Patterns and Processes of Pasture to Crop Conversion in Brazil: Evidence from Mato Grosso State
Download
Key Findings
Key Findings:
- We researched pasture to crop conversion using mixed methods.
- Roughly half of cropland expansion from 2002–13 occurred on cattle pastures.
- Land suitability weakly explained patterns of cropland expansion on pasture.
- Institutions, degradation, and agricultural density were key controls on conversion.
The rate and location of cropland expansion onto cattle pastures in Brazil could affect global food security, climate change, and economic growth. We combined mapping, statistical modeling, and qualitative methods to investigate patterns and processes of pasture to crop conversion (P2C) in Mato Grosso State (MT), Brazil, a globally important center of agricultural production. P2C land constituted 49% of cropland expansion from 2000 to 2013. For a random sample of ̃250 m pixels in MT, we estimated a regression model skilled at predicting P2C land in the rest of the state as a function of cattle ranching suitability, cropping suitability, and P2C conversion costs. Surprisingly, just 1/7 of pasture agronomically suitable for cultivation had undergone P2C. Hedonic regressions revealed that agronomic characteristics of land were associated with less than 20% of the variation in cropland suitability. Instead, the majority of the variation stemmed from a combination of proximity to agricultural infrastructure, characteristics of neighboring lands, and time fixed effects. The weak relationship between agronomic characteristics of land and P2C location suggests a less certain future for P2C than projections made with agronomic models. Consequentially, complications may arise for greenhouse gas mitigation policies in Brazil predicated on widespread expansion of cropland on pasture vs. natural areas. Our follow-up qualitative research shows that because P2C has often involved land rentals or sales, poorly functioning land institutions may have constrained P2C. Locally poor land quality, omitted from agronomic P2C predictions, can either catalyze or constrain P2C by limiting returns to ranching, farming, or both. Interventions to control rates and locations of P2C should take these insights into account.
More like this from Mathematica
Featured Publication
Incorporating Behavioral Science in USAID Economic Analysis
How do you apply evidence?
Take our quick four-question survey to help us curate evidence and insights that serve you.
Take our survey