Updated Brazilian’s Georeferenced Soil Database – An Improvement for International Scientific Information Exchanging

Benedetti, Marcelo Muniz; Curi, Nilton; Sparovek, Gerd; Filho, Amaury de Carvalho; Silva, Sérgio Henrique Godinho

doi:10.5772/29627

Cited by 7 publications

(5 citation statements)

References 7 publications

(3 reference statements)

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“…Purchase prices for agricultural machinery were compiled from local traders, while operational costs of machinery were estimated using the methodology of the Brazilian National Supply Company (CONAB 2010). Information on soils was taken from the georeferenced soil database of Brazil (Muniz et al 2011) and from official socio-ecological zoning maps produced by the Mato Grosso State Secretary of Planning (SEPLAN 2011).…”

Section: Input Data and Model Parameterizationmentioning

confidence: 99%

Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso, Brazil? Results from bioeconomic microsimulation

et al. 2017

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The need to balance agricultural production and environmental protection shifted the focus of Brazilian land-use policy toward sustainable agriculture. In 2010, Brazil established preferential credit lines to finance investments into low-carbon integrated agricultural systems of crop, livestock and forestry. This article presents a simulation-based empirical assessment of integrated system adoption in the state of Mato Grosso, where highly mechanized soybean-cotton and soybean-maize doublecrop systems currently prevail. We employ bioeconomic modeling to explicitly capture the heterogeneity of farmlevel costs and benefits of adoption. By parameterizing and validating our simulations with both empirical and experimental data, we evaluate the effectiveness of the ABC Integration credit through indicators such as land-use change, adoption rates and budgetary costs of credit provision. Alternative scenarios reveal that specific credit conditions might speed up the diffusion of low-carbon agricultural systems in Mato Grosso.

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Section: Input Data and Model Parameterizationmentioning

confidence: 99%

Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso, Brazil? Results from bioeconomic microsimulation

et al. 2017

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“…The estimated coefficients for fixed effects of the GLS he‐sp model of grain yield are given in Table 2, showing that among the soil attributes, only pH has a significant impact on yield because the main factor that impacts crop yields in most Brazilian agricultural fields is soil acidity (Amado et al, 2009; Dalchiavon et al, 2011; Nogara et al, 2011). This can be explained because low pH values are often associated with high Al 3+ contents in Latosols (Oxisols according to the U.S. soil taxonomy), which are prevalent throughout many of the cropped regions of Brazil (Muniz et al, 2011). Crop season had a large impact on yield, as indicated by the significant coefficients for year that varied substantially with time.…”

Section: Resultsmentioning

confidence: 99%

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

et al. 2013

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Effective site‐specific management requires an understanding of the soil and environmental factors influencing crop yield variability. Moreover, it is necessary to assess the techniques used to define these relationships. The objective of this study was to assess whether statistical models that accounted for heteroscedastic and spatial‐temporal autocorrelation were superior to ordinary least squares (OLS) models when evaluating the relationship between corn (Zea mays L.) yield and soil attributes in Brazil. The study site (10 by 250 m) was located in São Paulo State, Brazil. Corn yield (planted with 0.9‐m spacing) was measured in 100 4.5‐ by 10‐m cells along four parallel transects (25 observations per transect) during six growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. Ordinary least squares, generalized least squares assuming heteroscedasticity (GLShe), spatial‐temporal least squares assuming homoscedasticity (GLSsp), and spatial‐temporal assuming heteroscedasticity (GLShe‐sp) analyses were used to estimate corn yield. Soil acidity (pH) was the factor that most influenced corn yield with time in this study. The OLS model suggested that there would be a 0.59 Mg ha–1 yield increase for each unit increase in pH, whereas with GLShe‐sp there would be a 0.43 Mg ha–1 yield increase, which means that model choice impacted prediction and regression parameters. This is critical because accurate estimation of yield is necessary for correct management decisions. The spatial and temporal autocorrelation assuming heteroscedasticity was superior to the OLS model for prediction. Historical data from several growing seasons should help better identify the cause and effect relationship between crop yield and soil attributes.

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“…The soil and vegetation composition provide important context for understanding the sediment routing system (Figures 2A, 3A). The soils are dominantly planosols in the lowland flooded savanna, whereas the cerrado region contains mostly ferralsols and arenosols plus luvisols in the southern cerrado (Benedetti et al, 2011). Lithosols, planosols, vertisols, and solonetz are present in limited geographic extent in the Pantanal Basin (Figure 2A).…”

Section: Geological Settingmentioning

confidence: 99%

“…Where sandy soils form, the removal of feldspars likely occurs during periods of prolonged subaerial exposure and leaching. In contrast, the planosols are characterized by hydromorphized conditions and develop in the lowland regions (Benedetti et al, 2011). Both arenosols and ferralsols favor the generation of quartz-rich sediments, especially when combined with high temperature and precipitation encountered in the north and central areas of the basin (Sileshi et al, 2022).…”

Section: Chemical Weathering As a Secondary Control On Sand Compositionmentioning

confidence: 99%

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Source-to-sink controls on modern fluvial sands in the Pantanal back-bulge basin (Brazil)

Lo,

Silva,

Kuerten

et al. 2023

Sedika

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Sedimentological processes are underexplored in retro-arc basin segments of source-to-sink systems, particularly in the distal back-bulge depozone. The extant Pantanal Basin of Latin America is a large sub-tropical continental back-bulge for which the boundary conditions on riverine sedimentation (tectonics, climate, phytogeography) are reasonably understood. Here, we use a new database of riverbed sand samples (n = 97) to investigate the modern provenance of the Pantanal Basin. Petrographic analysis of grain-mounted thin sections and Gazzi-Dickinson point counting were used to assess the framework composition of the sands, and a pour point analysis based on geographic information systems (GIS) defined the hinterland geological environment for each sample. Six provenance domains were defined: (a) lowlands, (b) Amazon craton, (c) Rio Apa craton, (d) plateau, (e) South Paraguay Belt, and (f) North Paraguay Belt. Most Pantanal samples are quartzose non-orogenic detritus (Qt88\F5\L7). Rivers draining the Paraguay Belt highlands carry more lithic grains derived from carbonate parent rocks. The occurrence of plagioclase grains is rare. However, river sands where >30% of the total assemblage was K-feldspar were present downstream of foliated metamorphic outcrops of the Rio Apa craton. Sand relatively rich in fine K-feldspar grains in the medial Taquari River megafan were attributed to channel avulsion, incision, and reworking of floodplain deposits. Statistical analysis suggests that bedrock lithology and average annual rainfall are important controls on fluvial sands in the Pantanal Basin, with watershed slope, temperature, and dilution playing secondary roles in the spatial distribution of dominant grain types. This study provides new actualistic data that aim to improve interpretations of the geological record.

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Updated Brazilian’s Georeferenced Soil Database – An Improvement for International Scientific Information Exchanging

Cited by 7 publications

References 7 publications

Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso, Brazil? Results from bioeconomic microsimulation

Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso, Brazil? Results from bioeconomic microsimulation

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Source-to-sink controls on modern fluvial sands in the Pantanal back-bulge basin (Brazil)

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