Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/3/2009
Publication Date: 1/3/2010
Citation: Ortiz, B.V., Perry, C., Vellidis, G., Sullivan, D.G. 2010. Geostatistical Modeling of the Spatial Variability and Risk Areas of Southern Root-knot Nematodes in Relation to Soil Properties. Geoderma. 156:243-252.
Interpretive Summary: Site-specific management (SSM) of cotton (Gossypium hirsutum L.) fields at risk for southern root-knot nematode [Meloidogine Incognita (Kofoid & White) Chitwood] (RKN) infection may offer producers better management of on-farm resources and optimization of profitability. However, successful implementation of SSM requires some knowledge of how soil properties and RKN vary within the field. The objectives of this study were to 1) develop and evaluate maps of RKN distributions during three different sampling events, 2) determine the relationship between RKN distributions and soil properties, and 3) delineate “risk-based” management zones for SSM of RKN. Soil physical and chemical properties, along with RKN samples, were collected from two cotton fields in southern Georgia, USA, in 2006 using a 0.25 ha grid. Soil physical properties included soil texture measured via electrical conductivity (VERIS 3100) and elevation data collected using a real-time kinematic (RTK) GPS receiver. Soil chemical properties included P, K, Ca, Mg, and soil pH. Geostatistical analyses indicated that RKN populations were spatially dependent within sampling distances of 130 m and that clusters of high RKN densities remained stable throughout the growing season. Because RKN distributions were spatially dependent and highly correlated with electrical conductivity, electrical conductivity measurements facilitated the delineation of RKN risk areas.
Technical Abstract: Site-specific management (SSM) of cotton (Gossypium hirsutum L.) fields at risk for southern root-knot nematode [Meloidogine Incognita (Kofoid & White) Chitwood] (RKN) infection may offer producers better management of on-farm resources and optimization of profitability. However, it requires the study of RKN spatio-temporal variability and the identification of surrogate data spatially correlated with its occurrence. The objectives of this study were to (i) evaluate the spatial dependency of RKN distribution over time; (ii) establish the relationship between RKN occurrence and the spatial variability of soil properties; and (iii) delineate areas at risk for RKN based on surrogate data. The spatial relations between soil physical properties (apparent soil electrical conductivity - ECa, elevation, and slope) and soil chemical properties (P, K, Ca, Mg, and soil pH) on RKN population were studied in two cotton fields in southern Georgia, USA, in 2006. Soil samples for RKN population and chemical properties were collected at the center of a grid (0.25 ha cell size) delineated at both fields. Changes in RKN population were evaluated three times during the growing season. ECa was measured using the VERIS® 3100 implement and elevation data were collected with a real-time kinematic (RTK) GPS receiver mounted on the tractor pulling the VERIS® 3100. The spatio-temporal variability of RKN was studied through semivariograms. The spatial correlation between RKN and soil properties was studied through canonical correlation and cross-correlograms. Soil properties highly correlated with RKN population were entered into an ordinary logistic regression model to create a risk map showing the probability for RKN population over 100 second stage juveniles/100 cm3 of soil, a threshold value commonly used in Georgia to determine whether or not to apply nematicides. Semivariograms indicated that RKN samples were correlated over a range of 130 m and the location of clusters with high RKN population remain stable though time. RKN population was highly correlated with soil physical properties and the correlation with soil chemical properties was soil texture mediated. ECa (deep) showed advantages as surrogate data for RKN. The aggregated pattern of RKN facilitated the segregation of RKN risk areas based on low values of ECa.