Spatial Analysis of Rice Blast in China at Three Different Scales

Monitoring yellow rust progression during spring critical wheat growth periods using multi-temporal Sentinel-2 imagery

BACKGROUND

Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control.

RESULTS

Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively.

CONCLUSIONS

Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.

Disease spectrum and its molecular characterisation in the lentil production system of lower-Indo Gangetic plains

Lentil is a food legume grown in the Indo-Gangetic plains including lower Gangetic Bengal (LGB). Lentil productivity in this zone is severely impeded because of the prevalence of several biotic cues. Plausible reports regarding the status of disease scenario and the associated risk factors are missing. Therefore, judicious crop management strategies are lacking. An intensive survey of 267 farmers' fields was conducted over 3 years in major lentil-growing districts of LGB to evaluate the disease incidence and prevalence. Additional insights were generated, apprehending isolation and characterisation of associated pathogens through spore morphology and molecular markers as well as elucidating the role of biophysical factors in influencing disease development. Climate change has shifted the disease dimension of lentil and precipitated new disease complexes of great risk, which was reflected through geospatial mapping results in the present study. The prevalence of three major diseases, namely collar rot (Sclerotium rolfsii), lentil blight complex (LBC) incited by both Alternaria and Stemphylium, and lentil rust (Uromyces viciae-fabae), was ascertained through cultural and molecular studies and contextualised through pathogenicity appraisal. This study is the first to investigate the complex mixed infection of Alternaria alternata and Stemphylium botryosum, successfully isolating S. botyrosum in India, and confirming the pathogens through sequencing by using internal transcribed spacer (ITS) primers and Stemphylium-specific Glycerol-3-phosphate dehydrogenase 1 (gpd1) and gpd2 primers. Unlike late planting, early planting promoted collar rot infestation. LBC and rust incidence were magnified in late planting. Soil texture resulted in the spatial distribution of collar rot disease. The surveyed data also highlighted the potential role of resistant cultivars and cropping pattern intervention to ensure associational resistance towards addressing the disease bottleneck in lentil.

Development of Two Early Forecasting Models for Predicting Incidence of Rice Panicle Blast in China

Early forecasting of rice panicle blast is critical to the management of rice blast. To develop early forecasting models for rice panicle blast, the relationship between the seasonal maximum incidence of rice panicle blast (PBx) and the PBx in the preceding crop, weather conditions, location, and acreage of susceptible varieties was analyzed. Results revealed that PBx in the preceding crop, acreage of the susceptible varieties in class (SVC), altitude, weather conditions 120 to 180 days before the PBx date (dbPBx) and 30 to 90 dbPBx were significantly correlated with the PBx. Subsequently, a logistic model and a two-step hurdle model were developed to predict rice panicle blast. The logistic model was developed to predict whether the PBx was 0 or not based on the preceding PBx, altitude, acreage of susceptible varieties, the longest stretch of days with soil temperatures between 16 and 24°C for the period 120 to 150 dbPBx, and the longest stretch of rainy days in the period 120 to 180 dbPBx. The hurdle model predicted if the PBx was greater than 0 at the first step, and if the prediction was greater than 0, then a regression model was developed for predicting PBx based on the preceding PBx, SVC, altitude, and weather data 180 to 30 dbPBx. Validation with the test datasets showed that the logistic model could correctly predict whether PBx was 0 at a mean test accuracy of 78.39% and that the absolute prediction error of PBx by the two-step hurdle model was smaller than 6.16% for 90% of the records. The model developed in this study will be helpful in management decisions for rice growers and policy makers and offer a useful basis for further studies on the epidemiology and forecasting of rice panicle blast.

Comparison of projected rice blast epidemics in the Korean Peninsula between the CMIP5 and CMIP6 scenarios

Recently, the International Panel for Climate Change released the 6th Coupled Model Intercomparison Project (CMIP6) climate change scenarios with shared socioeconomic pathways (SSPs). The SSP scenarios result in significant changes to climate variables in climate projections compared to their predecessor, the representative concentration pathways from the CMIP5. Therefore, it is necessary to examine whether the CMIP6 scenarios differentially impact plant-disease ecosystems compared to the CMIP5 scenarios. In this study, we used the EPIRICE-LB model to simulate and compare projected rice blast disease epidemics in the Korean Peninsula using five selected family global climate models (GCMs) of the CMIP5 and CMIP6 for two forcing scenarios. We found a similar decrease in rice blast epidemics in both CMIP scenarios; however, this decrease was greater in the CMIP6 scenarios. In addition, distinctive epidemic trends were found in North Korea, where the rice blast epidemics increase until the mid-2040s but decrease thereafter until 2100, with different spatial patterns of varying magnitudes. Controlling devastating rice blast diseases will remain important during the next decades in North Korea, where appropriate chemical controls are unavailable due to chronic economic and political issues. Overall, our analyses using the new CMIP6 scenarios reemphasized the importance of developing effective control measures against rice blast for specific high-risk areas and the need for a universal impact and vulnerability assessment platform for plant-disease ecosystems that can be used with new climate change scenarios in the future.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10584-022-03410-2.

Spatial distribution and identification of potential risk regions to rice blast disease in different rice ecosystems of Karnataka

Rice is a globally important crop and highly vulnerable to rice blast disease (RBD). We studied the spatial distribution of RBD by considering the 2-year exploratory data from 120 sampling sites over varied rice ecosystems of Karnataka, India. Point pattern and surface interpolation analyses were performed to identify the spatial distribution of RBD. The spatial clusters of RBD were generated by spatial autocorrelation and Ripley’s K function. Further, inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) approaches were utilized to generate spatial maps by predicting the values at unvisited locations using neighboring observations. Hierarchical cluster analysis using the average linkage method identified two main clusters of RBD severity. From the Local Moran’s I, most of the districts were clustered together (at I > 0), except the coastal and interior districts (at I < 0). Positive spatial dependency was observed in the Coastal, Hilly, Bhadra, and Upper Krishna Project ecosystems (p > 0.05), while Tungabhadra and Kaveri ecosystem districts were clustered together at p < 0.05. From the kriging, Hilly ecosystem, middle and southern parts of Karnataka were found vulnerable to RBD. This is the first intensive study in India on understanding the spatial distribution of RBD using geostatistical approaches, and the findings from this study help in setting up ecosystem-specific management strategies against RBD.

Remote Sensing of Diseases

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host-pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.