United States Agricultural Price Index: Received by Farmers: Food Commodities data was reported at 88.800 2011=100 in Oct 2018. This records a decrease from the previous number of 90.600 2011=100 for Sep 2018. United States Agricultural Price Index: Received by Farmers: Food Commodities data is updated monthly, averaging 101.000 2011=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 126.000 2011=100 in Apr 2014 and a record low of 81.000 2011=100 in Feb 2010. United States Agricultural Price Index: Received by Farmers: Food Commodities data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

Graph and download economic data for Export Price Index (End Use): Agricultural Commodities (IQAG) from Mar 1985 to May 2025 about end use, agriculture, exports, commodities, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Farm Products (WPU01) from Jan 1913 to May 2025 about agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

This dataset aggregates daily wholesale price data for a wide spectrum of agricultural commodities traded across India’s regulated markets (mandis). It captures minimum, maximum, and modal prices, enabling detailed analysis of price dispersion and volatility over time. Data is sourced directly from the AGMARKNET portal and made available under the National Data Sharing and Accessibility Policy (NDSAP). With over 165,000 views and nearly 400,000 downloads, it’s a cornerstone resource for economists, agronomists, and data scientists studying India’s commodity markets.

This dataset provides daily wholesale minimum, maximum, and modal prices for a wide variety of agricultural commodities across India’s mandis, sourced from the AGMARKNET portal and published on Data.gov.in under NDSAP, with records dating back to 2013 and updated as of 19 May 2025 via a REST API; it includes key fields like Arrival_Date, State, District, Market, Commodity, Variety, Min_Price, Max_Price, and Modal_Price, making it ideal for time-series analysis, price-trend visualizations, and commodity forecasting.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Breaker Stock and Checks and Undergrades (WPU017108) from Dec 1991 to May 2025 about checkable, stocks, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

Argentina Commodities Prices Index: USD: Agriculture data was reported at 163.412 Dec2001=100 in Oct 2018. This records an increase from the previous number of 159.657 Dec2001=100 for Sep 2018. Argentina Commodities Prices Index: USD: Agriculture data is updated monthly, averaging 163.687 Dec2001=100 from Jan 1997 (Median) to Oct 2018, with 262 observations. The data reached an all-time high of 314.574 Dec2001=100 in Aug 2012 and a record low of 89.400 Dec2001=100 in Jul 1999. Argentina Commodities Prices Index: USD: Agriculture data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.I032: Commodities Prices Index.

Agricultural Producer Prices (APP) are prices received by farmers for their produce at the farm gate; i.e. at the point where the commodity leaves the farm. APP do not cover the costs after the farm gate; e.g. transportation cost from the farm gate to the nearest market or first point of sale, warehousing costs, processing costs and market charges (if any) for selling the produce. FAOSTAT gives free access to food and agriculture data for over 245 countries and territories and covers all FAO regional groupings from 1961 to the most recent year available. Annual APP data are provided from 1991 to the previous year for over 160 countries and about 200 commodities, and monthly APP data are provided from January 2010 to December of the previous year for over 60 countries and about 200 commodities. APP are used, along with production data, to estimate value of production for a country, by commodity and in total, as well as Producer Price Indexes, which measure price inflation. APP also enable analysts to analyse price transmission and volatility.

Succeed in the agricultural commodities market place with LSEG's Agriculture Data, including global cash price data, agriculture flows, and more.

Nepal Wholesale Price Index: Annual: Agricultural Commodities (AC) data was reported at 385.100 1999-2000=100 in 2018. This records a decrease from the previous number of 386.100 1999-2000=100 for 2017. Nepal Wholesale Price Index: Annual: Agricultural Commodities (AC) data is updated yearly, averaging 202.000 1999-2000=100 from Jul 2001 (Median) to 2018, with 18 observations. The data reached an all-time high of 386.100 1999-2000=100 in 2017 and a record low of 98.200 1999-2000=100 in 2001. Nepal Wholesale Price Index: Annual: Agricultural Commodities (AC) data remains active status in CEIC and is reported by Nepal Rastra Bank. The data is categorized under Global Database’s Nepal – Table NP.I0032: Wholesale Price Index: 1999-2000=100: Annual.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Golden Delicious Apples (WPU01110211) from Dec 1991 to May 2025 about agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

As an essential part of daily life, the drastic fluctuations in agricultural commodity prices significantly impact producers’ motivation and consumers’ quality of life, further exacerbating market uncertainty and unsustainability. The ability to scientifically and effectively predict agricultural commodity prices is of great significance for the rational deployment of market mechanisms, the timely adjustment of supply chains, and the promotion of food policy adjustments. This paper proposes a sustainable hybrid model SV-PSO-BiLSTM which integrates Seasonal-Trend decomposition procedure based on Loess (STL), Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. This innovative approach first performs seasonal decomposition of the original data using the STL method, then applies the VMD method for double decomposition of the residual components, reconstructs the data based on sample entropy, and finally predicts agricultural commodity market prices using the BiLSTM network model optimized by the PSO algorithm. This paper investigates the market price dynamics of four agricultural commodities (chili, garlic, ginger, and pork) and one agricultural financial derivative (soybean futures). The experimental results indicate that the proposed SV-PSO-BiLSTM hybrid model achieves average values of 0.2241 for root mean square error (RMSE), 0.1665 for mean absolute error (MAE), 0.0207 for mean absolute percentage error (MAPE), and 0.9851 for the coefficient of determination (R2). These results surpass those of other comparative models, demonstrating stronger generalization, reliability, and stability. The research findings can provide effective guidance for the reasonable regulation of agricultural commodity market prices and further promote the healthy and sustainable development of the agricultural commodity industry.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Grapefruits (WPU01110101) from Jan 1947 to May 2025 about agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

The World Bank’s Commodity Price historical data and forecasts are published quarterly, in January, April, July and October. The price forecasts go up to 2030. Topics: Agriculture & Rural Development

Global Agricultural Commodity market size is expected to reach $293.91 billion by 2029 at 5.8%, segmented as by soybeans, non-gmo soybeans, gmo soybeans

Agricultural price shocks strongly affect farmers' income and food security. It is therefore important to understand and anticipate their origins and occurrence, particularly for the world's main agricultural commodities. In this study, we assess the impacts of yearly variations in regional maize productions and yields on global maize prices using several statistical and machine-learning (ML) methods. Our results show that, of all regions considered, Northern America is by far the most influential. More specifically, our models reveal that a yearly yield gain of +8% in Northern America negatively impacts the global maize price by about –7%, while a decrease of –0.1% is expected to increase global maize price by more than +7%. Our classification models show that a small decrease in the maize yield in Northern America can inflate the probability of maize price increase on the global scale. The maize productions in the other regions have a much lower influence on the global price. Among the tested methods, random forest and gradient boosting perform better than linear models. Our results highlight the interest of ML in analyzing global prices of major commodities and reveal the strong sensitivity of maize prices to small variations of maize production in Northern America.

This dataset contains historical data on chili prices and weather conditions in Kota Singkawang. It includes monthly records of various chili prices, shallot and garlic prices, rainfall levels, number of rainy days, and inflation rates. This dataset is a cleaned and merged version of several publicly available datasets from Statistics Indonesia (BPS). See the attached README file for detailed sources and descriptions.

This Data is associated to the paper "PREDICTION OF FOOD COMMODITY PRICES IN KOTA SINGKAWANG USING MACHINE LEARNING: A COMPARATIVE STUDY OF RANDOM FOREST, LINEAR REGRESSION, AND XGBOOST" by Lestari, D. , Bangun, E., Gaol, F. and Matsuo, T.

https://data.apps.fao.org/catalog/dataset/3392bf08-35ed-4354-b5dd-a22707ffe4d1/resource/342d547e-93d1-487a-a2ca-7d1b5900c41c/download/market-prices.jpeg" alt="">

Agricultural commodity prices are an indicator of changes in supply and demand, and as such, can detect abnormal conditions that need to be brought to attention. Price monitoring supports well-functioning international and national markets through the provision of timely and transparent market information, and constitutes a basis for evidence-based decision making and food security strategies. Past price volatility events have put in evidence the value of timely market information and analysis in order to mitigate the negative effects on low-income groups of population whose expenditure on food represents a large proportion of their total expenses. FAO plays a key role in monitoring, analysing and disseminating food price data along the food supply chain, from producer to consumer through both domestic as well as international markets.

The Over-the-Counter (OTC) Agricultural Product Trading Platform market is experiencing robust growth, driven by increasing demand for efficient and transparent trading solutions within the agricultural sector. This market is characterized by a complex network of buyers and sellers engaging in bilateral transactions outside of regulated exchanges. While precise market size data for 2025 is unavailable, based on industry reports and comparable market segments showing growth rates in the 5-10% range, a reasonable estimation for the 2025 market size would be in the range of $500-700 million USD. Let's assume a conservative estimate of $600 million for this analysis. Considering a Compound Annual Growth Rate (CAGR) of approximately 7% (a reasonable estimate given the inherent volatility of agricultural markets and technological advancements driving efficiency), the market is projected to reach a value of approximately $1 billion USD by 2033. Key drivers include the increasing adoption of digital platforms for trading, the need to manage price volatility, and the growing importance of supply chain optimization across the agricultural value chain. Trends such as the rise of blockchain technology for enhanced security and traceability, alongside the increasing use of data analytics for informed trading decisions, are shaping the market's future. However, several restraints also exist, including regulatory complexities varying across regions, cybersecurity concerns associated with digital platforms, and the potential for market manipulation. The competitive landscape is highly fragmented, with various global players vying for market share. Companies like GAIN Global Markets Inc., AxiTrader Limited, LMAX Global, IG Group, and others are key players in this market, offering diverse trading solutions tailored to different agricultural commodities and client needs. Segment analysis requires more granular data, but likely categories include commodity type (e.g., grains, coffee, livestock), platform type (e.g., web-based, mobile), and geographic region. Future growth will depend on addressing these restraints, embracing technological advancements, and developing robust regulatory frameworks that promote fair and transparent trading practices.