Effect of Priestia megaterium inoculation on Urochloa ruziziensis grass under water stress conditions

• Grant number: 25/04205-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2025
• End date: November 30, 2025
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Pastures and Forage Crops
• Principal Investigator: Guilhermo Francklin de Souza Congio
• Grantee: Pedro Zanon de Britto
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

With the advancement of agricultural technologies, the use of biological inputs to support crop development, protection, and productivity has become increasingly common. Bacteria of the Bacillus genus play a significant role in these advancements, as they have shown promising results in plant growth promotion, pathogen and nematode control, and soil quality improvement. Among the bacteria that can perform these functions, the species Priestia megaterium (Syn. Bacillus megaterium) has stood out. It is a motile, gram-positive bacterium with a relatively large cell size. Its agronomic classification is as a nematicide; however, it also offers other potential benefits, such as plant growth promotion and mitigation of water stress. The use of biologicals in pastures still requires extensive research in the current scenario. Based on this, the present study aims to evaluate the effect of P. megaterium applied via seed treatment and foliar spraying on the tiller population density and forage production of Urochloa ruziziensis in a greenhouse. The experiment will be conducted at ESALQ/USP, Piracicaba, SP, from February to July 2025. The treatments will consist of different doses of the GNC0010 strain, inoculated via seed treatment (Exp. 1) and foliar spraying (Exp. 2) in U. ruziziensis grown in a greenhouse. The experimental design will be completely randomized, with five replications (pots). Data will be analyzed using RStudio® software, employing the analysis of variance (ANOVA) model.