Meiyan Wang

Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate the formation of erosion gullies, resulting in significant soil and water loss. Investigating the distribution of these waterlogged areas is crucial for comprehending the erosion patterns of sloping cropland and preserving black soil resource. In this study, we built varied models based on two stages (one using only deep learning methods and the other combining object-based image analysis (OBIA) with deep learning methods) to identify waterlogged areas using high-resolution remote sensing data. The results showed that the five deep learning models using original remote sensing imagery achieved precision rates varying from 54.6% to 60.9%. Among these models, the DeepLabV3+-Xception model achieved the highest accuracy, as indicated by an F1-score of 53.4%. The identified imagery demonstrated a significant distinction in the two categories of waterlogged areas: sloping cropland erosion zones and erosion risk areas. The former had obvious borders and fewer misclassifications, exceeding the latter in terms of identification accuracy. Furthermore, the accuracy of the deep learning models was significantly improved when combined with object-oriented image analysis. The DeepLabV3+-MobileNetV2 model achieved the maximum accuracy, with an F1-score of 59%, which was 6% higher than that of the model using only original imagery. Moreover, this advancement mitigated issues related to boundary blurriness and image noise in the identification process. These results will provide scientific assistance in managing and reducing the impact in these places.

Soil degradation in the Mollisol region of Northeast China is ubiquitous partly due to poor soil hydraulic characteristics. Improving soil hydraulic characteristics thus delivers benefits to soil sustainability. In this study, the effects of organic amendments on soil hydraulic characteristics were explored at the laboratory scale. Soil samples were subjected to three low‐cost and eco‐friendly organic amendments, including corn straw juice (CSJ), fulvic acid (FA), and humic acid (HA). The soil infiltration capacity, soil water characteristic curve (SWCC) and soil water retention capacity were determined by using a steel‐ring method and a centrifuge method. The parameters of the SWCC were fitted by a van Genuchten (VG) model, and the specific water capacity [C(h)] was calculated. In addition, bulk density (BD), macroporosity, and soil organic matter (SOM) were measured, and the relationships between the variables and processes were evaluated. The results indicated that the soil infiltration capacity (i.e., initial infiltration rate, cumulative infiltration, and steady infiltration rate) was significantly increased in the CSJ and FA treatments (p < 0.05) but decreased in the HA treatment compared to the control (CK) treatment. All the selected organic amendments improved the soil water release and supply capacity, and the CSJ2 treatment showed the best effect. The incorporation of CSJ, FA, and HA significantly improved the soil water retention capacity by increasing the saturated soil water content, field capacity, and plant available water capacity (p < 0.05). Such changes were significantly associated with macroporosity and SOM (p < 0.05). In this sense, our results showed that the CSJ treatment with 4 L m⁻² and 50% volumetric concentration could be an effective soil amendment to improve the soil hydraulic characteristics in Mollisols and deserves further research.

Deriving an accurate three‐dimensional (3D) spatial distribution of heterogeneous soil pollutants at contaminated sites using traditional spatial interpolation methods, such as inverse distance weighting (IDW), is challenging; especially when only limited borehole data are available. This study presents a novel IDW‐COV method, where weighting is determined by optimizing relative importance between feature distances of covariates and spatial distances to soil sample locations. The method was tested by mapping the 3D distributions of Cr (VI) and total Cr at a Cr salt production workshop (Site A) and a legacy Cr slag stacking site (Site B). The results were compared with those of IDW, the soil land inference model (SoLIM), and the SoLIM combined with IDW method (SoLIM‐IDW). The proposed IDW‐COV method returned the highest accuracy, with R ² values of 0.83 for Cr (VI) in Site A and 0.75 for total Cr in Site B, compared with 0.65 and 0.57 for IDW, 0.16 and 0.58 for SoLIM, and 0.73 and 0.61 for SoLIM‐IDW. This study highlights the importance of considering differences in explanatory power between multidimensional covariate and geographical spatial distance when incorporating multisource auxiliary data into weighted average estimators and provides guidance for mapping 3D distributions of heterogeneous pollutants from sparse soil borehole data .

BACKGROUND: The availability of soil nitrogen (N) decreases as the structure of agricultural soils degrades. Traditional methods focus on organic amendments that indirectly affect the porosity and N content of soil. Due to the low efficiency of such amendments, new materials, particularly highly porous materials, are needed to improve the quality of soil, which has opened new directions. RESULTS: The addition of 2 to 7 mm of porous clay ceramic (PLC) significantly increased the fresh weight of Brassica chinensis . The soil aeration porosity (>50 μm) increased by 0.69% on average in response to 1% PLC application. Soil NO ₃ ⁻ ‐N, NH ₄ ⁺ ‐N and mineral N increased by 3.3, 1.3 and 4.6 mg kg ⁻¹ on average, respectively, following a 1% PLC application rate. The initial N content of the high PLC treatments was the lowest in the incubation experiment. The parameters of soil N mineralization, i.e. potentially mineralizable N ( N ₀ ), the first‐order rate constant ( k ) and the mineralization composite index ( N ₀ × k ), increased obviously as the amount of PLC increased. Porosities larger than 1000 μm were significantly more positively correlated with the parameters of soil N mineralization than those N ₀ values had significant positive relationships with the fresh weights in double seasons. CONCLUSION: The application of PLC increased soil aeration and enhanced the availability of soil N, which yielded large vegetable harvests in clayey soils in the short term. © 2022 Society of Chemical Industry.

Soil structure and organic carbon are key factors that reflect soil quality in sustainable agricultural production. Many studies have concentrated on soil aeration under intensive anthropogenic agricultural practices, but the relationship between soil aggregate stability and soil organic carbon (SOC) functional groups is poorly understood, particularly in degraded greenhouse vegetable fields converted from paddy fields. To solve this problem, soil samples were collected from local vegetable fields and adjacent paddy fields. Conventional open‐air vegetable fields and greenhouse vegetable fields, both with a 6–8‐year cultivation history following conversion from paddy fields, were considered. SOC functional groups were characterised by ¹³ C cross‐polarisation magic‐angle‐spinning nuclear magnetic resonance ( ¹³ C CPMAS NMR). The results showed that the mean weight diameter (MWD) values of water‐stable soil aggregates in the open‐air vegetable fields and greenhouse vegetable fields decreased by 64.6% and 66.7%, respectively, compared with those in the paddy fields. Similar results were obtained for the soil aggregate distribution, and soil aggregates in the greenhouse vegetable fields and open‐air vegetable fields were mainly in the 0.25–2 mm size classes. Among all SOC functional groups, O‐alkyl C exhibited the greatest decrease after the conversion of paddy fields to vegetable fields, no matter whether greenhouse vegetable fields or open‐air vegetable fields. The degradation of soil structure decreased the physical protection of SOC functional groups by soil aggregates, and therefore, decreased the total SOC content and labile C functional group fractions in bulk soil. At the soil aggregate scale, the decrease of O‐alkyl C mainly occurred in microaggregates (0.053–0.25 mm), while aromatic C decreased significantly in macroaggregates (>0.25 mm) in the greenhouse vegetable fields converted from paddy fields. Correlation analysis showed that O‐alkyl C was significantly positively related to MWD and the proportion of large soil macroaggregate (>2 mm). This result suggested that soil aggregates become fragile in long‐term vegetable cultivation after conversion from paddy fields, and a large loss of O‐alkyl C might be the main reason for this phenomenon. Highlights: Soil aggregation was significantly decreased in conventional vegetable fields converted from paddy fields. SOC accumulation was closely positively related to soil macroaggregate formation. O‐alkyl C mainly decreased in microaggregates, with the opposite observed for aromatic C, in soil of greenhouse vegetable fields. The large loss of O‐alkyl C may explain the degradation of soil aggregates.

Soil iron (Fe) performs vital functions in the biogeochemical cycles of soil environments. The amount and profile allocation of various Fe parameters can be used as sensitive indicators of soil development and pedogenic processes. This study aimed to evaluate the potential of ground‐based hyperspectral imaging (HSI: 400–1010 nm) spectroscopy to predict and map six Fe parameters indicative of pedogenic processes: total Fe (Fe _t ), dithionite‐citrate‐bicarbonate (DCB)‐extracted Fe (Fe _d ), oxalate‐extracted Fe (Fe _o ), weathering index (Fe _W ), active ratio (Fe _A ) and crystallinity ratio (Fe _C ). In total, 17 intact soil profiles at a depth of 100 ± 5 cm were collected to acquire HSI images. Four non‐linear machine learning techniques, namely, random forest (RF), XGBoost, CatBoost and support vector machine regression (SVMR), were implemented and compared with linear partial least squares (PLS) to identify the models with the best performance for different soil Fe parameters. Our results indicate that the four non‐linear machine learning models outperformed PLS for most soil Fe parameters, with low root mean square error (RMSE) and high Lin's concordance correlation coefficient (LCCC) values. Based on the testing set, SVMR showed better performance over the other tested models for Fe _t (RMSE _P = 2.645 g kg ⁻¹ , LCCC _P = 0.89), Fe _d (RMSE _P = 0.972 g kg ⁻¹ , LCCC _P = 0.97), Fe _o (RMSE _P = 0.273 g kg ⁻¹ , LCCC _P = 0.97), Fe _W (RMSE _P = 0.035, LCCC _P = 0.97), Fe _A (RMSE _P = 0.033, LCCC _P = 0.97) and Fe _C (RMSE _P = 0.031, LCCC _P = 0.97). According to the LCCC _P values, soil Fe _t was predicted to be in substantial agreement by SVMR, and the other soil Fe predictions were considered to be in near perfect agreement. Moreover, SVMR required lower computational costs. Given these results, the combination of HSI spectroscopy and SVMR is recommended due to its more reliable estimation and profile mapping of the selected soil Fe parameters than that of PLS, RF, XGBoost and CatBoost. Highlights: Hyperspectral imaging was used to map six soil Fe parameters of intact profiles. Nonlinear machine learning models were compared to select the most suitable model. Nonlinear techniques outperformed the linear PLS model in most cases. SVMR showed higher comprehensive performance than other models. Hyperspectral imaging combined with SVMR is recommended to map soil Fe in profiles.

Greenhouse vegetable production (GVP) is developing rapidly and farming practices strongly affect soil quality. However, quantitative evidence of the differences in soil macropore structure between organic and conventional greenhouse vegetable cultivation is still unavailable. In this study, we sampled paired organic and conventional greenhouse vegetable soils from the tilled and plough pan layers at sample sites south of Nanjing, China. Using industrial computed tomography (CT) scanning technology to measure soil macropore characteristics, we examined the effects of different farming practices on soil macropore structure, as well as the main factors that influenced soil macropore characteristics. Organic greenhouse vegetable (OGV) production was associated with a greater quantity and quality of soil macropores compared with conventional greenhouse vegetable (CGV) production. The total macroporosity in the tilled soil layer averaged 16.9% in OGV soils, more than twice that of CGV soils. OGV soils exhibited an improved pore size distribution with a greater relative abundance of transmission pores (50–500 μm) and a decreased relative abundance of large macropores (>1,000 μm). Pore size and shape data suggested that OGV soils had fewer soil cracks but more biopores than CGV soils in the tilled layer, as well as more root biopores in the plough pan layer. Furthermore, macropore morphology was improved in both the tilled and plough pan layers of OGV soils. In particular, macropore connectivity was nearly 10 times greater in the tilled layer of OGV soils. Soil organic matter (SOM) content was the dominant factor influencing most macropore characteristics (total macroporosity, connectivity, and relative porosity of 100–1,000 μm pores, all small macropores and elongated medium macropores). We suggest that greater SOM content and consequently better soil macropore structure in OGV soils was dependent not only on greater organic manure input, but also on the non‐application of chemical fertilizer. Highlights: Farm management affects soil macropore structure in greenhouse vegetable production. Compared soil macropore structure in organic and conventional greenhouse vegetable cultivation. OGV had greater quantity and quality of soil macropores than CGV. Greater SOM in organic farming contributed to the better soil macropore structure.