Глобальный форум по продовольственной безопасности и питанию (Форум FSN)

Certainly, FSN must take into account new emerging technologies. However, it has to take into account the specific challenges that affect some farming communities living mainly in rural areas such as ignorance, inaccessibility (cost, knowledge, geographically, etc.). Those emerging technologies can obviously affect positively the issues of climate change, droughts, by providing timely accurate information to anticipate some challenges. But how to do it differently as it is done today. How can meteorological information and data be regularly and promptly produced and be available usefully to smallholder farmers to avoid and mitigate risks. Technology is one thing, but some issues like strong institutions, governance systems, infrastructure, research, women and young farmers’ inclusion, data management, etc.

Section 1:

Example 1 of the matrix: seems very general – I think examples have to be much more concrete to be helpful as an illustration.

Section 2:

On poor data quality, p 19: may add the following: often data is inconsistent, e.g. in spelling of commodities (e.g. “soybeans” and “soyabeans” in different types of FAOSTAT), etc. – this also causes problems. Furthermore, data is sometimes only available in pdf-format or in excel-sheet organised in such a way as to make systematic use by data processing and analysis programs particularly tedious.

Generally, may also put a focus regarding the data challenges to see some of them as a problem of data science and not too related to food systems – thus improvements could be sourced from the vast expertise in data science any by the help of data experts – with no specific relation to food systems. Thus, also explicitly try to learn from existing large data users on how they solve the data problems – e.g. big data in astronomy, particulate physics, neuroscience; in large companies (Amazon, etc.), in social media companies, etc.

Section 3:

Section 3.1.2 states: «Thus, while technological advances may reduce cost and widen the reach of surveys, the social divide may lead to the underrepresentation of those with poorer digital access and literacy (LeFevre et al., 2021) Policies and interventions that are based on such data generated from skewed sampling are therefore not useful to the unrepresented stakeholders who may have the utmost need for data-driven policy and support (Bell et al., 2017; LeFevre et al., 2021).» - true, but biased sampling is often a key mistake in any data collection and one should be very aware of this also in most traditional approaches for collecting data with the aim to gain a representative picture – as all too often the sampling strategy chosen does not allow this – the new digital devices may add an additional reason for bias to this, but being aware of it, this can be dealt with – what I want to emphasize here: biased sampling is also a problem in all other cases, where no new technologies are involved, and awareness of it needs to be increased also there. – This is taken up in section 3.3.2, so I would refer to 3.3.2 here in 3.1.2, and also vice-versa.

 “3.1.5. Lack of stakeholder engagement

Finally, the 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. These limitations to the access and use of data for improved decision-making, make it difficult to advocate for further funding and commitments towards the collection and analysis of food security and nutrition data.”

Regarding the quote above I would say, that this very much depends on the problem at hand and solutions identified and the data needed to implement those – stakeholder interaction is not always needed, or, if needed, it has to be specified in more detail. Thus, data usability is not in all cases limited if stakeholders have not been involved in survey planning – depends on the goals and topic.

Section 3.3, p36: the following is indeed a key challenge, one has to work on: “– reveals the overall scarcity of a minimally sufficient, statistical and quantitative analytic literacy, needed to ensure the validity of the results presented and their proper use.” – even more than getting more data – we have to assure that the data we already get is of good quality, and that the people analysing it know what they do and what can and cannot be done with the data at hand. – There is e.g. a gap in literacy on how to set up useful data structures: relational database, etc. – as you quote Rosenberg at the end of the intro to 3.3. Take the data on the Infoods-page, for example – the tables are in excel and all the tables look somewhat too very different – and they do not follow the relational database guidelines, so it is difficult to work with them. This would be a first and easy step to improve. – in more detail, in Infoods, where are e.g. cells with values but also an index for a footnote besides the value, or there are merged cells, thus disrupting the matrix structure, etc. – there are empty cells implicitly to be filled with the last previous entry in the same column, etc……some tables are available in pdf-format only, etc. – so this is a very sub-optimal data structure.

3.3. Lack of data processing and analytical capabilities – important section.

The sections 3.3.1 – 3.3.6  are very important, please invest on those to make them as helpful as possible. – One input on proxies: sometimes, the art of choosing an indicator is to avoid overly costly data collection requirements while still being able to make statements about the topic of interest. – Wisely chosen proxies can be very helpful – but it is a challenge to identify those – but it is often worth the effort.

May also add a section on “robustness” – not in the sense of uncertainty or noise (3.3.1) – but relating to how good the data has to be for supporting advice on actions to be taken. In some cases, there are “robust” patterns that can be identified from a range of approaches and without much sensitivity to changes in values of relevant parameters – thus, in such cases, data requirements are much lower than in cases, where results are very sensitive to which value a specific indicator may take. – Identifying these robust areas can be very helpful, as it reduces costs for data collection while still ensuring the possibility to derive advice for actions to be taken that will lead to the intended outcome with high certainty. – I would add such aspects to the framework presented at the beginning, e.g. giving explicit advice, on how to refine step 2 on data of the 4 steps above:

Given the priority, problem to be solved, question to be answered: which data is needed; then: which data is already there and which has to be collected. From the data that needs to be collected: identify first this data, which is useful in such a robust way as just described: are there parts of the priority/problem/question, where solutions seem to be quite clear, robust to how detailed the data is – then first go for them. Also, try to identify the big leverage points that may provide much effect on the basis of relatively less data, and do not focus on minor aspects, that may lead to incremental improvements but require large quantities of data.

Related to this, maybe some thoughts on the following statement: section 3.1.2: «For example, new data analytic architectures that generate farm and field level data allows farmers and stakeholders to monitor processes and make a decision for the precision livestock farming. (Fote et al., 2020). The use of these advanced technologies provides a level of granularity and immediate access to data that was lacking in traditional surveys.» true – but the first question again needs to be: which data is needed? There is some danger that the possibility to collect some more granular, detailed data at lower costs results in collecting it – without a clear aim and without a clear rationale that this data really contributes to increased food security. Thus, also with new technologies and with the huge potential of cell-phone-based data collection etc. – the first step always needs to be (as indicated in the framework) – which data is needed to solve which problem. Then the decision is taken on how to collect it.

Section 4:

This is somewhat confusingly structured and superficial.

As stated there, there are many new technologies, approaches, etc. that produce data. But these could be named in relation to how data is generated today – but which of them is then useful has to be decided on the basis of the framework introduced: what is the problem, which data is needed, how is it collected: there, sensors of the IoT may become important – or not. So I would much more locate this discussion on how to collect data as an instrumental discussion to what is needed than as a self-contained description of what is out there. Whether sensors of IoT or crowdsourcing is the best source of data strongly depends on what is needed. Related to the source of data can then be discussed, which requirements arise to transform the data into information – but also there, it should be strongly guided by the needs. Furthermore, the chapter, as it is now, covers a variety of concepts that are not all related to this step of data-to-information, or in very different ways. The Block-Chain, for example, plays a totally different role in this than Virtual Reality or social media: so I would also here differentiate much more in relation to the needs. May even add this as a step in the framework suggested above, given that there is a deluge of data and extracting information from it gets more and more challenging: i.e. between “2. Data” and “3. Translation” may add a separate step: “X.Information” – thus highlighting the crucial need to very explicitly think about and discuss how there is information gained from the data available – always guided by what is needed –

The steps may then look as follows: 1. Problem; 2. Information needed to solve it; 3. Data needed to get this information; 4. How to collect and analyse this data; 5. Translation, et… - thus, the information step may should be addressed earlier, before collecting data, as it is the focus of interest, and only when knowing which information is needed, we are able to collect the adequate data.   

Similarly for 4.1.3 “processing data” – this is not a goal in itself, thus address it again in closest connection to the goals formulated, and it is a service which becomes a topic due to the huge amount of data available and the related challenges to process it to extract the information needed. – But this can be addressed on a purely technical level.

Chapters 4.2. on new tech and 4.3. on how these support FSN are much too general – here, I would rather provide 2-4 in depth examples, presented in considerable detail, to illustrate certain key aspects of this in concrete cases, than providing extensive lists and references of examples without further contents.

4.4 and 4.5 are very important, but they could also be combined, each time discussing the risks and the mitigation approaches together, not in separate subsections.  

Section 5:

Governance: this is also a central issue, I have not much to add here, beside the following point:

One aspect that could be important is to think about where data collection and analysis can be AVOIDED – e.g. by sort of “self-organised” actions on a very small-scale level. Take e.g. a remotely organised extension service based on cell-phone pictures of pests and diseases and their damages, respectively – such a system can work well without collecting and analysing the data in detail – it requires a functioning cell-phone infrastructure as well as enough and well-educated farm advisors. Thus, the answer to a problem related to pest outbreaks in a region may not be to necessarily collect data but to establish a good remotely organized advisory system (I use this example just to illustrate my point – there will be better examples). – Clearly, some data is needed at the beginning (on which pests are there, etc.) – but what I want to emphasise is that in the framework of  1. Priority – 2. Data – 3. Translation – 4. Utilization – the data part can be really small – really only as much as needed. – Clearly, in such a context, more data can be collected to have better information for other cases, or maybe to better manage the given case – but again, it whould be driven by the problems to be solved and not  by the possibility that data can be collected relatively easy.

Thus, I would say, that a guiding principle should be to always collect as few data as possible to address the stated problem with the identified data need – this then also simplifies the data governance.  

Some further general comments:

• May make a stronger statement somewhere at the beginning of the report, in the following direction: all these new data technologies, etc. are only a tool in the FSN context and not a goal in itself. I have the feeling that we sometimes tend to give it too much significance. We definitely should avoid adopting an approach that implicitly runs somewhat as follows: “we have the technology X – so let’s see what we can solve with it and how we can apply it.” – As displayed in section 1, the course of action really needs to be as follows: “we have problem Y, then identify which technology is most adequate to solve it!” – such thoughts could be emphasized somewhat more, I think.
• The report goes quite far from data, information and analysis into discussion of physical devices and physical aspects (e.g. EWaste, section 4.4.5)), which I would not have expected from the title and goals of the report; may rephrase to really focus on the data/info/analysis part only and drop the rest; or broaden the rest and then also state this at the beginning of the report and include things such as 3-d-printing of spare parts to mend broken machines, while avoiding the need for complicated and time-consuming transport to remote areas, etc.

Related to this is the following: I think it is somewhat unclear, whether the focus of the report should be on data and data analysis for FSN (as stated at the beginning) or whether digitalization is also a central aspect (as here and there in the text). I would more clearly separate them – as data and data analysis is one specific aspect on gaining information for management and policy design, while digitalization is more about certain TOOLs to implement this and agronomic practices, etc.. Data and data analysis is about how to get information on the situation, and digital technologies can partly help with this, but many other tools can contribute there as well – depending on the goals.  

It may also be helpful to separate information provision approaches from data collection and analysis – e.g. virtual reality etc. may be stronger as educational tools than for data analysis.

Its great to see an FAO overview and review of digital technology and agricultural information systems. We're obviously in a time of rapid information systems change with all the excitement, trepidation and transitional expense that involves!

Should some food composition database data collection schemes like INFOODS be encouraged to revisit  a much more federated / central repository internet-based vision to support data integration especially as many food items are gaining global and cross-cultural exposure?

About: "the lack of political will and hesitancy to share sensitive information prevents the collection of data such as moderate food insecurity" - I find it helpful to emphasize the distinction between vocabulary standards and sharable or private data.  A proprietary knowledge layer may exist on top of the vocabulary layer, but this does not preclude companies and agencies from using a generic data specification language throughout their own operations and those of partners, to facilitate easier data sharing when the need arises (via regulatory compliance, aquisition, public health emergency, etc.).

Somewhere between "4.1.1. Producing and collecting data" and "4.1.2. Transforming data into information" could exist a paragraph on standardized vocabulary, including open source SKOS vocabularies and OWL ontologies that are able to straddle domains of interest, such as units of measurement (https://units-of-measurement.org/), taxonomy, chemistry, farming practice etc. .  The concept of "nanopublications" and "micromodels" mentioned as ways of stating observations and low-level data models which are more easily agreed upon and can therefore evolve into explicit or defacto data standards.

If one considers that what data is about is entirely conveyed by language, then it becomes an essential piece of a FAIR future vision to have a common open source language that describes datasets down to the field level in order to support the automated determination of comparable information, and its analysis.  This is the problem that ontology technology lends itself to by providing global term identifiers and machine readable framework for categorizing terms (and by proxy, datasets) as materials, processes, qualities, roles, capabilities, etc.  This also helps overcome "4.4.4. Insufficient capacity and inequities" as the open source data specification language attracts a common set of freely available tools and training materials, thus reducing hidden costs mentioned below too.

One hidden cost in the general "digital technology" revolution that arises especially in "4.4.7. Interoperability of data" is the existence of so many (data) language variations that require translators (technologists) one must hire for building mappings between systems, mappings that are often not elegantly designed to avoid rapid obsolescence. The other hidden cost is the slow pace of harmonizing existing vocabularies or curating new terms in them. Another key challenge an agency must confront is whether it should take on curation of multiple domains of vocabulary, or should plan to delegate vocabulary domains out to a more distributed curation governance context.  Cost reduction occurs only after further consolidation, as well as domain vocabulary completion are reached. 

Under Table 2 b) "Semantic Web", you might like to refer to the international open source vocabulary community building around OBO Foundry to supply diet (ONS, https://doi.org/10.1186/s12263-018-0601-y), nutrition (CDNO, cdno.info, paper in review, previous reference https://acsess.onlinelibrary.wiley.com/doi/10.1002/csc2.20092), nutritional study (ONE, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628051/), food composition (FCD, https://wikifcd.wiki.opencura.com, paper under review), food product (FOODON, foodon.org), chemistry (CHEBI), agricultural (AGRO), and environmental vocabulary (ENVO) terms, factors and micromodels, among others. The joint discussion group activity can be found in: https://github.com/FoodOntology/joint-food-ontology-wg/ and here is a slideshow that highlights activity in the last few years: https://docs.google.com/presentation/d/1lh6ndpo-QGU920Gzvqzr04L1DVe3frf…, with a paper under review.

As an aside, this needs edit: "In these cases, strengthening regional collaborations and the establishment of reference. In such cases, strengthening regional collaborations and the establishment of laboratories”

Thanks for opportunity to comment, and I appreciate reading other's feedback too.

Feedback

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

CVK: National guidelines for food security and nutrition will help the member states unable to prepare such guidelines on their own. There are several reports, some are annual published by WHO, FAO, UNICEF covering the data on food production, food security and nutrition and analysis[1-19]. Several academic research teams around the globe are publishing the role of healthy diets and nutrition[20-40]. There is no national guidance on pathway to reach Sustainable Developments Goals 2.1 and 2.2. WHO global guidance needs to be tailored to the specific member state’s requirements considering local situation (geopolitical, economic, armed conflicts etc.). Non-member states may consider guidelines of the nearest neighbouring member state.

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

CVK: Data science, data collection, data-based approach and data analysis is over represented in relation to their importance. In food security and nutrition, newly collected data and analysis may not be adding value, moreover matching newly collected data with already reported data from FAO may lead to controversies. One of these two could be incomplete, inaccurate, not current, or may not be a reliable indicator of what it is intended to represent. Data collection and analysis does not change consumer behaviour. For example, the COVID-19 pandemic might have worsened the situation of children with undernourishment, but the number of children with undernourishment has been increasing even before pandemic[41]. Data-driven approach play an important role in “business analytics.”[42] but not to food security and nutrition. Data collection and analysis should be followed with an action plan to change consumer behaviour. Game theory provides a mathematical framework for determining what behaviour is rational for agents interacting with each other in a partially observable environment43. Multiple Agents Influence Diagrams (MAID)[43], a tool in game theory is useful in goal-oriented approach. Exploring MAID role in progress towards SDG2 by 2030 is worth an attempt.

CVK: Conceptual framework in v0-draft is not connecting global targets to national and subnational consumer behaviour, businesses, and other local actors. Sensible consumption, sustainability, resilience to climate change and weather fluctuations, leaving no one behind, availability, accessibility, gender equality are some of the important topics in the food system design. These are under- represented in the proposed framework. In many countries the data collection priorities are changed with the change of regime effecting the quality of the data.

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

CVK: Success stories and examples- Reported success stories/examples should be scalable to SDG 2 which requires sustainability, availability, access, utilization and stability. Problems in few nations like armed conflicts, draughts should be analysed with specific agents in MAID for respective country/neighbouring countries /region. Many programs depending on funding from international organisations end the moment funding ends.

Are any facts or conclusions refuted, questionable or assertions with no evidence-base?

“Food systems have failed us”

CVK: Above statement is questionable.

“The scientific targets for healthy diets and sustainable food systems are integrated into a common framework, the safe operating space for food systems, so that win-win diets (ie, healthy and environmentally sustainable) can be identified. We propose that this framework is universal for all food cultures and production systems in the world, with a high potential of local adaptation and scalability. Application of this framework to future projections of world development indicates that food systems can provide healthy diets (ie, reference diet) for an estimated global population of about 10 billion people by 2050 and remain within a safe operating space. However, even small increases in consumption of red meat or dairy foods would make this goal difficult or impossible to achieve. Within boundaries of food production, the reference diet can be adapted to make meals that are consistent with food cultures and cuisines of all regions of the world.”[44]

Total production of primary crops in 2018 is 9.1 billion tonnes. With about one-third of the total, cereals were the main group of crops produced in 2018, followed by sugar crops (24 percent) and vegetables (12 percent). Oil crops, fruit, and roots and tubers each accounted for 9 to 11 percent of the total (Source: FAOSTAT https://doi.org/10.4060/cb1329en-fig20 ).

Production of cereals, roots and tubers far exceeds human requirement for projected10 billion population by 2050. Rice, wheat, corn, potatoes and tubers production in 2018 is about 4800 billion Kg whereas the requirement for 10 billion people (adults) at 232 grams per day or about 85 Kgs per head per year amounts to 850 billion Kg. About one third of 4800 billion Kg, that is 1600 billion Kg is used as animal feed. Modifying the plant product processing to direct human consumption (retaining nutrients in tact) will reduce the animal feed production. Reducing 5% production of cereals, roots, tubers and sugar every year till 2030 reduces global greenhouse gas emissions and fresh water use by 50%. [45]

References:

UN agencies publications:

1. WORLD FOOD AND AGRICULTURE 2020 STATISTICAL YEAR BOOK, Food and Agriculture Organization of the United Nations Rome, 2020

2. UNICEF/WHO/World Bank Group – Joint Child Malnutrition Estimates 2019 edition.

3. FAO. 2018. The State of World Fisheries and Aquaculture 2018 meeting the sustainable development goals. Rome. Licence: CC BY-NC-SA 3.0 IGO.

4. OILCROPS, FOOD OUTLOOK, 5 JUNE 2020.

5. Development Initiatives, 2018. 2018 Global Nutrition Report: Shining a light to spur action on nutrition. Bristol, UK: Development Initiatives.

6. Global action plan on physical activity 2018–2030: more active people for a healthier world, World Health Organization 2018.

7. Physical activity; November 2020, WHO.

8. FAO, IFAD, UNICEF, WFP and WHO. 2020. The State of Food Security and Nutrition in the World 2020. Transforming food systems for affordable healthy diets. Rome, FAO. https://doi.org/10.4060/ca9692en.

9. Plates, pyramids and planets Developments in national healthy and sustainable dietary guidelines: a state of play assessment Carlos Gonzalez Fischer & Tara Garnett, Published by the Food and Agriculture Organization of the United Nations and The Food Climate Research Network at The University of Oxford, 2016.

10. Global Recommendations on Physical Activity for Health, 5–17 years old, World Health Organization 2011.

11. Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old, World Health Organization and the United Nations Children’s Fund (UNICEF), 2019.

12. FAO/WHO Scientific Update on carbohydrates in human nutrition: introduction, C Nishida and F Martinez Nocito, European Journal of Clinical Nutrition (2007) 61 (Suppl 1), S1–S4.

13. World health statistics overview 2019: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2019 (WHO/DAD/2019.1). Licence: CC BY-NC-SA 3.0 IGO

14. Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases (2002 : Geneva, Switzerland) Diet, nutrition and the prevention of chronic diseases: report of a joint WHO/FAO expert consultation, Geneva, 28 January -- 1 February 2002. (WHO technical report series; 916)

15. WHO Global Database on Child Growth and Malnutrition, WHO/NUT/97.4 HO/NUT/97.4, WHO Geneva,1997.

16. Mensink, RP. Effects of saturated fatty acids on serum lipids and lipoproteins: a systematic review and regression analysis. Geneva: World Health Organization; 2016.

17. WHO healthy_diet_fact_sheet_394

18. Essential nutrition actions: mainstreaming nutrition through the life-course. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO

19. Guidelines on food fortification with micronutrients/edited by Lindsay Allen ... [et al.]. World Health Organization and Food and Agriculture Organization of the United Nations 2006.

Research publications:

20. The healthiness and sustainability of national and global food based dietary guidelines: modelling study, Marco Springmann, Luke Spajic, Michael A Clark, Joseph Poore, Anna Herforth, Patrick Webb, Mike Rayner, Peter Scarborough, BMJ2020;370:m2322.

21. Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study , Christopher J LMurray, Alan D Lopez, Lancet 1997; 349: 1498–1504.

22. Healthy diets from sustainable food systems A Bioversity International Initiative. Bioversity International Headquarters Maccarese (Fiumicino), Italy.

23. Strategies and interventions for healthy adolescent growth, nutrition, and development, Dougal Hargreaves, Emily Mates, Purnima Menon, Harold Alderman, Delan Devakumar, Wafai Fawzi, Geva Greenfield, Weeam Hammoudeh, Shanshan He, Anwesha Lahiri, Zheng Liu, Phuong Hong Nguyen, Vani Sethi, Haijun Wang, Lynnette M Neufeld, George C Patton, Published Online November 29, 2021 https://doi.org/10.1016/ S0140-6736(21)01593-2.

24. Global, regional, and national estimates and trends in stillbirths from 2000 to 2019: a systematic assessment; Lucia Hug, Danzhen You, Hannah Blencowe, Anu Mishra, Zhengfan Wang, Miranda J Fix, Jon Wakefield, Allisyn C Moran, Victor Gaigbe-Togbe, Emi Suzuki, Dianna M Blau, Simon Cousens, Andreea Creanga, Trevor Croft, Kenneth Hill, K S Joseph, Salome Maswime, Elizabeth M McClure, Robert Pattinson, Jon Pedersen, Lucy K Smith, Jennifer Zeitlin, Leontine Alkema, as members of the UN Inter-agency Group for Child Mortality Estimation and its Core Stillbirth Estimation Group, Lancet 2021; 398: 772–85

25. Future Food Systems: For people, our planet, and prosperity, SEPTEMBER 2020.

26. Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain, Dianhui Mao, Fan Wang , Zhihao Hao and Haisheng Li Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China; Int. J. Environ. Res. Public Health 2018, 15, 1627; doi:10.3390/ijerph15081627.

27. A Global Review of Food-Based Dietary Guidelines; Anna Herforth, Mary Arimond, Cristina Álvarez-Sánchez, Jennifer Coates, Karin Christianson, and Ellen Muehlhoff; Adv Nutr 2019;10:590–605; doi: https://doi.org/10.1093/advances/nmy130.

28. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail Marco Springmann, Keith Wiebe, Daniel Mason-D’Croz, Timothy B Sulser, Mike Rayner, Peter Scarborough; Lancet Planet Health 2018; 2: e451–61.

29. Ultra-Processing or Oral Processing? A Role for Energy Density and Eating Rate in Moderating Energy Intake from Processed Foods Ciarán G Forde,1,2 Monica Mars,3 and Kees de Graaf; Curr Dev Nutr 2020;4:nzaa019.

30. Food systems transformations, ultra-processed food markets and the nutrition transition in Asia; Phillip Baker and Sharon Friel, Globalization and Health (2016) 12:80 DOI 10.1186/s12992-016-0223-3.

31. Nutritional Benefits of Dairy Proteins, Published by: Dairy for Global Nutrition c/o U.S. Dairy Export Council.

32. Affordability of the EAT–Lancet reference diet: a global analysis, Kalle Hirvonen, Yan Bai, Derek Headey, William A Masters, Lancet Glob Health 2019, Published Online, November 7, 2019

33. Diets for a Better Future: Rebooting and Reimagining Healthy and Sustainable Food Systems in the G20, EAT report, 2020.

34. “Food Planet Health: Healthy Diets From Sustainable Food Systems”, Summary Report of the EAT-Lancet Commission, 2019

35. Dietary energy density as a marker of dietary quality in Swedish children and adolescents: the European Youth Heart Study, E Patterson, J Wa¨rnberg, E Poortvliet, JM Kearney and M Sjo¨stro¨m, European Journal of Clinical Nutrition (2010) 64, 356–363

36. Enhance local production for local consumption, Solution Cluster 5.2.1, UNFSS 2021.

37. Nutritional resilience and production for self consumption of rural communities with backyard poultry, home gardens & farmers’ & community markets, 34, farmers' forum, India, Solution Cluster 5.2.1, UNFSS 2021.

38. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis Hannah Blencowe, Julia Krasevec, Mercedes de Onis, Robert E Black, Xiaoyi An, Gretchen A Stevens, Elaine Borghi, Chika Hayashi, Diana Estevez, Luca Cegolon, Suhail Shiekh, Victoria Ponce Hardy, Joy E Lawn*, Simon Cousens*, Lancet Glob Health 2019 Published Online May 15, 2019 http://dx.doi.org/10.1016/ S2214-109X(18)30565-5

39. Institute for Health Metrics and Evaluation (IHME). Findings from the Global Burden of Disease Study 2017. Seattle, WA: IHME, 2018.

40. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 GBD 2017 Diet Collaborators, Published Online April 3, 2019 http://dx.doi.org/10.1016/ S0140-6736(19)30041-8.

41. The Sustainable Development Goals Report 2021, United Nations.

42. Simchi-Levi: OM Forum—OM Research: From Problem-Driven to Data-Driven Research Manufacturing & Service Operations Management 16(1), pp. 2–10, 2014.

43. Multi-agent influence diagrams for representing and solving games ; Daphne Koller , and Brian Milch; Games and Economic Behavior 45 (2003) 181–221; www.elsevier.com/locate/geb

44. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems, Walter Willett, Johan Rockström, Brent Loken, Marco Springmann, et al, Lancet 2019; 393: 447–92.

45. Mission Food System 2030: A Disruptive Transformation to Meet Sustainable Development Goals, Kameswararao Chiruvolu, January 2022. (attached).

After having carefully gone through the highlights of the V0 draft: 

Major omission: the data collection should include metrological approach for crops that is drought monitoring, early warning. The data collection must be given to trained persons. The questionnaire must be very much simplified.

The academic sector should give training and workshops to increase semi-skilled and skilled workers in the food processing industry to address food security, since the food waste level of developing countries is more than 30%. Adequate training in the grain management system is needed. 

There is insufficient fund allocation in the national statistical plan. 

On nutrition: identifying the native variety of grains and populating the merits to the farmer level for breeding. 

Dear Sir/Madam,

Having gone through the highlights of the V0 draft report, I'm sharing my personal view in the following manner:

In terms of data collection and utilisation and for equity and inclusion, the global community and international organizations need to seriously engage indigenous knowledge and innovations from the grassroot and local levels. This will provide a better opportunity to gain an appropriate insight into the current state of world food security and to agree on and design international action to improve it, leaving no one behind.

We need to decolonize our food systems by promoting indigenous food heritages. In countries with diverse geographical, climate and weather conditions, government  and citizens generated data should not only be based on a particular geo-location. I have observed and noticed that researchers and policy-makers rely mostly on regions that favour their interest or are convenient for them, this attitude has led to monocropping, near-extinction, non-accessibility and unavailability of most staple foods that serve as food and raw materials for the people at the bottom of pyramids in these countries.

For a more effective decision-making for food security and nutrition and to inform policies for the transformation of food systems, especially in developing countries; we need to improve equity in access to data for FSN policies and decisions at grassroot and local levels with regards to cultural diversity and geographical locations.

Dear Sir/Madam,

It is an interesting document and captures many important things. However, I like if it elaborates more about the following:

• In the case of societies where the practice of regular data recording is not available, the responses for a survey mainly based on recall basis and their willingness in providing truth data. However, from my personal experience, survey respondents, not few, are in difficulty to explain quantitative data such as revenue and expenditure data due to recalling problem and while others deliberately hide it. In this regard, some researchers pay some amount of money as form of compensation for respondents wasting time which aims to motivate the respondents to provide true information. Therefore, it is good if this document could cover such issues at least how compensation or incentives have an effect on the data collection
• There is sometimes understating or overstating of data problem. For instance, it is sometimes observed that there is a gap among different research institutions, donor organizations funded research projects, and government agencies reveal different with wide gap of national level results related to food security, nutrition security. In this case, there is a challenge among data users to select which one is relatively accurate. Therefore, this report should indicate some hints on what tactics should be followed as a solution.
• It would also be good the report should elaborate the extent of data quality using digital technologies particularly in the data collection vis-à-vis with traditional systems referring developed and developing countries.

Data collection and analysis is only as valuable as the parameters that are addressed. If important parameters are overlooked, important information that can guide future programs to more effective efforts can easily be lost and may have been. When it comes to food security and nutrition, I would like to suggest a couple important interwoven parameters that have been historically overlooked. Once these parameters are addressed, the data collected and analyzed, substantial improvements might be possible to the basic approach to both food security and improved nutrition.

1. First, is the operational feasibility of innovation. The agronomists, including myself, with their small plot technology do an excellent job of determining what is physical possible in an area, but say nothing about operationally feasible of their results particularly for smallholder farmers heavily relying on manual labor. That is who in the agronomic development technology transfer process is responsibility to determine:

a. The labor or access to mechanization needed to complete various agronomic activities within the estimated time allocated;

b.  If that labor or access to mechanization is available;

c.  If not available, what are the rational compromises farmers should make to adjust the innovations to their limited operational capacity; and

d.  How close to the current practices do these compromises come?

From an agronomic data collection prospective there is a simple proxy value to measure operational feasibility. That is timing of operations particularly the spread of basic crop establishment, which is often 8 weeks or more. This is well past the normal estimate of only 2 weeks and well beyond when recommended top dressing of fertilizer and good weeding is overdue. This data is easy to collect through farmer interview or simple field observations. The difficulty here is that this has been long noted, but attributed to lack of motivation or risk avoidance, and the need for extension programs to badger smallholders on the importance of early planting. Something they are most likely aware of as their very livelihood depends on it. This of course assume early planting is fully discretionary and labor is infinitely available. Is that realistic?? Isn’t it time to assume delayed planting is non-discretionary and focus programs toward enhancing the operational capacity of smallholder so they can get their crops planted in a timelier manner??!!

https://webdoc.agsci.colostate.edu/smallholderagriculture/OperationalFeasibility.pdf

https://webdoc.agsci.colostate.edu/smallholderagriculture/BrinksDrudgery.pdf

2.  A major factor in the operational feasibility is the caloric energy balance of smallholder farmers to undertake a full day of diligent agronomic field work. It is interesting to note that we have historically recognized that smallholder farmers are poor and hungry, but rarely factored hungry as a major hinderance to crop production. We have also rarely even collected data on how many calories smallholder have access to. Why has this not been done?? What little data is available show daily caloric intake of between 2000 – 2500 kcal. If you subtract the 2000 kcal/day estimate of basic metabolism, where is the energy for agronomic field work?  With these limited dietary calories how many diligent hours of field work are possible, and what will this do to the time it takes to get basic crop establishment? To do a full day of diligent agronomic field work takes in excess of 4000 kcal. Are our extension efforts compelling people to exert more effort than the calories they have access to? If so, does that qualify for Genocide? How close are we? Should this be referred to the International Criminal Court in The Hague as a potential “Crime Against Humanity”? Very provocative, but just how accurate is it?? Should we enhance the effort to collect information of available calories, analysis that in terms of potential hours of diligent agronomic effort, impact on crop husbandry, and food security? Also, how would the need for calories to meet economic opportunity impact on the nutrition programs promoting more diversified diets? Which should get the highest priority? I fear it will be for higher levels of caloric energy to optimize economic opportunity.

https://agsci.colostate.edu/smallholderagriculture/calorie-energy-balance-risk-averse-or-unger-exhasution/

https://agsci.colostate.edu/smallholderagriculture/ethiopia-diet-analysis/

https://agsci.colostate.edu/smallholderagriculture/affordability-of-improved-nutrition-while-optimizing-economic-opportunities/

Just a plea for more support for the Poshan, IFPRI and Bill Gates supported approach for increasing the domestic ability to carry out data collection/surveys.

One of the unintended effects of the huge amount of international assistance for agriculture/rural development has been to promote employment in asset creation. Jobs in data collection/evaluation and training have consequently become "cinderellas" or even punishment postings. Even in situations where funds are allocated for such purposes these are often later diverted to civil works.

Reading the draft, one has the uneasy feeling that the hope is that much can be achieved through new digital and remote technologies so that it may be possible to leap-frog the tiresome bit of having staff on the ground, supplemented by the odd mission flown in or in extreme cases helicopter surveys.

What is required is a continuing, constant, indigenous capacity to collect good quality data and this is a very important goal that should get priority financial support. 

FAO, 2022. Foro Global sobre Seguridad Alimentaria y Nutrición: el modelo productor-innovador

(MP-I) 

El MP-I es una alternativa viable para coadyuvar a superar la crisis alimentaria, social, de salud y ambiental, que vive la humanidad que se expresa en: pobreza alimentaria que afecta a 3 mil millones de habitantes, desigualdad social, migraciones, los más de cinco millones de muertes ocasionado por la pandemia de la Covid 19 y en el calentamiento del planeta que ha derivado en sequias, inundaciones, incendios, el derretimiento de casquetes polares, la acidificación de mares, etcétera, que empeoran los problemas mencionados. El MP-I fue sistematizada como propuesta de política pública para conseguir la autosuficiencia de maiceros de subsistencia y la soberanía alimentaria de México en maíz, frijol y grano de calabaza, tres alimentos básicos de los mexicanos.  

La soberanía alimentaria, permite acceder al derecho a la alimentación tutelado en la Declaración Universal de Derechos Humanos de las Naciones Unidas desde 1948 y por el artículo cuarto constitucional de México. Conseguirla, exige producir granos en cantidades suficientes, de forma sostenible, que sean saludables, nutritivos y culturalmente idóneos.   

El MP-I resume 20 años de trabajo y resultó de una rigurosa evaluación de las tecnologías progresivas (campesinas) y radicales (modernas) aplicadas en el manejo de maíz de secano. Esta evaluación permitió identificar experiencias de manejo agroecológico innovadoras y exitosas a escala local, asumidas como el MP-I (ver https://bit.ly/3BAIs3o).  

Todas las experiencias exitosas identificadas manejaron el maíz como milpa donde convergieron tecnologías campesinas y modernas trenzadas en un diálogo de saberes. Estas tecnologías, aunque distintas, se integraron entre sí. Se trata de lo que Piaget (1975) llamó el proceso de diferenciación-integración de elementos (tecnológicos) desigualmente desarrollados en el proceso histórico que devienen en una innovación con características productivas superiores a las tecnologías que le dieron origen.  

La teoría-praxis más importante aplicada en el manejo de la milpa es la biodiversidad que resulta de la asociación de cultivos, donde se siembran juntos, al menos, maíz, frijol y calabaza, capaz de reproducir una gran diversidad y abundancia de flora y fauna organizados en "pisos de plantas" que imitan la funcionalidad de los “pisos de plantas” de los ecosistemas naturales. En su construcción, la mano y sabiduría indígena-campesina han aplicado el principio de biomimesis que es “una ciencia, saber, arte, y tecnología que asume a la naturaleza como pedagoga para de ella aprender formas, procesos y organización para imitarla sin dañarla” (Tamayo, 2013. https://bit.ly/3pgsg22).  

Estos pisos de plantas albergan una gran abundancia y diversidad de flora y fauna que cumplen dos funciones esenciales para el campesino y la humanidad. La primera, recrea fuerzas productivas que proceden de miles de sinergias, pensadas como conectividades creativas, que origina lo que Capra (1998. https://bit.ly/3FgLM65) llamó “la trama de la vida activada por redes dentro de redes” y que en la milpa cuajan en una mayor produccion de granos que satisfacen necesidades biológicas y culturales de las familias campesinas, así como en la captura de gases efecto invernadero, de nitrógeno, de agua, de suelo, de materia orgánica, etcétera.    

De la segunda función de la biodiversidad deriva la resiliencia y sostenibilidad de la milpa, porque ahí coexisten lo que Capra denominó “especies superpuestas” o “especies redundantes” según Walker, constituidas por una multiplicidad de especies con funciones ecológicas análogas que reproducen una redundancia de relaciones. En caso de ocurrir un siniestro climatológico extremo, estas especies redundantes pueden recuperar la funcionalidad y productividad del agroecosistema (resiliencia). Un cultivo resiliente será sostenible a través del tiempo. 

Los resultados obtenidos de cinco estudios de caso subrayan las regularidades empíricas que representan las fortalezas del MP-I: a) emplean más tecnologías campesinas y, por ello, su manejo es más económico; b) obtienen un rendimiento por hectárea que duplica, en promedio, el de los maiceros menos eficientes que siembran el maíz como monocultivo; c) la mayoría de maiceros, independientemente de su productividad, se caracterizan porque conocen el manejo de la milpa, son minifundistas, viven en condiciones de pobreza extrema, y su acceso a los medios de producción es mínimo. Esta similitud de condiciones facilitaría la implementación del MP-I como política pública.  

El MP-I como política pública consta de cuatro etapas: a) identifica los territorios especializados en la produccion de maíz de secano; b) evalúa las tecnologías aplicadas en el manejo de maíz e identifica a los maiceros eficientes y su patrón tecnológico; c) establece faros agroecológicos para escalar horizontal y verticalmente a MP-I, y d) transforma los recursos orgánicos en compostas y bioinsumos que existen en las urbes para devolverlos al campo con el fin de potenciar la fertilidad de suelos agrícolas y la producción de granos.  

Se calcula que en México se siembran cerca de tres millones de hectáreas con milpa, que producen alrededor de tres millones de toneladas de maíz en promedio. Si se aplica el MP-I, se podrían producir cerca de nueve millones de toneladas de maíz, suficientes para alimentar a 54 millones de personas y garantizar la autosuficiencia alimentaria de la población rural. A este volumen, hay que sumarle otros 30 millones de toneladas de maíz al año, producidas por maiceros en transición y comerciales, que pueden alimentar a otros 90 millones de personas, aptos para garantizar la soberanía alimentaria del país.  

El MP-I, también aportaría cerca de 750 mil toneladas de frijol y 600 mil toneladas de semilla de calabaza, así como una gran cantidad de arvenses comestibles, que han sido parte esencial de una dieta nutritiva y saludable para las familias indígena-campesina.  

Para los milperos que viven en pobreza extrema, el MP-I constituye los cimientos sobre los cuales se puede construir una vida digna, plena, feliz, autogestionaria, fundada en un trabajo útil socialmente necesario, que produzca bienes concretos útiles, que satisfagan necesidades biológicas y culturales auténticas de las familias indígenas y campesinas. Para la humanidad el M-PI es una propuesta agroecológica que puede ayudarnos a transitar hacia al bioceno, que apunta hacia el horizonte utópico de una nueva era de la humanidad donde se valore, respete y cuide la vida en su diversidad biológica y cultural (Rozzi, 2019. https://bit.ly/3170bQd).

Dr. Miguel Ángel Damián Huato

Académico del Centro de Agroecología-Icuap; premio estatal de ciencia y tecnología, 2011; miembro del SNI-II y de la Academia Mexicana de Ciencias. Tercer lugar del 4º Premio Nacional “Dip. Francisco J. Mújica”, Cedrssa. Coordinador y profesor solidario de la maestría en Agroecología, Territorio y Soberanía Alimentaria, Cesder, Zautla, Pue.