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Detailed information on the spatio-temporal changes of cropland soil organic carbon (SOC) can significantly contribute to the improvement of soil fertility and mitigate climate change. Nonetheless, information and knowledge on the national scale spatio-temporal changes and the corresponding uncertainties of SOC in Chinese upland soils remain limited. The CENTURY model was used to estimate the SOC storages and their changes in Chinese uplands from 1980 to 2010. With the Monte Carlo method, the uncertainties of CENTURY-modelled SOC dynamics associated with the spatial heterogeneous model inputs were quantified. Results revealed that the SOC storage in Chinese uplands increased from 3.03 (1.59 to 4.78) Pg C in 1980 to 3.40 (2.39 to 4.62) Pg C in 2010. Increment of SOC storage during this period was 370 Tg C, with an uncertainty interval of −140 to 1110 Tg C. The regional disparities of SOC changes reached a significant level, with considerable SOC accumulation in the Huang-Huai-Hai Plain of China and SOC loss in the northeastern China. The SOC lost from Meadow soils, Black soils and Chernozems was most severe, whilst SOC accumulation in Fluvo-aquic soils, Cinnamon soils and Purplish soils was most significant. In modelling large-scale SOC dynamics, the initial soil properties were major sources of uncertainty. Hence, more detailed information concerning the soil properties must be collected. The SOC stock of Chinese uplands in 2010 was still relatively low, manifesting that recommended agricultural management practices in conjunction with effectively economic and policy incentives to farmers for soil fertility improvement were indispensable for future carbon sequestration in these regions.
Xiaoyu Liu; Yongcun Zhao; Xuezheng Shi; Shihang Wang; Xiang Feng; Fang Yan. Spatio-temporal Changes and Associated Uncertainties of CENTURY-modelled SOC for Chinese Upland Soils, 1980–2010. Chinese Geographical Science 2021, 31, 126 -136.
AMA StyleXiaoyu Liu, Yongcun Zhao, Xuezheng Shi, Shihang Wang, Xiang Feng, Fang Yan. Spatio-temporal Changes and Associated Uncertainties of CENTURY-modelled SOC for Chinese Upland Soils, 1980–2010. Chinese Geographical Science. 2021; 31 (1):126-136.
Chicago/Turabian StyleXiaoyu Liu; Yongcun Zhao; Xuezheng Shi; Shihang Wang; Xiang Feng; Fang Yan. 2021. "Spatio-temporal Changes and Associated Uncertainties of CENTURY-modelled SOC for Chinese Upland Soils, 1980–2010." Chinese Geographical Science 31, no. 1: 126-136.
We collected 682 topsoil samples (0–20cm) from agricultural lands of Luhe County in East China, and analyzed the spatial distribution patterns and potential sources of four major heavy metals. High Pb and Cr were mainly in the southeast adjacent to the Yangtze River, and Cd were characterized by an increasing trend from northwest to southeast, while high Hg mainly occurred in the areas near downtown. Spatially-continuous sources dominated the soil heavy metal concentrations. Contributions of spatially-continuous natural source (soil parent material) to Cr and Cd were 97.0% and 77.7%, respectively, whereas contributions of spatially-continuous anthropogenic source such as diffuse pollution to Pb and Hg were 75.7% and 86.7%, respectively. The distance to factories was the most influential anthropogenic factor for localized anomaly patterns of Pb, Cd, and Cr, while the intensive agricultural land uses associated with the rapid urban expansion were particularly relevant to the anomaly patterns of Hg.
Xianghua Xu; Xidong Zhang; Yuxuan Peng; Renying Li; Cuiying Liu; Xiaosan Luo; Yongcun Zhao. Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China. Bulletin of Environmental Contamination and Toxicology 2021, 106, 33 -39.
AMA StyleXianghua Xu, Xidong Zhang, Yuxuan Peng, Renying Li, Cuiying Liu, Xiaosan Luo, Yongcun Zhao. Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China. Bulletin of Environmental Contamination and Toxicology. 2021; 106 (1):33-39.
Chicago/Turabian StyleXianghua Xu; Xidong Zhang; Yuxuan Peng; Renying Li; Cuiying Liu; Xiaosan Luo; Yongcun Zhao. 2021. "Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China." Bulletin of Environmental Contamination and Toxicology 106, no. 1: 33-39.
Principal component analysis‐multiple linear regression (PCA‐MLR) is usually used to weaken the multicollinearity effects among auxiliary variables in regression prediction. However, both PCA and MLR in this model are only built on variable space rather than geographical space. When used in the spatial prediction of soil properties, PCA‐MLR usually cannot effectively capture the spatially non‐stationary structures among auxiliary variables and spatially non‐stationary relationships between the target variable and principal component scores. Moreover, PCA‐MLR may ignore the potentially valuable regression residual. To address these limitations, this study first proposed geographically weighted principal component analysis‐geographically weighted regression kriging (GWPCA‐GWRK) for the spatial prediction of soil alkaline hydrolyzable nitrogen (AN) in Shayang County, China. Then, the spatial prediction accuracy of GWPCA‐GWRK was compared with those of the following five models: ordinary kriging (OK), co‐kriging (CoK), PCA‐MLR, PCA‐GWR, and GWPCA‐GWR. Results showed that (i) eight variables were determined as auxiliary data by geodetector; (ii) the spatially non‐stationary relationships among the eight auxiliary variables were revealed by the results of the local correlation analysis, Monte Carlo test, and GWPCA; (iii) GWPCA‐GWRK provided the lowest prediction error (RMSE = 18.80 mg kg−1, MAE = 12.79 mg kg−1) and highest LCCC (0.75); (iv) relative improvement accuracies over the traditionally‐used OK were 19.74% for GWPCA‐GWRK, 16.42% for GWPCA‐GWR, 8.09% for PCA‐GWR, −3.67% for PCA‐MLR, and 4.70% for CoK. It is concluded that the proposed GWPCA‐GWRK model is an effective spatial predictor, which can adequately extract the main information of the multiple auxiliary variables in a large‐scale area. This article is protected by copyright. All rights reserved
Jian Chen; Mingkai Qu; Jianlin Zhang; Enze Xie; Yongcun Zhao; Biao Huang. Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA‐GWRK. Soil Science Society of America Journal 2020, 85, 879 -892.
AMA StyleJian Chen, Mingkai Qu, Jianlin Zhang, Enze Xie, Yongcun Zhao, Biao Huang. Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA‐GWRK. Soil Science Society of America Journal. 2020; 85 (3):879-892.
Chicago/Turabian StyleJian Chen; Mingkai Qu; Jianlin Zhang; Enze Xie; Yongcun Zhao; Biao Huang. 2020. "Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA‐GWRK." Soil Science Society of America Journal 85, no. 3: 879-892.
Understanding the spatiotemporal distribution of soil organic carbon (SOC) and its controlling factors is extremely important for improving soil quality and developing sustainable management practices. We quantified spatiotemporal variations in SOC in three typical regions (Shuyang, Rugao, and Shanghai) in southeastern China during 1981–2011, by using geographically weighted regression (GWR), and explored the drivers with a geographical detector method. A total of 219 topsoil samples were collected in the three regions to measure the SOC in 2011, and a total of 109 SOC data for 1981 were obtained from the soil survey reports of Shuyang, Rugao, and Shanghai, which involved in the database of the second national soil survey of China. The results showed that the mean SOC contents in 2011 were 14.68 g kg−1, 9.55 g kg−1, and 18.00 g kg−1 in Shuyang, Rugao, and Shanghai, respectively. The topography (q = 0.60) and the sand content of the soil (q = 0.70) were the main drivers of the spatial variability in the SOC in Shuyang and Rugao, while the carbon inputs (q = 0.68) predominantly explained the spatial heterogeneity of the SOC in Shanghai. Significant increases in SOC storage occurred in Shuyang and Rugao from 1981 to 2011, with increase rates of 0.55 t ha−1 yr−1 and 0.26 t ha−1 yr−1, respectively. Land use change (dryland farming to rice cultivation) was identified as the largest driver of the SOC increases in Shuyang and Rugao (q values of 0.16 and 0.09, respectively), followed by increasing carbon inputs (0.14 and 0.07). However, the SOC storage in Shanghai rapidly decreased at a rate of −0.38 t ha−1 yr−1 during 1981–2011. The land use change from wetlands to rice cultivation was the primary reason for the decreasing SOC (q = 0.24), and a net decrease in carbon inputs between 1981 and 2011 was another main driver of the reduction in the SOC in Shanghai (q = 0.14). Our results from this study provide important information on the spatiotemporal changes in SOC and its drivers to the scientific community and decision-makers, for the development of management strategies to sustain soil fertility in many areas with rapid economic development and increasing populations.
Enze Xie; Yanxia Zhang; Biao Huang; Yongcun Zhao; Xuezheng Shi; Wenyou Hu; Mingkai Qu. Spatiotemporal variations in soil organic carbon and their drivers in southeastern China during 1981-2011. Soil and Tillage Research 2020, 205, 104763 .
AMA StyleEnze Xie, Yanxia Zhang, Biao Huang, Yongcun Zhao, Xuezheng Shi, Wenyou Hu, Mingkai Qu. Spatiotemporal variations in soil organic carbon and their drivers in southeastern China during 1981-2011. Soil and Tillage Research. 2020; 205 ():104763.
Chicago/Turabian StyleEnze Xie; Yanxia Zhang; Biao Huang; Yongcun Zhao; Xuezheng Shi; Wenyou Hu; Mingkai Qu. 2020. "Spatiotemporal variations in soil organic carbon and their drivers in southeastern China during 1981-2011." Soil and Tillage Research 205, no. : 104763.
Cadmium (Cd) accumulations in crops and the effects of the related soil factors on them are critical to developing precise soil management measures for food safety. Traditionally-used non-spatial multiple linear regression (MLR) cannot adequately model the spatially varying effects of the related soil properties on Cd accumulations in crop (or soil). Moreover, the traditionally-used methods for exploring the spatial accumulation characteristics (e.g., ordinary kriging) and the effects of other factors on Cd accumulations (e.g., MLR) are sensitive to outliers. In this study, robust geostatistics, enrichment index, and bioavailability index were first used to explore the spatial accumulation characteristics of Cd in wheat grain (wheat-Cd), Cd in rice grain (rice-Cd), and soil DTPA-extractable Cd (DTPA-Cd) in Jintan County, a typical rice-wheat rotation area in China. Then, robust geographically weighted regression (RGWR), established in geographic space rather than variable space, was used to explore the spatially varying relationships between Cd accumulations and the corresponding main influential factors determined by stepwise regression. Last, the modelling accuracy of RGWR was compared with those of basic GWR and MLR. Results showed that (i) outliers affected the spatial predictions of soil total Cd, soil DTPA-Cd, wheat-Cd, and rice-Cd and robust variograms should be used; (ii) the enrichment index of wheat grain was significantly higher than that of rice grain in almost the whole study area; (iii) the areas with the high bioavailability index of soil Cd mainly located in the southeast, southwest, and centre of the study area; (iv) RGWR acquired higher modelling accuracy than GWR and MLR; (v) the spatially varying relationships between Cd accumulations and the corresponding influential factors were revealed by RGWR, which cannot be determined by MLR. The methods suggested in this study provided more precise spatial decision support for soil management measures to guarantee main agricultural product safety in large-scale areas.
Mingkai Qu; Jian Chen; Biao Huang; Yongcun Zhao. Exploring the spatially varying relationships between cadmium accumulations and the main influential factors in the rice-wheat rotation system in a large-scale area. Science of The Total Environment 2020, 736, 139565 .
AMA StyleMingkai Qu, Jian Chen, Biao Huang, Yongcun Zhao. Exploring the spatially varying relationships between cadmium accumulations and the main influential factors in the rice-wheat rotation system in a large-scale area. Science of The Total Environment. 2020; 736 ():139565.
Chicago/Turabian StyleMingkai Qu; Jian Chen; Biao Huang; Yongcun Zhao. 2020. "Exploring the spatially varying relationships between cadmium accumulations and the main influential factors in the rice-wheat rotation system in a large-scale area." Science of The Total Environment 736, no. : 139565.
The conversion of cereal to vegetable represents a significant shift in land use in China, and it causes significant changes in soil properties. Most studies have only focused on chemical or biological properties; few have investigated soil structure. Soil structure, especially macropore space, is very important for plant growth because of its relation to important soil functions and processes, such as gas diffusion and water permeability. The objective of this study was to assess the effect of land use conversion from rice to vegetable on soil macropores (>50 μm) measured by computed tomography (CT) and to examine the relationships between CT-measured pore characteristics and soil chemical properties. By using space instead of time, we sampled three land uses – rice/wheat rotation (RWR), open-field vegetable (OFV) and plastic-greenhouse vegetable (PGV) – in a tilled and plow pan layer in a suburban area of Nanjing, China, and analyzed the basic physicochemical properties and CT-measured macropore characteristics. The results showed that the tilled layer soil had a significant response to the land use change. The macroporosity decreased from 11.5% under RWR to 8.0% under OFV and 5.8% under PGV, and the decreased portion consisted mainly of elongated large macropores (>1000 μm). In addition, the macropore morphology of vegetable fields also showed degradation, with a higher degree of anisotropy (DA) and lower fractal dimension (FD) and connectivity compared to those under RWR, but PGV experienced a higher degree of degradation than did OFV. This study also showed that soil structure degradation was significantly correlated with decreasing soil organic matter (SOM). Increasing the amount of organic fertilizer applied might improve the SOM content and therefore improve the soil structure. Based on the linear regression equation, adding 1 g of SOM per kilogram of soil can improve the macroporosity by 0.54 m3 m−3.
Meiyan Wang; Shengxiang Xu; Chao Kong; Yongcun Zhao; Xuezheng Shi; Naijia Guo. Assessing the effects of land use change from rice to vegetable on soil structural quality using X-ray CT. Soil and Tillage Research 2019, 195, 104343 .
AMA StyleMeiyan Wang, Shengxiang Xu, Chao Kong, Yongcun Zhao, Xuezheng Shi, Naijia Guo. Assessing the effects of land use change from rice to vegetable on soil structural quality using X-ray CT. Soil and Tillage Research. 2019; 195 ():104343.
Chicago/Turabian StyleMeiyan Wang; Shengxiang Xu; Chao Kong; Yongcun Zhao; Xuezheng Shi; Naijia Guo. 2019. "Assessing the effects of land use change from rice to vegetable on soil structural quality using X-ray CT." Soil and Tillage Research 195, no. : 104343.
The impacts of rapid industrialization on agricultural soil cadmium (Cd) accumulation and their potential risks have drawn major attention from the scientific community and decision-makers, due to the high toxicity of Cd to animals and humans. A total of 812 topsoil samples (0–20 cm) was collected from the southern parts of Jiangsu Province, China, in 2000 and 2015, respectively. Geostatistical ordinary kriging and conditional sequential Gaussian simulation were used to quantify the changes in spatial distributions and the potential risk of Cd pollution between the two sampling dates. Results showed that across the study area, the mean Cd concentrations increased from 0.110 mg/kg in 2000 to 0.196 mg/kg in 2015, representing an annual average increase of 5.73 μg/kg. Given a confidence level of 95%, areas with significantly-increased Cd covered approximately 12% of the study area. Areas with a potential risk of Cd pollution in 2000 only covered 0.009% of the study area, while this figure increased to 0.75% in 2015. In addition, the locally concentrating trend of soil Cd pollution risk was evident after 15 years. Although multiple factors contributed to this elevated Cd pollution risk, the primary reason can be attributed to the enhanced atmospheric deposition and industrial waste discharge resulting from rapid industrialization, and the quick increase of traffic and transportation associated with rapid urbanization.
Xianghua Xu; Jiaying Qian; Enze Xie; Xuezheng Shi; Yongcun Zhao. Spatio-Temporal Change and Pollution Risk of Agricultural Soil Cadmium in a Rapidly Industrializing Area in the Yangtze Delta Region of China. International Journal of Environmental Research and Public Health 2018, 15, 2743 .
AMA StyleXianghua Xu, Jiaying Qian, Enze Xie, Xuezheng Shi, Yongcun Zhao. Spatio-Temporal Change and Pollution Risk of Agricultural Soil Cadmium in a Rapidly Industrializing Area in the Yangtze Delta Region of China. International Journal of Environmental Research and Public Health. 2018; 15 (12):2743.
Chicago/Turabian StyleXianghua Xu; Jiaying Qian; Enze Xie; Xuezheng Shi; Yongcun Zhao. 2018. "Spatio-Temporal Change and Pollution Risk of Agricultural Soil Cadmium in a Rapidly Industrializing Area in the Yangtze Delta Region of China." International Journal of Environmental Research and Public Health 15, no. 12: 2743.
Process-based models have been successfully applied to predict long-term changes in soil organic carbon (SOC) at plot scales, but considerable uncertainties are still introduced into regional or national extrapolations due to the lack of spatially explicit information on the model input parameters. Using the CENTURY model we predicted SOC changes in the uplands of Northeast China during the period from 1980 to 2050 and provided 95% confidence intervals regarding the uncertainties associated with variability in the key input parameters. Regional SOC estimation predicted by CENTURY was reliable for the uplands of Northeast China when considering the uncertainty associated with heterogeneous key input parameters. SOC stocks were estimated to be 0.99, 0.88 and 0.87 Pg C in 1980, 2010 and 2050, with 95% confidence intervals ranging from 0.69 to 1.31, 0.66 to 1.11, and 0.69 to 1.07 Pg C, respectively. Overall, the upland soils of Northeast China functioned as a carbon source from 1980 to 2010, with a net decrease of 106 (9–207) Tg C. The SOC losses mainly occurred where SOC contents were high (Heilongjiang Province and eastern Jilin Province). However, assuming unchanged management, whether the uplands of Northeast China will serve as a carbon sink/source over the next 40 years remains uncertain. Information collection on the most influential input parameters (the initial SOC content and clay content) is critical to reduce uncertainty and to provide meaningful information for decision makers.
X. Y. Liu; Y. C. Zhao; X. Z. Shi; Y. Liu; S. H. Wang; D. S. Yu. Uncertainty in CENTURY-modelled changes in soil organic carbon stock in the uplands of Northeast China, 1980–2050. Nutrient Cycling in Agroecosystems 2018, 113, 77 -93.
AMA StyleX. Y. Liu, Y. C. Zhao, X. Z. Shi, Y. Liu, S. H. Wang, D. S. Yu. Uncertainty in CENTURY-modelled changes in soil organic carbon stock in the uplands of Northeast China, 1980–2050. Nutrient Cycling in Agroecosystems. 2018; 113 (1):77-93.
Chicago/Turabian StyleX. Y. Liu; Y. C. Zhao; X. Z. Shi; Y. Liu; S. H. Wang; D. S. Yu. 2018. "Uncertainty in CENTURY-modelled changes in soil organic carbon stock in the uplands of Northeast China, 1980–2050." Nutrient Cycling in Agroecosystems 113, no. 1: 77-93.
The impacts of rapid industrialization on spatio-temporal changes in soil pH have drawn major attention from the scientific community and decision-makers. A total of 1185 topsoil samples (0–20 cm) were collected from southern Jiangsu Province of China in 1980, 2000, and 2015, and the geostatistical sequential Gaussian simulation (SGS) was used to quantify the spatio-temporal patterns of changes in soil pH and identify the areas with significantly-declined pH. A significant topsoil acidification occurred in major croplands of the study area, with mean decrements of 0.56 and 0.52 units over the periods of 1980–2000 and 2000–2015, respectively. A significant decrease in pH occurred in the areas surrounding Taihu Lake during 1980–2000, and a further decrease was evident predominantly in the northeast during 2000–2015. Over the entire 35-year period, the soil pH for approximately 79% of the study area declined significantly. The decline over the first two decades (1980–2000) was largely attributed to increased chemical fertilizer inputs and the development of industries, while the further enhanced industrial emissions resulting from the significant expansion of factories were the primary reason for the accelerated soil acidification in the subsequent 15 years (2000–2015). Results from this study may serve as a basis for developing management strategies to achieve better soil quality in similar areas moving toward industrialization.
Enze Xie; Yongcun Zhao; Haidong Li; Xuezheng Shi; Fangyi Lu; Xiu Zhang; Yuxuan Peng. Spatio-temporal changes of cropland soil pH in a rapidly industrializing region in the Yangtze River Delta of China, 1980–2015. Agriculture, Ecosystems & Environment 2018, 272, 95 -104.
AMA StyleEnze Xie, Yongcun Zhao, Haidong Li, Xuezheng Shi, Fangyi Lu, Xiu Zhang, Yuxuan Peng. Spatio-temporal changes of cropland soil pH in a rapidly industrializing region in the Yangtze River Delta of China, 1980–2015. Agriculture, Ecosystems & Environment. 2018; 272 ():95-104.
Chicago/Turabian StyleEnze Xie; Yongcun Zhao; Haidong Li; Xuezheng Shi; Fangyi Lu; Xiu Zhang; Yuxuan Peng. 2018. "Spatio-temporal changes of cropland soil pH in a rapidly industrializing region in the Yangtze River Delta of China, 1980–2015." Agriculture, Ecosystems & Environment 272, no. : 95-104.
Iron (Fe) occurs in almost all soils and the analysis of various forms of Fe in the soil is of great pedological interest. Very little is known, however, about how visible and near-infrared (Vis-NIR) spectroscopy performs in intact soil cores of paddy fields for quantifying Fe concentrations. Our objective was to evaluate the feasibility of Vis-NIR spectroscopy of intact soil cores for rapid determination of the four Fe forms: total Fe (Fet), pyrophosphate-extractable Fe (Fep), dithionite-citrate-bicarbonate extractable Fe (Fed), and oxalate-extractable Fe (Feo). A total of 148 intact soil cores in Yujiang County, China, were sampled, and Vis-NIR spectra (350–2500 nm) were sectioned and scanned on four horizontal surfaces (5-cm depth intervals) of each soil core in the laboratory. Partial least squares regression (PLSR) and support vector machine regression (SVMR) models were compared using 70% of the section samples for calibration and 30% for independent validation. Results showed that the nonlinear SVMR models performed better than the PLSR models for the predictions of all Fe forms. The SVMR models produced the best predictions in the independent validation set for Fed (RMSEP = 2.223; R2P = 0.88; RPDP = 2.86), Feo (RMSEP = 0.994; R2P = 0.85; RPDP = 2.59), Fet (RMSEP = 3.693; R2P = 0.82; RPDP = 2.32), and Fep (RMSEP = 0.086; R2P = 0.79; RPDP = 2.17). It was concluded that Vis-NIR spectroscopy coupled with SVMR is suitable for quantitatively determining different Fe forms in intact soil cores of paddy fields. Copyright © 2018. . Copyright © by the Soil Science Society of America, Inc.
Shengxiang Xu; Yongcun Zhao; Meiyan Wang; Xuezheng Shi. Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy. Soil Science Society of America Journal 2018, 82, 1497 -1511.
AMA StyleShengxiang Xu, Yongcun Zhao, Meiyan Wang, Xuezheng Shi. Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy. Soil Science Society of America Journal. 2018; 82 (6):1497-1511.
Chicago/Turabian StyleShengxiang Xu; Yongcun Zhao; Meiyan Wang; Xuezheng Shi. 2018. "Quantification of Different Forms of Iron from Intact Soil Cores of Paddy Fields with Vis-NIR Spectroscopy." Soil Science Society of America Journal 82, no. 6: 1497-1511.
Shengxiang Xu; Yongcun Zhao; Meiyan Wang; Xuezheng Shi. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma 2018, 310, 29 -43.
AMA StyleShengxiang Xu, Yongcun Zhao, Meiyan Wang, Xuezheng Shi. Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma. 2018; 310 ():29-43.
Chicago/Turabian StyleShengxiang Xu; Yongcun Zhao; Meiyan Wang; Xuezheng Shi. 2018. "Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy." Geoderma 310, no. : 29-43.
Falü Qin; Yongcun Zhao; Xuezheng Shi; Shengxiang Xu; Dongsheng Yu; Falv Qin. Uncertainty and Sensitivity Analyses for Modeling Long-Term Soil Organic Carbon Dynamics of Paddy Soils Under Different Climate-Soil-Management Combinations. Pedosphere 2017, 27, 912 -925.
AMA StyleFalü Qin, Yongcun Zhao, Xuezheng Shi, Shengxiang Xu, Dongsheng Yu, Falv Qin. Uncertainty and Sensitivity Analyses for Modeling Long-Term Soil Organic Carbon Dynamics of Paddy Soils Under Different Climate-Soil-Management Combinations. Pedosphere. 2017; 27 (5):912-925.
Chicago/Turabian StyleFalü Qin; Yongcun Zhao; Xuezheng Shi; Shengxiang Xu; Dongsheng Yu; Falv Qin. 2017. "Uncertainty and Sensitivity Analyses for Modeling Long-Term Soil Organic Carbon Dynamics of Paddy Soils Under Different Climate-Soil-Management Combinations." Pedosphere 27, no. 5: 912-925.
Shengxiang Xu; Yongcun Zhao; Meiyan Wang; Xuezheng Shi. Determination of rice root density from Vis–NIR spectroscopy by support vector machine regression and spectral variable selection techniques. CATENA 2017, 157, 12 -23.
AMA StyleShengxiang Xu, Yongcun Zhao, Meiyan Wang, Xuezheng Shi. Determination of rice root density from Vis–NIR spectroscopy by support vector machine regression and spectral variable selection techniques. CATENA. 2017; 157 ():12-23.
Chicago/Turabian StyleShengxiang Xu; Yongcun Zhao; Meiyan Wang; Xuezheng Shi. 2017. "Determination of rice root density from Vis–NIR spectroscopy by support vector machine regression and spectral variable selection techniques." CATENA 157, no. : 12-23.
Shengxiang Xu; Yongcun Zhao; Xuezheng Shi; Meiyan Wang. Rapid Determination of Carbon, Nitrogen, and Phosphorus Contents of Field Crops in China Using Visible and Near-Infrared Reflectance Spectroscopy. Crop Science 2016, 57, 475 -489.
AMA StyleShengxiang Xu, Yongcun Zhao, Xuezheng Shi, Meiyan Wang. Rapid Determination of Carbon, Nitrogen, and Phosphorus Contents of Field Crops in China Using Visible and Near-Infrared Reflectance Spectroscopy. Crop Science. 2016; 57 (1):475-489.
Chicago/Turabian StyleShengxiang Xu; Yongcun Zhao; Xuezheng Shi; Meiyan Wang. 2016. "Rapid Determination of Carbon, Nitrogen, and Phosphorus Contents of Field Crops in China Using Visible and Near-Infrared Reflectance Spectroscopy." Crop Science 57, no. 1: 475-489.
Based on the spatial distribution maps of the soil AN and NAR, vulnerability areas with a low available concentration and low/high availability ratio of soil nitrogen were delineated based on different thresholds of the soil AN and NAR.
Mingkai Qu; Weidong Li; Chuanrong Zhang; Biao Huang; Yongcun Zhao. Spatial assessment of soil nitrogen availability and varying effects of related main soil factors on soil available nitrogen. Environmental Science: Processes & Impacts 2016, 18, 1449 -1457.
AMA StyleMingkai Qu, Weidong Li, Chuanrong Zhang, Biao Huang, Yongcun Zhao. Spatial assessment of soil nitrogen availability and varying effects of related main soil factors on soil available nitrogen. Environmental Science: Processes & Impacts. 2016; 18 (11):1449-1457.
Chicago/Turabian StyleMingkai Qu; Weidong Li; Chuanrong Zhang; Biao Huang; Yongcun Zhao. 2016. "Spatial assessment of soil nitrogen availability and varying effects of related main soil factors on soil available nitrogen." Environmental Science: Processes & Impacts 18, no. 11: 1449-1457.
Process-based models such as CENTURY have been extensively validated for simulating soil organic carbon (SOC) dynamics at the homogeneous plot scale. However, considerable uncertainty may exist when upscaling a simulation from the plot scale to a larger scale because of variation in the model inputs. The objectives of this study were to assess the uncertainty of CENTURY-modeled SOC and to identify the most influential model inputs in various upland regions of China. Global sensitivity analysis was used to explore the sensitivity of CENTURY-modeled SOC to seven key model inputs. The uncertainties of the SOC simulated using various model inputs and climate-soil-management conditions were evaluated at 21 long-term monitoring sites located across upland areas in China. The identified sensitive model inputs differed among regions and periods due to diverse climate-soil-management conditions; nevertheless, initial SOC content (SOCi), soil clay content, and crop residue removal rate (Residuerr) were the most influential inputs. The site-to-region upscaling uncertainties remained moderately large (±42.7, ±49.4, and ±69.3 % at the 90, 95, and 100 % confidence levels, respectively) when currently available observation data were used. Therefore, the collection of detailed information on soil properties and crop residue removal, particularly legacy soil data such as the SOCi and clay content, is important for reducing the uncertainties in SOC modeling. Data on SOCi, Residuerr, and clay content need to be collected prior to other input data to reduce input-related uncertainty and thus to provide more reliable SOC assessment at the regional or national scale in China.
Xiaoyu Liu; Yongcun Zhao; Xuezheng Shi; Yang Liu; Shihang Wang; Dongsheng Yu. Sensitivity and uncertainty analysis of CENTURY-modeled SOC dynamics in upland soils under different climate-soil-management conditions: a case study in China. Journal of Soils and Sediments 2016, 17, 85 -96.
AMA StyleXiaoyu Liu, Yongcun Zhao, Xuezheng Shi, Yang Liu, Shihang Wang, Dongsheng Yu. Sensitivity and uncertainty analysis of CENTURY-modeled SOC dynamics in upland soils under different climate-soil-management conditions: a case study in China. Journal of Soils and Sediments. 2016; 17 (1):85-96.
Chicago/Turabian StyleXiaoyu Liu; Yongcun Zhao; Xuezheng Shi; Yang Liu; Shihang Wang; Dongsheng Yu. 2016. "Sensitivity and uncertainty analysis of CENTURY-modeled SOC dynamics in upland soils under different climate-soil-management conditions: a case study in China." Journal of Soils and Sediments 17, no. 1: 85-96.
Soil organic carbon (SOC) is spatially heterogeneous. Understanding SOC variability as a function of varying scale is important for accurately estimating the SOC stock. We selected three zones in the Huang-Huai-Hai agricultural region of China to define temperature (T Zone), precipitation (P Zone) and temperature + precipitation (PT Zone) gradients, respectively. The zonal differences in SOC variability as a function of increasing scale were examined. The results demonstrated that the SOC stock varied substantially among the different zones. The coefficient of variation (CV) of the SOC stock was more elevated in the PT Zone and was influenced by scale level. The mean CV increased by 12.5%, 4.6% and 2.9% from 1C to 12C for PT, T and P Zone, respectively. Zonal SOC variability differences were not obvious at small scale, with the CV ratio consistently less than 0.003 in the three zones; however, they became detectable at higher scales (6C and 12C), with the CV ratio showing as: PT Zone > T Zone > P Zone. SOC zonal variability must be considered to reduce uncertainty for soil carbon stock estimation.
Meiyan Wang; Shengxiang Xu; Yongcun Zhao; Xuazheng Shi. Climatic effect on soil organic carbon variability as a function of spatial scale. Archives of Agronomy and Soil Science 2016, 63, 375 -387.
AMA StyleMeiyan Wang, Shengxiang Xu, Yongcun Zhao, Xuazheng Shi. Climatic effect on soil organic carbon variability as a function of spatial scale. Archives of Agronomy and Soil Science. 2016; 63 (3):375-387.
Chicago/Turabian StyleMeiyan Wang; Shengxiang Xu; Yongcun Zhao; Xuazheng Shi. 2016. "Climatic effect on soil organic carbon variability as a function of spatial scale." Archives of Agronomy and Soil Science 63, no. 3: 375-387.
Process-based modeling is a powerful tool for identifying predominant factors that influence soil organic carbon (SOC) so that rational management decisions can be made for improving soil fertility and increasing carbon sequestration in agricultural soils. To achieve these goals, a sensitivity and uncertainty analysis of modeling output is required. Based on the data collected at a long-term observation site with a 21-year rice–bean planting history, two global sensitivity analysis (SA) methods and six uncertainty analysis (UA) methods were applied and compared for the DeNitrification–DeComposition (DNDC) model to simulate inter-annual SOC changes (dSOC). Morris and Sobol’ SA methods were used to identify the most influential parameters, and then the dSOC uncertainty intervals estimated by Morris, Sobol’, Morris and a joint use of Monte Carlo (MC) with the five most sensitive parameters considered (Morris-MC-5Fac), Morris and a joint use of MC with the four most sensitive parameters considered (Morris-MC-4Fac), DNDC embedded Most Sensitive Factor method (DNDC-MSF), and the user-modified Most Sensitive Factor method with the five most sensitive parameters considered (MSF-user-5Fac) were compared. Sensitivity analysis results indicated that the Morris and Sobol’ methods produced similar sensitivity indices for 11 DNDC model input parameters. The initial SOC, bulk density, amount of manure applied, ratio of crop residue incorporated into soils, and amount of chemical fertilizer applied are the five most influential parameters on dSOC uncertainty at the site. In our case, Morris is recommended for a pure sensitivity analysis due to its low model running cost compared to that of Sobol’. Among the six uncertainty analysis methods, the Sobol’ estimated uncertainty result is considered the most reliable, followed by that estimated with Morris-MC-5Fac, MSF-user-5Fac, Morris-MC-4Fac, Morris, and DNDC-MSF. DNDC-MSF has the lowest model running cost, followed by Morris, MSF-user-5Fac, Sobol’, Morris-MC-4Fac, and Morris-MC-5Fac. Considering both the reliability of the uncertainty analysis results and the model running costs, MSF-user-5Fac is the most efficient. However, it should be noted that MSF-user-5Fac is not suitable for models in which the input parameters and modeling outputs are not monotonically related. For the latter models, Sobol’ is a better choice. The information in this paper benefits DNDC users by providing guidance in proper sensitivity and uncertainty analysis method selection.
Falv Qin; Yongcun Zhao; Xuezheng Shi; Shengxiang Xu; Dongsheng Yu. Sensitivity and uncertainty analysis for the DeNitrification–DeComposition model, a case study of modeling soil organic carbon dynamics at a long-term observation site with a rice–bean rotation. Computers and Electronics in Agriculture 2016, 124, 263 -272.
AMA StyleFalv Qin, Yongcun Zhao, Xuezheng Shi, Shengxiang Xu, Dongsheng Yu. Sensitivity and uncertainty analysis for the DeNitrification–DeComposition model, a case study of modeling soil organic carbon dynamics at a long-term observation site with a rice–bean rotation. Computers and Electronics in Agriculture. 2016; 124 ():263-272.
Chicago/Turabian StyleFalv Qin; Yongcun Zhao; Xuezheng Shi; Shengxiang Xu; Dongsheng Yu. 2016. "Sensitivity and uncertainty analysis for the DeNitrification–DeComposition model, a case study of modeling soil organic carbon dynamics at a long-term observation site with a rice–bean rotation." Computers and Electronics in Agriculture 124, no. : 263-272.
Shengxiang Xu; Xuezheng Shi; Meiyan Wang; Yongcun Zhao. Determination of rice root density at the field level using visible and near-infrared reflectance spectroscopy. Geoderma 2016, 267, 174 -184.
AMA StyleShengxiang Xu, Xuezheng Shi, Meiyan Wang, Yongcun Zhao. Determination of rice root density at the field level using visible and near-infrared reflectance spectroscopy. Geoderma. 2016; 267 ():174-184.
Chicago/Turabian StyleShengxiang Xu; Xuezheng Shi; Meiyan Wang; Yongcun Zhao. 2016. "Determination of rice root density at the field level using visible and near-infrared reflectance spectroscopy." Geoderma 267, no. : 174-184.
Data on the spatial distribution of soil organic matter (SOM) are important for the spatio-temporal modeling of soil organic carbon dynamics and soil carbon sequestration potential estimates. A total of 175 topsoil samples (0–20 cm) were collected from a typical black soil area in central Hailun County in northeastern China. Seven sampling design schemes, ordinary kriging (OK), and regression kriging (RK) were applied to the re-sampled SOM data for predicting the spatial distribution of SOM. The results showed that single sampling designs, such as simple random, stratified random (STR), and conditional Latin hypercube (CLH), produced poor estimates of SOM, while hybrid sampling designs, such as uniform distribution of point pairs for variogram estimation combined with spatial coverage, STR combined with spatial coverage (STRC), and CLH combined with spatial coverage (CLHC), had a higher predicting accuracy when the sample size was relatively small (≤262). For square grid sampling, a higher predicting accuracy could be achieved only when the sample size was sufficiently large (i.e., ≥402). The inclusion of prior knowledge or SOM-related secondary data in the sampling design and the trade-off between the even and uneven distribution of sampling points are especially important for designing a small-size sampling scheme. Moreover, although the SOM-predicting accuracy of RK was not as good as OK in this study, increasing the sample size may improve the predicting accuracy of SOM. Therefore, the optimal sampling design and spatial predicting method are both important for the predictive mapping of SOM spatial distribution in this area.
Yongcun Zhao; Xianghua Xu; Kang Tian; Biao Huang; Nan Hai. Comparison of sampling schemes for the spatial prediction of soil organic matter in a typical black soil region in China. Environmental Earth Sciences 2015, 75, 4 .
AMA StyleYongcun Zhao, Xianghua Xu, Kang Tian, Biao Huang, Nan Hai. Comparison of sampling schemes for the spatial prediction of soil organic matter in a typical black soil region in China. Environmental Earth Sciences. 2015; 75 (1):4.
Chicago/Turabian StyleYongcun Zhao; Xianghua Xu; Kang Tian; Biao Huang; Nan Hai. 2015. "Comparison of sampling schemes for the spatial prediction of soil organic matter in a typical black soil region in China." Environmental Earth Sciences 75, no. 1: 4.