Increased climate pressure on the agricultural frontier in the Eastern Amazonia–Cerrado transition zone

Marengo, José A.; Jiménez, Juan Carlos; Espinoza, Jhan-Carlo; Cunha, Ana Paula; Aragão, Luiz E. O. C.

doi:10.1038/s41598-021-04241-4

Cited by 80 publications

(71 citation statements)

References 66 publications

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“…The SDG indicator 15.3.1 revealed several land degradation hotspots in the Amazon basin between 2001 and 2020, most of which were located in the Brazilian states of Pará, Mato Grosso, Amazonas, and Rondônia. This result ties well with previous studies wherein such land degradation hotspots were identified (Anderson et al, 2018;Arima et al, 2021;Marengo et al, 2022).…”

Section: Long-term Land Degradation In the Amazon Basinsupporting

confidence: 92%

“…Usually, this would lead to short-term declines in rainfed crop yields and progressive land degradation (Sims et al, 2019). This assumption is supported by recent studies highlighting the impacts of large monocultures and fragmented landscapes on biodiversity in the southwestern Amazon basin (de Almeida et al, 2020;Marengo et al, 2022).…”

Section: Long-term Land Degradation In the Amazon Basinmentioning

confidence: 82%

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Drought variability and land degradation in the Amazon River basin

et al. 2022

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The Amazon River Basin (ARB) plays an essential role in global climate regulation. Recent studies have revealed signs of increasing drought conditions in different parts of the basin. Although human activities have degraded large areas, little work has been done to assess whether prolonged drought may exacerbate land degradation. Among different methods for monitoring land degradation, the Sustainable Development Goal (SDG) indicator 15.3.1 adopted by the United Nations Convention to Combat Desertification (UNCCD) allows a comprehensive assessment of the impacts of land degradation due to its multi-factor nature and scalability. The aims of this study are twofold: 1) to assess the status of land degradation using the SDG indicator 15.3.1 from 2001 to 2020 in the Amazon basin; and 2) to explore the relationship between the detectability of land degradation using the UNCCD approach and long-term drought severity. The Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and self-calibrating Palmer Drought Severity Index (scPDSI) were used as drought indices. The results revealed 757,704 km2 (12.67% of the basin) as degraded land, which was reflected by a downward trend in land productivity dynamics followed by the combined downward trend in land productivity, Soil Organic Carbon (SOC) degradation, and land cover degradation. The largest land degradation hotspot was identified along the southwestern boundary of the Amazon River Basin. Furthermore, there was strong evidence that the detection of land degradation through SDG indicator 15.3.1 is sensitive to long-term drought, particularly when applied to rainfed croplands.

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Section: Long-term Land Degradation In the Amazon Basinsupporting

confidence: 92%

Section: Long-term Land Degradation In the Amazon Basinmentioning

confidence: 82%

Drought variability and land degradation in the Amazon River basin

et al. 2022

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“…These human conditions, together with climate change, were the basis for developing impact projections of the erosivity of rains in the AW region, alerting to possible scenarios of soil erosion (Santos et al, 2022). According to Marengo et al (2022), the transition zone between the Amazon and Cerrado biomes is marked by strong pressures from agricultural frontiers, and in the face of climate change, such adverse conditions cause a delay in the rainy season, decreased precipitation, humidity and evapotranspiration, and an increase in dry days.…”

Section: La Niñamentioning

confidence: 99%

Flow variability in the Araguaia River Hydrographic Basin influenced by precipitation in extreme years and deforestation

Gomes

Nascimento

Pereira

et al. 2022

Rev. Bras. Ciênc. Ambient.

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The climatic extremes and the dynamics of land use and cover can cause changes in river flow. The objective of this work was to analyze the flow of the Araguaia River under the effects of extreme years associated with the dynamics of land use in the Araguaia Watershed (AW) from 1981 to 2019. The land use and land cover product were based on the MapBiomas Project classification, imported from the Google Earth Engine. The measured rainfall and flow data were obtained from the National Water Agency. In contrast, the estimated rainfall was based on the data Climate Hazards Group InfraRed Precipitation with Stations. The precipitation climatology showed the lowest values (1,464.9–1,720.4 mm) in the south-central sector, and the highest (1,720.4–2,014.6 mm) rainfall amounts were observed in the north sector. However, it was identified in the five pluviometric stations with a high variability of precipitation, with an emphasis on the extreme years. Such wet and dry years were marked by a large difference in water availability. There was an intense reduction of the Amazon and Cerrado biomes by 31,641.8 and 42,618.9 km², respectively, mainly due to the expansion of 18,936.1 km² of agricultural activities and 47,494 km² of pasture. The fluviometric variability showed a decreasing trend, mainly in the past 15 years. Public actions, such as the intensification of environmental policies, monitoring focusing on the most compromised and strategic areas such as the headwaters of the Araguaia River, can minimize the impacts caused by climate extremes and deforestation.

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“…MATOPIBA presents, at the same time, the largest undisturbed remnants of Cerrado vegetation and a quarter of the Cerrado soybean area [21,23], equally important for environmental and economic issues, respectively. Nonetheless, over the last decade, the combination of climate change and land-use change has severely increased drought conditions in the region, which contribute to a higher fire risk, mainly during the fire season [24], and jeopardize biodiversity and food security.…”

Section: Introductionmentioning

confidence: 99%

Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil

Pletsch

Körting

Morita³

et al. 2022

Remote Sensing

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MATOPIBA is an agricultural frontier, where fires are essential for its biodiversity maintenance. However, the increase in its recurrence and intensity, as well as accidental fires can lead to socioeconomic and environmental losses. Due to this dual relationship with fire, near real-time (NRT) fire management is required throughout the region. In this context, we developed, to the best of our knowledge, the first Machine Learning (ML) algorithm based on the GOES-16 ABI sensor able to detect and monitor Active Fires (AF) in NRT in MATOPIBA. To do so, we analyzed the best combination of three ML algorithms and how long it takes to consider a historical time series able to support accurate AF predictions. We used the most accurate combination for the final model (FM) development. The results show that the FM ensures an overall accuracy rate of approximately 80%. The FM potential is remarkable not only for single detections but also for a consecutive sequence of positive predictions. Roughly, the FM achieves an accuracy rate peak after around 20 h of consecutive AF detections, but there is an important trade-off between the accuracy and the time required to assemble more fire indications, which can be decisive for firefighters in real life.

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Increased climate pressure on the agricultural frontier in the Eastern Amazonia–Cerrado transition zone

Cited by 80 publications

References 66 publications

Drought variability and land degradation in the Amazon River basin

Drought variability and land degradation in the Amazon River basin

Flow variability in the Araguaia River Hydrographic Basin influenced by precipitation in extreme years and deforestation

Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil

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