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

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.

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.

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.

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.

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.

The global commodity trading services market is experiencing robust growth, driven by increasing globalization, fluctuating commodity prices, and the need for efficient supply chain management. The market size in 2025 is estimated at $2 trillion, exhibiting a Compound Annual Growth Rate (CAGR) of 6% between 2025 and 2033. This growth is fueled by several key factors. Firstly, the rising demand for raw materials across various sectors, including metals, energy, and agriculture, is creating lucrative opportunities for commodity trading firms. Secondly, technological advancements in areas like data analytics and blockchain technology are improving transparency, efficiency, and risk management within commodity trading, further stimulating market expansion. Finally, the increasing complexity of global supply chains necessitates the expertise of specialized commodity traders to navigate market volatility and ensure secure and timely delivery of goods. The market is segmented by commodity type (metals, energy, agricultural, and others) and by the size of the businesses served (large enterprises and SMEs). While large enterprises dominate the market currently, the SME segment shows strong potential for future growth as businesses increasingly rely on external expertise for commodity sourcing. The geographical distribution of the commodity trading services market is diverse, with North America, Europe, and Asia Pacific representing the major regions. However, emerging markets in Asia and Africa are showing significant growth potential due to rapid industrialization and rising consumer demand. Competitive pressures within the industry are high, with numerous large multinational corporations vying for market share. These companies, including Vitol, Glencore, Trafigura, Mercuria, and Cargill, possess extensive global networks, strong financial capabilities, and deep expertise in risk management, allowing them to dominate the market. Nevertheless, smaller, specialized trading firms are also finding success by focusing on niche markets or employing innovative trading strategies. The overall outlook for the commodity trading services market remains optimistic, with continued growth expected over the coming years, albeit with some potential challenges related to geopolitical instability and regulatory changes.

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

The Over-the-Counter (OTC) agricultural product trading platform market is experiencing robust growth, driven by increasing global demand for agricultural commodities and the need for efficient trading solutions. The market's expansion is fueled by several key factors, including the rising adoption of digital technologies for trading, the growing preference for flexible and customized trading options offered by OTC platforms, and the increasing volatility in agricultural commodity prices, making efficient risk management crucial. Furthermore, the expanding e-commerce infrastructure and improved internet connectivity in emerging economies are facilitating greater participation in OTC agricultural trading. While challenges such as regulatory uncertainty and cybersecurity risks exist, the overall market outlook remains positive, with a projected Compound Annual Growth Rate (CAGR) of approximately 15% between 2025 and 2033. This growth is anticipated across various segments, including grains, oilseeds, and livestock products, with significant regional variations influenced by factors such as production levels, consumption patterns, and government policies. The competitive landscape is characterized by a mix of established financial institutions like GAIN Global Markets Inc., IG Group, and Saxo Bank, as well as newer entrants leveraging technology to gain market share. The market's growth will be significantly influenced by advancements in blockchain technology for secure and transparent transactions, the increased use of data analytics for improved price forecasting, and the development of more sophisticated risk management tools. The major players in the OTC agricultural product trading platform market are strategically investing in technological advancements and expanding their product offerings to cater to a diverse client base. This includes developing user-friendly trading platforms, offering competitive pricing, and providing comprehensive risk management solutions. Furthermore, collaborations and mergers and acquisitions are expected to reshape the competitive landscape, leading to consolidation within the industry. The market's success will also hinge on addressing regulatory challenges and fostering greater transparency and trust among participants. Geographic expansion, particularly into emerging markets with significant agricultural production and consumption, will be a key driver of growth. While the market is likely to face short-term fluctuations driven by global economic conditions and geopolitical events, the long-term prospects remain promising, suggesting substantial opportunities for existing and new market participants.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Almonds (WPU01190102) from Dec 1991 to May 2025 about nuts, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

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.

Commodity Index: Multi Commodity Exchange of India: Future Price: Agriculture data was reported at 2,854.700 2001=1000 in 10 Dec 2018. This records an increase from the previous number of 2,831.800 2001=1000 for 07 Dec 2018. Commodity Index: Multi Commodity Exchange of India: Future Price: Agriculture data is updated daily, averaging 2,200.475 2001=1000 from Jun 2005 (Median) to 10 Dec 2018, with 3904 observations. The data reached an all-time high of 3,716.580 2001=1000 in 16 Apr 2012 and a record low of 1,277.850 2001=1000 in 28 Jun 2005. Commodity Index: Multi Commodity Exchange of India: Future Price: Agriculture data remains active status in CEIC and is reported by Multi Commodity Exchange of India. The data is categorized under India Premium Database’s Financial Market – Table IN.ZF004: Commodity Index.

Corn fell to 393.37 USd/BU on July 14, 2025, down 0.66% from the previous day. Over the past month, Corn's price has fallen 9.52%, and is down 2.69% 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 July of 2025.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Eggs for Fresh Use (WPU017107) from Dec 1991 to May 2025 about eggs, 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.

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.

Overview The September edition of Agricultural commodities contains ABARES latest outlook for Australia's key agricultural commodities in 2018-19, which updates the outlook released in June 2018. Key…Show full descriptionOverview The September edition of Agricultural commodities contains ABARES latest outlook for Australia's key agricultural commodities in 2018-19, which updates the outlook released in June 2018. Key Issues • In 2018-19 the value of farm production is forecast to be relatively unchanged at $60 billion. • Dry conditions are affecting agricultural production in eastern Australia, but strong forecast production in Western Australia, rising grain prices, high livestock prices and a lower Australian dollar are providing support to farm incomes. • Export prices are forecast to increase by around 3% in 2018-19, driven by a decline in the global supply of grains and strong demand for meat products. • Downside risks to Australian agriculture include uncertainty around the duration of the drought in impacted areas, the timing and amount of rain in other regions, and possible disruption to world agricultural markets stemming from protectionist trade measures. Commodity production forecasts • The value of crop production is forecast to decrease by 3 per cent to $30 billion in 2018-19. ◦ The decline is expected to be driven by a forecast decline in area planted in the eastern states. Drought conditions across eastern Australia restricted planting opportunities for crops, such as barley, canola and wheat. ◦ Higher forecast prices for canola, coarse grains, cotton and wheat are expected to mitigate the impact of lower crop volumes on the value of production. ◦ Wine grape and sugar production are forecast to rise as producing areas have been less affected by drought. The value of sugar production is nevertheless forecast to decline due to weak international prices. ◦ Horticultural production has increased following a warm winter, boosting production of a range of fruits and vegetables • The value of livestock production is forecast to increase by 2 per cent to $30 billion in 2018-19. ◦ Drought in the eastern states has increased cattle and sheep turn-off, lifting meat production and leading to a forecast reduction in herd size. ◦ Dairy production is forecast to increase, as processors continue to offer relatively high milk prices. However, the production response is likely to be dampened by increasing feed and fodder costs. ◦ Wool production is forecast to be lower, constrained by lower flock numbers and poor grazing conditions. Commodity export forecasts • Export earnings for farm commodities are forecast to be $47 billion in 2018-19, down 5 per cent from $49 billion in 2017-18 • The decline in export earnings is largely due to lower exportable supplies of canola, coarse grains, pulses and wheat and increased domestic demand for grain. Agricultural export prices, measured by the index of unit export returns, are forecast to increase by 3% in 2018-19. ◦ Export earnings are forecast to decline in 2018-19 for canola (down 39 per cent), coarse grains (24 per cent), wheat (10 per cent), sugar (9 per cent), wool (2 per cent) and wine (1 per cent). Export earnings for beef and veal and live feeder/slaughter cattle are unchanged. • Export earnings are forecast to be supported by strong demand from Asia and advanced economies for Australian livestock and livestock products. Higher prices for wheat, coarse grains and cotton are also expected to support earnings. ◦ In 2018-19 export earnings are forecast to rise for lamb (up 17 per cent), rice (14 per cent), mutton (13 per cent), cotton (9 per cent), cheese (6 per cent) and rock lobster (3 per cent). • Export earnings for fisheries products are forecast to increase by 2 per cent in 2018-19 to $1.6 billion, after increasing by an estimated 10 per cent in 2017-18. Assumptions underlying this set of commodity forecasts Forecasts of commodity production and exports are based on global and domestic demand and supply assumptions. • On the demand side, stronger world economic growth will translate to higher per person incomes in most of Australia's export markets, supporting stronger demand. ◦ World economic growth is assumed to be 3.9 per cent in 2018 and 2019. ◦ Economic growth in Australia is assumed to be 3.0 per cent in 2018-19. ◦ The Australian dollar is assumed to average US74 cents in 2018-19, lower than the assumed average of US78 cents in 2017-18. • On the supply side, Australian agricultural production prospects are assumed to be below average. ◦ Dry conditions are forecast to have significant implications for crop yields and livestock production cycles in the eastern states. Uncertainties that could affect agricultural commodity production and export growth include supply shocks in Australia or international markets (such as natural disasters, drought and disease outbreaks) or unexpected economic events that affect trade and economic growth. Boxes on agricultural issues Evolving EU biodiesel policies • Proposed changes to the EU renewable fuels policy could increase demand for Australia's canola exports in the short to medium term. • Since 2010-11 the European Union has been the largest export market for Australian canola. Most canola is imported to produce renewable transport fuel.

Overview The report provides updated commodity forecasts as well as articles on the EU sugar industry, EU almond industry and investment on Australia's farms.

Key Issues Commodity forecasts
• The gross value of farm production is forecast to increase by 3.3 per cent to around $58.4 billion in 2016-17, following an estimated 3.8 per cent increase to $56.5 billion in 2015-16. If realised, the gross value of farm production in 2016-17 would be around 13 per cent higher than the five-year average of $51.8 billion between 2011-12 and 2015-16 in nominal terms. • The gross value of livestock production is forecast to remain largely unchanged at $29.2 billion in 2016-17, following an estimated 7.7 per cent increase in 2015-16. • The gross value of crop production is forecast to increase by 6.6 per cent to $29.2 billion in 2016-17. This mainly reflects forecast increases in the gross values of horticulture, cotton and oilseed production offsetting forecast decreases in the gross values of wheat and barley production. • Export earnings from farm commodities are forecast to fall slightly to $44 billion in 2016-17, following an estimated 1.3 per cent increase in 2015-16 to $44.5 billion. • The agricultural commodities for which export earnings are forecast to fall in 2016-17 are beef and veal (-12 per cent), dairy products (-1 per cent), live feeder/slaughter cattle (-4 per cent), chickpeas (-32 per cent) and mutton (-17 per cent). Export earnings for wheat are expected to remain largely unchanged. • The forecast decreases in 2016-17 are expected to be partly offset by forecast rises in export earnings for wool (6 per cent), sugar (21 per cent), wine (1 per cent), cotton (40 per cent), lamb (3 per cent) and canola (43 per cent). • Export earnings for fisheries products are forecast to increase by 8 per cent to $1.7 billion in 2016-17, after increasing by an estimated 7 per cent in 2015-16.

Economic assumptions underlying this set of commodity forecasts

In preparing this set of agricultural commodity forecasts: • World economic growth is assumed to be 2.8 per cent in 2016 and 3.3 per cent in 2017. • Economic growth in Australia averaged 2.9 per cent in 2015-16 and is assumed to average 2.7 per cent in 2016-17. • The Australian dollar is assumed to average US73 cents in 2016-17, largely unchanged from the estimated average for 2015-16.

Articles on agricultural issues

The EU sugar industry
• The EU sugar market is highly regulated, with domestic price support, export subsidies and import restrictions. This stocktake of EU domestic and trade policies that support the EU sugar market will inform government and industry in the lead up to the commencement of Australia-EU FTA negotiations. • Australia's access to the EU sugar market is constrained by a country-specific tariff rate quota. Any improved access through either lower tariffs or increased quota would improve Australia's competitiveness in that market.

The EU almond industry
• The European Union is the world's largest consumer and importer of almonds and has been a growing export market for Australian almonds since 2005-06. • However, significant benefits from an Australian-EU FTA are unlikely for Australian almonds because import tariffs are already low and equal to those faced by the United States, the main supplier to this market.

Investment in Australian farms
• Investment in Australian farms is substantial, with net additions of non-land capital items on broadacre and dairy farms currently worth around $2 billion a year. • Farm operators and their spouses provide most of the capital used to fund the ownership and operation of Australian farms; corporate investors account for less than one-fifth of total capital. The strength of the family farm business structure suggests that corporate investors are unlikely to significantly displace family farmers in the near future.