Padrões de crescimento econômico dos municípios do MATOPIBA

Ribeiro, Luiz Carlos de Santana; Lôbo, Ariana Souza; Silva, Lizandra Duarte da; Andrade, Nathália Francelina Santos

doi:10.1590/1806-9479.2020.212613

Cited by 10 publications

(12 citation statements)

References 19 publications

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“…On the other hand, underdeveloped municipalities are located mainly in the Matopiba region, as well as parts of northern Minas Gerais. These corroborate (Bolfe et al, 2016;Bragança, 2018;Ribeiro et al, 2020), which characterized Matopiba as a region with lower socioeconomic development compared to the other parts of Cerrado. Despite this, the authors emphasize that, due to the advancement of the agricultural frontier, the region has presented higher economic growth rates, a fact that has diminished the disparities.…”

Section: Resultssupporting

confidence: 75%

“…However, if considered only Tocantins and Bahia, the percentage would go up to approximately 60%, indicating a per capita income spatial heterogeneity between the regions that comprise the Matopiba Bolfe et al (2016). Between 2010 and 2015, MATOPIBA presented a real GDP growth rate of 5.1% per year, higher than the Brazilian growth rate of 1.5% Ribeiro et al (2020).…”

Section: The Cerrado Biome and The Agricultural Frontier Expansionmentioning

confidence: 83%

“…In this context, the present paper aims to verify if there is a relationship between economic development, -which is correlated with the increase in agricultural production Bragança (2018); Ribeiro et al (2020) and the Cerrado biome deforestation, with a special focus on the Matopiba, due to the agricultural frontier expansion in the region. In general, the literature points to a significant relationship for Cerrado, with economic development leading to deforestation, especially in the initial stages of development Colusso et al (2012); Stege and Barros (2019); .…”

Section: Introductionmentioning

confidence: 99%

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Socioeconomic Development and Deforestation in the Cerrado Biome, Brazil: Spatial Interactions and Heterogeneity

Barros

Stege

2022

RBERU

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This paper investigates the relationship between socioeconomic development and deforestation in the Cerrado biome, mainly focusing on the current Brazilian agricultural frontier of Matopiba. More specifically, we search for spatial interactions, heterogeneity, and spatial clusters among the municipalities using the exploratory analysis of spatial data (ESDA). Due to the many possible socioeconomic variables related to deforestation decisions, we create a Socioeconomic Development Index (SDI) that synthesizes all possible influences using factorial analysis from multivariate statistics. We also grouped cluster analysis municipalities with dissimilar deforestation and economic development patterns. The results show that the biome’s economic development and deforestation are spatially concentrated and heterogeneous and are negatively associated for the majority of Cerrado municipalities both in the spatial and cluster analysis, indicating that socioeconomic development may be an inhibitor of deforestation.

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Section: Resultssupporting

confidence: 75%

Section: The Cerrado Biome and The Agricultural Frontier Expansionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

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Socioeconomic Development and Deforestation in the Cerrado Biome, Brazil: Spatial Interactions and Heterogeneity

Barros

Stege

2022

RBERU

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“…De outro, emergiu um número significativo de pequenos, médios e grandes produtores que não mediram esforços em aprofundar a relação agricultura e indústria. As escalas ampliadas de produção, decorrentes do alargamento da integração agricultura e indústria, promoveram a exclusão de pequenos produtores que não conseguem acompanhar esse processo (SCHNEIDER; WAQUIL, 2001).…”

Section: Figura 1 -Síntese Da Ocupação Do Território Do Rio Grande Do...unclassified

Dimensão e dinâmica do processo produtivo e territorial da agropecuária no Rio Grande do Sul pós-2003

Cunha

Espíndola

2021

REVANPEGE

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A diversidade dos produtos da agropecuária do Rio Grande do Sul institui-se em um dos principais setores econômicos geradores de renda no estado. Entre 2009-2018, o valor bruto da produção dos principais bens agrícolas sul-rio-grandenses cresceu de R$ 47,2 bilhões para R$ 53,8 bilhões. Nas exportações, o valor gerado, em 2018, foi de US$ 12,2 bilhões, o que representa 58,05%, do valor total do estado, e 12%, do valor total dos agronegócios brasileiros. Sendo assim, o objetivo central deste texto é identificar o desempenho produtivo da agropecuária do Rio Grande do Sul, com base nos indicadores de área plantada, valor bruto da produção, produtividade e participação na pauta exportadora do estado, uma vez que, esse dinamismo veio acompanhado de alterações significativas nas estruturas produtivas territoriais. Desse modo, o processo de concentração/desconcentração, disseminação e exclusão estabeleceu uma nova especialização produtiva territorial, especialmente, na soja e na pecuária bovina.

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“…The MATOPIBA was officially recognized as an agricultural frontier through Decree number 8447 of 6 May 2015, which deals with the Agricultural Development Plan for the region to promote and coordinate public policies aimed at sustainable economic development based on agricultural activities and livestock [27]. In the last 20 years, the considerable increase in soybean production resulting from the expansion of cultivated areas and productivity significantly increased the Gross Domestic Product (GDP) of the municipalities in the region [27,28]. Studies on the agricultural expansion taking place in Cerrado areas, specifically in the MATOPIBA region, investigated in the present case, are thus essential.…”

Section: Introductionmentioning

confidence: 99%

Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis

et al. 2022

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This study aimed to evaluate the interaction of environmental variables and Water Use Efficiency (WUE) via multivariate analysis to understand the importance of each variable in the carbon–water balance in MATOPIBA. Principal Component Analysis (PCA) was applied to reduce spatial dimensionality and to identify patterns by using the following data: (i) LST (MOD11A2) and WUE (ratio between GPP-MOD17A2 and ET-MOD16A2), based on MODIS orbital products; (ii) Rainfall based on CHIRPS precipitation product; (iii) slope, roughness, and elevation from the GMTED and SRTM version 4.1 products; and (iv) geographic data, Latitude, and Longitude. All calculations were performed in R version 3.6.3 and Quantum GIS (QGIS) version 3.4.6. Eight variables were initially used. After applying the PCA, only four were suitable: Elevation, LST, Rainfall, and WUE, with values greater than 0.7. A positive correlation (≥0.78) between the variables (Elevation, LST, and Rainfall) and vegetation was identified. According to the KMO test, a series-considered medium was obtained (0.7 < KMO < 0.8), and it was explained by one PC (PC1). PC1 was explained by four variables (Elevation, LST, Rainfall, and WUE), among which WUE (0.8 < KMO < 0.9) was responsible for detailing 65.77% of the total explained variance. Positive scores were found in the states of Maranhão and Tocantins and negative scores in Piauí and Bahia. The positive scores show areas with greater Rainfall, GPP, and ET availability, while the negative scores show areas with greater water demand and LST. It was concluded that variations in variables such as Rainfall, LST, GPP, and ET can influence the local behavior of the carbon–water cycle of the vegetation, impacting the WUE in MATOPIBA.

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Padrões de crescimento econômico dos municípios do MATOPIBA

Cited by 10 publications

References 19 publications

Socioeconomic Development and Deforestation in the Cerrado Biome, Brazil: Spatial Interactions and Heterogeneity

Socioeconomic Development and Deforestation in the Cerrado Biome, Brazil: Spatial Interactions and Heterogeneity

Dimensão e dinâmica do processo produtivo e territorial da agropecuária no Rio Grande do Sul pós-2003

Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis

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