Soybeans Price History: Monthly Global Price Since 1992

Soybeans are the world’s dominant oilseed, crushed into protein meal for animal feed and into oil for food and biodiesel. This dataset tracks the IMF Primary Commodity Prices soybean benchmark (US, FOB Gulf), published monthly in US dollars per metric ton and distributed via FRED under the code PSOYBUSDM, with continuous coverage since 1992. Brazil and the United States dominate production and exports, while China is by far the largest importer.

Dataset: Soybeans Price (1992–2026) · Updated 2026-03-01

Source: IMF Primary Commodity Prices · International Monetary Fund (via FRED)

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns. Each row represents one calendar month.

Column names match the CSV headers exactly.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats — monthly observations covering more than three decades.

FRED Direct CSV Access

The underlying data is published in the Federal Reserve Economic Data (FRED) database under the series code PSOYBUSDM, sourced from the IMF Primary Commodity Prices dataset:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=PSOYBUSDM

The Eco3min dataset mirrors the same monthly series, packaged in a stable, versionable CSV with consistent column names — designed for direct ingestion in Python, R, or any data pipeline. The URL never changes, making it suitable for automated scripts.

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/soybeans-price.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/soybeans-price.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["soybeans_price"].describe())

df.plot(x="date", y="soybeans_price", title="Soybeans Price", legend=False)

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/soybeans-price.csv"
df <- read_csv(url)

head(df)
summary(df$soybeans_price)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

The IMF reports the soybean price in US dollars per metric ton, based on US soybeans (No.1 Yellow), FOB Gulf of Mexico.

Values are monthly averages, which smooth the intra-month swings visible in daily futures and physical quotes. The series begins in 1992.

This dataset is updated monthly via an automated pull from the FRED API (series PSOYBUSDM) by an Eco3min pipeline running on GitHub Actions, which regenerates the cleaned CSV and Excel files and refreshes the page metadata.

Data Quality & Provider Notes

The IMF/benchmark price is a widely cited physical-market reference. A few provider-specific points matter when using this series.

• Release latency. The IMF publishes Primary Commodity Prices monthly, typically in the first week of the following month. FRED ingests the update shortly after, and Eco3min mirrors it with a monthly pull. The series is not a real-time price.
• Monthly average vs futures spot. This series is a monthly average. It will differ from any single exchange settlement, and a monthly average necessarily understates intra-month peaks.
• Revisions. Prices are market-derived and not subject to the vintage revisions of survey-based macro series, though the IMF can restate recent observations.
• Alternative sources. ICE futures and the originating auction or indicator bodies provide higher-frequency or contract-specific quotes.

Common Pitfalls When Using This Series

1. Confusing nominal and real prices. This series is nominal. Comparing an early-1990s reading to a recent one without adjusting for cumulative inflation overstates the real change. Deflating by CPI gives the true purchasing-power move.
2. Reading the monthly average as a market price. Headlines quote exchange futures; this dataset reports the monthly benchmark average. The two diverge most during fast-moving rallies.
3. Unit confusion. This series is in US dollars per metric ton; CBOT soybean futures are quoted in US cents per bushel, so comparison requires conversion (1 metric ton ≈ 36.74 bushels).

Historical Regimes

1992–2006 — Gradual rise. Growing Chinese demand for feed and crush slowly lifted soybeans.

2007–2008 — Food-crisis surge. The broad agricultural boom carried soybeans higher.

2012 — Drought record. The 2012 US Midwest drought drove soybeans to record highs.

2013–2019 — Decline and trade war. Surpluses and the 2018-2019 US-China trade war (Chinese tariffs on US soybeans) pressured prices.

2020–2022 — China rebound and Ukraine. Resurgent Chinese buying and the 2022 supply shock lifted soybeans back toward record territory.

2023–2026 — Big harvests. Large Brazilian and US crops pulled prices lower.

Related Macroeconomic Datasets

Commodity Price Hub

This dataset is part of the Eco3min commodity price repository — energy, metals, agricultural softs, and grains, all sourced from IMF Primary Commodity Prices via FRED.

Explore the Commodity Price Hub

Sources

• International Monetary Fund — Primary Commodity Prices, Global Price of Soybeans
• Federal Reserve Bank of St. Louis — FRED database, series PSOYBUSDM
• US Gulf soybean export quotations — basis underlying the IMF series

Dataset Reference

Eco3min Research. (2026). Soybeans Price — Global Monthly Price (1992-2026). Source: IMF Primary Commodity Prices via FRED.<br> https://eco3min.fr/en/soybeans-price-history-dataset/