AI and genetics are paving the way for sustainable corn farming, offering a path to reduce fertilizer use and improve environmental outcomes.
New research from New York University reveals how artificial intelligence (AI) and genetics can help grow corn with less fertilizer. The study combines machine learning with molecular biology to improve nitrogen use efficiency in corn. This is crucial for reducing the environmental impact of agriculture.
The researchers used machine learning to identify gene groups, called regulons, that regulate how plants absorb and use nitrogen. These regulons are activated or deactivated by transcription factors, acting as genetic switches. By analyzing genes shared between corn and *Arabidopsis thaliana*, they identified genetic patterns predicting which hybrid seedlings would use nitrogen most efficiently.
This allows for selecting corn variants that require less nitrogen fertilizer. Less fertilizer reduces production costs and minimizes environmental damage, such as water contamination and greenhouse gas emissions. The findings enable predicting nitrogen efficiency in corn seedlings, saving time and resources for farmers.
This approach complements other innovations like the N-Fix technology from the University of Nottingham, which uses bacteria to fix atmospheric nitrogen within plant cells. Both AI-driven genetic selection and microbial symbiosis aim to reduce the ecological footprint of intensive agriculture. Ultimately, these advancements represent a shift towards smarter, biologically-driven agricultural practices.
Reducing nitrogen fertilizer use is essential for feeding a growing global population sustainably. AI and genetic insights offer a promising route to a more environmentally friendly and efficient agricultural system. This ensures food security without harming the planet.