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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #309473

Research Project: Application Technologies to Improve the Effectiveness of Chemical and Biological Crop Protection Materials

Location: Crop Production Systems Research

Title: Relationships between microsclerotia content and hyperspectral reflectance data in soybean tissue infected by Macrophomina phaseolina

Author
item Fletcher, Reginald
item Smith, James - Rusty
item Mengistu, Alemu
item Ray, Jeffery - Jeff

Submitted to: American Journal of Plant Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/9/2014
Publication Date: 12/17/2014
Citation: Fletcher, R.S., Smith, J.R., Mengistu, A., Ray, J.D. 2014. Relationships between microsclerotia content and hyperspectral reflectance data in soybean tissue infected by Macrophomina phaseolina. American Journal of Plant Sciences. 5:3737-3744.

Interpretive Summary: Soybean is an important crop throughout the United States and the world. Charcoal rot disease, caused by the fungus Macrophomina phaseolina, suppresses soybean yields in the United States and throughout world. Alternative methods are needed to assess the severity of charcoal rot disease in soybean plant tissue. Scientists at the USDA-ARS, Crop Production Systems Research Unit and Crop Genetics Research Unit, Stoneville, MS, demonstrated in a laboratory-based study that moderate to very strong correlations existed between near-infrared light reflectance properties and microsclerotia (tiny black masses of fungal tissue) content in ground stem and root tissue of soybean plants. Decreases in tissue near-infrared light reflectance values were related with increases in tissue microsclerotia content of soybean plants. The immediate benefit of this research has been to provide improved information on the correlation of light reflectance measurements and microsclerotia content of soybeans, opening new avenues to explore light reflectance measurements as a tool to assess charcoal rot disease severity of soybean plants and to evaluate the technology as a decision support tool in soybean breeding programs.

Technical Abstract: Alternative methods are needed to assess the severity of charcoal rot disease [Macrophomina phaseolina (Tassi) Goid] in soybean [Glycine max (L.)] plant tissue. The objective of this study was to define the relationship between light reflectance properties and microsclerotia content of soybean stem and root tissue. Understanding that relationship could lead to using spectral reflectance data as a tool to assess the severity of charcoal rot disease in soybean plants, thus reducing human bias associated with qualitative analysis of soybean plant tissue and cost and time issues connected with quantitative analysis. Hyperspectral reflectance measurements (400-2490 nm) were obtained with a non-imaging spectroradiometer of non-diseased and charcoal rot diseased ground stem and root tissue samples of six soybean genotypes (‘Clark’, ‘LD00-3309’, ‘LG03-4561-14’, ‘LG03-4561-19’, ‘Saline’, and ‘Y227-1’). Relationships between the reflectance measurements and tissue microsclerotia content were evaluated with Spearman correlation (rs) analysis (p < 0.05). Moderate (rs = ±0.40 to ±0.59), strong (rs = ±0.60 to ±0.79), and very strong (rs = ±0.80 to ±1.00) negative and positive statistically significant (p < 0.05) monotonic relationships were observed between tissue spectral reflectance values and tissue microsclerotia content. Near-infrared and shortwave-infrared wavelengths had the best relationships with microsclerotia content in the ground tissue samples, with consistent results obtained with near-infrared wavelengths in that decreases in near-infrared spectral reflectance values were associated with increases in microsclerotia content in the stem and root tissue of the soybean plants. The findings of this study provided evidence that relationships exist between tissue spectral reflectance and tissue microsclerotia content of soybean plants, supporting spectral reflectance data as a means for assessing variation of microsclerotia content in soybean plants. Future research should focus on the modelling capabilities of the selected wavelengths and on the feasibility of using these wavelengths in machine learning algorithms to differentiate non-diseased from charcoal rot diseased tissue.