Portal:Complex Systems Digital Campus/FOOD

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the repository for Open Questions, Challenges and Ressources of the
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FOOD
From smart agrOecOnomy to smart fooD



Great Challenge[edit]

Human Augmentation of Ecosystems[edit]

Summary for policymakers[edit]

This challenge is based on a perspective article[1] that explains why an eventual synthesis of professional silos such as agronomy, ecology, food, and medical sciences alone is not sufficient to overcome the complexity of the diet-health-environment trilemma and emphasizes the importance of driving fundamental participatory change in food production with deliberate enhancement of biodiversity and ecosystem functions to prevent the anticipated ecological regime shift and associated social collapse.

The standard viewpoint of ecologists that "better prediction leads to better decisions" is not proactive enough to prevent the global state shift because it disregards the massive developmental pressure of agriculture based on the fundamental trade-off between biodiversity and productivity.

Current rates of species extinction along with demographic pressure far outpace the innovations of monoculture-based production, such as the amelioration of yield gaps and its scaling-out strategies, which promotes technically precise resource management but is fundamentally inconsistent with biodiversity protection.

Furthermore, in order to properly situate a variety of evidence in a unified perspective of human and environmental health, recently emerging multi-scale omics studies in food and medical sciences need to incorporate the conditions that affect the metabolite of products and the associated loss and formation of biodiversity—typically, the culture conditions of crops and the various ecological factors that support food production.

In the past, traditional agricultural societies were able to achieve major relief from food shortage by producing a wide variety of comestible plants and livestock through breed improvement. However, this came at the cost of enormous burdens related to input-intensive production—resource depletion, the destruction of ecosystems and erosion of topsoil, and the consequent deviation of products’ metabolite from the evolutionary nurtured state—all of which imposed metabolic aberration on humans.

The integrative countermeasure to these problems, known in the field as the anthropogenic augmentation of ecosystems through primary food production, aims to utilize the positive legacies of agrobiodiversity and assistive technologies in an integrative cycle of human and ecosystem health. It coincides with important international incentives of environmentally responsible development, such as the mainstreaming of biodiversity in food production, in situ conservation and the exploration of untapped plant genetic resources, and further extends its reach towards dynamic adaptive strategies for the establishment of highly functioning, useful, and valuable ecosystems. The augmentation scenario can be distinguished from conventional scenarios of reducing environmental load (preservation, conservation, mitigation etc.) in terms of the enhanced level of biodiversity and ecosystem services compared with an uncontrolled natural ecosystem, and high compatibility with local economic activities based on sustainable utilization of natural resources.

To establish effective policy-science interfaces, links to exact scientific terms and references are provided through this challenge to facilitate the crucial coordination of cross-sectional task forces that should work to oversee the efforts of existing expert committees. Under such transdisciplinary and integrative measures, organizational efforts from public and private sectors should be integrated and be reflected in government and regional policies on the multi-territorial scale so as to realize a symbiotic Earth where human society and Anthropocene ecosystems can both survive and evolve through collaborative interactions.

Description[edit]

Current food production systems require fundamental reformation in the face of population growth, climate change, and degradation of health and the environment. Over the course of human history, every agricultural system that has emerged has featured some sort of trade-off between productivity and environmental load. These trade-offs are causing the planet to exceed the boundaries of its biogeochemical cycles and are triggering an unprecedented extinction rate of wild species, thus pushing global ecosystems to the brink of collapse. In this era, characterized as it is by human activity that can profoundly influence climate and the environment (i.e., the Anthropocene epoch), tipping points can be either negative or positive. While a negative tipping point can produce sudden, rapid, and irreversible deterioration of social and environmental systems, a positive tipping point can produce improved health and sustainable social-ecological systems. The key to promoting positive global tipping points is a thorough understanding of human activity and life history on an evolutionary scale, along with the comprehensive integration of science and technology to produce intelligent policies and practices of food production (in a way that conforms to 17 UN SDGs i.e. mutual wellbeing together and with Nature), particularly in the developing world. Simply increasing the efficiency and scale of monoculture-intensive agriculture is unlikely to drive social-ecological change in a positive and sustainable direction. A new solution to the health-diet-environment trilemma must be developed to achieve a net positive impact on biodiversity through the anthropogenic augmentation of ecosystems based on the ecological foundation of genetic, metabolic, and ecosystem health. Taken together, the great challenge of FOOD flagship studies the fundamental requirements for sustainable food production on the molecular, physiological, and ecological scales, including evolutionary and geological insights, in an attempt to identify the global conditions needed for the primary food production to ensure we survive this century. Particular emphasis is placed on how to make extensive use of this planet’s genetic resources without irretrievably losing them.


Board[edit]

  • Masatoshi FUNABASHI (chair | masa_funabashi"at"csl.sony.co.jp)

Bibliography[edit]

M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.


Challenges[edit]

Understanding the Scale of Scientific and Industrial Domains and Associated Human Impacts on Food Production[edit]

Description[edit]

We start by mapping the current scientific and industrial domains in order to define the path toward sustainable social-ecological systems.

Fig. 1: Scale of scientific and industrial domains and associated human impacts on food production.[2]

Fig. 1 shows the scale of scientific and industrial domains and associated human impacts on food production.[3] Horizontal axis represents the degree of technological complexity required for realization. Vertical axis is the spatial-temporal scale involved for the maintenance from small (bottom) to large (top) scale, in which experimental systems in science and production modes in industry can be represented as in vitro, in vivo, in cultura, and in natura conditions.

Most biological studies are conducted with in vitro and in vivo experiments in confined environments, while public health studies such as cohort analysis focus on in cultura products without questioning the foundation of agriculture. Agronomy focuses on the optimization in cultura, such as high-precision resource management and genetically modified cropping systems, accelerating urbanization supported by the large-scale monoculture system, while ecology mainly treats preservation and conservation in natura. As a solution for future food production, anthropogenic augmentation of ecosystems is situated at the top right, which combines enhanced agricultural biodiversity with the support of information and communication technologies (ICT), making use of various biological resources in dense and mixed polyculture situations without external material inputs.


Reformation of Multi-Scale Omics with respect to the "Hidden Reef Model"[edit]

Description[edit]

The complexities underlying the evidence construction in food science create the burden of “devil’s proof” in obtaining an integrated view beyond experimental conditions and data limitations. One cannot test a hypothesis that distinguishes true causal factors without a sufficiently comprehensive setting that extensively involves potentially related variables. Separated efforts in different disciplines methodologically omit the possibility of discovering a unified framework where the variables in different fields are coupled and mutually affect each other in real situations. In order to ease this burden and establish a multi-scale integrative model, various terminologies in the food and medical sciences such as “risk factors” and “beneficial components” should be integrated with explicit representation of latent variables in the background, represented in Fig. 2 (a1-3) as the “hidden reef model” that integrates observable (red and green circles) and latent (blue line) variables in biological study.[4]

Fig. 2. (a1-a3): “Hidden reef model” that integrates observable (red and green circles) and latent (blue line) variables in biological study. (b1-b3): Balance model of food variables with respect to evolutionary stable state (ESS, set as the green circles). (c): Relationship between human and ecosystem health and farming methods.[5]

(a1): Observable elements of an organism (genotype, nutrition, biomarkers, etc.) are aligned horizontally with the value in vertical axis, normalized as risk (+, red circles) and beneficial (-, green circles) factors with respect to metabolic state conditioned by the net effect of latent variables (blue line).

(a2): Example of drug treatment and associated side effects.

(a3): Example of ideal treatment in relation to the change in environmental factors.


Fig. 2 (b1-b3) show how the in natura-in cultura distinction could serve as an interface to explain the diet-health-environment trilemma, specifically, as a balance model of food variables with respect to evolutionary stable state (ESS) incorporating the hidden reef model in Fig. 2 (a1-a3).

(b1-b3): Balance model of food variables with respect to evolutionary stable state (ESS, set as the green circles). X-axis is the concentration (content per unit weight) of food variable divided by physiological effects or environmental requirement for production. Y-axis is actual amount of the food intake.

(b1): The case of in natura ESS, set as the standard for (b2, b3).

(b2, b3): Example of conversion to in cultura environment: micronutrient deficiency (left shift of red circles in b2) associated with calorie overtake (right shift of a red circle in b3). The terms sufficiency, deficiency, and excess in the legends describe the signification of blue and red rectangle surfaces when X values represent the concentration per physiological requirement of the food variables. As a different application, if we discuss the environmental impact of food by plotting the value of concentration per environmental requirement on X, these surfaces should be reinterpreted as the amount of necessary (blue rectangles in b2 and b3), reduced (red rectangles in b2), and excess (red rectangle in b3) loads, respectively.


The relationship between human and ecosystem health and farming methods is schematized in Fig. 2 (c), providing reference to the modes of agricultural production and consequent health benefits and risks.

Horizontal axis aligns the farming methods from the physiological (right) to ecological (left) optimum of plant communities, which corresponds to fewer (right) to higher (left) regulation services with high risk (red) and benefit (green) to ecosystem health.

Vertical axis is the state of human health or that of a single species, from risk (bottom, red) to benefit (top, green), which converges to ESS on the benefit side through evolution (orange arrow), conforming to (a3). Through the historical development of farming systems toward the physiological optimum of a single crop, human longevity and basic nutrients in the food matrix have been dramatically improved (upper right cyan arrow), while problems of micronutrient deficiency and calorie excess became newly dominant as system dysfunction, typically pervading non-communicable diseases (NCD) (lower right red arrow).

On the other hand, intensification toward the ecological optimum beyond the natural state, such as synecological farming (Synecoculture), creates a new integrative approach that has the potential to address both human and ecological health as positively interacting solutions (upper left green arrow). The augmentation scenario may be associated with new evolutionary pressure on the human population reflected by demographic transition and food and ecosystem changes (lower left cyan arrow). Conflicting directions between human and ecosystem health are indicated by cyan arrows.


Preventing and Reversing Agriculture-Induced Regime Shifts with in natura Augmentation of Ecosystems[edit]

Description[edit]

One potential outcome of the Anthropocene trajectory, if we succeed in achieving such augmentation of ecosystems through the majority of primary food production, especially on the part of smallholders in the developing world, is that it could be a major driving force to sustain our social-ecological systems. Fig. 3 (a1-a3) shows a possible mechanism and scenario for the prevention and reversal of the ecological regime shift.

Fig. 3. Possible scenario of prevention and reversal of ecological state shift (a1-a3) and expected outcome on ecosystem services (ES) (b1-b4).[6]

(a1): Relation between the evolution of Earth system, agricultural degradation, terraforming, and augmentation of the ecosystem with respect to ecological state.

(a2): Typical phase diagram of the ecological state shift with conventional (blue) and augmentation (red) scenarios in response to anthropogenic forcing such as agricultural land conversion, industrial pollution, and urbanization.

(a3): Estimated dynamics of the prevention and reversal of ecological state shift.


Fig. 3 (b1-b4) shows the expected yield of ecosystem services (ES) with pricing mechanisms under different scenarios of development, conservation, and augmentation. All figures represent the supply-demand curve of ecosystem services according to [7] with X-axis: Quantity and Y-axis: Price.

(b1): Pricing of ES in case of natural supply without human-induced degradation.

(b2): Pricing of ES in case of degraded supply under anthropogenic forcing.

(b3): Pricing of ES in a conservation scenario where humans pay a cost for the recovery of natural ecosystems to the level of natural supply.

(b4): Pricing of ES in the augmentation of ecosystems beyond conservation.


Ecology of Augmented Ecosystems[edit]

The prediction of the future ecological state and the transformation of food production into sustainable modalities should work in parallel, with greater and urgent importance placed on the substitution process of conventional agriculture. The complexity of social-ecological interactions in the augmentation scenario (i.e., the open systems) does not allow us to simply separately combine simulated projections of human activities and reactions of the climate system (i.e., the closed systems), although this is mainly what has been done in the prediction of global warming scenarios by IPCC.[8] Furthermore, the reactions of vegetation seasonality are much more complex than those related to the climate system, and we are also faced with the impossibility of estimating future trends117, which calls for the possibility of measurement with crowd-sourced solutions such as citizen science [9] [10] [11]. Once successful augmentation of ecosystems takes place, predictions based on past data should be completely revised upward, and further prediction will require new investigation into emerging mechanisms that may be in a different alternative stable state of ecosystem.

The ecology of augmented ecosystems, which includes multi-scale interventions and interactions between human society, technological solutions, legal frameworks, natural and modified ecosystems, and the health profiles of humans and wildlife, must be established through real-time on-site management of open complex systems.[12] [13]

Three-step development of scientific reductionism for clarifying complex systems phenomena[edit]

Description[edit]

Fig. 4. Three steps of scientific reductionism for clarifying complex systems phenomena.[14]

In Fig. 4, the target system subject to control is depicted with green elements with green arrows showing the interactions between them. The intervention system through which we control the target system is represented with blue elements and interactions, which also represent the modification of “sea surface” (blue line in the Hidden Reef model Fig. 2(a1-a3)). The effect of the intervention on the target system is shown with orange arrows. Typical examples of target and intervention systems related to the food industry are included in each figure.

(a): Elementary reductionism where the target system is controlled with a single element. A typical example is the internal metabolic state of a human affected by nutrition supplements and drugs. Scientific methodologies are based on a single molecular determinant paradigm and it is possible to apply rigid statistical testing such as a randomized controlled trial (RCT). It can provide solutions to problems based on the deficiency and/or excess of the elements.

(b): Systems theory based on an isolated definition of the systems. Not only the target system but also the intervention form stable systems with constant internal dynamics. The consistency of dynamics enables modeling at the level of macroscopic system properties (as represented by red circles) such as mean values in the food matrix; traditional community-based food systems; standardized production methods of farming; systematized medical treatments; and other fixed protocol of intervention and subsequent examination of the comprehensive effect on the target system. Scientific methodologies call for the measurement of system-wide characteristics, such as the variation of system responses; hierarchical orders of correlation statistics;[15] and extraction of effective contexts where the system manifests desirable performances in response to a sequence of interventions. The macroscopic variables can include latent variables that escape from element-level analyses, as well as the emergent properties of system dynamics.

Newly approachable examples with this model include public health issues such as malnutrition and epidemic outbreaks in short time scale, and can provide partial solutions to such system-dysfunction problems.

(c): Management of open complex systems. Beyond scientific formalization, key real phenomena are mostly in the open environment and are dynamically changing their structure. The systems interact with other components in the outside environment (black dotted circles, which also correspond to latent variables (blue line) in the Hidden reef model Fig. 2 (a1-a3) and temporally change their composition (from dashed to solid red circles) through bidirectional interactions (orange arrows) between intervention and target systems. Rigorous modeling is limited in such situations, but it is still possible to refine the working hypothesis through constant renewal of the model with internal observation.[16] Measurements, statistical analysis, modeling, and human experience should work together in order to achieve better prediction and controllability.[17] The closed systems modeling in (b) is only an approximation of open systems to isolated subsystems with temporally stable structures.

Typical phenomena that can only be addressed with such open systems framework reside in long-term recursive interactions between human society and natural ecosystems, with highest complexity of system dysfunction risks: food system change including culture condition change, typically the transition between in natura and in cultura conditions;[18] lifestyle change in face of demographic change and industrialization;[19] food crises that emerge from the entanglement of various dynamic stresses, such as climate change and natural disasters, crop and market failure, poverty and insecurity, and political instability;[20] trans-generational and life course factors of chronic diseases[21] and zoonoses;[22] and social-ecological effects of climate change ranging from recent global warming[23] to human evolution.[24] Human augmentation of ecosystems also becomes manageable with the open systems framework.


Bibliography[edit]

ICT Applications[edit]

MMS: Megadiversity Management System[edit]

· ICT platform for Open Systems Data Analytics

· System that can be applied to the management of the overall system with high diversity and utilities (e.g. Synecoculture farming method)

Open Systems Data Analytics[edit]

· Data analysis method for the open system science

· Perform the following procedure (Fig. 5)

1. Define the problem and its area

2. Define the variables and collect the data

3. Construct a useful model for management (including causal and statistical models)

4. If it is difficult to interpret the causal relationship in the model, there is a possibility that an important latent variable exists. Guess them and add new variable data as much as possible to the data set. Also, select the data and models which performs most significantly.

5. Repeat the 3-4 processes until satisfying results (consensus) are obtained.

6. Build predictions, interventions, and/or theories.

Fig. 5. Dynamical assessment framework as an example of the ICT architecture for Open Systems Data Analytics and a prototype of MMS. [25]


· The MMS represents system implementation guidelines for Open Systems Data Analytics.

· A Reference Implementation by the Synecoculture farming project is being developed as open source (AGPL v3). (The next update is scheduled at spring 2019)


Applications to be implemented[edit]

Synecocultrue Support System (initial conception)[edit]

・Acquire useful vegetation data through the cooperation of stakeholders around the world (citizen science)

・Construct a database of species that can grow in each climate zone. Perform the following analysis and support activities.

1. Extract promoting and inhibiting conditions of target species and apply them to increase diversity

2. Suggest species that can grow in a given area based on the database of growth conditions for each species and combinations of species

P2P matching of production and sales[edit]

・Support of matching between synecoculture producers and consumers

・Support of matching between seed producers and farms (conditional to seed regulation law)

Food truck value chain analysis[edit]

・Information support system for maximizing profit and efficiency of scientific data collection with a food truck business model


Databases in view of integration[edit]

Les AMAP - Associations pour le maintien d'une agriculture paysanne

Yuka - L'application mobile qui scanne votre alimentation

料理レシピ クックパッド

KEGG: Kyoto Encyclopedia of Genes and Genomes

Global Biotic Interactions

Climate Engine

Image Database HANABACHI Based on the Japanese Bees

九州大学昆虫学データベース(KONCHU)

植物和名ー学名インデックス YList

Tree of Life Web Project (ToL)


FOOD Africa[edit]

Preparation of African Project of Synecoculture[edit]

Steps of collaboration:[edit]

1. Make the list of objectives for Africa: sustainable and inclusive agriculture -being the first toward a new agro-ecology - agro-industry-creation of new jobs-transforming rural economy-

2. Create the link to Synecoculture documents and create concrete arguments

3. Launch an international project with African Union

4. [IMPORTANT] Prepare the annexed document of Synecoculture to propose to UN conventions by African Union at CCCP-COP25 2019 and CBD-COP15 2020

The annexed documents are associated with the articles in the conventions and provide the guideline of practice in each country.

Meeting occasions:[edit]

1. CS-DC portal "Big Blue Button" - each month. (next meeting on January 14, 2019) -

2. Meeting with African Union at Addis Ababa (5 - 8 September)

Prepare documents for African Union, CCCP-COP25 and CBD-COP15

3. 5th African forum on Synecoculture, 19-20 September 2019 at Ouagadougou, Burkina Faso

4. CCS'19 in Singapore (26 September - 1 October 2019)

References:[edit]

--- By Masa FUNABASHI --------

Human augmentation of ecosystems: objectives for food production and science by 2045

proceedings-of-the-1st-african-forum-on-synecoculture

proceedings-of-the-2nd-african-forum-on-synecoculture

proceedings-of-the-3rd-african-forum-on-synecoculture

Annexe documents of CBD-COP14 and CCCP-COP24

--- End Masa FUNABASHI -------

--- By Maurice TANKOU --------

--- End Maurice TANKOU -------

Board[edit]

  • Masatoshi FUNABASHI (chair | masa_funabashi"at"csl.sony.co.jp)
  • Paul BOURGINE (chair | )
  • Maurice TANKOU (chair | )
  • André TINDANO (chair | )
  • Patrice ZERBO (chair | )
  • Elvis Paul N. TANGEM (chair | )


  1. M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.
  2. M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.
  3. M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.
  4. M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.
  5. M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.
  6. Funabashi, M. et al. Foundation of CS-DC e-laboratory: open systems exploration for ecosystems leveraging. First Complex Systems Digital Campus World E-Conference 2015. 351-374 (2015).
  7. 1. Costanza, R. et al. The value of the world's ecosystem services and natural capital. Nature 387, 253-260 (1997).
  8. Smith, P. et al. in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Edenhofer, O. et al.) 2014: Agriculture, Forestry and Other Land Use (AFOLU). (Cambridge University Press, 2014).
  9. Funabashi, M. et al. Foundation of CS-DC e-laboratory: open systems exploration for ecosystems leveraging. First Complex Systems Digital Campus World E-Conference 2015. 351-374 (2015).
  10. Funabashi, M. Open systems exploration: An example with ecosystems management. First Complex Systems Digital Campus World E-Conference 2015, 223-243 (2017).
  11. iNaturalist. https://www.inaturalist.org/
  12. Tokoro, M. Open Systems Science: A Challenge to Open Systems Problems. CS-DC world e-conference in First Complex Systems Digital Campus World E-Conference 2015 213-221 (Springer International Publishing, 2017).
  13. Funabashi, M. et al. Foundation of CS-DC e-laboratory: open systems exploration for ecosystems leveraging. First Complex Systems Digital Campus World E-Conference 2015. 351-374 (2015).
  14. M. Funabashi "Human Augmentation of Ecosystems: Objectives for food production and science by 2045." npj Science of Food, 2018.
  15. Funabashi, M. Network Decomposition and Complexity Measures: An Information Geometrical Approach. Entropy 16, 4132-4167 (2014).
  16. Tokoro, M. Open Systems Science: A Challenge to Open Systems Problems. CS-DC world e-conference in First Complex Systems Digital Campus World E-Conference 2015 213-221 (Springer International Publishing, 2017).
  17. Funabashi, M. Citizen Science and Topology of Mind: Complexity, Computation and Criticality in Data-Driven Exploration of Open Complex Systems. Entropy 2017, 19, 181 DOI: 10.3390/e19040181 (2017).
  18. Funabashi, M. Food Components as Markers Linking Health and Environment: Statistical Invariance Analysis of in natura Diet. American Journal of Bioscience and Bioengineering 3, 183-196 (2015).
  19. Ulijaszek, S. J. Human eating behaviour in an evolutionary ecological context. Proc. Nutr. Soc. 61, 517-526 (2002).
  20. FAO (Food and Agriculture Organization). Global report on food crises 2017. http://www.fao.org/3/a-br323e.pdf (FAO, 2017).
  21. Kuh, D. L. & Ben-Shlomo, Y. A Life Course Approach to Chronic Disease Epidemiology; Tracing the Origins of Ill-health from Early to Adult Life. (Oxford University Press, 1997).
  22. One Health Initiative. http://www.onehealthinitiative.com/
  23. Petherick, A. A note of caution. Nat. Clim. Change. 2, 144-145 (2012).
  24. Verginelli, F., Aru, F., Battista, P. & Mariani-Costantini, R. Nutrigenetics in the light of human evolution. J. Nutrigenet Nutrigenomics 2, 91-102 (2009).
  25. M. Funabashi “Open Systems Exploration: An Example with Ecosystems Management” First Complex Systems Digital Campus World E-Conference 2015, Springer Proceedings in Complexity, 2017, Pages 223-243.