DRR Dhan 58, a Seedling Stage Salinity Tolerant NIL of Improved Samba Mahsuri Shows Superior Performance in Multi-location Trials.

BACKGROUND: Improved Samba Mahsuri (ISM) is an elite, high-yielding, bacterial blight resistant, fine-grained rice variety with low glycaemic index. It is highly sensitive to salt stress, particularly at seedling stage, which significantly reduces its yield potential in coastal areas. A salinity tolerant QTL, Saltol, associated with seedling stage tolerance was previously mapped on chromosome 1 (10.6-11.5 Mb) from the Indian landrace, Pokkali and is effective in different genetic backgrounds. The objective of this study was to enhance salinity tolerance of ISM by incorporating the Saltol QTL through marker-assisted backcross breeding using the breeding line, FL478 (Pokkali/IR29). RESULTS: Foreground selection was carried out at each generation using five Saltol-specific markers and three bacterial blight resistance genes, Xa21, xa13 and xa5. Background selection was conducted using 66 well distributed polymorphic SSR markers and at the BC3F2 generation, a single plant with maximum recurrent parent genome recovery (95.3%) was identified and advanced to the BC3F4 generation. Based on bacterial blight resistance, seedling stage salinity tolerance and resemblance to ISM, four advanced breeding lines were selected for testing in replicated experiments near Hyderabad, India. A promising near-isogenic line, DRR Dhan 58, was evaluated in multi-location trials-coastal salinity and it showed significant salinity tolerance, resistance to bacterial blight disease, high yield and excellent grain quality during the 2019 and 2020 trials. DRR Dhan 58 was 95.1% similar to ISM based on genotyping with the 90 K SNP chip. Whole genome resequencing analysis of Pokkali and FL478 which were salinity tolerant checks, ISM and DRR Dhan 58 showed a high degree of relatedness with respect to the candidate gene loci for Saltol and OsSKC1 (Shoot K+ Concentration 1). CONCLUSION: DRR Dhan 58, possessing Saltol and three bacterial blight resistance genes (Xa21, xa13 and xa5) in the genetic background of the Indian mega-variety of rice, Samba Mahsuri, was developed for potential cultivation in areas prone to seedling stage salinity, as well as areas with endemic bacterial blight disease. This entry had a 24% yield advantage over the recurrent parent ISM under coastal saline conditions in multi-location trials and was recently released for commercial cultivation in India.

Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.

Identification and development of salinity tolerant genotypes and varieties are one of the promising ways to improve productivity of salt-affected soils. Alternate methods to achieve this are required as the conventional methods are time-consuming and often difficult to handle large population of genotypes. In this context, hyperspectral remote sensing could be one of the rapid, repeatable and reliable methods. The aim of the present study is to develop non-invasive high-throughput phenotyping techniques for salinity stress monitoring in rice. Spectral signature of leaf samples from 56 salinity stress tolerant and sensitive rice genotypes were collected at maximum tillering and flowering stage in visible and near-infrared (VNIR) domain. The spectral reflectance data and rice leaf potassium, sodium, calcium, magnesium, iron, manganese, zinc and copper concentration were analyzed for optimum index identification and multivariate model development. Novel hyperspectral indices sensitive to leaf nutrient status as affected by salinity stress were identified. The correlation coefficient during calibration and validation of the optimized indices varied between 0.34-0.63 and 0.36-0.66, respectively. To develop multivariate model, solo partial least square regression (PLSR), PLSR- and principal component analysis (PCA)-combined machine learning models were tested. The results revealed that the performance of PLSR-combined models was the best followed by PCA-based model while indices based model was found to be least accurate. The results obtained in the present study showed potential of hyperspectral remote sensing for non-destructive phenotyping of salinity stress.

Identification of mega-environments and rice genotypes for general and specific adaptation to saline and alkaline stresses in India.

In the present study, a total of 53 promising salt-tolerant genotypes were tested across 18 salt-affected diverse locations for three years. An attempt was made to identify ideal test locations and mega-environments using GGE biplot analysis. The CSSRI sodic environment was the most discriminating location in individual years as well as over the years and could be used to screen out unstable and salt-sensitive genotypes. Genotypes CSR36, CSR-2K-219, and CSR-2K-262 were found ideal across years. Overall, Genotypes CSR-2K-219, CSR-2K-262, and CSR-2K-242 were found superior and stable among all genotypes with higher mean yields. Different sets of genotypes emerged as winners in saline soils but not in sodic soils; however, Genotype CSR-2K-262 was the only genotype that was best under both saline and alkaline environments over the years. The lack of repeatable associations among locations and repeatable mega-environment groupings indicated the complexity of soil salinity. Hence, a multi-location and multi-year evaluation is indispensable for evaluating the test sites as well as identifying genotypes with consistently specific and wider adaptation to particular agro-climatic zones. The genotypes identified in the present study could be used for commercial cultivation across edaphically challenged areas for sustainable production.