Aims To investigate the diversity of eco-distinct isolates of Magnaporthe oryzae for their morphological, virulence, and molecular diversity and relative distribution of five Avr genes. Methods and Results Fifty-two M. oryzae isolates were collected from different rice ecosystems of southern India. A majority of them (n=28) formed a circular colony on culture media. Based on the disease reaction on susceptible cultivar (cv. HR-12), all 58 isolates were classified in to highly-virulent (n=28), moderately-virulent (n=11), and less-virulent (13) types. Among the 52 isolates, 38 were selected for deducing ITS sequence diversity. For deducing phylogeny, another set of 36 isolates from other parts of the world was included, which yielded two distinct phylogenetic clusters. We identified eight haplotype groups and 91 variable sites within the ITS sequences, and haplotype-group-2 (Hap_2) was predominant (n=24). The Tajima’s and Fu’s Fs neutrality tests exhibited many rare alleles. Further, PCR analysis for detecting the presence of five Avr genes in the different M. oryzae isolates using Avr gene-specific primers in PCR revealed that Avr-Piz-t, Avr-Pik, Avr-Pia, and Avr-Pita were present in 73.68%, 73.68%, 63.16%, and 47.37% of the isolates studied, respectively. Whereas, Avr-Pii was identified only in 13.16% of the isolates. Conclusions Morpho-molecular and virulence studies revealed the significant diversity among eco-distinct isolates. PCR detection of Avr genes among the M. oryzae population revealed the presence of five Avr genes. Among them, Avr-Piz-t, Avr-Pik, Avr-Pia were more predominant. Significance and Impact of study The study documented the morphological and genetic variability of eco-distinct M. oryzae isolates. This is the first study demonstrating the distribution of the Avr genes among the eco-distinct population of M. oryzae from southern India. The information generated will help plant breeders to select appropriate resistant gene/s combinations to develop blast disease-resistant rice cultivars.

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India.

Introduction: Sapota (Manilkara zapota L.) is a major tropical fruit crop prone to damage by bud borer (Anarsia achrasella), seed borer (Trymalitis margarias), and fruit rot caused by Phytophthora species. Climatic variability strongly influences these biotic stresses, yet long-term temporal patterns remain poorly quantified.Methods: A decade-long dataset (2014–2022) from 21 major sapota-growing districts of Maharashtra, India, was analyzed to study pest and disease dynamics. Statistical and machine learning approaches, including ARIMA, SARIMA, and VAR time-series models, along with Random Forest feature importance analysis, were applied to quantify climatic influences and forecast severity trends. Correlation analyses were used to assess weather–pest/disease associations.Results: Trend analysis revealed fluctuating bud and seed borer damage, while Phytophthora disease severity remained relatively stable. Bud borer incidence was positively correlated with rainfall (r = 0.69), seed borer with maximum temperature (r = 0.47), and Phytophthora with minimum temperature (r = 0.64). The ARIMA model provided accurate forecasts for bud borer (MSE = 8.03) and Phytophthora (MSE = 0.20), while the VAR model performed best for seed borer (MSE = 17.96). Random Forest analysis identified minimum temperature as the most critical driver of bud borer and Phytophthora severity, whereas relative humidity was most influential for seed borer.Discussion: The integration of statistical and machine learning models provides robust insights into sapota pest and disease epidemiology under climatic variability. These findings highlight the importance of temperature, humidity, and rainfall in shaping pest–pathogen interactions and provide predictive tools to design timely, targeted, and climate-resilient management strategies for sapota cultivation.