Rhizodeposition of tropical forage grasses: a 14C pulse-labeling study

• Grant number: 18/14218-1
• Support type: Scholarships abroad - Research Internship - Post-doctor
• Effective date (Start): August 20, 2018
• Effective date (End): November 30, 2018
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: Ciro Antonio Rosolem
• Grantee: Eduardo Mariano
• Supervisor abroad: David Leonard Jones
• Home Institution: Faculdade de Ciências Agronômicas (FCA). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Local de pesquisa : Bangor University, Wales
• Associated to the scholarship: 17/02517-1 - Nitrogen dynamics in a production system using Brachiaria as a cover plant, BP.PD

Abstract

The release of organic C compounds from roots into their surrounding environment is paramount for the C dynamics in the soil. Root exudates play a critical role as a source of C and energy for soil rhizosphere microorganisms. However, our knowledge on rhizodeposition and rhizosphere-related processes is still very limited due to the complex plant-microorganism-soil interaction. In addition, rhizodeposition depends on the plant species and changes with plant growth stage. The use of isotope-based approaches allow us to trace the C flow through the plant to the soil (i.e., rhizodeposition). Our objective is to quantify the C rhizodeposition and composition of the soil microbial community of three forage grass species [Guinea grass (Megathyrsus maximum), palisade grass (Urochloa brizantha), and ruzigrass (U. ruziziensis)] at two growth stages. Forage plants will be pulse-labeled with 14CO2 at 30 and 45 d after germination on a mesocosm experiment. A mathematical model will be used to estimate the rhizodeposition-to-root ratio. The microbial community of the rhizosphere soil will be measured by phospholipid fatty acid analysis. Two-way ANOVA will be conducted to evaluate the influence of forage species and plant growth stages on 14C recoveries and microbial community. A principal component analysis will be used to elucidate major variation patterns.