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. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

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 31.98% and 71.05% compared to traditional methods and wet direct seeding, respectively. Using vermicompost alone as a nutrient source not only boosted yields by 21.31% over conventional fertilizers but also enhanced the rice's nutritional value and cooking quality. Moreover, soils treated with vermicompost showed higher dehydrogenase activity, indicating better soil health. Economically, mechanical transplanting with vermicompost was the most beneficial, yielding the highest net returns and benefit-cost ratios in both years studied. This approach presents a viable model for improving the sustainability of aromatic rice production globally, emphasizing the economic and environmental advantages of adopting mechanical planting techniques and organic fertilization methods.

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

Fifteen different genotypes of greater yam (Dioscorea alata) 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 87.85% from different genotypes, starch ranged from 47 to 80.67%, crude fat ranged from 0.6 to 2.32%, crude fibre ranged between 1.10 and 4.09%, anthocyanin content of genotypes ranged from 1.01 to 3.25 mg/g, beta-carotene content ranged between 0.97 and 1.88 µg/g. The antinutrients namely Diosgenin and Tannin ranged from 0.28 to 0.93% and 0.923 mg/100 g to 2.447 mg/100 g, respectively. RAPD analysis was also done by the help of 18 RAPD markers: OPA1, OPA2, OPA3, OPA13, OPB1, OPB6, OPB7, OPM1, OPM2, OPM4, OPM7, OPM11, OPM12, OPM13, OPM15, OPM16, OPM17 and OPM19 from which an average of 9.7 loci were detected with an average of 4.72 polymorphic loci which is 48.65% polymorphism per loci and 48.70% average polymorphism. From the overall study, it can be conferred that the study revealed highest carbohydrate content in NGY-3 (87.85%), fat (2.32%) and crude fiber content (4.09%) in NGY-11, ß-carotene in NGY-7 (1.88 µg/g), anthocyanin in NGY-4(3.25 mg/g). Lowest tannin (0.923 mg/100 g) and diosgenin (0.28%) was found in NGY-6, and NGY-7 respectively. For each of the biochemical parameters, the varieties with the optimum values may be cultivated. As the molecular studies revealed NGY-2 and NGY-3 have 96% similarities, they may be the duplicate of the same genotypes which can be studied further for better germplasm conservation.