Conference on Applied Statistics in Agriculture

DETERMINING THE EFFECTIVESNESS OF INCLUDING SPATIAL INFORMATION INTO A NEMATODE/NUTSEDGE PEST COMPLEX MODEL

An experiment was performed in 2005-2006 to determine if a nematode-resistant variety of alfalfa (Medicago sativa L.) can effectively reduce the pest complex consisting of yellow and purple nutsedge (YNS, Cyperus esculentus L. and PNS, C. rotundus L.) and the southern rootknot nematode (SRKN, Meloidogyne incognita (Kofoid & White) Chitwood). The alfalfa field, which had a history of severe infestation from both species of nutsedge and SRKN, was divided into 1m x 2m quadrats. In May, July and September of each year, eighty quadrats were randomly selected and counts of PNS, YNS and a soil sample (analyzed for the count of juvenile SRKN) were taken from each quadrat. Poisson regression models were fitted to see if information about YNS and PNS counts could be used to predict juvenile SRKN counts. In this study, two different ways to incorporate spatial information of quadrat locations within the field were examined to try to reduce over-dispersion in the original regression models. Spatial coordinates were first treated as fixed effects and then second, in separate models, as random effects using various spatial variance-covariance structures. Models with spatial coordinates as both fixed and random effects failed to converge, possibly because of small (n=80) sample size. The results of spatial models were compared to the original Poisson models, but there was not an effective way of comparing random-effects models with fixed-effects models. For this data, the use of spatial information did not improve the original model consistently. This may be partly because of the nature of the experiment. As hoped, the alfalfa crop effectively reduced YNS, PNS, and SRKN counts. The spatial information was generally more useful earlier in the experiment when the YNS, PNS, and SRKN populations were denser.