Rice (Oryza sativa L.) serves as a staple food for more than half of the world’s population, particularly in Asia, yet its productivity is often constrained by rice blast caused by Pyricularia oryzae (syn. Magnaporthe oryzae). The management of rice blast is complicated owing to high genetic variability of the pathogen and the emergence of fungicide-resistant strains. To address this, a two-year field investigation was undertaken at a known rice blast hotspot in Jammu and Kashmir, India, to assess the comparative performance of ten fungicidal treatments, including both single-site and combination formulations, against leaf and neck blast in the aromatic and highly susceptible cultivar Mushk Budji. The results indicated that combination fungicides, particularly Fluopyram + Tebuconazole and Propiconazole + Tricyclazole, produced the most significant reductions in leaf blast incidence, disease severity, and neck blast intensity, while simultaneously resulting in the highest grain yields. The research thrusts over the urgent need for integrated disease management strategies that incorporate combination fungicides with multiple modes of action to counteract the M. oryzae and delay resistance development. Future perspectives call for surveillance of fungicide resistance, the development of novel multi-target compounds, and integration of chemical control with genetic and agronomic innovations to ensure durable rice blast management and global food security.
Meteorological drought, a recurrent manifestation of climate variability, poses significant challenges to sustainable water resource management and agricultural planning in India. This study investigates the comparative performance of stochastic and artificial intelligence (AI)-based models for forecasting meteorological drought using monthly precipitation data (1981–2021) from Sagar and Chhatarpur districts of Madhya Pradesh, India. The primary objective is to identify the most reliable model capable of capturing complex temporal dependencies in the Standardized Precipitation Index (SPI) series. A suite of models, including Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) networks, were employed. The LSTM demonstrated superior predictive accuracy, achieving reductions in root mean square error (RMSE) of 71.4
Climate change has a very grave and multidimensional menace to the agricultural systems of the Global South where livelihoods are heavily dependent on natural resources that are sensitive to climatic parameters. The paper will discuss sustainable resource management as one of the solutions to establishing climate-resilient agricultural systems, and in particular, water-use efficiency, soil conservation practices, and application of climate-smart agriculture. With the aid of the fixed-effects and system GMM estimations, the analysis of the impact of climatic variability and resource management policies on the agricultural productivity is measured by using panel data of selected economies in the Global South between 2000–2022. The results show that fluctuation in rainfalls and temperature variations is interrelated in delivering low crop yield, but successful water management, effective soil utilization, and adoption of agriculture sensitive to climate are very positive. It is also estimated by interaction that sustainable resource management reduces the negative impacts of climatic stress, and hence, agrarian resilience is enhanced. The findings indicate that integrative resource control, institutional support and policy model flexibility are important in enhancing agricultural sustainability amid increasing climate uncertainty. The study offers empirically grounded data on the policymakers that are keen on enabling resilient and sustainable agricultural transformation in the Global South.
The rice moth, Corcyra cephalonica (Stainton), is an important stored-product insect whose growth and developmental characteristics may vary with rearing substrate and environmental conditions. Although morphometric information is available for the species under different rearing conditions, stage-specific morphometric data for C. cephalonica developing on walnut kernels remain limited, warranting systematic documentation under laboratory conditions. The study documented morphometric variation across developmental stages of the rice moth, Corcyra cephalonica (Stainton), reared on walnut kernels under laboratory conditions. Morphometric observations were recorded for the egg, successive larval instars, pupa, and adult using a stereomicroscope. Five specimens from each developmental stage were measured, and mean values were calculated for the recorded characters. Egg length and breadth were 0.48 and 0.34 mm, respectively. Larval body dimensions increased progressively across successive instars, with mean length rising from 1.62 mm in the first instar to 11.50 mm in the sixth instar, while mean breadth increased from 0.37 to 2.08 mm. The pupa measured 10.22 mm in length and 1.82 mm in breadth, showing a slight reduction in length relative to the sixth larval instar. Adult females were marginally larger than males, with mean body lengths of 11.25 and 10.88 mm, respectively, and wing spans of 15.68 and 15.61 mm. Overall, the observations showed a clear stage-wise progression in body size during larval development, followed by the expected transition to pupal and adult forms. The findings provide condition-specific baseline morphometric information for C. cephalonica reared on walnut kernels and may support identification and description of its developmental stages under comparable laboratory conditions.
