| 09 Jan 2026
AIM-ALPHA v1.0: A partial equilibrium model of global agriculture and land use at basin-level resolution
Abstract. This paper introduces AIM-ALPHA v1.0, a global partial equilibrium model that explicitly represents agricultural markets and land-use dynamics with high spatial resolution at the national and subnational river-basin levels. The model covers 166 countries on the demand side and 400 production units on the production side, balancing supply, demand, prices, trade, and land allocation across 23 agricultural commodities from 2015 to 2100. AIM-ALPHA is a comprehensive framework that analyses global socioeconomic and environmental challenges such as food security, climate change, biodiversity loss, and their mitigation. This paper describes the model structure, evaluates performance against historical data, and demonstrates application via a case study on the impacts of climate-change mitigation on global food security. Validation shows that AIM-ALPHA reproduces the historical statistics around 2015 and remains within ±10 % of observed values up to 2020, confirming numerical consistency. The baseline projections are broadly consistent with those of other integrated assessment and agricultural economic models. We also present a scenario analysis using a 1.5 °C climate-change mitigation pathway, which incorporates global carbon pricing, bioenergy expansion, and afforestation. The results show that agricultural production declines while food prices rise, reducing calorie intake, particularly in developing regions. The country-level resolution of the model reveals considerable heterogeneity within aggregated regions, with several developing countries in Africa and Asia facing particularly large reductions in food availability. Such distributional effects are largely obscured in more regionally aggregated models. These findings highlight the trade-offs between climate-change mitigation and food security and the importance of national-level assessments that capture socioeconomic and spatial heterogeneity. AIM-ALPHA serves as a comprehensive platform when exploring linkages between human and environmental systems and analysing sustainable land-use transitions and policy design at national and regional levels.
General Evaluation
This manuscript presents AIM-ALPHA v1.0, a global partial equilibrium model designed to simulate agricultural production, land use, trade, and prices, with explicit representation at national and river-basin levels. The model integrates supply, demand, and land allocation decisions across multiple commodities and regions, and is positioned as a tool for analyzing long-term interactions among agriculture, climate mitigation, and food security. The paper provides an overview of the model structure, core assumptions, and computational framework, and evaluates model performance against historical statistics. The authors demonstrate the model’s application through a climate mitigation scenario and emphasize that fine spatial resolution allows the identification of distributional effects that are masked in more aggregated models. The use of a GAMS/MCP formulation and the provision of model code are also notable strengths, as they support transparency and reproducibility in principle.
Overall, the manuscript introduces a relevant and timely modeling framework, and the development of a basin-level, globally consistent partial equilibrium model represents a potentially valuable contribution to the literature. The model has clear potential applications in food security analysis, land-use policy assessment, and integrated climate-economy studies. The paper is generally well organized and provides substantial technical material. However, despite the ambitious scope and policy relevance of the framework, several aspects of the documentation, validation, and methodological transparency require further clarification and strengthening. In particular, more rigorous evaluation of model performance, clearer exposition of the underlying economic structure, and a deeper discussion of uncertainties and limitations are needed to enhance the credibility and usability of the model. Addressing these issues would substantially improve the manuscript’s contribution to the GMD community.
Major Comments
(1) Positioning, Contribution, and Literature Coverage
The manuscript does not sufficiently situate AIM-ALPHA within the existing literature on spatially explicit partial equilibrium and land-use models. While MAgPIE and GLOBIOM are mentioned, other closely related modeling frameworks, such as GTAP-AEZ-RB, SIMPLE-G, and GTAP-SIMPLEG, are not discussed. Methodologically, AIM-ALPHA appears to share substantial similarities with the GTAP-AEZ and SIMPLE modeling traditions, particularly in its treatment of land heterogeneity, spatial disaggregation, and market equilibrium. In addition, the authors rely on USDA-based crop elasticities that are widely used in these existing models. Given these overlaps, the manuscript would benefit from a more systematic comparison with these frameworks. A more comprehensive literature review and explicit discussion of similarities, differences, and added value are needed to clarify the model’s contribution.
(2) Economic Representation
Figure 3 presents price on the horizontal axis and quantity on the vertical axis, which is inconsistent with standard economic conventions, where price is typically shown on the vertical axis. While this may seem minor, it raises concerns about the authors’ familiarity with standard economic representations and modeling practices. The authors should ensure that the economic interpretation of the diagram is correct and consistent with the broader literature. More generally, the paper would benefit from stronger engagement with established economic modeling conventions.
(3) Model Transparency, Documentation, and Structural Assessment
The manuscript provides extensive code and scripts, but very limited formal documentation of the model structure. There are essentially no explicit equations describing behavioral relationships, market-clearing conditions, or closure rules. As a result, it is currently impossible for reviewers and readers to evaluate the internal logic and structural consistency of the model. Without a clear mathematical representation, one cannot assess whether the implementation faithfully reflects the intended economic structure. For a GMD paper, this is a significant limitation. The authors should provide a formal description of the core model equations, including supply and demand systems, land allocation, trade representation, and equilibrium conditions. This is necessary to allow independent assessment and reproducibility. In future publications, you can refer to this paper for equations.
(4) Baseline Replication and Validation
The main evidence for model validation appears to be Figure 10, which compares estimated values with historical FAOSTAT data. However, this comparison raises important concerns. The base year of the model is 2015. In standard partial equilibrium models implemented in GAMS using MCP formulations, the benchmark equilibrium should “exactly” replicate the base-year data when no shocks are applied. This is a fundamental property of calibrated equilibrium models. Figure 10 indicates substantial deviations between modeled and observed values even in the base year, with many observations falling outside narrow tolerance ranges. This suggests that the model does not properly replicate its calibration data. In many established modeling frameworks (e.g., GEMPACK, GAMS/MCP and MPSGE-based models), baseline consistency is routinely verified through numeraire checks and zero-shock simulations, which reproduce the reference dataset exactly. Deviations at the benchmark stage often indicate structural inconsistencies, coding errors, or incomplete calibration.
The authors should therefore: Explain why exact base-year replication is not achieved; Clarify whether the reported discrepancies arise from deliberate modeling choices or technical limitations; and provide additional diagnostic checks demonstrating numerical consistency. Without convincing evidence of proper benchmark replication, the credibility of scenario results is difficult to assess.