A Two-Factor Empirical Deterministic Response Surface Calibration Model for Site-Specific Predictions of Lime Requirement

A Two-Factor Empirical Deterministic Response Surface Calibration Model for Site-Specific... This paper describes the development of an empirical deterministic two-factor response surface model for the Woodruff lime-requirement buffer (WRF). The model may be used to produce variable-rate lime requirement maps, or to predict lime requirements in real-time. Hence it may be suitable as a component of a decision support system (DSS) for the site-specific management of acid soil. The models' predictions were compared to those of a one-factor response surface, and those of a linear regression. The models tested were validated against soil-CaCO3 incubations using a statistical jackknifing procedure for error and bias estimations. The Akaike Information Criterion (AIC) was used to ascertain the best model in terms of goodness of fit and parsimony. The two-factor response surface model produced the best lime requirement estimates, followed by the single-factor model, then the conventional linear regression. The advantages of the response surface models are their improved prediction accuracy, and their flexibility in the choice of any target pH (from pH 5.5 to 7) without the need for excessive calibrations. The uncertainty of the model was assessed using data from an agricultural field in Kelso, New South Wales, Australia. Block kriged maps of soil pH measured in 0.01 M CaCl2 (pHCaCl2), WRF buffer pH (pHbuffer) and lime requirements to a target pH of 7 were produced, to compare their spatial distributions. Finally the economic and agronomic benefits of site-specific liming were considered. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

A Two-Factor Empirical Deterministic Response Surface Calibration Model for Site-Specific Predictions of Lime Requirement

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Kluwer Academic Publishers
Copyright © 2000 by Kluwer Academic Publishers
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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