Smoothing Data with Cubic Splines1
- B. A. Kimball2
Agronomic data frequently requires smoothing in order to obtain a reliable functional relationship for interpolating, predicting, or determining the rate of change of one variable with respect to another. To test whether cubic spline functions could provide satisfactory smoothing, the necessary equations were derived, computer programs written, and several sets of soil temperature and water content data were smoothed.
Cubic spline smoothing displayed the following, advantages: 1) Because spline functions are defined piecewise, they can represent any variable arbitrarily well over wide ranges of the other. 2) The data can be obtained at unequal intervals, so high sampling rates can be used where changes are rapid and low rates where they are slow. 3) Additionally, the gradients derived from cubic spline functions are smoothly joined parabolas, not the abruptly joined straightline segments characteristic of parabolic spline smoothing.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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