Article
Geosciences, Multidisciplinary
Muhammad Abdul Munnaf, Abdul Mounem Mouazen
Summary: External factors can negatively affect the accuracy of predicting soil organic carbon using online visible and near-infrared spectroscopy. This study compared the performance of four algorithms and found that EPO and OSC models provided better prediction accuracy.
Article
Soil Science
Songchao Chen, Jie Xue, Zhou Shi
Summary: Ensemble modelling (EM) is widely used in soil information prediction, and a new method called spectral-guided ensemble modelling (S-GEM) has been proposed to improve soil spectroscopic prediction. The results show that S-GEM outperforms EM in predicting soil properties using vis-NIR spectra. Therefore, S-GEM has a high potential to enhance the accuracy of soil spectroscopic prediction.
Article
Soil Science
Tong Lei, Da-Wen Sun
Summary: An attention-based spectra encoding-spectra/property decoding model was proposed for soil Vis-NIR spectral analysis to address the issue of inconsistent calibration models due to differences in instruments and measurement methods worldwide. By introducing the attention mechanism, the model can achieve joint calibration regardless of input spectral data length and soil sample type. The model design focuses on wavelengths combination and performed better than traditional PLSR and CNN encoding models on various datasets.
Article
Soil Science
Gafur Gozukara, Yakun Zhang, Alfred E. Hartemink
Summary: This study in the Driftless Area of Wisconsin focused on different parent materials in soils, specifically loess and terra rossa, using decision tree models and geochemical indicators. It was found that pXRF and vis-NIR spectra can accurately distinguish these parent materials, with pXRF spectra showing slightly higher overall accuracy.
Article
Soil Science
Ya Liu, Yuanyuan Lu, Danyan Chen, Wei Zheng, Yuxin Ma, Xianzhang Pan
Summary: The application of visible and near-infrared spectroscopy for predicting soil properties is cost-effective and time-efficient. Traditional methods focused on removing moisture effects from the spectra, resulting in the loss of valuable information. This study proposes a novel approach, using fractional-order derivative technique and one-dimensional convolutional neural network, to directly predict soil moisture and a range of soil properties.
Article
Soil Science
Songchao Chen, Hanyi Xu, Dongyun Xu, Wenjun Ji, Shuo Li, Meihua Yang, Bifeng Hu, Yin Zhou, Nan Wang, Dominique Arrouays, Zhou Shi
Summary: This study evaluated and compared the impact of two validation strategies on model performance at different scales, suggesting one-time random sampling or k-fold cross validation for large datasets and k-fold cross validation and/or repeated random sampling for small datasets would be more robust for spectral predictive model evaluation.
Article
Agriculture, Multidisciplinary
Gafur Gozukara, Sevda Altunbas, Orhan Dengiz, Alper Adak
Summary: Soil electrical conductivity (EC) and pH are crucial for managing agricultural productivity. This study investigated the impact of soil to water ratios and sampling strategies on predicting EC and pH using individual and combined visible near infrared (Vis-NIR) and portable X-ray fluorescence (pXRF) spectra. The results showed that using appropriate soil to water ratios and sampling strategies, individual Vis-NIR spectra can achieve the highest prediction accuracy for EC and pH.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Spectroscopy
Liusan Wang, Rujing Wang
Summary: In this study, the prediction of soil pH in lime concretion black soil using Vis-NIR spectroscopy and extreme learning machine (ELM) was investigated. By employing optimized continuous wavelet transform (CWT) and four spectral feature selection methods, the CARS-ELM method demonstrated superior prediction accuracy and computational efficiency, with the selected variables indicating the relationship between soil pH and iron oxides and organic matter.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Soil Science
Ernest Afriyie, Ann Verdoodt, Abdul M. Mouazen
Summary: This study explored the possibilities of estimating soil aggregate stability indices using vis-NIR spectroscopy and found that all three indices can be predicted with satisfactory accuracy from the models. This opens up opportunities for in-situ deployment of vis-NIR spectroscopy in soil protection and management.
SOIL & TILLAGE RESEARCH
(2022)
Article
Spectroscopy
Bo Yu, Changxiang Yan, Jing Yuan, Ning Ding, Zhiwei Chen
Summary: Visible and near-infrared (Vis-NIR) spectroscopy technique has been recognized as a cost-effective, rapid, non-destructive alternative to traditional soil physicochemical analysis to estimate soil properties. This study proposes a method to select characteristic wavelengths with optimal spectral resolution to improve the prediction performance. By using a 'two-step' wavelength selection method and the artificial bee colony (ABC) algorithm, a better prediction accuracy for soil properties was obtained compared to using full-spectra models.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2023)
Article
Soil Science
Meihua Yang, Songchao Chen, Dongyun Xu, Yongsheng Hong, Shuo Li, Jie Peng, Wenjun Ji, Xi Guo, Xiaomin Zhao, Zhou Shi
Summary: The large-scale soil spectral library (SSL) provides abundant information for predicting soil properties, but using SSL for predicting soil information from in situ spectra is still a challenge. This study compared different strategies for predicting soil organic matter (SOM) using SSL and found that the mean squared Euclidean distance (msd) is an optimal indicator for selecting representative samples. The recommended strategy depends on the availability of in situ and dry spectra. These findings contribute to efficient SOM prediction in situ by integrating large-scale SSL.
Article
Soil Science
Yuanyuan Yang, Qianqian Chen, Wu Yu, Zhou Shi
Summary: This study aims to analyze the spatial distribution of soil bacteria in Southeast Tibet, China using digital soil mapping technology. Multiple statistical and machine learning algorithms were used, and the results showed that convolutional neural networks performed the best in explaining the variations in bacterial abundance and diversity. Soil properties such as total nitrogen, carbon to nitrogen ratio, and temperature were found to be important factors regulating bacterial community distribution.
Article
Environmental Sciences
Anna A. Paltseva, Maha Deeb, Erika Di Iorio, Luana Circelli, Zhongqi Cheng, Claudio Colombo
Summary: The successful use of visible and near-infrared reflectance spectroscopy analysis combined with principal component regression and partial least-square regression was applied to detect different forms of lead. Linear discriminant analysis was used to classify soil into different categories of lead contamination risks. Different models were used to predict and compare total and bioaccessible lead concentrations.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Geosciences, Multidisciplinary
Gafur Gozukara, Mert Acar, Ekrem Ozlu, Orhan Dengiz, Alfred E. Hartemink, Yakun Zhang
Summary: A soil quality index (SQI) is used to manage soil for sustainable agriculture and ecology. This study characterized soil properties and SQI in a specific profile in Turkey, compared interpolation methods for mapping the distribution of soil properties and SQI, and evaluated the feasibility of using Vis-NIR and pXRF spectra to predict soil properties and SQI. The results showed significant variations of soil properties and SQI within the soil profile, with Vis-NIR having better prediction performances for some indicators and pXRF having better prediction performances for others. The combination of Vis-NIR and pXRF had the best prediction performance for SQI.
Article
Ecology
Timothy D'Angelo, Jacqueline Goordial, Melody R. Lindsay, Julia McGonigle, Anne Booker, Duane Moser, Ramunas Stepanauskus, Beth N. Orcutt
Summary: The phyla Nitrospirota and Nitrospinota have received significant research attention due to their unique nitrogen metabolisms. These phyla are common inhabitants of marine and terrestrial subsurface environments and have diverse physiologies. Basal clades primarily inhabit these subsurface environments and have smaller, more densely coded genomes compared to later-branching clades. Both phyla share many traits inferred to be present in their respective common ancestors. Later-branching groups have genome expansions that include gene clusters performing unique nitrogen metabolisms. Modern subsurface environments represent a genomic repository for ancestral metabolic traits.
Article
Biodiversity Conservation
Xiaowei Guo, Xiali Mao, Wu Yu, Liujun Xiao, Mingming Wang, Shuai Zhang, Jinyang Zheng, Hangxin Zhou, Lun Luo, Jinfeng Chang, Zhou Shi, Zhongkui Luo
Summary: Soil biogeochemical processes exhibit depth-dependent responses to climate change. This study presents an innovative and cost-effective approach of field incubation of intact soil cores to explore this depth dependence. The results indicate that soil respiration responds significantly to climate shifts induced by translocation experiments, but this response is independent of soil depth.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Multidisciplinary Sciences
Xinyi Li, Yuanyuan Yang, Haoming Li, Yangyang Jia, Zefan Liu, Zhou Shi, Chaofeng Shen
Summary: By combining single-cell isolation and resuscitation-promoting factor addition, potential low-temperature biphenyl-degrading bacteria were successfully isolated from the Qinghai-Tibet Plateau, providing microbial resources for the prevention and control of biphenyl pollution in the plateau and other cold areas.
CHINESE SCIENCE BULLETIN-CHINESE
(2023)
Article
Environmental Sciences
Xianglin Zhang, Jie Xue, Yi Xiao, Zhou Shi, Songchao Chen
Summary: Soil visible and near-infrared (Vis-NIR, 350-2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis. The study evaluated seven variable selection algorithms and three predictive algorithms in predicting soil properties using a regional soil Vis-NIR spectral library. The results showed that Cubist outperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties when using the full spectra. The study provides valuable insights for predicting soil information using spectroscopic techniques and variable selection algorithms.
Article
Environmental Sciences
Yefeng Jiang, Huading Shi, Lina Yi, Songchao Chen, Yin Zhou, Jieliang Cheng, Mingxiang Huang, Zhou Shi
Summary: By analyzing 188 peer-reviewed articles published between 2004 and 2022, it was found that potentially toxic elements in soils from industrial and mining sites in China pose a public health risk. The concentrations of eight elements, including As, Cd, Hg, and Pb, were significantly higher than background values, and a considerable proportion of the examined sites exceeded soil risk screening values. The study also demonstrated the ecological and health risks associated with these elements, highlighting the need for control measures.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Environmental Sciences
Bifeng Hu, Modian Xie, Hongyi Li, Rebin He, Yue Zhou, Yefeng Jiang, Wenjun Ji, Jie Peng, Fang Xia, Zongzheng Liang, Wanming Deng, Junjie Wang, Zhou Shi
Summary: The study investigates the spatio-temporal variation of soil nutrients and soil organic matter (SOM) in farmland over Jiangxi Province in Southern China. Based on a dataset of soil samples collected between 2005 and 2012, the study examines the changes in SOM, available nitrogen (N), phosphorus (P), potassium (K), pH, and cation exchange capacity. The results indicate significant temporal trends in the concentrations of SOM, available P, available N, and available K, with climate and soil management practices playing a dominant role in determining soil fertility.
JOURNAL OF SOILS AND SEDIMENTS
(2023)
Article
Horticulture
Ki-Ho Son, Han-Sol Sim, Jae-Kyoung Lee, Juhwan Lee
Summary: Leaf temperature sensing integrated into a conventional smart farm system was developed and demonstrated in two greenhouse farms in South Korea. Monitoring leaf temperatures allowed for optimization of photosynthetic efficiency and resulted in increased crop yield, particularly in the morning and at high leaf temperatures. The study also emphasized the need for leaf temperature models in smart greenhouse farming that consider nutrient and water supplies.
Article
Environmental Sciences
Xueyao Chen, Zhige Wang, Yulin Shangguan, Jie Yu, Bifeng Hu, Qiaohui Shen, Jie Xue, Xianglin Zhang, Zhou Shi
Summary: In this study, a machine learning model (Deep Forest) coupled with satellite data (TROPOMI and OMI) and meteorological datasets was used to estimate the surface ozone concentration in China during the COVID-19 pandemic. The results showed that the ozone concentration was higher in Eastern China and reached its peak in summer, with a significant decline in summer 2020 compared to summer 2019. The study demonstrated the reliability of TROPOMI data in estimating surface ozone concentration and provided insights into the formation mechanisms of surface ozone in China, which can support air environment management decision-making.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Biodiversity Conservation
Yanyu Wang, Wenqiang Wu, Hancheng Guo, Qianqian Chen, Hanyi Xu, Tieli Xie, Zhou Shi
Summary: Understanding past and present biodiversity patterns in China is crucial for development planning and biodiversity management. Satellite data analysis, specifically the Dynamic Habitat Indices (DHIs) using MODIS input, can effectively characterize the spatial distribution of species and support biodiversity conservation efforts. This study evaluated different surrogates for annual species richness in China and analyzed the trends and triggers of DHI variation from 2003 to 2018. The results showed that DHI Cum and DHI Min had strong explanatory power for estimating species richness, while DHI Var performed poorly. The best-performing individual DHI was GPP-DHI Cum. Trend analysis revealed a decrease in habitat appropriateness for species in areas with high suitability over time, while there was an increase in habitat appropriateness in areas with low suitability. The correlation factors for biodiversity variation varied spatially, with factors such as solar radiation, temperature, precipitation, and human activities influencing biodiversity changes differently in different regions. These findings contribute to a better understanding of biodiversity dynamics and can inform biodiversity conservation efforts and policy-making.
ECOLOGICAL INDICATORS
(2023)
Article
Ecology
Hongfen Teng, Songchao Chen, Bifeng Hu, Zhou Shi
Summary: Accurate detection and attribution of changes in global peak vegetation growth at the annual scale are essential. This study found widespread greening in response to climate warming, but climate change is not the sole cause of global greening. The study used the Cubist model to investigate the relationship between peak vegetation growth and environmental variables, showing that climate, atmospheric components, terrain properties, and soil properties all play a role in explaining greening/browning spatial variation. Future projections suggest enhanced vegetation greening globally, while browning changes are predicted in certain forest types and areas influenced by human land use.
ECOLOGICAL INFORMATICS
(2023)
Review
Environmental Sciences
Yadong Guo, Zhenzhong Zeng, Junjian Wang, Junyu Zou, Zhou Shi, Songchao Chen
Summary: This review provides a concise framework for understanding the impact of climate change on soil organic carbon (SOC) dynamics. While valuable insights have been gained, there are still knowledge gaps that need to be addressed. Future research should focus on standardizing organismal traits, SOC fractions, and the interactions and biochemical pathways of biological communities. By integrating multidisciplinary knowledge and utilizing new technologies and methodologies, the accuracy of models can be enhanced, providing a scientific foundation for mitigating climate change.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Soil Science
Jie Xue, Xianglin Zhang, Songchao Chen, Rui Lu, Zheng Wang, Nan Wang, Yongsheng Hong, Xueyao Chen, Yi Xiao, Yuxin Ma, Zhou Shi
Summary: This study investigates the potential of visible near-infrared and mid-infrared spectroscopy, as well as three model averaging methods, in predicting soil health properties. The results show that the combination of mid-infrared spectroscopy and the S-GEM model performs the best in predicting soil organic matter and pH.
Article
Green & Sustainable Science & Technology
Bifeng Hu, Modian Xie, Renbin He, Zhou Shi, Yin Zhou, Hanjie Ni, Hongyi Li
Summary: This study reveals the spatio-temporal heterogeneity of cropland ecosystem services and identifies its main driving factors in Jiangxi Province, China. The results show that the CESV has been steadily increasing over the past two decades, with vegetation and terrain factors contributing the most to its variation. Factors such as saturated soil hydraulic conductivity, clay content, elevation, slope, leaf area index, and vegetation cover fraction are positively correlated with CESV, while population density and nighttime light index are negatively correlated. This research provides important insights and guidance for sustainable cropland ecosystem management and development.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Environmental Sciences
Yuanyuan Yang, Qianqian Chen, Yin Zhou, Wu Yu, Zhou Shi
Summary: This study explored the biogeographic patterns and mechanisms of soil bacterial communities in maintaining soil carbon balance in an alpine timberline ecosystem in Southeast Tibet. The results showed significant differences in soil bacterial community composition and functions between shrubland and coniferous forest, which were related to soil temperature and C:N ratio. These differences can improve the predictive power of the carbon feedback process in terrestrial ecosystems.
JOURNAL OF SOILS AND SEDIMENTS
(2023)
Article
Environmental Sciences
Xianglin Zhang, Jie Xue, Songchao Chen, Nan Wang, Tieli Xie, Yi Xiao, Xueyao Chen, Zhou Shi, Yuanfang Huang, Zhiqing Zhuo
Summary: This study used Quantile Regression Forest to map the spatial distribution of soil organic carbon in cropland in the Northeast China Plain. The results showed that SOC increased overall from the southern area to the northern area, and decreased with depth. Climate, position, and organism were identified as the dominant controlling factors. Additionally, higher uncertainty was observed in certain areas.
Article
Green & Sustainable Science & Technology
Hongyi Li, Renbin He, Jie Hu, Yue Zhou, Modian Xie, Wanming Deng, Junjie Wang, Wanru Zhao, Shuangshuang Zhang, Yefeng Jiang, Zongzheng Liang, Lan Luo, Bifeng Hu, Zhou Shi
Summary: The aim of this study was to identify conservation priority zones (CPZs) and investigate the driving factors of ecosystem services (ESs) in order to prevent environmental degradation. Using biophysical models and linear regression, the spatiotemporal variation of typical ESs was assessed, CPZs were determined using ordered weighted averaging and conservation efficiency index, and the dual-factor interaction on ESs in CPZs was revealed using geo-detector. The study provides critical theoretical and practical support for the optimization and management of protected areas to improve ecosystem conservation.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Soil Science
C. Beraud, F. Piola, J. Gervaix, G. Meiffren, C. Creuze des Chatelliers, A. Delort, C. Boisselet, S. Poussineau, E. Lacroix, A. A. M. Cantarel
Summary: This study investigated the soil factors influencing the development of biological denitrification inhibition (BDI) and found that initial soil moisture, ammonium concentration, and the initial abundance of certain microbial genes play significant roles in BDI development. Additionally, the research highlighted the relevance of biotic factors in explaining BDI and proposed the use of procyanidin concentration from plant belowground system as a new proxy for measuring BDI intensity.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Yizhu Qiao, Tingting Wang, Qiwei Huang, Hanyue Guo, He Zhang, Qicheng Xu, Qirong Shen, Ning Ling
Summary: Soil microbial community coalescence, the mixing and interaction of microbial communities, has been found to enhance the stability and complexity of rhizobacterial networks, leading to improved plant health and biomass. This study investigated the effects of different degrees of bacterial community coalescence on plant disease resistance by mixing soils from healthy and diseased habitats for watermelon planting. The results showed that mixing in more healthy soil reduced the plant disease index and increased biomass by improving the stability and complexity of the rhizobacterial network. Core taxa Nitrospirillum and Singulisphaera were enriched in the rhizosphere from healthy soils and played important roles in disease suppression and regulating the positive cohesion and modularity of the networks. Overall, these findings provide insights into the potential mechanism of microbial community coalescence for improving plant microbial community function and suggest new tools for enhancing plant fitness via soil microbiota mixing.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Mengqiu He, Shending Chen, Lei Meng, Xiaoqian Dan, Wenjie Wang, Qinying Zhu, Zucong Cai, Jinbo Zhang, Pierfrancesco Nardi, Christoph Mueller
Summary: Maize genotypes directly affect gene expression and nitrogen uptake capacity. The feedback between maize genotypes and soil nitrogen transformations, as well as their regulations on nitrogen uptake capacity, have been studied. The findings suggest that maize genotypes play a central role in regulating these feedbacks, which are important for maize breeding and enhancing maize production.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Ke Shi, Jiahui Liao, Xiaoming Zou, Han Y. H. Chen, Manuel Delgado-Baquerizo, Zhengming Yan, Tingting Ren, Honghua Ruan
Summary: Through rewilding, microbial extracellular and cellular residues can continuously accumulate in soils and significantly contribute to soil organic carbon sequestration. Extracellular residues are mainly driven by fine root biomass, while cellular residues are mainly driven by soil nitrogen and organic carbon content.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Sensen Chen, Ying Teng, Yongming Luo, Eiko Kuramae, Wenjie Ren
Summary: This study comprehensively assesses the effects of NMs on the soil microbiome through a global meta-analysis. The results reveal significant negative impacts of NMs on soil microbial diversity, biomass, activity, and function. Metal NMs, especially Ag NMs, have the most pronounced negative effects on various soil microbial community metrics.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Shareen K. D. Sanders, Gerard Martinez-De Leon, Ludovico Formenti, Madhav P. Thakur
Summary: Collembolans, the diverse group of soil invertebrates, are affected by anthropogenic climate warming, which alters their diversity and density. In addition to abiotic stressors, changes in food availability, specifically the abundance of saprotrophic and mycorrhizal fungi, influence Collembola responses to climate warming. Collembolans prefer saprotrophic fungi but rely on mycorrhizal fungi when food sources are scarce. Understanding the mechanisms behind these dietary shifts in warm-dry and warm-wet soil conditions is crucial for predicting the impact of climate change on Collembola-fungal interactions.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Wimonsiri Pingthaisong, Sergey Blagodatsky, Patma Vityakon, Georg Cadisch
Summary: A study found that mixing high-C/N ratio rice straw with low-C/N ratio groundnut stover can improve the chemical composition of the input, stimulate microbial growth, decrease the loss of residue-derived carbon in the soil, and reduce native soil carbon and nitrogen consumption.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)
Article
Soil Science
Jiachen Wang, Jie Zhao, Rong Yang, Xin Liu, Xuyuan Zhang, Wei Zhang, Xiaoyong Chen, Wende Yan, Kelin Wang
Summary: Nitrogen is vital for ecosystem productivity, restoration, and succession processes. This study found that legume intercropping was more effective than chemical nitrogen fertilizers in promoting the complexity and stability of the soil micro-food web, as it increased microbial and nematode communities and enhanced energy flow patterns.
SOIL BIOLOGY & BIOCHEMISTRY
(2024)