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Coregionalized Single‐ and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth
Author(s) -
Banerjee Sudipto,
Johnson Gregg A.
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2006.00535.x
Subject(s) - computer science , bayesian probability , geospatial analysis , gaussian , growth curve (statistics) , spatial analysis , data mining , statistics , artificial intelligence , mathematics , cartography , geography , physics , quantum mechanics
Summary Modeling of longitudinal data from agricultural experiments using growth curves helps understand conditions conducive or unconducive to crop growth. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data that help understand spatial patterns. A particularly common problem is capturing spatial variation in growth patterns over the entire experimental domain. Statistical modeling in these settings can be challenging because agricultural designs are often spatially replicated , with arrays of subplots, and interest lies in capturing spatial variation at possibly different resolutions . In this article, we develop a framework for modeling spatially varying growth curves as Gaussian processes that capture associations at single and multiple resolutions. We provide Bayesian hierarchical models for this setting, where flexible parameterization enables spatial estimation and prediction of growth curves. We illustrate using data from weed growth experiments conducted in Waseca, Minnesota, that recorded growth of the weed Setaria spp. in a spatially replicated design.