Statistical and machine learning models for location-specific crop yield prediction using weather indices.

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions.

Enhancing aromatic rice production through agronomic and nutritional management for improved yield and quality.

To meet the growing international demand for aromatic rice, this study, conducted at Uttar Banga Krishi Viswavidyalaya in Cooch Behar, West Bengal, aimed to enhance the yield and quality of the 'Tulaipanji' rice cultivar through advanced establishment methods and the use of organic nutrients over two years. The research tested three planting techniques: mechanical transplanting, wet direct seeding (using a drum seeder), and traditional methods, alongside four nutrient management strategies: vermicompost, farmyard manure, a mix of both, and conventional fertilizers. Findings revealed that mechanical transplanting significantly increased yield by over 31.

Exploring of greater yam ( L.) genotypes through biochemical screening for better cultivation in south Gujarat zone of India.

Fifteen different genotypes of greater yam ( ) NGY-1, NGY-2, NGY-3, NGY-4, NGY-5, NGY-6, NGY-7, NGY-8, NGY-9, NGY-10, NGY-11, NGY-12, NGY-13, NGY-14 and Da-199 were procured from different places of south Gujarat, like Valsad, Navsari, Surat and Anand. Among the biochemical parameters total carbohydrate ranged between 51.87 and 87.