Purchase Order commodity line level detail for City of Austin Commodities/Goods purchases dating back to October 1st, 2009. Each line includes the NIGP Commodity Code/COA Inventory Code, commodity description, quantity, unit of measure, unit price, total amount, referenced Master Agreement if applicable, the contract name, purchase order, award date, and vendor information. The data contained in this data set is for informational purposes only. Certain Austin Energy transactions have been excluded as competitive matters under Texas Government Code Section 552.133 and City Council Resolution 20051201-002.

Weekly Commodity Prices are made up of four excel spreadsheets and graphs split into commodity groups. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Commodity Price Movements

If you require datasets in a more accessible format, please contact prices@defra.gsi.gov.uk.

The Producer Price Index (PPI) is a family of indexes that measures the average change over time in selling prices received by domestic producers of goods and services. PPIs measure price change from the perspective of the seller. This contrasts with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's perspective. Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution costs. There are three main PPI classification structures which draw from the same pool of price information provided to the BLS by cooperating company reporters: Industry classification. A Producer Price Index for an industry is a measure of changes in prices received for the industry's output sold outside the industry (that is, its net output). The PPI publishes approximately 535 industry price indexes in combination with over 4,000 specific product line and product category sub-indexes, as well as, roughly 500 indexes for groupings of industries. North American Industry Classification System (NAICS) index codes provide comparability with a wide assortment of industry-based data for other economic programs, including productivity, production, employment, wages, and earnings. Commodity classification. The commodity classification structure of the PPI organizes products and services by similarity or material composition, regardless of the industry classification of the producing establishment. This system is unique to the PPI and does not match any other standard coding structure. In all, PPI publishes more than 3,700 commodity price indexes for goods and about 800 for services (seasonally adjusted and not seasonally adjusted), organized by product, service, and end use. Commodity-based Final Demand-Intermediate Demand (FD-ID) System. Commodity-based FD-ID price indexes regroup commodity indexes for goods, services, and construction at the subproduct class (six-digit) level, according to the type of buyer and the amount of physical processing or assembling the products have undergone. The PPI publishes over 600 FD-ID indexes (seasonally adjusted and not seasonally adjusted) measuring price change for goods, services, and construction sold to final demand and to intermediate demand. The FD-ID system replaced the PPI stage-of-processing (SOP) system as PPI's primary aggregation model with the release of data for January 2014. The FD-ID system expands coverage in its aggregate measures beyond that of the SOP system by incorporating indexes for services, construction, exports, and government purchases. For more information, visit: https://www.bls.gov/ppi

Daily market prices of agricultural commodities across India from 2001-2025. Contains 75+ million records covering 374 unique commodities and 1,504 varieties from various mandis (wholesale markets). Commodity Like: Vegetables, Fruits, Grains, Spices, etc.

Cleaned, deduplicated, and sorted by date and commodity for analysis.

Column Schema

Data sourced from the Government of India's Open Data Platform.

License: Government Open Data License - India (GODL-India) https://www.data.gov.in/Godl

The import and export price index (in U.S. dollars) of Taiwan by sector includes classification indices of agricultural products, processed agricultural products, and industrial products.

Agmarknet India Commodity Prices (October 2024 – August 2025)

🧩 Overview

This dataset contains daily agricultural commodity price data scraped from the Agmarknet (Government of India) portal for the period October 2024 to August 2025.
It provides granular market-level data across multiple states, including information on commodity, variety, grade, and minimum, maximum, and modal prices (in Rs./Quintal).

With over 1.1 million records, this dataset offers valuable insights into agricultural price fluctuations and regional market dynamics in India.

📅 Time Period

October 2024 – August 2025

📊 Dataset Columns Description

🌾 Data Source

Data has been scraped from the official Agmarknet portal maintained by the Directorate of Marketing & Inspection (DMI) under the Ministry of Agriculture and Farmers Welfare, Government of India.
👉 https://agmarknet.gov.in/

📈 Possible Use Cases

• Agricultural price forecasting using machine learning
• Commodity-wise seasonal and regional trend analysis
• Correlation between price volatility and climatic or policy factors
• Decision support for farmers and agri-businesses
• Market intelligence and demand-supply analysis

🧮 Dataset Statistics

• Records: 1,118,899
• Features: 11
• Coverage: 8 Indian states and 1160 markets
• Frequency: Daily

⚖️ License

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
You are free to use, share, and adapt this dataset for any purpose, provided that you give appropriate credit and share derivatives under the same license.

📢 Citation

If you use this dataset in your research, please cite it as:

Anish (2025). Agmarknet India Commodity Prices (October 2024 – August 2025).
Retrieved from https://agmarknet.gov.in/

🧠 Acknowledgments

Special thanks to the Ministry of Agriculture & Farmers Welfare, Government of India, for maintaining open access to Agmarknet data, which enables valuable research and innovation in agricultural analytics.

This table contains 5 series, with data for years 1972 - 2010 (not all combinations necessarily have data for all years), and was last released on 2010-05-12. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Commodity (5 items: Total; all commodities; Food; Total excluding energy; Energy ...).

This paper attempts to describe and explain the long-term evolution of wage inequality in imperial China, covering over two millennia from the Han dynasty to the Qing dynasty (202 BCE-1912 CE). Based on historical government records of official salaries, commodity prices, and agricultural productivity, we convert various forms of salaries to equivalent rice volumes and comparable salary benchmarks. Wage inequality is measured by salary ratios and (partial) Gini coefficients between official and peasant classes as well as within the official class. The inter-class wage inequality features an “inverted U-u” pattern—first rose before the Tang dynasty and then declined afterwards (the “inverted U” trends) with “inverted u” dynastic cycles. The intra-class wage inequality has a secular decline trend. We propose a unified framework incorporating technological, institutional, political, and social (TIPS) mechanisms to explain both long-term and short-term patterns. It is concluded that the technological mechanism dominated the rise of wage inequality, while the political mechanism (emperor-bureaucracy power tensions) drove the decline.

This dataset shows the Import price index (2010=100) by commodity sections (SITC 4), 2010 - 2015 (Jan-Dec), Malaysia (Monthly)FootnoteStarting 2016, the import price index (2010 = 100) by commodity section (SITC 4) is no longer published.Weight: Sections WeightTotal 100.00Food 5.52Beverages & Tobacco 0.40Crude, Materials, Inedible 3.67Mineral Fuels, Lubricants,etc. 10.47Animal & Vegetables Oils & Fats 1.41Chemicals 8.31Manufactured Goods 11.46Machinery & Transport Equipment 51.60Miscellaneous Manufactured 5.24Miscellaneous Transactions & Commodities 1.92Source : Department of Statistics, Malaysia

International merchandise trade price and volume indexes, grouped by North American Product Classification System (NAPCS) section. Users have the option of selecting Imports or Exports, as well as Paasche or Laspeyres price indexes, or Laspeyres volume indexes. Data are unadjusted and seasonally adjusted, on a Customs and Balance of Payments basis, at an annual frequency.

The Canada Gazette is the official newspaper of the Government of Canada. Learn about new statutes, new and proposed regulations, administrative board decisions and public notices. To enhance transparency and accessibility, Global Affairs Canada (GAC) has proactively published relevant content to the Open Government Portal, making these records easier to find, search, and reuse by the public, researchers, and civil society. Please note, any releases after 2024-11-04 have not been published. For official records or any inquiries related to these notices, please consult the Canada Gazette website - https://gazette.gc.ca/

The data shows grain prices at select inland origin points and export destination ports and the price spread between them. More specifically, this dataset compares interior prices of corn in Illinois and Nebraska with the Gulf; Iowa and Gulf soybean prices; Kansas and Gulf hard red winter wheat; and North Dakota and Portland hard red spring wheat.

This dataset shows the Export price index (2010=100) by commodity section (SITC 4), 2005 - 2015 Malaysia (Annual). Footnote Starting 2016, the export price index (2010 = 100) by commodity section (SITC 4) is no longer published. Weight: Sections Weight Total 100.00 Food 2.46 Beverages & Tobacco 0.40 Crude, Materials, Inedible 2.91 Mineral Fuels, Lubricants,etc. 16.24 Animal & Vegetables Oils & Fats 8.61 Chemicals 6.14 Manufactured Goods 8.17 Machinery & Transport Equipment 44.88 Miscellaneous Manufactured 9.61 Miscellaneous Transactions & Commodities 0.59 Source : Department of Statistics, Malaysia No. of Views : 299

Monthly index time series statistics on the classification of consumer price commodities in Taipei City

USDA Economic Research Service (ERS) compares prices paid by consumers for food with prices received by farmers for corresponding commodities. This data set reports these comparisons for a variety of foods sold through retail food stores such as supermarkets and super centers. Comparisons are made for individual foods and groupings of individual foods-market baskets-that represent what a typical U.S. household buys at retail in a year. The retail costs of these baskets are compared with the money received by farmers for a corresponding basket of agricultural commodities.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.

This table contains 7 series, with data starting from 1972 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Commodity (7 items: Total; all commodities; Metals and Minerals; Energy; Total excluding energy ...).

This data product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat).

Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat

Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.)

This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page.

Spreadsheet Models

For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for:

1. the national-level season-average price received by farmers and
2. the implied counter-cyclical payment rate.

Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.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: Webpage with links to Excel files For complete information, please visit https://data.gov.

To accomplish our goal of the research, we selected Tangail district as our study population and draw a sample size for data collection from government and non-government employees using questionnaires and gathered primary data from 250 residents of Tangail. After a careful literature review, employees’ job status selected as dependent variable and the key economic indicators, such as inflation rates, consumer spending patterns, wage trends, seeking additional sources of income, employees' mental health, involvement in collective bargaining or labor action, and optimism about long-term prospects referred as independent variables.