Soil pore structure is highly variable with soil management practices, and plays an important role in nutrient availability. However, the relationships between soil pore characteristics (pore connectivity and pore size distribution) and nitrate release from soil organic matter or applied fertilizer are still unclear. This study aimed to identify how soil pore structure affects nitrate release under three fertilization regimes: Control (no fertilization), NPK (mineral NPK fertilization), and OF (organic fertilization). Soil samples were subjected to three physical disturbances (Intact, Repacked, and Compacted) for each fertilization treatment to alter soil pore structure characteristics which were quantified using computed tomography (CT). Nitrate release derived from soil organic matter (Ndfs) and applied fertilizer (Ndff) was distinguished with 15N labelled urea and leached during an incubation. The results showed a decrease in cumulative Ndff in the NPK and OF treatments, compared to the Control, while an increase in cumulative Ndfs in the OF treatment, compared to the Control. Cumulative Ndff was higher in the Repacked treatment, but lower in the Compacted treatment, than in the Intact treatment. Correlation analysis showed that cumulative Ndfs was positively influenced by soil organic carbon content (SOC) and ammonia-oxidizing bacteria abundance, while cumulative Ndff was negatively influenced by SOC. Furthermore, cumulative Ndfs was not associated with soil pore characteristics; however, cumulative Ndff was positively associated with macroporosity, macropore connectivity, and the porosities of pores with diameters in the ranges of 100–500 μm and 500–1000 μm. Path analysis indicated that 100–500 μm pores indirectly influenced the potential and rate of Ndff by modulating water holding capacity and air permeability. Our findings provide a novel perspective, indicating that soil pore structure characteristics significantly influence nitrate release from applied fertilizer rather than that from soil organic matter, with porosity of 100–500 μm being particularly effective in influencing nitrate release from applied fertilizer.
{"title":"The role of soil pore structure on nitrate release from soil organic matter and applied fertilizer under three fertilization regimes","authors":"Renjie Ruan, Zhongbin Zhang, Ting Lan, Yaosheng Wang, Wei Li, Huan Chen, Xinhua Peng","doi":"10.1016/j.still.2024.106396","DOIUrl":"https://doi.org/10.1016/j.still.2024.106396","url":null,"abstract":"Soil pore structure is highly variable with soil management practices, and plays an important role in nutrient availability. However, the relationships between soil pore characteristics (pore connectivity and pore size distribution) and nitrate release from soil organic matter or applied fertilizer are still unclear. This study aimed to identify how soil pore structure affects nitrate release under three fertilization regimes: Control (no fertilization), NPK (mineral NPK fertilization), and OF (organic fertilization). Soil samples were subjected to three physical disturbances (Intact, Repacked, and Compacted) for each fertilization treatment to alter soil pore structure characteristics which were quantified using computed tomography (CT). Nitrate release derived from soil organic matter (Ndfs) and applied fertilizer (Ndff) was distinguished with <ce:sup loc=\"post\">15</ce:sup>N labelled urea and leached during an incubation. The results showed a decrease in cumulative Ndff in the NPK and OF treatments, compared to the Control, while an increase in cumulative Ndfs in the OF treatment, compared to the Control. Cumulative Ndff was higher in the Repacked treatment, but lower in the Compacted treatment, than in the Intact treatment. Correlation analysis showed that cumulative Ndfs was positively influenced by soil organic carbon content (SOC) and ammonia-oxidizing bacteria abundance, while cumulative Ndff was negatively influenced by SOC. Furthermore, cumulative Ndfs was not associated with soil pore characteristics; however, cumulative Ndff was positively associated with macroporosity, macropore connectivity, and the porosities of pores with diameters in the ranges of 100–500 μm and 500–1000 μm. Path analysis indicated that 100–500 μm pores indirectly influenced the potential and rate of Ndff by modulating water holding capacity and air permeability. Our findings provide a novel perspective, indicating that soil pore structure characteristics significantly influence nitrate release from applied fertilizer rather than that from soil organic matter, with porosity of 100–500 μm being particularly effective in influencing nitrate release from applied fertilizer.","PeriodicalId":501007,"journal":{"name":"Soil and Tillage Research","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1016/j.still.2024.106384
Lea Epple, Oliver Grothum, Anne Bienert, Anette Eltner
Camera-based soil surface change measurement is a cost-efficient and non-invasive approach to assess soil erosion. A challenging aspect in this context is the obscuring of the sediment yield by subsidence phenomenon such as soil consolidation and compaction in the beginning of a rainfall event (masking effect). Based on the camera elevation changes and measured field observations, we develop an approach to estimate these masking effects and to approximate a correction function. We therefore conduct ten rainfall simulations (3 m x 1 m) on different agricultural slopes, measuring runoff and sediment concentration. With a time-lapse camera system, we generate high resolution digital elevation models every 20 s. An s-shaped curve is fitted via non-linear regression for every rainfall simulation. We use the variables of these functions as well as a combination of the different field observations – bulk density, soil moisture, grain size distribution, total organic carbon, slope steepness, surface cover and surface roughness – as input values for an adjustment. We are able to estimate the masking effects at the beginning of rainfall events as functions of soil and plot characteristics and therefore offer a potential to increase the informative value of camera-based soil erosion measurements on agricultural fields.
基于相机的土壤表面变化测量是一种经济有效的非侵入性土壤侵蚀评估方法。在这种情况下，一个具有挑战性的方面是，在降雨事件开始时，沉降现象（如土壤固结和压实）掩盖了产沙量（掩蔽效应）。基于相机高程变化和实测的野外观测，我们开发了一种估计这些掩蔽效应和近似校正函数的方法。因此，我们在不同的农业斜坡上进行了10次降雨模拟（3 m x 1 m），测量径流和沉积物浓度。使用延时相机系统，我们每20 秒生成高分辨率数字高程模型。通过非线性回归对每次降雨模拟拟合出s型曲线。我们使用这些函数的变量以及不同现场观测的组合——容重、土壤湿度、粒度分布、总有机碳、坡度、地表覆盖和地表粗糙度——作为调整的输入值。我们能够估计降雨事件开始时的掩蔽效应作为土壤和地块特征的函数，因此有可能增加基于相机的农田土壤侵蚀测量的信息价值。
{"title":"Decoding rainfall effects on soil surface changes: Empirical separation of sediment yield in time-lapse SfM photogrammetry measurements","authors":"Lea Epple, Oliver Grothum, Anne Bienert, Anette Eltner","doi":"10.1016/j.still.2024.106384","DOIUrl":"https://doi.org/10.1016/j.still.2024.106384","url":null,"abstract":"Camera-based soil surface change measurement is a cost-efficient and non-invasive approach to assess soil erosion. A challenging aspect in this context is the obscuring of the sediment yield by subsidence phenomenon such as soil consolidation and compaction in the beginning of a rainfall event (masking effect). Based on the camera elevation changes and measured field observations, we develop an approach to estimate these masking effects and to approximate a correction function. We therefore conduct ten rainfall simulations (3 m x 1 m) on different agricultural slopes, measuring runoff and sediment concentration. With a time-lapse camera system, we generate high resolution digital elevation models every 20 s. An s-shaped curve is fitted via non-linear regression for every rainfall simulation. We use the variables of these functions as well as a combination of the different field observations – bulk density, soil moisture, grain size distribution, total organic carbon, slope steepness, surface cover and surface roughness – as input values for an adjustment. We are able to estimate the masking effects at the beginning of rainfall events as functions of soil and plot characteristics and therefore offer a potential to increase the informative value of camera-based soil erosion measurements on agricultural fields.","PeriodicalId":501007,"journal":{"name":"Soil and Tillage Research","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soil organic matter (SOM) mapping in salinized areas is crucial for scientific guidance on soil salinization. However, accurately mapping SOM is challenging due to the intricate interplay between soil moisture content (SMC) and soil salt content (SSC), which significantly influences soil spectra. Unlike prior research that has separately examined the impacts of moisture or salinity, this study delves into the combined effects of these factors on SOM spectra. The objective of this study is to develop and validate several spectral optimization algorithms at both the laboratory and satellite levels. In October 2020, a study was conducted using 291 ground-truth data to examine the impact of various moisture-salt stages (seven moisture stages, five salt stages) on hyperspectral data. Spectrum mechanism responsive for soil moisture and salinity were analyzed through spectral curves, correlation, and analysis of variance (Anova, AOV), and spectrum mechanism responsive for soil moisture and salinity model were built. Following that, the spectra were optimized using piecewise direct normalization (PDS)-AOV, non-negative matrix factorization (NMF)-AOV, and orthogonal signal correction (OSC)-AOV. The SOM prediction models were then built by integrating these optimized spectra with Stacking ensemble machine learning algorithms (RF, GBM, ANN). Eventually, the lab-optimized spectra were merged with satellite multispectral images to create new image (named REC) for SOM mapping. The results indicated varying impacts of SMC and SSC on spectra, particularly between 1400 nm to 2000 nm, revealing the influence of moisture-salt interaction; the best optimization algorithm (OSC-AOV) with Stacking mitigated the effect of moisture-salt coexistence on spectra (the R2 and RPD of the best models elevated by 0.005–0.267, 0.020–0.374 respectively, RMSE reduced by 0.137–1.817 g/kg); implementing this algorithm on REC significantly improved the accuracy of SOM mapping (R2 elevated by 0.185–0.259, RMSE reduced by 2.615–3.203 g/kg). This study extensively investigated the effects of moisture and salinity on spectra, spanning from laboratory to satellite, offering a novel approach to understanding and addressing the complexities in SOM mapping in salinized environments.
{"title":"Recognizing and reducing effects of moisture-salt coexistence on soil organic matter spectral prediction：From laboratory to satellite","authors":"Danyang Wang, Yayi Tan, Cheng Li, Jingda Xin, Yunqi Wang, Huagang Hou, Lulu Gao, Changbo Zhong, Jianjun Pan, Zhaofu Li","doi":"10.1016/j.still.2024.106397","DOIUrl":"https://doi.org/10.1016/j.still.2024.106397","url":null,"abstract":"Soil organic matter (SOM) mapping in salinized areas is crucial for scientific guidance on soil salinization. However, accurately mapping SOM is challenging due to the intricate interplay between soil moisture content (SMC) and soil salt content (SSC), which significantly influences soil spectra. Unlike prior research that has separately examined the impacts of moisture or salinity, this study delves into the combined effects of these factors on SOM spectra. The objective of this study is to develop and validate several spectral optimization algorithms at both the laboratory and satellite levels. In October 2020, a study was conducted using 291 ground-truth data to examine the impact of various moisture-salt stages (seven moisture stages, five salt stages) on hyperspectral data. Spectrum mechanism responsive for soil moisture and salinity were analyzed through spectral curves, correlation, and analysis of variance (Anova, AOV), and spectrum mechanism responsive for soil moisture and salinity model were built. Following that, the spectra were optimized using piecewise direct normalization (PDS)-AOV, non-negative matrix factorization (NMF)-AOV, and orthogonal signal correction (OSC)-AOV. The SOM prediction models were then built by integrating these optimized spectra with Stacking ensemble machine learning algorithms (RF, GBM, ANN). Eventually, the lab-optimized spectra were merged with satellite multispectral images to create new image (named REC) for SOM mapping. The results indicated varying impacts of SMC and SSC on spectra, particularly between 1400 nm to 2000 nm, revealing the influence of moisture-salt interaction; the best optimization algorithm (OSC-AOV) with Stacking mitigated the effect of moisture-salt coexistence on spectra (the R<ce:sup loc=\"post\">2</ce:sup> and RPD of the best models elevated by 0.005–0.267, 0.020–0.374 respectively, RMSE reduced by 0.137–1.817 g/kg); implementing this algorithm on REC significantly improved the accuracy of SOM mapping (R<ce:sup loc=\"post\">2</ce:sup> elevated by 0.185–0.259, RMSE reduced by 2.615–3.203 g/kg). This study extensively investigated the effects of moisture and salinity on spectra, spanning from laboratory to satellite, offering a novel approach to understanding and addressing the complexities in SOM mapping in salinized environments.","PeriodicalId":501007,"journal":{"name":"Soil and Tillage Research","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-15DOI: 10.1016/j.still.2024.106236
Xiaoting Xie, Hengnian Yan, Yili Lu, Lingzao Zeng
Information on soil temperature is crucial for modeling hydrological and climatic processes. Nevertheless, direct measurements of soil temperature are usually rather limited in space, leading to an urgent need for improved spatial resolution. To address this issue, a Physics-Informed Neural Networks (PINN) method for estimating soil temperature () profile variations was proposed in this study. This method combines the advantages of Deep Neural Networks (DNN) in modeling complex non-linear relationships and physical laws for more robust predictions. The performance was evaluated using in-situ annual soil at depths of 5 cm, 10 cm and 20 cm on a maize field in Northeast China. Cross-validation was used, a PINN was used to derive the new data at unobserved depth from observations at the other two depths. The results demonstrated that the performance of the PINN was superior to the commonly used process-based method and a DNN for all situations. Compared to the traditional method, the PINN achieved a 0.69°C and 0.39°C reduction in root-mean-square error (RMSE) for estimates at 10 cm and 20 cm depths, respectively, under plowed tillage condition, while it could also accurately estimate at 5 cm depth with RMSE of 0.56 °C. In addition, the PINN does not require inputs of soil thermal properties e.g., apparent thermal diffusivity (κ), as the space and time-dependent κ values could also be learned during the training process. The results presented here demonstrated that a PINN could successfully utilize limited observation data to estimate unknown soil profiles, and solve some challenging problems beyond the reach of existing methods in simulating soil thermal dynamics.
{"title":"Simulating field soil temperature variations with physics-informed neural networks","authors":"Xiaoting Xie, Hengnian Yan, Yili Lu, Lingzao Zeng","doi":"10.1016/j.still.2024.106236","DOIUrl":"https://doi.org/10.1016/j.still.2024.106236","url":null,"abstract":"Information on soil temperature is crucial for modeling hydrological and climatic processes. Nevertheless, direct measurements of soil temperature are usually rather limited in space, leading to an urgent need for improved spatial resolution. To address this issue, a Physics-Informed Neural Networks (PINN) method for estimating soil temperature () profile variations was proposed in this study. This method combines the advantages of Deep Neural Networks (DNN) in modeling complex non-linear relationships and physical laws for more robust predictions. The performance was evaluated using in-situ annual soil at depths of 5 cm, 10 cm and 20 cm on a maize field in Northeast China. Cross-validation was used, a PINN was used to derive the new data at unobserved depth from observations at the other two depths. The results demonstrated that the performance of the PINN was superior to the commonly used process-based method and a DNN for all situations. Compared to the traditional method, the PINN achieved a 0.69°C and 0.39°C reduction in root-mean-square error (RMSE) for estimates at 10 cm and 20 cm depths, respectively, under plowed tillage condition, while it could also accurately estimate at 5 cm depth with RMSE of 0.56 °C. In addition, the PINN does not require inputs of soil thermal properties e.g., apparent thermal diffusivity (κ), as the space and time-dependent κ values could also be learned during the training process. The results presented here demonstrated that a PINN could successfully utilize limited observation data to estimate unknown soil profiles, and solve some challenging problems beyond the reach of existing methods in simulating soil thermal dynamics.","PeriodicalId":501007,"journal":{"name":"Soil and Tillage Research","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141768699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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