Agronomy Journal Journal of Natural Resources and Life Sciences Education
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Published online 17 June 2005
Published in Agron J 97:1082-1096 (2005)
DOI: 10.2134/agronj2004.0130
© 2005 American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA
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Spatial Analysis of Precision Agriculture Treatments in Randomized Complete Blocks

Guidelines for Covariance Model Selection

Nan Honga, Jeffrey G. Whiteb,*, Marcia L. Gumpertzc and Randy Weiszd

a Dep. of Crop and Soil Science, 116 ASI Building, The Pennsylvania State Univ., University Park, PA, 16802
b Dep. of Soil Science, North Carolina State Univ., Raleigh, NC 27695-7619
c Dep. of Statistics, North Carolina State Univ., Raleigh, NC 27695-8203
d Dep. of Crop Science, North Carolina State Univ., Raleigh, NC 27695-7620



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Fig. 1. Schematic representation of the best-fit covariance model selection procedure for RCB analyses in the presence of spatial correlation. The LRT denotes likelihood ratio {chi}2 test. AIC denotes Akaike Information Criterion.

 


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Fig. 2. (a) Field layout of the randomized complete block design at the Lower Coastal Plain Tobacco Research Station, Kinston, NC. Adjacent plots with the same shading pattern are in the same block (three plots per block). The treatment number is indicated in each plot; (b) wheat forage yield map; and (c) corn grain yield map. RYE = Realistic Yield Expectation N management; FA = field-average N management; SSNM = site-specific N management.

 


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Fig. 3. Spatial correlation of wheat forage and corn grain yield illustrated by the isotropic semivariograms of the original observations (h), the residuals from fitting a model with just fixed effects e(h), and the RCBiid model-fit residuals e(h)RCB. The semivariances were divided by 100000 before plotting.

 





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