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This article in SSSAJ

  1. Vol. 43 No. 3, p. 450-455
     
    Received: Feb 6, 1978
    Published: May, 1979


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doi:10.2136/sssaj1979.03615995004300030006x

Variability of Mechanical Impedance in a Tilled One-hectare Field of Norfolk Sandy Loam1

  1. D. K. Cassel and
  2. L. A. Nelson2

Abstract

Abstract

The spatial variability of field-measured values of mechanical impedance makes it difficult to detect significant differences in impedance effected by different management and/or tillage practices. This study was conducted to examine sources of cone index (CI) variation and to develop models to predict CI in a 1-ha field of Norfolk sandy loam for three different tillage practices.

Cone index measurements, obtained using a hydraulic cone penetrometer, were collected in four randomized blocks (B) for each of three tillage treatments imposed prior to planting. Tillage treatments were mold-board plowed plus disked (N); chiselplowed (C); and subsoiled plus bedded (S). Penetrometer probes in each plot were taken at seven positions (P) on four transects normal to the soybean [Glycine max (L.) Merr.] rows and to a depth (D) of 41 cm. The maximum CI value within the 0- to 14-, 14- to 28-, and 28- to 41-cm depth increments for each probe was used in the statistical analyses.

The best predictive model for each tillage treatment was selected based upon the correlation coefficient, coefficient of variation, standard deviation, and the normality of the residuals as tested by the Kolmogorov-Smirov statistic. Using these statistics, it was determined that the CI data must undergo a log transformation prior to model development. As much as 40% of the observed spatial variability for treatment N was explained by the full model. The depth variable (D) was the only significant term. The D, P, and P × D terms in the CI model were significant at the 0.01 probability level for treatment S and the full model containing these terms accounted for 71% of the variation. Finally, the D and B terms were significant at the 0.01 probability level and the model accounted for 63% of the spatial variability encountered in treatment C.

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