**Revolutionizing Corn Production: AI and Genetics Unite to Boost Nitrogen Efficiency**
In a groundbreaking effort to enhance agricultural sustainability, New York University scientists are leveraging artificial intelligence to identify the complex genetic networks controlling nitrogen use efficiency in corn. This innovative approach aims to help farmers optimize crop yields while minimizing the environmental and financial impacts of excessive fertilizer use.
Over the past five decades, advancements in plant breeding and fertilizers have significantly increased crop yields. However, only about 55% of the nitrogen applied to fields is utilized by crops, with the remainder posing serious environmental risks. Unused nitrogen can contaminate groundwater, leading to harmful algae blooms, and is converted into nitrous oxide, a potent greenhouse gas[1][2][5].
The United States, as the world’s leading corn producer, faces significant challenges due to corn’s low nitrogen use efficiency. This inefficiency not only burdens farmers with high fertilizer costs but also jeopardizes soil, water, and air quality[1][4].
To address these challenges, NYU researchers have developed a novel process integrating plant genetics with machine learning. By analyzing data from both corn and Arabidopsis, a model organism in plant biology, they identified sets of genes—known as “regulons”—that are crucial for nitrogen use efficiency. These regulons are controlled by transcription factors, which are regulatory proteins that activate or repress gene expression[1][3].
The researchers used RNA sequencing to measure how genes in corn and Arabidopsis respond to nitrogen treatment. They then trained machine learning models to identify nitrogen-responsive genes conserved across both species, as well as the transcription factors regulating these genes. This approach allowed them to predict nitrogen use efficiency in field-grown corn varieties with greater accuracy[1][4].
By identifying and modifying genes associated with nitrogen use efficiency, farmers can select or engineer corn varieties that require less fertilizer. This not only reduces costs but also mitigates environmental pollution and greenhouse gas emissions[1][3][5]. The use of molecular markers at the seedling stage can help select the most efficient hybrids, further optimizing nitrogen use[1].
The integration of AI and genetics in agriculture offers a promising future for sustainable farming practices. By enhancing nitrogen use efficiency, scientists can help farmers achieve higher yields while minimizing environmental harm, paving the way for a more sustainable agricultural industry.
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