Agronomy and Horticulture Department


Date of this Version

Summer 8-5-2013


Perez-Hernandez, O. 2013. MULTIFACTORIAL ANALYSIS OF MORTALITY OF SOYBEAN CYST NEMATODE (Heterodera glycines Ichinohe) POPULATIONS IN SOYBEAN AND IN SOYBEAN FIELDS ANNUALLY ROTATED TO CORN IN NEBRASKA. Ph.D. Dissertation. University of Nebraska-Lincoln. Lincoln, NE. 178 pages.


A DISSERTATION Presented to the Faculty of The graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Agronomy (Plant Pathology), Under the Supervision of Professor Loren J. Giesler. Lincoln, Nebraska: August, 2013

Copyright (c) 2013 Oscar Pérez-Hernández


The soybean cyst nematode (SCN; Heterodera glycines Ichinohe) is the most economically important pathogen of soybean in the U.S. The effect of annual corn rotation, soil properties, weather, and agronomic factors on SCN population densities was quantified in 45 fields in Nebraska over three years. SCN population densities (eggs/100 cm3 of soil) in each field were determined before (Pi) and after (Pf) annual corn rotation. Average SCN population density reduction was 50.62%. Multivariate analysis was used to describe the relationship of soil texture (% of sand, silt, and clay), Pi, and Pf. Two principal components explained 92% of the variability in the data set. The first component was represented by texture and accounted for 60.5% and the secondwas represented by Pi and Pf and explained 31.5%. Cluster analysis identified two groups of fields: one group with predominantly sandy soil (57 to 95%) and the other with predominantly silty soil (23 to 61%). SCN Pi was significantly higher in the sandy group than in the silty group (F = 271.19, P

The SCN Pf was modeled using an initial set of eight predictors. A negative binomial regression model with the log link function was applied to a 35-field training data set and a final model was selected. This model was used to estimate the nematode population density after annual corn rotation in the training data set and its prediction power was 82.1%. This predicting capability was confirmed in a validation data set in which the model’s predicting capability was 79.6%.

Intra and interplot spatial variability of SCN population densities was analyzed in three experimental areas and its relationship with soybean yield was examined. SCN population densities had an aggregated pattern, showing spatial dependence with those of adjacent plots. The β-binomial distribution adequately described data of incidence and suggested that SCN population density aggregation also occurred within plots. The SCN reproduction factor was not related to the number of SCN-positive cores per plot nor was it related to soybean yield in two soybean varieties assessed, one resistant and one susceptible.

Advisor: Loren J. Giesler