This dataset provides a comprehensive and up-to-date collection of futures related to corn, oat, and other grains. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular grain at a predetermined price on a future date.

Use Cases: 1. Crop Yield Predictions: Use machine learning models to correlate grain futures prices with historical data, predicting potential harvest yields. 2. Impact Analysis of Weather Events: Implement deep learning techniques to understand the relationship between grain price movements and significant weather patterns. 3. Grain Price Forecasting: Develop time-series forecasting models to predict future grain prices, assisting traders and stakeholders in decision-making.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/agriculture-arable-barley-bread-265242/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Maximum price reached during the trading session. 4. Low: Minimum traded price during the day. 5. Close: Market's closing price. 6. Volume: Number of contracts traded during the session. 7. Ticker: Unique market quotation symbol for the grain future. 8. Commodity: Specifies the type of grain the future contract represents (e.g., corn, oat).

Wheat rose to 504.50 USd/Bu on October 17, 2025, up 0.40% from the previous day. Over the past month, Wheat's price has fallen 3.77%, and is down 11.92% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on October of 2025.

Prices are a fundamental component of exchange and have long been important to the functioning of agricultural markets. Grain prices are closely related to grain transportation, where the supply and demand for grain simultaneously determines both the price of grain, as well as the demand for grain transportation.

This data has corn, soybean, and wheat prices for a variety of locations. These include origins—such as Iowa, Minnesota, Nebraska, and many others—and destinations, such as the Pacific Northwest, Louisiana Gulf, Texas Gulf, and Atlantic Coast.

The data come from three sources: USDA-AMS Market News price reports, GeoGrain, and U.S. Wheat Associates. Links are included below. GeoGrain offers granular data for purchase. The GeoGrain data here is an average of those granular prices for a given state (and the "Southeast" region, which combines Arkansas, Mississippi, and Alabama).

This is one of three companion datasets. The other two are grain basis (https://agtransport.usda.gov/d/v85y-3hep) and grain price spreads (https://agtransport.usda.gov/d/an4w-mnp7). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price).

This dataset provides a comprehensive record of daily mandi (agricultural market) prices for five of India's most important food crops — Onion, Tomato, Potato, Wheat, and Rice — across all Indian states and nearly every district. The data spans a complete two-year period, from June 6, 2023 to June 6, 2025, making it a rich resource for time-series analysis, price forecasting, and agriculture-based policy insights.

India’s agricultural markets are dynamic and influenced by a variety of factors such as seasonal variations, supply chain disruptions, regional demand, government interventions (like MSP), weather patterns, and global commodity trends. This dataset helps capture that dynamic by offering cleaned, structured, and standardized price data directly sourced from the official Agmarknet portal — a government-run platform under the Ministry of Agriculture & Farmers Welfare, Government of India. This dataset download from Agmarknet Webside and modify then
DATASET:https://www.agmarknet.gov.in/
Each record in the dataset reflects:

🌾 Dataset Highlights
✅ Time Span:

Daily data for 2 years: from 2023-06-06 to 2025-06-06

✅ Top 5 Indian Crops Covered:

🧅 Onion

🍅 Tomato

🥔 Potato

🌾 Wheat

🍚 Rice

✅ Geographic Coverage:

All 28 states and 8 union territories

Almost all districts with their corresponding mandi (market) names

✅ Price Details (Rs./Quintal):

Min_price: Minimum price for the day

Modal_price: Most commonly traded price

Max_price: Maximum price for the day

💡 Possible Use Cases
Crop price trend analysis

Forecasting and time series modeling

Agri-market insights for farmers & traders

Machine learning and data science training

📁 Column Descriptions
Column : Description
sl_no : Sequential row ID
STATE : Name of the Indian state
District Name : District name
Market Name :Mandi/market location
Commodity : Crop name (onion, wheat, etc.)
Variety : Variety/type of crop
Grade : Grading (e.g., FAQ)
Min_price : Minimum price in Rs./Quintal
Modal_price : Modal price in Rs./Quintal
Max_price : Maximum price in Rs./Quintal
Price Date : Date of price entry (YYYY-MM-DD)

This dataset contains Ontario wheat grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data from January 1, 2025 to September 30, 2025. Data for October 1, 2025 to December 31, 2025 will be added as it becomes available.

The period of the study was characterised by major improvements in financial institutions and transport. The objectives of the study were to see how pricing behaviour changed as a result of this. Since the prices are weekly and by county, it is possible to see how seasonality and regional patterns changed over time. Quantifying the effects of these changes should enable us to see their relative importance in promoting economic growth. The general aim of our research has been to increase our understanding of market integration and its effect on economic performance - particularly in the context of financial markets and commodity markets. Our specific objectives were (i) to create a major dataset of eighteenth and nineteenth century grain prices which can be used within our research project and which could also potentially be useful for other researchers; (ii) to estimate an econometric model to quantify the changes in financial markets, especially the spread of country banks (iii) to estimate an econometric model to measure the effects of transport networks; (iv) to use our results from to estimate the social welfare gains from market integration and to relate the improvements in market integration to the major changes in agricultural practice.

This data set contains Ontario wheat grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data from January 1, 2024 to December 31, 2024.

Graph and download economic data for Global price of Wheat (PWHEAMTUSDM) from Jan 1990 to Jun 2025 about wheat, World, and price.

Data obtained from the National Bureau of Statistics in Abuja, Nigeria on monthly prices for select markets in Kebbi State, Nigeria for January 2000 to December 2016.

China Buyout Price: Avg of Grain with Different Grades: Mixed Wheat data was reported at 118.640 RMB/50 kg in Dec 2024. This records a decrease from the previous number of 120.240 RMB/50 kg for Nov 2024. China Buyout Price: Avg of Grain with Different Grades: Mixed Wheat data is updated monthly, averaging 118.595 RMB/50 kg from Jan 2007 (Median) to Dec 2024, with 216 observations. The data reached an all-time high of 157.010 RMB/50 kg in Dec 2022 and a record low of 72.480 RMB/50 kg in Jul 2007. China Buyout Price: Avg of Grain with Different Grades: Mixed Wheat data remains active status in CEIC and is reported by Price Monitoring Center, NDRC. The data is categorized under China Premium Database’s Price – Table CN.PA: Price Monitoring Center, NDRC: Buyout Price: Agricultural Product.

The goal of this study is to examine the significance of grain prices for economic and social history of the early modern period and its role for the evaluation of socio-economic phenomena. Grain prices in Cologne serve as an example because at least within Germany, it has the most frequently reported data and for a time period of 267 years, weekly data is available. The study is not about the specific economic history of Cologne; based on the Cologne series edited by Ebeling/Irsigler 1976/77 the general significance of grain prices is discussed. Grain prices are also put into a broader framework to acknowledge their meaning. The study contains grain prices from at the earliest 1500 to at the latest 1800 as nominal prices as well as converted in grams of silver. Due to these standards the classification of the territory needs to be limited on the German territory. The previous evaluation of grain price series from Cologne are summarized and critically discussed. The discussion is focused on data source base, how this base was chosen and the relevant institutional features of grain provision in Cologne. The question, in how far grain prices fulfill the required condition to serve as indicators for economic development, can be reconstructed not only looking at studies on development of prices. Especially the importance of grain in alimentation of the early modern period, the consumption of grain and the quantitative restriction of grain demand are essential parts of the analysis. Until long into the 19th century grain prices were influenced by strong political regulations. Therefore it will be analyzed as well which economic policy measures concretely influenced grain prices and in how far these issues are covered in the theoretical economic literature. The transition of the grain market in the middle of the 18th century marks the beginning of the scientific investigation on prices and their history. The analysis of the statistical material and of the underlying sources until the present age can only be undertaken for some basic works, due to the richness of material. Subdivision of the study (list of tables): - Indices of nominal rye prices in Germany (1500 - 1800) - Rye prices in Germany in g silver/ hectoliters (1500 - 1800) Ziel dieser Untersuchung ist es, die Aussagefähigkeit von Getreidepreisen für die Wirtschafts- und Sozialgeschichte der Frühen Neuzeit sowie ihre entscheidende Rolle für die Beurteilung sozioökonomischer Phänomene näher zu beleuchten. Als Beispiel dienen die Kölner Getreidepreise, die zumindest für Deutschland die dichteste Überlieferung aufweisen und für einen Zeitraum von 267 Jahren wöchentliche Umsatzorientierungen enthalten. Dabei geht es nicht um die spezifische Entwicklung innerhalb der Kölner Wirtschaftsgeschichte, sondern anhand der von Ebeling/Irsigler 1976/77 edierten Kölner Reihen werden allgemein die Aussagefähigkeit von Getreidepreisen erörtert. Weiterhin erfolgte deren Einordnung in einen größeren Rahmen, um ihre Stellung zu würdigen. Die Untersuchung enthält Getreidepreise deutscher Städte von frühestens 1500 bis längstens 1800 als Nominalpreis sowie die in Gramm Silber umgerechneten Preise. Aufgrund dieser Vorgaben muss sich eine Einordnung auf den deutschen Raum beschränken. Die bisherige Auswertung der Kölner Getreidepreis- und -Umsatzreihen wird zusammengefasst und kritisch kommentiert, wobei vorrangig nach der Quellengrundlage, ihrem Zustandekommen und den damit verbundenen institutionellen Grundlagen der Kölner Getreideversorgung gefragt wird. Die Frage, inwieweit Getreidepreise die Voraussetzungen erfüllen, um der ihnen zugeschriebenen Rolle als Indikatoren der wirtschaftlichen Entwicklung gerecht zu werden, lässt sich nicht nur anhand des Studiums von Preisverläufen rekonstruieren. Vor allem die Bedeutung des Getreides in der Ernährung der frühen Neuzeit, der Getreideverbrauch und die quantitative Eingrenzung der Getreidenachfrage sind wesentliche Bestandteile dieser Analyse. Der Getreidepreis unterlag bis weit in das 19. Jahrhundert hinein stärksten politischen Reglementierungen, somit wird hier ebenfalls untersucht, welche wirtschaftspolitischen Maßnahmen konkret auf die Getreidepreise einwirkten und inwieweit diese Problematik in der wirtschaftstheoretischen Literatur behandelt wird. Der Mitte des 18. Jahrhunderts erfolgte Wandel in der Einstellung zum Getreidehandel markiert den Beginn der „wissenschaftlichen“ Erforschung der Preise und ihrer Geschichte. Die Untersuchung des statistischen Materials, sowie der grundlegenden Quellen und deren Auswertungen bis in die Gegenwart kann sich im Hinblick auf die Fülle des Materials nur auf die grundlegenden Arbeiten beschränken. Untergliederung der Studie (Tabellenliste): - Indizes der nominalen Roggenpreisen in Deutschland (1500-1800) - Roggenpreise in Deutschland in g Silber / Hektoliter (1500 - 1800)

Note: Updates to this data product are discontinued. This data set provides farmgate and wholesale prices for select organic and conventional fruits and vegetables, wholesale prices for organic and conventional poultry (broilers) and eggs, as well as f.o.b. and spot prices for organic grain and feedstuffs. Prices are based on those reported by USDA Agricultural Marketing Service Market News, Organic Food Business News, and USDA National Agricultural Statistics Service.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

Corn fell to 422.07 USd/BU on October 20, 2025, down 0.10% from the previous day. Over the past month, Corn's price has risen 0.08%, and is up 3.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on October of 2025.

A "spread" can have multiple meanings, but it generally implies a difference between two comparable measures. These can be differences across space, across time, or across anything with a similar attribute. For example, in the stock market, there is a spread between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept.

In this dataset, spread refers to differences in prices between two locations, an origin (e.g., Illinois, Iowa, etc.) and a destination (e.g., Louisiana Gulf, Pacific Northwest, etc.). Mathematically, it is the destination price minus the origin price.

Price spreads are closely linked to transportation. They tend to reflect the costs of moving goods from one point to another, all else constant. Fluctuations in spreads can change the flow of goods (where it may be more profitable to ship to a different location), as well as indicate changes in transportation availability (e.g., disruptions). For more information on how price spreads are linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6).

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain basis (https://agtransport.usda.gov/d/v85y-3hep). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price, and there needs to be both an origin and a destination to have a price spread).

The origin and destination prices come from the grain prices dataset.

This dataset contains Ontario corn grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data from January 1, 2025 to September 30, 2025. Data for October 1, 2025 to December 31, 2025 will be added as it becomes available.

This dataset consists of growth and yield data for each year when maize (Zea mays, L., also known as corn in the United States) was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data.Resources in this dataset:Resource Title: 1989 Bushland, TX, east maize growth and yield data. File Name: 1989_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: This dataset consists of growth and yield data for one of the seasons when maize was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. There are separate spreadsheets for the east (NE and SE) lysimeters and fields, and for the west (NW and SW) lysimeters and fields. The spreadsheets contain tabs for data and corresponding tabs for data dictionaries. Typically there are separate data tabs and corresponding dictionaries for plant growth during the season, crop growth stage, plant population, manual harvest from replicate plots in each field and from lysimeter surfaces, and machine (combine) harvest, An Introduction tab explains the tab names and contents, lists the authors, explains conventions, and lists some relevant references.Resource Title: 1990 Bushland, TX, east maize growth and yield data. File Name: 1990_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1990 East.Resource Title: 1994 Bushland, TX, east maize growth and yield data. File Name: 1994_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 East.Resource Title: 1994 Bushland, TX, west maize growth and yield data. File Name: 1994_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 West.Resource Title: 2013 Bushland, TX, west maize growth and yield data. File Name: 2013_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 West.Resource Title: 2016 Bushland, TX, east maize growth and yield data. File Name: 2016_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 East.Resource Title: 2016 Bushland, TX, west maize growth and yield data. File Name: 2016_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 West.Resource Title: 2018 Bushland, TX, west maize growth and yield data. File Name: 2018_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 West.Resource Title: 2013 Bushland, TX, east maize growth and yield data. File Name: 2013_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 East.Resource Title: 2018 Bushland, TX, east maize growth and yield data. File Name: 2018_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 East.

This data product contains statistics on wheat-including the five classes of wheat: hard red winter, hard red spring, soft red winter, white, and durum-and rye. Includes data published in the monthly Wheat Outlook and previously annual Wheat Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series. Most data are on a marketing year basis, but some are calendar year.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

Taiwan Farm Price: Coarse Grain & Special Crop: Sweet Corn data was reported at 27.020 NTD/kg in Oct 2018. This records an increase from the previous number of 26.510 NTD/kg for Sep 2018. Taiwan Farm Price: Coarse Grain & Special Crop: Sweet Corn data is updated monthly, averaging 13.740 NTD/kg from Jan 2003 (Median) to Oct 2018, with 190 observations. The data reached an all-time high of 55.270 NTD/kg in Nov 2016 and a record low of 5.460 NTD/kg in Sep 2003. Taiwan Farm Price: Coarse Grain & Special Crop: Sweet Corn data remains active status in CEIC and is reported by Council of Agriculture, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.P002: Farm Price: Coarse Grain and Special Crop.

Table 1: Grain Transport Cost Indicators

This data set contains Ontario feed grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.This dataset includes data from January 1, 2024 to December 31, 2024.