A team of researchers at New York University has made a groundbreaking discovery that combines artificial intelligence (AI) and genetics to improve nitrogen use efficiency in corn. This innovative approach has the potential to reduce the amount of nitrogen fertilizers used in farming, resulting in cost savings for farmers and a minimized environmental impact.
The researchers developed a novel process that integrates plant genetics with machine learning to identify genes that govern nitrogen use efficiency in corn. By analyzing data from corn and Arabidopsis, a small flowering weed, the machine-learning model can identify groups of genes, known as regulons, that work together to control nitrogen use efficiency.
These regulons are activated or repressed by transcription factors, which are regulatory proteins. The model can predict how efficiently nitrogen is used in field-grown corn varieties based on the expression levels of specific genes. This breakthrough can help farmers identify corn hybrids with high nitrogen use efficiency, reducing the need for fertilizers and minimizing environmental pollution.
The benefits of this technology are twofold. By reducing fertilizer use, farmers can save money and decrease their environmental footprint. Excess nitrogen from fertilizers can contaminate groundwater and contribute to greenhouse gas emissions. Additionally, improving nitrogen use efficiency can lead to increased crop yields, helping to meet the world's growing food demands.
This technology has far-reaching implications for the future of agriculture. It can be applied to other crops, enabling farmers to grow more sustainable and resilient crops. The integration of AI and genetics can pave the way for precision agriculture, where crops are tailored to specific growing conditions, reducing waste and improving yields.
