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Title: AN AUTOREGRESSION MODEL FOR A PAIRED WATERSHED COMPARISON

Author
item Meek, David
item Dinnes, Dana
item Jaynes, Dan
item Cambardella, Cynthia
item Colvin, Thomas
item Hatfield, Jerry
item Karlen, Douglas

Submitted to: Applied Statistics In Agriculture Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2000
Publication Date: 12/1/2000
Citation: MEEK, D.W., DINNES, D.L., JAYNES, D.B., CAMBARDELLA, C.A., COLVIN, T.S., HATFIELD, J.L., KARLEN, D.L. AN AUTOREGRESSION MODEL FOR A PAIRED WATERSHED COMPARISON. APPLIED STATISTICS IN AGRICULTURE CONFERENCE PROCEEDINGS. 2000. P. 223-231.

Interpretive Summary:

Technical Abstract: Analysis of water quality data from a paired watershed design is needed to determine if a best fertilizer management practice reduces a specific water quality variable compared to a conventional fertilizer management practice. This study examines an existing recommended method of analysis for paired watershed designs, simple analysis of covariance (ANCOVA) on time aggregated data, then via a comparison, offers two autoregression analyses (AR) as alternatives. The first and simpler approach models the sequence of paired differences and estimates its 95% confidence band. The second approach develops individual watershed AR models then examines the joint 95% confidence interval about the predicted difference. A reliability analysis on the water quality data reveals that the data for the controlled watershed, i.e., the covariate, has a sizable measurement error, a factor that is not considered in the usual ANCOVA model. The AR methods avoid the measurement error and other inherent problems with the published recommended method. Graphically both AR analyses are similar and reveal three distinct trend phases: a period of continued similarity; a period of transition; and a period of sustained change. The AR model for the sequence of paired differences is easy to use and interpret because its trend model of splined linear segments readily defines each response phase; hence, we recommend it over alternatives. It offers water resources researchers an effective and readily adoptable analysis option.