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Soil organic carbon (SOC) loss from intensive agriculture represents a major global concern. Consequently, strategies to improve soil management to mitigate or abate SOC losses and enhance carbon (C) sequestration are urgently needed. Nutrient availability, especially nitrogen (N) and phosphorus (P), regulates soil C cycling and storage. While N effects are well studied, less is known about how soil P status and different fertilizer types affects SOC dynamics. This laboratory incubation assessed how two common P fertilizers, diammonium phosphate (DAP) and single superphosphate (SSP), affected microbial activity and C immobilization in the zone of soil directly around the fertilizer granule (prillosphere) across three contrasting agricultural soils (Inceptisol, Vertisol, Alfisol). Soils were amended with DAP or SSP granules and C turnover assessed with ¹⁴C-labeled glycine, malic acid or glucose, alongside unfertilized controls. After three weeks, soil pH, electrical conductivity (EC), Olsen-P and microbial C use efficiency (CUE) were measured. DAP increased pH in the Inceptisol (acidic soil), while SSP decreased pH in all soils. Both fertilizers increased EC and Olsen-P, but SSP enhanced Olsen-P more than DAP. Cumulative ¹⁴CO₂ emissions were 19–20 % higher with P fertilizers compared to the control, with DAP stimulating faster initial C mineralization rates than SSP, except in the Alfisol. P addition reduced microbial CUE by 23–34 % across all soils and substrates versus the unfertilized control. We ascribe this reduction in CUE to an alleviation of nutrient limitation or a fertilizer-induced osmotic stress. The co-addition of N either in DAP or glycine did not alter the P-induced CUE response suggesting that P was more important than N in regulating microbial CUE in these soils. We conclude that P fertilization increased short-term C turnover and may lead to reduced C storage in soil, however, further long-term (>1 year) research is needed to identify optimum P management strategies to minimize C losses in agricultural soils.

Linear erosion channel (LEC) devastates arable land and significantly contributes to soil loss in agricultural watersheds. In the presence of a less- or non-erodible layer, channel widening governs the erosion process once the channel bed incises to this layer, accompanied by failure block generation and transport. Current knowledge on channel widening, however, is limited due to the lack of robust and efficient methods to capture the rapid sidewall expansion process. Laboratory experiments were designed to simulate the channel widening process with an initial channel width of 10 cm. Two packed soil beds with a non-erodible layer and two slope gradients (5 % and 11 %) were subjected to the inflow rate of 0.67 L/s. Images were captured by mounted digital cameras and automatically transformed into orthophotos. Channel edges and failure blocks were automatically detected by deep learning algorithm in a newly developed Channel-DeepLab network model based upon DeepLabv3+ platform. The procedure includes learning samples labelling, data augmentation, model construction, training, and validation. Sediment discharge and changes in channel width, geometry of channel edges, and failure blocks were measured. The results indicate that initial period is critical for erosion prediction and remediation due to its small sidewall failure interval, high channel expansion rate and sediment discharge. Channel surface area has great potential on accumulated sediment discharge prediction. The slope section that witnessed the fastest channel widening rate migrated downwards when slope gradient increased from 5 % to 11 %. The total number and area of the failure blocks increased with time, while the collapse frequency of the sidewalls decreased. Upstream reach experienced the highest sidewall collapse frequency and rate of disaggregation and transport, while the downstream reach experienced the highest total number of failure blocks. A time lag was found between sidewall collapse and sediment discharge, which increased as time progressed, attributing to decreased runoff erosivity as the flow velocity decreased. Results of this study will provide methodological support for channel sidewall and streambank retreat monitoring, realizing the automatic detection of channel edges and efficient output of rapid sidewall expansion process with high temporal and spatial precision. Future work can be focused on broadening the applicability of the Channel-DeepLab network model and quantifying the delayed response process between sidewall failure and sediment discharge.

Soil sensing enables rapid and cost-effective soil analysis. However, a single sensor often does not generate enough information to reliably predict a wide range of soil properties. Within a case-study, our objective was to identify how many and which combinations of soil sensors prove to be suitable for high-resolution soil mapping. On a subplot of an agricultural field showing a high spatial soil variability, six in-situ proximal soil sensors (PSSs) next to remote sensing (RS) data from Sentinel-2 were evaluated based on their capabilities to predict a set of soil properties including: soil organic carbon, pH, moisture as well as plant-available phosphorus, magnesium and potassium. The set of PSSs consisted of ion-selective pH electrodes, a capacitive soil moisture sensor, an apparent soil electrical conductivity measuring system as well as passive gamma-ray-, X-ray fluorescence- and near-infrared spectroscopy. All possible combinations of sensors were exhaustively evaluated and ranked based on their prediction performances using model stacking. Over all soil properties, data fusion demonstrated a considerable increase in prediction accuracy. Five out of six soil properties were predicted with an R² ≥ 0.80 with the best sensor fusion model. Nonetheless, the improvement derived from fusing an increasing number of PSSs was subject to diminishing returns. Sometimes adding more PSSs even decreased prediction performances. Gamma-ray spectroscopy and near-infrared spectroscopy demonstrated to be most effective, both as single sensors or in combination with other sensors. As a single sensor, RS outperformed three out of six PSSs. RS showed especially potential for fusion with single PSSs but was of limited benefit when multiple PSSs were fused. Model stacking proved to be more robust than using single base-models because sensor performances were less model-dependent.

Soil visible-near-infrared (vis–NIR) spectroscopy offers a rapid, uncontaminated, and cost-efficient method for estimating physicochemical properties such as soil organic carbon (SOC). The development of soil spectral libraries (SSLs) and localized modeling methods has significantly improved the selection of appropriate modeling sets from SSLs for soil analysis. Nevertheless, most studies assume that the SSLs sufficiently cover the target samples for prediction. This study challenges this assumption by investigating the feasibility of using an SSL to predict SOC accurately in a local area when the dataset to be predicted (156,800 samples) vastly exceeds the SSL capacity (3755 samples). We utilized 1-meter-deep whole-soil profile and employed spectral similarity and continuum-removal (SS-CR) calculation to construct a Local dataset from the SSL, with a Global subset serving as a baseline for comparison. The effectiveness of partial least-squares regression (PLSR) and random forest (RF) algorithms in establishing quantitative relationships between spectra and SOC content was evaluated. Our results demonstrated that the Local model, with significantly fewer samples (1116), achieved higher predictive accuracy than the Global model. Both Global (R² = 0.80, RMSE = 0.74 %) and Local (R² = 0.83, RMSE = 0.75 %) models, developed using the RF algorithm, not only exhibited excellent accuracy but also enabled detailed and cost-effective characterization of the spatial distribution of SOC. Thus, leveraging SSLs enhances the cost-efficiency and predictive capacity of vis–NIR spectral analysis, particularly in handling large datasets at a local scale, underscoring the value of localized approaches in soil spectroscopy.

Soil organic matter (SOM) is critical for soil fertility, crop growth, and plays an important role in the global carbon cycle and climate change. Therefore, spatial prediction of SOM is important to rational soil resource utilization, agricultural production, and ecological environment management. However, large-area SOM mapping research heavily relies on legacy soil data, and large-scale recent SOM mapping may not be possible or have lower accuracy due to limited or less recent data availability. In this study, we aimed to improve SOM prediction and mapping accuracy by combining legacy data with limited recent data. Three models, namely, partial least squares regression (PLSR), random forest (RF), and one-dimensional convolutional neural network (1D-CNN), were applied and compared. The results showed that combining legacy and recent data effectively improved SOM prediction accuracy compared to using only recent data. Among the three modeling methods, 1D-CNN exhibited superior performance, with an averaged determination coefficient of the prediction (R²) of 0.58, a root mean square error (RMSE) of 4.56 g/kg, and a ratio of performance to interquartile distance (RPIQ) of 2.05. The predicted SOM content for both legacy (1980 s) and recent (2010 s) periods showed similar spatial distribution patterns throughout the Huanghuaihai Plain. Generally, there was a noticeable trend of increasing SOM content from northwest to southeast, with higher values observed in Jiangsu and lower values concentrated in Henan, Hebei, and Shandong regions within the study area. Over time, SOM contents in the Huanghuaihai Plain showed an increasing trend, with an average increase of 5.90 g/kg from legacy to recent period. This study provides a promising approach for improving SOM prediction and mapping accuracy at large scales, particularly when recent data availability is limited.

Tillage and soil compaction affect soil properties, processes, and state variables influencing soil health, hydrodynamics, and crop growth. Assessing soil compaction levels using traditional methods, such as soil sampling and penetration resistance, is inefficient for scaling up from plot to field scales. Geophysical methods like Ground-penetrating Radar (GPR) and Electromagnetic Induction (EMI) are becoming prominent for assessing soil properties and state variables in agriculture due to their ability to overcome the limitations of traditional methods. However, a research gap exists in non-destructively estimating bulk density changes related to tillage and soil compaction. This study aimed to (1) assess the influence of soil compaction on GPR and EMI responses in boreal podzolic soil and (2) develop and evaluate prediction models to determine soil bulk density using GPR and EMI. The experiment was conducted by compacting loamy sand-textured soil using a lawn roller. GPR data were collected to determine the soil dielectric constant (K_r) and the direct ground wave amplitude (A_DGW), along with EMI-measured apparent electrical conductivity (EC_a) under three compaction levels (no, four and ten roller passes). Relationships between K_r, A_DGW and EC_a and the average bulk density of 0–0.30 m depth at three compaction levels were tested. A Random Forest (RF) regression approach was employed to identify the most significant variables for predicting bulk density. Simple and multiple linear regression (SLR and MLR, respectively) models were developed using EC_a and K_r and were subsequently evaluated. Results revealed significant differences between the measured bulk density and geophysical data across the tested compaction levels. During the model development, SLR and MLR showed R² > 0.65, and the model evaluation showed a root mean square error of < 0.14 g/cm³. This study highlights the potential of using GPR and EMI for the non-destructive prediction of bulk density in the agricultural landscape. However, further research is needed to explore the applicability and limitations of this approach across varying water contents, electrical conductivities, and soil types.