Corn fell to 383.01 USd/BU on August 8, 2025, down 0.39% from the previous day. Over the past month, Corn's price has fallen 4.07%, and is down 3.04% 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 August of 2025.

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.

Wheat traded flat at 514.50 USd/Bu on August 11, 2025. Over the past month, Wheat's price has fallen 4.99%, and is down 4.15% 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 August of 2025.

Soybeans fell to 967.25 USd/Bu on August 8, 2025, down 0.46% from the previous day. Over the past month, Soybeans's price has fallen 4.14%, and is down 3.74% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Soybeans - values, historical data, forecasts and news - updated on August of 2025.

SNAP (Soil Nutrient Assessment Program), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that provides an estimate of plant-available nutrients that the soil naturally provides. Soil test fertilizer recommendations have long been predicated upon response curves generated from fertility trials across the country. These response curves have been compared to relative yield which provide probability ranges for a response to varying fertilizer inputs. Category responses include very low, low, adequate, high or very high inversely related to probability of a response to various inputs of nitrogen, phosphate, and potassium (N, P, and K). New soil test methods, increases in computing power and access to the internet have enabled development of an interactive tool that is based on plant available NPK from both the inorganic fraction and organic pool of the soil. The new methods provide an estimate of plant available nutrients that the soil naturally provides, which has largely been ignored for decades. Since we have access to large datasets we can calculate the amounts of NPK required growing crops in lbs NPK per bu of the desired crop. For example, it requires 100 lbs of N, 50 lbs P2O5, 50 lbs K2O to grow 100 bu corn. These are the base numbers from which we subtract the soil test data after converting from the analytical ppm to Lbs P2O5 or lbs K2O. This is a straight subtraction. It also eliminates the need for "calibration data" since the soil tests reflect the soils inherent fertility. Using the example above, of 100, 50, 50 of N, P, and K required and soil test results of 25, 35, 45 then the fertilizer needed would be 75 N, 15 P2O5 and 5 K2O. This is a simple approach that doesn't get lost in relative yield-crop response curves that have been used for decades from differing geographical areas. This tool will include current fertilizer prices, soil test inputs, and crop based county averages for the last 15 years that will predict the chances of making the yield goal the user inputs compared to historical yield data for their county and calculate the fertilizer cost with and without soil testing compared to user input yield goal and county average. This tool will allow the user via the internet to produce a more straightforward approach to realistically planning next year's fertilizer inputs and associated cost. It will also show the benefits of soil testing for increased fertilizer efficiency and reduced environmental impact. Resources in this dataset:Resource Title: Website Pointer to SNAP - Soil Nutrient Assessment Program. File Name: Web Page, url: https://snap.brc.tamus.edu/Home/Index The web dashboard interface for estimating local yield based on field location (state/county), crop (, area, and yield goal; and soil NPK test results (lb/acre), Results returned illustrate local yield, fertilizer cost/acre, fertilizer needed (lb/acre), and overall chance of success (%).

The Future Farm Project was established to re-examine and improve the way in which soil and crop sensors, supplemented by other sources of useful/available data, are used to inform decisions about input management and to provide a way of automating the process from data acquisition, through analysis, to the formulation and implementation of decision options. In particular, using nitrogen (N) fertilizer management as a ‘use-case’, the project sought to enable enhanced grower confidence in N decision making through the adaptive generation of site-specific management models. A key element of these is their increased and improved use of in-season field monitored data (soil, crop, climatic), historic on-farm data, external public and private data and automation of decision rules in software that may potentially be linked to real-time application equipment. This was considered important given the pre-project perception that a lack of farmer confidence in precision agriculture-based decision making was constraining adoption of precision agriculture (PA) approaches to management of grains-based farming systems. This lack of adoption was in spite of the potential of PA approaches as a counter to farm labour shortages, the need to optimise resource use efficiency as a means of maintaining or enhancing farm profitability and the finding through an exhaustive modelling exercise, that the error associated with prediction of N fertilizer requirement based on expected yield was of the order of 50 kg N/ha. Future Farm was co-funded by GRDC and involved CSIRO (as lead research organisation) along with the University of Sydney, University of Southern Queensland, Queensland University of Technology and Agriculture Victoria. The project made us of both 'core' and 'satellite' field sites across the major grain-growing regions; core sites were the major project resource, whereas satellite sites were those where we collaborated opportunistically with farmers running their own strip trials. The dataset comprises data collected in-field at these various sites (using soil and crop analysis or through the use of proximal crop or soil sensors) or acquired through remote sensing or from publicly available sources (eg weather data, soil information systems); historical data were also acquired. It also includes data gathered through the use of yield monitors and protein sensors on the farmers' harvesters. For the latter reason along with other privacy issues, access to the dataset is restricted. Further information about Future Farm is available at https://grdc.com.au/resources-and-publications/grdc-update-papers/tab-content/grdc-update-papers/2022/02/better-targeted,-more-precise-fertiliser-decisions-as-a-counter-to-rising-fertiliser-prices-focussing-on-3-of-the-6-rs and on other relevant GRDC webpages. Code is available at: https://bitbucket.csiro.au/projects/FUTUREFARM

Supplementary files for article "Spatial inequality in prices and wages within a late-developing economy: Serbia, 1863–1910"Serbia emerged as a small independent nation-state in the economic periphery of nineteenth-century Europe. This article leverages uniquely abundant town-level data to examine spatial inequality in prices and wages within this late-developing economy. I first build a new dataset on prices of traded and household goods, and wages of skilled and unskilled workers for a panel of 42 urban settlements in Serbia in the period from 1863 to 1910. I apply the welfare ratio approach to calculate real wages of day labourers and masons. Second, I find strong spatial convergence in grain prices and costs of living, but divergence in wages, both nominal and real. Lastly, I investigate the determinants of price convergence and wage divergence with panel-data models. The results suggest that falling transport costs decreased price gaps between locations, whereas rising population differences increased inter-urban wage gaps.© The Authors, CC BY 4.0

Rice rose to 12.74 USD/cwt on August 8, 2025, up 0.28% from the previous day. Over the past month, Rice's price has fallen 3.08%, and is down 14.16% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rice - values, historical data, forecasts and news - updated on August of 2025.

Examing the impact of China-US trade friction on China’s grain import resilience can provide valuable insights for China and other developing countries in achieving food security. On the basis of defining the theoretical connotation of China’s grain import resilience, this study employs data from 2013 to 2023 and applies a difference-in-differences (DID) approach to quantitatively analyzes the impact of China US trade frictions on China’s grain import resilience. The study shows that the trade friction between China and the United States has an obvious negative impact on the resilience of China’s grain imports. During the U.S.-China trade friction period, China’s grain import prices rose, price volatility increased, import volume declined, import tempo changed, and the degree of import diversification declined. This shows that China’s grain import resilience, recovery capacity and transformation and upgrading capacity need to be improved. Developing countries can enhance their food supply capacity by adopting measures such as diversifying import sources, expanding domestic production, promoting regional cooperation, and establishing strategic reserve systems. Specific strategies may include actively cultivating new trade partnerships, strengthening irrigation and water conservancy infrastructure, and modernizing grain storage facilities.

This data collection includes experimental and modelling data collected from the Southern Farming Systems Project (SFS) in NSW. The aim of the project was to improve farming systems efficiency in Southern NSW by exploring strategies like increased diversity with legumes, different nitrogen (N) fertilisation strategies, and early sowing for dual-purpose crops versus grain-only systems. The data was collected over 6-years at each of four sites, Wagga Wagga, Greenethorpe, Urana, and Condobolin from 2018 to 2023. The different rotational strategies implemented at each site were fully phased and the data provided describes: the environment, crop management including herbicide, pesticide and fertiliser applications, yield, change in soil water and soil nitrogen, grain quality and system economics. The experimental datasets include measurements of crop establishment, crop production (grain yield, yield components and grain quality), soil water and mineral nitrogen (measured down to 2 m depth at both pre-sowing and post-harvest each year), record of sowing and agrochemicals application (herbicide, pesticide, fungicide and synthetic fertiliser), biotic stress (weed and disease pressure), system economics (variable costs, grain prices and gross margin); grazing (for early-sowing grazed systems only); site characterisation and daily weather data. The modelling datasets include two sets of modelling using APSIM at all four sites, one set was 66-years (1959-2023) long-term modelling of a range of alternative farming systems vs. a baseline at each site and the other set was annual modelling of the individual experimental plots during 2018-2023.

Lineage: Data was collected from field experiments as part of the Southern Farming Systems (SFS) project in NSW. Modelling data was simulated using the Agricultural production systems sIMulator APSIM-Classic version 7.10 Build r4221 build date 14-Feb-2024. https://www.apsim.info

Ethanol rose to 1.78 USD/Gal on August 8, 2025, up 0.42% from the previous day. Over the past month, Ethanol's price has risen 2.74%, and is up 0.14% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Ethanol - values, historical data, forecasts and news - updated on August of 2025.

Oils Price Index in World increased to 166.80 Index Points in July from 155.70 Index Points in June of 2025. This dataset includes a chart with historical data for World Oils Price Index.