Spatial distribution of soil organic carbon concentrations in grassland of Ireland

McGrath, D.; Zhang, Chaosheng

doi:10.1016/s0883-2927(03)00045-3

Cited by 161 publications

(79 citation statements)

References 17 publications

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“…Outliers, especially spatial outliers, in the data set can make the variogram exhibit erratic behavior (McGrath and Zhang, 2003). The spatial outliers are the values that are obviously different from the values of their surrounding locations (Lalor and Zhang, 2001).…”

Section: Spatial-outlier and Spatial-cluster Analysesmentioning

confidence: 99%

“…Considering the positively skewed distribution of soil P (Tab. 2), more robust statistics such as median is recommended as its representative mean value, while the arithmetic mean (average) should be discarded for this variable (McGrath and Zhang, 2003). The raw pH values ranged from 4.72 to 6.36, and the average was equal to the median and geometric mean, with a small standard-deviation value, revealing a possible normal distribution.…”

Section: Descriptive Parameters Of the Raw-data Setsmentioning

confidence: 99%

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Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Tunney

Zhang

2010

Z. Pflanzenernähr. Bodenk.

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The spatial variation of soil test P (STP) in grassland soils is becoming important because of the use of STP as a basis for policies such as the recently EU-introduced Nitrate Directive. This research investigates the spatial variation of soil P in grazed grassland plots with a long-term (38 y) experiment. A total of 326 soil samples (including 14 samples from an adjacent grasswood buffer zone) were collected based on a 10 × 10 m 2 grid system. The samples were measured for STP and other nutrients. The results were analyzed using conventional statistics, geostatistics, and a geographic information system (GIS). Soil test P concentrations followed a lognormal distribution, with a median of 5.30 mg L -1 and a geometric mean of 5.35 mg L -1 . Statistically significant (p < 0.01) positive correlation between STP and pH was found. Spatial clusters and spatial outliers were detected using the local Moran's I index (a local indicator of spatial association) and were mapped using GIS. An obvious low-value spatial-cluster area was observed on the plots that received zero-P fertilizer application from 1968 to 1998 and a large high-value spatial-cluster area was found on the relatively high-P fertilizer application plots (15 kg ha -1 y -1 ). The local Moran's I index was also effective in detecting spatial outliers, especially at locations close to spatial-cluster areas. To obtain a reliable and stable spatial structure, semivariogram of soil-P data was produced after elimination of spatial outliers. A spherical model with a nugget effect was chosen to fit the experimental semivariogram. The spatial-distribution map of soil P was produced using the kriging interpolation method. The interpolated distribution map was dominated by medium STP values, ranging from 3 mg to 8 mg L -1 . An evidently low-P-value area was present in the upper side of the study area, as zero or short-term P fertilizer was applied on the plots. Meanwhile, high-P-value area was located mainly on the plots receiving 15 kg P ha -1 y -1 (for 38 y) as these plots accumulated excess P after a long-term P-fertilizer spreading. The high-or low-value patterns were in line with the spatial clusters. Geostatistics, combined with GIS and the local spatial autocorrelation index, provides a useful tool for analyzing the spatial variation in soil nutrients.

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Section: Spatial-outlier and Spatial-cluster Analysesmentioning

confidence: 99%

Section: Descriptive Parameters Of the Raw-data Setsmentioning

confidence: 99%

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Tunney

Zhang

2010

Z. Pflanzenernähr. Bodenk.

Self Cite

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“…A better understanding of soil quality variation as influenced by human activities is important in order to improve sustainable land use management practices (McGrath & Zhang 2003), provide early warning of adverse trends, identify problem areas (Bindraban et al 2000) and provide a valuable base against which subsequent and future measurements can be evaluated. The objectives of this study were to evaluate soil quality variation in a typical agricultural ecosystem of the YRD region, from 1980 to 2004, using Zhangjiagang County as the study area, and to assess the effects of human activities on soil quality variation.…”

Section: Introductionmentioning

confidence: 99%

Impacts of agricultural land management on soil quality after 24 years: a case study in Zhangjiagang County, China

Huang

Darilek

2011

New Zealand Journal of Agricultural Research

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“…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%

“…These changes might have influenced the SOM level and spatial distribution patterns in agricultural systems. a better understanding of the spatiotemporal variability of SOM and related factors is important for refining agricultural management practices and improving sustainable land use (Mcgrath & Zhang 2003). it provides a valuable baseline against which subsequent and future measurements can be evaluated (Liu et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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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Spatial distribution of soil organic carbon concentrations in grassland of Ireland

Cited by 161 publications

References 17 publications

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Impacts of agricultural land management on soil quality after 24 years: a case study in Zhangjiagang County, China

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

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