Evaluation of Physicochemical Grain Traits in Promising Rice Lines in Comparison with Local Check Varieties

Introduction:Rice is Iran's second most strategic crop after wheat, serving as a staple food with significant economic and cultural importance.Its high consumption highlights its key role in food security, household diets, and the nation's agricultural economy.Among the various attributes valued by Iranian consumers, cooking and eating quality traits such as grain appearance, elongation after cooking, texture, and aroma are paramount.These traits directly affect consumer preferences and market demand, thereby playing a critical role in determining the commercial success of a variety.Therefore, a major goal of rice breeding programs in Iran is to combine the cooking and eating quality of local varieties with the high yield of improved lines.In this context, the present study was conducted to evaluate the grain quality characteristics of a set of rice lines developed through backcrossing and crossing strategies involving both local and improved varieties, aiming to identify lines with improved eating quality suitable for further development and release. Materials and Methods:The experiment was carried out during the 2022 cropping season at the research farm of the Rice Research Institute of Iran in Rasht.A total of 40 genotypes, including 36 advanced lines and four check varieties (Anam, Hashemi, Domsiah, and Hasansarai), were evaluated in a randomized complete block design with three replications.Genotypes were cultivated in plots of 3 3 meters.Transplanting was performed at a spacing of 20 20 cm with single seedlings at four weeks of age, corresponding to the three-to four-leaf stage.After harvesting, grain quality traits were measured, including paddy length and width, milled grain length and width, cooked grain length, elongation ratio, amylose content, and gelatinization temperature.Paddy length and width, as well as milled grain length and width, were measured using a photo enlarger device.For each trait, the mean of measurements taken on five random samples was recorded.Statistical analyses, including analysis of variance (ANOVA), mean comparisons, cluster analysis, and measuring person correlation coefficients, were performed using SAS and SPSS software and packages "metan" and "ggpubr" in R to assess variability and identify superior genotypes. Results and Discussion:The analysis of variance revealed significant differences (P<.05) among the genotypes for all evaluated traits, indicating the presence of substantial genetic variation that can be exploited in breeding programs.