Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Precise nutrient management for enhancing crop yield is possible through delineating soil management zones. Generally, the fertilizer recommendations followed use a blanket application for a larger area without considering the soil spatial variability. This may lead to low fertilizer application in pockets of less nutrient content and vice versa. Therefore, this study aims to develop soil management zones (MZs) adopting geostatistical and fuzzy clustering techniques in the Alwarthirunagiri block of the Thoothukudi district in Southern India. One hundred and seventy-one surface samples were collected from a study area of 2760 ha. The collected soils were processed and characterized by available macronutrients and micronutrients. The coefficient of variation of the soils varied from low (9.72%) to high (74.60%). Ordinary kriging and semivariogram analysis showed wide variation in the soil characteristics within the study site, with a spatial dependence ranging from moderate to strong. Four management zones were demarcated based on fuzzy performance index and normalized classification entropy using PCA and fuzzy K-means clustering. The study results indicated that the soil properties differed significantly under different management zones and provided potential site-specific fertilizer management options. The management zone map could be useful to the farmers to adopt precise management of nutrients for different zones.

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. Between 2015 and 2023, information on rice blast occurrence was gathered weekly from three locations (Thanjavur, Tirunelveli, and Coimbatore), together with relevant meteorological data like temperature, humidity, rainfall, sunshine, evaporation, and sun radiation. The associations between the occurrence of rice blast and environmental factors were investigated using stepwise regression analysis, descriptive statistics, and correlation. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. The best prediction accuracy was given by the ANN, which outperformed SVR and INGARCHX in every location, according to the results. The complicated and non-linear relationships between meteorological variables and disease incidence were well-represented by the ANN model. The Diebold–Mariano test further demonstrated that ANNs are more predictive than other models. This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. The application of these models can help farmers make timely decisions to minimize crop losses. The findings suggest that machine learning models offer promising potential for accurate disease forecasting and improved rice management.