Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China

Sun, Bо; Zhou, Shenglü; Zhao, Qian

doi:10.1016/s0016-7061(03)00078-8

Cited by 274 publications

(199 citation statements)

References 16 publications

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“…in recent years, interest has grown in studying the spatiotemporal variability of S oM because of its extreme importance in agricultural development and environment health (goovaerts 1998;Sun et al 2003;Liu et al 2006). Changes in SoM are attributable to both natural processes and to human activities (e.g., soil management practices, fertilisation, and crop rotation) (Turner & Lambert 2000;Sun et al 2003) and the importance of human activities has been widely recognised.…”

Section: Introductionmentioning

confidence: 99%

“…Changes in SoM are attributable to both natural processes and to human activities (e.g., soil management practices, fertilisation, and crop rotation) (Turner & Lambert 2000;Sun et al 2003) and the importance of human activities has been widely recognised. Land-use changes, including deforestation, biomass burning, draining wetlands, ploughing, the use of mineral fertilisers, and other agricultural practices, are regarded as the main factors causing the loss of soil organic carbon and the emission of Co 2 to the atmosphere (Zhang & Mcgrath2004).…”

Section: Introductionmentioning

confidence: 99%

“…Land-use changes, including deforestation, biomass burning, draining wetlands, ploughing, the use of mineral fertilisers, and other agricultural practices, are regarded as the main factors causing the loss of soil organic carbon and the emission of Co 2 to the atmosphere (Zhang & Mcgrath2004). geostatistics has been successfully applied to quantify the distribution of the spatial patterns and changes in SoM (frogbrook & oliver 2001 ;Sun et al 2003;Wei et al 2006;huang et al 2007). geostatistics is based on the theory of a "regionalised variable" (Matheron 1963), i.e., one that is distributed in space with spatial coordinates.…”

Section: Introductionmentioning

confidence: 99%

“…The concepts of geostatistics are explained in several textbooks (Cressie 1993;goovaerts 1997;Clark & harper 2000;Webster & oliver 2001). Several studies have specifically examined the spatial variability in SoM using geostatistical techniques and most of them have focused on largescale variability (Mapa & Kumaragamage 1996;Mcgrath & Zhang 2003;Liu et al 2006;huang et al 2007), or on the methodology for studying spatial variability (goovaerts 1998;Sun et al 2003). only a few studies on small-scale areas have been reported (Wang & gong 1998;Sun et al 2003;Wei et al 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have specifically examined the spatial variability in SoM using geostatistical techniques and most of them have focused on largescale variability (Mapa & Kumaragamage 1996;Mcgrath & Zhang 2003;Liu et al 2006;huang et al 2007), or on the methodology for studying spatial variability (goovaerts 1998;Sun et al 2003). only a few studies on small-scale areas have been reported (Wang & gong 1998;Sun et al 2003;Wei et al 2006). in addition, relatively few studies have characterised the temporal changes in SoM and related factors at a regional scale, although many studies have been conducted at the plot level, monitoring SoM over the long term (Clark et al 1998;aref & Wander 1998;bhandari et al 2002;Jiang et al 2006).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Temporal and spatial variability of soil organic matter in a county scale agricultural ecosystem

Yuan

Chai

Gao

et al. 2007

New Zealand Journal of Agricultural Research

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In agricultural ecosystems, soil organic matter (SOM) is a major determinant and indicator of soil fertility and quality, and is closely related to crop productivity. This paper examines the temporal and spatial variation of SOM in Pinggu district at a county scale using data for 1980 and 2005 and traditional statistics, geostatistics, and geographic information system (GIS) analyses. Pinggu district, Beijing, China is an area characterised by changing farming practices and land uses. A lognormal distribution feature was observed for both datasets, and the difference between them was significant (t-test). The geostatistical characteristics were analysed using a GIS platform. andrelative parameters were developed. The spatial distribution maps for 1980 and 2005 were generally similar, with the SOM content increasing gradually from south-west to north-east. The difference between the two maps was determined using map algebraic functions and was found to show considerable spatial change. Changes in farming practices and land use are thought to be the main reason for the temporal change. Therefore, a combination of geostatistics and GIS map algebra provides a useful tool for examining spatiotemporal changes in the environmental sciences and may detect features that are not discernible when only conventional statistics are used.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Temporal and spatial variability of soil organic matter in a county scale agricultural ecosystem

Yuan

Chai

Gao

et al. 2007

New Zealand Journal of Agricultural Research

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Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Wang

Zhang

et al. 2018

Earth Surf Processes Landf

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Near soil surface characteristics change significantly with vegetation restoration, and thus, restoration strategies likely affect soil erodibility. However, few studies have been conducted to quantify the effects of vegetation restoration strategies on soil erodibility in regions experiencing rapid vegetation restoration. This study was conducted to evaluate the effects of vegetation restoration strategies on soil erodibility, reflected by soil cohesion (Coh), penetration resistance (PR), saturated conductivity (Ks), number of drop impacts (NDI), mean weight diameter of soil aggregates (MWD), and soil erodibility K factor on the Loess Plateau. One slope farmland and five 25‐year‐restored lands covered by old world bluestem, korshinsk peashrub, shrub sophora, sea‐buckthorn, and black locust were selected as test sites. The old world bluestem was restored via natural succession, while the other four lands were restored by artificial planting. A comprehensive soil erodibility index (CSEI) was produced by a weighted summation method to quantify the effects of vegetation restoration strategies on soil erodibility completely. The results showed that Coh, Ks, NDI, and MWD of the five restored lands were greater than those of the slope farmland. However, the PR and K of the five restored lands were less than those of the slope farmland. CSEI varied greatly under different restoration strategies, from 1 to 0.214. Compared with the control, these indices decreased on average by 68.2%, 78.6%, 72.7%, 75.8%, and 62.8% for old world bluestem, korshinsk peashrub, shrub sophora, sea‐buckthorn, and black locust, respectively. The variation in soil erodibility was significantly influenced by biological crust thickness, bulk density, organic matter content, plant litter density, and root mass density. Shrub‐lands via artificial planting, especially korshinsk peashrub, were considered the most effective restoration strategies to reduce soil erodibility on the Loess Plateau. The results are helpful for selecting vegetation restoration strategies and asking their benefits in controlling soil erosion. © 2018 John Wiley & Sons, Ltd.

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Diagnosing Drainage Problems in Coastal Areas Using Machine‐Learning and Geostatistical Models

Darzi‐Naftchali

Karandish

Asgari

2017

Irrigation and Drainage

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This study focuses on diagnosing drainage problems in the coastal areas of Iran by using geostatistical methods, support vector machines (SVMs) and the adaptive neuro‐fuzzy inference system (ANFIS). Groundwater level (WD) and quality were monitored at 37 shallow wells scattered over a 25 000 ha area at different times. Using prepared raster maps of pH, ESP, EC and WD by the best method, drainage problems were categorized into eight classes. Both SVM and ANFIS models significantly improved predicted data for pH, ESP, EC and WD compared with geostatistical models, while SVMs provided slightly better results which were used for further analysis. More than 60% of the area needs drainage to lower the groundwater table in pre‐planting and post‐harvest periods, while during the growing seasons, more than about 72% of the area requires drainage for salinity control. Based on the results, identifying drainage problems at basin scale is possible with cost‐efficient machine learning models with minimum time and data requirements and investment for detailed field surveys. Copyright © 2017 John Wiley & Sons, Ltd.

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Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China

Cited by 274 publications

References 16 publications

Temporal and spatial variability of soil organic matter in a county scale agricultural ecosystem

Temporal and spatial variability of soil organic matter in a county scale agricultural ecosystem

Soil erodibility as impacted by vegetation restoration strategies on the Loess Plateau of China

Diagnosing Drainage Problems in Coastal Areas Using Machine‐Learning and Geostatistical Models

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