Rice Genomics and Genetics 2024, Vol.15, No.5, 287-296 http://cropscipublisher.com/index.php/rgg 289 3.3 Genomic and transcriptomic insights Advancements in genomic and transcriptomic technologies have provided deeper insights into the genetic basis of nutritional traits in rice. High-throughput sequencing and genome-wide association studies (GWAS) have identified key genes and regulatory networks involved in nutrient uptake, transport, and storage. For example, a study using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population identified significant QTLs and SNP markers for iron and zinc biofortification, co-locating with known metal homeostasis genes such as OsMTP6, OsNAS3, and OsVIT1 (Descalsota et al., 2018). Additionally, transcriptomic analyses have revealed genes differentially expressed under micronutrient stress conditions, offering potential targets for genetic manipulation to enhance nutrient content (Reis et al., 2018; Raza et al., 2019; Dhaliwal et al., 2022). 4 Role of Agronomic Practices 4.1 Fertilizer management for nutrient enrichment Fertilizer management is crucial for enhancing the nutritional quality of rice. Agronomic biofortification with nitrogen (N) and selenium (Se) has shown significant improvements in grain quality. For instance, the application of Se combined with N fertilization increased the reserve protein fractions in rice seeds, such as glutelin and globulin, and enhanced the grain Se content within safe limits, thereby potentially increasing daily Se intake for human health (Reis et al., 2018). Additionally, remote sensing approaches have been employed to optimize nitrogen fertilizer recommendations, demonstrating that machine learning and vegetation indexes can effectively predict nitrogen nutrient indices and leaf area indices, leading to more precise and reduced nitrogen application (Zhang et al., 2023). Zinc (Zn) fertilization, combined with appropriate water management, has also been shown to increase grain-Zn concentration, although the effect is relatively small and requires complementary strategies for significant improvements (Tuyogon et al., 2016). 4.2 Water management and nutrient uptake Water management practices significantly influence nutrient uptake and the nutritional quality of rice. Alternate wetting and drying (AWD) techniques have been found to enhance grain-Zn concentration more consistently than Zn fertilization alone, indicating the importance of water management in nutrient uptake (Tuyogon et al., 2016). In rainfed environments, nutrient demand is closely linked to water availability, necessitating the characterization of environments based on resource limitations to improve nutrient-use efficiency (Lafitte, 1998). Regenerative agriculture practices, which include deficit irrigation, have also been shown to improve the micronutrient concentrations in crops, including rice, by enhancing soil health and crop resilience (Manzeke-Kangara et al., 2023). 4.3 Crop rotation and soil health improvement Crop rotation and the use of organic fertilizers play a vital role in improving soil health and, consequently, the nutritional quality of rice. Organic fertilization has been shown to significantly increase soil organic carbon (SOC) and available nutrients, leading to improved leaf area index, chlorophyll content, and total biomass accumulation, which are favorable for grain filling and yield (Liu et al., 2021). Regenerative agriculture practices, which often include crop rotations and organic inputs such as composts and manures, have been associated with increased micronutrient concentrations in rice grains, particularly zinc (Manzeke-Kangara et al., 2023). These practices contribute to building soil health, enhancing nutrient availability, and improving the overall sustainability of rice production systems. 5 Key Nutritional Traits and Underlying Genes 5.1 Genetic basis of protein content The protein content in rice is a critical nutritional trait, especially for populations that rely heavily on rice as a staple food. Research has identified several quantitative trait loci (QTLs) associated with protein content in rice. For instance, a study on aromatic rice germplasm identified a significant QTL for protein content on chromosome 1, which exhibited a variance of 7.89% and a LOD score of 2.02 (Islam et al., 2020). Additionally, the integration of metabolomics and transcriptomics has revealed differentially expressed genes (DEGs) that correlate with
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