Soil Organic Matter Models Research Articles

Estimating Soil Organic Matter Content in Desert Areas Using In Situ Hyperspectral Data and Feature Variable Selection Algorithms in Southern Xinjiang, China

Soil organic matter (SOM) is a key factor for evaluating soil fertility. Rapidly monitoring organic matter content in desert soil can provide a scientific basis for the rational development and utilization of reserve arable land resources. Although spectral inversion accuracy for SOM under laboratory-controlled conditions is high, it is time-consuming and costly compared to the in situ spectroscopic determination method. However, in situ spectroscopy causes losses in accuracy due to interference from external environmental factors (e.g., the surface roughness of soil, changes in weather conditions, atmospheric water vapor, etc.). Therefore, reducing or removing the interference of external environmental factors to improve the accuracy of in situ spectroscopy for estimating SOM is challenging. In this study, visible and near-infrared (Vis-NIR) in situ spectral data were collected from 135 topsoil (0–20 cm) samples in a desert area of northwestern China, and organic matter content was measured. Three spectral pre-processing methods—the standard normal transform (SNV), reciprocal logarithm (log(1/R)) and normalization (NOR)—combined with three feature variable selection methods—the particle swarm algorithm (PSO), ant colony algorithm (ACO) and simulated annealing (SA) algorithm—were used to filter the spectral feature bands of SOM, and then partial least squares regression (PLSR), a back propagation neural network (BPNN) and a convolutional neural network (CNN) were used to construct the estimation models of SOM. The results indicated that the SNV could enhance the spectral information related to SOM and improve the accuracy of model estimation, and it was one of the most effective spectral pretreatment methods. Compared with the model constructed with the full-band spectroscopy method, the feature variable selection method could effectively improve the estimation accuracy of the Vis-NIR in situ spectroscopy model. The most obvious improvement was found with PSO, where R2 and RPD were improved by more than 0.34 and 0.16, respectively, and RMSE was reduced by more than 0.29 g kg−1. The accuracy of the CNN model was higher than that of the BPNN and PLSR models, both for the inversion model of SOM built from full-band spectral data and the bands selected by the characteristic variable selection method. SNV-PSO-CNN is the optimal hybrid model for in situ spectral measurement of SOM (R2 = 0.71, RPD = 1.88, RMSE = 1.67 g kg−1) and can realize the quantitative in situ spectral inversion of SOM in desert soils.

Using PRISMA Hyperspectral Satellite Imagery and GIS Approaches for Soil Fertility Mapping (FertiMap) in Northern Morocco

Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P2O5), and potassium (K2O) content over a cultivated area in Khouribga, northern Morocco. These soil nutrients were estimated using (i) the random forest (RF) algorithm based on feature selection methods, including feature subset evaluation and feature ranking methods belonging to three categories (i.e., filter, wrapper, and embedded techniques), and (ii) 107 soil samples taken from the study area. The results show that the RF-embedded method produced better predictive accuracy compared with the filter and wrapper methods. The model for SOM showed moderate accuracy (Rval2 = 0.5, RMSEP = 0.43%, and RPIQ = 2.02), whereas that for soil P2O5 and K2O exhibited low efficiency (Rval2 = 0.26 and 0.36, RMSEP = 51.07 and 182.31 ppm, RPIQ = 0.65 and 1.16, respectively). The interpolation of RF-residuals by ordinary kriging (OK) methods reached the highest predictive results for SOM (Rval2 = 0.69, RMSEP = 0.34%, and RPIQ = 2.56), soil P2O5 (Rval2 = 0.44, RMSEP = 44.10 ppm, and RPIQ = 0.75), and soil K2O (Rval2 = 0.51, RMSEP = 159.29 ppm, and RPIQ = 1.34), representing the best fitting ability between the hyperspectral data and soil nutrients. The result maps provide a spatially continuous surface mapping of the soil landscape, conforming to the pedological substratum. Finally, the hyperspectral remote sensing imagery can provide a new way for modeling and mapping soil fertility, as well as the ability to diagnose nutrient deficiencies.

Microbial carbon use efficiency along an altitudinal gradient

Soil microbial carbon-use efficiency (CUE), described as the ratio of growth over total carbon (C) uptake, i.e. the sum of growth and respiration, is a key variable in all soil organic matter (SOM) models and critical to ecosystem C cycling. However, there is still a lack of consensus on microbial CUE when estimated using different methods. Furthermore, the significance of many fundamental drivers of CUE remains largely unknown and inconclusive, especially for tropical ecosystems. For these reasons, we determined CUE and microbial indicators of soil nutrient availability in seven tropical forest soils along an altitudinal gradient (circa 900–2200 m a.s.l) occurring at Taita Hills, Kenya. We used this gradient to study the soil nutrient (N and P) availability and its relation to microbial CUE estimates. For assessing the soil nutrient availability, we determined both the soil bulk stoichiometric nutrient ratios (soil C:N, C:P and N:P), as well as SOM degradation related enzyme activities. We estimated soil microbial CUE using two methods: substrate independent 18O-water tracing and 13C-glucose tracing method. Based on these two approaches, we estimated the microbial uptake efficiency of added glucose versus native SOM, with the latter defined by 18O-water tracing method. Based on the bulk soil C:N stoichiometry, the studied soils did not reveal N limitation. However, soil bulk P limitation increased slightly with elevation. Additionally, based on extracellular enzyme activities, the SOM nutrient availability decreased with elevation. The 13C-CUE did not change with altitude indicating that glucose was efficiently taken up and used by the microbes. On the other hand, 18O-CUE, which reflects the growth efficiency of microbes growing on native SOM, clearly declined with increasing altitude and was associated with SOM nutrient availability indicators. Based on our results, microbes at higher elevations invested more energy to scavenge for nutrients and energy from complex SOM whereas at lower elevations the soil nutrients may have been more readily available.

Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China

Soil organic matter (SOM) is a key index of soil fertility. Visible and near-infrared (VNIR, 350–2500 nm) reflectance spectroscopy is an effective method for modeling SOM content. Characteristic wavelength screening and spectral transformation may improve the performance of SOM prediction. This study aimed to explore the optimal combination of characteristic wavelength selection and spectral transformation for hyperspectral modeling of SOM. A total of 219 topsoil (0–20 cm) samples were collected from two soil types in the East China. VNIR reflectance spectra were measured in the laboratory. Firstly, after spectral transformation (inverse-log reflectance (LR), continuum removal (CR) and first-order derivative reflectance (FDR)) of VNIR spectra, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variables elimination (UVE) algorithms. Secondly, the SOM prediction models were constructed based on the partial least squares regression (PLSR), random forest (RF) and support vector regression (SVR) methods using the full spectra and selected wavelengths, respectively. Finally, optimal SOM prediction models were selected for two soil types. The results were as follows: (1) The CARS algorithm screened 40–125 characteristic wavelengths from the full spectra. The UVE algorithm screened 105–884 characteristic wavelengths. (2) For two soil types and full spectra, CARS and UVE improved the SOM modeling precision based on the PLSR and SVR methods. The coefficient of determination (R2) value in the validation of the CARS-PLSR (PLSR model combined with CARS) and CARS-SVR (SVR model combined CARS) models ranged from 0.69 to 0.95, and the relative percent deviation (RPD) value ranged from 1.74 to 4.31. Lin’s concordance correlation coefficient (LCCC) values ranged from 0.83 to 0.97. The UVE-PLSR and UVE-SVR models showed moderate precision. (3) The PLSR and SVR modeling accuracies of Paddy soil were better than those for Shajiang black soil. RF models performed worse for both soil types, with the R2 values of validation ranging from 0.22 to 0.68 and RPD values ranging from 1.01 to 1.60. (4) For Paddy soil, the optimal SOM prediction models (highest R2 and RPD, lowest root mean square error (RMSE)) were CR-CARS-PLSR (R2 and RMSE: 0.97 and 1.21 g/kg in calibration sets, 0.95 and 1.72 g/kg in validation sets, RPD: 4.31) and CR-CARS-SVR (R2 and RMSE: 0.98 and 1.04 g/kg in calibration sets, 0.91 and 2.24 g/kg in validation sets, RPD: 3.37). For Shajiang black soil, the optimal SOM prediction models were LR-CARS-PLSR (R2 and RMSE: 0.95 and 0.93 g/kg in calibration sets, 0.86 and 1.44 g/kg in validation sets, RPD: 2.62) and FDR-CARS-SVR (R2 and RMSE: 0.99 and 0.45 g/kg in calibration sets, 0.83 and 1.58 g/kg in validation sets, RPD: 2.38). The results suggested that the CARS algorithm combined CR and FDR can significantly improve the modeling accuracy of SOM content.

Spatial distribution of soil nutrients and evaluation of cultivated land in Xuwen county.

It is of great significance to promote the quantitative research of soil science and the implementation of precision agriculture. On this basis, taking Xuwen County as the research object, this paper comprehensively analyzed the soil characteristics of cultivated land in Xuwen County and clarify the soil nutrient content and spatial distribution characteristics of cultivated land in Xuwen County, this paper comprehensively applied the methods of geostatistics, geographic information system (GIS) and fuzzy mathematics, and referred to the cultivated land quality grade standard (GB/T 33469-2016), to analyze the soil characteristics and evaluate the soil fertility of this region. The results show that the optimal interpolation model of soil pH and available phosphorus (AP) is a Gaussian model, and the optimal interpolation model of soil organic matter (SOM), available nitrogen (AN) and available potassium (AK) is a J-Bessel model. In addition, the spatial correlation of AK is weak, whereas pH, SOM, AN and AP show moderate spatial correlation. The proportion of excellent, good, average, medium and poor comprehensive fertility index are 26.00%, 32.67%, 19.33%, 19.00% and 3.00%, respectively. The overall level of soil fertility in Xuwen County is above the average, and the fertility quality presents an obvious trend of high in the South and low in the North. Areas that above average fertility are mainly distributed in Maichen Town, Qujie Town, Nanshan Town and Chengbei Town. The results can provide theoretical basis for improving the utilization rate of chemical fertilizer, fine management of cultivated land and ecological environment in this region, which can help in decision-making of precision fertilization.

A well‐established fact: Rapid mineralization of organic inputs is an important factor for soil carbon sequestration

AbstractWe have read with interest an opinion paper recently published in the European Journal of Soil Science (Berthelin et al., 2022). This paper presents some interesting considerations, at least one of which is already well known to soil scientists working on soil organic carbon (SOC), that is, a large portion (80%–90%) of fresh carbon inputs to soil is subject to rapid mineralization. The short‐term mineralization kinetics of organic inputs is well‐known and accounted for in soil organic matter models. Thus, clearly, the long‐term predictions based on these models do not overlook short‐term mineralization. We point out that many agronomic practices can significantly contribute to SOC sequestration. If conducted responsibly whilst fully recognising the caveats, SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it, whilst at the same time delivering other co‐benefits such as reduced soil erosion and enhanced biodiversity.Highlights Rapid mineralization of organic inputs is an important factor for soil carbon sequestration. Mineralization kinetics of organic inputs are well‐known and accounted for in soil organic matter models. Many agronomic practices can contribute significantly to SOC sequestration. SOC sequestration can lead to a win‐win situation where agriculture can both contribute to the mitigation of climate change and adapt to it.

Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)

BackgroundLarge uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.ResultsResults showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.ConclusionsThe output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty.

Impacts of Ground Fissures on Soil Properties in an Underground Mining Area on the Loess Plateau, China

Mining-induced ground fissures are the main type of geological disasters found on the Loess Plateau, China, and cause great impacts on the soil properties around ground fissures. However, little research has been conducted on the quantitative relationship between ground fissures and changes in soil properties. To address this, 40 ground fissures in the Yungang mining area, Datong City, Shanxi Province, China, were investigated, and changes in soil properties (soil organic matter, soil moisture, field capacity, bulk density, soil porosity, and grain compositions) were revealed by the difference in soil properties between the edge and contrast points around ground fissures. Redundancy analyses were used to illustrate the relationships between the value (Si_DV) and percentage (Si_DP) of the difference in soil properties between the edge and contrast points, as well as the ground fissures. The characteristics of ground fissures that had a significant correlation according to Pearson correlation analysis with Si_DP were selected and analyzed via multivariate linear fitting model, random forest model, and Back Propagation (BP) neural network model, respectively. Results show that soil organic matter, soil moisture content, bulk density, field capacity, and the content of clay at the edge points were significantly less than those at the contrast points; conversely, soil porosity at the edge points was significantly greater. The average percentage of the difference between the edge points and contrast points of ground fissures in these six properties was 15.27%, while soil moisture content showed the greatest change (20.65%). The Si_DP was significantly correlated with the width, slope, and vegetation coverage of ground fissures; however, the vegetation coverage was the determining factor. BP neural network model had the greatest performance in revealing the relationships between ground fissures and changes in soil properties. The model for soil organic matter had the highest accuracy (R2 = 0.89), and all others were above 0.5. This research provides insights into the quantitative relationship between ground fissures and their impacts on soil physical properties, which can be used in conjunction with remote sensing images to rapidly assess soil erosion risks caused by mining on a large scale, given that soil physical properties are closely related to topsoil stability.

浙江清凉峰自然保护区土壤肥力指标空间变异及其影响因素

PDF HTML阅读 XML下载 导出引用 引用提醒 浙江清凉峰自然保护区土壤肥力指标空间变异及其影响因素 DOI: 10.5846/stxb202103310832 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 浙江省自然科学基金项目（LY20C160004）；浙江清凉峰国家级自然保护区管理局森林土壤资源调查编研项目（H20190328） Spatial variability of soil fertility properties and its affecting factors of Qingliangfeng Nature Reserve, Zhejiang Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为了探究清凉峰自然保护区内华南野生梅花鹿主要栖息地千顷塘的土壤肥力指标的空间变异规律及其相关影响因子，采用经典统计学和地统计学方法对千顷塘土壤pH、有机质、全磷、全氮、全钾含量进行了系统分析，并探讨了其主要影响因子。结果显示：千顷塘保护区内土壤整体呈酸性，区域内有机质、全磷、全氮、全钾含量均值分别为72.44、0.45、1.91、23.16 g/kg，所有指标均呈现中等程度变异。半方差分析结果表明，指数模型为pH、全磷、全氮、全钾的最佳拟合模型，球状模型为有机质最佳拟合模型，pH、全磷、全钾空间自相关强烈，显示其空间变异主要受结构性因素影响，有机质和全氮空间自相关程度中等，说明随机因素存在一定影响。全局Moran's I指数检验结果显示，有机质、全磷和全氮存在显著空间自相关，在空间上呈现聚集分布。所有肥力指标的空间分布较为连贯，条带状分布特征显著，总体呈现由边缘向中间降低趋势；相关性分析显示，土壤有机质、全磷、全氮、全钾与海拔、容重和植被类型呈显著相关性；回归分析表明，在所有环境因子中，容重对各类元素的方差解释最大，是影响千顷塘土壤肥力因子空间异质性的主控因素。研究可为南方丘陵地区人为扰动较小的林地土壤肥力特征及影响因子解译提供重要科学意义。 Abstract:In order to explore the spatial heterogeneity of soil fertility properties and its affecting factors in Qianqingtang, the main habitat of wild sika deer of South China, in Qingliangfeng Nature Reserve. Based on the sampling data,using traditional statistics and geostatistical methods, the soil pH, soil organic matter (SOM), total phosphorus (TP), total nitrogen (TN), and total potassium (TK) were systematically analyzed. Moreover, the importance of the environmental factors influencing the variability and accumulation of soil fertility properties were studied. The results indicated that the soil in Qianqingtang Reserve was acidic on the whole, and the average contents of SOM, TP, TN and TK in the studied area were 72.44 g/kg, 0.45 g/kg, 1.91 g/kg and 23.16 g/kg, respectively. The content of each properties is relatively high, basically in the rich level. The coefficients of variation (C.V.s) of all studied soil fertility properties were at a moderate level. The results of semi-variance analysis indicated that the exponential model was the best-fit model for pH, TP, TN and TK while spherical model was the best-fit model for SOM. In addition, pH, TP and TK had strongly spatial autocorrelations which represented that their spatial variability was mainly dominated by structural factors. SOM and TN showed moderately spatial autocorrelations which indicated that the random factors had a certain influence. The results of the global Moran's I index test showed that SOM, TP and TN had significant spatial autocorrelation, and presented an aggregated distribution in space which corresponded to the calculation results of the variable range. Kriging interpolation results showed the spatial distribution of all kinds of soil fertility properties were coherent and the trend of stripe distribution was obvious. The spatial distribution regularity showed a similarity of each soil fertility properties to some extent, the distribution showed a significant downward trend from the edge to the middle. The results of correlation analysis revealed that SOM, TP, TN, and TK in the study area had significant correlation with altitude, bulk density and stand types. Regression analysis showed that bulk density explained the highest variance of elements among all environmental factors, indicating that bulk density was the dominant controlling factor affecting the spatial heterogeneity of soil fertility properties in Qianqingtang Reserve. However, the overall explanatory degree of the research factors was low, indicating that the spatial variation of soil fertility properties was also affected by other factors. This study can provide important scientific information related to soil property characteristics and influencing factors in the southern hilly area with little human disturbance. 参考文献 相似文献 引证文献

Mapping Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on the Northern Songnen Plain

Soil organic matter (SOM) plays a critical role in agroecosystems and the terrestrial carbon cycle. Thus, accurately mapping SOM promotes sustainable agriculture and estimations of soil carbon pools. However, few studies have analyzed the changing trends in multi-period SOM prediction accuracies for single cropland soil types and mapped their spatial SOM patterns. Using time series 7 MOD09A1 images during the bare soil period, we combined the pixel dates of training samples and precipitation data to explore the variation in SOM accuracy for two typical cropland soil types. The advantage of using single soil type data versus the total dataset was evaluated, and SOM maps were drawn for the northern Songnen Plain. When almost no precipitation occurred on or near the optimal pixel date, the accuracies increased, and vice versa. SOM models of the two soil types achieved a lower root mean squared error (RMSE = 0.55%, 0.79%) and mean absolute error (MAE = 0.39%, 0.58%) and a higher coefficient of determination (R2 = 0.65, 0.75) than the model using the total dataset and resulted in a mean relative improvement (RI) of 30.21%. The SOM decreased from northeast to southwest. The results provide reference data for the accurate management of cultivated soil and determining carbon sequestration.

Regional mapping of soil organic matter content using multitemporal synthetic Landsat 8 images in Google Earth Engine

Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development, especially in fertile black soil areas. The present study proposed a regional-scale high spatial resolution (30 m) SOM mapping method based on multitemporal synthetic images. The study area is located on the Songnen Plain of Northeast China. First, all available Landsat 8 surface reflectance (SR) data during the bare soil period (April and May) from 2014 to 2019 in the study area were screened in the Google Earth Engine (GEE), and the cloud mask was constructed. The median, average, maximum, and minimum values of the image set were synthesized according to single-year multimonth, multiyear single-month and multiyear multimonth time ranges, and the spectral index of the synthesized image was constructed. Second, the bands and spectral indices of different synthetic images were used as input to establish a random forest (RF) model of SOM prediction, and the accuracies of different spatial prediction models of SOM were compared to evaluate the optimal regional remote sensing prediction model of SOM. The following results were show. 1) The use of the spectral index combined with the image band as input had a greater improvement in the accuracy of SOM prediction than the use of only the image band. 2) Compared to the average, maximum and minimum synthesized images, the median synthesized image had higher accuracy in SOM prediction. 3) More years of synthesized images provided more robust SOM prediction results. 4) May was the best time window for SOM mapping on the Songnen Plain. This study presents a large-scale and high spatial resolution SOM mapping method that is suitable for black soil areas in Northeast China and extends the application of GEE in digital soil mapping.

Applying CCB to predict management change affected long‐term SOM turnover of the Extended Static Fertilization Experiment in Bad Lauchstädt

AbstractLong‐term field experiments (LTEs) are invaluable in improving understanding of soil organic matter (SOM) turnover, as some of the involved processes have proceeded over centuries. Prediction of such slow carbon fluxes depends especially on the initialization of slow‐reacting model pools and requires monitoring for a very long time for evaluation. This study reports soil organic matter (SOM) modelling covering more than 100 years for a dataset of 36 treatments of the “Extended Static Experiment” that is based on the classical “Static Experiment Bad Lauchstädt.” The experimental scheme includes different fertilization treatments where the plots are arranged in two field stripes. A special feature of this experiment is the management change for each stripe after seven decades of continuous cultivation that includes a considerable alteration of organic amendments on different SOM levels. The Candy Carbon Balance (CCB) model was calibrated for soil organic carbon (SOC) dynamics regarding initial SOC content and one parameter describing the physical protection of SOM. Calibration was performed for each plot individually, for each stripe, and for the whole experiment, resulting in a relative root mean square error (rRMSE, (−)) of 0.050, 0.058 and 0.064, respectively. Model predictions for soil organic nitrogen (SON) dynamics were satisfactory using the calibration results for SOC and the average C/N ratio from all observations within this dataset (11.8) for model initialization. The model error for SON prediction reached from rRMSE = 0.09 (whole experiment) over rRMSE = 0.085 (each stripe) to rRMSE = 0.084 (each plot). Calibration results suggest some heterogeneity between the two field stripes. Restarting the model after the management change in 1978 using the plot‐specific calculated SOC value from previous simulations for the whole time period provided different results of predicted SOC trends and values, suggesting that the precondition of SOM in steady state was still not fulfilled after seven decades.Highlights SOC and SON were modelled for 36 treatments over more than one century. Model restarts after seven decades indicated that SOM pools were not in steady state. Modelling SON required only an additional input of the average C/N ratio of SOM Effect of serious fertilization changes can be well predicted by the tested model setup

Accounting for soil architecture and microbial dynamics in microscale models: Current practices in soil science and the path ahead

AbstractMacroscopic models of soil organic matter (SOM) turnover have faced difficulties in reproducing SOM dynamics or in predicting the spatial distribution of carbon stocks. These models are based on a largely inadequate linear response of soil microorganisms to bulk concentrations of nutrients and it is clear that a new approach to SOM modelling is required. Introducing explicit microbial activity and organic matter (OM) reactivity in macroscopic models represents a challenge because of the fine spatial scales at which the processes occur. To get a better grasp on interactions that take place at the microscale, a new generation of SOM models have been developed at the spatial scale of the soil microenvironments where microorganisms evolve. These models are well adapted to challenge traditional hypotheses about the influence of soil architecture on soil microbial activity. Soil architecture provides the stage for a dynamic spatial accessibility of resources to microbes and the emergence of interactions between the actors in SOM decomposition. In this context, we review microscale models of microbial activity that have been designed for soils and soil analogues. To understand how these models account for spatial accessibility, we look in detail at how soil microenvironments are described in the different approaches and how microbial colonies are spatialized in these microenvironments. We present the advantages and disadvantages of the developed strategies and we discuss their limits.HighlightsWe review the state of the art in the development of microscale models of soil microbial processes.Microscale models have integrated the tremendous progress in knowledge of OM cycling in soils.We categorize modelling approaches by how they deal with complexity of soil architecture.Modelling benchmarks are required to properly balance complexity and data uncertainty.

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model

Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generally captured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystem respiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) was well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

Estimation of soil organic matter content based on CARS algorithm coupled with random forest

Soil organic matter (SOM) is an important index used to evaluate soil fertility and nutrient availability, and it is also an important component of precision agriculture. In this study, in order to quickly and efficiently estimate the SOM content of farmland soil, we took 190 farmland soil samples in Jingbian County and measureed the SOM content of the samples in the lab and collected the corresponding Vis-NIR spectroscopy data. Based on the six pretreatment methods, a competitive adaptive weighting algorithm (CARS) is used for characteristic wavelength selection. Random forest (RF) regression is used to establish the predictive SOM model. The results indicate that after the CARS algorithm screens the different spectral variables, the optimal variable sets of the seven spectral variables are 15, 40, 30, 23, 20, 26, and 23, respectively. The accuracy of the model is improved after the CARS algorithm screens the different spectral variables. A total of 15 characteristic variables from the 2151 spectral wavelengths were used as the optimal spectral variable subset; RF shortened the training time required during the SOM modeling process and dramatically improved the model’s accuracy and predictive ability, and the R2 of the validation set increased from 0.21 to 0.96, and the RPD increased from 0.46 to 3.02. The RPIQ increased from 1.25 to 4.41. Among the tested models, the CR-RF model produced the best results. The R2 and RMSE values of the calibration set are 0.91 and 0.49, and the R2, RMSE, RPD, and RPIQ values of the validation set are 0.96, 0.51, 3.02, and 4.41, respectively. Accurate prediction of the SOM of the cultivated layer in the study area was realized.

Spatiotemporal Assessment of GHG Emissions and Nutrient Sequestration Linked to Agronutrient Runoff in Global Wetlands

AbstractWetlands play a key role in regulating global greenhouse gas (GHG) emissions but anthropogenic impacts on nutrients may severely alter this balance. Recent assessments indicate that almost 22% of the global wetland area may be affected by agricultural runoff. In this work, we developed and applied a dynamic mechanistic reaction network model of soil organic matter linking the carbon (C), nitrogen (N), and sulfur (S) cycles at 0.5° × 0.5° spatial resolution across the globe. The model was used to estimate GHG emissions and nutrient sequestration rates in wetlands, driven by environmental stressors including N, P, and S fertilization. Wetland annual GHG emissions are estimated to be 136 ± 12.5 Tg C‐CH4, 589 ± 45.8 Tg C‐CO2, and 0.3 ± 0.04 Tg N‐N2O; in contrast, C, N, and S annual sequestration rates are estimated to be 576 ± 88.1 Tg C, 20 ± 4.4 Tg N, and 7.4 ± 0.8 Tg S, between 2000 and 2017. N fertilization inputs were responsible for 13% N2O emissions in wetlands in the Northern Hemisphere, while tropical wetlands were major reservoirs for C, N, and S. Temperature, net primary productivity, and methanogenic microorganisms exert the major control on GHG emissions. Wetland CH4 and CO2 emissions were found to have a hysteretic relationship with seasonal soil temperature, but not N2O. A global‐scale assessment is pivotal for best nutrient management practices, reducing nutrient losses, and controlling gas emissions.

Exploring the structure and dynamics of proteins in soil organic matter.

Alongside inorganic materials, water, and air, soil organic matter (SOM) is one of the major components of soil and has tremendous influence on the environment given its vital role in the carbon cycle. Many soil dwelling organisms like plants, fungi and bacteria excrete proteins, whose interaction with SOM is poorly understood on an atomistic level. In this study, molecular dynamics simulations were used to investigate selected proteins in soil models of different complexity from simple co‐solvent molecules to Leonardite humic acids (LHA). We analyzed the proteins in terms of their structural stability, the nature and strength of the interactions with their surroundings, as well as their aggregation behavior. Upon insertion of proteins in complex SOM models, their structural stability decreased, although no unfolding or disruption of secondary structure was observed. The interactions of proteins and SOM were primarily governed by electrostatic forces, often in form of hydrogen bonds. However, also weaker van der Waals forces made a significant contribution to the total interaction energies. Moreover, we showed that even though the molecular structure and size of SOM molecules varied, the functional groups of SOM ordered around the protein in a similar pattern. Finally, the number of aggregates formed by proteins and SOM molecules was shown to be primarily proportional to the size of the latter. Strikingly, for varying protein net charges no changes in the formation of aggregates with the strongly negatively charged LHA were observed.

Predicting soil organic matter nitrogen mineralization

AbstractThe objectives of this study were to develop a model for soil organic matter (SOM) N mineralization that required minimal inputs, to test the model using independent datasets, and to determine if the results could be used in adjusting commercial N fertilizer recommendations. A first‐order model was adjusted monthly for differences in soil temperature and moisture. A daily time step was used to allow N mineralization estimates from one date to another. Simulations began in the month where temperature exceeded 10 °C. Model predictions were compared with those in published studies in northern California and Illinois. In the first California study, model predictions were near observed values in four of five locations. In the second California study, model predictions were within the 95% confidence interval of those based on laboratory incubation corrected for field temperature and moisture regimes. In a third study, unfertilized corn (Zea mays L.) yields were compared with seasonal N mineralization predictions plus N from the prior crop for 15 Illinois soils. The result was 32.0 kg corn kg−1 seasonal available N as compared with the typical value of 67.5 kg corn kg−1 commercial N fertilizer. Nitrogen mineralization predictions for Illinois soils were related to total N, suggesting that an equation could replace simulation modeling for a given soil–location combination. It is proposed that the N mineralization model, which has readily obtainable inputs (total N, bulk density, monthly weather), could be used in conjunction with field studies to refine commercial N fertilizer recommendations.