Determination of spatial distribution patterns of clay and plant available potassium contents in surface soils at the farm scale using high resolution gamma ray spectrometry
Pracilio, G., Adams, M.L., Smettem, K.R.J. and Harper, R.J. (2006) Determination of spatial distribution patterns of clay and plant available potassium contents in surface soils at the farm scale using high resolution gamma ray spectrometry. Plant and Soil, 282 (1-2). pp. 67-82.
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Variation in dryland crop yield is often related to underlying soil properties such as water availability and soil fertility. There are often significant difficulties in adequately defining the spatial distribution of such properties at the farm scale. Gamma ray spectrometry (radiometrics) is a relatively new soil sensing technique that can potentially address this by improving the mapping of soil texture and plant available potassium (bic-K). Three sites North Nolba, South Nolba and Summerset were investigated using exploratory linear correlation analysis. Mapping analysis was focused on the Summerset site. In contrast to the two Nolba sites, the soils from Summerset had sufficient soil texture range and parent material conditions that allowed for calibrations to be developed. Soil properties were mapped at Summerset using multivariate linear regression and tree-based models with radiometric, topographic and location data as the inputs. A multivariate linear regression analysis using radiometric data was associated with greater than 70% of the variance in bic-K and soil texture at Summerset. Field checked maps indicated that up to 66% and 60% of the variation in clay and bic-K contents respectively, could be predicted. The overall lowest map errors in root mean square error (RMSE) were 2.4 dag/kg clay and 103 mg/kg bic-K contents. This study concludes that for a site with weathered soils of sufficient soil texture range, radiometrics can reliably predict clay and plant available potassium contents. Radiometrics has practical farm scale applications at a precision that is useful for understanding potential yield variation across a farm.
|Publication Type:||Journal Article|
|Publisher:||Kluwer Academic Publishers|
|Copyright:||© Springer 2006.|
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