Spatial variability of Southeastern U.S. Coastal Plain soil physical properties: Implications for site-specific management

Duffera, Miressa; White, Jeffrey G.; Weisz, Randy

doi:10.1016/j.geoderma.2006.08.018

Cited by 147 publications

(98 citation statements)

References 25 publications

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“…The spatial dependence of K s has been also reported for surface soil (0-15 cm) in an agricultural field located in southern New Mexico (Sharma et al, 2011; Table 10). The important difference between different datasets presented in Table 10 is that spatial variability of K s was reported as moderate by Shukla et al (2004) and Iqbal et al (2005), weak by Duffera et al (2007) and strong by Sharma et al (2011). Looking at the CV (Table 2 for some of the data), in these studies K s was reported always as highly variable.…”

Section: Spatial Variability Of Saturated Hydraulic Conductivitymentioning

confidence: 86%

“…Table 10. Spatial variability of saturated hydraulic conductivity (K s * ) presented using semivariogram parameters for an experimental farm, Austria (Shukla et al, 2004), a 162-ha cotton field Perthshire, Mississippi, USA (Iqbal et al, 2005), a 12-ha field in Kingston, North Carolina, USA (Duffera et al, 2007) and a 40-ha field in Las Cruces, New Mexico, USA (Sharma et al, 2011 (Shukla et al, 2004;Duffera et al, 2007); and cm d -1 (Iqbal et al, 2005;Sharma et al, 2011); § NSR: Nugget to sill ratio (%); S is strong spatial dependence (NSR<25%), M is moderate spatial dependence (25%<NSR<75%) and W is weak spatial dependence (NSR>75%) using the criteria suggested by Cambardella et al (1994) The short-term temporal variability of K s in the 3, 14 and 32-year fields resulted from seasonal changes and crop management practices (e.g., irrigation of soybeans, flooding of rice, tillage, disking), while the short-term variability of K s in the prairie resulted from climate influences on the biological activities of the grasses and microorganisms. Although not consistent, in general a pattern emerged and K s values started increasing from spring until early summer, remained similar until early winter and then decreased.…”

Section: Temporal Variability Of Saturated Hydraulic Conductivitymentioning

confidence: 99%

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Variability of Hydraulic Conductivity Due to Multiple Factors

Deb

Shukla

2012

American Journal of Environmental Sciences

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Soil properties are greatly influenced by intrinsic factors of soil formation as well as extrinsic factors associated with land use and management and vary both in time and space. Intrinsic variability is caused by the pedogenesis and usually takes place at large time scales. The variability caused by extrinsic factors could take effect relatively quickly and could not be treated as regionalized. Saturated hydraulic conductivity is one of the most important soil properties for soil-water-plant interactions, water and contaminant movement and retention through the soil profile. It is a critically important parameter for estimation of various other soil hydrological parameters necessary for modeling flow through the naturally unsaturated vadose zone. Among different soil hydrological properties, saturated hydraulic conductivity is reported to have the greatest statistical variability, which is associated with soil types, land uses, positions on landscape, depths, instruments and methods of measurement and experimental errors. The variability of saturated hydraulic conductivity has a profound influence on the overall hydrology of the soil system. Therefore, focus of this review is centered on the variability of saturated/unsaturated hydraulic conductivity due to a large number of factors. This study reviews recent experimental and field studies addressing the measurements and variability of hydraulic conductivity. A synthesis of a large amount of data available in literature is presented and the possible sources of the variability and its implications are discussed. The variability of a soil hydraulic conductivity can be expressed by range, interquartile range, variance and standard deviation, coefficient of variation, skewness and kurtosis. The spatial and temporal variability of hydraulic conductivity and the influences of sample support, measurement devices/methods, soils, land uses and agricultural management on hydraulic conductivity are evaluated. Methods of measurements strongly impact variability, for example, saturated hydraulic conductivity measured using a single ring may produce significantly different mean and standard errors than those measured using a double ring. The sample support can also influence the variability, for example, increasing or decreasing the size of the infiltrometer rings can change the mean and variability of the saturated hydraulic conductivity. Similarly, hydraulic conductivity measured in the field could show a much larger variability than those measured in the laboratory. The spatial and temporal variations of hydraulic conductivity and interactions among soil characteristics, land uses, agricultural management, climatic and environmental conditions and measurement methods are rather complex, which should take into account multiple factors discussed in this review. Decisions and choices made by investigators during sampling, sampling designs, availability of resources, number of investigators involved in sampling and analysis, skill level of investigators, type and qua...

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Section: Spatial Variability Of Saturated Hydraulic Conductivitymentioning

confidence: 86%

Section: Temporal Variability Of Saturated Hydraulic Conductivitymentioning

confidence: 99%

Variability of Hydraulic Conductivity Due to Multiple Factors

Deb

Shukla

2012

American Journal of Environmental Sciences

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“…Precision agriculture seeks to identify, analyze, and manage spatial and temporal variability within fields in order to optimize profitability, sustainability, and environmental protection (Robert et al, 1996;Duffera et al, 2007). At present, the use of site-specific management zones, rather than the traditional whole field approach, is a popular approach for farm managers to manage field variability on a site-specific basis.…”

Section: Introductionmentioning

confidence: 99%

Determination of potential management zones from soil electrical conductivity, yield and crop data

Shi

et al. 2008

J. Zhejiang Univ. Sci. B

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Abstract:One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper, the variables of soil electrical conductivity (EC) data, cotton yield data and normalized difference vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources, and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georeferenced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and productivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone, and management zone 3 presented the highest nutrient level and potential crop productivity, whereas management zone 1 the lowest. Based on these findings, we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils, and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.

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“…The geostatistical parameters describing soil properties from adata set were listed in Table 3.Regression coefficients (R 2 )suggested that all models were best fitowing to the R 2 value (greater than 0.5) of the best-fitted model (Duffera et al, 2007).…”

Section: Geostatisticsmentioning

confidence: 99%