Bakanae or foot rot disease caused by Fusarium fujikuroi [teleomorph: Gibberella fujikuroi (Sawada) Ito] is emerging as a serious disease in rice. The disease causes both quantitative and qualitative losses to the grains under the field conditions. Breeding for resistance to Bakanae disease is a promising strategy to manage this emerging disease. In this study, we used a population of 168 F14 recombinant inbred lines (RILs) derived from two indica rice parents Pusa 1342, a highly resistant variety and Pusa Basmati 1121, a highly susceptible variety to map quantitative trait loci (QTLs) governing resistance against Bakanae disease. The disease reaction of 168 F14 RILs were measured on the seedlings inoculated using Fusarium fujikuroi culture using high-throughput screening protocol under glasshouse conditions. Utilizing inclusive composite interval mapping, three QTLs governing resistance to Bakanae were identified, namely qBK1.1, qBK1.2 and qBK1.3 which accounted 4.76, 24.74 and 6.49 % of phenotypic variation, respectively. The major effect QTL designated qBK1.2 was mapped in 0.26 Mb region between RM5336 and RM10153. A total of 55 annotated genes were identified within the identified QTL region qBK1.2. The novel QTLs identified in this study are useful resource for efficiently breeding rice cultivars resistant to Bakanae disease. This is the first report on identification of QTLs governing resistance against Bakanae in rice using inclusive composite interval mapping strategy in a RIL population.

Drought stress at the reproductive stage in rice is one of the most important cause for yield reduction, affecting both productivity and quality. All Basmati rice varieties, including the popular cultivar “Pusa Basmati 1 (PB1)” is highly sensitive to reproductive stage drought stress (RSDS). We report for the first time, improvement of a Basmati cultivar for RSDS tolerance, with the introgression of a major quantitative trait locus (QTL), “qDTY1.1” into PB1. The QTL was sourced from an aus variety, Nagina 22 (N22). A microsatellite (simple sequence repeat (SSR)) marker “RM 431” located at telomeric end (38.89 mb) of chromosome 1, and located within a 1.04 mb QTL region was employed for foreground selection for qDTY1.1 in the marker assisted backcross breeding process. A set of 113 SSR markers polymorphic between N22 and PB1 were utilized for background selection to ensure higher recurrent parent genome recovery. After three backcrosses followed by five generations of selfing, eighteen near isogenic lines (NILs) were developed, through combinatory selection for agro-morphological, grain and cooking quality traits. The NILs were evaluated for three consecutive Kharif seasons, 2017, 2018 and 2019 under well-watered and drought stress conditions. RSDS tolerance and yield stability indicated that P1882-12-111-3, P1882-12-111-5, P1882-12-111-6, P1882-12-111-7, P1882-12-111-12, P1882-12-111-15 and P1882-12-111-17 were best in terms of overall agronomic and grain quality under RSDS. Additionally, NILs exhibited high yield potential under normal condition as well. The RSDS tolerant Basmati NILs with high resilience to water stress, is a valuable resource for sustaining Basmati rice production under water limiting production environments.

Reproductive stage drought stress (RSDS) is detrimental for rice, which affects its productivity as well as grain quality. In the present study, we introgressed two major quantitative trait loci (QTLs), namely, qDTY2.1 and qDTY3.1, governing RSDS tolerance in a popular high yielding non-aromatic rice cultivar, Pusa 44, through marker-assisted backcross breeding (MABB). Pusa 44 is highly sensitive to RSDS, which restricts its cultivation across drought-prone environments. Foreground selection was carried out using markers, RM520 for qDTY3.1 and RM 521 for qDTY2.1. Background selection was achieved with 97 polymorphic SSR markers in tandem with phenotypic selection to achieve faster recurrent parent genome (RPG) recovery. Three successive backcrosses followed by three selfings aided RPG recoveries of 98.6% to 99.4% among 31 near isogenic lines (NILs). Fourteen NILs were found to be significantly superior in yield and grain quality under RSDS with higher drought tolerance efficiency (DTE) than Pusa 44. Among these, the evaluation of two promising NILs in the multilocational trial during Kharif 2019 showed that they were significantly superior to Pusa 44 under reproductive stage drought stress, while performing on par with Pusa 44 under normal irrigated conditions. These di-QTL pyramided drought-tolerant NILs are in the final stages of testing the All India Coordinated Rice Improvement Project varietal trials for cultivar release. Alternately, the elite drought-tolerant Pusa 44 NILs will serve as an invaluable source of drought tolerance in rice improvement.

Rice germplasm is a rich resource for discovering genes associated with salt tolerance. In the current study, a set of 96 accessions were evaluated for seedling stage salinity tolerance and its component traits. Significant phenotypic variation was observed among the genotypes for all the measured traits and eleven accessions with high level of salt tolerance at seedling stage were identified. The germplasm set comprised of three sub-populations and genome-wide association study (GWAS) identified a total of 23 marker–trait associations (MTAs) for traits studied. These MTAs were located on rice chromosomes 1, 2, 5, 6, 7, 9, and 12 and explained the trait phenotypic variances ranging from 13.98 to 29.88 %. Twenty-one MTAs identified in this study were located either in or near the previously reported quantitative trait loci (QTLs), while two MTAs namely, qSDW2.1 and qSNC5 were novel. A total of 18 and 13 putative annotated candidate genes were identified in a genomic region spanning ~200 kb around the MTAs qSDW2.1 and qSNC5, respectively. Some of the important genes underlying the novel MTAs were OsFBA1,OsFBL7, and mTERF which are known to be associated with salinity tolerance in crops. These MTAs pave way for combining salinity tolerance with high yield in rice genotypes through molecular breeding.

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R² = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.