São Miguel Do Oeste

Municipality in Santa Catarina State in Brazil

Exports (2019): $61.6M, Rnk 434/2197

Top Destination (2019): $39.6M, China

Top Export (2019): $53.4M, Pig Meat

Imports (2019): $26M, Rnk 378/2197

Top Origin (2019): $25.2M, Paraguay

Top Import (2019): $24.3M, Corn

Overview: This page contains the latest international trade data for São Miguel do Oeste, including export and import data.

Exports: In 2019, Brazil's São Miguel Do Oeste exported $61.6M, making it the 434th largest exporter out of the 2197 exporters in Brazil. In 2019 the top exports of São Miguel Do Oeste were Pig Meat ($53.4M), Lifting Machinery ($2.3M), Washing and Bottling Machines ($1.25M), Wood Carpentry ($844k), and Processed Bones ($824k).

Imports: In 2019, Brazil's São Miguel Do Oeste imported $26M, making it the 378th largest importer out of the 2197 importers in Brazil. In 2019 top imports of São Miguel Do Oeste were Corn ($24.3M), Soybean Meal ($878k), Artificial Vegetation ($687k), Unsaturated Acyclic Monocarboxylic Acids ($49.2k), and Iron Wire ($31.7k).

Yearly Exports

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Value
Depth

Top Destination (2019): China, $39.6M

Top Product (2019): Pig Meat, $53.4M

In 2019 the top export destinations of São Miguel Do Oeste were China ($39.6M), Chile ($12M), Japan ($5.36M), Argentina ($2.03M), and Peru ($650k).

In 2019 the top exports of São Miguel Do Oeste were Pig Meat ($53.4M), Lifting Machinery ($2.3M), Washing and Bottling Machines ($1.25M), Wood Carpentry ($844k), and Processed Bones ($824k).

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Yearly Imports

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Value
Depth

Top Origin (2019): Paraguay, $25.2M

Top Import (2019): Corn, $24.3M

In 2019 the top import origins of São Miguel Do Oeste were Paraguay ($25.2M), China ($779k), United States ($7k), and Germany ($5.28k).

In 2019 the top imports of São Miguel Do Oeste were Corn ($24.3M), Soybean Meal ($878k), Artificial Vegetation ($687k), Unsaturated Acyclic Monocarboxylic Acids ($49.2k), and Iron Wire ($31.7k).

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This section shows forecasts for total exports and imports from São Miguel do Oeste. The forecast is based in a Long Short-Term Memory Model constructed using monthly trade data.

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