Abstract Previous research has established the usefulness of remotely sensed vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to characterize the spatial dynamics of agriculture in the state of Mato Grosso (MT), Brazil. With these data it has become possible to track MT agriculture, which accounts for ~85% of Brazilian Amazon soy production, across periods of several years. Annual land cover (LC) maps support investigation of the spatiotemporal dynamics of agriculture as they relate to forest cover and governance and policy efforts to lower deforestation rates. We use a unique, spatially extensive 9-year (2005–2013) ground reference dataset to classify, with approximately 80% accuracy, MODIS VI data, merging the results with carefully processed annual forest and sugarcane coverages developed by Brazil’s National Institute for Space Research to produce LC maps for MT for the 2001–2014 crop years. We apply the maps to an evaluation of forest and agricultural intensification dynamics before and after the Soy Moratorium (SoyM), a governance effort enacted in July 2006 to halt deforestation for the purpose of soy production in the Brazilian Amazon. We find the pre-SoyM deforestation rate to be more than five times the post-SoyM rate, while simultaneously observing the pre-SoyM forest-to-soy conversion rate to be more than twice the post-SoyM rate. These observations support the hypothesis that SoyM has played a role in reducing both deforestation and subsequent use for soy production. Additional analyses explore the land use tendencies of deforested areas and the conceptual framework of horizontal and vertical agricultural intensification, which distinguishes production increases attributable to cropland expansion into newly deforested areas as opposed to implementation of multi-cropping systems on existing cropland. During the 14-year study period, soy production was found to shift from predominantly single-crop systems to majority double-crop systems.

Citation: Kastens JH, Brown JC, Coutinho AC, Bishop CR, Esquerdo JCDM (2017) Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil. PLoS ONE 12(4): e0176168. https://doi.org/10.1371/journal.pone.0176168 Editor: Krishna Prasad Vadrevu, University of Maryland at College Park, UNITED STATES Received: December 8, 2016; Accepted: April 6, 2017; Published: April 28, 2017 Copyright: © 2017 Kastens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: JK, CB, and JB were funded by grants from the National Science Foundation (https://nsf.gov) (EPS – 0903806, with matching support from the State of Kansas through the Kansas Board of Regents; GSS – 1227160). JB was funded by the Fulbright Scholar Program (http://www.cies.org/) and the Fundação de Amparo à Pesquisa do Estado de São Paulo (http://www.fapesp.br/en/) (FAPESP – 2015/19437-5). CB was funded by the College of Liberal Arts and Sciences of the University of Kansas (http://college.ku.edu/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Previous studies have successfully applied vegetation index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to the study of agriculture, deforestation, and land change dynamics at the regional scale in the Brazilian Amazon, especially in the state of Mato Grosso (MT), which is one of the most dynamic agricultural frontiers in the world. Land cover (LC) datasets produced from MODIS have been used to study a wide range of human and environmental dynamics including land change impacts on biogeochemical cycling [1,2], fire frequency [3,4], and water quality [5,6]; physical geographic limits on agricultural practices [7–9]; regional socio-economic conditions [10,11]; and relationships among agricultural intensification, deforestation, and conservation [5,12–17]. Predominantly through LC classification, MODIS data also have been used in assessments of policy and governance efforts such as the “Soy Moratorium” (hereafter SoyM) designed to stem deforestation [18–26]. SoyM is an ongoing effort supported by environmental organizations and large agribusiness companies that involves a pledge not to purchase soy from areas deforested in the Amazon biome after July 24, 2006 [27]. In May 2016, SoyM was renewed indefinitely. MT accounts for approximately 85% of the soy grown in the Amazon biome [24]. Some of the most relevant research related to soy production has focused on the fate of deforested lands to determine the extent that deforestation is driven by soy production. The studies rely on data from the Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES; http://www.obt.inpe.br/prodes/index.php) produced by Brazil’s National Institute for Space Research (INPE) to identify what areas of the Brazilian Amazon have been deforested each year going back to 1988 but with the most detailed data available beginning in 2000. Researchers typically then use MODIS data to classify post-deforestation lands to determine whether cropland or pasture replaced forests. In [17] it is reported that crop production had become a significant factor in deforestation because an increasing amount of cropland was replacing forests. The authors also report a correlation between increased cropland area and soy price, implying that increasing demand for soy had a role in causing deforestation. In a related study [16], this trend is reassessed with similar methods, and it is concluded that deforestation for soy production decoupled after the 2007 crop year (CY2007 = Aug 06 –Jul 07) based on a comparison between soy profitability and deforested land use. The authors describe a number of national and state government-led initiatives that could explain the drop in deforestation and subsequent decoupling with soy production, and they also mention industry-led initiatives such as SoyM as a potential factor in diminishing incentives to deforest for soy production. In [24] the authors argue that the SoyM should be renewed (which it eventually was) and extended spatially to the Cerrado (savanna) biome, based on their analysis of property-level impacts. Post-deforestation land use analysis using MODIS indicated a sharp decrease in deforestation for soy production, which they attribute in part to SoyM. The present study contributes to the pursuit of policy-relevant research on agriculture and deforestation dynamics in the Amazon using remotely-sensed satellite data and GIS analyses. Building from our previous class separability study [28], we produce a 14-year LC time series for MT with a class structure that includes multiple soy and cotton classes in addition to others. By contrast, mapping efforts used in [17] and [16] did not seek to distinguish the type of cropland detected with MODIS, making the necessary assumption that cropland is most likely soy. This was avoided in [24] where a dataset was created following previous research [25, 26] that does claim to specifically map soy, but this was only for the part of their analysis that is in the Amazon (humid forest) biome. Their Cerrado biome analysis was based on a separate dataset in which all identified cropland is assumed to be soy. Finally, INPE’s PRODES data, though an extremely valuable research tool, is not without ambiguities and difficulties in its use. It is essential to describe how one processes the PRODES data to ensure results can be reproduced, as there has been a substantial lack of consistency among PRODES-based deforestation annual time series that have been independently developed for use in previous studies (S1 Fig). The key outcomes of this work are three-fold. (1) Results from our PRODES-based deforestation analysis are more consistent with a large, direct SoyM effect on Amazon deforestation decline in MT than has been previously reported. (2) We create an updateable 14-year LC map set for MT and make it available for others to use. (3) We provide a detailed assessment of basic soy and deforestation dynamics in MT with attention to SoyM impacts. For map production, we use downloadable MODIS Normalized Difference Vegetation Index (NDVI) time series data [28] and a random forest (RF) classification model [29]. The RF model is developed using a farmer interview-based ground reference dataset that is unprecedented in its spatial and temporal coverage, with minimal reliance on the less rigorous technique of visual interpretation of high resolution imagery to generate reference data. Following the image classification, INPE forest and sugarcane map data are overlaid to provide additional class detail. Model and map accuracy are examined using traditional metrics, probabilistic methods, federal crop statistics, and a spatially extensive, multi-year roadside dataset. We then utilize the map set to examine impacts of SoyM on deforestation and forest-to-soy conversion, to look at post-deforestation land use tendencies, and to examine the degree to which increased agricultural production in MT is being driven by conversion of forest to cropland (horizontal intensification) as compared to elevating production on existing cropland (vertical intensification; adapted from [28]).

Study area MT covers approximately 904,000 km2 and is located in the center of the South American continent (Fig 1). Three official biomes comprise MT: the Pantanal wetland in the southwest (61,000 km2), the humid tropical forests of the Amazon in the north (484,000 km2), and the Cerrado (360,000 km2), a tropical savanna that extends from east to west through the center of the state. MT experiences a hot, semi-humid to humid climate (Koppen Aw), with a marked dry season from May through October. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 1. Study area (Mato Grosso). Forest and cropland geography as of CY2014 are shown along with biome boundaries and the locations of the ground reference and roadside data points. https://doi.org/10.1371/journal.pone.0176168.g001 Much of the state’s soils are old, deep, and nutrient poor. With inputs of fertilizer and lime, well adapted seed varieties, and favorable market conditions in recent years, MT increasingly has become a major source of agricultural production within Brazil, already a recognized agricultural superpower. According to official statistics published by the Brazilian Institute of Geography and Statistics (IBGE), during the crop year that ended in 2014, MT produced 28%, 21%, and 54% of Brazilian soybeans, corn, and cotton, respectively, making it the most productive Brazilian state for each of these major crops. The practice of increasing productivity through double cropping (sequentially growing and harvesting two commercial crops per year on the same land) has become increasingly prevalent across the region since 2000, where a second crop (called safrinha in Brazil) (predominantly corn) is planted after soy.

Conclusion In this study we developed a 14-year land cover time series for the state of Mato Grosso, Brazil. MODIS NDVI and a spatiotemporally extensive ground reference dataset were utilized to develop a random forest classification model that was used to map the entire state for the 2001–2014 crop years. Annual INPE PRODES forest and Canasat sugarcane coverages were processed and incorporated into the maps, along with static urban and water information. We found a more abrupt reduction of the deforestation rate in MT immediately following implementation of SoyM than has been reported in previous studies, consistent with claims that SoyM has played a role in reducing pressure to deforest [23,24,38]. In MT we observed a 5.7-fold decrease in annual deforestation rate post-SoyM (CY2007-2014) compared to pre-SoyM (CY2001-2006). We also observed a marked decline in forest-to-soy conversion rate post-SoyM compared to pre-SoyM, another expected outcome of SoyM. Looking at 1 to 5-year lags following deforestation, deforested areas ended up as soy at a rate 2.4 times greater pre-SoyM than post-SoyM. These findings suggest that the policy is helping eliminate the incentive to eventually use newly deforested lands for soybean production. However, the situation remains complex; in the property-level analysis of [24], the authors show that hundreds of “soy properties” in MT experienced post-SoyM deforestation in violation of Brazil’s Federal Forest Code, though they remained in compliance with SoyM because they did not plant soy in the deforested areas. By analyzing total production increases attributable to vertical and horizontal intensification, we can account for the fact that mechanized agriculture may expand its area and yields both on recently deforested land and on other non-forest areas, especially existing cropland [39]. Focusing on soy, we found a pronounced increase in vertical cropping intensification over the study period. While our maps indicate that total soybean plantings have increased by a factor of 2.4 between CY2001 and CY2014, the total number of commercial crops harvested from soybean fields has increased by a factor of 3.5 due to increased use of double cropping. In terms of total number of commercial crops per soy pixel, we find this number has increased from 1.1 in CY2001 (indicating single crop soybeans to be the largely dominant management practice) to 1.6 in CY2014 (indicating that the majority of soybean crops are now being followed by a second commercial crop in the same growing season). We found that soy producers in MT are intensifying production on existing soy fields (vertical) at 2.8 times the relative rate that they are expanding soy production to areas deforested before SoyM (horizontal). Understanding how this rate varies across space, with municipalities, watersheds, vegetation zones, etc. as the units of analysis, allows for exploring both the human and environmental dynamics that bring about intensification. A number of location-specific factors will also warrant consideration, such as production potential (agricultural aptitude) and market access, in addition to local and regional yield trends (e.g. MT soy, corn, and cotton yields from IBGE have increased by 0.3% yr-1, 4.5% yr-1, and 0.4% yr-1, respectively, where these values are the 14-year linear trend slopes divided by the 14-year average yields). Our land cover map data are provided with this article (S3 Dataset). This is the first time such a detailed and extensive land cover dataset is available to the public covering this region of the world that has attracted so much attention concerning both the development of agriculture there and the policies designed to save remaining tropical forests. Policy researchers, land change scientists, non-governmental and governmental agencies, and the public can now take advantage of this reliable land cover dataset for various applications without having to produce the data themselves.

Acknowledgments Thanks are extended to the many farmers in Mato Grosso and representatives of the APROSOJA cooperative who participated in and facilitated our fieldwork.

Author Contributions Conceptualization: JK JB AC CB. Data curation: AC JK CB JE JB. Formal analysis: JK CB JB AC JE. Funding acquisition: JB AC JK. Investigation: JK JB AC CB JE. Methodology: JK CB JB. Project administration: JB AC JK. Resources: AC JK JB. Software: JK CB. Supervision: JB AC JK. Validation: JK CB JB AC. Visualization: JK CB. Writing – original draft: JK JB CB. Writing – review & editing: JK JB AC CB JE.