Forests globally have been disappearing at an alarming rate. These losses have had major consequences for Earth’s climate, biogeochemical and water cycles, and biodiversity. While deforestation and forest degradation are a global phenomenon, the loss of tropical forests has been particularly pronounced.
For example, one of the most extensively forested countries in the world, and the focus of our assignment here, is Brazil: a country that has seen major forest loss within the Amazonian Forest region over the past several decades. One of the main causes of deforestation globally, and in Brazil particularly, is agricultural expansion: the conversion of natural forested landscapes into food production systems. Agricultural expansion can occur for multiple reasons, including subsistence livelihoods (i.e., small scale agriculture), though major forest loss is commonly attributable to very large-scale industrial agriculture. In the case of Brazil, two primary agricultural land-uses – namely cattle and soybean production – are a major cause of deforestation.
Our understanding of contemporary environmental issues has been greatly enhanced by greater accessibility to large, robust environmental datasets. Open access data is also often regarded as a means by which science may become more democratized and inclusive.
Many open access data sources exist, including those focused on climate change and biodiversity. In this lab, we will focus on open access data from the United Nations Food and Agricultural Organization (the FAO). The FAO is among the world’s leading international organization that (among other tasks) monitors and measures relationships between land-use and land-use change, agricultural productivity, food security, and climate change. The FAO database—called FAOSTAT—is free to access and has this type of data for all regions and countries of the world (Figure 1).
Figure 1. Landing page for the UN FAOSTAT website (left panel), alongside a snapshot of the multiple environmental datasets houses in the database.
FAOSTAT houses a tremendous amount of data on everything from rates of CO2 emissions, to amount of land under forest cover, to agricultural production trends. All of this data, in turn, is reported by individual countries and consolidated in a single, easy-to-use database. As a result, the FAOSTAT represents a large, open access database that can be used to assess questions (as in this lab) focused on the causes and consequences of environmental change.
Peer-reviewed Academic Literature in Brief
In addition to open access data, the gold standard for scientific reliability remains peer-reviewed literature. In short, peer-review is a process by which any scientific article is critically evaluated by experts in field, prior to appearing in a scientific journal. This is quite different from media reports, popular scientific magazines like National Geographic, or (of course) social media; in these cases, information is not vetted by experts in the field prior to dissemination.
The peer-review process is onerous and will be discussed in the lab. Though generally, peer-reviewed articles remain the most reliable source of scientific information. There are hundreds of peer-reviewed journals each with their own area of focus, though some are more general and “high profile.” Among the world’s most rigorous and influential peer-reviewed journals are called Science and Nature (Figure 2): these are the journals where major scientific breakthroughs are reported.
At the same time, important scientific findings are also reported in “discipline-specific” journals, which tend to focus more narrowly on certain topics such as forest management, climate change, or food security. These are the types of journals that will likely be of most use, for our lab focused explicitly on land-use change and deforestation in Brazil.
Figure 2. Covers of four peer-reviewed journals including high-profile general science journals (Nature, Science), and discipline-specific journals (Forest Ecology and Management, Journal of Applied Ecology). All of these journals are likely to contain information related to deforestation in the Amazon.
Procedure for Laboratory 4
Part 1. Land-use change in Brazil since 1995.
This is a multiple step process involving three different datasets from FAOSTAT; two of these datasets will be downloaded from an online source, and then the third will be created by merging these two downloaded datasets.
Step 1. Visit the FAO data portal. Specifically, this link (www.fao.org/faostat) will take you to the home page of FAOSTAT.
Step 2. Click on the “Explore Data” tab, which then accesses the specific data options. Here, you will see a number of different major themes.
Step 3. Locate “Land, Inputs and Sustainability” section. Click to open the options within this theme. Once the options open, click on the “Land” data tab. Then, open the “Land use” option. Here you will note, this was updated in early July 2023.
Step 4. A new page will open that allows you to download datasets for multiple indicators related to land-use, across different countries, at different time periods.
Step 5. First, we will extract the amount of forest land in Brazil. Do so by requesting data for:
A) “Brazil” (in the top left panel).
B) “Area” (in the top right panel).
C) “Forest Land” (in the bottom left panel). Note: updates to the FAO data differentiate “Forest Land”, “Naturally Regenerating Forest”, and “Planted Forest”. We will make use of “Forest Land” only (so you can deselect the others).
D) Select years from 1995-2022 (in the bottom right panel; please note you have to select these years manually, by clicking/ highlighting each year individually).
Step 6. Download the data and open it in Excel. Note the data will be downloaded as a comma separated file (“.csv”), but should open in Excel when you click on it.
Step 7. From this “forest land” dataset, you will only need the columns entitled “Year”, “Unit”, and “Value”; rename the “Value” column to “Forest Area.”
Step 8. Follow steps 5-6 above again. Except this time, in step 4C select “Agricultural Land”. Note: Agricultural land is also disaggregated in multiple categories. Simply select “Agriculture” or “Agricultural land” from the list (they are the same).
Step 9. From this “agricultural land” dataset, you will only need the columns entitled “Year”, “Unit”, and “Value”; rename the “Value” column to “Agricultural Land.”
Step 10. Merge these datasets together into a single excel file, so that you have one file with the following headers: “Year”, “Forest Land”, “Agricultural Land”, and “Units.” This is your working dataset for Part 1 questions, and will be expanded in Part 2, so review and save it carefully.
Part 2. The role of cattle and soy production in driving land-use change in Brazil since 1995.
Step 1. Again, visit the Food and Agricultural Organization of the United Nations data portal (www.fao.org/faostat).
Step 2. Here, click on the “Explore Data” tab. This time, locate the “Production” section. Once expanded, click on the “Crops and Livestock Products” data tab.
Step 3. Download the data for:
A) “Brazil” (in the top left panel).
B) “Stocks” (in the top right panel).
C) Click the ““Live animals> (List)” tab in the bottom left panel. Then, in the expanded list, select “cattle.”
D) Select years from 1995-2022 (in the bottom right panel).
Step 4. Download the data, and open it in Excel. Note the data will be downloaded as a comma separated file (“.csv”), but should open in Excel when you click on it.
Step 5. From this “cattle production” dataset, you will only need the columns entitled “Year” and “Value”; rename the “Value” column to “Cattle production.”
Step 6. Add this data to your previous dataset (i.e., Created in Part 1, Step 10).
Step 7. Go back to Part 2, Step 2.
Step 8. Download the data for:
A) “Brazil” (in the top left panel).
B) “Area harvested” (in the top right panel).
C) “Soya beans” (in the bottom left panel). Note: This is found by opening the sub-menus under the “Crops primary > (List)” tab.
D) Select years from 1995-2021 (in the bottom right panel).
Step 9. Download the data, and open it in Excel. Note the data will be downloaded as a comma separated file (“.csv”), but should open in Excel when you click on it.
Step 10. From this “soy production” dataset, you will only need the columns entitled “Year”, “Units”, and “Value”; rename the “Value” column to “Soybean area.”
Step 11. Add this data to your dataset (i.e., last updated in Part 2, Step 6).
Tip: at the end of these data retrieval steps, you should have:
· Requirement 1. A single merged dataset with all of the data columns from A) Part 1 Step 10, B) Part 2 Step 6, and C) Part 2 Step 11.
· Requirement 2. Performed at least a cursory review the assignment questions below on Pages 9-10. Evaluate how your dataset will be used to answer these questions.
Part 3. Interpreting your results with scientific literature.
This step of the lab entails locating scientific papers that will further support your data analysis. In this section you will do a search of the peer-reviewed scientific literature to answer one final question on the implications of your findings.
Step 1. Visit Google Scholar, an online scientific literature search engine (scholar.google.ca).
Step 2. In the search bar, enter a combination of terms that are relevant to the overall theme of this lab (e.g., “deforestation”, “Brazil”, “agriculture”, “agricultural conversion”, “cattle”, “soy”, etc.). Individual search terms can be linked by including a “+” sign between them.
Step 3. Refine the search to include only papers that have been published since 2015.
Step 4. Note that for certain articles, to access these articles you will have to set up remote access through your UofT accounts. Information on this is accessible here: https://onesearch.library.utoronto.ca/faq-keywords/campus-access
Step 5. Look through the paper titles, keeping assignment Question 10 (Page 10 below) in mind.
Laboratory 4 Assignment
Assignment 4 is 27 marks total and is worth 15% of your final grade.
Please submit a PDF version of your assignment on Quercus under “Lab 4 submission”. Your lab 4 assignment is in 2 weeks (so please consult Quercus for the individualized deadlines).
Part 1 Questions
Question 1. Create a table in Microsoft Word that shows all your data from Part 1 of the assignment. This data table also should include a brief (i.e., one- or two sentence) title (1 mark).
Question 2. Create a graph (namely, an XY Scatter Graph) that shows how forest area (on the Y-axis) has changed through time (on the X-axis) in Brazil. Include a main title, axes labels, and a linear trend line in your graph. In two or three sentences, explain what this graph is telling us. This explanation should explicitly identify which is the independent variable, and which is the dependent variable (3 marks).
Question 3. Create a new graph (again, an XY Scatter Graph) that shows how agricultural area (on the Y-axis) has changed through time (on the X-axis) in Brazil. Include a title, axes labels, and a linear trend line in your graph. In two or three sentences, explain what this graph is telling us. This explanation should explicitly identify which is the independent variable, and which is the dependent variable (3 marks).
Part 2 Questions
Question 4. Create a table in Microsoft Word that shows your final dataset created in Part 2, Step 11. This data table also should include a brief (i.e., one- or two sentence) title (1 mark).
Question 5. Create a graph that shows how cattle production has changed through time in Brazil. Include a title, axes labels, and a linear trend line in your graph. In two or three sentences, explain what this graph is telling us. This explanation should explicitly identify which is the independent variable, and which is the dependent variable (3 marks).
Question 6. Create a graph that shows how soybean production has changed through time in Brazil. Include a title, axes labels, and a linear trend line in your graph. In two or three sentences, explain what this graph is telling us. This explanation should explicitly identify which is the independent variable, and which is the dependent variable (3 marks).
Synthesis Questions
Question 7. Create a graph that shows how forest land has changed in relation to cattle production through time in Brazil. Include a title, axes labels, and a linear trend line in your graph. In two or three sentences, explain what this graph is telling us. This explanation should explicitly identify which is the independent variable, and which is the dependent variable (3 marks).
Question 8. Create a graph that shows how forest land has changed in relation to soybean area through time in Brazil. Include a title, axes labels, and a linear trend line in your graph. In two or three sentences, explain what this graph is telling us. This explanation should explicitly identify which is the independent variable, and which is the dependent variable (3 marks).
Question 9. Based on the graphs created in Questions 7 and 8, which agricultural factor appears to have a more pronounced relationship with deforestation in Brazil (2 mark).
Question 10. What are the likely short- and long-term environmental consequences of converting forest lands to either soybean/cattle production. Specifically, if Brazil’s forests continue to be converted/ lost, how can we expect biodiversity, climate, or other ecosystem services (such as freshwater provisioning) to change?
To answer this question then, choose two papers from your search that address this question (note: these papers do not have to cover all the environmental consequences of land-use change, just one or two aspects is sufficient). Provide a full citation for these two papers, and use the findings from those papers to answer this question in detail. Your answer should be roughly 8-10 sentences (+ the two references; 5 marks).