CropRotationViz: An open-source R framework for multi-scale crop rotation analysis and visualization

Abstract

Crop rotation analysis is fundamental to understanding agricultural landscape dynamics, yet the complexity of multi-annual, spatially explicit agricultural data presents significant analytical challenges. A key difficulty lies in tracking field parcels whose boundaries change over time through administrative splits, mergers, or reorganizations. This process requires robust spatial intersection approaches that can reconcile multi-temporal polygon geometries. Here, we present CropRotationViz, an open-source R package that provides a detailed framework for processing, analyzing, and visualizing crop rotation patterns across multiple spatial and temporal scales. The package implements a modular architecture consisting of (i) a spatial processing pipeline with automated data validation, crop nomenclature standardization through a customizable crop classification aggregation tool, and geometric handling of temporal field boundary changes through multi-year polygon intersection, (ii) interactive multi-annual flow and diversity visualizations, including Sankey diagram and structural diversity mapping, and environmental context integration, and (iii) three distinct visualization interfaces optimized for different analytical needs: a dynamic rendering mode for detailed exploration, a pre-rendered fast visualization mode for rapid regional comparisons, and a field-level interface for individual parcel inspection. The framework bridges complex geospatial data processing and user-friendly visualization through a dual-access architecture offering both graphical user interfaces and a programmatic API, making advanced crop rotation analysis accessible to researchers and practitioners across disciplines. Privacy-preserving spatial aggregation to administrative or hydrological boundaries enables analysis of sensitive field-level data while producing outputs suitable for stakeholder distribution. The framework is validated using 7 years of field-level crop data from two German federal states, encompassing approximately 2.8 million hectares and over 200,000 parcels annually, processed in under 1 hour on standard hardware. Applied to these data, the framework reveals that soil potential alone explains 61% of the variance in structural crop rotation diversity, highlighting the decisive role of edaphic constraints in shaping agricultural management.