Modeling climate change impact in Brazil's soybean culture

• Grant number: 22/14886-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2023
• End date: July 31, 2027
• Field of knowledge: Agronomical Sciences - Agronomy
• Principal Investigator: Marcos Silveira Buckeridge
• Grantee: Janaina da Silva Fortirer
• Host Institution: Instituto de Biociências (IB). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated scholarship(s): 25/15362-2 - Integrative transcriptomic meta-analysis and climate change-based soybean yield modelling, BE.EP.DR

Abstract

Climate change (CC), resulting from the high emission of CO2 in the atmosphere, already increases extreme events with high temperatures and prolonged periods of drought and rain, affecting agricultural species; development, and productivity. Soybean is vital in the Brazilian agricultural economy, with Brazil being its largest producer. In 2021, 135 million tons were produced. However, most research related to CC only considers isolated factors of temperature, water stress, and CO2 in soybean yield. Studies that combine two or more factors in soy are in the minority. This difficulty results from the complexity and costs of an experiment that involves the imposition of all three factors simultaneously. To reduce data processing costsand time, computational strategies that simulate and modulate the effect of CC on crops areneeded. Mathematical models predict plant responses to these environmental change factors and mimic the action of one, two, or more factors that affect productivity. This project aims toinvestigate the future impacts of CC on Brazilian soybean production through modeling. The study will be carried out with physiological data and 40 libraries of transcripts of target genesthat regulate the individual, and the combined effects of high CO2, water stress, and hightemperature. Such data were produced in our laboratory. In addition, we will performbibliometric, transcriptomic, meta-analyses, and network analyses to support the development ofmodels that more accurately predict soybean responses (at the physiological and general level) to CC. With these analyses, we hope to be able to predict soybean cultivation scenarios and propose study targets for plant biotechnology to develop new cultivars for crop adaptation to CC. (AU)