← Latest papers
📄 earth science

Parcel-Level Deforestation Hazard Rates in the Paraguayan Chaco: A Multi-Scale Empirical Analysis of Annual Clearing Dynamics on Large Cattle Ranches

This paper analyzes parcel-level deforestation dynamics in the Paraguayan Chaco to demonstrate how compressed clearing cycles on active cattle ranches generate high annual hazard rates that, when scaled by the fraction of actively converting parcels, reconcile with broader regional statistics and inform REDD+ baseline calculations.

Original authors: William C. Schuman, Ionuț-Alexandru Ciocîrlie

Published 2026-09-15
📖 5 min read🧠 Deep dive

Original authors: William C. Schuman, Ionuț-Alexandru Ciocîrlie

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the vast, dry forests of the Paraguayan Chaco, a quiet tension has grown between two ways of counting lost trees. On one side, regional maps show the forest shrinking at a steady, manageable pace, with thousands of hectares disappearing each year as cattle ranches expand. On the other side, satellite images of individual ranch properties tell a different story: when a rancher decides to clear land, they do it with startling speed, often removing a massive chunk of forest in just a single year. This discrepancy has led to a serious debate about carbon credit projects designed to protect these forests. Critics argue that if you look at how fast a single ranch clears its land, you might conclude the entire region is being destroyed much faster than it actually is, leading to an overestimation of the threat and potentially flawed conservation credits. The core question is whether these fast, intense bursts of clearing on individual farms are an anomaly or a sign of a much larger, more aggressive crisis.

To resolve this, researchers William Schuman and Ionuț-Alexandru Ciocîrlie turned their attention to the specific mechanics of how ranchers operate in this landscape. They focused on twenty-two large cattle ranches in the western part of the Chaco, an area where the law allows for the conversion of forest into pasture, provided a certain percentage of trees is kept as a legal reserve. Using satellite data from 2012 to 2020, they tracked exactly how much forest was cleared on each property and how long that clearing took. They found that when a ranch is in the middle of an active development phase, the clearing rate is indeed intense. On average, these active ranches removed nearly fifteen percent of their total land area every single year. This number is high, and it is right where the critics expected it to be. However, the researchers discovered that this high number only applies to the small slice of ranches that are actually working on clearing land at that specific moment.

The key to understanding the full picture lies in the timing and the scale of the operation. The study revealed that clearing is not a slow, steady drip across the entire region; it is a front-loaded event. When a rancher begins to convert forest to pasture, they typically do the heavy lifting in the first one or two years, clearing a massive amount of ground before moving on or pausing. By the third year, the pace slows down dramatically. This means that while a specific ranch might be a clearing machine for a short time, most other ranches in the region are sitting idle, waiting for their turn, or are already finished. The researchers calculated that at any given year, only about twelve percent of the eligible ranches are in this active, high-speed clearing phase. The other eighty-eight percent are not clearing anything significant that year.

By combining these two facts—the high speed of active ranches and the low number of ranches active at any one time—the researchers were able to bridge the gap between the local and the regional. They showed that if you take the fast clearing rate of the active farms and multiply it by the small fraction of farms that are actually active, the result perfectly matches the slower, steady rate seen on the regional maps. The high numbers from individual ranches are real, but they are diluted by the fact that most ranches are not clearing land in any given year. This finding suggests that the concern about overestimating the threat based on parcel-level data is unfounded, provided that the math correctly accounts for how many ranches are actually working at once.

The study also looked at a larger group of forty-six ranches to ensure their initial findings were not just a fluke of a small sample. Even with this expanded group, the pattern held true: the most aggressive clearing happened in short bursts, and the average rate of clearing across all ranches, when adjusted for how many were active, remained consistent with regional records. The researchers noted that the specific number of fifteen percent per year is an average of the most active properties and that the actual rate varies widely depending on the size of the ranch and the capital available to the owner. Smaller, well-funded ranches tended to clear faster, while larger or less capitalized ones cleared more slowly.

Ultimately, the work provides a clear explanation for why the numbers look different depending on how you count. The rapid deforestation seen on individual ranches is a real phenomenon driven by the economic need to establish pasture quickly, but it does not mean the entire forest is vanishing at that same speed. The forest loss is a series of intense, short-lived events scattered across a vast landscape, rather than a continuous, uniform wave. By reconciling the local intensity with the regional average, the study offers a way to build more accurate conservation baselines that reflect the true dynamics of the Chaco without exaggerating the risk or underestimating the speed of change where it is actually happening.

Drowning in papers in your field?

Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.

Try Digest →