Evaluating vegetation indices for precision phenotyping of quantitative stripe rust reaction in wheat

Abstract

Wheat production and productivity is widely affected by stripe rust infection. Resistance to this disease governed by additive-gene effect is one of the recent strategies being adopted in wheat breeding programme. While the present phenotyping approaches for scoring, the plants reaction often vary from person to person. A novel, repeatable and reliable approach can enhance the efficiency of determining genetic variability in large number of genotypes. Taking into consideration the emerging non-invasive tools to assess thephysiological status of plants. The present investigation was undertaken to explore utility of optical measurements to variation of the stripe rust reaction in wheat genotypes. One hundred and twenty Indian wheat genotypes representing released varieties, elite genotypes, genetic stocks, and local landraces were used for the study. The stripe rust epidemics in the field were initiated with Yr27-virulent P. Striiformisrace 78S84. The Area under the Disease Progress Curve (AUDPC) values were calculated from four weekly visual estimates of disease severity which ranged from 0 to 2077. Normalized difference vegetation index (NDVI), Chlorophyll content index (CCI) and Plant Canopy temperature (CT) were recorded twice, 7 days apart, when disease severity approached maximum values on the susceptible controls. The results indicate that the temporal ground-based NDVI is most effective in studying quantitative rust reaction with a significant regression coefficient (r2=0.63) between AUDPC and NDVI data followed by chlorophyll content index (r2=0.37) and canopy temperature (r2=0.21).