In today's data-driven agricultural landscape, breeders and growers face the challenge of managing vast and often sparse datasets, requiring them to navigate missing information and complex data correlations, necessitating innovative solutions like advanced decision-support tools, machine learning models, and improved data capture practices to optimize resource management.

Some members of the team at Cucumber recently attended the evokeAG conference in Brisbane. During a panel discussion on the optimal management of nature capital, Michelle Gortan of the Macdoch Foundation emphasized the importance of systems thinking in understanding the broader value and inherent causality between our food systems and the proper governance of our natural capital, a topic that we feel very passionate about here at Cucumber. The panel’s insights highlighted how integrated approaches are essential for balancing the health of people, economies, and the environment—a concept echoed in resources like the Natural Capital Primer.

Natural capital encompasses the world's stocks of natural resources—such as geology, soils, air, water, and all living organisms—that provide essential goods and services to humanity, from tangible products like food, water, and minerals to vital ecosystem functions like climate regulation, water filtration, and pollination. A systems thinking perspective reveals the intricate interdependencies within these assets, illustrating how the health of one component influences the resilience of the entire system. For instance, healthy soil supports plant growth, which in turn provides food, raw materials, and habitats, while its degradation triggers cascading effects that disrupt ecological stability and economic security.

Changing climatic patterns and biodiversity loss further threaten the productive capacity of natural resources, impacting biosecurity and established supply chains. Concepts like Planetary Health and One Health stress the need for integrated approaches that balance the well-being of people, animals, and ecosystems 1. By embracing systems thinking, we can navigate these complexities, fostering collaborative solutions that sustain and regenerate natural capital for future generations.

At first sight, these seemingly discrete sub-systems of this illustrated One Health ecosystem do not seem to be clearly correlated. However, in 1931 the Russian evolutionist Paul Terentjev, described how in organisms within nature there are correlations among measurable characteristics of seemingly discrete elements, making these elements a cluster, or pleiades 2. This became known as a 'correlation pleiades', which can be discovered throughout nature's complex systems, but often veiled as hidden patterns or forbidden links 3. Discovering such ‘hidden links’ (not all relationships are easily recognisable) are very important since they might also serve as network drivers and resemble 'organic' network control systems, which if better understood, could support much improved decision-making capabilities 4.

However, when these network drivers are not properly understood, we can often experience sudden and unforeseen consequences. Complex systems have a habit of backfiring in unforeseen ways, which is equally true in wildlife conservation. One such non-linear feedback mechanism are called sliding reinforcers 5,

In conservation contexts, sliding reinforcers can include such phenomena as the construction of infrastructure (roads may allow an area to benefit from ecotourism, but also facilitate the entrance of undesirable influences, such as poaching); gradual changes in the demands of resource users (e.g., unreasonable expectations for increases in hunting quotas that were originally designed to facilitate responsible co-management); pesticide and fertilizer use (small amounts can improve crop yields, but overuse harms the environment); and deliberately introduced exotic species, such as biocontrol agents, that are initially beneficial but subsequently become invasive. Feedbacks may influence the values of two variables of interest in the same direction simultaneously, meaning that correlations and other simple statistics are not suited to detecting them 5.

In an era of unprecedented environmental challenges, understanding the complex interdependencies within our natural ecosystems is more crucial than ever. Systems thinking offers a powerful framework for interpreting these complexities, enabling us to co-create resilient natural capital that is both beneficial for indigenous custodians and stewards of the land (such as farmers and growers) but also consumers.

The Paradigm Effect and New Ways of Thinking

The Tuli Circle Example from Africa

Historically, transformative ideas that challenge established paradigms often face significant resistance. Thomas Kuhn's concept of "paradigm shifts" highlights how prevailing beliefs can impede the acceptance of new knowledge 6. A compelling example is the work of Allan Savory, who observed environmental degradation in regions like the Tuli Circle in Africa.

Contrary to the prevailing belief that removing livestock would restore these fragile ecosystems, Savory discovered that strategic grazing by large herds could regenerate land 7. This insight contradicted established scientific understanding and faced initial opposition, illustrating the paradigm effect's influence on our willingness to embrace new solutions.

Adaptive Management and Real-Time Feedback Loops

An Example – Our Greenhouse Project

Cultivating technology-enabled real-time feedback loops is critical for effectively governing natural capital, which operates as a complex adaptive system and is constantly evolving. Here at Cucumber we employ a range of innovative sensing technologies, ranging from MEMS through to novel photonic systems, which ensures granular data provenance.

These advanced tools enable us to conserve the informational flow from the biome through to decision-making leverage points (or “decision constellations” as we like to call them), enhancing our ability to respond to environmental changes promptly and accurately define root-causes within a system. This enhances our ability to design-in resilience and respond rapidly to evolving circumstances, such as pathogens and pests. Conversely, interpreting the molecular realm, such as Biogenic Volatile Organic Compounds, enables us to interpret the informational signalling from crops and forests, which unlocks a new realm of data that is often missed or lost.

Volatile organic compounds are organic compounds released by plants that play crucial roles in defence mechanisms, signalling, and ecosystem interactions 8–11. Understanding these emissions can enhance agricultural practices by providing real-time health assessments of crops. Tomato plants emit various VOCs, including terpenes like isoprene, monoterpenes, and sesquiterpenes 12–16. These compounds are pivotal for plant communication and stress responses, making them valuable bio-proxies for event-monitoring that occur at the resource, with use cases such as optimal growth, emissions tracking and pathogen detection.

BioCanvas Platform

Interpreting On-Field Events via Bio-Physical Signalling i.e. BVOCs

Fingerprinting Foods for Traceability within Supply Chains

Fostering Adaptable and Agile Supply Chains

A Kiwifruit Industry Example

In today's data-driven agricultural landscape, breeders and growers face significant challenges in managing and utilizing vast amounts of data. Breeders are tasked with sifting through extensive, often inconsistent datasets to select individuals for advancement, a process complicated by missing information, fuzzy correlations between stages, and the need to balance trade-offs between traits. Inefficiencies arise from dispersed data and incomplete records, making it difficult to optimize breeding selections and manage resources effectively. Additionally, integrating genetic and phenotypic data to extract actionable insights remains a critical hurdle. Addressing these pain points requires innovative solutions such as advanced decision-support tools, machine learning models, and improved data capture practices to streamline analysis, enhance decision-making, and drive efficiencies in breeding programs.

• The Missing Data Points: Conventional data-capture methods often overlook critical data points, leading to an incomplete understanding which represents itself as ‘loss of information’ within supply chain ecosystems. This inherently limits the information available for decision-making.

• Supply Chain ‘Wearables’ as Trustworthy Bio-Proxies: There is a pressing need for cost-effective, portable detection systems that yield data which can link with other contextual data sources, including domain-specific data ontologies, to create a comprehensive view of ecosystem health and optimal productive state. This can help unpack ‘the hidden links’ and provide more complete decision constellations.

• Bi-lateral Compliance and Export Readiness: There is a critical need for solutions that can automate and augment compliance reporting, via means such as digital certificates (e.g. certificates of low prevalence), providing stakeholders with assurance that practices are compliant with local and global standards.

Systems thinking underpins adaptive management by promoting iterative cycles of action, monitoring, and insight 17, 18.

Scaling the Stratum - Biogenic Data as a ‘Product’ with Provenance

In today's data-driven world, biogenic data – information derived from biological processes – is an invaluable asset. However, much of this data is lost or underutilized. Nature, in contrast, is an efficient custodian of information, conserving data through mechanisms like the mass-energy-information equivalence principle, as manifested through phenomena such as Hawking radiation 19. This reality suggests that the flow of information is just as fundamental as the flow of energy and matter in maintaining the integrity of natural systems.

Physics has also evolved to reflect this understanding on the nature of information. As Mark Buchanan noted in his book Nexus 21,

"Physicists, in particular, have entered into a new stage of their science and have come to realize that physics is not only about physics anymore, about liquids, gases, electromagnetic fields, and physical stuff in all its forms. At a deeper level, physics is really about organization – it is an exploration of the laws of pure form."

The implication is that the network, or the informational architecture of relationships and interactions, is as crucial as the physical entities involved. This understanding naturally extends to biological systems, where the pathways and networks governing biochemical interactions are pivotal.

The conservation of information and its flow through various strata – from the molecular level to the biome – reflects an overarching organizational principle. In this sense, biological data becomes more meaningful not just as isolated entities but as part of a broader, interconnected system. This perspective aligns with systems biology, which emphasizes that the behavior of biological systems can't be understood merely by studying individual components. Instead, the interactions and regulatory networks that connect these components are paramount 22.

Emulating nature, which excels at preserving and conserving information, can help us minimize data loss and enhance the utility of biogenic data for governance and decision-making. In informational terms, we must treat biogenic data as a “product” to be conserved, ensuring its provenance and integrity throughout the bio-informational stratum.

The journey from molecular data to biome-level insights involves scaling this informational stratum. By integrating molecular data with environmental context, we can discern patterns and trends in ecosystem responses to various stressors. This holistic approach bridges the gap between molecular-level changes and their ecological impacts, facilitating accurate and reliable data-driven narratives.

Scaling the stratum allows us to build data-driven stories that encompass the complexity of natural ecosystems. These stories are constructed by interpreting molecular data within the broader context of environmental conditions, species interactions, and human influences. These data-driven narratives empower stakeholders with actionable insights that support sustainable ecosystem management. They bridge the gap between scientific research and practical decision-making, fostering a collaborative approach to addressing biomonitoring challenges 23.

References

1.            Morrison, M. et al. A growing crisis for One Health: Impacts of plastic pollution across layers of biological function. Front. Mar. Sci. 9, 980705 (2022).

2.            Berg, R. L. The Ecological Significance of Correlation Pleiades. Evolution 14, 171–180 (1960).

3.            Izquierdo-Palma, J., Arizmendi, M. del C., Lara, C. & Ornelas, J. F. Forbidden links, trait matching and modularity in plant-hummingbird networks: Are specialized modules characterized by higher phenotypic floral integration? PeerJ 9, e10974 (2021).

4.            Gautam, M. K. et al. A Comprehensive Review of the Evolution of Networked Control System Technology and Its Future Potentials. Sustainability 13, 2962 (2021).

5.            Cumming, G. S. A Review of Social Dilemmas and Social‐Ecological Traps in Conservation and Natural Resource Management. CONSERVATION LETTERS 11, e12376 (2018).

6.            Kuhn, T. S. & Hacking, I. The Structure of Scientific Revolutions. (University of Chicago press, Chicago, 2012).

7.            Savory, A. & Butterfield, J. Holistic Management: A Commonsense Revolution to Restore Our Environment. (Island Press, Washington, 2016).

8.            Sanaei, A. et al. Changes in biodiversity impact atmospheric chemistry and climate through plant volatiles and particles. Commun Earth Environ 4, 445 (2023).

9.            Laothawornkitkul, J., Taylor, J. E., Paul, N. D. & Hewitt, C. N. Biogenic volatile organic compounds in the Earth system. New Phytologist 183, 27–51 (2009).

10.          Dudareva, N., Klempien, A., Muhlemann, J. K. & Kaplan, I. Biosynthesis, function and metabolic engineering of plant volatile organic compounds. New Phytologist 198, 16–32 (2013).

11.          Vivaldo, G., Masi, E., Taiti, C., Caldarelli, G. & Mancuso, S. The network of plants volatile organic compounds. Sci Rep 7, 11050 (2017).

12.          Lee Díaz, A. S., Rizaludin, M. S., Zweers, H., Raaijmakers, J. M. & Garbeva, P. Exploring the Volatiles Released from Roots of Wild and Domesticated Tomato Plants under Insect Attack. Molecules 27, 1612 (2022).

13.          Li, J., Di, T. & Bai, J. Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars. Molecules 24, 2594 (2019).

14.          Nawrocka, J., Szymczak, K., Skwarek-Fadecka, M. & Małolepsza, U. Toward the Analysis of Volatile Organic Compounds from Tomato Plants (Solanum lycopersicum L.) Treated with Trichoderma virens or/and Botrytis cinerea. Cells 12, 1271 (2023).

15.          Takayama, K. et al. Emission index for evaluation of volatile organic compounds emitted from tomato plants in greenhouses. Biosystems Engineering 113, 220–228 (2012).

16.          Dehimeche, N., Buatois, B., Bertin, N. & Staudt, M. Insights into the Intraspecific Variability of the above and Belowground Emissions of Volatile Organic Compounds in Tomato. Molecules 26, 237 (2021).

17.          Allen, W. & Kilvington, M. An Introduction to Systems Thinking and Systemic Design – Concepts and Tools.

18.          Boardman, J. & Sauser, B. Systems Thinking: Coping with 21st Century Problems. (CRC Press, Boca Raton, FL, 2008).

19.          Vopson, M. M. The mass-energy-information equivalence principle. AIP Advances 9, 095206 (2019).

20.          Lévy, P. The Semantic Sphere 1: Computation, Cognition and Information Economy. (Iste, London, 2011).

21.          Buchanan, M. Nexus: Small Worlds and the Groundbreaking Science of Networks. (W.W. Norton, New York, 2002).

22.          Kim, Y. et al. Quantum Biology: An Update and Perspective. Quantum Reports 3, 80–126 (2021).

23.          Makiola, A. et al. Key Questions for Next-Generation Biomonitoring. Front. Environ. Sci. 7, 197 (2020).