Spatial analysis of soybean canopy response to soybean cyst nematodes (Heterodera glycines) in eastern Arkansas: An approach to future precision agriculture technology application.

摘要

Heterodera glycines Ichinohe, commonly known as soybean cyst nematode (SCN) is a serious widespread pathogen of soybean in the US. Present research primarily investigated feasibility of detecting SCN infestation in the field using aerial images and ground level spectrometric sensing. Non-spatial and spatial linear regression analyses were performed to correlate SCN population densities with Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) derived from soybean canopy spectra. Field data were obtained from two fields; Field A and B under different nematode control strategies in 2003 and 2004. Analysis of aerial image data from July 18, 2004 from the Field A showed a significant relationship between SCN population at planting and the GNDVI (R2=0.17 at p=0.0006). Linear regression analysis revealed that SCN had a little effect on yield (R2 =0.14, at p=0.0001, RMSEP=1052.42 kg ha-1) and GNDVI (R 2=0.17 at p=0.0006, RMSEP=0.087) derived from the aerial imagery on a single date. However, the spatial regression analysis based on spherical semivariogram showed that the RMSEP was 0.037 for the GNDVI on July 18, 2004 and 427.32 kg ha-1 for yield on October 14, 2003 indicating better model performance. For July 18, 2004 data from Field B, a relationship between NDVI and the cyst counts at planting was significant (R2=0.5 at p=0.0468). Non-spatial analyses of the ground level spectrometric data for the first field showed that NDVI and GNDVI were correlated with cyst counts at planting (R 2=0.34 and 0.27 at p=0.0015 and 0.0127, respectively), and GNDVI was correlated with eggs count at planting (R2= 0.27 at p=0.0118). Both NDVI and GNDVI were correlated with egg counts at flowering (R 2=0.34 and 0.27 at p=0.0013 and 0.0018, respectively). However, paired T test to validate the above relationships showed that, predicted values of NDVI and GNDVI were significantly different. The statistical evidences suggested that variability in vegetation indices was caused by SCN infestation. Comparison of estimators such as -2 RLL, AIC, and BIC of non-spatial and spatial models affirmed that incorporating spatial covariance structure of observations improved model performances. These results demonstrated a limited potential of aerial imaging and ground level spectrometry for detecting nematode infestation in the field. However, it is strongly recommended that more multisite-multiyear trials must be performed to establish and validate empirical models to quantify SCN population densities and their impact on soybean canopy reflectance.