, Example 4: Consider adding an example related to the intersection of food security and water security or an example related to ensuring food security in the context of poor sanitation or lacking clean water.
• P. 19 of 63: We do not agree that anonymizing the data will make it appropriate to be freely accessible. This is true for personally identifiable information (PII), but not for business confidentiality. Will later drafts of the report address confidential business information, including that collected by the government for regulatory, oversight, or other purposes?
• P. 20 of 63, Box 2: We agree with the premise of Box 2 that drawing crisis, fragility, and conflict data into the FSN data context would be useful. The fact that a country has had multiple humanitarian crises over time can be argued to be a salient data point in informing discussion of FSN development objectives. Without endorsing any particular data source, examples of sources of fragility and conflict data might include the Fragile States Index and the Heidelberg Institute for International Conflict Research’s Conflict Barometer.
• P. 21 of 63: Table 1 begins with a list of multi-country sources of data for FNS. Suggest starting with a list of available multi-country validated measures that can be used in data collections in Table 1.
  • One such measure that is missing from the report is the Food Insecurity Experience Scale (FIES). The FIES can be implemented in national data collections relatively inexpensively as it is a set of 8 survey questions, that are already translated into more than 170 languages. The scale has already been validated as an experiential measure of food insecurity and included successfully in other surveys. Further, FAO already has resources on their website for survey implementation and data analysis https://www.fao.org/in-action/voices-of-the-hungry/fies/en/
  • Another measure that is missing is the Water Insecurity Experience Scales. Access to safe water is closely related to food and nutrition outcomes, and the scale has already been validated. https://www.ipr.northwestern.edu/wise-scales/about-the-scales/index.html
• P. 30 of 63, para 2: “Both the European Food Safety Authority (EFSA) and Codex Alimentarius have databases containing…” – Inappropriate to call out one government’s database in this context.
• P. 39 of 63: We are glad to see reference to the potential role of artificial intelligence and machine learning, particularly as a tool for data interpretation and forecasting.

Responses to Guiding Questions

The following comments are organized around question 8 (although some of the comments may overlap with the other questions)

8. Please provide your feedback on the following:

a. Are there any major omissions or gaps in the V0-draft?

• As highlighted in the data life cycle/ data value chain, analyses of FSN data as well as dissemination of results to end users is of great importance and empirical assessment of FSN outcomes is hard. This is partly because of how FSN outcome is measured and what proxies are used to measure FSN outcomes, as it often the case in practice. For example, it is common to see various studies use varying measures of FSN outcomes, although most would agree on the conceptualization of the FSN outcomes. We find in the literature varying measures that seem to refer to the same FSN outcomes, including, but not limited to, Food Insecurity Experience Scale (FIES), the level of calorie consumed (e.g., 2100 Kcal/capita/day, and some other measures indicating the level and intensity of food and nutrition insecurity (food gap and food severity index) etc. Therefore, it will be imperative to develop a commonly agreed upon metric for measuring food security that is simple and less data intensive to help facilitate data driven decision making. The implication is that while designing data collection for FSN purposes, it will be vital to use demand driven and end user informed approach and incorporate this into the data value chain / data life cycle.
• In the conceptual framework, example in Figure 1, “Individual food security and nutrition outcomes:” it may be important to highlight the level of disaggregation (by household members as well as by gender), which may have repercussions to many aspects of the data life cycle.
• In terms of the Data Value Chain, it could prove useful to add ‘model and analyze data’ as part of the 4 components of the data life cycle (Figure 2). This is important for the conceptual framework because the modeling aspect of data life cycle informs the type of data to be collected and how it should be handled as well. 
• BOX 2: may also include some coverage in relation to countries with complete lack of data reporting systems (e.g., the Democratic People's Republic of Korea (DPRK), the State of Eritrea, etc.…)
• The introduction of new data related technologies are disruptive by their nature and there is so much unknown, especially going forward. As we go forward, newer and better digital technologies will only accelerate this disruption. Regarding the AI/ML and agricultural data in general, and FSN data, it will be good to think about putting the right data strategy along the data value chains to take advantage of future opportunities and challenges that will surely be presented because of these technologies. It is important to think beyond what is currently possible and prepare our data system to reinvent every aspect of the FSN. It is important to have agile and robust data system and well-trained workforce to handle the impact of AI’s impact in the future. 

b. Are topics under- or over-represented in relation to their importance?

• Although the text contains a note on the ‘Lack of coordination between agencies (Section 3.1.3), more could be done here including some existing examples. For example, the lessons from the U.S. Government’s global food and nutrition security initiative (https://www.feedthefuture.gov/) provides a good example of how multiple stakeholders are brought together to achieve shared objective of sustainably reducing global hunger and malnutrition, also addressing agency and sustainability aspects of FSN. The U.S. Government’s global food and nutrition security initiative as indicated in the website states that the initiative was developed by 12 U.S. Government agencies and departments, with the input of multi-sectoral partners to present an integrated, multi-disciplinary approach to combating the root causes of hunger, malnutrition, and poverty in the target countries around the world. 

• Moreover, the use of women’s empowerment, although briefly mentioned in the draft report, could be elaborated a bit more to include examples and evidence of the positive linkages between women’s empowerment (agency) and FSN indicators.
• On Page 17 of 63 of the report, “EXAMPLE (3): Emergency / conflict situation in which healthy dietary intake is compromised”, the following could be added:
  • Example (5) food consumption and dietary intake level by children (and women)
  • Example (6) women’s empowerment (agency) and intrahousehold food consumption and allocation  
• More could be said about ‘capacity and inequities’ (Section 4.4.4: Insufficient capacity and inequities), especially as one of the growing demands in the wake of such newer and better data related technologies is the manpower needed to make sense of the data. Training and upgrading the human capacity aspects could be strengthened.
• Regarding chapter 5, “INSTITUTIONS AND GOVERNANCE FOR DATA COLLECTION, ANALYSIS, AND USE”, although well outlined, it can be expanded and enriched. This aspect has always been important, but it will become even more so with the increasing introduction of new data related technologies. Specifically, the draft report may need to include a dedicated section or subsection on existing data governance conceptual frameworks (example see Abraham et al. 2019), applicable to FSN, or propose a new one that should help clarify outcomes and expectations.
• Comprehensive and up to date country level estimates on price and income elasticities, and in general supply and demand dynamics as well as comprehensive price information on food consumed by consumers is necessary. This is of utmost importance, including its use for accurate assessments of economic, social, and environmental changes related to FSN.
• Presentation of new and emerging technologies: This can be elaborated with some more relevant applications for FSN, including using machine learning in combination with publicly available data sets such as LSMS data and remote sensing data for poverty predictions and other applications in low-income countries with inherent data quality problems. One important data point that is hard to collect is prices of commodities. The use of emerging technologies such as smart phones in combination with other tools could be leveraged to collect data. The key point is to empower individual consumers to report data points, if appropriate incentives are put in place, directly to central systems where information could be aggregated and harmonized for use.
• Food accessibility: food security has been mostly measured by income, especially in the low- and middle-income countries. Food insecurity, in terms of availability and nutrition intake does occur in the developed nations. It would be a good to have a central database that captures food deserts across countries. This data could be collected from grassroot (local) level.
• Rural-Urban food price dispersion: This is a variable that can provide information about differences in rural and urban dwelling across countries. Lack of infrastructures connecting rural and urban dwellings create differences in food cost and accessibility. This is important for food aid policy towards low-income countries. Since most countries’ port of entry for food aid shipments is in major cities. Lack of infrastructure linking the rural communities can also affect accessibility to these food aid provisions. 
• Food waste and post-harvest loss: Reliable data on food waste (food loss) and post-harvest loss would be beneficial to researchers and policy makers.

c.Are there any redundant facts or statements that could be eliminated from the V0-draft?

• Although timely and relevant, the generic use of terms such as machine learning and AI related technologies seems to be overly used and may need to be refined.
• Finally, regarding the contents in Table 1 Existing initiatives on data for FSN, some additional data sources to consider are:

• USDA ERS - Food Consumption and Nutrient Intakes: ERS provides data on food consumption and nutrient intake by food source and demographic characteristics
• USDA ERS - Food Availability (Per Capita) Data System: Food Availability (Per Capita) Data System-The ERS Food Availability (Per Capita) Data System (FADS) includes three distinct but related data series on food and nutrient availability for consumption: food availability data, loss-adjusted food availability data, and nutrient availability data.
• Data.gov: The home of the U.S. Government’s open data. Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more

References:

Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424-438.

NEW ZEALAND COMMENTS ON V0 DRAFT OF HLPE REPORT ON DATA COLLECTION AND

ANALYSIS TOOLS FOR FOOD SECURITY AND NUTRITION

General New Zealand comments:

Re: “food systems have failed us” - as for our comments in previous HLPE and CFS papers, and in

the context of the UNFSS – the paper needs to have a balanced narrative around food systems to

reflect the achievements of food systems as well as the gaps and problems. We prefer the OECD

Narrative in its “Making Better Policies for Food Systems” report “the frequent claim that food systems

are “broken” overlooks important achievements across all three dimensions, although important

challenges exist and require urgent attention”.

We also note the premise in the introduction that that data and information might be part of the

problem, in the sense that, despite the incredible amount of data and information available nowadays,

these are not sufficiently timely, accurate or relevant, or are not properly analysed and used to guide

the actions of all agents involved in the management and functioning of food systems’. Part of this

needs to acknowledge that in many cases, uncertainty, inconsistency and/or misinterpretation of the

data can result in the scale and nature of the problem being overstated or incorrectly stated. This

relates to our concerns around the “food systems have failed us” narrative being too simplistic, and the

lack of nuance around the example of meat consumption used in the report.

For the most up to date analysis of data issues and uncertainty concerned with comparison of food

items through Life Cycle Assessment (LCA) methodologies we recommend that the authors refer to

the recently published FAO report “Integration of environment and nutrition in life cycle assessment of

food items: opportunities and challenges” (Read the Publication Here). Many relevant aspects of the

report relating to uncertainty and data intersect with the issues in the HLPE paper. Refer in particular

to Chapter 4 for data related issues with LCAs, in particular section 4.5.

We welcome that there is a reference to data on Traditional and indigenous knowledge in the report.

The UNFSS process rightly acknowledged that data on traditional and indigenous worldviews are

often not well translated in scientific terms and this pertains to data as well. Ongoing engagement with

indigenous peoples and recognition of indigenous worldviews is needed in order to capture these

perspectives in data and food systems, food security and nutrition indicators.

We consider that the report would benefit from more discussion of the range of actors within domestic

food systems that are collecting FSN data (i.e. across local government, central government,

academia, private sector, indigenous groups etc.) including the incentives they have for doing so.

Policy makers need a good understanding of these incentives if they are to encourage integrated data

systems that are capable of providing useful insights into the performance of food systems.

The report could also provide further context around how food systems data is currently, and should

be, aligned with national data strategies (the ‘macro level/distal determinant’ level from the conceptual

framework). This could help with exploration later in the document of the policy approaches for

ensuring institutional settings support FSN data systems. For instance, some governments have a

centralised function (such as out of its statistical agency) that have a mandate for this national data

system and advancing it (New Zealand has a Chief Data Steward position that is responsible for

releasing the Government Data Strategy and Road Map).

Conceptual Framework

Comments

Generally, the methodology in the conceptual framework used makes sense (evidence, data,

disseminate etc.) however based on Example 1 the application is too high a level to support national

level policy-making in any detail. National level capacity constraints are well outlined in the wider

document, however further thought needs to go into how to support policy makers to utilise the

framework given the level of complexity, uncertainty, context specificity and data gaps they face in

reality. Many of the points in the table are very complex in their own right, and there is the added

complexity of interpretation of data, and analysis of trade-offs.

We have a number of questions and suggestions on the table:

• The document (and table) does not adequately capture the essential role of international trade to support food security and nutrition – with an apparent singular focus on local food systems/farmers markets etc. International trade can help improve the “matching” of supply and demand. Trade not only enables food to move from surplus to deficit regions, but also will be necessary to ensure the efficient and sustainable use of global food and agricultural resources. However, import tariffs for agricultural goods remain higher than for industrial goods, creating distortions which limit this “matching” function of international agricultural trade. The supporting role of international trade and impact of import tariffs and harmful subsidies on food security and nutrition is currently absent from the analysis, and should be part of any country data gathering and analysis relating to food security and nutrition using a systems approach.
• The links between nutrition and sustainable production and the assumptions around these need further clarification. The problem identified (too high or low meat consumption leading to poor health outcomes) and assumptions need further clarification (i.e. is “unsustainable” meat production assumed to reduce the nutritional content of the meat? Are environmental externalities (water, GHG emissions) linked to certain livestock systems assumed to be linked to particular health outcomes? (or simply to the consumption preferences of some consumers?).
• The current approach does not reflect the holistic approach to policy, which the rest of the report is promoting. I.e. Under a systems approach poor health outcomes would be identified in populations, then the full range of factors contributing to this would be identified (including interactions and feedback loops) – policy makers should then look at interventions holistically, considering all relevant factors. Meat consumption would be one of those factors in some populations, but there will be many other factors (dietary and otherwise) contributing to health outcomes. It does not reflect a ‘systems’ approach to policy making to frame the example using one factor. Our first preference would be for the report instead use an example from real life (i.e. within x country it was found that y. If this framework was used, then z…). If the preference is to keep it hypothetical, then perhaps the example could be nuanced to reflect the above point around multiple factors contributing to health outcomes.

2. The report adopts the broader definition of food security, proposed by HLPE in 2020, which

includes the two dimensions of agency and sustainability, alongside the traditional four of

availability, access, utilization and stability.

Comments

There was a range of differing views on broadening the definition of food security in the 2020

HLPE report development process. For example, the issue of including Agency as one of the

elements was a contentious issue, and this paper notes that it is difficult to measure through

current data. The paper currently does not cover the rationale and implications in depth and

requires referral back to the 2020 HLPE paper.

a. What type of data will be most useful in measuring food security dimensions such as “agency” and “sustainability”?

Comments

For sustainability, the LCA report we refer to in our general comments provides a useful detailed

analysis of the current state of play regarding knowledge gaps, inconsistencies and assumptions in LCA analysis and data.

4. The report discusses capacity constraints at local, national and global levels, with a special

focus on statistical and analytical capacity.

Comments

We support comments on page 15 around the importance of engagement with stakeholders when

using digital technologies to address ethical concerns and ensure accuracy ground trothing at farm

level (He Waka Eke Noa example below). We agree with 3.1.5 on “usability of the data is limited when

stakeholders have not been involved in the survey planning and there is inadequate dissemination or

access to information on what data is available and how it can be used by the stakeholder”.

We support the comment on page 18 on the findings of the Independent Evaluation on FAO’s support

to countries and the need for better capitalising on regional statistical expertise. For the Pacific region,

we are glad that the FAO is engaging with the Pacific Data Hub at the Secretariat for the Pacific

Community (SPC).

7. Drawing on HLPE reports and analysis in the wider literature, in the next draft the report will

outline examples of potential policy pathways to address challenges to data collection and

analysis tools for FSN.

Comments

We suggest that this work should consider and build upon relevant OECD reports that overlap with

this area including:

• Overcoming evidence gaps on food systems, OECD 2021
• Making better policies for food systems, OECD 2021 – especially chapter 3.2.

Several ongoing initiatives within New Zealand relate to the points under question 7. We are happy

to provide further information on specific initiatives if it would be helpful to the report.

• He Waka Eke Noa is a partnership between the New Zealand Government, Industry and Māori to work towards pricing agricultural emissions. The programme that will equip farmers and growers with the information, tools and support they need to reduce emissions and build resilience to climate change. Part of the workplan includes ‘Developing criteria, methodologies and definitions for calculating on-farm emissions and a system for farm-level emissions accounting and reporting.’
• Integrated farm planning is a complementary programme to He Waka Eke Noa that seeks to pull management practices and information on business planning; animal welfare; biosecurity; employee wellbeing and management (including health and safety); agricultural greenhouse gas emissions; freshwater; intensive winter grazing; biodiversity; waste management; nutrient management; adverse event plan (to ensure an agribusiness can keep operating during a storm); Te Mana o te Wai; consents and permits; food safety.
• The Sustainable Food and Fibre Futures fund: which supports problem-solving and innovation in New Zealand’s food and fibre sectors by co-investing in initiatives that make a positive and lasting difference. This includes initiatives that will help farmers and growers, including Māori to better collect and utilise data.

Māori (the indigenous peoples of New Zealand) collect and hold data relating to FSN separate to and in partnership with Government. Should the authors be interested in such examples then we can reach out to see what examples may be appropriate to contribute.

Dear Sir or Madam,

Thanks for this important discussion.

My contribution is as follows:

1a.  No. Food security and Nutrition is profoundly diverse. A holistic framework is required, in order to capture all the components of the FSN system. That framework is the Sustainable Livelihoods Framework (SLF). The Sustainable Livelihoods Framework, if implemented well, would significantly transform global food systems.

1b. No. Because it is not holistic. Too much relevant data would not be collected.

1c. No. The four-stage-cycle does not mention critical issues, such as assessing information needs, team formation, planning, finding and using data, data collection techniques, etc. Further expounded in the FAO e-agriculture strategy guide.

1d. The Costa Rica Food and Nutrition Policy.

2a. No. Because it is not holistic.

2b. Data on production (and productivity), storage, marketing, business, finance, policy and legal factors.

3a. Yes.

3b. No. Trends such as climate change, pandemics, pestilences, empowerment, governance, and root causes of food and nutrition insecurity.

3c. Technology is empowering. Digital technologies would enable communities on the global continuum to effectively and efficiently participate in food security and nutrition, if properly applied. Inclusion and Innovation result in empowerment.

4a. No. Capacity constraints emerge from underlying or root causes. Also, there are connectivity and content hindrances. These include poor basic infrastructure, poor production and storage services, poor marketing and business services, poor financial services, and lack of or inappropriate policy and legal framework.

5a. Yes. Others: Unmanned Aerial Vehicles, Nanotechnology.

5b. Data is collected on: (i) Basic infrastructure. (ii) Production and storage services. (iii) Marketing and business services. (iv) Financial services. (v) Policy and legal framework. In each of these stages, the FSN data value chain is applied, in accordance with conventional data collection and analysis practice.

5c. See (b).

5d. Respect for human rights, good stewardship (including professionalism), gender equality, good governance, persons with special needs, non-discrimination, social protection.

6a. Yes, Transparency and accountability; responsive service delivery; authentic institutions; and the rule of law.

6b. Risks of data-driven technologies: (i) Subjective algorithms. (ii) Unpredictable behaviour of advanced technologies. (iii) 'Usurping' of human work. (iv) Lack of conscience. (iv) Data privacy (v) Data security.

6c. (i) Equitable benefit-sharing (ii) Governance. (iii) Climate change adaptation and/or mitigation. (iv) Empowerment of communities. (v) Value chain approach. Note: These factors are intricately woven.

6d. -  The United Nations Food and Agriculture Organization (FAO). - Conflict Analysis and Management.

6e. Financing needs: Often context-specific and subject to assessment. But, in general terms, financing for basic infrastructure, production, marketing etc., as aforementioned.

Financial mechanisms and tools: Policies, markets and institutions which enhance FSN.

7a. Data on the following: (i) Demographics. (ii) Economics (iii) Technologies. (iv) Politics. (v) Institutions. (vi) Culture.

7b. Data on the following (country): In addition to the above (7a), data on infrastructure, production, storage, marketing, business, finance, and policy and legal frameworks.

References:

• Community Food Security Assessment Toolkit (United States Department of Agriculture)
• Community-based Adaptation to Climate Change (Participatory Learning and Action-60).
• Costa Rica and its commitment to sustainability. In: Challenges for food and nutrition security in the Americas.
• Data Management and Mapping tools and systems for food security; Food and Agriculture Organization, Project GCP/RAS/247/EC.
• Empowerment theory, research, and application; American Journal of Community Psychology, 1995.
• Factors affecting implementation of good government governance (GGG) and their implications towards performance accountability; International Journal of Business and Social Science, 2013.
• Food Security and Food Production Systems; . In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
• Food security and Nutrition Data Collection Framework: Inter-agency Social Protection Assessment (ISPA)
• Innovation for Inclusive Growth; Organization for Economic Cooperation and Development, 2015.
• Quantitative methods for integrated Food and Nutrition Security measurements.
• Sustainable Livelihoods Guidance Sheets; Department for International Development, 1999.
• The Costa Rica Food Security and Nutrition Policy.
• The Food and Agriculture Organization E-Agriculture Strategy Guide.
• The Future of Food and Agriculture: Trends and Challenges (Food and Agriculture Organization)

Dear HLPE Steering Committee and Project Team,

Thank you very much for this timely draft report which is a welcome addition to the HLPE lineup of reports. Please find attached the comment matrix with comments and suggestions highlighted. I hope they are useful for the continued refinement of the document.

Kind regards,

Abram J. Bicksler

NSPED, FAO

First of all, allow me to express my appreciation to the HLPE Steering Committee and the project team for producing this first draft report on data collection and analysis tools.

The comments and observations submitted here are on behalf of the European Commission Services. Several collogues who have reviewed the draft have contributed their input during an internal consultation process, the results of which can be found in the attached document.

Dear HLPE on FSN,

Thanks for sharing the draft of this very timely initiative. It is truly appreciable. Please find my feedback in the attached file and hope that you would consider the points highlighted.

Best wishes,

Masud Parvage

Dear High-Level Panel of Experts on Food Security and Nutrition,

Congratulations for the initiative for developing a comprehensive and cross-cutting data process framework. I am providing comments on behalf of the WHO Department of Nutrition and Food Safety (NFS).

The final product, I believe, can provide significant contribution to the food security and nutrition. NFS is interested to contribute in more details, in special within the context of WHO's data governance framework, so that it can be an alignment. The WHO framework is been developed in partnership with some of the key stakeholders for global health estimates, such as Health Data Collaborative. I attach two slides that describe the steps involved in that case.

Before specific comments, I would like to highlight the fact that food safety only appears very late in the document, while perhaps it should be one of the cross-cutting areas, put more in evidence. It is an area that is of increasing interest in terms of data collection, and it would profit of being a more significant part of this kind of document.

My initial comments are attached for your consideration. Overall, I had difficulties to understand  Chapter 1 main objective – for the proposed framework. Would that be to define pathways to build evidence for decision making through setting research priorities? Or the actual steps in terms of data process harmonization? Or both?

Chapter 2 on the potential sources for data and current initiatives is very useful, however I thought the text per se are too much focused on limitations rather than opportunities.

Chapter 3 is my favorite, as it describes at lengthy all our every-day challenges in collating and analysing data, and common issues with data processing all areas face. Section 3.1.1 contains useful information but heavy to read, with long paragraphs. Perhaps is possible to break a bit or provide less details… In Section 3.3, I believe one needs to be careful not to put all "sophisticated" analysis in one basket with a rotten potato. Robust analyses can be critical to address key gaps and several can be made interpretable if communicated in the right way. When assumptions used can be explained in a transparent manner and are aligned with evidence, sophisticated analysis hold well and help the building of good and so well-needed evidence.

In turn, in Chapter 4, innovation is encouraged and this is good, I believe. Perhaps bringing more the idea that standardized methods to carry out these new forms of data collection are needed, as well as how to make use of them without jeopardizing quality of evidence gathered.

Thank you very much for the opportunity for all of us to contribute to this report. I look forward to further collaboration and the next version.

Kind regards,

Elaine Borghi, PhD

Unit Head, Monitoring Nutrition Status and Food Safety Events Unit

Department of Nutrition and Food Safety, World Health Organization

Geneva, Switzerland