Mapping Land Degradation in Europe: A Comprehensive Analysis of Agricultural Impact

Study Area

This European analysis includes 40 continental states, with 27 being EU member states. The study excludes seven countries: Vatican, Iceland, Belarus, Ukraine, Republic of Moldova, and transcontinental states Russia and Turkey. This exclusion is due to reasons such as small size (Vatican, which has no agricultural areas) or a lack of geospatial data for the examined land degradation processes in the other six countries. The 40 countries cover about 5 million square kilometers, roughly half of Europe, with a combined agricultural area of approximately 2.10 million square kilometers, constituting about 42% of the total area. Over half of this agricultural land (~1.14 million square kilometers) consists of arable land.

Data Selection

We selected twelve land degradation processes representative of agricultural environments in Europe. These processes are critical for illustrating the degradation of agricultural landscapes both in Europe and globally. Each process shows how various biophysical mechanisms negatively affect land productivity. The degradation processes included are documented through specialized literature, detailing their disruptive effects leading to decreased agricultural productivity.

Data Acquisition/Preparation

For this study, we processed twelve geospatial databases related to the selected degradation processes. We gathered final form databases for six processes, including water erosion, soil organic carbon loss, soil salinization, soil acidification, soil compaction, and soil pollution via pesticides. For the other six processes, we refined or modeled data using existing sources to achieve final outputs. Soil nutrient imbalances, groundwater decline, vegetation degradation, and aridity were among these processes.

For nitrogen data, we identified surplus and deficit conditions based on nitrogen soil content. For phosphorus, we assessed both soil availability and budget conditions to define imbalances threatening agricultural productivity. Soil pollution was measured through individual layers of nine heavy metals, which were classified and processed to highlight areas at risk of contamination.

Final Data Modelling

All twelve processed spatial databases underwent refinement to create a combined dataset at a 500-meter resolution. We categorized the data into two classes: “Non-critical” and “Critical.” The “Critical” class indicates high or severe degradation conditions and was mapped based on established thresholds documented in literature.

The Land Multi-degradation Index (LMI) was then created, reflecting the simultaneous presence of degradative processes at the pixel level. This index was analyzed for all agricultural classes, with a focus on arable lands essential for food security in Europe. LMI results were produced for all 40 countries, despite some missing data for certain processes in a few nations.

Data Quality and Limitations

The data quality was evaluated on three main aspects. First, the reliability of the acquired datasets is backed by the studies referenced in this paper. Second, the data for the modeled processes came from trustworthy sources. Third, we selected only statistically significant trends for new data layers in this study, limiting errors.

However, some limitations exist. Different spatial resolutions among datasets, varying metric types, and critical threshold selection may affect data quality. Additionally, the equality of contributions from all underlying factors in the LMI could overlook the varying impacts of different degradation processes. Missing data for certain countries may also understate the extent of land degradation.

Data Uncertainty and Sensitivity

To assess variable effects on the binary classification for the LMI map, we conducted an uncertainty and sensitivity analysis. This involved random simulations adjusting the thresholds for each input map. The results were then analyzed to determine the influence of each process on the overall classification, providing insight into how different thresholds affect the assessments of land degradation indices.

By ensuring clarity and precision in this analysis, we aim to present findings that contribute to understanding land degradation impacts across Europe effectively.