The spatial prediction of soil mineral N and potentially available N using elevation

Baxter, S.J.; Oliver, M. A.

doi:10.1016/j.geoderma.2005.04.013

Cited by 65 publications

(30 citation statements)

References 19 publications

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“…Methods such as co-kriging, kriging with external drift, regression kriging or simple regression (e.g. Baxter and Oliver, 2005;Bhatti et al, 1991;Delin and Söderström, 2003;McBratney et al, 2000) can be used with such ancillary data to improve the accuracy of estimates, but they still require adequate data for the principle variable, which is seldom the case in practice. Therefore, it is important to find methods that increase the number of observations without increasing the cost.…”

Section: Introductionmentioning

confidence: 99%

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

Wetterlind¹,

Stenberg²,

Söderström³

2008

Precision Agric

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The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared (MIR) reflection from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a costeffective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.

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Section: Introductionmentioning

confidence: 99%

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

Wetterlind¹,

Stenberg²,

Söderström³

2008

Precision Agric

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“…Various studies investigated relationships between soil chemical properties and landscape features using advances in soil-landscape modeling (e.g., Moore et al, 1993;Gessler et al, 1995;Florinsky et al, 2002;Park and Burt, 2002;Baxter and Oliver, 2005;Henderson et al, 2005), but few dealt with the tropics (e.g., Holmes et al, 2005). Soil-landscape modeling helps to predict the spatial patterns of soil properties and to regionalize point observations by quantifying the relationship between environmental variables and soil properties of interest.…”

Section: Introductionmentioning

confidence: 99%

Soil nutrient–landscape relationships in a lowland tropical rainforest in Panama

Barthold

Stallard

Elsenbeer

2008

Forest Ecology and Management

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“…several kriging methods (Baxter & oliver 2005;Bourennane et al 2006;goovaerts 2000). in addition, in terms of the correlation thresholds between the target variables and the secondary variables, goovearts (2000) concluded that accounting for elevation using multivariate geostatistical algorithms generally reduces the univariable kriging prediction error as long as the correlation coefficient is larger than 0.75. a similar correlation threshold has been reported by asli & Marcotte (1995) who further concluded that the introduction of secondary information in estimation seems worthwhile only for correlations above 0.4.…”

Section: * Linmentioning

confidence: 99%

Accuracy and uncertainty of spatial patterns of soil organic matter

Chai¹,

Huang²,

Yuan³

2007

New Zealand Journal of Agricultural Research

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Accurate quantification of the spatial patterns of soil organic matter (SOM) is essential for both SOM management and for the application of SOM models. The objective of this study is to determine whether elevation could be used to increase the accuracy of spatial predictions and the corresponding prediction uncertainty of soil organic matter. The sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were compared with respect to the accuracy of predictions as well as to the uncertainty inherent in the spatial prediction of soil organic matter. The SGS algorithm accounted for only the SOM data. The SGCS accounted for both SOM data and intensive elevation data. The root mean square errors revealed that the more accurate simulations were those accounting for intensive elevation inform ation by the SGCS method for the two areas compared with SGS. As regards modelling local uncertainty, SGCS performed better at modelling prediction uncertainty than SGS. In addition, the results of assessing the standard deviation confirmed that the exhaustive elevation data could be used to reduce the spatial uncertainty of SOM by SGCS compared with the SGS algorithm.

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The spatial prediction of soil mineral N and potentially available N using elevation

Cited by 65 publications

References 19 publications

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

Soil nutrient–landscape relationships in a lowland tropical rainforest in Panama

Accuracy and uncertainty of spatial patterns of soil organic matter

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