Brazil's Soy Moratorium (SoyM) was the first voluntary zero-deforestation agreement implemented in the tropics and set the stage for supply-chain governance of other commodities, such as beef and palm oil [supplementary material (SM)]. In response to pressure from retailers and nongovernmental organizations (NGOs), major soybean traders signed the SoyM, agreeing not to purchase soy grown on lands deforested after July 2006 in the Brazilian Amazon. The soy industry recently extended the SoyM to May 2016, by which time they assert that Brazil's environmental governance, such as the increased enforcement and national implementation of the Rural Environmental Registry of private properties (Portuguese acronym CAR) mandated by the Forest Code (FC) (1), will be robust enough to justify ending the agreement (2). We argue that a longer-term commitment is needed to help maintain deforestation-free soy supply chains, as full compliance and enforcement of these regulations is likely years away. Ending the SoyM prematurely would risk a return to deforestation for soy expansion at a time when companies are committing to zero-deforestation supply chains (3).

The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.

Carbon Dioxide Information Analysis Center

The production of soy is one of the most important economic activities in the Brazilian Amazon, though the expansion of this industry has come at the cost of huge swaths of forest. Since 2006, the private firms that buy and trade soybeans globally have assumed a key role in ensuring that soy producers comply with forest protection policies, including the Soy Moratorium and public policies banning the use of illegally deforested land. We used evidence from field interviews and a GIS of property boundaries and soy-production areas to describe the private sector governance process and to characterize the variety of property arrangements underlying soy production in Mato Grosso, the leading soy-producing state in the Brazilian Amazon. These increasingly complex property arrangements include ownership of multiple properties by a single producer, use of rental properties owned by others, and soy and cattle production on a single property. This complexity could create loopholes allowing soy associated with deforestation to enter the supply chain. Comprehensive soy-governance strategies that include more robust procedures for verifying the provenance of soy across all properties, that account for the entire property rather than only the area planted to soy, and that use more transparent verification systems could achieve greater reductions in deforestation.

Does environmental policy aiming to reduce deforestation induce displacement of existing agricultural activities? To shed light on this question, we exploit a difference-in-differences strategy with a distance-based treatment to examine whether two policies in the Brazilian Amazon, the Soy Moratorium and the Zero-Deforestation Cattle Agreements, have displaced production or deforestation into neighboring regions. Our results show evidence that the Soy Moratorium induced soy spillovers onto previously cleared land—mainly pasture—in the less regulated ecosystem. The spillovers from the Cattle Agreements, three years after the Soy Moratorium, resulted in increased deforestation.