JSM 2004 - Toronto

Abstract #302126

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Activity Number: 121
Type: Contributed
Date/Time: Monday, August 9, 2004 : 12:00 PM to 1:50 PM
Sponsor: Biometrics Section
Abstract - #302126
Title: Regression Neural Nets and Trees for Modeling Factors Effecting Cotton Yield
Author(s): Andy Mauromoustakos*+
Companies: University of Arkansas
Address: 101 AGRX, Fayetteville, AR, 72701,
Keywords: regression ; neural nets ; trees
Abstract:

Meloidogyne incognita and Thielaviopsis basicola population densities and influential soil factors were evaluated in 2001 and 2002 in a natural infested field to determine their effects on 2002 yield using various statistical models. A 15-acre field was subdivided into 512 plots (100ft x 4 rows) and sampled for M. incognita in April, May, July, and October; T. basicola in April and October; and soil fertility and soil texture in April. Telone II was applied at 0, 1.5, 3, and 4.5 gal/acre for nematode control comparisons for both years. Plots were aggregated and averaged (from 512 to 40) based on four soil textural ranges (0-30 % sand, 31-45 % sand, 46-60 % sand, and >60 % sand) and the aforementioned Telone II applications. Statistical analyses were conducted using JMP SAS software. Statistical models fitted to the data included multiple regression, neural networks, and partitions trees. The data variables used by all three models included percent sand, Telone II, M. incognita (April 2002), and T. basicola (October 2001). Results of all three models explained at least 89 % of the variability of the 2002 yield.

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