1 Introduction

Rice (Oryza sativa L.) is a fundamental staple food for more than half of the world's population, particularly in Asia, Africa, and Latin America. It provides a significant portion of the daily caloric intake and is a primary source of nutrition for billions of people (Yu et al., 2009). In many developing countries, rice contributes at least 20% of dietary protein and 3% of dietary fat, making it an essential component of the diet.

Globally, rice is cultivated on approximately 166.1 million hectares, with an annual production of around 745.2 million tonnes (Gregorio, 2002). In India alone, rice is grown on 43.5 million hectares, producing 90 million tonnes annually, which accounts for 23% of the country's gross cropped area (Naik et al., 2020). These statistics underscore the critical role of rice in global food security and the agricultural economy.

Rice is not only a staple food but also a significant source of essential nutrients. It provides 43% of the caloric requirement and 20%~25% of agricultural income in many regions (Xu et al., 2016). However, the nutritional quality of rice can be enhanced through breeding and genetic engineering to address micronutrient deficiencies, such as iron and zinc, which are crucial for human health. Biofortification efforts have shown promise in increasing the concentration of these essential minerals in rice grains (Naik et al., 2020).

The economic impact of rice yield is profound, as it directly influences food security and the livelihoods of millions of farmers. High-yielding rice varieties are essential to meet the growing global demand and to ensure economic stability in rice-producing regions (Meena et al., 2023). The development of hybrid rice varieties has demonstrated significant yield increases, contributing to global food security (Das et al., 2020).

Recent research has focused on the genetic relationship between grain yield and nutrient content in rice. Studies have identified quantitative trait loci (QTLs) that influence both yield and nutritional components, such as protein and fat content. The integration of genomics and metabolomics in breeding programs has further enhanced the predictability of hybrid yield and nutritional quality, the identification of promising breeding lines with high iron and zinc content, as well as high yield, highlights the potential for developing biofortified rice varieties.

2 Genetic Factors Affecting Nutritional Content

2.1 Main nutritional components of rice

As one of the most important staple foods in the world, rice has an important impact on human health. The main nutrients of rice include carbohydrates, proteins, fats, vitamins and minerals. Carbohydrates are the main component of rice, accounting for about 70%~80% of its total weight, mainly in the form of starch. Starch is not only an important source of energy for the human body, but also determines the texture and taste of rice during cooking. Secondly, protein is the second most important component of rice, accounting for about 7%~8% of the total weight. Although rice has a relatively low protein content, it has high bioavailability, especially a relatively high lysine content, which is very important for the growth and development of humans, especially children (Mamata et al., 2018).

In addition, rice contains a small amount of fat, accounting for about 0.5%~1% of the total weight. These fats are mainly concentrated in the rice bran layer, including essential fatty acids such as linoleic acid and linolenic acid. In terms of vitamins, rice, especially brown rice, is rich in B vitamins, such as thiamine (vitamin B1), riboflavin (vitamin B2) and niacin (vitamin B3), which play an important role in human metabolism and nervous system function (Yu et al., 2009). Rice also contains a variety of minerals, such as potassium, magnesium, phosphorus and zinc. These minerals are not only important components of bones and teeth, but also participate in a variety of physiological processes in the body, including enzyme activation, acid-base balance and nerve conduction. The main nutrients of rice are diverse and balanced, and have a profound impact on human health. By understanding these nutrients, we can better understand the importance of rice in our daily diet and focus on optimizing these nutrients during rice breeding and cultivation (Mamata et al., 2018).

2.2 Genetic variation and nutritional content

Genetic variation plays a vital role in the formation of crop nutrients. Genetic variation includes natural mutations and artificial selection in the genome, both of which can significantly affect the nutritional quality of crops. In natural mutations, random variations in genes may cause an increase or decrease in certain nutrients. For example, certain genetic variants in rice can increase iron and zinc levels in the grain, which could have important implications for solving the global problem of micronutrient deficiencies.

Artificial selection introduces specific genetic variations through breeding techniques to improve the nutritional content of crops. Modern molecular breeding techniques, such as gene editing and genomic selection, can precisely manipulate genetic variations to enhance the content of targeted nutrients. For example, through CRISPR-Cas9 gene editing technology, specific genetic sites can be precisely introduced or deleted in rice to increase the content of key nutrients such as vitamin A, iron, and zinc. Genetic variations can also affect anti-nutritional factors in crops, such as phytic acid and tannins, which inhibit the body's absorption of nutrients. Reducing or eliminating these anti-nutritional factors through selection and breeding can significantly increase the nutritional value of crops.

Genetic variation affects the nutritional content of crops through multiple pathways. Whether through natural mutation or artificial selection, genetic variation provides a wide range of possibilities and important tools for crop nutritional improvement. This not only helps improve the quality of agricultural products, but also has a profound impact on human nutrition, health and food security. Rice’s genetic diversity plays a crucial role in determining its nutritional content. Rice has significant genetic variation in traits related to nutritional content. This diversity is crucial for breeding programs aimed at improving the nutritional quality of rice (Xing and Zhang, 2010). QTL mapping has identified many loci related to rice nutritional components. For example, QTL related to grain iron, zinc and protein content have been identified, providing valuable targets for biofortification (Samonte et al., 2006). Specific QTL, such as qZn4 and qMn6.2, have been shown to affect both yield and micronutrient content, highlighting the potential to improve these traits simultaneously.

2.3 Molecular mechanisms and pathways

Understanding the molecular mechanisms underlying nutrient synthesis and regulation is critical for developing nutritionally enhanced rice varieties. The synthesis of nutrients in rice is controlled by a complex network of genes. For example, the expression of genes involved in starch and protein synthesis significantly impacts the nutritional quality of rice grains. Studies have profiled the expression patterns of genes involved in these pathways, providing insights into their regulation and potential targets for genetic manipulation (Ajmera, 2017).

Transcription factors play a pivotal role in regulating nutrient metabolism in rice. These proteins can activate or repress the expression of genes involved in nutrient synthesis, thereby influencing the nutritional content of the rice grains. Advances in functional genomics have identified several key transcription factors that regulate these processes, offering new avenues for improving the nutritional quality of rice through genetic engineering (Duan et al., 2022). The genetic factors affecting the nutritional content of rice are multifaceted, involving natural genetic diversity, QTLs, and molecular mechanisms. By leveraging this knowledge, breeding programs can develop rice varieties with enhanced nutritional profiles, addressing global nutritional deficiencies and improving public health.

3 Genetic Factors Affecting Yield

3.1 Rice yield composition

Grains per panicle is a key determinant of rice yield. Genetic factors influencing this trait have been extensively studied. For example, the DEP1 locus has been identified as a major quantitative trait locus (QTL) that increases grain number per panicle by enhancing meristem activity6. Furthermore, constitutive expression of cell wall invertase genes has been shown to increase grain number and thus overall grain yield (Pujar et al., 2020). Particle size and weight are also important factors affecting yield. The GS2 locus encoding growth regulatory factor 4 (OsGRF4) has been identified as a key genetic determinant of grain size. Rare alleles of GS2 result in larger cells and increased cell numbers, thereby increasing grain weight and yield. Furthermore, studies have shown that grain weight is highly correlated with yield, and this relationship is consistent across various rice varieties.

Plant height and biomass are closely related to yield. QTL analyzes have identified several loci affecting these traits. For example, five QTL related to plant height were detected, some of which also contributed to grain yield and biomass. The relationship between plant height and yield is complex, with some QTL contributing to both traits, while other QTL mainly affect plant height5. Furthermore, biomass production and allocation are critical for yield, and specific QTL were identified for these traits.

3.2 Genetic determinants of yield

High-yielding rice varieties often have specific genetic markers that contribute to their superior performance. For example, the DEP1 locus is common in many high-yielding Chinese rice varieties and is associated with increased grain yield. Likewise, the GS2 locus has been introduced into various rice varieties to improve grain weight and yield (Das et al., 2020). These genetic markers provide valuable tools for breeding programs aimed at developing high-yielding rice varieties.

QTL play a vital role in yield improvement by regulating various yield-related traits. For example, QTL related to grain yield, biomass, and harvest index have been identified, with some of these QTL colocalizing on specific chromosomes, suggesting a genetic basis for increased yield through enhanced biomass or increased harvest index5. In addition, QTL affecting grain yield and nutritional composition have been identified, with certain regions on chromosomes 6 and 10 being particularly important.

3.3 Molecular pathways affecting yield

Photosynthetic efficiency is a key factor affecting yield, Genetic engineering methods target genes involved in photosynthesis to increase yields. For example, constitutive expression of cell wall invertase genes has been shown to increase photosynthetic efficiency, thereby increasing grain yield and starch content (Li et al., 2013). Stress resistance genes also play an important role in determining yield, especially under adverse environmental conditions. Although specific stress tolerance genes are not highlighted in the data presented, it is known that breeding for stress tolerance can significantly improve yield stability and overall productivity.

The genetic factors that affect rice yield are multifaceted, involving the number of grains per panicle, grain size and weight, plant height, biomass and other traits. High-yielding varieties often have specific genetic markers, and QTL play a crucial role in increasing yield. Molecular pathways, including those related to photosynthetic efficiency and stress tolerance, further influence yield outcomes. These insights provide valuable opportunities for breeding programs aimed at improving rice yield and nutritional content (Sakamoto and Matsuoka, 2008).

4 Breeding Strategies to Improve Nutritional Content and Yield

4.1 Traditional breeding methods

Traditional breeding methods, such as cross breeding and selection, have been fundamental in improving both the nutritional content and yield of rice. These methods involve the deliberate crossing of different rice varieties to combine desirable traits from each parent, followed by selection of the best-performing progeny over successive generations. One of the key strategies in traditional breeding is the identification and utilization of quantitative trait loci (QTLs) that influence both yield and nutritional content. For instance, a study involving 209 recombinant inbred lines derived from a cross between indica rice Xieqingzao B and Milyang 46 identified 22 QTLs affecting traits such as grain yield, protein content, and fat content. Notably, two QTL clusters were found on chromosomes 6 and 10, which were responsible for multiple traits, indicating the potential for simultaneous improvement of yield and nutritional content through targeted breeding (Huang et al., 2009).

Efforts to enhance the micronutrient density in staple crops like rice have also been pursued through conventional breeding approaches. Studies have shown that it is possible to exploit genetic variation in seed concentration of essential minerals such as iron and zinc without negatively impacting yield. This approach involves selecting varieties with high mineral content and combining these traits with high-yield characteristics through cross breeding and selection (Li et al., 2019).

Marker-assisted selection (MAS) has emerged as a powerful tool in traditional breeding, allowing for the precise selection of desirable traits based on genetic markers. This method has been particularly effective in improving grain yield and nutritional content. For example, MAS has been used to identify and introgress major QTLs for yield and yield component traits, leading to significant improvements in rice productivity. The use of well-characterized QTLs through introgression and gene pyramiding has proven effective, especially under abiotic stress conditions (Xing et al., 2010).

Biofortification, a process of increasing the nutritional content of crops through breeding, has been a focus of rice breeding programs. QTL mapping for grain yield and micronutritional traits has identified several QTLs that influence both yield and nutrient content. For instance, QTLs for zinc, manganese, and copper content have been identified, with some QTLs showing synergistic effects on both yield and micronutrient content, suggesting the potential for simultaneous improvement through QTL pyramiding (Yu et al., 2009). Breeding for improved nitrogen utilization efficiency (NUE) has shown promise in enhancing both yield and grain protein concentration. Studies have demonstrated significant variation in NUE among different rice genotypes, with positive correlations between NUE, grain yield, and grain protein concentration. These findings suggest that selecting for high NUE can lead to rice varieties that are both high-yielding and nutritionally superior (Guo and Ye, 2014).

Traditional breeding methods, including cross breeding, selection, and the use of marker-assisted selection, have been instrumental in improving the nutritional content and yield of rice. By leveraging genetic variation and QTL mapping, breeders can develop rice varieties that meet the dual goals of high yield and enhanced nutritional quality. These strategies, combined with biofortification and improved nutrient use efficiency, offer promising avenues for the development of nutritionally superior and high-yielding rice varieties.

4.2 Breeding strategies to improve nutritional content and yield

Marker Assisted Selection (MAS) has emerged as a powerful tool in modern rice breeding, enabling the precise introduction of desirable traits into rice cultivars. MAS leverages DNA markers that are closely linked to target genes or quantitative trait loci (QTLs) to facilitate the selection process. This method has been particularly effective in improving traits such as disease resistance, abiotic stress tolerance, and yield components.

For instance, MAS has been successfully employed to pyramid multiple resistance genes into a single rice variety, enhancing its resilience against various biotic and abiotic stresses. A study demonstrated the pyramidization of genes/QTLs for resistance to blast, gall midge, submergence, and salinity in an elite rice cultivar, resulting in lines that showed high levels of resistance to these stresses (Matsubara et al., 2016). Similarly, MAS has been used to introduce QTLs associated with grain number and yield-related traits, such as Gn1a and Dep1, into rice plants, thereby enhancing yield (Rana et al., 2019).

The integration of MAS with conventional breeding approaches has also been shown to be effective. For example, marker-assisted backcross breeding has been used to integrate major genes or QTLs with large effects into widely grown varieties, providing opportunities to develop high-yielding, stress-resistant, and better-quality rice cultivars (Zhou et al., 2018). The use of cost-effective DNA markers derived from fine-mapped positions of important agronomic traits further enhances the efficiency of MAS.

Genomic selection (GS) is another advanced breeding technology that utilizes genome-wide markers to predict the breeding value of individuals. Unlike MAS, which focuses on specific markers linked to target traits, GS considers the entire genome, making it a more comprehensive approach. GS has the potential to accelerate the breeding process by enabling the selection of superior genotypes at an early stage. This method has been particularly useful in addressing complex traits that are controlled by multiple genes, such as yield and nutritional content. The integration of next-generation sequencing technologies has further enhanced the accuracy and efficiency of GS by providing high-resolution mapping of QTLs and facilitating the identification of causal genes (Rana et al., 2019).

CRISPR/Cas9 and other gene-editing technologies have revolutionized plant breeding by allowing precise modifications of the rice genome. These technologies enable the targeted editing of specific genes to enhance desirable traits or eliminate undesirable ones. Gene editing has been successfully applied to improve various traits in rice, including yield, disease resistance, and nutritional content. For example, CRISPR/Cas9 has been used to knock out genes associated with negative traits, thereby enhancing yield and stress tolerance. The ability to make precise edits in the rice genome opens up new possibilities for developing rice varieties with improved nutritional content and yield (Tripathy, 2021).

4.3 Integrated breeding approach

An integrated breeding approach combines traditional and modern methods to achieve both high nutritional content and high yields. This approach leverages the strengths of each method to overcome their individual limitations. Traditional breeding can be used to create a diverse genetic base, while MAS and genomic selection can accelerate the identification and propagation of superior genotypes (Huang et al., 2009).

Multi-trait selection involves the simultaneous selection for multiple desirable traits, such as yield, nutritional content, and stress resistance. This method requires a comprehensive understanding of the genetic architecture of these traits and their interactions. Studies have shown that it is possible to combine high yield with improved nutritional quality through careful selection and breeding strategies (Inthapanya et al., 2000).

The use of advanced molecular markers and genomic tools facilitates the efficient selection of multi-trait genotypes, ensuring that new rice varieties meet the diverse needs of farmers and consumers. The integration of traditional and modern breeding methods offers a robust strategy for improving both the nutritional content and yield of rice. By leveraging the strengths of each approach, breeders can develop rice varieties that are not only high-yielding but also nutritionally superior, addressing the dual challenges of food security and nutritional deficiency.

5 Case Studies

In a multi-site study, two rice mapping populations (MTU1010/Suraksha and MTU1010/Jalpriya) were tested at two to three sites, and the results showed that zinc content was significantly affected by the environment. Previous studies have also found significant environmental variation in zinc content in rice germplasm, high-zinc transgenic rice, and mapping populations.

The study found that in Swarna/Chittimuthyalu and IR64/Chittimuthyalu recombinant inbred lines (RILs), there was a significant negative correlation (P<0.001) between yield per plant and zinc content, but not in MTU1010/Suraksha and MTU1010/Jalpriya populations. This correlation was found (Figure 1). In general, lines with the highest zinc content produced lower yields and vice versa. Although most studies reported a negative relationship between grain zinc content and yield, a few reported positive results. The dilution effect of nutrient concentration in plant tissues as dry matter increases is commonly observed in cereals, explaining the inverse relationship between zinc content and yield (Figure 1) (Sanjeeva Rao et al., 2020).

Although negative correlations were observed when performing association studies on the entire mapping population, individual high-producing and zinc-rich recombinants were also found. In addition to high zinc content, biofortified rice varieties should also have comparable yields to existing cultivars. Currently in India, there is no special price for biofortified rice grains, so farmers have no incentive to grow these varieties. Therefore, farmers are likely to accept biofortified varieties only if their yields are comparable to existing popular cultivars. Cooking quality is also an important factor in rice variety release and adoption (Figure 1) (Sanjeeva Rao et al., 2020). For biofortified rice varieties to be promoted and used, they should have a zinc content of≥35 mg/kg without yield loss and have ideal cooking quality. However, this combination rarely occurs in germplasm, thus requiring conscious breeding efforts to develop biofortified rice varieties with desirable attributes (Figure 1).

Salt-alkali stress can have a significant impact on the growth and development of rice. High salt concentrations lead to high permeability and ionic imbalance in the rhizosphere environment. Plants absorb a large amount of Na+, which enters the outer cells of the roots from the soil solution, then enters the root xylem, and is finally transported to the leaves through the stems (Munns and Tester, 2008). The large accumulation of Na+ will inhibit the absorption of other nutritional elements such as K+, leading to ion toxicity (Assaha et al., 2017). Low external water potential causes osmotic stress in root cells, prompting cells to accumulate compatible solutes to reduce their water potential, thereby maintaining cell volume and turgor pressure. This process also helps to keep stomata open, increase CO₂ concentration, and alleviate the inhibition of photosynthesis (Türkan and Demiral, 2009). However, salt-alkali stress will destroy this balance, leading to the accumulation of reactive oxygen species (ROS) in plants, damaging the biofilm system, and ultimately causing plant wilting.

In the study of Ganapati et al. (2022), the ion regulation and signaling mechanisms of rice seedlings under salt-alkali stress were demonstrated. Na⁺ is absorbed from the roots into the plant through specific channels and transporters, and then transported to different tissues through xylem and phloem. In this process, OsSOS1 excludes Na⁺ through Na⁺/H⁺ anti-transport on the plasma membrane to maintain a lower cellular Na⁺/K⁺ ratio, thereby improving salt tolerance. OsHKT1,5 regulates Na⁺ transport in roots and maintains Na⁺/K⁺ balance, which is a key factor in salt-alkali tolerance. Other transporters such as OsHKT2,1, OsHKT2,3a, OsHKT1,4, OsHAK1, OsAKT1, OsKAT1 and OsGORK work together in different parts and organelles to regulate the balance of Na⁺ and K⁺ and ensure the normal function of cells.

Calcium signaling plays a key role in response to salt-alkali stress. Ca₂⁺-binding proteins adapt to environmental changes by regulating plasma membrane H⁺-ATPase and other ion channels. The SOS signaling pathway, including SOS₂ and SOS₃ proteins, is involved in regulating Na⁺ emission and calcium signaling. In addition, salt-alkali stress causes the accumulation of reactive oxygen species (ROS) and damages the cell membrane system. Plants use a series of antioxidant mechanisms to alleviate the toxic effects of ROS and maintain cell homeostasis (Figure 2) (Ganapati et al., 2022).

The ion homeostasis regulation mechanism of rice under salt-alkali stress is a complex process involving multiple ion channels, transporters and signaling pathways. Research on these mechanisms not only helps to reveal the basic physiological mechanisms of plants in response to stress, but also provides important theoretical foundations and breeding strategies for the future development of salt-alkali tolerant crops. By delving deeper into these mechanisms, we can better understand how plants survive in saline environments and develop more resistant and productive crop varieties.

6 Challenges and Future Directions

Balancing nutrient content and yield in rice presents a significant challenge due to the genetic complexity involved. The primary obstacle lies in the fact that many genes regulating nutrient content and yield are often intertwined and may exhibit pleiotropic effects, where a single gene influences multiple traits. Enhancing nutrient content, such as increasing zinc or iron levels, can sometimes inadvertently affect yield negatively. This phenomenon, known as the yield-nutrient trade-off, poses a critical hurdle for breeders. Therefore, it is essential to develop strategies that can simultaneously optimize both traits without compromising one for the other.

Managing genetic trade-offs requires a nuanced understanding of the underlying genetic mechanisms and their interactions. Advanced molecular techniques, such as genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping, can help identify key genetic regions associated with both high nutrient content and high yield. Additionally, the integration of CRISPR/Cas9 gene-editing technology provides a precise tool to manipulate specific genes, potentially mitigating negative trade-offs. By targeting and modifying specific alleles, breeders can enhance desired traits while minimizing adverse effects on yield.

The study of the literature on nutrient content and yield in rice highlights several critical findings. Genetic relationships between grain yield and nutrient contents, such as protein and fat, have been identified, with significant negative correlations observed between nutrient contents and yield traits like brown rice recovery and grain yield (Inthapanya et al., 2000). Nitrogen utilization efficiency (NUE) has been shown to have significant positive effects on grain yield, with variations in NUE among different rice genotypes. Quantitative trait loci (QTL) mapping has revealed numerous QTLs associated with both grain yield and micronutrient contents, suggesting potential for biofortification breeding (Yu et al., 2009).

Studies have also demonstrated that increasing nitrogen rates can significantly enhance grain yield, although the genetic improvement's contribution to yield increases may be less than previously believed (Samonte et al., 2006). The negative correlation between yield and protein concentration in cereals has been consistently observed, emphasizing the challenge of improving both yield and nutritional quality simultaneously. Genotypic differences in nutrient uptake and utilization efficiency further contribute to variations in grain yield under different fertilization conditions. Additionally, the association between grain zinc and iron content with seed yield suggests potential for breeding micronutrient-rich rice varieties without compromising yield. The development of green super rice varieties with high nutrient use efficiency through phenotypic selection under varied nutrient conditions has also shown promise (Duan et al., 2020).

Future research should focus on several key areas to further enhance the nutrient content and yield in rice. There is a need for more comprehensive studies on the genetic basis of nutrient content and yield traits, particularly through advanced QTL mapping and genome-wide association studies (Duan et al., 2022). Exploring the potential of combining high NUE with other desirable traits, such as disease resistance and drought tolerance, could lead to the development of more resilient rice varieties. The integration of conventional breeding techniques with modern molecular breeding and genetic engineering approaches should be prioritized to achieve significant improvements in both yield and nutritional quality (Singh et al., 2021). Additionally, research should investigate the socio-economic impacts of adopting nutritionally enhanced rice varieties, particularly in developing countries where rice is a staple food.

The potential impacts of improving nutrient content and yield in rice on global food security and nutrition are profound. Enhancing the nutritional quality of rice can address micronutrient deficiencies in populations that rely heavily on rice as a staple food, thereby improving overall health outcomes (Jewel et al., 2019). Moreover, increasing rice yield through genetic and agronomic interventions can contribute to food security by ensuring a stable and sufficient food supply. The integration of advanced breeding techniques and genetic engineering holds promise for developing rice varieties that meet the dual goals of high yield and enhanced nutritional quality, ultimately contributing to sustainable agricultural practices and improved livelihoods for farmers worldwide. Continued research and collaboration among scientists, breeders, and policymakers will be essential to realize these benefits and address the challenges associated with improving rice nutrition and yield.

Acknowledgments

The author extends sincere thanks to two anonymous peer reviewers for their feedback on the manuscript, whose suggestions have contributed to the improvement of the manuscript.

Conflict of Interest Disclosure

The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Ajmera S., 2017, Character association analysis for grain iron and zinc concentrations and grain yield components in rice genotypes, International Journal of Pure and Applied Bioscience, 5(4): 940-945.

https://doi.org/10.18782/2320-7051.5694

Ashokkumar K., Govindaraj M., Karthikeyan A., Shobhana V., and Warkentin T., 2020, Genomics-integrated breeding for carotenoids and folates in staple cereal grains to reduce malnutrition, Frontiers in Genetics, 11: 414.

https://doi.org/10.3389/fgene.2020.00414

Assaha D.V.M., Ueda A., Saneoka H., Al-Yahyai R., and Yaish M.W., 2017, The role of Na⁺ and K⁺ transporters in salt stress adaptation in glycophytes, Front Physiol., 8(2): 509.

Das P., Adak S., and Majumder A., 2020, Genetic manipulation for improved nutritional quality in rice, Frontiers in Genetics, 11: 776.

https://doi.org/10.3389/fgene.2020.00776

Duan L., Wu T., Li X., Huang, D., Li X., Wen X., Chen P., Xie J., and Hu B., 2022, QTL detection for grain yield and micro-nutrition contents in rice (Oryza sativa) using an interspecific backcross population derived from Dongxiang wild rice (Oryza rufipogon), Crop and Pasture Science, 73(11): 1253-1263.

https://doi.org/10.1071/CP22039

Ganapati R.K., Naveed S.M., Zafar S., Wang W.S., and Xu J.L., 2022, Saline-alkali tolerance in rice: physiological response, molecular mechanism, and QTL identification and application to breeding, Rice Science, 29(5): 412-434.

Gregorio G., 2002, Progress in breeding for trace minerals in staple crops, The Journal of Nutrition, 132(3): 500S-502S.

https://doi.org/10.1093/JN/132.3.500S

Guo L., and Ye G., 2014, Use of major quantitative trait loci to improve grain yield of rice, Rice Science, 21(2): 65-82.

https://doi.org/10.1016/S1672-6308(13)60174-2

Huang X., Qian Q., Liu Z., Sun H., He S., Luo D., Xia G., Chu C., Li J., and Fu X., 2009, Natural variation at the DEP1 locus enhances grain yield in rice, Nature Genetics, 41(4): 494-497.

https://doi.org/10.1038/ng.352

Inthapanya P., Sihavong P., Sihathep V., Chanphengsay M., Fukai S., and Basnayake J., 2000, Genotype differences in nutrient uptake and utilisation for grain yield production of rainfed lowland rice under fertilised and non-fertilised conditions, Field Crops Research, 65(1): 57-68.

https://doi.org/10.1016/S0378-4290(99)00070-2

Jayaprakash T., Reddy T., Babu V., and Bhave M., 2017, Association analysis of protein and yield related traits in f3 population of rice (Oryza sativa L.) crosses, International Journal of Current Microbiology and Applied Sciences, 6: 2476-2485.

https://doi.org/10.20546/IJCMAS.2017.608.293

Jewel Z., Ali J., Mahender A., Hernandez, J., Pang Y., and Li Z., 2019, Identification of quantitative trait loci associated with nutrient use efficiency traits, using SNP markers in an early backcross population of rice (Oryza sativa L.), International Journal of Molecular Sciences, 20(4): 900.

https://doi.org/10.3390/ijms20040900

Li B., Liu H., Zhang Y., Kang T., Zhang L., Tong J., Xiao L., and Zhang H., 2013, Constitutive expression of cell wall invertase genes increases grain yield and starch content in maize, Plant Biotechnology Journal, 11(9): 1080-1091.

https://doi.org/10.1111/pbi.12102

Li R., Li M., Ashraf U., Liu S., and Zhang J., 2019, Exploring the relationships between yield and yield-related traits for rice varieties released in China from 1978 to 2017, Frontiers in Plant Science, 10: 543.

https://doi.org/10.3389/fpls.2019.00543

Li S.L., Zheng H.Y., and Wang L., 2020, Application and prospect of gene editing technology in crop breeding, Biotechnology Bulletin, 36(11): 209-221.

Mamata K., Rajanna M., and Savita S., 2018, Assessment of genetic parameters for yield and its related traits in f2 populations involving traditional varieties of rice (Oryza sativa L.), International Journal of Current Microbiology and Applied Sciences, 7(1): 2210-2217.

https://doi.org/10.20546/IJCMAS.2018.701.266

Matsubara K., Yamamoto E., Kobayashi N., Ishii T., Tanaka J., Tsunematsu H., Yoshinaga S., Matsumura O., Yonemaru J., Mizobuchi R., Yamamoto T., Kato H., and Yano M., 2016, Improvement of rice biomass yield through QTL-based selection, PLoS One, 11(3): e0151830.

https://doi.org/10.1371/journal.pone.0151830

Meena D., Kumar M.S., and Soni R., 2023, Assessment of genetic diversity for grain yield in rice (Oryza sativa L.) genotypes under humid south eastern plain of Rajasthan, India, International Journal of Plant and Soil Science, 35(18): 971-977.

https://doi.org/10.9734/ijpss/2023/v35i183361

Munns R., Tester M., 2008, Mechanisms of salinity tolerance, Annu Rev. Plant Biol., 59(1): 651-681.

Naik S., Raman A., Nagamallika M., Venkateshwarlu C., Singh S., Kumar S., Singh S., Ahmed H., Das S., Prasad K., Izhar T., Mandal N., Singh N., Yadav S., Reinke R., Swamy B., Virk P., and Kumar A., 2020, Genotype×Environment interactions for grain iron and zinc content in rice, Journal of the Science of Food and Agriculture, 100(11): 4150-4164.

https://doi.org/10.1002/jsfa.10454

Perween S., Kumar A., Singh S., Endra S., Kumar M., and Kumar R., 2020, Genetic variability parameters for yield and yield related traits in rice (Oryza sativa L.) under irrigated and drought stress condition, International Journal of Current Microbiology and Applied Sciences, 9(2): 1137-1143.

https://doi.org/10.20546/ijcmas.2020.902.133

Pujar M., Pujar M., Govindaraj M., Gangaprasad S., Kanatti A., and Shivade H., 2020, Genetic variation and diversity for grain iron, zinc, protein and agronomic traits in advanced breeding lines of pearl millet (Pennisetum glaucum L.) for biofortification breeding, Genetic Resources and Crop Evolution, 67: 2009-2022.

https://doi.org/10.1007/s10722-020-00956-x

Rana N., Rahim M., Kaur G., Bansal R., Kumawat S., Roy J., Deshmukh R., Sonah H., and Sharma T., 2019, Applications and challenges for efficient exploration of omics interventions for the enhancement of nutritional quality in rice (Oryza sativa L.), Critical Reviews in Food Science and Nutrition, 60(19): 3304-3320.

https://doi.org/10.1080/10408398.2019.1685454

Sakamoto T., and Matsuoka M., 2008, Identifying and exploiting grain yield genes in rice, Current Opinion in Plant Biology, 11(2): 209-214.

https://doi.org/10.1016/j.pbi.2008.01.009

Samonte S., Wilson L., Medley J., Pinson S., McClung A., and Lales J., 2006, Nitrogen utilization efficiency: relationships with grain yield, grain protein, and yield-related traits in rice, Agronomy Journal, 98(1): 168-176.

https://doi.org/10.2134/AGRONJ2005.0180

Sanjeeva Rao D., Neeraja C.N., Madhu Babu P., Nirmala B., Suman K., Rao L.V.S., Surekha K., Raghu P., Longvah T., Surendra P., Kumar R., Babu V.R., and Voleti S.R., 2020, Zinc biofortified rice varieties: challenges, possibilities, and progress in India, Front Nutr., 7: 26.

Singh U., Dixit S., Alam S., Yadav S., Prasanth V., Singh A., Venkateshwarlu C., Abbai R., Vipparla A., Badri J., Ram T., Prasad M., Laha G., Singh V., and Kumar A., 2021, Marker assisted forward breeding to develop a drought, bacterial leaf blight, and blast-resistant rice cultivar, The Plant Genome, 15(1): e20170.

https://doi.org/10.1002/tpg2.20170

Tripathy S., 2021, Phenotyping and association analysis of grain zinc and iron content with seed yield in diverse local germplasm of rice, Indian Journal of Agricultural Research, 1: 6.

https://doi.org/10.18805/ijare.a-5843

Türkan I.T., 2009, Demiral, Recent developments in understanding salinity tolerance, Environ Exp. Bot., 67(1): 2-9.

Xing Y., and Zhang Q., 2010, Genetic and molecular bases of rice yield, Annual Review of Plant Biology, 61(1): 421-442.

https://doi.org/10.1146/annurev-arplant-042809-112209

Xu S., Xu Y., Gong L., and Zhang Q., 2016, Metabolomic prediction of yield in hybrid rice, The Plant Journal: for Cell and Molecular Biology, 88(2): 219-227.

https://doi.org/10.1111/tpj.13242

Yu Y., Li G., Fan Y., Zhang K., Min J., Zhu Z., and Zhuang J., 2009, Genetic relationship between grain yield and the contents of protein and fat in a recombinant inbred population of rice, Journal of Cereal Science, 50(1): 121-125.

https://doi.org/10.1016/J.JCS.2009.03.008

Zhou J., Xin X., He Y., Chen H., Li Q., Tang X., Zhong Z., Deng K., Zheng X., Akher S., Cai G., Qi Y., and Zhang Y., 2018, Multiplex QTL editing of grain-related genes improves yield in elite rice varieties, Plant Cell Reports, 38: 475-485.