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This article in AJ

  1. Vol. 98 No. 3, p. 554-561
     
    Received: May 30, 2005
    Published: May, 2006


    * Corresponding author(s): gjschw2@uky.edu
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doi:10.2134/agronj2005.0164

Univariate Distribution Analysis to Evaluate Variable Rate Fertilization

  1. E. M. Pena-Yewtukhiwa,
  2. Gregory J. Schwab *b and
  3. L. W. Murdockc
  1. a Division of Plant and Soil Sciences, West Virginia Univ., P.O. Box 6108, Morgantown, WV 26506
    b Dep. of Agronomy, Univ. of Kentucky, N-122 Agric. Sci. North, Lexington, KY 40546
    c Univ. of Kentucky Research & Education Center, 1205 Hopkinsville Street, P.O. Box 469, Princeton, KY 42445-0469

Abstract

Technological advances in precision fertilization such as yield monitors and remote sensing are increasing the density of samples collected and decreasing the scale inputs can be managed in the field. A compounding problem is that fertilizer applications can often be made at a much smaller scale than yield data can be collected. Analytical tools such as ANOVA and geostatistics can be used on high-density data sets; however, these analytical tools do not provide all the information required to test research ideas. An alternative to solve this problem is the use of statistics not traditionally applied to precision agricultural experiments. The objective of this study was to determine if univariate distribution (population statistics) analysis is useful in the study of wheat (Triticum aestivum L.) yield response to variable-rate N fertilization strategies using active optical sensors in red and near-infrared bands (NDVI [normalized difference vegetative index] sensors). Measurements of NDVI and fertilizer applications were on a 0.56-m2 basis, while wheat yield data were collected at a 2-m2 scale. Classical ANOVA was conducted to compare treatment effects. Analysis of univariate distributions for NDVI and wheat yield monitor data sets was used to further evaluate the effect of the treatments. In addition to a significant effect on the mean NDVI and yield, fetilizer staregies affected the normality, median, mode, skewness, and kurtosis of the resulting NDVI and yield distributions. Unlike ANOVA, the analyses of univariate distributions provided an insight on those portions of the NDVI and yield populations responsible for changes in the mean.

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