Mapping the cattle industry in Brazil's most dynamic cattle-ranching state: Slaughterhouses in Mato Grosso, 1967-2016.

The state of Mato Grosso is Brazil's agribusiness powerhouse with a cattle herd of 30.2 million head in 2017. With land use patterns heavily influenced by beef production, which requires substantial land inputs, the state is a key target for environmental conservation. Yet the spatial and temporal dynamics of slaughterhouses in Mato Grosso remain largely unknown due to data limitations. Here, we provide a novel method to map slaughterhouse expansion and contraction. We analyzed the opening and closing of 133 plants between 1967 and 2016 in Mato Grosso and estimated the geographic locations and slaughter volumes. This was achieved by triangulating across multiple data sources including a registry of 21 million companies, government records of three million slaughter transactions (Portuguese acronym GTA), and high resolution satellite imagery. Our study is the first to include longitudinal information and both inspected (for food quality) and uninspected slaughterhouses. The results show that 72 plants operated in 2016 through 52 holding companies. By measuring geographic distances between active plants and pasture areas, we documented a 29% increase in the density of plants during 2000-2016, showing an expansion of the cattle slaughter infrastructure. We identified three periods of expansion: 1967-1995, with 15.1% of the plant openings; 1996-2003, with 24.6%; and 2004-2016, with 60.3%. While closings likely occurred throughout the period studied, no data were available prior to 2002. We estimated a minimum value for the volume of uninspected slaughter as 2-3% for 2013-2016. We conclude by discussing potential applications of the data, a deidentified version of which is made available through an online repository. The method developed here can be replicated for the whole country, which would increase our understanding of the dynamics of cattle slaughter and their impact on land use.

Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation.

Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts' selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal "blacklist" that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies that are implemented in just a few locations.