Chinese Scientists Decipher Genetic Code for Increased Maize Yield
Chinese agricultural scientists have identified and cloned a gene region that stably increases maize yield by 10-16%. This discovery offers new avenues for enhancing crop productivity.

A research group led by Professor Tian Feng at China Agricultural University has successfully identified and cloned the world's first quantitative trait locus (QTL) associated with maize harvest index, a key factor in grain production. The study, published in the journal Cell, also reveals the evolutionary patterns of the maize harvest index.
The research pinpointed a critical molecular module, HI1-BRR1, which governs the allocation of carbon and nitrogen within the plant. Field trials conducted in multiple locations across China, including Liaoning, Hainan, and Beijing, demonstrated a stable yield increase of 10% to 16% when this mechanism was optimized.
The harvest index measures the efficiency with which a plant converts its total biomass into grain yield. The team observed a significant increase in this index during maize's domestication process, from its wild ancestor to modern varieties. The HI1 element, a non-coding sequence, acts as a distant enhancer that regulates the expression of the BRR1 gene. Enhancing BRR1 expression was shown to simultaneously improve harvest index, biomass, and grain yield without compromising protein content or stalk strength.
This research indicates that maize domestication has not only reshaped its physical structure but also its physiological resource allocation. This "physiological domestication" plays a crucial role in achieving high yields. Furthermore, increased BRR1 expression demonstrated potential for improving maize's tolerance to low nitrogen conditions, suggesting broader applications in challenging agricultural environments.
These findings suggest that manipulating the HI1-BRR1 pathway could lead to more robust and productive maize cultivation globally. The ability to consistently increase yields by 10-16% through genetic understanding holds significant implications for food security and agricultural efficiency.