The Dengue incidence has increased over the past ten years in India. Almost half of the world’s population, about 4 billion people, live in areas with a risk of Dengue. So, forecasting the Dengue cases will help to take preventive measures to overcome the disease. In this study Dengue cases of India has been forecast using ARIMA and ARIMA-GARCH model. Annual data from year 1996 to 2023 has been used to develop the model. Goodness of fit measures has been used to compare the traditional ARIMA and hybrid ARIMA-GARCH model. From the results, ARIMA (1,1,1)-GARCH (1,1) outperformed the ARIMA (1,1,1) model.

Keywords: Dengue, Stationarity, Heteroskedasticity, volatility, ARIMA model, ARCH and GARCH model.

This paper is an attempt to classify different crop yields of Coimbatore district using multivariate techniques. Fifteen different crop yields including food and cash crops from the year 1970 to 2021 have been used for the analysis. A comparative study using Principal Component analysis and Cluster analysis has been carried out for classifying different crop yields. Using both Principal Component analysis and Cluster analysis, four groups have been classified. The Principal Component analysis performed better than Cluster analysis in the classification of crop yields. The four groups obtained from classification are Pulses, Cereal grains, Seasonal cash crops and Annual cash crops.

Keywords: Multivariate Techniques, Crop Yield, Agriculture, Classification, Sustainability.

Wheat, a crucial global staple, faces significant threats from leaf rust, a disease caused by the fungus Puccinia triticina. Identified by orange-brown pustules on leaves, leaf rust can drastically reduce wheat yield and quality. Historically, rust has affected crops for centuries, with notable epidemics in India since the 18th century. The disease spreads from both southern and northern regions of India, with initial outbreaks in December. The fungus’s adaptability and windborne spores make it a persistent challenge, necessitating a multifaceted management approach. Key strategies include developing and using resistant wheat varieties with specific resistance genes (Lr genes), continuous monitoring of pathogen populations, and employing cultural practices such as crop rotation, debris removal, and strategic planting. Chemical controls, like fungicides, are effective when applied early and rotated to prevent resistance. Biological controls using beneficial microorganisms are being researched for their potential to suppress the fungus. Integrated Disease Management (IDM) combines these methods for comprehensive protection, supported by Decision Support Systems (DSS) that use weather and disease models for timely interventions. Future advances in plant breeding and biotechnology, including CRISPR, promise faster development of resistant varieties. Collaboration among stakeholders is essential for effective disease management and sustainable wheat production.

Keywords: Epidemiology, Integrated Disease Management (IDM), Leaf rust, wheat.

Seed varietal identification is a critical procedure in modern agriculture that has a big impact on crop production results and sustainability. This article examines different approaches to seed variety identification, highlighting the importance of seed varieties in maintaining crop quality, increasing productivity, and satisfying a range of agricultural demands. Conventional techniques, such as the Distinctness, Uniformity, and Stability (DUS) test and the Grow-Out Test (GOT), rely on morphological traits. Chemical techniques, such as the Peroxidase and Phenol Tests, provide accurate and timely results. High precision is achieved in differentiating closely related types using molecular techniques like Polymerase Chain Reaction (PCR) and biochemical techniques like electrophoretic analysis. Maintaining the genetic purity, legitimacy, and quality of seed supply is critical for regulatory compliance, market trust, and selective breeding. These identification procedures play a major role in ensuring these qualities. By employing these methods, farmers and researchers can make informed decisions that support sustainable agricultural practices and boost overall productivity.

Keywords: Genetic purity, Grow-out test (GOT), Seed varietal identification, Selective breeding, Sustainable agriculture

The cropping pattern plays a vital role in determining the level of agricultural production and reflects the agricultural economy of an area or region. The present study was conducted to know the changes in the cropping pattern of the selected districts of Karnataka using Markov chain analysis. Crop yield data were collected from the District Statistical Office, Vijayapura and District Statistical Office, Dharwad for the period of 1981-82 to 2020-21. Markov chain analysis is used to study the changes that occurred in the cropping pattern of agricultural crops. In Vijayapura District, the study reveals notable retention in grape, jowar, and cotton cultivation, with grapes showing the highest retention rate at 66%. Conversely, maize exhibited minimal retention at 4%. Crop transitions indicated substantial losses from grapes to cotton and complete displacement of onion by maize. In Chikkaballapur District, maize, along with other crops, exhibited strong retention, while pomegranate showed no retention. Crop shifts demonstrated exchanges between various crops, with maize and other crops largely retaining their areas. These findings offer valuable insights for agricultural planning, highlighting the persistence and transformation of crop cultivation in response to climatic conditions and regional preferences over four decades.

Keywords: Cropping pattern, Markov chain analysis, Transitional probability