Machine learning can pinpoint “genes of importance” that help crops to grow with less fertilizer, according to a new study published in Nature Communications.
New York University (NYU) researchers and collaborators in the U.S. and Taiwan researchers demonstrated that genes whose responsiveness to nitrogen are evolutionarily conserved between two diverse plant species – Arabidopsis, a small flowering plant widely used as a model organism in plant biology, and varieties of corn, America’s largest crop – significantly improved the ability of machine learning models to predict genes of importance for how efficiently plants use nitrogen.
The researchers conducted experiments that validated eight master transcription factors as genes of importance to nitrogen use efficiency. They showed that altered gene expression in Arabidopsis or corn could increase plant growth in low nitrogen soils, which they tested both in the lab at NYU and in cornfields at the University of Illinois.
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“Now that we can more accurately predict which corn hybrids are better at using nitrogen fertilizer in the field, we can rapidly improve this trait. Increasing nitrogen use efficiency in corn and other crops offers three key benefits by lowering farmer costs, reducing environmental pollution, and mitigating greenhouse gas emissions from agriculture,” said study author Stephen Moose, Alexander Professor of Crop Sciences at the University of Illinois at Urbana-Champaign.
Moreover, the researchers proved that this evolutionarily informed machine learning approach can be applied to other traits and species by predicting additional traits in plants, including biomass and yield in both Arabidopsis and corn. They also showed that this approach can predict genes of importance to drought resistance in rice.