Geoderma最新文献_第9页

Soil total carbon as a key factor affects soil biota attributes in plant mixtures over time: A meta-analysis 土壤总碳是影响植物混合土壤生物群属性的关键因素：一项荟萃分析

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117125

Huiling Zhang , Jinshan Cai , Xu Yang , Jing Huang , Xuan Zhou , Dima Chen

Studies have determined that soil biota have distinct responses to plant richness. However, the potential mechanisms that regulate soil biota (microbes and fauna) attributes (biomass, activity, and abundance) to plant mixtures over experimental time are still unclear. By conducting 1594 paired observations of the impacts of plant mixture on soil biota attributes and its corresponding potential drivers from 179 studies, we found that plant above- and belowground biomass and total biomass were significantly increased by 35.0%, 52.9%, and 48.6% under plant mixture, respectively. Soil pH decreased significantly by 0.8% with experimental time. The responses of soil microbial attributes were more sensitive than soil fauna abundances under plant mixture over time. On average, soil microbial respiration and microbial biomass increased by 11.6% and 12.1%, respectively, in plant mixtures across all ecosystem types. For soil fauna community, only the abundance of herbivores showed a significant increase of 20.4% to plant mixtures. The response of above- and belowground biomass, total biomass, the ratio of carbon to nitrogen, and pH showed positive relationships with most specific microbial attributes, while mean annual precipitation, mean annual temperature, and the response of soil total nitrogen and NO₃^–-N showed negative relationships with them in response to plant mixtures. The abundance of soil fauna was secondarily affected by the changes of soil abiotic properties. Taken together, the response of soil total carbon had a strong effect on soil biota attributes. Changes in belowground biomass and total biomass showed negative relationships with specific soil fauna abundance, while soil total carbon, nitrogen, pH, and soil moisture showed positive relationships with specific soil fauna abundance. However, only herbivore abundance showed significant differences across different ecosystems. Our analysis illustrates the distinct responses of soil biota attributes to plant mixtures and their potential influencing factors, thereby benefiting the sustainability of soil biota biodiversity in the face of plant richness loss.

研究已经确定土壤生物群对植物丰富度有不同的响应。然而，在实验时间内调节土壤生物群（微生物和动物）属性（生物量、活性和丰度）对植物混合物的潜在机制仍不清楚。通过179项研究中1594项植物混合处理对土壤生物群属性的影响及其潜在驱动因素的配对观测，我们发现植物混合处理使土壤地上生物量、地下生物量和总生物量分别显著增加了35.0%、52.9%和48.6%。随着试验时间的延长，土壤pH值显著下降0.8%。混合植物对土壤微生物属性的响应比土壤动物丰度的响应更敏感。在所有生态系统类型的植物混合中，土壤微生物呼吸和微生物生物量平均分别增加了11.6%和12.1%。在土壤动物群落中，只有草食动物的丰度比混合植物显著增加20.4%。地上、地下生物量、总生物量、碳氮比和pH与大多数特定微生物属性呈正相关，而年平均降水量、年平均气温以及土壤全氮和NO3——N对植物混合的响应与它们呈负相关。土壤动物的丰度受土壤非生物性质变化的次生影响。综上所述，土壤全碳的响应对土壤生物群属性有很强的影响。地下生物量和总生物量的变化与特定土壤动物丰度呈负相关，土壤全碳、全氮、pH和土壤水分的变化与特定土壤动物丰度呈正相关。不同生态系统间只有草食动物丰度存在显著差异。本研究揭示了土壤生物群属性对植物混合的不同响应及其潜在影响因素，从而有利于在植物丰富度丧失的情况下土壤生物群多样性的可持续性。

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引用次数: 0

Bedrock modulates the elevational patterns of soil microbial communities 基岩调节了土壤微生物群落的海拔格局

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117136

Xianjin He , Ruiqi Wang , Daniel S. Goll , Laurent Augusto , Naoise Nunan , M.D. Farnon Ellwood , Quanzhou Gao , Junlong Huang , Shenhua Qian , Yonghua Zhang , Zufei Shu , Buhang Li , Chengjin Chu

Elevational gradients are often used to reveal how soil microorganisms will respond to climate change. However, inconsistent microbial distribution patterns across different elevational transects have raised doubts about their practical applicability. We hypothesized that variations in bedrock, which influence soil physical and chemical properties, would explain these inconsistencies. We therefore investigated soil microbial communities (bacterial and fungal) along two adjacent elevational transects with different bedrocks (granite vs. slate) in a subtropical forest. Our findings reveal that soil microbial communities are shaped by complex interactions between bedrock type and environmental factors along elevational gradients. Bacterial biomass was higher on slate, whereas fungal biomass was higher on granite. On granite, both bacterial and fungal biomass increased with elevation, whereas divergent patterns were observed on slate, likely due to the distinct soil properties or combinations of properties influencing microbial biomass on each bedrock. Bedrock and elevation strongly influenced microbial beta-diversity, with beta-diversity on granite driven primarily by soil total phosphorus and moisture, and on slate by soil organic carbon and pH. In contrast, alpha-diversity was impacted less by bedrock and elevation, but its relationship with environmental factors varied markedly between bedrock types. Overall, our results highlight the critical influence of bedrock in determining soil microbial community structure along elevational gradients and their potential responses to climate change.

高程梯度通常被用来揭示土壤微生物将如何应对气候变化。然而，不同海拔横断面上不一致的微生物分布模式使人们对其实际应用性产生了怀疑。我们假设，影响土壤物理和化学性质的基岩变化可以解释这些不一致。因此，我们对亚热带森林中基岩（花岗岩和板岩）不同的两个相邻海拔断面的土壤微生物群落（细菌和真菌）进行了调查。我们的研究结果表明，土壤微生物群落是由基岩类型和海拔梯度环境因素之间复杂的相互作用形成的。板岩上的细菌生物量较高，而花岗岩上的真菌生物量较高。在花岗岩上，细菌和真菌的生物量都随着海拔的升高而增加，而在板岩上则观察到不同的模式，这可能是由于每种基岩上影响微生物生物量的不同土壤特性或特性组合造成的。基岩和海拔对微生物的贝塔多样性有很大影响，花岗岩上的贝塔多样性主要受土壤总磷和水分的影响，而板岩上的贝塔多样性则受土壤有机碳和 pH 值的影响。相比之下，α-多样性受基岩和海拔的影响较小，但其与环境因素的关系在不同基岩类型之间存在明显差异。总之，我们的研究结果凸显了基岩在决定海拔梯度土壤微生物群落结构及其对气候变化的潜在响应方面的重要影响。

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引用次数: 0

Comparison of three quantification methods used to detect biochar carbon migration in a tropical soil: A 4.5-year field experiment in Zambia 用于检测热带土壤中生物炭碳迁移的三种量化方法的比较：赞比亚4.5年的田间试验

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117153

Jing Lyu , Alfred Obia , Gerard Cornelissen , Jan Mulder , Andreas Botnen Smebye , Andrew R. Zimmerman

Understanding the stability and movement of biochar in soil is pivotal for its effective use in soil improvement and carbon sequestration projects. Building on a previous study that evaluated the migration of three size fractions of maize biochar carbon (BC) after 4.5 years in a Zambian loamy sand soil using δ¹³C isotopes, this study compares the results with those using chemothermal oxidation (CTO) and benzene polycarboxylic acid (BPCA) biomarkers. While the δ¹³C method registered the most BC in the application layer (0–7 cm), it detected less BC in lower layers (7–30 cm, 3.2–7.9 % downward migration), and with a greater variance, than the other two methods. The BPCA method detected relatively more BC in the lower layers (9.1–20.2 % downward migration), particularly for fine-sized biochar. It also detected the most BC in the control soil plot and outside the experimental block, which suggests either its efficiency in fine biochar detection or an issue with false positive detection. The CTO method, though less sensitive in detecting fine biochar particle BC, was strongly correlated with δ¹³C isotope results, thus representing a cost-effective and simpler alternative to the other BC quantification methods. These findings underscore the necessity of methodological consideration in biochar C quantification to ensure accurate assessment of its distribution and stability. This is a pressing need for correct assignment of climate mitigation credits. More field studies should be carried out involving multiple biochar types and quantification methods to refine our understanding of biochar C dynamics in soil.

了解生物炭在土壤中的稳定性和运动对其在土壤改良和固碳工程中的有效利用至关重要。先前的一项研究利用δ13C同位素评估了玉米生物炭碳（BC）在赞比亚壤土中4.5年后的三种大小组分的迁移，该研究在此基础上，将结果与使用化学热氧化（CTO）和苯多羧酸（BPCA）生物标志物的结果进行了比较。δ13C法在表层（0 ~ 7 cm）测得的BC最多，而在下层（7 ~ 30 cm）测得的BC较少，向下迁移3.2% ~ 7.9%，且差异较大。BPCA方法在较低的层中检测到相对较多的BC（向下迁移的9.1 - 20.2%），特别是对于细粒度的生物炭。它在对照土壤地块和实验地块外检测到最多的BC，这表明它在精细生物炭检测方面的效率很高，或者存在假阳性检测的问题。CTO方法虽然在检测细颗粒生物炭BC时灵敏度较低，但与δ13C同位素结果密切相关，因此代表了一种成本效益高且更简单的BC定量方法。这些发现强调了在生物炭C定量中考虑方法学的必要性，以确保准确评估其分布和稳定性。这是正确分配气候缓解信用额度的迫切需要。需要开展更多涉及多种生物炭类型和量化方法的实地研究，以完善我们对土壤中生物炭C动态的理解。

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引用次数: 0

Machine learning-based pseudo-continuous pedotransfer function for predicting soil freezing characteristic curve 基于机器学习的拟连续土壤传递函数预测土壤冻结特征曲线

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117145

Sangyeong Park , Yongjoon Choe , Hangseok Choi , Khanh Pham

Unfrozen water plays a crucial role in thermophysical processes occurring in frozen ground. Measurement difficulties require approximate approaches to describe the relationship between unfrozen water content (θ) and soil temperature, known as soil freezing characteristic curve (SFCC). Despite significant progress, model characteristics, freezing-thawing hysteresis, and phase equilibrium remain challenging. This study developed an alternative approach to estimate θ using a pedotransfer function (PTF) implemented with extreme gradient boosting (XGB). The XGB-PTF model was trained using SFCC data available in the literature, and cooperative game theory was utilized to assess potential impacts on θ predictions. The performance of the XGB-PTF was rigorously evaluated and compared with two high-performance empirical models. Significant reductions in root mean square error and mean absolute error of 42% and 55%, respectively, demonstrated the superiority of the XGB-PTF. The XGB-PTF’s usability was also verified by experimental validation. A notable advantage of the proposed model is its capacity to provide a credible range containing the actual θ with a 95% confidence level. Coupling the XGB-PTF with game theory indicated that the primary factors influencing the SFCC were in order of porosity (n), initial saturation degree (S_r), and clay fraction (F_clay) for fine-grained soils, while for coarse-grained soils, the order is F_clay, n, and S_r. Furthermore, insights derived from game theory aligned with previous experimental studies concerning the phase transition of pore water across various temperature ranges. The proposed XGB-PTF, with its straightforward predictors, efficiency, and transparency, is expected to serve as a versatile tool for advancing SFCC studies.

未冻水在冻土热物理过程中起着至关重要的作用。测量困难需要近似的方法来描述未冻水含量（θ）和土壤温度之间的关系，称为土壤冻结特性曲线（SFCC）。尽管取得了重大进展，但模型特性、冻融滞后和相平衡仍然具有挑战性。本研究开发了一种使用极端梯度增强（XGB）实现的pedotransfer函数（PTF）来估计θ的替代方法。XGB-PTF模型使用文献中的SFCC数据进行训练，并利用合作博弈论评估对θ预测的潜在影响。对XGB-PTF的性能进行了严格的评估，并与两种高性能的经验模型进行了比较。均方根误差和平均绝对误差分别显著降低42%和55%，证明了XGB-PTF的优越性。XGB-PTF的可用性也通过实验验证。所提出的模型的一个显著优点是它能够以95%的置信水平提供包含实际θ的可信范围。将XGB-PTF与博弈论相结合表明，细粒土的孔隙度(n)、初始饱和度（Sr）、粘粒分数（Fclay）依次为影响孔隙水相变的主要因素，粗粒土的影响因素依次为Fclay、n、Sr。此外，博弈论的结论与前人关于孔隙水在不同温度范围内相变的实验研究相一致。提出的XGB-PTF具有直接的预测、效率和透明度，有望成为推进SFCC研究的通用工具。

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引用次数: 0

Unraveling edaphic, environmental, and management drivers of soil microbial communities via ester-linked fatty acid methyl esters using a multilocation agroecosystem study 利用多地点农业生态系统研究，通过酯链脂肪酸甲酯揭示土壤微生物群落的土壤、环境和管理驱动因素

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117158

Wayne R. Roper , Verónica Acosta-Martínez , Kristen S. Veum , Christopher J. Burgess , Jennifer M. Moore , Daniel K. Manter , Catherine E. Stewart , Bryan D. Emmett , Mark A. Liebig , Matthew H.H. Fischel , R.Michael Lehman , José G. Franco , Jane M.F. Johnson , Sharon Weyers , Maysoon M. Mikha , Kristin M. Trippe , Jude E. Maul , Robert S. Dungan , Hero T. Gollany , Thomas F. Ducey , Catherine L. Reardon

Microbial communities are essential to soil functions within agroecosystems. Understanding interactions between agricultural management and soil biological properties is important for sustainability, however, broadscale inferences on these interactions are challenged by differences in site-specific characteristics. To identify the effects of conservation management on soil microbial communities, we conducted a multi-location study of 15 sites across the United States, which varied in crop management strategies and climate and edaphic characteristics. Microbial community composition was assessed by ester-linked fatty acid methyl esters (EL-FAME) with biomarkers for gram-negative bacteria, gram-positive bacteria, actinobacteria, saprotrophic fungi, and arbuscular mycorrhizal fungi. Among the edaphic characteristics considered in this study, soil organic C (SOC) was more correlated with EL-FAME than pH and clay content. Reduced tillage, cover cropping, and manure increased total EL-FAME and SOC, whereas crop diversity had no significant effect. Abundance of bacterial fatty acid biomarkers had stronger relationships to SOC (r² = 0.64–0.65) than fungal biomarkers (r² < 0.23), but fungi exhibited more sensitivity to management than bacteria. Though some fatty acids were sensitive to management across locations, manure had the overall largest effect on EL-FAMEs. This study revealed a strong response of the microbial community to conservation management practices regardless of location, but the magnitude differed across locations. Additionally, SOC and moisture deficit were key drivers of site-specific responses. Our multilocation study supports the utility of EL-FAMEs as an important soil health indicator that should be considered in national soil health assessments.

微生物群落对农业生态系统中的土壤功能至关重要。了解农业管理和土壤生物特性之间的相互作用对可持续性很重要，然而，对这些相互作用的广泛推断受到场地特定特征差异的挑战。为了确定保护管理对土壤微生物群落的影响，我们在美国各地的15个地点进行了多地点研究，这些地点在作物管理策略和气候和土壤特征方面存在差异。微生物群落组成通过酯链脂肪酸甲酯（EL-FAME）与革兰氏阴性菌、革兰氏阳性菌、放线菌、腐养真菌和丛枝菌根真菌的生物标志物进行评估。在本研究考虑的土壤特征中，土壤有机碳（SOC）与EL-FAME的相关性大于pH和粘土含量。减少耕作、封种和施肥可提高土壤有机碳和EL-FAME总量，而作物多样性对土壤有机碳的影响不显著。细菌脂肪酸生物标志物丰度与SOC的关系（r2 = 0.64-0.65）强于真菌生物标志物(r2 <；0.23)，但真菌对管理的敏感性高于细菌。虽然一些脂肪酸对不同地点的管理很敏感，但粪便对EL-FAMEs的总体影响最大。该研究揭示了不同地点的微生物群落对保护管理措施的强烈响应，但不同地点的响应程度不同。此外，土壤有机碳和水分亏缺是土壤特异响应的关键驱动因素。我们的多地点研究支持EL-FAMEs作为一个重要的土壤健康指标的效用，应该在国家土壤健康评估中加以考虑。

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引用次数: 0

The fusion of machine olfactory data and UV–Vis-NIR-MIR spectra enabled accurate prediction of key soil nutrients 机器嗅觉数据和UV-Vis-NIR-MIR光谱的融合使关键土壤养分的准确预测成为可能

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117161

Shuyan Liu , Lili Fu , Xiaomeng Xia , Jiamu Wang , Yvhang Cao , Xinming Jiang , Honglei Jia , Zengming Feng , Dongyan Huang

Conventional approaches for evaluating soil nutrients typically involved lengthy and resource-intensive analytical procedures, rendering them inadequate for large-scale and high-throughput testing. To address these limitations, this study proposed an innovative solution based on sensor data fusion to predict the content of key soil nutrients. The proposed methodology entailed collecting olfactory data after soil pyrolysis using gas sensors and spectral data from soil samples utilizing ultraviolet–visible-near infrared (UV–Vis-NIR) and mid-infrared (MIR) techniques. Three fusion strategies including series and parallel modes were designed to effectively amalgamate the gathered data and supplemented with machine learning algorithms to predict the content of key soil nutrients. Tested a testing set consisting of 33 soil samples. The findings demonstrated that introducing a self-attention procedure into the series splicing fusion strategy significantly improved the predictive performance. This highlights the synergistic benefits of integrating information from olfactory and spectral data sources. Predicting multiple nutrient contents within the framework of the multi-layer perceptron combined with random forest (MLP-RF) fusion model showed superior performance, with the coefficient of determination (R²) ranging from 0.80 to 0.96. The predictive validity for the content of fundamental nutrients and available nutrients in the soil can benefit from the combination of biological and structural information captured by olfactory data and chemical information provided by spectroscopy.

评估土壤养分的传统方法通常涉及冗长和资源密集的分析程序，使其不适合大规模和高通量的测试。为了解决这些限制，本研究提出了一种基于传感器数据融合的创新解决方案来预测关键土壤养分的含量。提出的方法包括利用气体传感器收集土壤热解后的嗅觉数据，以及利用紫外-可见-近红外（UV-Vis-NIR）和中红外（MIR）技术收集土壤样品的光谱数据。设计了串联和并行三种融合策略，有效融合收集到的数据，并辅以机器学习算法来预测关键土壤养分的含量。测试了一个由33个土壤样品组成的测试集。研究结果表明，在序列拼接融合策略中引入自关注过程可显著提高预测性能。这突出了从嗅觉和光谱数据源整合信息的协同效益。多层感知器结合随机森林（MLP-RF）融合模型对多种养分含量的预测效果较好，决定系数（R2）在0.80 ~ 0.96之间。嗅觉数据捕获的生物和结构信息与光谱学提供的化学信息相结合，可以提高土壤中基本养分和速效养分含量的预测有效性。

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引用次数: 0

Driving mechanisms of taxonomic and functional community composition of Collembola during subalpine succession 亚高山演替中弹线虫分类和功能群落组成的驱动机制

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117156

Yan Zhang , Ajuan Zhang , Zheng Zhou , Ting-Wen Chen , Xueyong Pang , Stefan Scheu

Plant succession dramatically alters both aboveground vegetation and belowground conditions, impacting the organisms residing in the soil. However, the extent to which the taxonomic and functional community composition of soil animals is shaped by the same biotic and environmental factors and their relative importance remains unclear. Here, we considered plant community characteristics, abiotic soil factors, and food-web factors as potential drivers for the taxonomic and functional community composition (based on life forms) of Collembola during plant succession in the subalpine region of southwest China. Our results show that Collembola abundance and richness were lower in grassland, shrubland, and primary forest compared to secondary forest (birch forest). Temperature and moisture were identified as pivotal factors influencing Collembola fitness in grassland, while soil pH was a key factor in primary forest. Overall, abiotic soil factors (i.e., pH, C/N, and temperature), played predominant roles in shaping both the taxonomic and functional community composition of Collembola. Plant community characteristics (i.e., plant richness and litter biomass) were subdominant drivers in structuring functional community composition. By contrast, food-web factors (i.e., fungal biomass and fungi-to-bacteria ratio as bottom-up factors, and predatory mites as top-down factor) exerted a minor impact. Further, functional community composition was generally more closely related to variations in soil abiotic factors and plant community traits than taxonomic community composition. These findings highlight the priority importance of soil abiotic factors over plant community characteristics and food web factors in structuring soil mesofauna communities and emphasize the importance of trait-based approaches for understanding the mechanisms underlying soil animal communities.

植物演替极大地改变了地上植被和地下条件，影响了居住在土壤中的生物。然而，土壤动物的分类和功能群落组成在多大程度上受到相同的生物和环境因素的影响，以及它们的相对重要性尚不清楚。在此，我们认为植物群落特征、非生物土壤因子和食物网因子是影响西南亚高山地区线虫在植物演替过程中分类和功能群落组成（基于生命形式）的潜在驱动因素。结果表明：与次生林（白桦林）相比，草地、灌丛和原生林中弹线虫的丰度和丰富度均较低；温度和湿度是影响草地弹虫适宜性的关键因素，土壤pH是影响原生林弹虫适宜性的关键因素。总体而言，非生物土壤因子（pH、C/N和温度）在线虫的分类和功能群落组成中起主导作用。植物群落特征（即植物丰富度和凋落物生物量）是构建功能群落组成的次显性驱动因素。相比之下，食物网因子（即真菌生物量和真菌细菌比为自下而上因子，掠食性螨为自上而下因子）的影响较小。功能群落组成与土壤非生物因子和植物群落性状的关系比分类群落组成更密切。这些发现强调了土壤非生物因子在构建土壤中游动物群落中的重要性，而不是植物群落特征和食物网因子，并强调了基于性状的方法对理解土壤动物群落机制的重要性。

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引用次数: 0

Separating fast from slow cycling soil organic carbon – A multi-method comparison on land use change sites 快循环和慢循环土壤有机碳的分离——土地利用变化地点的多方法比较

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117154

Marcus Schiedung , Pierre Barré , Christopher Peoplau

Soil organic carbon (SOC) is significantly affected by land use change (LUC). Consequently, LUC is a major controlling factor of total SOC contents and SOC pool dynamics. Several methods have been developed to assess distinct SOC pools, which includes particle size separation, thermal analysis and soil reflectance mid-infrared spectroscopy. All of which are considered to have a potential as high through put methods to generate large datasets. Here, we used 23 sites covering six different types of LUC to assess differences in fast and slow cycling SOC derived from three approaches. We used i) particle size fractionation to obtain coarse (>50 µm) and fine (<50 µm) SOC fractions; ii) thermal Rock-Eval® 6 analysis in compilation with the PARTY_SOCv2.0_EU model to estimate active and stable SOC pools and iii) mid-infrared spectroscopy to determine the relative SOC composition and derive fast (aliphatic compounds) and slow (aromatic/carboxylic compounds) cycling SOC pools. The particle size SOC fractions and thermal SOC pools showed similar dynamics but differed substantially in the magnitude with LUC. The fine SOC fraction contained around two-thirds of the total SOC across all land uses and was strongly responsive by nearly matching the relative changes of total SOC (slope of 0.76 and R² = 0.91). Therefore, the fine fraction SOC might be more dynamic than considered until now. In comparison, the stable SOC pool calculated using PARTY_SOCv2.0_EU was less responsive to the relative changes (slope of 0.43 and R² = 0.72) and contained around 40 % of the total SOC. This underlines that both physical and thermal approaches separate biogeochemically distinct pools. The qualitative assessment by mid-infrared spectroscopy related well to the thermal SOC pools but not to the particle size fractions. The initial land-use SOC composition, as a ratio of the corresponding fast and slow cycling SOC pool, can be a suitable predictor for SOC evolution. This was particularly true for thermal and mid-infrared spectroscopy derived SOC pools. We show that three conceptually different methods (physical, thermal and mid-infrared spectroscopic) are suitable to determine SOC pool changes for a large diversity of LUC, but the sensitivity of the individual pools can differ strongly, depending on the method.

土壤有机碳（SOC）受土地利用变化（LUC）影响显著。因此，土壤有机碳含量是土壤有机碳总含量和有机碳池动态的主要控制因素。目前已经开发了几种方法来评估不同的有机碳库，包括粒度分离、热分析和土壤反射率中红外光谱。所有这些都被认为具有作为高吞吐量方法生成大型数据集的潜力。在这里，我们使用了覆盖6种不同类型LUC的23个站点来评估三种方法得出的快循环和慢循环SOC的差异。我们使用i)粒度分馏获得粗（>50µm）和细（<50µm） SOC分数；ii)使用PARTYSOCv2.0EU模型编译热Rock-Eval®6分析，以估计活跃和稳定的SOC池；iii)中红外光谱测定相对SOC组成，并得出快速（脂肪族化合物）和缓慢（芳香/羧基化合物）循环的SOC池。颗粒级有机碳组分和热有机碳池的动态变化与陆面碳变化相似，但在量级上存在较大差异。在所有土地利用中，土壤有机碳精细组分约占总有机碳的三分之二，对土壤有机碳的相对变化响应强烈（斜率为0.76,R2 = 0.91）。因此，精细组分SOC可能比目前所认为的更具动态性。相比之下，使用PARTYSOCv2.0EU计算的稳定SOC池对相对变化的响应较小（斜率为0.43,R2 = 0.72），约占总SOC的40%。这强调了物理和热方法将不同的生物地球化学池分开。中红外光谱定性评价与热固碳池相关，但与颗粒级别无关。初始土地利用有机碳组成作为相应的快循环和慢循环有机碳库的比值，可以作为土壤有机碳演变的合适预测因子。对于热光谱和中红外光谱衍生的SOC池来说尤其如此。我们发现三种概念上不同的方法（物理、热光谱和中红外光谱）适用于确定LUC多样性大的SOC池变化，但单个池的灵敏度可能存在很大差异，这取决于方法。

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引用次数: 0

Airborne radiometric data for digital soil mapping of peat at broad and local scales 大尺度和局部尺度泥炭数字土壤制图的航空辐射测量数据

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117129

Dave O’Leary , Colin Brown , Jim Hodgson , John Connolly , Louis Gilet , Patrick Tuohy , Owen Fenton , Eve Daly

Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.

泥炭土的土壤有机质（SOM）含量高，是公认的碳储存地。泥炭土的空间分布已成为许多研究的焦点，并与泥炭地制图密切相关。泥炭土的精确地图有许多具有国际重要性的应用，例如，气体排放清单报告或土壤有机碳储量核算。传统的测绘方法包括原位土壤螺旋钻取样或泥炭探测（深度），而现代方法也包括卫星数据（光学和雷达）。然而，这两种方法都有局限性。传统的采样往往忽略了泥炭土和矿质土壤之间的边界和过渡区，而卫星数据只测量地表，可能无法穿透土地覆盖层，从而可能忽略了泥炭地下的区域，例如草地或森林。辐射测量学是对自然产生的伽马辐射的测量。泥炭土由于土壤含水量高而减弱了这种辐射。对于目前在爱尔兰的研究，对多年来获得的网格化航空辐射数据进行监督分类，使用神经网络模式识别来识别泥炭和非泥炭土壤区域。分类置信值用于确定这些土壤类型之间的过渡区，提供这种过渡的简化可视化。使用爱尔兰的点火损失率（LOI %）点数据和几个不同的（毯状沼泽、凸起沼泽、过渡区）地点进行验证，表明分类数据可以检测到从广泛到局部尺度的泥炭土的存在。航空地球物理方法，特别是航空辐射测量法，可以通过提供统一的数据和客观分析，弥补点测量精度与卫星数据空间覆盖之间的差距，从而识别泥炭土。由此产生的地图是了解爱尔兰泥炭土真实空间分布的一步，包括过渡区。

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引用次数: 0

Climate and gypsum parent material shape biocrust communities and moss ecology in the Chihuahuan and Mojave Deserts 气候和石膏母质塑造了奇瓦瓦沙漠和莫哈韦沙漠的生物群落和苔藓生态学

IF 5.6 1区农林科学 Q1 SOIL SCIENCE

Geoderma

Pub Date : 2025-01-01 DOI: 10.1016/j.geoderma.2024.117131

Katelyn G. Gobbie , Nicole Pietrasiak , Brian M. Jusko , Rebecca E. Drenovsky

Biological soil crust communities (biocrusts) establishing on gypsum soils have been well-documented for their prolific appearance and rich diversity of lichens and bryophytes. However, studies characterizing gypsum biocrusts have occurred primarily outside of the U.S., most of which lack comparisons to other soil types. We conducted intensive field surveys to evaluate the ground cover and frequency of biocrust functional groups and moss species on gypsum and non-gypsum soils in the U.S. regions with the most extensive gypsum outcrops, the northern Chihuahuan and eastern Mojave Deserts. Study sites were stratified by geomorphology and paired, so that every gypsum site was matched with a non-gypsum site in the same region. We employed canonical correspondence analysis (CCA) to relate the observed differences in biocrust abundance and composition across soil types to distinct environmental variables. Additionally, we assessed species richness of biocrust mosses on gypsum versus non-gypsum soils, as well as in the Chihuahuan versus Mojave Deserts. Our results indicate that differences in biocrust communities on gypsum and non-gypsum soils are predominantly due to gypsum’s profuse dark algal (mostly cyanobacteria-formed) rather than lichen and moss biocrusts in these two hot desert biomes. Biocrust functional groups did not exhibit distinct associations with environmental variables. However, moss species appear to be strongly influenced by environmental variables and exhibited differential preferences for substrate parent material. Moss species richness was greater on gypsum soils and, surprisingly, in the hottest and driest North American Desert, the Mojave. Differences in species richness across deserts were strongly correlated to mean annual and seasonal temperatures, as well as mean winter precipitation. Overall, our data suggest that environmental and climate conditions all play important roles in the ecology of biocrusts, specifically moss diversity and distribution, in the northern Chihuahuan and eastern Mojave Deserts of the U.S. More importantly, we emphasize that gypsum soils of the U.S. are unique refugia for moss-forming biocrusts.

建立在石膏土上的生物土壤结皮群落（biocrusts）因其丰富的外观和丰富的地衣和苔藓植物多样性而得到了充分的研究。然而，表征石膏生物结壳的研究主要发生在美国以外，其中大多数缺乏与其他土壤类型的比较。我们对美国奇瓦瓦北部和莫哈韦沙漠东部石膏和非石膏土壤进行了深入的实地调查，以评估石膏露头最广泛的地区，石膏和非石膏土壤上的地表覆盖、生物结皮功能群和苔藓物种的频率。研究地点按地貌分层并配对，使每个石膏遗址与同一地区的非石膏遗址相匹配。我们采用典型对应分析（CCA）将不同土壤类型生物结皮丰度和组成的差异与不同的环境变量联系起来。此外，我们还评估了石膏土壤和非石膏土壤以及奇瓦瓦沙漠和莫哈韦沙漠中生物结皮苔藓的物种丰富度。我们的研究结果表明，石膏和非石膏土壤的生物结壳群落差异主要是由于石膏中大量的暗藻（主要是蓝藻形成的）而不是地衣和苔藓生物结壳。生物外壳功能基团与环境变量的关系不明显。然而，苔藓物种似乎受环境变量的强烈影响，并表现出对基质母质的不同偏好。苔藓物种丰富度在石膏土壤上更大，令人惊讶的是，在最热和最干燥的北美沙漠莫哈韦沙漠。荒漠物种丰富度的差异与年平均气温、季节平均气温以及冬季平均降水量密切相关。总体而言，我们的数据表明，环境和气候条件在美国奇瓦瓦沙漠北部和莫哈韦沙漠东部的生物结皮生态学中发挥了重要作用，特别是苔藓的多样性和分布。更重要的是，我们强调美国石膏土是苔藓形成生物结皮的独特避难所。

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引用次数: 0