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

• aidsSystemData-DIB.xlsx contains the first combination of the USDA ERS data into a single tabular format. It also contains the formulas for the calculation of derived data. • aidsSystemData3.csv is the .xlsx file converted to .csv for import into R. • aidsvdataInbrief.R contains all code used to estimate and calculate elasticities. The following packages must be installed for the script: 'tidyverse', 'lubridate', 'micEconAids', and 'broom'. • Results.xlsx contains all results. Tabs are labeled as “results_” lags between price and quantity, and “h” or “m” to indicate Hicksian or Marshallian. • all.Rdata contains all results and intermediate objects.

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.

Overview

The data refers to Daily prices of various commodities in India like Tomato, Potato, Brinjal, Wheat etc. It has the wholesale maximum price, minimum price and modal price on daily basis. the prices in the dataset refer to the wholesale prices of various commodities per quintal (100 kg) in Indian rupees. The wholesale price is the price at which goods are sold in large quantities to retailers or distributors.

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Features of the dataset include:

• State: The state in India where the market is located.
• District: The district in India where the market is located.
• Market: The name of the market.
• Commodity: The name of the commodity.
• Variety: The variety of the commodity.
• Grade: The grade or quality of the commodity.
• Min Price: (INR) The minimum wholesale price of the commodity on a given day, per quintal (100 kg).
• Max Price: (INR) The maximum wholesale price of the commodity on a given day, per quintal (100 kg).
• Modal Price: (INR) The most common or representative wholesale price of the commodity on a given day, per quintal (100 kg).

1 INR = 0.012 USD (as on 17 August, 2023)

Use Cases

Market analysis: You can use this dataset to analyze trends and patterns in the wholesale prices of various commodities across different markets in India. This can help you understand factors that affect prices, such as supply and demand, seasonality, and market conditions. Commodity recommendation: Develop recommender systems that suggest the best markets or commodities for farmers or traders to sell or buy based on their location, preferences, and market conditions.

Licensed under the Government Open Data License - India (GODL) https://data.gov.in/government-open-data-license-india

Feel free to download the data and use it in your work. I will wait for interesting notebooks from your side. Thank you

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 study develops an integrated forecasting framework that combines Machine Learning (Random Forest), Deep Learning (LSTM), and Econometric (VECM) approaches to analyse the dynamic behaviour of Indian pepper and turmeric prices. The models incorporate major macroeconomic determinants, including GDP, Consumer Price Index (CPI), exchange rate, gold price, interest rate, trade volume, and foreign institutional investments (FII), to capture both non-linear and long-term relationships. Model performance was evaluated using RMSE, MAE, and MAPE metrics, alongside SHAP-based feature explainability analysis.

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.

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

Agri Commodity Trading Platforms Market Outlook

According to our latest research, the global Agri Commodity Trading Platforms market size reached USD 4.2 billion in 2024, demonstrating robust expansion driven by digital transformation across the agricultural sector. As per our analysis, the market is forecasted to grow at a CAGR of 12.7% between 2025 and 2033, reaching approximately USD 12.1 billion by 2033. This accelerated growth is primarily propelled by increased digital adoption among farmers and traders, the need for real-time price discovery, and the rising integration of advanced technologies such as artificial intelligence and blockchain within trading platforms. These factors collectively underscore the market's dynamic evolution and the growing importance of technology-driven solutions in agricultural commodity trading worldwide.

The growth trajectory of the Agri Commodity Trading Platforms market is significantly influenced by the global push towards digitalization in agriculture. As traditional trading methods face limitations related to transparency, efficiency, and scalability, digital platforms have emerged as vital tools for streamlining transactions and enhancing market access for stakeholders at every level of the value chain. The proliferation of smartphones and improved internet connectivity, especially in developing regions, has catalyzed the adoption of these platforms, enabling even smallholder farmers to participate in broader markets. Furthermore, the integration of advanced analytics, real-time pricing, and risk management tools has empowered users to make informed decisions, thereby reducing price volatility and transaction costs. This digital transformation is further supported by government initiatives and private sector investments aimed at modernizing agricultural supply chains and improving food security.

Another critical growth driver for the Agri Commodity Trading Platforms market is the increasing demand for transparency and traceability in food supply chains. With consumers and regulators placing greater emphasis on food safety and sustainability, trading platforms are incorporating blockchain and other traceability technologies to provide end-to-end visibility into the movement of agricultural commodities. This not only helps in building trust among buyers and sellers but also facilitates compliance with stringent international trade regulations. Additionally, the adoption of digital payment solutions and smart contracts has streamlined settlement processes, minimizing the risk of defaults and disputes. As a result, both large agribusinesses and small-scale producers are increasingly leveraging these platforms to access new markets, secure better prices, and enhance operational efficiency.

The market’s expansion is also fueled by the growing participation of institutional investors and financial intermediaries in agricultural commodity trading. The availability of sophisticated trading tools, data analytics, and risk management features on these platforms has attracted a diverse range of users, including hedge funds, banks, and cooperatives. This influx of institutional capital has improved market liquidity and price discovery, further incentivizing platform adoption. Moreover, the emergence of innovative business models such as platform-as-a-service and subscription-based offerings has lowered entry barriers for new market participants, fostering competition and innovation. These developments are expected to sustain the market’s momentum over the forecast period, with further acceleration anticipated as emerging technologies and regulatory frameworks mature.

From a regional perspective, the Asia Pacific region is expected to lead the Agri Commodity Trading Platforms market in terms of growth rate, driven by rapid digitalization, large agricultural output, and supportive government policies. North America and Europe continue to dominate in terms of market share, owing to advanced infrastructure, high internet penetration, and the presence of established agribusinesses. Meanwhile, Latin America and Middle East & Africa are witnessing steady growth as digital initiatives and investments in agri-tech gain momentum. Each region presents unique opportunities and challenges, shaped by local market dynamics, regulatory environments, and technological adoption rates.

Agricultural Market Price Hedging Platform Market Outlook

According to our latest research, the global Agricultural Market Price Hedging Platform market size reached USD 1.91 billion in 2024, driven by increased volatility in agricultural commodity prices and the growing adoption of digital risk management solutions. The market is demonstrating a robust expansion, registering a CAGR of 8.7% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 4.08 billion. This growth is primarily fueled by the surging need for advanced tools that enable farmers, agribusinesses, and commodity traders to hedge against price fluctuations, thereby ensuring profitability and financial stability in a dynamic global agricultural landscape.

One of the principal growth factors for the Agricultural Market Price Hedging Platform market is the increasing unpredictability in global agricultural commodity prices. Factors such as climate change, supply chain disruptions, geopolitical tensions, and fluctuating demand patterns have made price volatility a persistent challenge for stakeholders. As a result, there is a heightened demand for digital platforms that provide real-time market data, predictive analytics, and automated hedging strategies. These platforms empower end-users to make data-driven decisions and safeguard their revenues against adverse price movements. Furthermore, the integration of artificial intelligence and machine learning into these platforms is enhancing forecasting accuracy, making them indispensable tools for modern agriculture.

Another significant growth driver is the rapid digital transformation occurring within the agricultural sector. The proliferation of smartphones, increased internet penetration in rural areas, and the emergence of cloud-based solutions have collectively democratized access to sophisticated financial instruments. This digitalization trend is not only enabling large agribusinesses and commodity traders to optimize their risk management strategies but is also making it feasible for smallholder farmers and cooperatives to participate in price hedging activities. The availability of user-friendly interfaces, multilingual support, and mobile accessibility further accelerates adoption, bridging the gap between traditional farming practices and modern financial technologies.

Additionally, supportive government policies and initiatives aimed at stabilizing farm incomes are catalyzing the adoption of Agricultural Market Price Hedging Platforms. Many governments and agricultural organizations are actively promoting the use of risk management tools to mitigate the adverse effects of price volatility on rural livelihoods. Subsidies, training programs, and partnerships with fintech providers are fostering a conducive environment for market growth. Moreover, the increasing participation of institutional investors in agricultural commodities is enhancing market liquidity, thereby improving the effectiveness and appeal of hedging platforms. These collective efforts are expected to sustain the market’s upward trajectory over the coming years.

From a regional perspective, North America continues to dominate the Agricultural Market Price Hedging Platform market, accounting for a significant share due to its advanced technological infrastructure, high awareness levels, and the presence of major market players. Europe follows closely, with strong government support for agricultural innovation and risk management practices. The Asia Pacific region is witnessing the fastest growth, propelled by the digitalization of agriculture and increasing price volatility in emerging economies such as India and China. Latin America and the Middle East & Africa are also experiencing steady growth, driven by expanding agribusiness sectors and rising adoption of digital financial services. The regional dynamics underscore the global relevance and necessity of price hedging platforms in ensuring agricultural sustainability and profitability.

Component Analysis

The Agricultural Market Price Hedging Platform market is segmented by component into software and services, with each playing a pivotal role in the market’s overall growth and adoption. The software segment dominates the market, accounting for over 65% of the total revenue in 2024. This dominance is attributed to the increasing demand for advanced analytics, real-time data feeds, and automated trading function

Agricultural Market Price Hedging Platform Market Outlook

According to our latest research, the global agricultural market price hedging platform market size reached USD 1.38 billion in 2024. The industry is demonstrating robust momentum, with a recorded CAGR of 11.7% from 2025 to 2033. By the end of the forecast period, the market is expected to attain a valuation of USD 3.67 billion. This growth is primarily driven by the increasing volatility in global agricultural commodity prices and the rising need among farmers, agribusinesses, and cooperatives to manage price risks through sophisticated digital platforms.

One of the foremost growth factors propelling the agricultural market price hedging platform market is the heightened price volatility in the global agricultural commodities sector. Fluctuations in crop yields, weather uncertainties, and unpredictable geopolitical events have made price risk management more critical than ever. As a result, stakeholders across the agricultural value chain are turning to advanced hedging platforms to secure their revenues and stabilize operational planning. These platforms enable users to lock in prices, engage in futures and options trading, and utilize data-driven insights to make informed decisions. The proliferation of digital technologies and the integration of artificial intelligence and machine learning have further enhanced the accuracy and accessibility of these solutions, making them indispensable for modern agricultural operations.

Another significant driver for the growth of the agricultural market price hedging platform market is the increasing penetration of digital infrastructure in rural and semi-urban regions. Governments and private sector players are investing heavily in expanding internet connectivity and digital literacy among farmers and agribusinesses. This digital transformation is facilitating the adoption of cloud-based and on-premises hedging solutions, which offer real-time market data, predictive analytics, and seamless execution of hedging contracts. Additionally, the rise of mobile-based applications and user-friendly platforms has democratized access to sophisticated risk management tools, empowering smallholder farmers and cooperatives to participate in price hedging activities that were previously limited to large-scale traders and multinational agribusinesses.

Furthermore, regulatory reforms and supportive government policies are fostering the adoption of price hedging platforms in the agricultural sector. Many countries are recognizing the importance of risk management in ensuring food security and stabilizing farm incomes. As a result, there is a growing emphasis on creating transparent and efficient commodity markets, along with providing incentives for farmers and agribusinesses to use hedging instruments. The emergence of agri-fintech startups and collaborations between technology providers and agricultural cooperatives are also contributing to market expansion. These initiatives are not only enhancing the resilience of the agricultural sector but also driving innovation and competition among platform providers.

From a regional perspective, North America remains at the forefront of the agricultural market price hedging platform market, owing to its well-established commodity exchanges, advanced technological infrastructure, and high awareness among stakeholders. Europe follows closely, driven by strong government support and a mature agribusiness ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid digitalization of agriculture in countries such as India, China, and Australia. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as digital transformation and market liberalization gather momentum. The regional dynamics are shaped by factors such as the size of the agricultural sector, regulatory environment, and the level of technological adoption among end-users.

Solution Type Analysis

The so

This dataset provides the current daily prices of various commodities sourced from multiple markets (mandis) across different regions. It includes detailed information on the market names, commodity types, and their respective prices, offering a snapshot of real-time agricultural and other commodity market trends. The data is valuable for farmers, traders, and analysts to monitor price fluctuations, compare regional price variations, and make informed decisions. It offers insights into supply and demand dynamics, and market conditions, and helps in understanding the economic factors affecting commodity pricing. This dataset supports decision-making, price forecasting, and market research.

Agricultural Market Price Hedging Platform Market Outlook

According to our latest research, the Agricultural Market Price Hedging Platform market size was valued at $1.5 billion in 2024 and is projected to reach $4.2 billion by 2033, expanding at a CAGR of 12.1% during 2024–2033. The primary catalyst behind this robust growth is the increasing volatility in global agricultural commodity prices, which has driven stakeholders across the value chain—including farmers, agribusinesses, and commodity traders—to seek advanced risk management solutions. As market uncertainties intensify due to climate change, geopolitical tensions, and supply chain disruptions, the demand for sophisticated price hedging platforms that offer real-time analytics, automated trading, and customizable risk mitigation strategies is surging. This trend is further bolstered by digital transformation initiatives in agriculture, which are enabling seamless integration of financial tools with traditional farming operations, thereby enhancing accessibility and user engagement across diverse market participants.

Regional Outlook

North America currently commands the largest share of the global Agricultural Market Price Hedging Platform market, accounting for approximately 38% of total market value in 2024. The region’s dominance is attributed to its mature agricultural sector, widespread adoption of digital technologies, and a highly developed financial infrastructure that supports commodity trading and risk management. The presence of leading platform providers, robust regulatory frameworks, and a high concentration of large-scale agribusinesses further reinforce North America’s leadership. Additionally, proactive government policies fostering agri-tech innovation and risk mitigation have accelerated the uptake of hedging platforms, particularly among commercial farmers and cooperatives seeking to stabilize revenues amidst price fluctuations. The region’s established track record in deploying cloud-based solutions and integrating advanced analytics into agricultural operations continues to set the benchmark for global adoption.

Asia Pacific is emerging as the fastest-growing region in the Agricultural Market Price Hedging Platform market, projected to witness a CAGR of 15.7% through 2033. This exponential growth is fueled by rapid digitalization, expanding agricultural output, and increasing participation of smallholder farmers in organized commodity markets. Countries such as China, India, and Australia are investing heavily in agri-fintech infrastructure, supported by government-led initiatives to modernize supply chains and enhance market transparency. The proliferation of mobile-based trading platforms and tailored risk management solutions is making price hedging accessible to a broader base of end-users. Furthermore, rising cross-border trade and the integration of local markets with global commodity exchanges are amplifying the demand for sophisticated platform capabilities that cater to diverse crop categories and trading preferences.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of agricultural market price hedging platforms is steadily gaining momentum, albeit at a slower pace due to infrastructural and policy challenges. These regions are characterized by fragmented agricultural landscapes, limited access to digital infrastructure, and lower financial literacy among smallholder farmers. Nevertheless, targeted policy reforms, international development programs, and public-private partnerships are gradually bridging the digital divide. Localized demand for risk management tools is being driven by increasing exposure to global price shocks and the need for income stabilization. Regulatory harmonization and capacity-building initiatives are expected to catalyze platform penetration, particularly among cooperatives and commodity traders seeking to mitigate market uncertainties.

Report Scope

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: Tomatoes (WPU01130217) from Jan 1947 to Sep 2025 about agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

Discover the booming global commodity trading services market, projected to reach $3.1 trillion by 2033 with a 5% CAGR. This comprehensive analysis explores market size, drivers, trends, restraints, segmentation (metals, energy, agriculture, etc.), key players (Vitol, Glencore, Cargill), and regional insights. Learn about opportunities and challenges in this dynamic sector.

Abstract of associated article: The main purpose of this paper is to identify the effects of exogenous factors, which have been somewhat controversial, on the price links between the energy and agricultural commodity markets. Our study differs from other studies by employing multivariate normal mixture models to capture the structural properties of the price dependencies in the underlying states. This paper investigates price dependencies from both quantitative and structural perspectives. By analyzing the overall dependencies and structural heterogeneity in the empirical results, we conclude that the global financial crisis is the most influential shock on the price links between energy and agricultural commodities. Because price links are vulnerable to financial shocks, our results also suggest introducing state-based analysis to risk management and portfolio diversification across the energy and agriculture markets during times of turmoil.

Corn rose to 433.53 USd/BU on December 2, 2025, up 0.01% from the previous day. Over the past month, Corn's price has fallen 0.17%, but it is still 2.43% higher than a year ago, 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 December of 2025.

Wholesale and farm gate prices data for the agricultural commodities are retrieved from the European Commission’s DG Agriculture and Rural Development. DG-AGRI is responsible for the implementation of agriculture and rural development policy for the European Union. The monthly data stored is a weighted average based on the number of days in the week/month; the time horizon of the price series depends upon availability of the commodity in the country under analysis. The DG-AGRI dataset includes data on farm gate and wholesale prices for a number of agricultural products from the Cereals, Oils and Fats, Meat and Dairy farm sectors for EU Member States. Given the lack of the availability of many time series observations, a parallel dataset has been created that aims at filling the gaps in the price series. In this parallel dataset, outliers have been removed according to the following criteria: any observation very far from the mean remains in the price series in order not to distort data, while zero values, which must be incorrect, have been removed. Four geographic macro areas4 of European Union countries have been created based on the characteristics they share for instance, similar agricultural outputs and similar weather conditions. Missing values are estimated using a correlation-based approach that identifies which countries' price series can serve as indicators for others with missing data. First, correlation coefficients are calculated for the month-on-month percentage changes in food prices across different countries. These coefficients are then compared within the same geographic area and food category. To uncover additional patterns and connections, each individual series is also compared to aggregate series representing the average month-on-month percentage changes in prices. Ultimately, the most highly correlated price series is used to fill in the gaps, applying its month-on-month percentage change multiplied by the corresponding correlation coefficient to the last available price point. Four food aggregates are computed using the PPP consumption weights to aggregate the series to get to country series for each of the product groups requested. The European series are created from these country aggregates and weighted using HICP weights and index formula. For further information, please visit the European Commission Agricultural Markets Data Portal.