Apocarotenoid treatment to enhance maize vigor under salt and drought stress

Overview

Maize is the most important crop in America, yet it has limited tolerance to drought and salt stress, both of which are intensifying with climate change. This research program is developing novel apocarotenoid metabolites as treatments to enhance maize vigor and yield in farmland that requires heavy irrigation. The work builds on the lab's prior discovery of three root regulatory compounds (beta-cyclocitral, anchorene, and retinal) that have demonstrated the ability to promote plant growth and resilience during stress in rice. The current focus is translating these findings to maize and validating their performance under salt and drought conditions.

Technical specifications

• Core approach: Application of small-molecule apocarotenoid metabolites derived from lycopene and beta-carotene to maize to improve root architecture and stress tolerance.
• Candidate compounds: Beta-cyclocitral (bcc), anchorene, and retinal, with bcc as the primary candidate based on prior rice salt-stress results.
• Optimization method: Dose-response testing to identify optimal concentration ranges, followed by stress trials measuring primary, lateral, crown, and seminal root count and length.
• Data-driven refinement: Collaboration with Dr. Saeed Manshadi at SDSU applying neural network analysis to predict optimal and potentially combinatorial apocarotenoid concentrations.
• Target outcomes: Improved root development, enhanced vigor under salt and drought stress, and increased maize yield in irrigated farmland.

Technology readiness level

The program is at an early-to-mid stage of development. Optimal concentrations of beta-cyclocitral have been identified in maize under non-stress conditions, and work is now focused on testing performance under salt and drought stress. The underlying biology has been validated in rice, and the team is currently translating and optimizing the approach for maize using neural network-guided concentration prediction. Further validation in field-relevant conditions is planned.