12-04, 14:00–14:30 (America/Belem), Room I
Tropical forests host half of Earth’s biodiversity (Dirzo & Raven, 2003), 62% of global terrestrial vertebrate species (Pillay et al., 2022), and play a crucial role as a carbon sink (Mitchard, 2018). Despite their importance, every year, 3 to 4 million hectares of primary tropical forests are lost, mainly in Brazil, Indonesia, and the Democratic Republic of Congo (DRC) (Hansen et al., 2013; Seymour, 2022), contributing to 22% of total greenhouse gas (GHG) emissions worldwide along with agriculture, forestry and other land use (AFOLU) (IPCC, 2023).
Preventing deforestation requires understanding its root causes, particularly the capital availability to the farm sector. In many tropical countries, rural credit is available as loans at subsidized interest rates to improve agricultural production or support agricultural costs (Servo, 2019). However, these loans may be leading to more deforestation. Some studies have analyzed this issue on a municipal scale, but few peer-reviewed studies have linked rural credit to individual property-scale deforestation. Recently, the NGO Greenpeace (Greenpeace, 2024) and the Climate Policy Initiative (Mourão et al., 2024) published two studies showing the relationship between rural credit and deforestation. Understanding this relationship can improve public policies to prevent deforestation from happening even before it starts.
Methods
In this study, I used open data and FOSS4G to quantify the amount of Rural Credit released to rural properties that committed Deforestation. The datasets came from different open data sources. The Central Bank of Brazil provides data on rural credit on the SICOR System. The National Institute of Space Research (INPE) provides data on deforestation in the Terrabrasilis system. The Brazilian Forest Service provided data for each property's Rural Environmental Registry (CAR), providing their boundaries. The Brazilian Institute of Geography and Statistics (IBGE) provides data for administrative boundaries (state and municipality).
Using the Terra library in CRAN-R,. I processed the data sets from three states that contributed the most to deforestation: Rondônia, Mato Grosso, and Pará. I used a Spatialite database and QGIS Geographic Information System to check the results. The novelty here is that by using R scripts, it was possible to rebuild the relational database from SICOR in a geospatial environment, providing a reproducible environment. All steps are described below.
First, using R, all the data needed for the analysis was downloaded from their source and loaded into the R environment. The second step, still using R, was to recreate the SICOR, CAR, and PRODES Deforestation tables and populate them into a Spatialite (SQLite) database. This step provides a valuable tool for monitoring by both environmental agencies and the banks that provide loans for rural credit.
The next step was to intersect the deforestation data with the CAR property boundaries, calculating the amount of deforestation on each property using PRODES data between 2008 and 2023. Next, the total number of loans between 2013 and 2023 was identified for each property. All these steps were processed using the Terra library in R.
Results
In 1992, the Brazilian Parliament enacted Law 4,829, creating subsidies for rural credit, known as Safra Plan. The interest rates of the Safra Plan have always been significantly lower than those practiced in the market. In March 2019, for example, while the average interest rate on loans for non-rural purposes stood at 31.6% per year, rural credit was observed at 10.8% p.y. on market rates, and even lower with controlled rates observing an average rate of 6.1% p.y.(Servo, 2019) .
The results show that from 2013 to 2023, more than BRL 17 billion was loaned to properties with some deforestation in these three states (RO, PA, MT). Counting deforestation from August 2008 to July 2023, 8197 km² is the total amount of clearing in properties that received rural credit in those same three states, representing 8.5% of all deforestation for the period.
My first contact with Free and Open Souce Software was in 1994 when I started computer sciences at the Federal University of Santa Catarina (Brazil). I moved to the Geography Department at the State University of Santa Catarina in 1998, when I had my first contact with Spring (GIS open source), developed by the National Institute of Space Research (INPE). 2003, I started working in the Amazon Forest for the Brazilian Institute of Environment (IBAMA). One year later, I finished my master’s in Geomatics and project management at the University of Avignon (France). At that time, I started exploring the recently launched Quantum GIS. In 2008, I moved from the Amazon to Brazil’s capital, Brasília, to take the office as Environmental monitoring coordinator. During the following eleven years, one of my roles was to provide computational infrastructure for geospatial applications. Geoserver, PostGIS, GeoNetwork, and Leaflet were among my daily duties. In 2013, we decided to go 100% open source at IBAMA and QGIS was adopted as the main desktop GIS. In 2016 and 2018, I helped organize FOSS4G IS GOV, when, for the first time, several Brazilian governmental institutions gathered to exchange experiences in FOSS4G. In 2022, I started my PhD at the University of British Columbia, using a lot of FOSS4G in my research (of course!). In 2024, I actively participated in creating the Brazilian OSGEO Chapter. Besides, I love rock climbing, sailing, cycling, and yoga in my spare time.