PowerPoint Presentation

[Audio] In Modules 1 through 4, we mapped the present: the institutions, instruments, technologies, and sustainability frameworks that define how agricultural funding works today. In this module, we turn to what comes next. Not as prediction — no one can predict the funding environment of 2030 with confidence — but as structured strategic preparation..

[Audio] et me start with a question that might seem obvious: why do scenario planning at all? Why not simply extrapolate current trends and plan accordingly? The answer is that the funding environment is not a stable system being incrementally adjusted. It is a system being reshaped simultaneously by regulatory change, geopolitical shock, technological disruption, and climate pressure. The combination, speed, and interaction of these forces is genuinely uncertain. Extrapolation assumes tomorrow will resemble yesterday. Scenario planning assumes it might not. Scenario planning offers four specific advantages: First, it exposes blind spots. It forces explicit consideration of low-probability, high-impact events that planning teams typically ignore — geopolitical shocks, regulatory reversals, technological breakthroughs. The organisations that had thought through a scenario roughly resembling the COVID-19 pandemic fared significantly better than those who hadn't. The same applies to the Ukraine war's impact on EU agricultural investment priorities. Neither was predicted. Both had been scenario-mapped by some institutions. Second, it aligns stakeholders. Scenarios create a shared language for discussing uncertainty. Teams and organisations that have worked through futures together make faster, better-aligned decisions when disruptions actually occur. The conversation has already happened; activation is quicker. Third, it tests strategic resilience. Scenario analysis reveals which strategies work across multiple futures — what we call robust strategies — versus strategies that only work in one specific future, which are fragile. This distinction is critical for investment decisions made today that will play out over five to ten years. Fourth, it enables early signals monitoring. Each scenario comes with observable 'signposts' — measurable indicators that tell you which future is beginning to materialise. This turns planning into a live monitoring system. You are not waiting for the future to arrive; you are watching for it..

[Audio] Before we get into the five scenarios, let me walk you through the three analytical tools that underpin this module. PESTLE Analysis maps the macro-forces shaping the funding environment across six categories: Political: EU Green Deal trajectory, CAP post-2027 negotiations, food sovereignty mandates Economic: interest rate environment, VC pullback from riskier asset classes, public budget constraints Social: farmer digital literacy, food system trust, rural demographic shift Technological: AI in grant management, satellite MRV maturity, smart contract legal readiness Legal: CSRD and Data Act enforcement timelines, EU Taxonomy implementation pace Environmental: biodiversity loss urgency, drought frequency, carbon price volatility How to use it: Map each of these six force categories against your organisation's funding dependencies. Score each force as HIGH, MEDIUM, or LOW impact over a five-year horizon. Forces scoring HIGH across three or more scenarios become your strategic priorities — they are the forces you must respond to regardless of which future arrives. Technology Radar answers the question: which technologies should I invest in, trial, or wait on? The four positions are: ADOPT now: AI grant matching, satellite-verified carbon, parametric insurance — these are operational and competitively necessary TRIAL: Smart contract milestone disbursements, digital twin funding evidence — scaling and worth structured piloting ASSESS: Agricultural DAO governance, tokenised nature credits — signal value is real; deployment is premature HOLD: DeFi agricultural lending, full DAO cooperative replacement — regulatory uncertainty too high before 2030 How to use it: Place each relevant technology in one of four rings based on readiness and strategic relevance. Reassess quarterly. Move technologies inward as evidence builds. Risk-Opportunity Quadrant plots identified risks and opportunities on two axes — probability and impact — to determine your response: ACT NOW: CSRD Scope 3 cascade, EU Taxonomy alignment, AI proposal screening MONITOR: EU ETS agriculture inclusion, agricultural DAO legal framework, food security emergency funds MANAGE: Horizon Europe call complexity, reporting burden, carbon price short-term volatility DEPRIORITISE: DeFi lending, full DAO governance replacement before 2030 Together, these three tools give you the analytical infrastructure to navigate what follows..

[Audio] We have identified five plausible scenarios for the agricultural funding landscape through 2030. They are not mutually exclusive — elements of multiple scenarios may materialise simultaneously. But each represents a distinct dominant logic, a different answer to the question: what primarily drives capital allocation to digital agriculture? The five scenarios, mapped across time horizons: S1 — Blended Finance (Near: 2–4 years): Public grants become catalytic first-loss instruments. The weight of agricultural investment is carried by private capital, outcome-based payments, and impact bonds. S2 — Green Finance Shift (Near: 2–4 years): ESG alignment becomes the primary eligibility filter for agricultural capital. Carbon-linked subsidies, biodiversity credits, and water stewardship incentives dominate the funding landscape. S3 — Full Digital Transformation (Mid: 4–7 years): Technology becomes the funding infrastructure itself. Applications evaluated algorithmically, payments automated, compliance monitored in real time. S4 — Community-Driven Ecosystem (Long: 7–10 years): Cooperative DAOs, regional agro-innovation funds, and community-governed capital pools create a parallel funding architecture alongside institutional systems. S5 — Crisis-Driven Funding Shift (Unpredictable): Geopolitical disruption, climate shocks, or food security emergencies redirect agricultural investment priorities — rapidly, unpredictably, and at scale. I want to draw your attention to four short passages on this slide that describe what each scenario feels like at the strategic level — because these are not just structural descriptions; they are signals about the culture and logic that would govern funding decisions. For S1, the signal that this scenario has arrived is not a policy announcement. It is the first time a major European bank tells an agricultural SME that green alignment is a prerequisite for a standard business loan. That conversation is already happening in some Member States. The question is how quickly it becomes universal. For S3, this is a structural replacement of the human-mediated funding process with a technology-mediated one. The proposal is read first by a machine. This is not speculative — the EIC Accelerator already uses AI-assisted pre-screening, the EU's Integrated Administration and Control System already uses satellite imagery to verify CAP payments without field inspectors, and the European Blockchain Services Infrastructure is actively testing smart contract disbursements. For S4, the governance culture change it requires is a shift from centralised allocation to community ownership, from board decisions to on-chain voting, from institutional trust to cooperative trust. That does not follow the same timeline as a technology deployment. It follows the timeline of human institutions, which is slower and less predictable. For S5, the precedent is already established. Russia's invasion of Ukraine in February 2022 triggered immediate CAP emergency payments, restructured supply chain investment priorities, and put food security — largely absent from strategic EU investment narratives since the 1970s — back at the centre of the conversation. Institutions that had never considered emergency disbursement mechanisms suddenly needed them operational within weeks..

[Audio] What drives it: Four converging forces push toward this scenario. Institutional ESG mandates are directing pension and insurance capital into agricultural impact at increasing scale. Outcome-based contracting frameworks are maturing in EU public procurement practice. Grant budget pressure — across Member States and at the EU level — is forcing public funders to leverage private capital more efficiently rather than simply issuing more grants. And Development Finance Institutions — EIB, EBRD — are actively anchoring blended agri-tech investment funds, providing the credibility layer that attracts private co-investors. What the future state looks like: Public grants cover only first-loss tranches; private capital carries senior risk positions. Social Impact Bonds for regenerative agriculture and farmer transition become mainstream instruments. Outcome measurement infrastructure replaces output reporting across all funded activities. The fundamental logic shifts: we no longer fund activities, we fund results. Observable signals that this scenario is arriving: Watch for: InvestEU's agricultural window exceeding €5Bn in blended deal flow; the first EU-wide agricultural Social Impact Bond programme launched by the European Commission; a major agri-food corporate issuing outcome-linked supply chain transition finance; EIB increasing its agri-food Climate Bank Roadmap target to €10Bn by 2027; and pay-for-performance contracts beginning to replace activity-based CAP subsidy instruments. Strategic implication: If you are still designing projects primarily for output-based grant compliance, your model is already becoming outdated in this scenario. The organisations building outcome measurement infrastructure today are positioning themselves as the investable assets of 2027.

[Audio] Horizon: 2–4 years. ESG as the primary eligibility filter for agricultural capital. What drives it: CSRD's Scope 3 cascade reaches agri-food supply chains in full by 2026, creating mandatory demand for farm-level sustainability data at corporate scale. The EU Nature Restoration Law (2024) creates binding biodiversity finance obligations, opening new credit markets. The voluntary carbon market is maturing, with high-quality agricultural credits commanding premium prices. Post-2027 CAP eco-schemes are expanding with outcome-verified payment architecture. And the EU Green Bond Standard (2023) is channelling institutional capital toward Taxonomy-aligned activities with increasing volume. What the future state looks like: Carbon, biodiversity, and water credits form a stacked revenue layer for farmers — multiple income streams from the same verified sustainable practice. ESG data APIs become a commercial product that corporate clients pay for as a Scope 3 supply chain compliance tool. Agri-tech platforms are valued primarily on their verified environmental data assets, not their software. And green bond issuance for national agri-transition programmes becomes standard sovereign practice. Observable signals that this scenario is arriving: Watch for: post-2027 CAP allocating more than 40% of eco-scheme budget to verified outcome payments; the first EU-regulated credit standard adopted under the Nature Restoration Law; a major EU bank making Taxonomy alignment a lending prerequisite for agri-SMEs; and CSRD Scope 3 enforcement triggering measurable corporate pressure on agri-food suppliers. Strategic implication: Your ESG data architecture is not a compliance burden in this scenario — it is your primary commercial asset. The platforms that built verified data pipelines first will capture the institutional capital that arrives second..

[Audio] Horizon: 4–7 years. Technology becomes the funding infrastructure itself. What drives it: EU EBSI scales blockchain infrastructure to public grant management at Member State level. The EIC Accelerator's AI screening extends across all Horizon Europe instruments, establishing algorithmic evaluation as the standard. The EU Data Act creates mandated interoperability across agricultural data spaces, eliminating the siloed data environments that currently block automation. And administrative cost pressure forces public funders toward automated processing — not as an innovation agenda, but as a budget necessity. What the future state looks like: Grant applications are evaluated algorithmically within 48 hours of submission. An algorithmic credibility score — built from data quality, prior project performance, ESG metrics, and financial health — becomes a prerequisite for institutional investment, much as credit scores gate consumer lending today. Real-time compliance dashboards replace annual narrative reports entirely. Funder-innovator matching platforms surface calls before public announcement, giving algorithmically well-positioned projects a structural first-mover advantage. Observable signals that this scenario is arriving: Watch for: EIC AI screening success rates statistically diverging from manually-prepared proposals — the data will show this first; the first fully automated EU grant disbursement via smart contract publicly reported by an EU institution; a national paying agency eliminating on-site CAP inspections in favour of AMS-only verification; and a major EU bank mandating structured, machine-readable ESG data as a condition for agri-lending. Strategic implication: In this scenario, data quality is not just about credibility — it is the mechanism by which your project reaches decision-makers. Unstructured, non-interoperable, manually reported data is the equivalent of an unreadable application. Organisations that invest in structured, machine-readable, FAIR data infrastructure today are building their algorithmic reputation for 2028.

[Audio] Horizon: 7–10 years. Cooperative DAOs and community-governed capital as a parallel funding architecture. What drives it: Five forces converge over the longer term. EU MiCA regulation (2024) has created a legal framework that, over time, enables agricultural DAOs to operate with legal personality. Cohesion Policy decentralisation is devolving innovation fund governance to the regional level, creating institutional space for community allocation models. Disillusionment with centralised grant bureaucracy is accelerating interest in cooperative alternatives — particularly among farming communities that have long felt excluded from the grant ecosystem. Farmer digital literacy is improving through CAP digitalisation support programmes. And community carbon aggregation is creating a new economic incentive for collective governance — smallholder farmers who cannot individually access institutional carbon markets can do so collectively. What the future state looks like: Regional DAOs govern the allocation of pooled EU structural innovation funds. Cooperative digital voting replaces traditional board-governed farm fund structures. Community carbon pools aggregate smallholder credits for institutional market access. Platform fee models replace product sales for agri-tech serving DAO infrastructure. And farmer-governors hold both equity and governance tokens in platform ecosystems — aligning economic interest with governance responsibility. Observable signals that this scenario is arriving: Watch for: the first EU-legally-registered agricultural DAO under the MiCA framework; an ERDF programme formally delegating allocation decisions to a DAO-governed committee; a major farmer cooperative adopting on-chain governance for investment decisions; a Living Lab network launching a shared digital resource pool with tokenised governance; and a regional agro-innovation fund adopting community voting for portfolio selection. Strategic implication: This is the longest-horizon and most culturally dependent scenario. But the direction of travel is already visible in cooperative digital voting pilots and carbon aggregation models. Organisations that begin building governance literacy and community trust infrastructure now will be positioned to lead when this architecture becomes mainstream — rather than scrambling to adapt to it..

[Audio] Horizon: Unpredictable. Geopolitical disruption and climate shocks redirect investment priorities at speed. What drives it: The Russia–Ukraine war has already established the precedent: food security can be reframed as national security priority within weeks of a geopolitical event. Climate extreme event frequency is damaging yield stability across EU production regions, creating recurring emergency budget pressures. Supply chain fragility — demonstrated repeatedly since 2020 — is triggering strategic food production sovereignty investments. Emergency budget instruments deployed during COVID-19 and the energy crisis have demonstrated that rapid, large-scale capital reallocation is institutionally possible. And global food price volatility creates the political pressure that accelerates public intervention. What the future state looks like: Food security replaces climate as the primary stated driver of agricultural R&D investment. Emergency EU budget instruments are deployed at speed — months, not years. Climate adaptation overtakes climate mitigation as the primary agricultural funding priority: the emphasis shifts from reducing emissions to protecting yield stability and supply chain resilience. Short supply chains, local precision yield technology, and resilience-focused agri-tech attract premium valuations — redefining what 'strategic' means in the eyes of institutional investors. NATO and EU food security frameworks explicitly classify agri-tech as resilience infrastructure. Observable signals that this scenario is arriving: Watch for: EU activation of Article 219 emergency market measures for food supply disruption; a NATO/EU food security framework explicitly funding agri-tech as resilience infrastructure; major pension funds reclassifying agricultural land as a defensive asset class; Horizon Europe mid-term revision significantly increasing the food security mission budget; and national governments announcing strategic food technology reserve programmes. Strategic implication: This scenario is the most uncomfortable to plan for, because it requires holding a dual narrative — climate leadership and food security resilience — simultaneously. Projects that can demonstrate contribution to both are significantly more robust to a funding environment that could shift primary emphasis between these objectives within months. Narrative flexibility is itself a strategic asset.

[Audio] Turning scenarios into a early-warning system. . These are specific, measurable indicators that signal which scenario is beginning to materialise. The key signposts, and the scenarios they each point toward: InvestEU agri blended volume exceeding €5Bn → S1 (Blended Finance) CAP eco-scheme share of direct payments linked to verified ecological outcomes exceeding 40% → S1, S2 First EU-wide agricultural Social Impact Bond programme launched → S1, S2 (cross-scenario signal) EU Commission adopting a binding biodiversity credit framework under the Nature Restoration Law → S2 CSRD Scope 3 enforcement: EU formally sanctions the first large company for inadequate agri supply chain reporting → S2, S1 Voluntary carbon market price for high-quality agricultural credits exceeding €60/tCO₂e → S2, S1 EIC AI screening success rates statistically diverging from manually prepared proposals → S3 First fully automated EU grant disbursement via smart contract publicly reported → S3 ERDF programme formally delegating allocation decisions to a DAO-governed committee → S4 EU officially classifying precision agriculture technology as critical infrastructure → S5 EU activation of Article 219 emergency market measures for food supply disruption → S5 How to use this: Assign a team member or automated monitoring function to track these signposts quarterly. When two or more signposts for the same scenario move within a 6-month window, treat that as an activation signal for the corresponding contingency strategy. This converts scenarios from a planning exercise into a live decision-support system.

[Audio] This table is the analytical core of the module. It compares all five scenarios across five strategic dimensions — revealing both the differences and, critically, the robust commonalities. Three things stand out from this analysis: First, the evidence requirement shifts in every scenario, but all of them require evidence. The format changes — impact measurement in S1, dMRV in S2, structured data APIs in S3, on-chain records in S4, yield data in S5 — but the underlying requirement for independent, verifiable, structured data is constant across all five futures. Investing in data infrastructure is the most scenario-robust action available. Second, the failure mode in every scenario is a different version of the same problem. Not being ready means: an investor relations gap in S1, an ESG data gap in S2, algorithmic exclusion in S3, governance capture in S4, and mission misalignment in S5. In each case, the organisation that was not prepared is not simply slower — it is effectively excluded from the dominant capital allocation mechanism of that future. Third, narrative flexibility is a cross-scenario asset. In S2, your climate narrative is primary. In S5, your food security narrative is primary. In S1, your outcomes narrative is primary. Organisations that have only one story to tell about what they do are fragile. Those that can credibly articulate climate, food security, and social outcomes simultaneously are robust across the scenario set..

[Audio] For public administrators: In S1, pilot AI-assisted grant evaluation now. Early movers will set the standards that become mandatory by 2030 — regulatory first-mover advantage is real in EU policy processes. In S3, begin devolving innovation fund governance to regional community structures. Build the infrastructure before it becomes politically mandated — reactive adaptation is always more costly than proactive positioning. In S5, redesign programme architecture with public grants as catalytic first-loss instruments, not primary capital vehicles. The days of public money as the dominant funding source for agricultural innovation are numbered in every scenario except crisis. Cross-scenario robust action: Building outcome measurement infrastructure. This retains value in S1 (impact bonds require it), S2 (carbon and biodiversity credits require it), S3 (algorithmic scoring requires structured outcomes data), and S5 (emergency programmes require rapid impact demonstration). It is the highest-return single investment available to a public administrator right now. For agri-tech SMEs and startups: In S1, invest in data architecture interoperability now — blended finance structures require data that multiple institutional partners can access and audit simultaneously. In S2, build your green data API as a standalone commercial product. Your Scope 3 supply chain data is not just a monitoring output — it is something corporate clients will pay for as a compliance service. In S5, engage DFIs — EIB, EBRD — now as co-investors, not just grant sources. Blended finance relationships take 18–24 months to develop. By the time an emergency fund is activated, it is too late to start the relationship. Cross-scenario robust strategy: verified data + multi-credit capability (carbon, biodiversity, water) + smart contract architecture. These three capabilities retain value in S1, S2, and S5 simultaneously — they are the combination most resilient to scenario uncertainty. For researchers and academics: In S1, design research outputs to be algorithmically discoverable from day one. Clean data, structured metadata, SDG-tagged outputs. If a machine cannot find and classify your research, it will not surface in the AI-matched funding calls of 2028. In S2, ESRS AGRI — the agriculture-specific CSRD standard — is being developed now. Positioning your lab as a technical contributor means you help shape the metrics that will gate future funding, rather than having to retrofit to standards designed without your input. In S4, build food security framing into all proposals alongside climate framing. Dual narrative resilience is a strategic asset — a project that speaks only to climate mitigation is fragile in an S5 funding environment. Cross-scenario robust action: Publish research as FAIR data — Findable, Accessible, Interoperable, Reusable. This is the single action most robust across all five futures. In S3, FAIR data is the entry requirement for algorithmic matching. In S2, it enables carbon and biodiversity certification. In S1, it enables outcome verification. In S5, it enables rapid evidence-based policy response. There is no future in this analysis in which FAIR data is a disadvantage.

[Audio] Five things to carry forward from Module 5 — and from this programme. One: Scenarios are not predictions. They are structured tools for making better decisions under genuine uncertainty. Use them as a decision framework, not a forecast. Two: Five plausible 2030 futures exist simultaneously — Digital, Green, Community, Crisis, and Blended. Build resilience across all of them, not optimisation for one. Three: The most scenario-robust investments are verified data infrastructure, multi-credit capability, outcome measurement, and narrative flexibility. These retain value regardless of which future arrives. Four: Signpost monitoring converts scenarios into a live early-warning system. Build it now and act when signals accumulate — two signposts for the same scenario in six months is an activation threshold. Five: This is the summary statement of the entire programme. Verified data, interoperable architecture, and multi-credential capability retain strategic value across every plausible future. The organisations that build these capabilities now are not preparing for one future — they are becoming resilient to all of them. If there is one message to carry forward from this programme as a whole, it is this: the future of funding in digital agriculture is not about which instrument wins. It is about which organisations can produce credible, verified, machine-readable evidence of real-world impact — and do so continuously, at scale, under any regulatory and market framework that arrives.

[Audio] Thank you.. thank you!. TALLHEDA has received funding from the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101136578. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them..