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Abstract Private lands are important for conservation worldwide, but knowledge about their effectiveness is still insufficient. To help fill this important knowledge gap, we analyzed the impacts of a national policy for conservation on private lands in Brazil, a global biodiversity hotspot with high potential for nature-based climate solutions. Through the evaluation of over 4 million private rural properties from the Rural Environmental Cadastre, we found that the last policy review in 2012 mainly affected the Amazon Forest. The amnesty granted to 80% of landowners of small properties prevented the restoration of 14.6 million hectares of agricultural land with a carbon sequestration potential of 2.4 gigatonnes. We found that private lands exist within the limits of public conservation areas and that between 2003 and 2020 deforestation rates in these private lands were higher than those across all conservation areas. The Rural Environmental Cadastre can be an effective tool for managing forests within private lands, with potential to integrate governance approaches to control deforestation and mitigate climate change.more » « less
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Abstract Human–environment interactions within and across borders are now more influential than ever, posing unprecedented sustainability challenges. The framework of metacoupling (interactions within and across adjacent and distant coupled human–environment systems) provides a useful tool to evaluate them at diverse temporal and spatial scales. While most metacoupling studies have so far addressed the impacts of distant interactions (telecouplings), few have addressed the complementary and interdependent effects of the interactions within coupled systems (intracouplings) and between adjacent systems (pericouplings). Using the production and trade of a major commodity (soybean) as a demonstration, this paper empirically evaluates the complex effects on deforestation and economic growth across a globally important soybean producing region (Mato Grosso in Brazil). Although this region is influenced by a strong telecoupling process (i.e., soybean trade with national and international markets), intracouplings pose significant effects on deforestation and economic growth within focal municipalities. Furthermore, it generates pericoupling effects (e.g., deforestation) on adjacent municipalities, which precede economic benefits on adjacent systems, and may occur during and after the soybean production takes place. These results show that while economic benefits of the production of agricultural commodities for global markets tend to be localized, their environmental costs tend to be spatially widespread. As deforestation also occurred in adjacent areas beyond focal areas with economic development, this study has significant implications for sustainability in an increasingly metacoupled world.
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null (Ed.)Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.more » « less
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null (Ed.)Agricultural systems are heterogeneous across temporal and spatial scales. Although much research has investigated farm size and economic output, the synergies and trade-offs across various agricultural and socioeconomic variables are unclear. This study applies a GIS-based approach to official Brazilian census data (Agricultural Censuses of 1995, 2006, and 2017) and surveys at the municipality level to (i) evaluate changes in the average soybean farm size across the country and (ii) compare agricultural and socioeconomic outcomes (i.e., soybean yield, agricultural production value, crop production diversity, and rural labor employment) relative to the average soybean farm size. Statistical tests (e.g., Kruskal–Wallis tests and Spearman’s correlation) were used to analyze variable outcomes in different classes of farm sizes and respective Agricultural Censuses. We found that agricultural and socioeconomic outcomes are spatially correlated with soybean farm size class. Therefore, based on the concepts of trade-offs and synergies, we show that municipalities with large soybean farm sizes had larger trade-offs (e.g., larger farm size was associated with lower crop diversity), while small and medium ones manifest greater synergies. These patterns are particularly strong for analysis using the Agricultural Census of 2017. Trade-off/synergy analysis across space and time is key for supporting long-term strategies aiming at alleviating unemployment and providing sustainable food production, essential to achieve the UN Sustainable Development Goals.more » « less