Biological tillage by roots has been proposed as a sustainable strategy for subsoil structural recovery following compaction; however, the potential of perennial grassland mixtures remains insufficiently quantified. This study evaluated two well-established perennial grassland mixtures (three vs twelve species) using field-incubated ingrowth soil cores. Two root-contact conditions were imposed: direct root contact (DCR; roots allowed into cores) and isolated root contact (ICR; roots excluded by mesh). X-ray µCT was used to analyze the total CT-detectable pore space (CTtotal) and vertically continuous biopores (VCBio), before and after incubation. Following incubation, soil physical characteristics, root intensity, arbuscular mycorrhizal fungi (AMF) biomarkers (PLFA and NLFA C16:1ω5), and glomalin-related soil protein (GRSP-EE) were measured, followed by destructive assessment of structural quality. VCBio was more sensitive to root-induced structural changes than CTtotal. In DCR twelve-species mixture, macroporosity (mvrel) and macropore length density (ρL) derived from VCBio increased with incubation by 39% and 52%, respectively, while total macropore volume (mvCT) within the 0.24–0.5 mm diameter class increased 2.50 times with incubation. No significant increases in mvrel, ρL, mvCT from CTtotal were found. The DCR twelve-species mixture exhibited greater increases in CT-derived parameters than the DCR three-species mixture, indicating stronger biological subsoiling potential. This coincided with higher concentrations of AMF biomarkers PLFA and NLFA than all other treatments. Air-filled porosity and visual structural quality showed significant treatment effects. These results indicate that early subsoil recovery under perennial grassland mixtures is driven by changes in VCBio, with AMF biomarkers and soil physical characteristics responding at different rates.
The symmetric decreasing rearrangement of functions on $\mathbb{R}^n$ features in several seminal inequalities, such as the Pólya-Szegő inequality. The latter was shown by the authors to hold for all smoothing rearrangements, a class that includes the more general $(k,n)$-Steiner rearrangement, as well as others introduced by Brock and by Solynin. The theory of rearrangements and their associated set maps is developed, with an emphasis on approximation, particularly by polarizations. The Pólya-Szegő inequality holds with equality for polarizations, so is proved relatively easily for rearrangements that can be suitably approximated by them. One goal here is to show that the Brock rearrangements cannot be approximated in such a way. It turns out that under mild conditions, each set map associated with a rearrangement has in turn an associated contraction map from $\mathbb{R}^n$ to $\mathbb{R}^n$. With this new analytical tool, several general results on the approximation of rearrangements are also proved.
CONTEXT Chemical pesticides remain central to agricultural productivity but pose well documented risks to human health and ecosystems. While Integrated Pest Management (IPM) offers a more sustainable alternative, its adoption remains limited, partly due to the complex trade-offs that are not easily quantified and compared. OBJECTIVE This study presents the IPM Self-Assessment Tool (ISAT), a web-based application designed to enable transparent comparison of crop protection scenarios at the field level, integrating environmental, human health, agronomic, and economic indicators. METHODS A new harmonized pesticide risk indicator for Europe (PLEU – Pesticide Load Indicator for Europe) was developed. The PLEU covers 41 hazard metrics across three sub-indicators (Environmental Fate, Ecotoxicity, and Human Health). The PLEU was integrated into the ISAT alongside indicators for greenhouse gas (GHG) emissions and production costs, calculated using life cycle based methods. The tool was developed using data from ten European countries covering eight crops, with scenarios described by regional specialists and validated by farm advisors. RESULTS AND CONCLUSIONS The PLEU was calculated for 502 active ingredients (441 synthetic, 61 biopesticides) across eight pesticide groups. A case study on winter wheat in Germany showed that, relative to conventional practice, an IPM scenario replacing herbicide use by mechanical weeding reduced pesticide risk by up to 75%, but at the cost of GHG emissions comparable to the conventional scenario. The ISAT enables users to identify risk-driving active ingredients and explore lower-risk alternatives, supporting informed decision-making for IPM adoption. SIGNIFICANCE To our knowledge, ISAT is the first freely accessible, web-based tool integrating a harmonized pesticide risk indicator with GHG emissions and economic outcomes within a single, comprehensive field level scenario comparison. It provides a transparent decision-support platform that can help farmers, advisors, and researchers make informed decisions when designing and comparing crop protection strategies across diverse European cropping systems.
The short- and medium-term effects of agricultural soil compaction are well documented, but its long-term impacts and the role of biopores in subsoil recovery remain poorly understood. This study evaluated legacy effects of subsoil compaction on soil hydrophysical properties and root density and assessed the contribution of earthworm biopores to soil recovery. The compaction experiment was established in 1995 on a silt loam Haplic Luvisol by applying six passes of a wheel loader with a maximum wheel load of 5 Mg. Soil measurements were conducted immediately after compaction (1995) and repeated in 2019 and 2023. Measurements in 1995 included soil penetration resistance (PR), bulk density (BD), air-filled porosity (ɛₐ), and relative gas diffusivity (Ds/D0). Winter wheat root density was assessed in 2019. In 2023, PR, BD, ɛₐ, Ds/D0, saturated hydraulic conductivity (Ks), earthworm abundance, and subsoil structural quality (Ssq) were measured. Intact soil cores (30–35 cm) collected in 2023 were grouped according to the presence or absence of visible earthworm biopores. Compaction effects persisted after 28 years. Compared with the control, compacted soil had higher BD (+7.4%), PR (+78% at 20–40 cm), Ssq (+133%), and lower ɛₐ (−31.7%) and Ds/D0 (−33.1%). Root density, Ks, and earthworm abundance were statistically identical between treatments. These hydrophysical differences persisted strongly in samples without biopores, but largely disappeared in biopore-containing samples. Comparison with 1995 data indicated partial recovery of aeration and gas transport, especially in biopore-rich samples. These results show that subsoil compaction can persist for decades, but recovery of pore connectivity is strongly mediated by earthworm biopores.
CONTEXT Crop sequences are important for sustainable agriculture, yet we know relatively little about how they recur across individual fields or which information best predicts the crop grown each year. Denmark's national field records allow both questions to be examined over time. OBJECTIVE We identified recurrent crop sequences across Denmark and tested how well annual crop-family selection could be predicted from crop history, farm characteristics, management, prices, soil and climate. METHODS Using 10 years (2011−2020) of national field-level crop data (∼600,000 fields yr−1), we developed a heuristic algorithm to detect recurring crop sequence patterns. We then trained machine learning (LightGBM) and deep learning (TabNet) models to predict annual crop choices based on preceding crops and lagged management and farm structure predictors, and pedoclimatic conditions. Models were evaluated through forward-chaining validation and temporal holdout tests, and SHAP values were used to examine how LightGBM used each predictor. RESULTS AND DISCUSSION Crop sequences were largely dominated by cereals, with little diversification even in longer sequences. Crop selection patterns were strongly associated with the previous crops and farm typology, but pedoclimatic conditions and previous management played a minor role. The DL model achieved a slightly higher overall accuracy, particularly for dominant crops, while the ML model provided a more balanced performance across different crops and enabled interpretation through SHAP. SIGNIFICANCE The analysis separates two distinct tasks: identifying multi-year crop sequences and predicting the crop family grown each year. The sequence analysis shows why diversification cannot be assessed from sequence length or crop counts alone. The predictive models could help generate locally plausible sequences for agri-environmental simulations, reducing reliance on standard rotations that poorly reflect observed field histories.
