Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

Evaluation of Physicochemical and Cooking Characteristics of Rice (Oryza sativa L.) Landraces of Lamjung and Tanahun Districts, Nepal.

After the Green Revolution, the increase in the choice of modern varieties at the expense of landraces has become a major cause of varietal loss. The preference, choice, and the economy of rice (Oryza sativa L.) largely depend on its physicochemical and cooking properties, which are found to be superior for landraces than modern varieties. In this study, we assessed and evaluated milled rice of 30 rice landraces on their physicochemical and cooking characteristics which aim to promote the revival of old landraces. Six parameters of physical properties, four parameters of chemical properties, and five parameters of cooking properties were evaluated based on the standard protocols. Significant variations (p < 0.05) were found in all the properties that were evaluated. The result showed that the highest milling recovery was found in Indrabeli (75.55%) whereas the lowest was found in Kalo Masino (66.98%) and bulk density ranged from 0.81 g/cm3 to 0.88 g/cm3 showing not much variability. Although most of them were of medium grain type, their 1000 kernel weight varied between 12.62 g and 25.65 g. From the observed chemical properties, Pahelo Anadi (9.73 ± 0.55 mm) showed the highest gel consistency and lowest apparent amylose content (7.23 ± 0.36%). Also, 13% of landraces possessed strong aroma while noble cooking properties were showed by Thakali Lahare Marsi with the highest elongation ratio (2.41 ± 0.05) and by Chiniya with the lowest gruel solid loss (0.033 ± 0.03%) and minimum optimum cooking time (23.45 ± 0.03 min). In the principal component analysis, the first four principal components retained 73.8% of the variance. The first and second principal components were mostly related with the physical and chemical characteristics while the third and fourth principal components were concerned with cooking characters. Superior characters possessed by rice landraces can be further assessed for the breeding programs so that the cultivation of these cherished rice landraces can be enhanced.