Spatio‐temporal change of soil organic matter content of Jiangsu Province, China, based on digital soil maps

Sun, Xiaofang; Zhao, Yu-Guo; Wu, Yurong; Zhao, Mingsong; Wang, H.-L.; Zhang, Gan‐Lin

doi:10.1111/j.1475-2743.2012.00421.x

Cited by 23 publications

(14 citation statements)

References 29 publications

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“…Spatial variation of soil acidity along with other properties were characterized using a digital soil mapping approach at various scales 20,30–34 . There are studies that quantified the spatial and temporal changes of soil properties, including soil carbon and organic matter 31,32 , soil nutrients 19,35 , and soil moisture 36,37 . Spatio-temporal changes in soil acidity have been quantified using legacy soil test data at the national scale in South Korea 7 and at regional scales in England and Wales 38 , and in Victoria, Australia 9 .…”

Section: Introductionmentioning

confidence: 99%

Drivers of spatio-temporal changes in paddy soil pH in Jiangxi Province, China from 1980 to 2010

Guo

et al. 2018

Sci Rep

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The spatio-temporal distribution soil pH is critical for understanding the productivity and long-term sustainability of our agri-ecosystem. This study quantified the spatio-temporal distribution of paddy soil pH in Jiangxi province, China, and the potential driver of the change between 1980 and 2010. Data from the Soil Survey Information of Jiangxi province (1980s) and Jiangxi Soil Testing and Fertilizer Recommendation study (2010s) were collected and categorized into six pH ranges from strongly-acidic to strongly-alkaline with unit pH differences. Changes were calculated from the maps developed using the Pedological Knowledge base for 1980s data (without geolocation) and geostatistical methods for the 2010s data (geolocated). An overall 0.6-unit decrease and a major shift of soil pH from weakly-acidic (54% → 18%) to acidic (35% → 74%) was observed over the province in a scattered fashion with concentration in the central part and the Poyang Lake area. About half of the area under paddy cultivation went through acidification by at least one pH unit and 7% by at least 2 pH units, while 40% of the area remained unchanged. Excessive fertilizer application and acid-rain intensity contributed to the acidification. Thus, a more knowledge-based and comprehensive fertilizer management should be adopted to make paddy production sustainable in the province.

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Section: Introductionmentioning

confidence: 99%

Drivers of spatio-temporal changes in paddy soil pH in Jiangxi Province, China from 1980 to 2010

Guo

et al. 2018

Sci Rep

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“…Spatial outliers can be defined as an observation that is unusual compared to their neighbours (Lark et al, 2012). In soil science, spatial outliers have been widely discussed in previous studies (e.g., Lark, 2000;Zhao et al, 2007;Sun et al, 2012), because such observations could lead to exaggerated estimates of mapping uncertainty (Sun et al, 2012). In the air quality data, measurements might be spatially outlying due to influences of nearby emission sources, specific terrain of the surrounding area and/or biased monitoring devices due to mechanical or electrical malfunction.…”

Section: Introductionmentioning

confidence: 99%

Effect of spatial outliers on the regression modelling of air pollutant concentrations: A case study in Japan

Araki

Shimadera

Yamamoto

et al. 2017

Atmospheric Environment

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Land use regression (LUR) or regression kriging have been widely used to estimate spatial distribution of air pollutants especially in health studies. The quality of observations is crucial to these methods because they are completely dependent on observations. When monitoring data contain biases or uncertainties, estimated map will not be reliable. In this study, we apply the spatial outlier detection method, which is widely used in soil science, to observations of PM 2.5 and NO 2 obtained from the regulatory monitoring network in Japan. The spatial distributions of annual means are modelled both by LUR and regression kriging using the data sets with and without the detected outliers respectively and the obtained results are compared to examine the effect of spatial outliers. Spatial outliers remarkably deteriorate the prediction accuracy except for that of LUR model for NO 2. This discrepancy of the effect might be due to the difference in the characteristics of PM 2.5 and NO 2. The difference in the number of observations makes a limited contribution to it. Although further investigation

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“…For example, Sun et al . () reported regional‐scale spatial and temporal change of SOM using geostatistics, but there was no purpose indicated in their paper. Liao et al .…”

Section: Introductionmentioning

confidence: 99%

“…Some research has been conducted in this area. For example, Sun et al (2012) reported regional-scale spatial and temporal change of SOM using geostatistics, but there was no purpose indicated in their paper. Liao et al (2009) detailed regional increase in soil organic carbon from 1982 to 2004, but the influencing factors were not explored.…”

Section: Introductionmentioning

confidence: 99%

Driving forces of soil organic matter change inJiangsuProvince ofChina

Zhao

Zhang

et al. 2015

Soil Use and Management

Self Cite

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Soil organic matter (SOM) is a key property determining soil functions and a major form of carbon stored in soil. Understanding the spatial and temporal variability of SOM and the driving forces responsible for spatial and temporal changes is important to assess regional soil quality and carbon sequestration potential and, particularly, to establish better practices for land use and management. We evaluated the spatio‐temporal change in SOM content from 1979–1982 to 2006 and its driving forces in Jiangsu Province, East China, using geostatistics. The results showed that mean SOM content increased from 16.60 ± 8.50 to 18.31 ± 8.32 g/kg over a 26‐yr period. The maps of SOM generated by ordinary kriging represented the increasing trend from north to south across the province in the two periods. The level of SOM in 1979–1982 affected the pattern of change: the SOM increasing in areas initially with a small content while decreasing in areas having a large content. The map of SOM change showed that the rate of increase decreased from north to south within the province. Increased fertilizer application promoted crop production with more residual biomass being retained in the soil, which resulted in increased SOM content. Land use changes to paddy, upland or forest improved SOM content, whereas abandoning land reduced SOM content.

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Spatio‐temporal change of soil organic matter content of Jiangsu Province, China, based on digital soil maps

Cited by 23 publications

References 29 publications

Drivers of spatio-temporal changes in paddy soil pH in Jiangxi Province, China from 1980 to 2010

Drivers of spatio-temporal changes in paddy soil pH in Jiangxi Province, China from 1980 to 2010

Effect of spatial outliers on the regression modelling of air pollutant concentrations: A case study in Japan

Driving forces of soil organic matter change inJiangsuProvince ofChina

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