# Flexible crop rotation identification and classification:
New tool and application to France

Ugo Javourez, Baptiste Girault, Philippe Martin, Marie-Sophie Dedieu,
Olivier Therond

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Ugo Javourez, Baptiste Girault, Philippe Martin, Marie-Sophie Dedieu, Olivier Therond. Flexible crop rotation identification and classification: New tool and application to France. 8th International Farming System Design Conference, Aug 2025, Palaiseau, France. hal-05251440

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Submitted on 15 Sep 2025

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# Flexible crop rotation identification and classification: new tool and application to France

JAVOUREZ UGO<fnref n="1" />, GIRAULT BAPTISTE<fnref n="2" />, MARTIN PHILIPPE<fnref n="2" />, DEDIEU MARIE-SOPHIE<fnref n="3" /> and THEROND OLIVIER<fnref n="1" />

## Cropping systems, Circular pattern matching, Land Parcel Identification System, Crop sequence

### Introduction

Characterizing dominant crop rotations across spatial and temporal scales is essential for farming system design and modeling. Crop rotations influence key ecosystem services such as yield, nutrient cycling, and pest control (Sahajpal et al., 2014). They consist of recurrent crop successions on a field over time. In Europe, the Land Parcel Identification System (LPIS) is the most comprehensive source of field-level agricultural land-use data. In France, LPIS covers 94% of agricultural land, detailing crop successions for approximately nine million parcels in continuous sequences of increasing temporal depth (Girault and Martin, 2024). However, the very high diversity observed in crop sequences complicates identifying dominant rotations at territorial or higher levels.

Approaches identifying dominant crop rotations range from indirect statistical or machine learning-based reconstruction to direct rule-based or expert-based estimations (Leenhardt et al., 2012). Indirect methods often combine Markov chains with clustering techniques, generating artificial crop sequences that are statistically representative at the territorial level and further analyzed with agronomic expertise (e.g., average return time of starter crops, functional diversity; Nowak et al., 2022; Stein and Steinmann, 2018). Direct approaches identify existing recurrent patterns before assessing their representativeness. However, crop rotations are modified by climatic or socioeconomic factors, challenging algorithm convergence and reuse in cropping systems models (Bohan et al., 2021). The last large-scale attempt in France to identify field-level crop rotations using an ad-hoc direct approach (Leenhardt et al., 2012), identified predefined agronomically coherent rotations of up to three crops, aggregated into 13 classes.

Existing methods lack flexibility and reusability due to unavailable or case-specific code and reliance on expert calibration. A tool for systematically identifying and classifying crop rotations while maintaining adaptability for novel applications is needed.

### Methods

Our approach generalizes the rule-based algorithms from Leenhardt et al., (2012) and Sahajpal et al., (2014). It requires a crop sequence dataset, a crop codes mapping table, and outputs dominant crop rotations with their spatially explicit coverage. The procedure consists of five steps: (i) crop codes aggregation, (ii) crop sequence preprocessing, (iii) flexible crop rotation matching, (iv) selection of dominant crop rotations, and (v) distance assessment between crop sequences and rotations.

First, crop codes are aggregated to a tunable granularity level to reduce crop and sequence diversity. Sequences with perennial or unknown crops are discarded. Recurrent patterns covering at least 60% of a crop sequence's length are identified iteratively (e.g., for an eight-year dataset, patterns of up to four years are detected if covering at least five years). A unique

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

<footnote n="2">² Université Paris-Saclay, INRAE, AgroParisTech, UMR SADAPT, 91120 Palaiseau, France</footnote>

<footnote n="3">³ INRAE, US ODR, Castanet-Tolosan F-31326, France</footnote>

<footnote>*ugo.javourez@inrae.fr</footnote>
terminology (*P_f_{non-P}_%seq) captures the pattern (P), its coverage ratio (%seq), and flexible crops (non-P). Among different potential matching patterns, one is retained, with hierarchical criteria: maximizing coverage and preferring shorter and recently observed patterns. Circularity checks ensure equivalent patterns (e.g., A-B-C, B-C-A and C-A-B) are unified. Patterns P without flexible crops are considered rotations.

Dominant crop rotations are identified based on customizable selection criteria, such as limiting the number of rotations or setting a minimum representativity threshold. Each crop sequence (including those without any matching pattern) is then assigned to the most similar selected rotation using custom distance metric, typically the Hamming distance, which measures substitution costs between sequences. Ties are solved through random sorting.

The procedure was applied to the 2015-2022 metropolitan France crop sequence dataset (Girault and Martin, 2024), after filtering fields below 0.5 ha. LPIS crop codes were aggregated into 50 classes, and rotations selected to represent at least 50% of total arable land.

## Preliminary results

We illustrate results using two contrasting French regions: Meurthe & Moselle and Beauce. These regions include 66,900 and 45,900 crop sequences, but only 25,000 and 29,000 remain unique after crop code aggregation. Flexible crop rotations cover 85% and 87% of the selected area, with shorter %seq sequences predominant in Beauce. For instance, if the pattern-matching threshold were raised to 70% instead of 60%, flexible crop rotations would cover only 50% of Beauce's area. Identified flexible rotations (*P_f_{non-P}_%seq) maintain 80-90% uniqueness even after circular harmonization (15,400 and 19,800), but drops to 10-15% considering patterns P only (yielding 2300 and 3100 distinct rotations). The largest 11th and 80th crop rotations together cover 50% of the total area (Table 1) and represent 90% of crops and 70% of successive crop pairs observed at the territorial level. Full nationwide results, including sample size, calibration effects, and assessment of rotations' length and diversity, will be presented at the conference.

<table><caption>Table 1. Top five dominant crop rotations and their total area coverage</caption><thead><tr><th colspan="2">Meurthe & Moselle</th><th colspan="2">Beauce</th></tr></thead><tbody><tr><td>Grassland_100</td><td>28%</td><td>BTH-BTH-CZH</td><td>4.0%</td></tr><tr><td>BTH-BTH-ORH-CZH</td><td>15%</td><td>BTH-MIS</td><td>3.5%</td></tr><tr><td>BTH-ORH-CZH</td><td>8.8%</td><td>BTH-ORH-CZH</td><td>3.5%</td></tr><tr><td>BTH-BTH-BTH-MIE</td><td>4.2%</td><td>BTH-CZH</td><td>2.5%</td></tr><tr><td>BTH-MIE</td><td>4.0%</td><td>BTH-BTH-ORH-CZH</td><td>2.5%</td></tr></tbody><tfoot><tr><td colspan="4">BTH: soft winter wheat; ORH: winter barley; CZH: winter rape; MIE: silage maize; MIS: grain corn</td></tr></tfoot></table>

## Perspectives

Limited by data history availability, the proposed procedure missed here five-years and longer rotations. Comparing outputs with farmer decision-making and expert insights can enhance interpretability. Yet, the procedure identifies circular crop patterns from field data, simplifying complex crop successions into representative rotations, and can support scaling strategies for cropping and farming system models like STICS or MAELIA.
References

Bohan, D.A., Schmucki, R., Abay, A.T., Termansen, M., Bane, M., Charalabidis, A., Cong, R.-G., Derocles, S.A.P., Dorner, Z., Forster, M., Gibert, C., Harrower, C., Oudoire, G., Therond, O., Young, J., Zalai, M., Pocock, M.J.O., 2021. Chapter Five - Designing farmer-acceptable rotations that assure ecosystem service provision in the face of climate change, in: Bohan, D.A., Dumbrell, A.J., Vanbergen, A.J. (Eds.), Advances in Ecological Research, The Future of Agricultural Landscapes, Part III. Academic Press, pp. 169-244.

Girault, B., Martin, P., 2024. Séquences de culture, France, 2015-2022.

Leenhardt, D., Therond, O., Mignolet, C.C., 2012. Quelle représentation des systèmes de culture pour la gestion de l'eau sur un grand territoire ? Agron. Environ. Sociétés 2, 77-90.

Nowak, B., Michaud, A., Marliac, G., 2022. Assessment of the diversity of crop rotations based on network analysis indicators. Agric. Syst. 199, 103402.

Sahajpal, R., Zhang, X., Izaurralde, R.C., Gelfand, I., Hurtt, G.C., 2014. Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt. Comput. Electron. Agric. 108, 173-182.

Stein, S., Steinmann, H.-H., 2018. Identifying crop rotation practice by the typification of crop sequence patterns for arable farming systems – A case study from Central Europe. Eur. J. Agron. 92, 30–40.