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MAELIA for large-scale farming system design:
methodological approach

Ugo Javourez, Olivier Therond

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Ugo Javourez, Olivier Therond. MAELIA for large-scale farming system design: methodological approach. 8th International Farming System Design Conference, Aug 2025, Palaiseau (Université Paris-Saclay), France. hal-05251459

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# MAELIA for large-scale farming systems design: methodological approach

JAVOUREZ UGO<fnref n="1" /> and THEROND OLIVIER<fnref n="1" />

Scaling methods, Agent-based models, Integrated assessment, Agriculture

## Introduction

Integrated Assessment and Modeling (IAM) approaches aim to account for the complexity of agricultural systems (Ewert et al., 2011), and are recognized for their ability to assist stakeholders in shaping organizations of human activities, such as those within biomass-based chains of the bioeconomy. IAM combines multidisciplinary knowledge with local actors’ empirical expertise to create models that simulate and evaluate the consequences of various organizations under endogenous or exogenous changes (Hamilton et al., 2015). MAELIA is a high-resolution, agent-based IAM platform designed for multi-criteria analysis of territorial farming and bioeconomy systems (Therond et al., 2014). It simulates daily spatiotemporal dynamics of agricultural operations at the field level considering the diversity of farming strategies, soils and weather variability, and ecological processes. Currently, the largest MAELIA case study covers 40,000 ha and 1,150 farms (Tribouillois et al., 2022), with larger-scale assessments constrained by data availability and computational resources.

This study aims to develop a scaling strategy for applying MAELIA at the whole France scale to assess the sustainability of scalable solutions for producing, mobilizing and utilizing agricultural biomass while sequestering carbon in soils. The goal is to move beyond averaging approaches typically used at France (e.g., Afterres scenario) or regional scales (Billen et al., 2021). Previous bottom-up simulations, such as those using the STICS cropping systems model (Launay et al., 2021; Tibi and Therond, 2024) have been applied to a pedoclimatic unit mapping of metropolitan France (average 18 km² grid). However, MAELIA operates at the field level considering farm level constraints (e.g., work load for farming operations), posing additional scaling challenges. This work presents a methodological approach for applying MAELIA at the France level and discusses the implications and limitations.

## Methods

The scaling strategy aims to maintain computational costs at levels comparable to existing case studies, as new functionalities continue to be integrated to MAELIA. It also seeks to preserve the existing modelling algorithm by implementing a flexible scale change treatment on top of MAELIA. The proposed method involves selecting representative production situations and farming systems for simulation (preprocessing), assessing dynamics of these entities with MAELIA, and upscaling results to the local, regional up to national scales.

The approach is rooted in an information system and a configurable data treatment that manages nationwide inputs data to characterize and select entities to be simulated (Figure 1). First, France is partitioned into relatively homogenous agro-environmental zones. Within each zone, farming systems are clustered based on field-level (e.g., pedoclimatic and crop sequences) and farm-level variables (e.g., area, production orientation). The resulting farm typology highlights key differentiation criteria relevant to MAELIA simulations, such as the presence of irrigation or organic farming. Simulated farming systems are selected from each typological pool using modal values for each variable to ensure coherent farm simulation unit that integrates representative combinations of climate, soil, crop sequence and management, and irrigation.

<footnote n="1">¹ Université de Lorraine, INRAE, LAE, F-68000 Colmar, France</footnote>

<footnote>*ugo.javourez@inrae.fr</footnote>
Crop management for each production situation are modelled by identifying first the primary drivers of spatial differentiation. A procedure is developed to convert crop operations recorded in date-dose format into rule-based models (IF-THEN conditions) that trigger farming operations as needed by MAELIA. It requires characterizing machinery performances (e.g., tillage depth) and enabling conditions for operations (e.g., soil water content). Once simulations are performed for the selected entities, results are upscaled to larger levels (Ewert et al., 2011), with a quantitative assessment of embedded biases and uncertainties, and validation against historical data. Once calibrated, the approach can be used to evaluate the sustainability of nationwide alternative farming or bioeconomy system trajectories under various scenarios, incorporating reference case variations and alternative climate projections.

<figure><img src="image_3.png" /><figcaption>Figure 1. Key preprocessing steps of the MAELIA scaling strategy</figcaption></figure>

## Perspective

A major part of the procedure depends on consolidating the information system, with restricted data access currently under review. At the conference, we will demonstrate the country-scale feasibility of our approach by applying MAELIA to a representative sample of the 432 historical French agricultural zones (average area: 1,300 km²), using simplified crop practices and farm types. This work advances a tunable upscaling strategy for the agent-based MAELIA platform, designed to progressively relax selection constraints as computational barriers diminish. The strategy aims to represent French farming systems at scales ranging from approximately 50,000 fields and 1,000 farms upwards, unlocking flexible assessment of prospective agricultural scenarios through scales.
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References

Billen, G., Aguilera, E., Einarsson, R., Garnier, J., Gingrich, S., Grizzetti, B., Lassaletta, L., Le Noë, J., Sanz-Cobena, A., 2021. Reshaping the European agro-food system and closing its nitrogen cycle: The potential of combining dietary change, agroecology, and circularity. One Earth 4, 839–850.
https://doi.org/10.1016/j.oneear.2021.05.008

Ewert, F., van Ittersum, M.K., Heckelei, T., Therond, O., Bezlepkina, I., Andersen, E., 2011. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric. Ecosyst. Environ., Scaling methods in integrated assessment of agricultural systems 142, 6–17.
https://doi.org/10.1016/j.agee.2011.05.016

Hamilton, S.H., ElSawah, S., Guillaume, J.H.A., Jakeman, A.J., Pierce, S.A., 2015. Integrated assessment and modelling: Overview and synthesis of salient dimensions. Environ. Model. Softw. 64, 215–229.
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Launay, C., Constantin, J., Chlebowski, F., Houot, S., Graux, A., Klumpp, K., Martin, R., Mary, B., Pellerin, S., Therond, O., 2021. Estimating the carbon storage potential and greenhouse gas emissions of French arable cropland using high-resolution modeling. Glob. Change Biol. 27, 1645–1661.
https://doi.org/10.1111/gcb.15512

Therond, O., Sibertin-Blanc, C., Lardy, R., Gaudou, B., Balestrat, M., Hong, Y., Louail, T., 2014. Integrated modelling of social-ecological systems: The MAELIA high-resolution multi-agent platform to deal with water scarcity problems, in: Ecological Systems. Presented at the International Congress on Environmental Modelling and Software (iEMSs 2014), San Diego, California, United States, p. 2386.

Tibi, A., Therond, O., 2024. Ecosystem services provided by agricultural areas. Evaluation and characterisation approaches. Quae. https://doi.org/10.35690/978-2-7592-4015-9

Tribouillois, H., Constantin, J., Casal, L., Villerd, J., Therond, O., 2022. Introducing and expanding cover crops at the watershed scale: Impact on water flows. Agric. Ecosyst. Environ. 337, 108050.
https://doi.org/10.1016/j.agee.2022.108050