Quantum yield of non-regulated energy dissipation in PSII (Y(NO)) for early detection of leaf rust (Puccinia triticina) infection in susceptible and resistant wheat (Triticum aestivum L.) cultivars

Abstract

The development and optimization of protocols for the precise and pre-symptomatic detection of diseases, and non-invasive evaluation of genotype-specific pathogen resistance enabling selection of the more promising genotypes in breeding programmes are important and often overlooked topics in precision agriculture. The increasing pressure to minimize both production costs and the environmental impact of pesticides forces the search for rapid and objective methods of screening pathogen resistance. Using the non-destructive pulse amplitude modulated (PAM) chlorophyll fluorescence imaging technique, we hypothesized that not only disease detection but also discrimination between differences in the level of resistance of wheat cultivars to the leaf rust (Puccinia triticina Erics.) pathogen can be achieved. Experiments were conducted using the cultivars Dekan and Retro as representatives of a susceptible and a highly resistant genotype, respectively. Fluorescence measurements were carried out daily on the control and on plants inoculated with P. triticina until the first small red-brown pustules appeared in the centre of chlorotic spots. In response to pathogen inoculation, the fluorescence readings showed an early characteristic increase in Y(NO) in both resistant and susceptible cultivars. The susceptible cultivar, however, showed a more pronounced difference between Y(NO) values measured on the control and inoculated leaves as well as a distinct evolution over time. Accordingly, our results indicate that Y(NO) might be suitable for discriminating between wheat genotypes as early as 2 days after inoculation. Thus, the proposed protocol might be adopted as an additional tool for the early screening of new genotypes, especially in breeding programs that aim for high resistance to disease and low crop variability for precision agriculture. However, its implementation in experimental field plots requires improvement of the measurement system and establishment of appropriate algorithms for disease pattern recognition and data analysis.