Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating...

Unexploited genetic variation in soybean photosynthetic efficiency can be converted into yield gains by coupling high-throughput phenotyping with genomic-phenomic prediction within a G×E×M-oriented breeding framework built on heritable, physiologically meaningful traits. Improving crop yield through enhanced photosynthetic efficiency (PE) is a re-emerging and increasingly tractable objective for modern breeding, driven by recent advances in high-throughput field phenotyping, genomics, and data analytics. In C3 crops like soybean, radiation use efficiency (RUE) remains below the theoretical maximum, and evidence from cereals and legumes indicates that gains in RUE have contributed substantially to long-term yield improvement, suggesting unrealized potential in soybean. However, despite extensive physiological and genetic studies, PE has historically been difficult to integrate into breeding programs due to the complexity of photosynthetic processes and the historical lack of reliable high-throughput field-scale phenotyping and predictive breeding frameworks. This review synthesizes current knowledge on biological determinants of PE in soybean (leaf and canopy morphology, non-foliar photosynthesis), alongside natural genetic variation and the genetic architecture of photosynthesis (PS)-related traits. We summarize recent transgenic and functional studies that demonstrate the capacity to modify PS processes, while highlighting challenges of achieving durable yield benefits in real field conditions. We further review emerging high-throughput phenotyping (HTP) platforms, including unmanned aerial vehicles (UAVs)-assisted multi/hyperspectral imaging and ground-based portable sensors, that enable scalable assessment of PS-related traits across diverse field environments and management regimes. We argue that bridging the gap between photosynthetic potential and realized yield requires the integrating HTP with genomics, phenomic prediction and molecular breeding frameworks. We propose a pragmatic breeding pipeline that prioritizes standardized, biologically meaningful and heritable PE traits and proxy traits, leverages diverse germplasm for allele discovery, and applies integrated genomic-phenomic prediction frameworks that explicitly incorporate genotype × environment × management (G×E×M) interactions. By embedding PS-traits as complementary selection criteria within breeding pipelines, this multi-disciplinary approach offers a practical route to accelerate genetic gain and enhance soybean yield resilience.