Serious games can unlock farmer uptake of land‑use decision tools by making model assumptions transparent, demonstrating profitability–emissions trade‑offs, and fitting into farm workflows; however, the evidence is preliminary, coming mostly from small pilots and requiring larger field trials and validation to confirm real‑world effects on land use and emissions.
Serious games and decision support tools: Supporting farmer decision making for land use net zero.
Serious games can materially increase farmer uptake of land-use decision-support tools by enabling co-design, building trust, visualizing trade-offs, linking profitability to environmental outcomes, and integrating with workflows, but the supporting evidence is largely qualitative and from small-scale pilots.
Significant land use change is needed if global net zero targets are to be met. This is likely to involve changes to the way that farmers operate. Farmers will need appropriate tools to make decisions that deliver net zero goals while also maintaining their business enterprise. A range of decision support tools (DSTs) are increasingly available to support farmers to make informed decisions based on data and evidence. However, DST uptake in the context of net zero is limited. The chapter explores how ‘serious games’ are already being used as land use DSTs and to support the design and use of land use DSTs, and how through their continued use in this area serious games can help to address some of the challenges of DST uptake, particularly through co-design, establishing trust, visualisation of outcomes, demonstrating links between environmental action and profitability, and integration with other tools.
Summary
Main Finding
Serious games—interactive, simulation-based decision support tools—can materially increase farmer uptake of land-use decision support tools (DSTs) needed to meet global net zero targets by enabling co-design, building trust, visualizing outcomes, demonstrating profitability–environment links, and integrating with other tools. Current DST uptake for net zero remains limited; serious games address many behavioral, informational, and design barriers to wider adoption.
Key Points
- Problem framing: Achieving net zero requires significant land-use change and changes in farm management; farmers need practical DSTs that balance emissions goals with enterprise viability.
- Limited uptake: Existing DSTs are underused in net-zero contexts due to issues of trust, usability, lack of evidence linking actions to farm profitability, and poor integration into farmer workflows.
- Role of serious games:
- Co-design: Games facilitate participatory design with farmers and stakeholders, yielding tools that match on-farm decision contexts and preferences.
- Trust-building: Interactive, transparent simulations let users explore assumptions and model behavior, reducing skepticism about DST recommendations.
- Visualization: Dynamic, scenario-based visual outputs help users understand trade-offs over time (e.g., carbon sequestration vs. yields).
- Profitability linkage: Games can explicitly model economic outcomes alongside environmental metrics, showing how mitigation/adaptation actions affect enterprise resilience and income.
- Integration: Games can act as front-ends to underlying models and datasets or bridge multiple DSTs, improving interoperability and workflow fit.
- Remaining challenges: Ensuring scientific validity of game models, scaling co-design processes, measuring real-world behavioral change, and aligning incentives (policy/subsidies, markets) to encourage adoption.
Data & Methods
- Evidence basis: The chapter synthesizes literature and practice examples of DSTs and serious games in land-use planning and agricultural decision-making.
- Common empirical approaches reflected in the chapter:
- Case studies and deployed game prototypes used with farmer groups.
- Participatory workshops and co-design sessions to elicit requirements and test interfaces.
- Qualitative interviews and surveys assessing farmer perceptions, trust, and willingness to adopt DST outputs.
- Comparative demonstrations showing economic and environmental outcomes under alternative decisions/scenarios.
- Evaluation metrics discussed or implied:
- Usability and engagement (uptake, time in tool, repeat use).
- Comprehension and trust (self-reported understanding, confidence in recommendations).
- Behavioral intent and observed practice change (pilot implementation).
- Outcomes alignment (measured emissions, sequestration, yields, profitability) where pilots exist.
- Limitations noted: Heterogeneity of farmer contexts limits generalizability; many studies are small-scale or experimental, and long-term impact data are sparse.
Implications for AI Economics
- Reducing informational frictions: Serious-game DSTs can make model outputs (including AI-based recommendations) interpretable and actionable, thereby lowering barriers to adoption and improving the translation of technical advice into economic behavior.
- Behavioral design & incentive alignment: Co-designed games reveal farmer preferences and constraints, informing incentive schemes and policy design (e.g., payment levels, timing of subsidies) that better align private profitability with social emissions objectives.
- Evaluation of economic impact: Rigorous economic evaluation (RCTs, quasi-experiments) is needed to quantify how game-enhanced DSTs affect investment, land-use choices, emissions outcomes, and farm incomes.
- Model transparency & trust in AI: Games provide a human-centered interface for exposing model assumptions, uncertainty, and trade-offs—a practical pathway to increase trust in AI-driven recommendations in high-stakes economic decisions.
- Integration & markets: Embedding games within broader DST ecosystems (market platforms, precision-agriculture systems, carbon accounting services) could unlock monetization routes (carbon markets, ecosystem service payments) and reduce transaction costs.
- Scalability & targeting: AI can help personalize game scenarios to farm-specific data, improving relevance, but economics research must study cost-effectiveness of individualized vs. generic solutions and distributional impacts across farm sizes and regions.
- Policy and governance: Insights from co-design and game interactions can inform regulatory standards for DST validity, data governance, and accountability for AI-based land-use advice.
Actionable research priorities: - Run larger-scale field trials linking game use to observed land-use and economic outcomes. - Develop protocols for validating game-backed models against empirical on-farm data. - Study heterogeneity of impacts to target interventions where games most increase cost-effective emissions reductions. - Design incentive mechanisms (market or policy) that leverage game-demonstrated profitability co-benefits to accelerate adoption.
Assessment
Paper Typereview_meta
Evidence Strengthlow — Claims are supported primarily by case studies, pilot deployments, participatory workshops, qualitative interviews, and small-scale demonstrations rather than causal estimates; long-term impacts on behaviour, land use, emissions, and incomes are sparse or absent.
Methods Rigorlow — Synthesis of heterogeneous practice examples without systematic meta-analytic methods or pre-registered protocols; relies heavily on qualitative evidence and demonstrations rather than randomized or well-identified quasi-experimental evaluation.
SampleLiterature and practice examples including small-scale deployed game prototypes, participatory co-design workshops with farmer groups, qualitative interviews and surveys of farmers, and comparative demonstration scenarios; contexts are heterogeneous (different farm types, regions) with typically small samples and limited longitudinal follow-up.
Themesadoption human_ai_collab productivity governance
GeneralizabilityEvidence comes from small pilots and context-specific co-designs, limiting external validity across regions and farm types, Likely selection bias toward engaged or early-adopter farmers in studies, Short-term demonstrations dominate; long-term behavioural and land-use outcomes are largely unobserved, Variability in local institutions, markets, and subsidy regimes may alter effectiveness, Scalability of co-design processes and fidelity of game-simulations when deployed broadly is uncertain
Claims (14)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Serious games—interactive, simulation-based decision support tools—can materially increase farmer uptake of land-use decision support tools (DSTs) needed to meet global net zero targets by enabling co-design, building trust, visualizing outcomes, demonstrating profitability–environment links, and integrating with other tools. Adoption Rate | positive | medium | DST uptake (use/adoption rate), engagement with DSTs |
0.07
|
| Current uptake of DSTs for net zero remains limited because of issues of trust, usability, lack of evidence linking actions to farm profitability, and poor integration into farmer workflows. Adoption Rate | negative | high | DST adoption/use rates; reported barriers (trust, usability, integration) |
0.12
|
| Co-design through serious games facilitates participatory design with farmers and stakeholders, producing tools that better match on-farm decision contexts and preferences. Organizational Efficiency | positive | medium | Perceived relevance/fit of DSTs to on‑farm decisions; usability measures |
0.07
|
| Interactive, transparent simulations in games reduce skepticism by letting users explore assumptions and model behavior, thereby building trust in DST recommendations. Decision Quality | positive | medium | Trust/confidence in recommendations; self-reported skepticism |
0.07
|
| Dynamic, scenario-based visual outputs in serious games help users understand trade-offs over time (for example, carbon sequestration versus yields). Decision Quality | positive | medium | Comprehension of trade-offs; ability to reason about temporal outcomes |
0.07
|
| Serious games can explicitly model economic outcomes alongside environmental metrics, showing how mitigation/adaptation actions affect enterprise resilience and income. Firm Revenue | positive | medium | Profitability/income estimates, economic resilience indicators, environmental metrics (emissions/sequestration) |
0.07
|
| Games can act as front-ends to underlying models and datasets or bridge multiple DSTs, improving interoperability and workflow fit for farmers. Organizational Efficiency | positive | medium | Interoperability metrics, integration into farmer workflows, time/effort to use DST ecosystem |
0.07
|
| Ensuring scientific validity of game models, scaling co-design processes, measuring real-world behavioral change, and aligning incentives (policy/subsidies, markets) are remaining challenges to using serious games for DST uptake. Adoption Rate | negative | high | Model validity (accuracy vs. empirical data), scalability of co-design processes, observed behavioral change/adoption, policy alignment indicators |
0.12
|
| Many studies on serious-game DSTs are small-scale or experimental, and long-term impact data on behavioral change and emissions outcomes are sparse, limiting generalizability. Research Productivity | negative | high | Study scale/sample size, duration of follow-up, evidence on long-term behavior change and emissions outcomes |
0.12
|
| Serious-game DSTs can reduce informational frictions by making model outputs (including AI-based recommendations) more interpretable and actionable, lowering barriers to adoption and improving translation of technical advice into economic behavior. Adoption Rate | positive | medium | Interpretability (user understanding), adoption intentions, changes in decision-making behavior |
0.07
|
| Embedding games within broader DST ecosystems (market platforms, precision-agriculture systems, carbon accounting services) could unlock monetization routes (carbon markets, ecosystem service payments) and reduce transaction costs. Firm Revenue | positive | low | Participation in carbon markets/payments, transaction costs, monetization revenue |
0.04
|
| AI can help personalize game scenarios to farm-specific data, improving relevance, but the cost-effectiveness of individualized versus generic solutions and distributional impacts across farm sizes and regions require study. Inequality | mixed | low | Relevance/fit of scenarios, cost per unit of impact, distributional impacts across farm types/sizes |
0.04
|
| Rigorous economic evaluation (RCTs, quasi-experiments) is needed to quantify how game-enhanced DSTs affect investment, land-use choices, emissions outcomes, and farm incomes. Research Productivity | null_result | high | Investment decisions, land-use change, emissions (measured GHG outcomes), farm incomes |
0.12
|
| Actionable research priorities include running larger-scale field trials linking game use to observed land-use and economic outcomes, developing validation protocols for game-backed models against empirical on-farm data, studying heterogeneity of impacts, and designing incentive mechanisms that leverage game-demonstrated profitability co-benefits. Research Productivity | null_result | high | Observed land-use change, economic outcomes, validated model performance, heterogeneous treatment effects, effectiveness of incentive mechanisms |
0.12
|