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Activity Number: 531 - SPEED: Statistical Computing: Methods, Implementation, and Application, Part 2
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Consulting
Abstract #307953
Title: Incorporating Spatial Statistics into Routine Analysis of Agricultural Field Trials
Author(s): Julia Piaskowski* and Chad Jackson and Juliet Marshall and William J Price
Companies: University of Idaho and University of Idaho and University of Idaho and University of Idaho
Keywords: agriculture; spatial statistics; statistical consulting; R; SAS

Agricultural field experiments commonly employ standard experimental designs such as randomized complete block to control for field heterogeneity. However, there can be substantial spatial variation not fully captured by blocking, particularly in large experiments. Although spatial statistics have demonstrated effectiveness in controlling localized spatial variation, they are rarely integrated into analysis of agricultural field experiments. Our aim was to create demonstrations in R and SAS to enable routine incorporation of spatial statistics into agricultural field trials. We developed a set of comparative scripts that fit several spatial models and chose an optimal model based on a user-specified criterion such as AIC. To demonstrate this approach, wheat variety trial data from the University of Idaho comprising 140 varieties grown in 59 separate environments were obtained. We evaluated autoregressive, moving average and mixed models with spatial covariance structures. By developing these scripts, we aim to lower barriers-to-entry in both spatial statistics and programming so that researchers can incorporate spatial statistics into their field trial analysis.

Authors who are presenting talks have a * after their name.

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