Soil thermal conductivity (λ) is a key parameter influencing seed germination, root growth, and soil-atmosphere energy exchange. Accurate and energy-efficient in-situ measurement of λ is essential for optimising tillage practices and improving soil management. This study evaluates the thermal losses conduction (Qcond), convection (Qconv), and radiation (Qrad) as well as thermal efficiency (ηth) and specific energy consumption (Qcon) of an improved heat-pulse probe (HPP) under four tillage systems: no-tillage (NT), mouldboard plough (MP), rotary harrow (RH), and subsoiler (SP). Measurements were taken at three soil depths and four heating durations. Field experiments were conducted on clay loam soil (38% clay, 42% silt. 20% sand) in a semi-arid region using a randomised complete block design. Results showed that MP consistently achieved the highest ηth (86.10% at 0–10 and 20–30 cm) and lowest Qcon (104.30 W m−1 K−1 at 10–20 cm), representing a trade off with λ measurement accuracy, RH provided the most accurate measurements with (RMSE=0.041 0.041 W m−1K−1, R2 =0.94), while MP showed slightly lower accuracy in deeper layer (R2= 0.86 at 20–30 cm). SP required the most energy (128.55 W m−1K−1 at 0–10 cm). longer heating durations reduced ηth across all tillage systems due to increased thermal losses. The subsurface layer provided the most stable conditions for λ measurements. Statistical analysis confirmed significant effects of tillage systems, heating time, and depth on heat losses, λ, and ηth. The HPP measurements demonstrated strong agreement with pedotransfer function estimates (R² = 0.86–0.94). The highest accuracy of the HPP occurred in the 0–10 cm layer at RH systems (RMSE = 0.041 W m−1 K−1, MAE = 0.033 W m−1 K−1). The accuracy of HPP slightly declined as soil depth increased under MP and SP systems. The RH systems provided the most accurate measurements across all soil depths. In conclusion, MP combined with short heating durations provided the highest thermal efficiency and the lowest specific energy consumption, whereas RH achieved the highest agreement with the pedotransfer function (lowest RMSE and highest R²), indicating the best measurement performance. These findings provide practical guidelines for improving both the energy efficiency and measurement reliability of HPP applications in agricultural systems, with potential relevance to precision agriculture. Future research should validate the system across different soil types, climates, and cropping systems to broaden its applicability.
Maintaining soil quality is a key challenge for Mediterranean vineyards, where declining soil organic matter affects both productivity and ecosystem services. This study evaluated how service crops and organic amendments, alone and in combination, influence soil functioning, vine performance, and yield across six commercial vineyards in southern France. Four soil management strategies were tested over three years (2020–21; 2021–22; 2022–23): tillage (T), tillage + amendment (TA), service crop (C), and service crop + amendment (CA). Soil organic matter fractions, microbial biomass, mineralisation rates, and mineral nitrogen were measured, alongside vine growth, yield, and berry composition. Combining service crops and organic amendments (CA) consistently enhanced particulate organic matter (up to 0.45%), microbial biomass (up to 70 mg/kg), and mineralisation rates (above 300 mg kg⁻¹ ), suggesting strong combined effect on soil biogeochemical processes. Mineral-associated organic matter was less responsive, reflecting slower turnover. Increased microbial activity under CA was associated with higher organic inputs. Vine yield (2.18–3.48 kg per vine) and cluster number were mainly driven by interannual climatic variability, with no negative effects of soil management. Vine vigor was temporarily reduced during the first dry year, and grape nitrogen status (YAN) was slightly lower with service crops.These results show that interactions between soil management practices enhance soil functioning and nutrient cycling without compromising production, highlighting their role in supporting ecosystem services and agroecosystem resilience in Mediterranean vineyards.
Land management practices dictate soil physico-chemical and biological functions, and consequently, the agroecological benefits of soils. While several studies exist detailing the effects of cover crops on soil health parameters in annual systems, there is no clear understanding of microbial responses under perennial systems, especially after conversion to annual systems. A 20-year perennial fescue (Schendonorus arundinaceus) field was converted to annual systems including two levels of cover crops (cover crop vs no cover crop) and two levels of tillage (tillage vs no tillage). The cover crop included a mix of winter wheat (Triticum aestivum) and crimson clover (Trifolium incarnatum), while the tillage included disc plowing. Three and four years after conversion, soil samples were collected from no-till with cover crop (NT-CC), no-till with no cover crop (NT-NC), tillage with cover crop (Till-CC), and tillage with no cover crop (Till-NC) plots. Additional samples were also collected from an adjacent field under perennial fescue grass (PER). Soil physico-chemical properties were greater (p < 0.05) under PER and NT-CC than other management practices evaluated. As a result, total microbial biomass and the composition of microbial community were greater under PER and NT-CC than other management practices. Four years after conversion, the Gr- bacteria and fungi populations under PER were 49% and 59% greater than the average of all CC, and 51% and 133% greater under the average of all tillage managements, respectively. Further, the predato:prey ratio was 38% greater under PER than Till-CC and Till-NC managements. While a conversion from perennial to annual systems can result in soil health parameter degradation, NT-CC is a better annual alternative to perennial systems than other annual practices evaluated.
Accurate soil organic carbon (SOC) mapping is vital for sustainable agriculture in black soil regions. Although remote sensing offers an efficient and cost-effective approach for characterizing the spatial distribution of SOC, three major bottlenecks persist: single source data cannot fully represent SOC dynamics, traditional models struggle with complex nonlinearities, and spatial heterogeneity limits global models from reflecting local pedogenic mechanisms. To address these, this study developed a hybrid framework for the Songnen Plain integrating a zonal ensemble strategy with deep learning. Using Google Earth Engine, a dataset was constructed with multi-temporal Sentinel-1 radar, Sentinel-2 optical data, and topographic covariates from 2020 to 2024, covering both bare soil (April–May) and growing seasons (June–August). A prediction model combining Fuzzy C-Means (FCM) clustering and Long Short-Term Memory (LSTM) networks was developed using 640 topsoil samples. Results indicate: (1) the FCM-LSTM zonal ensemble achieved optimal performance on the independent hold-out test set (R² = 0.84, RMSE = 3.20 g kg⁻¹); (2) multi-temporal data fusion effectively enhanced accuracy; compared to relying solely on bare soil data, including growing season data increased the R² of Sentinel-2 and Sentinel-1 by 0.08 and 0.03, respectively; and (3) the synergistic use of multi-temporal radar and optical data, supplemented with environmental covariates, significantly improved SOC prediction. This study demonstrates the effectiveness of integrating multi-temporal remote sensing with a zonal ensemble strategy, providing a robust methodological template for high-precision tillage management and soil fertility preservation in critical black soil regions.
Soil infiltration is a critical process in regulating precipitation transformation, playing an essential role in sustaining water resources and ecosystem stability in water-limited regions. Although root systems are recognized as key drivers influencing soil hydrological processes, the dynamic changes in root growth and their texture-dependent effects on soil infiltration remain unclear. This study examined soil infiltrability across different soil textures and root growth stages through a simulated planting experiment. Three vegetation types, consisting of ryegrass (Lolium perenne L.), alfalfa (Medicago sativa L.) and a 1:1 alfalfa×ryegrass mixture were cultivated in three soil textures (sandy soil, silt loam soil and silty clay loam soil) on the Loess Plateau, with bare land served as a control. The changes in root and soil properties and infiltration rates were measured during four growth stages in April, June, August and October. Results indicated that planted grasslands effectively enhanced soil infiltration capacity, and the dynamics of infiltration rates in response to root growth exhibited distinct texture dependence. The infiltration rates of sandy soil and silty loam increased initially before decreasing during the growing season, while that of silty clay loam continued to rise. During the early growth stage, infiltration rates varied significantly across different soil textures, following the order of sandy > silty clay loam > silt loam. Soil texture contributed more independently to infiltration than root traits and soil physical properties, although this contribution weakened later and shifted to synergistic interaction with soil properties. As the growth period progressed, the independent contribution of root systems to infiltration gradually increased. Structural equation modeling revealed soil texture (clay and silt) exerted a significant total and direct negative effect on soil infiltration. The absolute value of total effect was smaller than that of the direct effect, primarily attributed to its indirect positive effects on soil organic matter (SOM) and porosity. Root traits primarily influenced infiltration rates indirectly by regulating SOM and porosity. These findings offer important insights into the root-mediated hydrological processes and the development of ecohydrological models.
Gas conductivity of soil is vital for evaluating soil aeration and regulating oxygen delivery to plants, while plant roots affect the gas conductivity of surrounding soil. Hence, root decay during long-term root development is expected to affect gas conductivity accordingly. However, relevant experimental data are limited and models are lacking. Since herbaceous plants are more susceptible to root decay, this study focuses on measuring gas conductivity in unsaturated soil containing decayed roots of vetiver grass, alongside optical microscopic observation on root structural changes, and developing a model for gas conductivity incorporating decay effects. Microscopic observations reveal that most decayed secondary lateral roots were destroyed, forming hollow pores as preferential pathways. In contrast, decayed adventitious roots and primary lateral roots retained their epidermis but became contracted and irregular, creating pores at the root-soil interface and also facilitating gas transport along with hollow interiors. Therefore, root decay significantly increased the gas conductivity of soil across varying degrees of saturation. Specifically, the soil containing decayed roots (with a root decay ratio of about 26%) exhibited a gas conductivity of 6.7 × 10⁻⁷ m/s at the degree of saturation of 0.34, which was approximately 52% higher than that of bare soil (4.4 × 10⁻⁷ m/s). Gas conductivity ranked highest for soil containing decayed roots, intermediate without roots, whereas lowest with fresh roots. This resulted from roots obstructing large pores during plant growth, which reduced gas conductivity despite also enhancing pore connectivity via highly connected root-supported pores, leaving pore network with high connectivity after root decay. As regards modelling, the maximum gas conductivity considering root decay was derived by introducing the effect of root decay on soil pores into a pore-based model. Good agreements were observed between the measurements and predictions, under the root mean square error (RMSE) of 0.22 and the scatter index (SI) of 0.03.
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.
Visible and near-infrared diffuse reflectance spectroscopy (vis-NIR) and X-ray fluorescence spectroscopy (XRF) are promising alternatives for rapid, reagent-free soil analysis that can help scale up soil organic carbon (SOC) monitoring for soil health and carbon credit reporting. We investigated whether combining laboratory-based vis-NIR and XRF spectra improves SOC prediction relative to vis-NIR alone, and whether the resulting performance is fit for purpose for monitoring SOC variability at farm scale. A national spectral library of 12,829 Brazilian soil samples with laboratory spectral readings was used to build global Cubist models, and eight local libraries (952 samples) were used to build local partial least squares (PLS) models. Two sensing scenarios were compared: vis-NIR alone and vis-NIR + XRF data fusion. Performance was evaluated using conventional accuracy metrics and a fitness-for-purpose framework based on analytical tolerances and percentile error profiles. The results support the added value of XRF as complementary information to vis-NIR for SOC prediction under both modelling strategies, with the largest relative improvement observed under the global-Cubist strategy (RMSE of 1.11 g kg–1 for vis-NIR+XRF, versus 1.52 g kg–1 for vis-NIR alone) and the best absolute performance achieved under the local-PLS strategy (RMSE of 0.97 g kg–1 with vis-NIR+XRF, versus 1.15 g kg–1 with vis-NIR alone). Using an application-specific analytical tolerance framework, sensor-based monitoring was fit-for-purpose mainly under medium and high within-farm SOC variability, but not under low variability. These results support sensor-based SOC inference as a practical tool for farm-scale monitoring.
Freeze-thaw cycles and raindrop splash are critical drivers of soil erosion, significantly affecting surface soil stability and sediment yield. However, the dynamics of inter-aggregate transformations under the influence of freeze-thaw and raindrop splash remain insufficiently understood. In this study, four size fractions (large aggregates, 5–2 mm; small aggregates, 2–0.25 mm; microaggregates, 0.25–0.053 mm; silt and clay particles, <0.053 mm) were labeled with rare earth element (REE) tracers to investigate their redistribution during simulated freeze-thaw cycles (0, 3, 6, 10, 15, and 25) and splash erosion (rainfall intensities: 60 and 90 mm h−1). The results showed that successive freeze-thaw cycles and raindrop splash reduced the proportion of > 0.25 mm aggregates while increasing the < 0.053 mm fraction, with the 2–0.25 mm aggregates decreasing by 44.6%-49.4% and the < 0.053 mm aggregates increasing by 14.75%-36%, indicating a net decline in aggregate stability. The 2–0.25 mm aggregate fraction was the major contributor to splash-eroded sediment, and its export increased significantly with rainfall intensity. Except for the 5–2 mm class, all other splash particles resulted from the breakdown and transformation of 2–0.25 mm aggregates. Residual aggregate turnover ranged from 0.2 h−1 to 0.8 h−1, dominated by breakdown, especially for the 5–2 mm aggregates, whose cumulative breakdown rate (46.96%-89.18%) was substantially higher than their formation rate. Additionally, organic carbon analysis revealed a decrease in SOC within the 5–2 mm aggregates (e.g., from 33 g kg−1 to 30 g kg−1 in cultivated land) but an increase in all other size fractions after splash erosion. This pattern was largely attributed to the fragmentation pathways and cumulative breakdown of the < 0.053 mm aggregates fraction. This study systematically clarifies the dynamic evolution of soil aggregates under freeze-thaw cycles and raindrop splash, along with their coupling to erosion and carbon transport, providing a theoretical basis for understanding erosion-induced soil degradation.
Green manure (GM) boosts sustainable agriculture by enhancing soil fertility and crop yields. However, the dynamics of nitrogen (N) release from different GM residues into soil and their supply patterns to wheat remain unclear. Clarifying residue N mineralization during the growing season and its contribution to soil N fractions and wheat is key to optimizing GM use strategies. An in-situ decomposition experiment using 15N-labeled soybean (SB) and sudangrass (SG) GM was conducted in a wheat field on the Loess Plateau. Soil and wheat samples were collected at different wheat growth stages. We determined GM-N mineralization, wheat N uptake, and yield at maturity. The contribution and recovery rate of residue N to soil total N (STN), particulate organic N (PON), microbial biomass N (MBN), available N (NH4⁺ and NO3⁻), and to the wheat were also measured. Our results showed that the GM-N mineralization rate and amount from SB consistently exceeded those from SG throughout the wheat growth stages. At maturity, compared to the CK, SB and SG treatments increased wheat N uptake by 112% and 47%, and yield by 65.30% and 39.09%, respectively. The contribution of SB-N to wheat was 8.23 times greater than that of SG. During the wheat season, SB exhibited higher soil N distribution and recovery rates than SG. Before green-up, SB showed greater distribution and recovery rates in PON and MBN than SG. However, during jointing and heading stages, SG demonstrated higher recovery rates in PON and MBN than SB. SB residue N was predominantly accumulated in PON and MBN pools in the early growth stage, which may act as transient N reservoirs and potentially contribute to subsequent wheat N uptake. In contrast, SG’s contribution was more consistent with direct N mineralization supplying available N. Correlation analysis revealed a positive relationship between GM-N contributions to soil N fractions and wheat N uptake, which was closely associated with yield formation. Legume residue N is initially retained in PON and MBN pools, and may subsequently contribute to plant N supply during peak wheat demand after green-up, thereby showing greater temporal synchrony. In contrast, the asynchrony of the non-legume residue N supply may be partly explained by intense early-stage microbial N immobilization competition and limited storage in soil organic N. This demonstrates the greater potential of legume GM for enhancing soil fertility and crop yields.
Organic amendments are widely adopted to improve soil fertility, yet how distinct soil organic carbon (SOC) fractions are differentially stabilized and how their underlying mechanisms are reorganized under organic management remain incompletely understood. Here, we integrate a global meta-analysis of 236 paired observations from 70 field experiments with nanoscale secondary ion mass spectrometry (NanoSIMS) imaging to disentangle dual, functionally distinct pathways of SOC stabilization under organic management. Organic amendments significantly increased SOC, particulate organic carbon (POC), and mineral-associated organic carbon (MAOC) by 38.9–40.3%, 81.0–95.5%, and 25.5–29.8%, respectively. The increased POC to SOC ratio and decreased MAOC to SOC and MAOC to POC ratios suggest that organic management preferentially enhanced the accumulation of particulate organic carbon relative to mineral-associated carbon. Meta-regression analyses revealed that increases in POC and MAOC were jointly driven by nitrogen availability and microbial biomass, identifying microbial transformation as a common entry point, while their subsequent stabilization diverged through distinct pathways. Specifically, POC accumulation was primarily associated with aggregation-driven physical inaccessibility, whereas MAOC formation was governed by microbial-mineral interactions and strongly associated with reactive iron oxides rather than bulk clay content. Consistent with these patterns, NanoSIMS imaging showed preferential MAOC enrichment in microbially active, intermediate-to-large pores (approximately 240–330 μm), while SOC in compost-amended soils was relatively enriched in aggregate-associated small pores, reflecting reinforced physical protection of particulate carbon. By integrating global evidence with pore-scale observations, this study reveals two complementary pathways governing SOC persistence under organic amendments, with POC stabilized primarily by aggregation-mediated physical protection and MAOC by microbial-mineral interactions.
