Hydrogen peroxide detoxifying enzymes show different activity patterns in host and non-host plant interactions with Magnaporthe oryzae Triticum pathotype.

Wheat blast caused by the hemibiotroph fungal pathogen Magnaporthe oryzae Triticum (MoT) pathotype is a destructive disease of wheat in South America, Bangladesh and Zambia. This study aimed to determine and compare the activities of antioxidant enzymes in susceptible (wheat, maize, barley and swamp rice grass) and resistant (rice) plants when interacting with MoT. The activities of reactive oxygen species-detoxifying enzymes; catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), glutathione S-transferase (GST), peroxidase (POX) were increased in all plants in response to MoT inoculation with a few exceptions. Interestingly, an early and very high activity of CAT was observed within 24 h after inoculation in wheat, barley, maize and swamp rice grass with lower H2O2 concentration. In contrast, an early and high accumulation of H2O2 was observed in rice at 48 hai with little CAT activity only at a later stage of MoT inoculation. The activities of APX, GST and POD were also high at an early stage of infection in rice. However, these enzymes activities were very high at a later stage in wheat, barley, maize and swamp rice grass. The activity of GPX gradually decreased with the increase of time in rice. Taken together, our results suggest that late and early inductions of most of the antioxidant enzyme activities occurs in susceptible and resistant plants, respectively. This study demonstrates some insights into physiological responses of host and non-host plants when interacting with the devastating wheat blast fungus MoT, which could be useful for developing blast resistant wheat.

Simulation of Wheat Productivity Using a Model Integrated With Proximal and Remotely Controlled Aerial Sensing Information.

A crop model incorporating proximal sensing images from a remote-controlled aerial system (RAS) can serve as an enhanced alternative for monitoring field-based geospatial crop productivity. This study aimed to investigate wheat productivity for different cultivars and various nitrogen application regimes and determine the best management practice scenario. We simulated spatiotemporal wheat growth and yield by integrating RAS-based sensing images with a crop-modeling system to achieve the study objective. We conducted field experiments and proximal sensing campaigns to acquire the ground truth data and RAS images of wheat growth conditions and yields. These experiments were performed at Gyeongsang National University (GNU), Jinju, South Gyeongsang province, Republic of Korea (ROK), in 2018 and 2019 and at Chonnam National University (CNU), Gwangju, ROK, in 2018. During the calibration at GNU in 2018, the wheat yields simulated by the modeling system were in agreement with the corresponding measured yields without significant differences (p = 0.27-0.91), according to two-sample t-tests. Furthermore, the yields simulated via this approach were in agreement with the measured yields at CNU in 2018 and at GNU in 2019 without significant differences (p = 0.28-0.86), as evidenced by two-sample t-tests; this proved the validity of the proposed modeling system. This system, when integrated with remotely sensed images, could also accurately reproduce the geospatial variations in wheat yield and growth variables. Given the results of this study, we believe that the proposed crop-modeling approach is applicable for the practical monitoring of wheat growth and productivity at the field level.