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Aims: For prevention and management of type II diabetes among rice eating population, consumption of rice with lower Glycemic Index (GI) is highly desirable as low GI food decreases plasma glucose levels and plasma insulin demand. As there is paucity of information on the GIs of commonly consumed rice varieties in India, the aim of this study was to determine GI of selected five rice varieties, and to find relationship of GI with their cooking and eating quality traits. Study Design, Place and Duration: In vivo experiment was conducted at PG&RC, PJTS Agricultural University, Hyderabad, India, during March, 2017 to February, 2018. During this period, cooking and eating quality traits of selected rice varieties were determined in ICAR- Indian Institute of Rice Research, Hyderabad, India. Methodology: GIs were determined by feeding10 fasted human subjects a fixed portion of foods and subsequently measuring plasma glucose of their blood samples at specific interval of time and GIs were calculated using standard formula. Cooking and eating quality traits were determined using standard procedure. Results: The estimated GI values were 56.38(SEM 1.57), 59.23(SEM 1.02), 71.73 (SEM 1.75), 87.40 (SEM 1.90), and 94.05 (SEM 1.21), respectively for Sampada, Dhanrasi, DRR Dhan 42, DRR Dhan 43, and Jarava. Alkali spreading value (ASV), Gel consistency (GC), and Water uptake (WU) values of these five varieties ranged from 4 to 6, 22 mm to 54.5 mm, and 80 ml/ 100 g to 255 ml/100 g, respectively. Conclusion: Two varieties were identified with moderate GI values (Sampada, 56.38 ± 1.57; Dhanrasi, 59.23± 1.02). These varieties can prove to be a healthier option to incorporate in nutritional therapeutic diet. It is also concluded that lower ASV, higher GC, and lower WU, are good predictors for lower GIs of rice varieties. These relationships may be used for screening of low GI rice cultivars in breeding programme.

A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations, two-stage STARMA approach was developed to predict rice yield in some of the most intensive national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is significantly different from classical approaches. The proposed STARMA-II approach is promising alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from local to regional levels.