Rice is a major cereal food crop to more than half of the global population (Khush and Jena, 2009; Roser et al., 2013), and rice production must be doubled by 2050 to meet the demand of the ever-growing global population (Yamori, 2013; Pandey et al., 2017). Current yield trends are insufficient to meet this demand, and declining land, water, labor, and nutrient resources pose further threats on global food security (Ray et al., 2013; Pradhan et al., 2019). Hence, increasing rice productivity is a key for achieving global food security.

Rice is cultivated in a wider range of environments and exposed to various abiotic and biotic stresses affecting yield (Pandey et al., 2017; Yugander et al., 2018). The changing climate aggravates the situation by increasing the occurrence of extreme weather events and changing the dynamics of pest and diseases. Abiotic stresses frequently affecting rice yields include drought, salinity, and submergence (Joneydi, 2012). Biotic stresses affecting rice yields include pests (brown plant hopper, leaf folder, stem borer, gall midge, etc.), pathogens (blast, bacterial leaf blight, false smut, etc.), and weeds. Crop losses due to major biotic stressors such as bacterial leaf blight, blast, and brown plant hopper are quite high (Hasan et al., 2015). In India, the area vulnerable to drought has increased by 57% since 1997, and the frequency of heavy rainfall and flash floods has increased by more than 80% since 2012 (The World Bank. 2023). Hence, a sustained increase in rice production depends on the development of resilient rice varieties tolerant to these biotic/abiotic stresses as well as requiring less labor, water, and nutrient resources.

Rice scientists persist in integrating crucial yield trait characteristics, including the 2Gs for the yield ceiling, by employing advanced breeding techniques, such as marker-assisted selection, haplotype-based breeding, and allele mining. The close proximity of the GW7 gene for grain weight and the DEP2 gene for grain number on chromosome 7 indicates the potential for simultaneous introgression of these two genes, provided that advantageous allelic variants exist in a single donor parent (Singh et al., 2024). Developing a multiple-stress-tolerant rice cultivar through conventional breeding approaches is practically very difficult. In conventional breeding, linkage drag continues to several generations and thus requires several backcross generations to eliminate undesirable traits. The discovery of major effect QTLs/genes controlling tolerance to major biotic/abiotic stresses, viz., drought, salinity, submergence, high temperature, bacterial leaf blight, blast, brown plant hopper, gall midge, low phosphorus, etc. ( Supplementary Table S1 ) and advances in marker genotyping have paved the way for pyramiding of multiple QTLs/genes in a single genetic background through marker-assisted breeding (Janaki Ramayya et al., 2021).

In case of drought tolerance, qDTY1.1 and qDTY2.1 from a Philippine upland rice, Apo, have been used by several researchers to develop drought-tolerant versions of popular rice varieties (Venuprasad et al., 2009; Vikram et al., 2011; Muthu et al., 2020). Despite the existence of limited genetic variation for salinity tolerance traits in rice, intensive research efforts have identified a rice genotype, Pokkali, exhibiting a high level of tolerance to coastal salinity in India. Using Pokkali, a consistent QTL “Saltol” regulating Na/K uptake was identified and used by several researchers for the genetic improvement of salinity tolerance in rice (Singh et al., 2018). The discovery and utilization of a flood-tolerant rice “FR13A” discovered a mega effect of QTL “Sub1” regulating submergence tolerance in rice, which paved the way for deployment of MABB in rice (Neeraja et al., 2007).

Among the biotic stresses, blast is one of the most serious diseases causing yield reduction by up to 50% (Qu et al., 2006). Among >500 QTLs linked to blast resistance, only ~30 genes have been cloned and characterized (Das and Rao, 2015; Wu et al., 2016; Zheng et al., 2016). Out of this, Pi9 and Pi54 are the major genes offering broad-spectrum resistance against diverse isolates of M. oryzae (Qu et al., 2006). Against bacterial leaf blight (BLB) capable of causing 20%–40% yield reduction (Yasmin et al., 2017), 43 resistant genes have been identified ( Supplementary Table S1 ). The combination of two or more resistant genes was found to be effective against prevailing strains of bacterial leaf blight in different locations of India. Pyramiding of xa13+ Xa21, either alone or in combination with other genes, viz., Xa2, Xa5, or Xa4, was proven effective in providing durable resistance to bacterial leaf blight. Gall midge is fast becoming a serious pest of rice in India, causing huge yield reduction. Several gall midge resistance genes have been identified (Gm1, Gm2, gm3, Gm4, Gm5, Gm6, Gm7, Gm8, Gm9, Gm10, and Gm11) and used in breeding rice against different biotypes of gall midge. Out of >10 Gm resistance genes, Gm4 was reported to be effective against major biotypes of Gm (Divya et al., 2018).

Developing “climate-smart rice” involves the development of rice varieties that can endure increasingly severe and harsh circumstances in the future (Reay and Reay, 2019). It is anticipated that climate-smart rice will contribute to a sustainable rise in rice output under environmental constraints that regularly arise in a single growing season (Bin and Zhang, 2023). Molecular marker techniques are more effective for transferring desired gene(s) in any combination(s) into any desired genetic backgrounds. Hence, molecular techniques offer novel prospects for the enhancement of rice yields amid changing pest/disease/abiotic stress conditions. Molecular breeders have developed many high-yielding rice varieties with resistance against several abiotic and biotic stress tolerances ( Supplementary Table S2 ). Multiple-stress-tolerant rice varieties can provide yield security in areas subjected to multiple stresses in a single season. However, there are only limited varieties with more than two tolerance/resistance traits (Dixit et al., 2020).

In this study, we attempted to pyramid eight different QTLs/genes conferring tolerance/resistance against drought (qDTY1.1 and qDTY2.1), salinity (Saltol), submergence (Sub1), blast (Pi9), bacterial blight (xa13 and Xa21), and gall midge (Gm4) in an elite genetic stock CBMAS 14065.

Most of the rice varieties with high yields are susceptible to both biotic and abiotic stressors. Conventional breeding methods have been utilized effectively to produce numerous crop varieties with improved yield, but conventional breeding faces challenges (time-consuming and labor-intensive process) in pyramiding multiple-component traits/genomic loci conferring tolerance to various biotic/abiotic stresses. Recent advances in genotyping platforms and marker-assisted breeding have provided opportunities to pyramid or mobilize multiple QTLs/genes from several donors simultaneously into a single genetic background (Ramalingam et al., 2017; Das et al., 2018; Kumar et al., 2018; Rana et al., 2019; Valarmathi et al., 2019; Dixit et al., 2020; Muthu et al., 2020; Pancaldi and Trindade, 2020). Similar approaches have been successfully demonstrated in mobilizing QTLs/genes for various biotic (blast, bacterial leaf blight, and gall midge) and abiotic (salinity and submergence) stresses in the genetic background of popular rice varieties, viz., Naveen, Lalat, and Tapaswini (Das and Rao, 2015; Kumar et al., 2018; Janaki Ramayya et al., 2021).

CBMAS 14065 was developed as a semi-dwarf version of Improved White Ponni, and during field testing, this culture was found to exhibit a high level of susceptibility to salinity, submergence, blast, and bacterial leaf blight. Hence, attempts were made to pyramid tolerance to salinity (Saltol), submergence (Sub1), bacterial leaf blight (xa13 and Xa21), blast (Pi9), and gall midge (Gm4) along with the drought tolerance loci qDTY1.1 and qDTY2.1 already present in it. Except for drought and salinity tolerance, gene-based markers were used to monitor the introgression of genetic loci controlling tolerance to submergence, bacterial blight, and blast. Adequate numbers of progenies (~800 to 1,000) were maintained in each generation for the effective selection of targeted gene combinations. In the IMF₄ generation, elite lines harboring all of the target genes/QTLs under homozygous conditions were identified and forwarded further through selfing. Under each generation, the presence of target loci was confirmed through foreground selection using trait-linked markers and yield potential.

Drought stress is a major problem affecting rice cultivation under both irrigated and rain-fed conditions, causing serious yield losses to the farmers. The discovery of major-effect QTLs, viz., qDTY1.1, qDTY2.1, qDTY12.1, etc., in Apo, Nagina 22, Adaysel, Way rarem, etc., enabled the deployment of marker-assisted breeding to improve drought tolerance or yield under drought in rice (Vikram et al., 2011; Ghimire et al., 2012; Mishra et al., 2013). In the present study, CBMAS 14065 and the inter-mated F₇ population of CBMAS 14065 harboring qDTY1.1 and qDTY2.1 were screened for their tolerance to dehydration using polyethylene glycol (6000). CBMAS 14065 and the inter-mated F₇ progenies pyramided with qDTY1.1 and qDTY2.1 recorded lesser reduction in their shoot length, root length, relative water content, root fresh biomass, root dry biomass, shoot fresh biomass, and shoot dry biomass during dehydration as compared to Improved White Ponni. This study has demonstrated the importance of drought-tolerant QTLs (qDTY1.1 and qDTY2.1) in alleviating drought-induced dehydration and other negative impacts similar to earlier reports (Venuprasad et al., 2009; Ghimire et al., 2012; Vikram et al., 2015).

Rice exhibits high sensitivity to salt stress at both vegetative and reproductive phases (Das et al., 2020). Intensive efforts in characterizing the salinity-tolerant Pokkali identified a QTL “Saltol” as regulating the uptake of cationic salts into the roots (Das et al., 2020). Since salinity is a major abiotic stress for rice, in the present study, a Pokkalai-derived RIL—namely, FL478—harboring the salinity-tolerant loci “Saltol” was used as a donor, and the introgression of “Saltol” loci conferred a comparable level of tolerance to salinity in the inter-mated F₇ progenies of CBMAS 14065. The inter-mated F₇ progenies of CBMAS 14065 and FL 478 recorded a relatively lesser reduction in their plant height and total dry matter accumulation when compared to that of the recurrent parent CBMAS 14065 and Improved White Ponni ( Table 5 ) and the total dry matter production. The above-mentioned results were similar to that of the earlier reports where the Saltol-locus-introgressed lines of popular rice varieties exhibited enhanced tolerance to salinity than their recurrent parents (Valarmathi et al., 2019; Singh et al., 2021).

About 12–14 Mha of rice crop in India is frequently affected by flash-flooding submergence, and the discovery of a major effect QTL “Sub 1” from flood-tolerant FR 13A paved the way to develop submergence-tolerant versions of popular rice varieties (Neeraja et al., 2007; Rahman et al., 2018). In the present study, efforts were made to pyramid the submergence-tolerant loci “Sub 1” from FR13A into CBMAS 14065 along with other biotic/abiotic-stress-tolerant QTLs/genes. All of the inter-mated F₇ progenies exhibited 90%–96% of recovery after 14 days of submergence stress, whereas the recurrent parent CBMAS 14065 showed 100% susceptibility ( Figure 5 ). Relative plant elongation percentage (RPE) was found to be high in the recurrent parent (123.42%) and found to be low in the donor parent and the inter-mated F₇ progenies (80.69% to 113.84%). The marker-assisted introgression of Sub1 loci was followed to develop several flood-tolerant rice varieties—namely, CR Dhan 801, Bahuguni dhan-1 and Bahugunidhan-2, Swarna, Samba Mahsuri, TDK Sub1, BR 11 Sub1, CR 1009 Sub1, and CO 43 Sub1, which are now extensively cultivated in different parts of India (Septiningsih et al., 2015; Rahman et al., 2018; Sandhu et al., 2019). In a similar manner, the present study has paved the way for the development of submergence-tolerant rice in the genetic background of CBMAS 14065, a derivative of a popular rice variety of South India—namely, Improved White Ponni.

Out of the several blast resistance genes, Pi9 is reported to exhibit broad-spectrum resistance to different pathotypes of the blast fungus (Liu et al., 2002; Luo et al., 2017). Large-scale testing of rice genotypes harboring different blast resistance genes demonstrated that the efficacy of Pi9 was sufficient enough to confer a high level of resistance to the most virulent Eastern Indian pathotype of blast fungus (Imam et al., 2014; Variar et al, 2019). Hence, efforts were made to pyramid Pi9 into CBMAS 14065 along with target QTLs for other biotic/abiotic stresses. The inter-mated progenies of CBMAS 14065 harboring Pi9 locus exhibited a high level of resistance to a local isolate of the blast pathogen (score = 0) as compared to CBMAS 14065 and the susceptible check CO 39 (Score = 7 to 9) ( Figure 6 ). Khanna et al. (2015) had successfully introgressed seven blast genes into Pusa Basmati 1 (PB1), and they discovered that, among the seven genes, the Pi9-carrying inter-mated population showed the highest resistance against the virulent races of blast diseases, particularly in hotspot areas. Furthermore, this study has opened up avenues for pyramiding other blast resistance like Pi54 which will further broaden the genetic base against blast.

The most realistic/practical way of enhancing resistance to bacterial leaf blight pathogen is to combine two or more R genes reported (Mundt et al., 1999). Among the numerous bacterial leaf blight genes reported, the broad-spectrum Xa21, in combination with xa13, demonstrated broad-spectrum resistance to diverse races of bacterial blight pathogen (Pradhan et al., 2016). In this study, IMF₇ progenies harboring xa13 and Xa21 showed a high level of resistance to local isolate(s) of bacterial blight infection when compared to the recurrent parent CBMAS 14065 ( Figures 7A, B ). The pyramided lines showed significantly lesser lesion length (<0.3 cm) when compared to the recurrent parent. Pyramiding of multiple QTLs/genes conferring resistance to various biotic and abiotic stresses through conventional breeding is difficult and consumes a longer time. Recent advances in marker technology, genotyping, and mating designs provided an opportunity to accelerate the development of multiple-stress-tolerant rice with significant reduction in time, space, labor, and money. In the present study, the development of early generation backcross progenies of CBMAS 14065 harboring different stress-tolerant QTLs and the planned inter-mating of backcross progenies combined with precision genotyping and phenotyping demonstrated the accelerated development of climate-resilient rice requiring less chemicals. Further evaluation of these progenies across environments will identify elite lines exhibiting a consistent performance across environments. These rice genotypes will also serve as genetic stocks for further pyramiding of new traits to enhance their performance and climate resilience.

The present study clearly demonstrated the success of MAS in combining tolerance to multiple biotic and abiotic stresses while maintaining a higher yield potential and the preferred grain quality. The developed population containing eight distinct QTLs/genes in the current study demonstrated superiority over recurrent parents for multiple abiotic and biotic stresses (drought, submergence, salinity, blast, bacterial leaf blight, and gall midge) as well as for use as genetic stock in future breeding programs.

The original contributions presented in the study are included in the article / Supplementary Material, Further enquires can be directed to the corresponding Authors.

RM: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing. VMo: Formal analysis, Investigation, Methodology, Writing – original draft. VMu: Investigation, Methodology, Writing – original draft. RK: Data curation, Software, Writing – original draft. RP: Data curation, Investigation, Writing – original draft. PS: Investigation, Validation, Writing – original draft. BA: Resources, Validation, Writing – original draft. VR: Resources, Supervision, Writing – original draft. SM: Project administration, Resources, Supervision, Writing – review & editing. RR: Methodology, Project administration, Resources, Writing – review & editing. HR: Methodology, Writing – review & editing. JN: Software, Validation, Writing – review & editing. MS: Project administration, Resources, Supervision, Writing – review & editing. GC: Resources, Validation, Writing – review & editing. GV: Project administration, Writing – review & editing.