Plant Gene and Trait 2025, Vol.16, No.3, 104-112 http://genbreedpublisher.com/index.php/pgt 108 4.5 Lessons learned for future yield improvement practices The experience with SRI offers several lessons for future yield improvement practices. First, the importance of integrated and interdisciplinary research is evident, as SRI’s success relies on the synergy between various agronomic practices (Stoop et al., 2002). Second, the adaptability of SRI to local conditions and its potential for resource savings make it a valuable model for sustainable agriculture (Satyanarayana et al., 2006; Majumder et al., 2019). However, addressing the labor-intensive nature of SRI and providing adequate training and support to farmers are crucial for its broader adoption and success (Dobermann, 2004). Future research should focus on optimizing SRI practices to reduce labor requirements and enhance its applicability across diverse agro-ecological settings (Thakur et al., 2023). 5 Synthesis of Key Findings 5.1 Comparative effectiveness of genetic, agronomic, and socio-economic approaches Genetic approaches, such as Meta-QTL and genome-wide association studies, have been instrumental in identifying stable QTLs and significant loci that control yield and yield-related traits under various conditions. For instance, Meta-QTL analysis has identified 61 stable QTLs for traits like grain weight and root architecture under water deficit conditions, which are crucial for breeding programs aimed at improving yield in non-flooded cultivation systems (Khahani et al., 2021). Similarly, genome-wide association studies have pinpointed significant loci for component traits like grains per panicle and tillers per plant, which indirectly enhance yield potential (Su et al., 2021). Agronomic approaches, particularly optimized nitrogen management, have shown significant promise in improving both yield and nitrogen use efficiency. Studies have demonstrated that reducing total nitrogen and late-stage nitrogen application can enhance rice eating quality and nitrogen use efficiency without significantly compromising yield (Cheng et al., 2021). Additionally, optimized management practices, including appropriate water and fertilizer management, have been shown to improve grain yield and nitrogen use efficiency by enhancing post-heading carbon and nitrogen metabolism (Deng et al., 2022). Socio-economic approaches, such as the development of decision support systems like Nutrient Expert (NE) for Rice, have also proven effective. These systems provide science-based fertilizer recommendations that improve yield and agronomic efficiency, thereby increasing profits for farmers. 5.2 Regional variations in yield improvement success Regional variations significantly influence the success of yield improvement techniques. In China, for example, traditional nitrogen management practices have been heavily reliant on high nitrogen input, which has led to environmental concerns and reduced eating quality. Adjusting these practices has shown promise in balancing yield and quality (Cheng et al., 2021). In contrast, in regions facing water deficit conditions, genetic approaches like Meta-QTL analysis have been more effective in identifying traits that enhance yield under stress (Khahani et al., 2021). In intensive irrigated systems in Asia, continuous agronomic and genetic interventions have been essential for sustaining high annual production. However, these systems have struggled to achieve the yield increases needed to meet growing global demand, highlighting the need for ongoing innovation and adaptation (Ladha et al., 2021). The variability in success across regions underscores the importance of tailoring yield improvement strategies to specific environmental and socio-economic contexts. 5.3 Identification of synergistic strategies Synergistic strategies that combine genetic, agronomic, and socio-economic approaches have shown the most promise for sustainable yield improvement. For instance, the integration of optimized nitrogen management with genetic improvements in super hybrid rice has led to significant gains in both yield and nitrogen use efficiency (Deng et al., 2022). Similarly, the use of decision support systems like Nutrient Expert, which incorporate agronomic data and genetic insights, has proven effective in enhancing yield and profitability (Xu et al., 2017). Moreover, the combination of genetic mapping techniques with agronomic practices has facilitated the identification of key traits and management practices that can be targeted for improvement. For example, the
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