December 2025
Digital twins are increasingly being explored in agriculture as a structured approach to consolidating and interpreting data from fields, crops, livestock operations, and farm equipment. By combining satellite imagery, in-field sensors, weather data, and predictive models, these systems create dynamic virtual representations of agricultural operations that support more informed decision-making. Early deployments focus on improving yield forecasts, monitoring crop and livestock development, identifying emerging stress or disease risks, and enabling more precise allocation of inputs such as water and fertilizer.
Adoption remains uneven, with many implementations operating as partial or function-specific twins rather than fully integrated, farm-wide models. Data quality, interoperability, and integration with existing farm management systems continue to limit scalability in some environments. As a result, most deployments remain constrained to specific use cases or pilot-scale implementations rather than end-to-end operational twins.
These challenges stem from practical and structural barriers. Many farms continue to struggle with basic data integration, and the quality and consistency of available data varies widely. In some regions, limited connectivity restricts real-time monitoring and data exchange. Equipment and software platforms often lack seamless interoperability, making it challenging to scale digital twin solutions beyond individual assets or fields. Cost remains another constraint, particularly for smaller operations with limited budgets for sensors, analytics platforms, and technical support. At a broader level, national or regional agricultural digital twins depend on consistent data standards and governance frameworks, many of which are still emerging.
The industry also faces a set of practical and operational risks. Models that rely heavily on historical data may perform poorly under atypical or extreme weather conditions. Automated recommendations can be misleading if underlying assumptions are opaque, or in rare cases where analytical models generate erroneous or misleading outputs. In addition, many farmers require training and support to adopt digital tools confidently and effectively. These issues do not undermine the value of digital twins, but they underscore the importance of realistic expectations, human oversight, and careful implementation.
Despite these challenges, digital twins align closely with broader trends in agriculture, including precision input management, remote sensing, autonomous equipment, and increasingly transparent supply chains. As the technology matures, digital twins have the potential to improve planning, reduce operational risk, and strengthen the resilience of farming systems.
In this report, we apply Tolaga Intelligence’s analytical frameworks to examine the evolving landscape for digital twins in agriculture. We assess agriculture’s relative maturity compared with other industry verticals, identify leading solution providers, and examine key use cases, real-world deployments, adoption challenges, and implications for producers, technology vendors, investors, and policymakers.
In agriculture, digital twins are applied across crop and field management, precision farming and resource optimization, livestock management, supply chain and operations, financial, insurance, and risk management, strategic planning and scenario modeling, research and development and breeding, and immersive training and visualization. Together, these applications reflect a shift toward more data-driven, predictive, and increasingly autonomous agricultural systems.
Given the complexity and diversity of agricultural environments, digital twin solution providers typically deliver targeted capabilities rather than fully integrated end-to-end systems. Most solutions focus on specific segments of the agricultural value chain, such as crop and field management, farm machinery twins for predictive maintenance, livestock management, precision input optimization, supply chain and operations, finance and insurance, strategic planning and scenario modeling, research and breeding, and immersive visualization and training.
In crop and field management, digital twins integrate real-time monitoring, advanced simulation, and predictive analytics to optimize production across the growing cycle. Virtual models simulate crop growth, forecast yields, and assess the impacts of weather variability and long-term climate change. These systems also support early detection of pests, disease, and crop stress, enabling proactive intervention before issues escalate.
Soil-focused digital twins incorporate moisture sensing, nutrient profiling, and soil health analytics to support precise fertilizer application and irrigation optimization. Farm machinery is increasingly mirrored within these virtual environments, enabling continuous performance monitoring, predictive maintenance, and the virtual testing of alternative field strategies. In controlled environments such as greenhouses, digital twins simulate and fine-tune lighting, humidity, temperature, and airflow to maximize productivity while minimizing energy and water consumption.
In practice, crop and field–level digital twins are often assembled by integrating multiple platforms, such as Bayer Climate FieldView, John Deere Operations Center, and Hexagon’s digital twin platform, which together enable persistent virtual representations of crops, fields, and operations. In parallel, some vendors deliver more targeted digital twins that focus on specific aspects of crop management, such as early detection of pests, diseases, and crop stress, soil moisture, nutrient profiling and irrigation optimization, greenhouse and controlled environment agriculture, and virtual field and equipment model integration.
CropX uses in-field soil sensors, weather data, and agronomic models to build digital representations of soil and crop conditions. These models detect early signs of crop stress related to moisture imbalance, nutrient deficiencies, and environmental conditions, enabling proactive intervention (e.g. in irrigation, nutrient management).
Taranis applies high-resolution aerial imagery and AI analytics to monitor crop health at the leaf level. Its platform continuously mirrors crop conditions in a virtual environment to identify early signals of pests, diseases, and plant and livestock stress. These insights are used operationally by growers and agronomists for targeted intervention, representing a specialized crop-health digital twin layer.
Arable's field-level monitoring systems integrate soil moisture, microclimate data, and crop growth metrics into predictive models that support irrigation and fertilizer optimization. The platform is publicly used in specialty crops and row crops to guide water and nutrient decisions through season-long virtual field modeling.
Through integrations with third-party soil sensor providers, Deere’s digital field models incorporate soil moisture and nutrient data to support variable-rate irrigation and fertilization. These continuously updated soil twins improve precision input management and reduce resource waste.
Priva develops greenhouse digital twins that simulate lighting, humidity, temperature, carbon dioxide levels, and airflow. These systems continuously adjust environmental controls to optimize plant growth while minimizing energy and water consumption. Priva's solutions are publicly deployed in commercial greenhouses worldwide.
Siemens has partnered with controlled environment agriculture (CEA) operators, such as 80 Acres Farms and Nemo’s Garden, to deploy digital twins of greenhouses and enclosed environments. These twins integrate environmental sensing, operational data, and simulation models to create persistent virtual replicas of growing environments, enabling optimization of lighting, climate control, energy use, and production workflows to improve productivity and operational efficiency.
Digital twins for farm machinery mirror tractors, harvesters, planters, and implements by continuously synchronizing machine telemetry, sensor data, and operational logs with persistent virtual equipment models. These machinery digital twins enable real-time performance monitoring, remote diagnostics, predictive maintenance, and lifecycle optimization, while also supporting the virtual testing of operational strategies before execution. When integrated with digital field and agronomic models, equipment twins improve execution precision, reduce unplanned downtime, and enhance operational efficiency across planting, spraying, and harvesting workflows.
John Deere deploys equipment-level digital twins through its Operations Center by mirroring farm machinery using continuous machine telemetry. These equipment twins support predictive maintenance, real-time performance monitoring, and remote diagnostics, and are integrated with digital field models to enable virtual testing of operational strategies under varying field conditions. Deere has publicly documented reductions in downtime and improvements in operational efficiency resulting from these capabilities.
AGCO’s Fuse Smart Farming platform maintains digital representations of machinery and implements optimize planting, spraying, and harvesting operations. By synchronizing machine telemetry and operational data with virtual equipment models, AGCO enables predictive maintenance and allows farmers to simulate field strategies such as planting depth, spacing, and nutrient delivery prior to execution, improving precision and reducing operational risk.
CNH Industrial deploys equipment-level digital twins through its AFS Connect (Case IH) and PLM Intelligence (New Holland) platforms, which mirror tractors, harvesters, and implements using continuous machine telemetry. These digital twins support remote diagnostics, predictive maintenance, and performance optimization, and are increasingly integrated with field and agronomic data to improve execution accuracy, reduce downtime, and extend machine lifecycles.
In livestock management, digital twins create data-driven virtual representations of animals and their surrounding environments. By integrating sensor data, environmental inputs, and AI-driven analytics, these systems continuously monitor animal health, behavior, and productivity. Digital twins can identify early indicators of disease, assess feeding efficiency, and simulate how changes in housing, nutrition, or environmental conditions may affect outcomes before actions are taken in the physical environment.
These predictive insights support more effective breeding programs, improved animal welfare, and more sustainable livestock operations. By reducing uncertainty and enabling earlier intervention, livestock digital twins also help lower veterinary costs, improve productivity, and reduce overall operational risk.
Allflex Livestock Intelligence deploys sensor-driven digital twins across dairy and beef operations by continuously modeling individual animals and herds using activity, rumination, temperature, and feeding data. These virtual representations enable early detection of health issues, optimization of reproduction cycles, and evaluation of nutrition and management strategies before changes are implemented on farm. By transforming real-time sensor inputs into predictive insights, Allflex’s digital twins help improve animal welfare, increase productivity, and reduce veterinary intervention and operational risk at scale.
Connecterra's Ida Cow platform functions as an AI-driven digital twin for dairy cattle, serving as continuously updated virtual models of individual animals that integrate sensor data, farm records, and environmental context. The system predicts emerging health issues, detects behavioral anomalies, and simulates how changes in feeding, housing, or workload may affect outcomes across lactation cycles. These capabilities support earlier intervention, improved breeding decisions, reduced antibiotic usage, and more consistent long-term herd performance.
Smartbow deploys ear-tag-based sensor systems that generate real-time digital twins of cattle herds, capturing spatial movement, activity patterns, and health indicators. These virtual models enable early disease detection, heat-stress analysis, and the prediction of breeding and calving events. By simulating herd behavior and environmental interactions, Smartbow’s digital twins aim to help farmers improve productivity, reduce labor requirements, and manage livestock more predictably across large-scale operations.
DeLaval's digital services platform integrates animal sensors, milking systems, feeding data, and barn environmental inputs to create combined animal-environment digital twins. These models allow farmers to assess how changes in ventilation, temperature, feeding regimes, or milking schedules affect animal health, comfort, and productivity. By linking animal performance directly to environmental and operational conditions, DeLaval’s digital twins support long-term optimization of resource use, welfare outcomes, and lifetime yield per animal.
SwineTech applies AI-driven digital twins in pig farming by modeling individual and group behavior to identify stress, aggression, and emerging health risks. Sensor and behavioral data are used to simulate how environmental or management changes influence welfare and productivity, enabling proactive intervention. These digital twins help reduce piglet mortality, improve welfare outcomes, and lower operational risk by addressing issues before they escalate in physical environments.
Digital twins play a central role in precision farming by unifying real-time agronomic, environmental, and operational data into continuously updated virtual field models. These twins allow farmers to optimize critical inputs such as water, nutrients, energy, and labor with a high degree of accuracy. Soil moisture, nutrient availability, canopy development, and weather conditions are continuously mirrored, enabling simulation of crop responses under different management strategies and environmental scenarios.
This intelligence drives precision irrigation, targeted fertilization, and variable-rate application, reducing waste while improving crop performance. Predictive analytics identify emerging inefficiencies, forecast future resource requirements, and recommend proactive adjustments. As a result, digital twins transform precision farming from a static optimization exercise into a dynamic, self-optimizing system that supports higher yields, lower costs, and improved sustainability across diverse cropping systems.
Beyond the farm gate, digital twins are increasingly positioned to support broader agricultural supply chains, with the longer-term ambition of creating virtual replicas of ‘farm-to-table’ ecosystems. These models aim to integrate data from fields, storage facilities, transportation networks, processing plants, and markets to enable stakeholders across the value chain to simulate logistics flows, identify bottlenecks, and optimize routing, storage, and scheduling decisions. While elements of this vision have been implemented by companies such as Hexagon, IBM, John Deere, SAP, and Maersk, current deployments typically address specific segments of the supply chain rather than delivering fully integrated end-to-end digital twins, reflecting the technical, organizational, and data-integration challenges inherent in connecting diverse systems and stakeholders.
Digital twins also underpin a growing set of financial, insurance, and risk management applications in agriculture. By creating virtual replicas of farms, crops, and livestock that can be simulated under varying environmental and operational conditions, these approaches enable data-driven assessments of performance, exposure, and future outcomes. These solutions draw on intelligence and virtual modeling capabilities from multiple technology providers to support underwriting, claims assessment, credit evaluation, and market risk analysis, even though they are typically not positioned or marketed explicitly as digital twins.
SatYield provides satellite-driven yield intelligence by combining Earth-observation data with AI-based predictive models to maintain continuously updated virtual representations of farms and crops. These models generate forward-looking yield forecasts and risk indicators that are integrated into insurance, lending, and trading decision systems, supporting underwriting, claims validation, credit assessment, and market intelligence.
Planet Labs provides high-frequency satellite imagery that is used by insurers, reinsurers, hedge funds, and commodity traders to build continuously updated digital representations of agricultural regions and crop systems. When combined with analytics and predictive models, these regional-scale virtual representations support assessment of climate exposure, yield variability, and systemic risk, informing insurance pricing, risk management, and commodity trading strategies.
Descartes Labs provides satellite- and climate-driven analytics that integrate Earth-observation data, weather information, and historical yield records to create continuously updated digital representations of agricultural systems. These models are used by traders, insurers, and financial institutions to assess climate exposure, forecast yields, stress-test production scenarios, and generate market intelligence.
ClimateAi provides climate-risk analytics that create forward-looking digital representations of farms and agricultural regions by integrating climate models, historical data, and geospatial intelligence. These models are used by insurers, lenders, and agribusiness investors to simulate future climate scenarios and assess impacts on crop viability, asset value, and financial performance, supporting underwriting, portfolio risk management, and long-term capital allocation.
Global reinsurers such as Swiss Re and Munich Re use advanced modeling frameworks that integrate weather and climate data, historical loss records, satellite observations, and crop models to create persistent virtual representations of agricultural production systems at regional and national scales. These models support scenario analysis, reinsurance pricing, capital allocation, and systemic risk assessment across global agricultural insurance portfolios.
Corteva provides agronomic data platforms that integrate yield forecasts, soil data, weather exposure, and management practices to support farm performance analysis and risk assessment. These insights can be shared with financial and insurance partners and incorporated into credit and underwriting models to enhance the evaluation of farm-level risk beyond static, historical assessments. While Corteva does not explicitly market these capabilities as financial digital twins, they reflect an emerging approach to dynamic farm risk and credit modeling.
Major commodity trading firms use advanced modeling frameworks that integrate satellite data, weather forecasts, logistics constraints, and historical production trends to create continuously updated digital representations of crop systems and supply regions. These models support scenario analysis and risk management by simulating future supply conditions, informing hedging strategies, price discovery, and market positioning.
Digital twins enable long-term strategic planning by allowing stakeholders to evaluate future outcomes through simulated farm and agricultural system models. These twins integrate weather, soil, crop, and management data to assess how different decisions, technologies, and environmental conditions influence productivity, resource use, emissions, and sustainability over time.
New practices, operating models, and technologies can be tested and compared in virtual environments, supporting more resilient planning and improved adaptation strategies. Government agencies and policymakers are increasingly applying agricultural digital twins to assess the impacts of regulatory changes, climate policies, and subsidy frameworks. By modeling outcomes such as emissions, water use, yield stability, and supply chain resilience, digital twins help balance sustainability objectives with food security, cost efficiency, and system-wide robustness.
U.S. Department of Agriculture programs, working with land-grant universities, operate large-scale agricultural modeling frameworks that integrate crop models, soil data, climate projections, and management practices to evaluate long-term productivity and sustainability outcomes. These systems simulate the impacts of climate change, conservation practices, irrigation strategies, and technology adoption on yields, water use, and emissions, and are used by policymakers to inform conservation incentives, subsidy design, and climate adaptation strategies at regional and national scales.
The European Commission and affiliated research bodies use integrated agricultural modeling frameworks to support strategic planning under the Common Agricultural Policy (CAP). These systems combine farm production data, climate scenarios, land-use models, and economic variables to simulate how regulatory changes, carbon reduction targets, and subsidy structures affect farm viability, food production, and environmental outcomes. While not explicitly described as digital twins, these models function as policy-oriented agricultural digital twins by enabling long-term evaluation of trade-offs between emissions reduction, biodiversity goals, farmer income, and food security across EU member states.
The Netherlands has developed a set of integrated agricultural, water, and environmental modeling systems that function as national-scale digital twins by simulating interactions between farming systems, water management, land use, and emissions. These models are used by government agencies to test scenarios related to nitrogen-reduction policies, land-use reform, and climate adaptation, and to evaluate long-term impacts on yields, water quality, emissions, and farm economics.
CSIRO develops integrated agricultural and climate modeling frameworks that function as digital twins by combining climate projections, crop and soil models, and farm management data to support long-term resilience planning. These systems are used to evaluate future climate scenarios, assess technology adoption pathways, and test alternative farming systems under conditions such as drought, heat stress, and water scarcity, informing government and industry decisions on investment, regional planning, and climate adaptation.
In research, development, and breeding, digital twins are accelerating innovation while reducing the cost, time, and risk associated with physical field trials. High-fidelity virtual replicas of crops, fields, and growing environments allow researchers to simulate how genetic traits respond to variations in climate, soil conditions, water availability, and management practices.
For example, these models support plant phenomics by linking genotype data to observable traits such as growth rates, stress tolerance, and yield potential. Breeders can evaluate thousands of scenarios digitally before committing to real-world trials, including stress-testing new seed varieties under extreme or future climate conditions that are difficult to reproduce consistently in the field. By shifting early-stage experimentation into simulated environments, digital twins shorten breeding cycles, improve selection accuracy, and support more targeted, data-driven crop improvement programs for both public research institutions and commercial seed developers.
While physical field trials remain essential for validation, digital twins increasingly shift early-stage experimentation into simulated environments, shortening breeding cycles and improving selection efficiency.
Bayer Crop Science uses integrated modeling and simulation approaches that function as digital twins across its R&D and breeding programs, simulating how genetic traits interact with soil, climate, and management variables across diverse growing environments. These virtual crop models combine genotype, phenotypic, and environmental data to predict yield potential, stress tolerance, and disease resistance under current and projected climate conditions, enabling earlier candidate selection, more targeted field trials, and improved breeding efficiency.
Corteva Agriscience uses integrated modeling and simulation approaches in its R&D and breeding programs to link genomic data with crop growth and environmental models. These tools allow researchers to evaluate how new traits may perform under different weather patterns, soil types, and agronomic practices, supporting earlier-stage screening and more targeted field trials.
Agmatix provides AI-driven analytics that integrate experimental data and sensor inputs to create virtual representations of plant behavior and phenotypic response. These models support simulation and interpretation of plant responses to environmental stressors such as drought, heat, and nutrient variability, accelerating hypothesis testing, improving field-trial analysis, and enabling more efficient trait discovery.
Syngenta uses integrated modeling and simulation approaches within its R&D and breeding workflows that function as digital twins by evaluating how seed varieties perform under different climatic and agronomic conditions. These virtual crop models assess yield stability, disease resistance, and resource efficiency across regions and future climate scenarios, informing breeding priorities, trial design, and the development of climate-resilient seed portfolios.
Leading agricultural universities, including the University of Arizona, Texas A&M University, University of Nebraska–Lincoln, University of Nottingham, Wageningen University & Research, ETH Zurich, and the University of Queensland, operate advanced phenomics platforms that function as digital twins by linking high-throughput sensing, imaging, and genetic data with virtual crop models. These systems enable researchers to evaluate trait expression under controlled and simulated conditions, including extreme or future climate scenarios, improving trait characterization, reducing experimental noise, and supporting more precise breeding decisions.
Digital twins are also being deployed as immersive visualization and training platforms. By combining real farm data with 3D and metaverse-style environments, these systems allow farmers, agronomists, policymakers, and other stakeholders to interact with complex agricultural systems in intuitive, accessible ways.
Virtual training environments enable users to explore fields, equipment, and management scenarios without operational risk, while immersive simulations help visualize how changes in weather, water use, crop selection, or input strategies affect productivity, sustainability, and risk over time. These tools are increasingly used for education, workforce training, and stakeholder engagement, improving understanding of farming practices, environmental trade-offs, and sustainability outcomes. At an operational level, advanced visualization highlights yield variability, soil conditions, water stress, and risk zones such as drought- or flood-prone areas, translating complex datasets into actionable insight across the agricultural value chain.
John Deere uses advanced simulation and digital modeling tools to support operator training, technician education, and equipment safety. These systems employ detailed virtual representations of agricultural machinery and field workflows within 3D environments, allowing users to practice operation, maintenance, and troubleshooting without operational risk. While not fully immersive, real-time digital twins of live farm operations, these training platforms apply digital twin principles to improve workforce skills, safety, and equipment utilization under a range of simulated field conditions.
Wageningen University & Research develops integrated digital agriculture platforms that combine agronomic data, crop and environmental models, and advanced visualization tools to create interactive virtual representations of farming systems. These environments are used for education, research collaboration, and stakeholder engagement, enabling exploration of management strategies, climate scenarios, and crop choices and improving understanding of system-level trade-offs among productivity, sustainability, and resource use.
Hexagon provides digital twin and geospatial intelligence platforms that support immersive 3D visualization of agricultural landscapes, infrastructure, and supply-chain systems. These environments are used primarily for planning, scenario analysis, and stakeholder communication, enabling users to explore spatial relationships between fields, storage facilities, transport routes, and processing sites. While not always positioned as immersive training tools, the visual and spatial perspective improves understanding of operational bottlenecks, risk exposure, and system-wide dependencies across agricultural and agri-supply-chain contexts.
Natural language processing (NLP) and AI tools were used to identify companies mentioned in the content corpus, measure their prevalence (BREADTH), and evaluate how frequently they appear alongside other companies (DEPTH). Of the 97 companies identified, the ranking of the top 10 is shown in the chart below.
John Deere applies digital twin methodologies across agricultural equipment, field operations, and agronomic workflows by maintaining persistent digital representations of machines, fields, and production activities within its Operations Center ecosystem. Using continuous machine telemetry, sensor data, and agronomic inputs, Deere mirrors farm equipment to enable real-time performance monitoring, predictive maintenance, and operational optimization, while integrating these equipment twins with digital field models to support planning, simulation, and execution of planting, spraying, and harvesting strategies. Together, these interconnected twins allow growers and advisors to evaluate scenarios, improve execution precision, reduce downtime, and make data-driven decisions across the production cycle, positioning John Deere as one of the most mature and operationally deployed digital twin providers in agriculture.
Microsoft Azure supports digital twins for agriculture primarily as a horizontal platform and enabling ecosystem, rather than as a vertically integrated farming solution. Through Azure Digital Twins, Azure IoT, Azure Maps, Azure AI, and cloud-scale data services, Microsoft enables agribusinesses, OEMs, researchers, and governments to build digital representations of farms, equipment, supply chains, and agricultural environments. These Azure-based digital twins integrate data from sensors, machinery telemetry, satellites, weather services, and enterprise systems to model crop performance, resource use, infrastructure, and operational workflows. In agriculture, Azure is commonly used to underpin solutions for precision farming, equipment monitoring, supply-chain traceability, sustainability and emissions tracking, climate-risk modeling, and scenario simulation. Rather than delivering a single agricultural digital twin product, Microsoft positions Azure as the foundational cloud and AI layer on which partners and customers construct domain-specific agricultural digital twins tailored to operations, finance, sustainability, and policy use cases.
NLP and AI techniques were used to identify and classify keywords and phrases in the content corpus into 28 topics. Their frequency was measured (BREADTH) and their inter-relationships analyzed (DEPTH). The chart below shows the top 10 topics.
The most prominent topics in online agricultural technology discourse today are precision agriculture, artificial intelligence (AI), and the Internet of Things (IoT). As digital twins remain nascent within the agricultural sector, the current emphasis on enabling technologies such as AI and IoT is unsurprising, as these form the foundational infrastructure required to support digital twin development. Over time, as digital twin adoption matures, attention is likely to shift from underlying technologies toward higher-level service capabilities, including pest and disease management, livestock monitoring, irrigation optimization, and other outcome-oriented applications beyond core precision agriculture.
Digital twins are emerging as a foundational capability in agriculture, but adoption remains uneven and largely use-case driven rather than system-wide. Across crop and field management, machinery monitoring, livestock operations, finance, supply chains, research, and policy planning, most deployments today function as partial or domain-specific digital twins rather than fully integrated representations of entire farming systems. This reflects both the inherent complexity of agricultural environments and persistent challenges around data quality, interoperability, connectivity, and cost.
Where digital twins are delivering the greatest near-term value is in decision support rather than automation. Yield forecasting, early detection of stress and disease, precision input optimization, predictive maintenance, risk assessment, and scenario modeling are already improving planning accuracy, reducing operational risk, and supporting more efficient resource use. In finance, insurance, and policy contexts, digital twin–like models are enabling more dynamic assessments of exposure and resilience, even when they are not explicitly branded as digital twins.
The report highlights a clear structural pattern in the market: horizontal platforms and large OEMs provide enabling foundations, while specialist providers deliver depth in specific domains. Companies such as John Deere and Microsoft Azure have established themselves as central digital twin enablers through persistent operational data, scalable cloud infrastructure, and ecosystem integration. At the same time, targeted providers focused on soil, crop stress, livestock health, climate risk, or breeding are advancing specialized twins that address high-value problems within constrained scopes.
Public-sector and research-led digital twins play a complementary role, particularly in strategic planning, climate adaptation, and food security. National and regional modeling frameworks operated by governments, universities, and international organizations increasingly function as policy-oriented digital twins, supporting long-term scenario analysis and trade-off evaluation. While these systems are not real-time or operational, they are becoming essential tools for navigating climate volatility, sustainability mandates, and systemic risk.
Looking ahead, the evolution of agricultural digital twins is likely to follow a progressive integration pathway. As data standards improve, connectivity expands, and confidence in model outputs grows, digital twins will move from isolated applications toward more interconnected systems that link production, operations, finance, and policy. However, human oversight, transparent assumptions, and realistic expectations will remain critical. Digital twins will not replace agronomic expertise or physical trials, but they will increasingly shape how decisions are tested, prioritized, and executed.
In this context, digital twins should be viewed not as a single technology deployment, but as a long-term transformation in how agricultural systems are modeled, understood, and managed. Their ultimate impact will depend less on technical sophistication alone and more on integration, governance, and the ability to translate complex data into trusted, actionable insight across the agricultural value chain.
