Bioscience Evidence 2024, Vol.14, No.6, 260-269 http://bioscipublisher.com/index.php/be 262 which adjusts the application rates of water, fertilizers, and other inputs based on the specific needs of different field zones (Hedley, 2015; Sachin et al., 2023a). VRT helps in optimizing input use, improving crop health, and increasing yields while minimizing environmental impact. 2.5 Automation and robotics in soybean production Automation and robotics are emerging technologies in precision agriculture, offering the potential to further enhance efficiency and productivity in soybean farming. Automated systems can perform tasks such as planting, weeding, and harvesting with high precision and consistency. Robotics can also be integrated with other precision agriculture tools, such as GPS and sensors, to perform site-specific management practices autonomously. These technologies reduce labor costs and increase operational efficiency, contributing to higher yields and better resource management (Hedley, 2015; Smidt et al., 2016). Precision agriculture technologies, including GPS, GIS, remote sensing, soil sensors, and automation, are transforming soybean farming by enabling more precise and efficient management of inputs. These technologies help optimize resource use, improve crop health, and increase yields, contributing to sustainable agricultural practices. The integration of these advanced tools into soybean production systems holds great promise for the future of agriculture. 3 Genetic and Environmental Interactions in Soybean Yield 3.1 Genetic improvement in soybean varieties Genetic improvement in soybean varieties has been a cornerstone of agricultural advancements, aiming to enhance yield potential and stability. Plant breeders have successfully released varieties with improved yield potential through performance-based selection, even without a complete understanding of the molecular mechanisms involved (Vogel et al., 2021). Recent studies have utilized machine learning and genetic optimization algorithms to model and optimize soybean yield by analyzing key yield component traits such as the number of nodes and pods per plant (Yoosefzadeh-Najafabadi et al., 2021). Additionally, genome-wide association studies (GWAS) and genomic selection (GS) have identified specific single nucleotide polymorphisms (SNPs) associated with yield and related traits, providing valuable markers for breeding programs (Ravelombola et al., 2021). The integration of conventional and molecular breeding techniques, including CRISPR-based genome editing, has opened new avenues for soybean yield and quality improvement (Figure 2) (Gai et al., 2021). Figure 2 The main breeding targets in soybean are yield, quality improvement and diseases resistance (Adopted from Gai et al., 2021)
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