Graph and download economic data for Producer Price Index by Commodity: Processed Foods and Feeds: Other Soybean Products, Including Isolates and Concentrates (WPU029201122) from Dec 2009 to Sep 2025 about processed, food, commodities, PPI, inflation, price index, indexes, price, and USA.

Headlines of Corn and Soybean

Headlines extracted from http://soybeansandcorn.com/

Monthly Average - Chicago Board of Trade (CBOT)

https://www.macrotrends.net/2531/soybean-prices-historical-chart-data https://www.macrotrends.net/2532/corn-prices-historical-chart-data

Content

The data source of time-series were obtained from the World Agricultural Supply and Demand Estimates (WAOB) from the United States Department of Agriculture (USDA) website.

The period ranges from January 2014 to December 2020. The attributes represent several time-series features, such as planted area, harvested area, yield, beginning stocks, imports, supply, demand (World), and other estimates from countries with the most significant corn and soybean production

The CBOT is a designated contract maker of the CME Group for the future exchange where agricultural commodity contracts are traded, and the prices charged at CBOT are a benchmark in worldwide prices.

We use the textual data extracted from the website Soybean & Corn Advisor. Since 2009, the website has provided daily news and information on soybean and corn production related to the South American growth cycles, climate, infrastructure, land use, ethanol, and alternative fuel production.

Files: - All_Headlines: All headlines from 2014 to 2020 on the website (http://soybeansandcorn.com). - All_News_Corn_Soybean: All headlines and News from 2014 to 2020 on the website (http://soybeansandcorn.com). - Headlines_Corn/Soybean: headlines that have the word corn/soybean. Label2: 0 is neutral or downtrend; 1 is uptrend. Label3: -1 is downtrend ; 0 neutral; 1 uptrend. A variation of 1% of the monthly average was used for the attribution of labels. - USDA Corn/soybean: reports WASDE; - prices_historical_corn/soybean: historical value from CBOT. Source: Macrotrends.

Acknowledgements

The WASDE data is extracted from (public domain) monthly reports produced by the U. S. Department of Agriculture. The Corn and Soybean Advisor website for offering daily textual content. The macrotends by offering historical value

Inspiration

This data set was designed for forecasting tasks that consider textual data.

Basis reflects both local and global supply and demand forces. It is calculated as the difference between the local cash price and the futures price. It affects when and where many grain producers and shippers buy and sell grain. Many factors affect basis—such as local supplies, storage and transportation availability, and global demand—and they interact in complex ways. How changes in basis manifest in transportation is likewise complex and not always direct. For instance, an increase in current demand will drive cash prices up relative to future prices, and increase basis. At the same time, grain will enter the transportation system to fulfill that demand. However, grain supplies also affect basis, but will have the opposite effect on transportation. During harvest, the increase in the supply of grain pushes down cash prices relative to futures prices, and basis weakens, but the demand for transportation increases to move the supplies.

For more information on how basis is linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6), and links below for research on the topic.

This data has corn, soybean, and wheat basis 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.

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) 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).

The cash price comes from the grain prices dataset and the futures price comes from the appropriate futures market, which is Chicago Board of Trade (CME Group) for corn, soybeans, and soft red winter wheat; Kansas City Board of Trade (CME Group) for hard red winter wheat; and the Minneapolis Grain Exchange for hard red spring wheat.

The purpose of this study was to establish best ways of improving household soybean processing and utilization in selected districts in the Eastern Province of Zambia. This was a concurrent triangulation study design, nested with a cross sectional survey and barrier analysis. Up to 1,237 households and 42 key informants participated in the quantitative and qualitative studies respectively. Quantitative data was analysed using Stata MP 15 software (StataCorp, College Station, TX, USA). NVIVO QSR10 software (QSRInt, Melbourne Australia) was used to organize qualitative data which was later analysed thematically. In this study whole soybean processing and utilization in eastern province was at 48%. However, accessibility to soybean for household consumption throughout the year was negligible (0.29%). Based on the food systems an interplay of factors influenced soybean processing and utilization. In the food environment, a ready-made Textured Soya Protein mainly imported [1,030/1237(83%)] and a milled whole soybean-maize blend AOR 816.37; 95%CI 110.83 to 6013.31 were preferred. Reports of labour intensity, hard to cook properties, coarse milling and beany flavour with associated anti-nutrients negatively influenced whole soybean utilization. In the enabling environment, soybean production AOR 4.47; 95%CI 2.82 to 7.08 increased the chances of utilization. Lack of inputs, poor access to affordable credit and lack of ingredients were deleterious to utilization. Low coverage of existing projects and poor access to technologies were other adverse factors. Among the Socioeconomic factors, a higher social hierarchy shown by owning a bed AOR 1.75; 95%CI 1.22 to 2.49, belonging to the Chewa community AOR 1.16; 95%CI 1.08 to 0 1.25, gender of household head particularly male AOR 1.94; 95%CI 1.21 to 3.13, off farm income and livestock ownership were supportive to soybean utilization. Unfavourable factors were; belonging to any of the districts under study AOR 0.76; 95%CI 0.58 to 0.98, lack of knowledge (55.65%), low involvement of the male folks AOR 0.47; 95%CI 0.30 to 0.73 and belonging to a female headed household AOR 1.94; 95%CI 1.21 to 3.13. Age, time and household size constraints as well as unreliable soybean output markets, lack of land, poor soils in some wards and poor soybean value chain governance were other negative factors. Immediately in the food environment there is need to boost milling of whole soybean while strengthening cooking demonstrations, correct processing, incorporation of soybean in the local dishes and conducting acceptability tests. In the enabling environment, there should be access to inputs, affordable credit facilities and subsidized mineral fertilisers. Post-harvest storage, collective action with full scale community involvement and ownership should be heightened. Socioeconomic approaches should target promotion of soybean processing and utilization among all ethnic groups, participation of male folks and female headed households as well as advocating for increased nutrition sensitive social protection. In the medium or long term, capacity building, market development, import substitution agreements, creation of new products, development of cottage industries, information exchange and inter district trade as well as more public-private partnerships and more local private sector players should be bolstered. Lastly farm diversification should be supported.