Impact of environmental factors on the spatiotemporal variability of soil organic matter: a case study in a typical small Mollisol watershed of Northeast China

Hu, Wei; Shen, Qian‐Hua; Zhai, Xingyu; Du, Shuli; Zhang, Xingyi

doi:10.1007/s11368-020-02863-1

Cited by 20 publications

(6 citation statements)

References 73 publications

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“…(Li et al, 2018) observed the highest correlation coe cient (0.735, P < 0.001) between TWI and SOCD when studying the distribution of SOC across topography. Furthermore, (Hu et al, 2021) found a signi cant negative correlation between TWI and SOC, which aligns with the ndings of this study. This relationship can be attributed to the in uence of microorganisms on SOC decomposition and nutrient mineralization, ultimately impacting soil fertility and quality.…”

Section: Discussionsupporting

confidence: 91%

Mechanisms driving spatial and temporal changes in soil organic carbon stocks in saline soils in a typical county of the western Songnen Plain, northeast China

Liang,

Wei,

Zhao

et al. 2024

Soil Res.

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Context The Songnen Plain is a crucial grain-producing region in China, and its western area, influenced by natural conditions and long-term human activities, faces severe issues of soil salinisation. In recent years, soil organic carbon (SOC) in saline-alkali soils has gained increasing attention as a material foundation for grain production and an essential carbon sink for mitigating climate change. However, the driving factors behind regional-scale changes in SOC in saline-alkali soils remain unclear. Aims This research was conducted to assess changes in soil organic carbon stocks and identify main driving factors in saline soils at 40-years scale. Methods Taking Tongyu County in the western part of the Songnen Plain as a case study, this research explored the spatiotemporal variation of soil organic carbon density (SOCD) and soil organic carbon storage (SOCS) from 1982 to 2022 using GIS. Random forest regression and structural equation modelling were employed to identify environmental factors influencing SOCD distribution in different soil layers. Key results (1) From 1982 to 2022, the average SOCD in the surface, subsurface, and bottom soil layers decreased by 0.65, 0.34, and 0.46 kg m−2, respectively, resulting in a total carbon storage reduction of 12.93 Tg C. (2) In 1982, the vertical distribution of SOCD was higher at the top and bottom layers and lower in the middle; however, by 2022, it exhibited a gradual decrease layer by layer. (3) Topographic factors only influenced surface SOCD, while the influence of environmental humidity and land use on SOCD decreased with increasing depth. Conclusions These findings provided valuable scientific insights for implementing regional soil carbon sequestration and soil nutrient conservation measures.

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Section: Discussionsupporting

confidence: 91%

Mechanisms driving spatial and temporal changes in soil organic carbon stocks in saline soils in a typical county of the western Songnen Plain, northeast China

Liang,

Wei,

Zhao

et al. 2024

Soil Res.

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“…Soil organic matter (SOM) is the collective term for various carbon-containing organic compounds in soil. It is an important indicator of soil fertility and farmland quality [3][4][5]. The accumulation and decomposition of SOM provide nutritional support for crop growth [6].…”

Section: Introductionmentioning

confidence: 99%

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

Wu,

Dou,

Lin

et al. 2023

Remote Sensing

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Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images.

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“…It is necessary to contact a large amount of soil for the raw materials and the fermentation process, which is critical for plant growth and ecosystem circulation (Zhou and Wang 2015). Previous research discovered that topography (Cho et al 2018) and parent material factors (Hu et al 2021) had a significant impact on the soil to obtain a large amount of organic matter, which determined the amount and structure of biomass in the soil (Wang et al 2013;Zhou and Wang 2015;Tripathi et al 2018). Luzhou is located in southern Sichuan and has various soil types, with purple-red and yellow loams predominating, according to palaeogeographical and evolutionary analysis.…”

Section: Gdemv3 30mmentioning

confidence: 99%

Investigate the mystery of Baijiu production region - Environmental factors for Luzhou (Sichuan, China)

Qingyan,

Yurun,

Fan

et al. 2023

Czech J. Food Sci.

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With a long tradition of brewing history, Chinese strong-flavour Baijiu (SFB) developed prosperously in the southwest, especially in Luzhou, and has existed for more than 300 years building craftwork and brewing techniques. As a product of the distillation of fermented grains, its production involves many microbial communities. During this time, microbial communities within production have burgeoned and evolved throughout the development history, enhancing unique texture and flavour. Meanwhile, environmental factors are critical constituents in the production, which provide the optimal conditions for processing procedures and various microorganisms, including externally controlled macro-environment and micro-environment of fermentation. By studying the correlation with environmental factors from macro and micro perspectives, we illustrated the bond with environmental symbiotic and holistic patterns of the intrinsic microbial community and fermentation characteristics. This review is essential to study and control the whole ecological conditions of the Baijiu fermentation process, aiming to gain insight into the dynamics of the microorganisms and ensure a traditional fermentation environment for quality control.

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Impact of environmental factors on the spatiotemporal variability of soil organic matter: a case study in a typical small Mollisol watershed of Northeast China

Cited by 20 publications

References 73 publications

Mechanisms driving spatial and temporal changes in soil organic carbon stocks in saline soils in a typical county of the western Songnen Plain, northeast China

Mechanisms driving spatial and temporal changes in soil organic carbon stocks in saline soils in a typical county of the western Songnen Plain, northeast China

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

Investigate the mystery of Baijiu production region - Environmental factors for Luzhou (Sichuan, China)

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