Pastures sustainable development in Brazil: spatially explicit soil multifunctionality modelling for a climate-changing world

• Grant number: 24/08446-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: October 01, 2024
• End date: February 28, 2027
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: Maria Victoria Ramos Ballester
• Grantee: Andres Mauricio Rico Gomez
• Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated research grant: 21/10573-4 - Center for Carbon Research in Tropical Agriculture (CCARBON), AP.CEPID
• Associated scholarship(s): 25/09804-2 - Multifunctionality of Brazilian pasturelands: A large-scale spatial assessment based on DSSAT simulations and digital soil mapping, BE.EP.DR

Abstract

Soil is considered a natural capital that produces a flow of goods and services for humanity. Given the potential of agroecosystems to provide ecosystem services, studying soil functions is fundamental to understand their role in environmental sustainability. Moreover, a spatial understanding of soil multifunctionality (SM) plays a key role in stakeholder steering and decision making. However, this type of approach requires a large volume of information. Therefore, this research aims to i) develop a methodology based on legacy and freely available data that allows high spatial resolution (30 m) estimation of SM (biomass production and water and climate regulation); ii) assessment of pastures environmental sustainability through spatially explicit information; and iii) define the impacts of future climate change scenarios, as defined by the IPCC, on SM in Brazilian pastures. We will use products derived from geotechnologies, legacy data (soil and climate), and climate projections to carry out simulations of Brachiaria growth using the DSSAT model. The output files from these simulations will serve as indicators of soil functionality, which we will integrate into a synthetic SM index. All data will be subjected to two main statistical approaches: descriptive analysis, to summarize and describe the data, and modeling, to test hypotheses and derive conclusions. Data analysis will be done using R, Python, and JavaScript software. Finally, it is expected that the information gathered in this project will contribute to the monitoring, verifying, and reporting of the ABC+ Plan. In addition, to understand what will be the contribution of the SM in Brazilian pastures to the fulfillment of the Sustainable Development Goals (SDG) linked to action against global climate change (SDG 13), zero hunger, and sustainable agriculture (SDG 2)