Physiological and spectral responses of sugarcane under biotic (Sphenophorus levis [Coleoptera: Curculionidae]) and abiotic (water restriction) stresses

• Grant number: 21/09645-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2022
• End date: February 29, 2024
• Field of knowledge: Agronomical Sciences - Agronomy - Plant Health
• Principal Investigator: Odair Aparecido Fernandes
• Grantee: João Rafael Silva Soares
• Host Institution: Faculdade de Ciências Agrárias e Veterinárias (FCAV). Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Jaboticabal , SP, Brazil
• Company: Universidade Estadual Paulista (UNESP). Campus de Jaboticabal. Faculdade de Ciências Agrárias e Veterinárias (FCAV)
• Associated research grant: 17/25258-1 - Engineering Research Center - Plant Health in Sugarcane, AP.PCPE

Abstract

Sugarcane is subjected to a variety of yield limiting stressors that can act individually or simultaneously during the development of plants. Most studies that assessed the impact of these stressors disregarded such interactions. Consequently, the purpose of this project is to determine the physiological and spectral responses of sugarcane plants under stresses caused by the attack of the Sugarcane Billbug Sphenophorus levis (Coleoptera: Curculionidade) and water restriction (drought). For that, infestations of S. levis larvae will be carried out on plants submitted to different water regimes. The photosynthesis rate, stomatal conductance, intercellular CO2 concentration, transpiration rate, vapor pressure deficit, leaf temperature, and sugarcane spectral reflectance will be analyzed during the occurrence of the insect pest and water stress. Spectral responses will be obtained through a hyperspectral sensor. Also, biomolecules usually associated with plant stress such as proline, malondialdehyde (MDA), hydrogen peroxide (H2O2), as well as the activities of Ascorbate Peroxidase (APX), Catalase (CAT), Superoxide Dismutase (SOD) will be determined. In addition, multiple flights with a Remotely Piloted Aircraft (RPA) carrying a multispectral camera will be performed to capture aerial images in an open environment. From the information collected, machine learning algorithms will verify whether it is possible to distinguish these stresses and better understand sugarcane's response to S. levis and water deficit attack. This information provides a basis for understanding how these stressors act on sugarcane and allow new strategies for assessing these stressors using remote sensing tools. (AU)