Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 2/5/2011
Publication Date: 5/2/2012
Citation: Cribben, C.D., Thomasson, J.A., Ge, Y., Korte, M.D., Morgan, C.L., Yang, C., Nichols, R.L. 2012. Ground-based technologies for cotton root rot control: an update. National Cotton Council Beltwide Cotton Conference. 481-486. Interpretive Summary: Cotton root rot (CRR) is a fungus affecting cotton in the southwestern U.S. and northern Mexico. The overall goal of this research is to develop ground-based technologies for early detection and site-specific management of CRR. In this study, plant reflectance, ground-based thermal images, surface soil moisture, temperature, and apparent electrical conductivity (ECa) were measured from three fields for two years in Thrall, Sinton, and San Angelo, Texas. Preliminary analysis showed that CCR incidence appears to be correlated with ECa and aerial images. These results can be used to develop strategies for site-specific treatment of this disease.
Technical Abstract: The overall goal of this research is to develop ground-based technologies for early detection and site-specific management of CRR (cotton root rot). Early detection could facilitate a more economical solution than those that might be used after plant infection had become more severe and widespread. Three cotton fields around CRR-prone areas of Texas have been the sites for two years of data collection. Freshly picked cotton leaves from healthy, disease-stressed, and dying or dead plants were scanned with an ASD VisNIR spectroradiometer. Surface soil moisture, temperature, and electrical conductivity were measured in each field with a Delta-T WET sensor. A thermal infrared camera was used to capture leaf canopy images of healthy and disease-stressed plants. A complete soil ECa (apparent electrical conductivity) survey was conducted for each field with an EM-38 sensor. Plant status was visually inspected and recorded to form a series of disease-progression maps in each field. Preliminary models relating ECa and remotely sensed NDVI (normalized difference vegetative index) levels to CRR incidence produced varying results, which may be improved through further analysis. Leaf spectra have also been evaluated with LDA (linear discriminant analysis) to classify their infection level with a 66% success rate. These data continue to be analyzed to (1) identify promising means for early detection of CRR, (2) relate disease occurrence to soil data, and (3) develop sound strategies for site-specific management of CRR.