Agriculture over the Horizon: A Synthesis for the Mid-21st Century

Abstract

Agriculture stands at a pivotal juncture in the twenty-first century, confronting the converging crises of climate change, biodiversity loss and rising food demand, even as it is increasingly recognised as part of the solution. This paper assesses the transformative potential of integrating three emerging paradigms—digital agriculture, regenerative agriculture and decommoditised agriculture—into a unified approach capable of delivering productivity, ecological restoration and economic viability. Digital agriculture deploys artificial intelligence, Internet of Things (IoT) networks and remote sensing to optimise inputs and sharpen decision-making. Regenerative agriculture seeks to rebuild soil function, enhance biodiversity and restore ecosystem processes through holistic, adaptive management. Decommoditised agriculture reorients value chains from bulk markets towards quality-differentiated systems that privilege direct producer–consumer relationships, value-added processing and regional market development, enabling price premiums and community resilience. We examine their convergence through the “3N” lens—net-zero greenhouse gas emissions, nature-positive outcomes and nutrition-balanced food systems. Integration creates clear complementarities: digital tools monitor, verify and optimise regenerative practices; regenerative systems provide the ecological foundation for sustainable intensification; and decommoditised models supply economic incentives that reward stewardship and nutritional quality. Persistent barriers include the digital divide, data governance, technical complexity and fragmented policy settings. Realising the benefits will require technology democratisation, interdisciplinary research, enabling regulation and farmer-centred innovation processes. We conclude that converging digital, regenerative and decommoditised approaches offers a credible and necessary pathway to resilient, sustainable and equitable agri-food systems.

1. Introduction

Agriculture stands at a transformative crossroads in the twenty-first century, embodying both the depth of the environmental crisis and the scale of the opportunity to repair it. Contemporary farming systems are rightly scrutinised for their contributions to greenhouse gas emissions, biodiversity loss, water pollution and soil degradation [1]. Yet, the same sector can be mobilised for environmental restoration, climate mitigation and sustainable development.

Pressures on the global food system are without recent precedent and demand structural change rather than incremental adjustment. Climate change is reshaping precipitation patterns, intensifying extremes and redrawing the geography of production [2]. In parallel, a biodiversity emergency is unfolding, with agricultural expansion a primary driver of habitat loss and species decline [3]. These ecological stresses intersect with powerful demographic and economic trends: a world population likely to reach 9.7 billion by 2050, rising incomes that favour more resource-intensive diets and growing consumer awareness of the links between diet, health and environmental impact [4]. Together, these dynamics define the pressure-opportunity space for agricultural transformation (Figure 1).

Agricultural science has responded with successive paradigms. Industrial agriculture delivered exceptional yield gains through mechanisation, synthetic inputs and genetic improvement, averting famine during decades of rapid population growth while revealing significant social and ecological costs [5]. Conservation agriculture targeted soil erosion and degradation using minimal tillage, permanent cover and diversified rotations [6]. Precision agriculture promised efficiency by aligning input use with spatial and temporal variability [7,8]. Most recently, regenerative agriculture has gained prominence as a holistic approach that restores ecosystem function while maintaining or improving productivity [9].

Alongside production-oriented shifts, alternative market models have questioned the commodity paradigm. Decommoditised agriculture, spanning direct-to-consumer channels, community-supported schemes and value-added processing, seeks to reconnect producers and consumers while capturing premiums linked to quality and production method [10].

Rather than treating these developments as discrete or competing, emerging research underscores the gains from integration, with the potential to address multiple challenges and deliver co-benefits [11]. This logic is captured in the “3N” framing of systems that are net-zero in greenhouse gas emissions, nature-positive in ecological outcomes and nutrition-balanced in outputs. Originating in the strategic plan of the Sydney Institute of Agriculture, the 3N model articulates three interconnected goals for agricultural systems: net-zero greenhouse gas emissions through reduced operational emissions and enhanced carbon sequestration; nature-positive outcomes that actively restore ecosystems and biodiversity; and nutrition-balanced outputs that prioritise diverse, nutrient-dense foods rather than caloric volume alone. We adopt this framing as the integrative objective function for the synthesis that follows.

The central argument of this paper is that integrating digital agriculture, regenerative agriculture and decommoditised agriculture provides a coherent and synergistic pathway for transformation. Each element reinforces the others: digital tools supply the monitoring and precision management required for regenerative systems; regenerative practices build ecological resilience that improves the reliability and payoff of digital optimisation; and decommoditised markets create durable incentives that sustain both digital innovation and regenerative adoption by rewarding transparency, quality and environmental stewardship.

This perspective builds directly on Sydney Institute of Agriculture’s “3N Agriculture” concept by operationalising the 3N goals—net-zero, nature-positive and nutrition-balanced—through an integrated system design that combines digital agriculture, regenerative agriculture and decommoditised agriculture and by linking the 3N framing both to a state-based view of regeneration [12] and to the farmscape function framework that makes regenerative potential legible and measurable across landscapes [13]. Relative to, which introduces 3N as a strategic framing, we (i) specify a compact, transferable indicator set for each “N”; (ii) connect those indicators to a synthesis logic and synergy matrix showing how digital, regenerative and decommoditised approaches reinforce one another; (iii) map the synergies to system leverage points (after Meadows, 1999 [14]); and (iv) illustrate feasibility across contrasting contexts (Australia, the USA, and China) with case vignettes tied explicitly to 3N outcomes. Accordingly, this manuscript is a standalone synthesis–position piece that complements—but does not duplicate—Sydney Institute of Agriculture’s concept: it translates the 3N vision into a practical integration blueprint, anchored in state-based regeneration and farmscape function measurement, with indicators, implementation pathways and policy levers [15].

2. Evolution of Agricultural Paradigms

Across the past century, agricultural development has proceeded through successive paradigm shifts shaped by prevailing risks, available technologies and societal priorities. Industrial agriculture emerged in the mid-twentieth century as a transformative force, recasting production through mechanisation, standardisation and intensive use of synthetic inputs [5,16]. The Green Revolution crystallised this model by introducing high-yielding varieties, chemical fertilisers and large-scale irrigation, together driving unprecedented gains in global output. A timeline of the paradigms and their characteristic levers/trade-offs is shown in Figure 2.

The productivity record was remarkable. Between 1961 and 2020, global cereal production rose almost threefold, from 877 million tonnes to 2.74 billion tonnes, while the cultivated area expanded by only 12 percent [17]. These gains underpinned a population increase from 3.1 to 7.8 billion people and coincided with a fall in the global prevalence of undernourishment from 37 percent to 8.9 percent [4].

The costs were also substantial. Agriculture now accounts for an estimated 10–12 percent of anthropogenic greenhouse gas emissions [18] and has contributed to widespread water pollution and soil degradation. The FAO estimates that 33 percent of agricultural soils are moderately to highly degraded [19]. Habitat conversion and intensification have driven biodiversity loss, contributing to what many describe as a sixth mass extinction [20].

New paradigms responded to these consequences. Conservation agriculture, developed through the 1970s and 1980s, sought to curb environmental impacts via minimal soil disturbance, permanent organic cover and diversified rotations [21]. Evidence shows reduced erosion and improved soil structure and biological health, although uptake has been uneven due to socioeconomic and technical barriers [22]. By the 1990s, precision agriculture emerged, enabled by GPS, remote sensing and data analytics. It promised to optimise inputs through site-specific management informed by detailed spatial and temporal variability within fields [7,8].

3. Digital Agriculture: Technology-Enabled Transformation

Digital agriculture signals a decisive shift towards data-driven, technology-enabled and interconnected farming systems. By coupling computational tools, advanced sensors and ubiquitous connectivity, it seeks to optimise production through real-time insight and automated decision-making. This domain spans precision technologies, autonomous machinery, AI-driven decision-support platforms and blockchain-based supply-chain management, underpinned by the convergence of the Internet of Things, AI, cloud computing and advanced sensing.

The market has expanded rapidly, with global digital agriculture projected to reach around USD 16.3 billion by 2025 [23]. Agricultural drones illustrate this pace of adoption, with market analyses reporting strong multi-year growth in the early-to-mid 2020s [23]. Deployed at scale, these platforms enhance crop monitoring, enable earlier pest and disease detection and support precision input application, often revealing problems before they are visible to the human eye [24].

IoT-enabled systems extend these capabilities across entire operations, providing continuous measurement of soils, microclimates, crop growth and livestock health [25]. Smart-farming programmes that integrate data from hundreds of sensors deliver whole-farm oversight and automate routine processes. Case studies report reductions in water use and labour while maintaining yields when digital tools are integrated coherently into management [24].

AI and machine learning further strengthen decision quality by analysing complex datasets, forecasting outcomes and optimising interventions at multiple spatial and temporal scales. Image-based disease identification models can exceed 95% accuracy in controlled studies, enabling targeted treatment and substantial reductions in pesticide use when coupled with site-specific management [26,27]. We summarise the salient adoption barriers and control levers in Figure 3.

Notwithstanding these advances, adoption is uneven. The digital divide constrains smaller farms, older farmers and regions with limited connectivity [28]. Concerns about data governance, including privacy, ownership and rights of use, continue to dampen trust [29]. Technical complexity and weak interoperability impede seamless integration, while high upfront costs, uncertain returns and ongoing subscription and support fees remain prohibitive for many operations [28,30,31]. Realising these gains also depends on enabling infrastructure and energy system integration across farms and regions [32].

4. Regenerative Agriculture: Ecological Restoration and System Resilience

Regenerative agriculture is a holistic paradigm that moves beyond sustainability to active restoration of ecosystem health, soil fertility and biodiversity while maintaining productive capacity. It works with, rather than against, ecological processes and prioritises the rebuilding of soil organic matter, strengthening of the water cycle, enhancement of biodiversity and sequestration of atmospheric carbon.

Its core principles improve soil function through organic matter accumulation, reduced disturbance and the promotion of biological activity [6,21]. Minimising disturbance through no-till systems can preserve soil structure, safeguard microbial communities and reduce erosion while improving water infiltration and aggregate stability [33]. Maintaining continuous cover via cover crops or residues protects against erosion, buffers temperature and supplies organic matter that sustains biological activity; such practices have been shown to cut erosion substantially and to raise soil organic carbon stocks in temperate croplands on average, with context-dependent variability [34]. Keeping living roots in the soil year-round supports microbial processes and nutrient cycling, and strategic livestock integration can improve nutrient distribution and diversify farm income. Maximising biodiversity above and below ground through diversified rotations, agroforestry and intercropping bolsters ecosystem function, resilience and productivity [35,36]. How these principles propagate through soil processes to system-level outcomes is shown in Figure 4.

A growing empirical base confirms these benefits. Meta-analyses report consistent SOC gains with cover crops in temperate systems [34], and diversified systems commonly show higher infiltration and improved water storage compared with conventional practices [37]. Perennial and agroforestry elements increase total carbon stocks, with sequestration distributed across biomass and soils and varying by climate and system [38,39]. Biodiversity gains are also notable, with diversified farms supporting substantially more species and multiple ecosystem services than monocultures [35,36]. Monitoring below-ground biodiversity remains method-constrained; environmental DNA (eDNA) and metabarcoding approaches provide useful proxies but face detection-limit, cost and spatial-representativeness issues, especially for mycorrhizal fungi [40]. Indicative effect sizes across common outcomes are summarised in

Table 1. Regenerative agriculture: Effect sizes at a glance (indicative ranges; context-dependent). (Source: Authors, adapted from [6,21,33,34,35,36,37,38,39]). Ranges are indicative of meta-analyses/reviews; effects vary with climate, soils, baseline condition, species mix and time in transition. Local monitoring is used for baselines and trajectories.

Outcome	Representative Metric	Typical Effect (Range)	Context/Notes
Soil Organic Carbon (SOC)	ΔSOC (t C ha−1 yr−1) under cover crops and low disturbance	+0.3 to +0.6 (mean ≈+0.56)	Top 0–30 cm; larger in temperate, fine-textured soils; multi-year accumulation
Infiltration	Change vs. conventional (%)	+30 to +60%	Cover crops ≈+35%; perennial systems ≈+60%; no-till alone often small/NS
Aggregate Stability	Water-stable aggregates (%)	+10 to +20%	Gains accumulate with years under cover/low disturbance
Runoff/erosion	Reduction vs. conventional (%)	−20 to −50%	Cover/rotation + reduced disturbance; larger on sloping/erodible soils
Above-ground biodiversity	Species richness (%)	+20 to +30%	Mean richness gains in diversified vs. simplified systems; taxa- and landscape-dependent
Carbon stocks (agroforestry, soil only)	ΔSOC (t C ha−1 yr−1)	+0.2 to +0.5	Hedgerows and alley-cropping typically positive; silvopastoral variable; above-ground biomass adds further C

.

Despite these advantages, adoption remains uneven. Transitioning demands specialised knowledge, yet advisory and education systems often remain tuned to conventional practices. Economic hurdles include upfront capital requirements, possible yield dips in the first two to three years and uncertainty about longer-term performance. Underdeveloped markets for regeneratively produced commodities also limit the ability to secure price premiums, slowing the scaling of this model.

5. Decommoditised Agriculture: Market Innovation and Value Capture

Decommoditised agriculture challenges the dominance of anonymous, standardised commodity markets by foregrounding quality, production methods, geographic origin and direct producer–consumer relationships. In this paper, decommoditised agriculture aligns with what the literature variously terms alternative food networks, short food supply chains and values-based supply chains. This paradigm strengthens farm incomes and system resilience through direct-to-consumer channels, value-added processing, certification schemes and regionalised supply networks.

Direct marketing has expanded markedly over the past three decades. In the United States, the number of farmers’ markets rose from 1755 in 1994 to more than 8600 by 2019, reflecting both increased farmer participation and sustained consumer demand for fresh, local and seasonal food [41,42]. Farmers’ markets typically allow producers to retain 80–90 percent of the retail price, compared with about 15–20 percent through conventional channels [43,44]. Community-supported agriculture models deepen these ties by creating shared financial commitments: members purchase a season’s share upfront, supplying working capital while sharing risk and reward. CSA operations in the United States grew substantially from the 1990s to the 2010s, although recent trends show more variable participation with competition and retention challenges [42,45].

Digital innovation has further accelerated online direct sales, a shift intensified by the COVID-19 pandemic and changing purchasing habits. Online platforms extend market reach beyond local catchments and provide tools for inventory management, payments and customer engagement. Farms that adopted online sales during the pandemic showed greater resilience and more stable revenues than those reliant on traditional outlets alone [13]. Value-added processing provides another pathway to retain margins and differentiate products, from simple washing and packing to more complex manufacturing. Such enterprises can raise farm revenues by 25–100 percent, but they demand investment in equipment, facilities and regulatory compliance [44,46].

The advantages extend beyond revenue growth. Direct channels consistently secure higher prices than commodity markets, with organic certification often commanding 20–50 percent premiums and local or artisanal designations delivering similar or greater uplifts [43,44,45]. Direct relationships can also buffer producers from global price volatility, creating more predictable income streams. Significant barriers remain. Producers need marketing, branding and business management capabilities that sit outside traditional skill sets. Infrastructure and capital requirements are substantial, particularly for value-added enterprises subject to food safety regulation. Market access is frequently constrained by geography since farmers’ markets draw primarily on nearby communities. Equity concerns persist as direct marketing can skew towards higher-income consumers, risking the entrenchment of social and economic inequalities within the food system [47,48].

Feasibility in Smallholder Economies: Designing for Transaction–Cost Compression

In smallholder-dominated systems, per-unit coordination and verification costs can be high when direct marketing is attempted farm by farm. International evidence indicates that decommoditised models become practical when four design levers are combined:

(1)

Collective market access (FPOs/co-ops) to convert fixed costs into shared services.

India’s national programme to form 10,000 Farmer Producer Organisations (FPOs) (2020–2027/28) targets aggregation for input procurement, marketing and basic processing. Government briefs and syntheses report higher producer shares and improved price realisation where managerial capacity and service breadth are in place [49,50].

(2)

Low-cost assurance via Participatory Guarantee Systems (PGSs).

Where third-party certification is uneconomic, PGSs provide community-based verification suited to short chains and CSAs, institutionalised in India and supported internationally. PGSs maintain traceability and transparency while lowering a major barrier to method-differentiated sales [51,52].

(3)

Digital platforms to reduce search, matching and logistics costs without erasing provenance.

China’s rural e-commerce clusters (e.g., Taobao village ecosystems) demonstrate that platform aggregation can increase household incomes and employment while sustaining origin branding; cluster formation lowers per-unit logistics and marketing costs over time—precisely the structural enabler that decommoditised channels require [53,54].

(4)

Buyer-anchored offtake where perishability is high.

Anchor-buyer arrangements (retail, institutional and platform-linked brands) stabilise cash flow and inventory risk while paying for specified attributes (variety, residue standards and provenance). Paired with (1)–(3), anchor demand makes premiums bankable rather than episodic [17,50].

Decommoditised agriculture is feasible for smallholders when treated as an ecosystem design problem—aggregation + low-cost assurance + platform logistics + selective anchor demand—rather than atomised farm retailing. Digital tools verify and coordinate; regenerative practice supplies the ecological and quality basis for differentiation; and redesigned market architecture captures value at tolerable cost.

Evidence from FPO programs and rural e-commerce clusters indicates meaningful compression of coordination/verification costs and improved price realisation where four levers co-exist. Figure 5 summarises indicative metrics and design bounds drawn from [49,50,51,52,53,54]; values vary by product and geography. Figure 5 summarises indicative metrics and design bounds drawn from [49,50,51,52,53,54], and

Table 2. Indicative design bounds for smallholder-oriented decommoditised systems. (Source: Authors, adapted from [49,50,51,52,55,56,57]).

Metric/Bound	Typical Value (Source)
Transaction cost reduction vs. atomised sales	In total, ≈30% lower marketing costs when selling via FPO/FPC channels vs. individual sales
Price realisation gain (farmer share, % of retail)	In total, ~+20–25% higher price realisation to members selling through FPOs/FPCs (evidence shows ≈+22%).
Minimum effective co-op/FPO scale (members or annual tonnage)	In total, ≥300 members in plains; ≥100 members in NE/hilly regions
Logistics radius for perishables (hours/km to sustain quality)	Same-day (≤8–12 h) delivery feasible for local ambient distribution; next-day (≤24 h) achievable at provincial scale with cold-chain/e-commerce integration (China pilots). Uses ~100–150 km for same-day ambient and ~300–500 km for next-day cold-chain as planning heuristics.

provides typical values and planning bounds by lever and context.

6. The 3N Model: An Integrated Framework

The “3N” model provides a coherent framework for agricultural transformation that addresses climate change, biodiversity loss and malnutrition together. It envisions farming systems that achieve net-zero greenhouse gas emissions, deliver nature-positive ecological outcomes and produce nutrition-balanced food. By aligning agricultural development with planetary boundaries and human well-being, the model seeks environmental sustainability without sacrificing economic viability.

The framework arises from the recognition that agriculture cannot solve environmental degradation, climate instability and malnutrition in isolation. The planetary boundaries concept supplies the scientific foundation, identifying biophysical thresholds within which humanity must operate to avoid destabilising Earth-system processes [1,58]. Agriculture influences at least six of the nine boundaries: climate change, biodiversity loss, biogeochemical flows, land-system change, freshwater use and atmospheric aerosol loading [1].

To keep the framework operational, we use a compact indicator set that travels across contexts: net-zero is indexed by soil-carbon change, on-farm fuel and electricity use, captured on-farm energy and net methane and nitrous oxide; nature-positive by soil and above-ground biodiversity, safeguards for threatened species and concentrations of heavy metals and pesticides in soils; and nutrition-balanced by protein, vitamin and trace-element profiles together with heavy-metal and pesticide residues and the nutrient profile of food products.

Net-zero. Agriculture is both a major emitter and a potential climate solution. It generates an estimated 10–12 percent of global anthropogenic emissions, largely from livestock, rice cultivation and fertiliser use [18]. Mitigation involves improving feed efficiency; adopting precision and enhanced-efficiency fertilisers; and integrating renewable energy. Carbon sequestration complements these measures, with soils offering the largest removal potential. Regenerative practices such as cover cropping, reduced tillage, diverse rotations and integrated livestock can raise soil organic carbon in many systems [33,34]. Agroforestry and perennial systems add further capacity, storing additional carbon in biomass and soils while providing habitat and erosion control [38,39].

Nature-positive. Beyond minimising harm, nature-positive systems actively restore ecological function by enhancing ecosystem services, including pollination, pest regulation, soil fertility and local climate moderation. Strategies span farm-scale interventions, such as diversification and habitat creation, through to landscape-scale networks that connect ecological corridors, supporting long-term productivity and resilience [35,36].

Nutrition-balanced. Reframing goals from calories to nutrient density addresses the global nutrition crisis. Prioritising diverse, nutrient-rich foods supports human health while reducing dependence on highly processed staples. Evidence from diversified and agroecological systems indicates potential improvements in vitamin, mineral and antioxidant profiles, illustrating the synergy between ecological integrity and nutritional outcomes [35,36].

These pillars are mutually reinforcing. Soil health underpins sequestration, biodiversity enhancement and nutrient-rich production. Diversified systems generally exhibit lower emissions, higher sequestration potential and stronger nutritional performance than monocultures. Taken together, the 3N model offers a science-based pathway for redesigning agriculture to meet twenty-first-century environmental and dietary imperatives [1,18,35,36,38,39,58]. We adapt this framework into a practical national typology in

Table 3. Adapting 3N to national settings: a practical typology. (Source: Authors). * “Advanced consolidated” = high-income, highly integrated agri-food systems with mature infrastructure and high digital penetration; concentrated processing/retail; simplified landscapes.

System Type	Main Barriers	Resource Needs	Policy Mix (Near + Long)
Advanced consolidated *	Data rights, vendor lock-in, biodiversity on simplified landscapes	Interop MRV, on-farm renewables, habitat networks	Data/interop standards → outcome-based incentives → biodiversity corridors, procurement for verified nutrition
Emerging smallholder	High per-unit coordination costs; limited capital/connectivity	Co-ops/FPOs, PGS, platform logistics, anchor demand, micro-finance	Grants and connectivity → blended finance and PGS → procurement and stacked ecosystem payments
Dual structures	Fragmented standards; uneven market access	Regional hubs (MRV, logistics, processing); staged standards	Bridge standards and hubs → scale outcome-based payments → integrate into Scope-3 procurement

.

7. Integration and Synthesis: Towards Transformative Agricultural Systems

The convergence of digital, regenerative and decommoditised agriculture creates reinforcing effects that amplify individual benefits while offsetting inherent limits. Operating across technical, operational, economic and social dimensions, integration enables outcomes that none of the paradigms can reliably deliver alone. The core enablement relationships among the three paradigms are mapped in

Table 4. Synergy matrix across digital, regenerative and decommoditised paradigms. (Source: Authors). Matrix showing how each paradigm enables the others: digital → regenerative (monitoring, verification, decision support), regenerative → digital (more stable biophysical baselines that raise ROI on optimisation), digital ↔ decommoditised (traceability/provenance and market access), regenerative → decommoditised (quality and environmental differentiation), decommoditised → regenerative/digital (premiums and cash-flow stability to fund adoption). Shading denotes relative strength of complementarity (darker = stronger).

	Digital Beneficiary	Regenerative Beneficiary	Decommoditised Beneficiary
Digital Enabler	-	MRV and sensing Variable-rate prescriptions Adaptive management Early warnings	Traceability/provenance Automated certification Lower transaction costs Market access and logistics
Regenerative Enabler	Stable biophysical baselines Higher signal-to-noise Richer training data Ground truth for models	-	Differentiated quality Verified eco-claims Nutritional profile gains Ecosystem-services credits
Decommoditised Enabler	Capex financing and cashflow Demand signals and feedback Incentives for data collection	Premiums fund transition Risk-sharing (CSA/contracts) Longer planning horizon	-

.

Digital technologies are powerful enablers of regenerative practice. Precision tools, remote sensing and AI-based decision support convert ecological principles into operational guidance, while real-time feedback supports adaptive management. Soil health sensors that track organic matter, biological activity and nutrient cycling provide the quantitative basis to optimise interventions and to demonstrate results to stakeholders.

Conversely, regenerative systems improve the performance of digital tools. Digital optimisation delivers the greatest value in stable, biologically active systems with predictable properties. By improving soil structure, boosting biological activity and increasing system stability, regenerative management raises the reliability and payoff of digital analytics and automation.

Digital innovation also underpins decommoditised models by enabling transparency, traceability and direct marketing. Blockchain verification, IoT-based production monitoring and online sales platforms supply the infrastructure for provenance assurance, reduce transaction costs and widen market reach. These systems allow producers to document and communicate environmental outcomes, production methods and product quality, supporting premium pricing [53,54,55,56,59].

Regenerative practices themselves create strong decommoditisation opportunities through environmental and quality differentiation. Rising demand for demonstrably positive production aligns with regenerative gains in soil health, carbon sequestration and biodiversity, while frequently reported improvements in nutritional profile add further product distinction [13,36,38,39,42,45].

Decommoditised channels then provide the economic engine for adoption. Premium prices, direct sales and value-added processing generate margins to finance technological investment and ecological transition. Durable consumer relationships, including community-supported agriculture and direct distribution, stabilise cash flow and enable strategic investment in both digital and regenerative innovations [13,42,45,49].

Key enablers include interoperable, farmer-controlled data platforms that protect data sovereignty while allowing secure exchange across tools and stakeholders. Advances in hyperspectral imaging, soil sensing and biodiversity monitoring are lowering the costs of verifying regenerative outcomes. AI-driven ecological management systems now combine real-time field data, weather forecasts and historical performance to deliver context-specific recommendations, aligning regenerative objectives with the precision and scalability of digital agriculture [29,30,31,59].

A complementary downstream perspective shows how verified regenerative signals can propagate beyond the farm gate into ingredient functionality, processing choices and Scope-3 reporting, preserving provenance “from soil to shelf” via a digital thread through supply chain [60]. We operationalise these integration pathways into three plausible end-date archetypes in

Table 5. Illustrative 3N-aligned archetypes (to ~2050). (Source: Authors). Purpose: To make plausible end-states concrete without prescribing a single blueprint, we sketch three 3N-aligned archetypes that differ by structure, technology intensity and market design. These are not exhaustive; they delimit a feasible set within which many hybrids will sit. * Farm size distribution: “Right-skewed, large mean” = many small/medium farms with a long upper tail of very large units; the mean exceeds the median. “Many small/medium; landscape partnerships” = predominantly small/medium holdings that scale via co-ops/FPOs, shared MRV/logistics hubs, and landscape-level partnerships (e.g., machinery rings, habitat corridors). “Bimodal” = two distinct size clusters; smallholders and large estates/agribusiness, with a thin middle; policies must address both segments.

Attribute	A: Platform-Enabled Consolidated	B: Bio-Regional Mosaic	C: Dual-Structure Transitioners
Farm size distribution *	Right-skewed, large mean	Many small/ medium landscape partnerships	Bimodal
Tech intensity	High	Moderate, shared	Mixed
Market config	Long chains + verified Scope-3	Short chains + value-added	Mixed; procurement as bridge
Core policy levers	Data/interop, energy, MRV, extension	Co-ops/PGS/logistics, anchor demand	Sequenced standards/finance
Main risks	Lock-in, equity	Scale, logistics	Fragmentation

.

7.1. Practical Examples: Integrated Implementations in Contrasting Contexts

7.1.1. Taranaki Farm (Australia): Water-First Design + Adaptive Grazing + Direct Sales as a Mutually Reinforcing Bundle

Taranaki Farm (≈160 ha, Central Victoria) integrates Keyline water harvesting (small high-point storages, lock-pipes and contour channels), planned multi-species grazing (cattle, sheep, pigs and poultry) and decommoditised channels (CSA/buying clubs) to convert landscape re-hydration and nutrient cycling into stable enterprise margins [61,62,63]. In synthesis terms, water-first infrastructure lifts soil moisture uniformity and groundcover; choreographed animal flows redistribute nutrients and suppress pests; and direct market access captures value from method and provenance—together reducing dependency on purchased inputs and commodity prices. The case demonstrates an operational pathway by which regenerative practice creates the ecological stability that makes digital monitoring and traceability worthwhile (even with lightweight tools), while decommoditised outlets monetise those verified attributes [61,62,63].

7.1.2. International Comparator (USA): White Oak Pastures’ Measured Climate Signal

An independent Quantis LCA of White Oak Pastures’ multi-species regenerative grazing reported a net farm-gate footprint for beef of c. −3.5 kg CO₂-e kg⁻¹ fresh meat when soil–carbon change is included, with stated uncertainties around permanence and enteric assumptions [64]. While biophysical and market contexts differ from Australia, this quantified result illustrates how regenerative design plus short value chains can shift the carbon balance of ruminant systems and strengthen a 3N-aligned narrative—provided monitoring boundaries and time horizons are transparent.

7.1.3. Jingdong Farm (China): Platform-Scale Integration of Production Data, Logistics and Market Access

JD.com’s Jiangsu initiatives couple IoT/vision systems (environmental and greenhouse monitoring, grading and UAV operations) with cold-chain logistics and e-commerce provenance to standardise quality and move differentiated produce at a national scale—an architecture that collapses transaction, verification and distribution frictions simultaneously. The agriculture-tech stack (asset digitisation, traceability and finance) underwrites both regenerative-consistent practices (by verifying processes/outcomes) and decommoditised positioning (by preserving origin/method signals along the chain) [55,56], complementing broader smart-agriculture implementations documented in China [56].

Taken together, these cases illustrate a common synthesis logic under different constraints: (i) ecological design builds stability and biological function; (ii) digital systems monitor, verify and coordinate flows; (iii) decommoditised channels pay for verifiable attributes and provenance. The bundle lowers risk, improves margins and aligns with 3N outcomes when verification boundaries are explicit, and transaction costs are jointly addressed by design and platforms.

8. Challenges and Future Pathways

Integrated agriculture has exceptional potential to transform food systems, yet delivery at scale is constrained by interlocking technical, economic, social and institutional barriers. Overcoming these constraints is essential if integrated approaches are to achieve the step change required in the twenty-first century.

A first constraint is the complexity of technical integration. Coordinating diverse technologies, management practices and knowledge systems, each with its own requirements, timescales and performance metrics, demands substantial capacity. Farmers are asked to operate advanced digital tools, apply ecological management principles and run direct-to-market strategies simultaneously, often juggling competing priorities and reconciling tensions between approaches.

Capability development is pivotal. Each paradigm—digital, regenerative and decommoditised agriculture—requires specialist competencies that are rarely addressed in conventional education or extension programmes. Digital agriculture calls for proficiency in data management, sensor calibration, software use and system troubleshooting. Regenerative agriculture depends on a deep understanding of ecological processes, soil biology and the dynamics of complex agroecosystems. Decommoditised agriculture relies on strong business skills, including marketing, customer relationship management and value-chain development [11,31,42,43,44,45].

Economic hurdles are most acute during transition. The combined costs of acquiring digital tools, implementing regenerative practices and building direct-marketing infrastructure can be prohibitive, particularly when returns are delayed. Regenerative shifts may involve temporary yield declines and higher managerial demands, while digital systems carry continuing maintenance and subscription costs. Market constraints further limit adoption. Infrastructure for verifying, processing and distributing products from integrated systems remains underdeveloped in many regions, and consumer recognition of associated value propositions is uneven. Certification for integrated production is still nascent, lacking the standardisation and brand recognition of established labels.

Targeted research and development is needed to produce affordable, user-friendly digital tools adapted to diversified, regenerative enterprises. Many current solutions are optimised for large-scale monocultures and are ill-suited to the complexity of integrated farms. Ecological research must also deepen our understanding of how specific practices drive measurable ecosystem outcomes across varied environmental contexts.

Finally, supportive policy and institutional frameworks are essential. Cross-sector coordination should reward the multiple public benefits of integrated agriculture while systematically removing adoption barriers. Policy needs to align local, regional and national regulations, from zoning and food safety to agricultural and environmental law. In parallel, robust data governance frameworks must protect farmer rights, with clear definitions of ownership, permitted use and privacy in the deployment of digital agriculture systems [29,30,31,59].

Transition Costs and Food Security Alignment

Transition economics can be assessed with a simple two-layer approach. Farm layer: Compare (i) capex/opex for digital kits and regenerative practice changes; (ii) transitional yield trajectories (including plausible 1–3-year dips where relevant); (iii) input substitutions (fertiliser, pesticides, water and energy); and (iv) price realisation (premiums, direct sales and public procurement). Evaluate net present value, internal rate of return and break-even premiums under sensitivity to weather and prices. Sector layer: use partial-equilibrium or CGE scenarios to test supply, price and nutrition outcomes under alternative adoption rates and policy mixes; optionally couple to simple emissions pathways to track the net-zero dimension. Together, these tools show how ecological restoration can align with stable output and access, avoiding the relegation of “green” production to niche markets. We map the concrete policy instruments to the three paradigms and their integration points in

Table 6. Policy tools mapped to paradigms and integration points. (Source: Authors). We map concrete instruments to digital, regenerative and decommoditised paradigms and to their integration points (traceability/MRV, provenance, Scope-3), noting sequencing and primary actors.

Instrument	Maps to	What It Does	Near Term (0–3 y)	Medium (3–7 y)	Long (7–15 y)	Primary Actors
Capex Subsidies/Rebates	Digital, regenerative	Lower entry costs (sensors, variable-rate, cover-crop gear)	Launch pilots, basic kits	Scale to priority regions	Taper as markets internalise	Nat./state gov, DFIs
Connectivity and interop standards	Digital, integration	Close rural connectivity gaps; open data standards	Rural 4G/LPWAN pushes	Interop/IDs; farmer-controlled data commons	Continuous upgrades	Gov, telcos, SDOs, co-ops
Blended finance (first loss)	Regenerative, integration	Crowd in private capital for transition	Facility design, guarantees	Scale instruments; outcome-based tranches	Mainstream into ag banking	DFIs, green banks, private
Carbon and ecosystem markets (MRV)	Regenerative, integration	Monetise removals/ co-benefits	Align protocols with 3N set	Stack biodiversity/water	Integrate with Scope-3	Standards bodies, registries
Land and data tenure reforms	Regenerative, digital	Secure incentives and data rights	Clarify data ownership/licensing	Land aggregation/commons models	Periodic review	Legislature, land boards
Public procurement	Decommoditised, integration	Create demand for verified products	Set 3N specs, pilots	Regional scaling	Long-term contracts	Gov depts., hospitals, schools
Value-added/processing grants	Decommoditised	Build local processing capacity	Seed grants	Scale viable hubs	Private finance takeover	Gov, RD agencies
Extension and vocational training	All	Build capacity and trust	Train-the-trainer programs	Curricula-embedded	Lifelong learning	Universities, extension, co-ops

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9. Limitations and Research Priorities

Scope and attribution. The reported effect sizes are context-dependent; separating practice effects from climate/market variability remains challenging, and publication bias cannot be excluded. Standardised monitoring (including below-ground biodiversity proxies such as eDNA) still faces cost and representativeness limits [40].

Digital constraints. Adoption is uneven due to capital costs, connectivity and skill gaps; interoperability and data rights issues constrain integration across tools and actors [28,29].

Transition economics. Regenerative transitions can involve short-term yield dips and learning costs; decommoditised channels require capabilities and infrastructure that many farms lack.

Equity and access. Direct-marketing models can skew towards higher-income consumers; programme design must avoid reproducing inequities [47,48].

Research priorities. (i) Long-horizon, multi-site trials that co-measure 3N indicators with cost/return data; (ii) affordable, farmer-controlled digital stacks with open standards and privacy-preserving verification; (iii) robust biodiversity metrics linking soil/above-ground diversity to yield stability and nutrition; (iv) transaction-cost compression for smallholders (co-ops/FPOs, PGS, platform logistics) with rigorous evaluation [49,50,51,52,53,54]; (v) policy experiments on outcomes-based payments tied to the compact 3N indicator set. Priority questions and methods are set out by domain in

Table 7. Research agenda by domain. (Source: Authors, adapted from [29,33,34,35,36,37,38,39,49,50,51,52,53,54]). Priority questions are grouped by biophysical science, socioeconomics, governance/institutions and MRV/data systems, with indicative methodological gaps.

Domain	Priority Questions	Methodological Gaps/Pointers
Biophysical science	How do diversified rotations and agroforestry co-determine SOC accrual, infiltration and biodiversity across climates? What permanence bounds apply?	Long-horizon, multi-site trials coupling 3N indicators with micro-economics; uncertainty propagation for SOC permanence; trait-based biodiversity metrics
Socioeconomics	What are transition cost curves and payback times under different price/credit regimes? How do premiums diffuse?	Partial-equilibrium and CGE scenarioing; difference-in-differences on adopters; real options for staged investment.
Governance/institutions	Which co-op/FPO and PGS designs minimise per-unit transaction costs while preserving provenance?	Field experiments on service-bundle designs; platform economics; policy labs on outcomes-based procurement
MRV/data systems	What is a minimum-viable MRV stack for smallholders? How to ensure comparability without coercion?	Interop standards, farmer-controlled data trusts; low-cost spectral/eDNA protocols; hierarchical models for cross-site comparability

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10. Conclusions

This analysis shows both the distinctive strengths of digital, regenerative and decommoditised agriculture and the transformative potential released when they are combined. Faced with the twenty-first-century imperatives of climate stability, biodiversity recovery and food security, an integrated approach offers a credible route to environmental restoration, durable farm economics and social equity while maintaining the productive capacity required to feed a growing population.

Digital agriculture harnesses IoT sensing, AI-enabled analytics, drones and precision application to optimise resources and improve decisions. It has delivered measurable gains in efficiency and monitoring, yet uptake is limited by capital costs, technical complexity, data-governance concerns and a persistent digital divide. Regenerative agriculture, grounded in ecological science, rebuilds soils, enhances biodiversity, sequesters carbon and strengthens resilience while sustaining yields; adoption is slowed by transition costs, the need for specialist knowledge, immature markets and difficulties in measuring outcomes across diverse contexts. Decommoditised agriculture reshapes value chains through direct sales, value-added processing and certification, securing premiums, rewarding stewardship and revitalising rural economies, but it faces constraints when pursued alone, including scalability limits, infrastructure gaps and the demands of sustained marketing and equitable access.

Together, these paradigms generate mutually reinforcing benefits. Digital systems provide monitoring, transparency and traceability that strengthen regenerative management and market credibility. Regenerative practices produce ecological gains and differentiated products that increase the returns to digital optimisation and support premium pricing. Decommoditised channels supply the stable revenues and incentives needed to underwrite long-term investment in both digital and regenerative systems.

Such integration advances the 3N model by linking improvements in soil health to net-zero emissions, nature-positive outcomes and nutrition-balanced diets. Positive feedback loops follow as successful integrated farms demonstrate feasibility, build farmer confidence and consumer trust and catalyse, enabling infrastructure and market ecosystems.

Realising this potential at scale requires coordinated action to lower the cost and raise the accessibility of technology, embed interdisciplinary education and extension, reform policies to reward multi-benefit outcomes and expand market pathways. Given the scale and urgency of environmental and social challenges, incremental change is insufficient. A fully integrated strategy offers a practical, transformational route to agricultural systems that are more resilient, more sustainable and more equitable, delivering enduring benefits for human well-being and planetary health.

Operational checklist (5 steps).

• Baseline 3N: Establish a compact baseline using the indicator set (SOC change; farm fuel/electricity and captured on-farm energy; net CH₄/N₂O; soil and above-ground biodiversity; threatened-species safeguards; soils/product contaminants; nutrient profile).
• Design for function: Map capacity–condition–regenerative potential across farmscape elements [42] and then select practices that close the largest functional gaps.
• Digitise and verify: Deploy lightweight digital stacks (IoT/RS, interoperable data hubs) to track the indicator set and enable farmer-controlled data sharing [29,59].
• Preserve provenance: Build decommoditised routes (direct sales, value-added and anchor buyers) and carry verified signals forwards (“soil-to-shelf”) in Scope-3 data flows [60].
• Align policy and finance: Tie outcome-based incentives and finance to compact indicators; include captured on-farm energy and grid interconnection status in plans [32].