Geospatial and temporal analyses of Bean pod mottle virus epidemics in soybean at three spatial scales

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.

Detection and discovery of plant viruses in soybean by metagenomic sequencing

BACKGROUND

Viruses negatively impact soybean production by causing diseases that affect yield and seed quality. Newly emerging or re-emerging viruses can also threaten soybean production because current control measures may not be effective against them. Furthermore, detection and characterization of new plant viruses requires major efforts when no sequence or antibody-based resources are available.

METHODS

In this study, soybean fields were scouted for virus-like disease symptoms during the 2016-2019 growing seasons. Total RNA was extracted from symptomatic soybean parts, cDNA libraries were prepared, and RNA sequencing was performed using high-throughput sequencing (HTS). A custom bioinformatic workflow was used to identify and assemble known and unknown virus genomes.

RESULTS

Several viruses were identified in single or mixed infections. Full- or nearly full-length genomes were generated for tobacco streak virus (TSV), alfalfa mosaic virus (AMV), tobacco ringspot virus (TRSV), soybean dwarf virus (SbDV), bean pod mottle virus (BPMV), soybean vein necrosis virus (SVNV), clover yellow vein virus (ClYVV), and a novel virus named soybean ilarvirus 1 (SIlV1). Two distinct ClYVV isolates were recovered, and their biological properties were investigated in Nicotiana benthamiana, broad bean, and soybean. In addition to infections by individual viruses, we also found that mixed viral infections in various combinations were quite common.

CONCLUSIONS

Taken together, the results of this study showed that HTS-based technology is a valuable diagnostic tool for the identification of several viruses in field-grown soybean and can provide rapid information about expected viruses as well as viruses that were previously not detected in soybean.

Temporal Progress and Spatial Patterns of Northern Corn Leaf Blight in Corn Fields in China

Northern corn leaf blight (NCLB), caused by Exserohilum turcicum, is a devastating disease of corn in China. To enhance our understanding of NCLB epidemiology, the temporal progress and spatial patterns of NCLB were investigated. A susceptible corn cultivar, Xianyu 335, was planted in a field in Beijing in 2016 and 2017. Leaf lesions of NCLB on each plant were counted twice a week during the growing seasons. In addition, temporal disease progress was monitored for 8 weeks in three commercial corn fields in each of Yanqing, Miyun, Daxing, and Haidian Districts of Beijing in 2017, and the spatial patterns of diseased plants and NCLB lesion counts per plant were assessed in three commercial corn fields with moderate to high NCLB incidence in Yanqing District. The results demonstrated that a logistic model was the most appropriate to describe the temporal progress of NCLB incidence. The initial disease incidence was the key factor affecting disease epidemics under various conditions in the four districts of Beijing during the study. The higher the initial incidence of NCLB, the higher the final incidence. Thus, the earlier in the season NCLB incidence attained 1%, the higher was the final disease incidence. Greater than 1.0 variance-to-mean ratios suggested that the leaf lesions of NCLB tended to be aggregated on a plant. According to results from join-counts, variance of moving window averages, and semivariogram analysis, diseased corn plants and lesion numbers on each plant were aggregated in the field. The clustered pattern of NCLB lesions and infected plants suggested that conidia produced locally on diseased plants were important for disease spread within the field. The aggregated pattern of diseased plants suggested that plants should be sampled from more sites in a field to accurately estimate incidence of NCLB.

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.

Spatial Analysis of Rice Blast in China at Three Different Scales

In this study, spatial analyses were conducted at three different scales to better understand the epidemiology of rice blast, a major rice disease caused by Magnaporthe oryzae. At the regional scale, across the major rice production regions in China, rice blast incidence was monitored on 101 dates at 193 stations from 10 June to 10 September during 2009 to 2014, and surveyed in 143 fields in September 2016; at the county scale, three surveys were done covering one to five counties in 2015 to 2016; and, at the field scale, blast was evaluated in six fields in 2015 to 2016. Spatial cluster and hot spot analyses were conducted in the geographic information system on the geographical pattern of the disease at regional scale, and geostatistical analysis was performed at all three scales. Cluster and hot spot analyses revealed that high-disease areas were clustered in mountainous areas in China. Geostatistical analyses detected spatial dependence of blast incidence with influence ranges of 399 to 1,080 km at regional scale and 5 to 10 m at field scale but not at county scale. The spatial patterns at different scales might be determined by inherent properties of rice blast and environmental driving forces, and findings from this study provide helpful information to sampling and management of rice blast.

Activity of Bacillus thuringiensis Cry1Ie2, Cry2Ac7, Vip3Aa11 and Cry7Ab3 proteins against Anticarsia gemmatalis, Chrysodeixis includens and Ceratoma trifurcata

Transgenic soybean producing the Cry1Ac insecticidal protein from the bacterium Bacillus thuringiensis is used to control larvae of the velvetbean caterpillar (Anticarsia gemmatalis Hübner) and the soybean looper [Chrysodeixis includens (Walker)]. The main threat to the sustainability of this technology is the development of resistance, which could be delayed by using pyramiding of diverse Bt insecticidal genes. We report high activity of Cry2Ac7 and Vip3Aa11 but not Cry1Ie2 against larvae of A. gemmatalis and C. includens. In addition, we also report anti-feeding activity of Cry1Ie2 and Cry7Ab3 in adults of the bean leaf beetle [Ceratoma trifurcata (Foster)], an alternative pest of soybean.

Bean pod mottle virus Time of Infection Influences Soybean Yield, Yield Components, and Quality

Bean pod mottle virus (BPMV) negatively affects soybean yield and quality, yet quantitative information on effect of time of BPMV infection on soybean yield and quality has not been reported. The impact of time of BPMV infection on soybean yield, yield components, and grain quality components were quantified during the 2006 and 2007 soybean growing seasons in Iowa. Soybean quadrats (30 cm in length) were established within soybean plots ('NB3001') that consisted of six rows and were 7.5 m long. Quadrats were sampled 9 times during the 2006 growing season and 10 times during the 2007 growing season, beginning 25 days after planting in both years. Sap was extracted from leaflet samples from each quadrat and tested for the presence or absence of BPMV by enzyme-linked immunosorbent assay. The day of year (DOY) and quadrat position when BPMV was first detected within each plot were recorded and mapped. Soybean yield, number of pods per plant, number of seed per pod, and 100-seed weight for each quadrat were determined. The relationship between time (DOY) of BPMV infection and soybean yield, soybean yield components, and soybean grain quality were then quantified using linear regression. DOY of BPMV infection within quadrats explained 89.7 and 57.9% of the variation in soybean grain yield in 2006 and 2007, respectively. Soybean yield damage functions (slopes) were 15.2 and 8.1 kg/ha per day, respectively, indicating that, for each day that BPMV infection was delayed, soybean yield increased by 15.2 kg/ha in 2006 and 8.1 kg/ha in 2007. The number of pods per plant increased by 0.15 pods for each day that BPMV infection was delayed (R² = 72.8%) in 2006 but there was no relationship in 2007. The 100-seed weight had a significant linear relationship with the DOY when BPMV was first detected within quadrats in 2006 (slope = 0.013, R² = 86.3%) but not in 2007. The percentage of mottled seed in 2006 decreased by 1% for each day that BPMV infection was delayed in 2006 (R² = 87.4%). Both protein and oil content were affected by the DOY that BPMV was first detected within quadrats in 2006 but not in 2007. This study demonstrated that time of BPMV infection can negatively affect soybean yield, yield components, and grain quality components when BPMV disease risk is high.

Landscape-scale disease risk quantification and prediction

The study of plant disease epidemics at a landscape scale can be extended to allow for predictions about disease occurrence at this scale. Examined within the context of the disease triangle, systems developed to incorporate information primarily about the pathogen and conditions conducive to the infection process. Parametric methods can be used to relate environmental conditions to disease, and specifically relate environment to the inoculum production, the resulting infection process, or both. Aspects relating to the presence or absence of the host plant within the landscape, or patterns of the host within the landscape, are much rarer in disease prediction, although analyses incorporating these factors have been conducted. Predictive systems at the landscape scale may concentrate only on the conditions for infection or possible migratory paths of pathogen propagules. Incorporation of all components of the disease triangle may be one way to improve these systems.