Although human anthrax has become rare, endemic outbreaks still occur in tropical countries, parts of South America and Europe. We report 23 cases of cutaneous anthrax due to an endemic outbreak of animal and human anthrax in South India. These patients were admitted to our hospital between July 1998 and July 2001. Children outnumbered adults and most of them had lesions on the exposed sites. The majority of patients reported the death of infected animals in the neighbourhood without any direct contact with dead animals. Hence, vector borne transmission was suspected in most of the cases. Diagnosis was confirmed by the presence of a typical ulcer with eschar, Gram-stained smears from ulcers and epidemiological evidence. Except for one fatal case, all patients responded to treatment.

The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of climatological data were subjected to count time series (Integer-valued Generalized Autoregressive Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN, and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX) and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice gall midge populations. Utilizing the presented efficient early warning system based on a robust statistical model to predict the build-up of gall midge population could greatly contribute to the design and implementation of both proactive and more sustainable site-specific pest management strategies to avoid significant rice yield losses.

The System of Rice Intensification (SRI) developed in Madagascar has spread to many parts of the world, including India. This study assessing insect pest prevalence on rice grown with SRI vs. conventional methods at multiple locations in India was prompted by reports that SRI-managed rice plants are healthier and more resistant to pest and disease damage. Field experiments were conducted under the All-India Coordinated Rice Improvement Project over a 5-year period. The split-plot design assessed both cultivation methods and different cultivars, hybrids and improved varieties. Across the eight locations, SRI methods of cultivation showed a lower incidence of stem borer, planthoppers, and gall midge compared to conventional methods. Whorl maggots and thrips, on the other hand, were observed to be higher. Grain yield was significantly higher with SRI management across all locations. Higher ash, cellulose, hemicellulose, as well as silica content in rice plants under SRI management could explain at least in part the SRI plants’ resistance to pest damage. Analysis of guild composition revealed that in SRI plots, there were more natural enemies (insect predators and parasitoids) present and fewer crop pests (phytophages). A meta-analysis that considered other published research on this subject revealed a lower incidence of dead hearts, white ear-heads, and leaf folders, along with higher grain yield, in SRI plots.

No abstract