Sugar rose to 16.56 USd/Lbs on July 11, 2025, up 1.83% from the previous day. Over the past month, Sugar's price has risen 1.84%, but it is still 13.76% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Sugar - values, historical data, forecasts and news - updated on July of 2025.

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Raw Sugar Market Size 2025-2029

The raw sugar market size is forecast to increase by USD 152.7 million, at a CAGR of 2.3% between 2024 and 2029.

The market is witnessing significant growth, driven primarily by the increasing demand for raw sugar in various food and beverage applications. This trend is being fueled by the expanding food industry, particularly in emerging economies, where sugar consumption is on the rise. Additionally, the emergence of e-commerce platforms has facilitated easier access to raw sugar for consumers and manufacturers, further boosting market growth. However, the high production cost of raw sugar poses a significant challenge for market participants. Producers must navigate this obstacle through efficient production methods, cost optimization, and strategic pricing to remain competitive in the market.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on innovation, cost reduction, and supply chain optimization. By staying agile and responsive to market trends, they can position themselves for long-term success in the dynamic the market.

What will be the Size of the Raw Sugar Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with various factors shaping its dynamics. Sugarcane and sugar beet supply and demand, production costs, and sustainability are key elements influencing market activities. Biofuel production from sugarcane bagasse and sugar beet residues adds complexity to the market. Sugarcane diseases and pests, as well as transportation challenges, can impact yields and prices. Sugarcane consumption is driven by various applications, including food and beverage industries, ethanol production, and pharmaceuticals. Organic sugar and fair trade sugar are gaining popularity, adding to the market's diversity. Sugarcane juice and molasses are used to produce syrups and other value-added products.

Sugarcane syrup and turbinado sugar cater to specific market segments. Sugarcane cultivation and harvesting techniques, as well as irrigation and fertilizer usage, influence production costs and quality. Sugarcane and sugar beet prices fluctuate based on supply and demand, with imports and exports playing a role in market equilibrium. Traceability and sustainability concerns are increasingly important, influencing consumer preferences and regulations. Sugarcane and sugar beet varieties, processing methods, and storage techniques also impact market trends. Overall, the market remains dynamic, with ongoing shifts in production, consumption, and market conditions.

How is this Raw Sugar Industry segmented?

The raw sugar industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Liquid sugar
  Crystallized sugar


Type

  Conventional
  Organic


Base

  Sugarcane-based
  Beet-based


Application

  Food & Beverage Industry
  Biofuel Production
  Pharmaceuticals
  Animal Feed
  Chemicals


End-use Industry

  Food Processing
  Beverage Production
  Ethanol Production
  Pharmaceutical & Personal Care
  Chemical Manufacturing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Product Insights

The liquid sugar segment is estimated to witness significant growth during the forecast period.

Liquid sugar, derived from raw sugar through the addition of water, is a popular choice among manufacturers due to its convenience and versatility. The sweetener's ability to dissolve quickly and evenly makes it an ideal ingredient for large-scale production of beverages, including carbonated soft drinks, sports drinks, and juices. Additionally, it is widely used in the baking industry for creating cakes, cookies, and pastries. The consistency and stability of liquid sugar enable manufacturers to control the texture and flavor of their products effectively. The sugar beet industry and sugarcane industry serve as the primary sources for raw sugar production.

Sugarcane cultivation, which includes irrigation, fertilization, and pest management, incurs significant production costs. Sugarcane diseases and pests pose challenges to the industry, affecting both yield and quality. Sugarcane bagasse and molasses are by-products used in biofuel production and ethanol manufacturing. Sugar beet cultivation, on the other hand, is practiced in regions with cooler climates. Sugarcane and su

📈 Daily Historical Stock Price Data for Dhampur Sugar Mills Limited (2002–2025)

A clean, ready-to-use dataset containing daily stock prices for Dhampur Sugar Mills Limited from 2002-07-01 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Dhampur Sugar Mills Limited Ticker Symbol: DHAMPURSUG.NS Date Range: 2002-07-01 to 2025-05-28 Frequency: Daily Total Records:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-dhampur-sugar-mills-limited-20022025.

Sugar Price Index in World decreased to 103.70 Index Points in June from 109.40 Index Points in May of 2025. This dataset includes a chart with historical data for World Sugar Price Index.

This statistic depicts the average annual prices for sugar from 2014 through 2026*. In 2023, the average price for sugar stood at 0.46 nominal U.S. dollars per kilogram.

In 2024, the global sugar market increased by 2.9% to $118.2B, rising for the fourth year in a row after three years of decline. Over the period under review, consumption, however, continues to indicate a relatively flat trend pattern. Over the period under review, the global market reached the peak level at $126.7B in 2012; however, from 2013 to 2024, consumption stood at a somewhat lower figure.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Sugar Futures Signal Potential Price Volatility for CRB Commodities Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is the Sugar Index a Sweet Spot for Investors?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dangote Sugar Refinery PLC reported NGN151.51B in Stock for its fiscal quarter ending in September of 2024. Data for Dangote Sugar Refinery PLC | DANGSUGA - Stock including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Sugar Index Poised for Volatility, Say Analysts

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Sugar-Based Excipients Market is segmented By Product Type (Actual Sugars, Sugar Alcohols, and Artificial Sweeteners) and Excipient Type (Powders/granules, Direct Compression Sugars)

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is the Sugar Index a Reliable Indicator of TR/CC CRB Performance?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Global sugar manufacturers have endured fluctuations in global sugar prices over the five years to 2024. Nonetheless, global sugar manufacturers' revenue is anticipated to strengthen at a CAGR of 5.6% to $83.2 billion over the five years to 2024, including a drop of 8.5% in 2024. Brazil is very influential in the industry's health. The country produces and exports the most sugar of any nation and is also the second-largest producer of ethanol, which is often produced from sugarcane. As energy prices have strengthened over the past five years, Brazil has expansively diverted more of its sugar stock toward ethanol production. Brazil's changing production and export levels have impacted the world supply of sugar, which, in turn, has disturbed world sugar prices. For example, prior to the current period, in 2011, when Brazil cut its production of sugar by 2.0 million tons, the world price of sugar shot up 25.6%; the following year, as Brazil boosted production by more than 2.0 million tons, the world price of sugar dropped 18.5%. These fluctuations in production, coupled with other countries following Brazil's lead and diverting their sugar stock toward ethanol production or other more valuable crops, have led revenue for the entire industry to endure intense volatility during the current five-year period. Profit, measured as earnings before interest and taxes, inched upward to 6.1% of revenue in 2024. These factors are expected to continue driving volatility in the world price of sugar and global sugar manufacturers' revenue over the five years to 2029. Despite ongoing fluctuations, the world price of sugar will moderately drop as global demand for sugar and sugar-heavy products dips, along with lower energy prices, which will likely prompt demand for alternative fuel sources, like ethanol. Also, as demand from developing nations continues to swell and as trade barriers are expansively removed, global production and international trade of sugar will strengthen. As a result of these factors, global sugar manufacturers' revenue will drop at a CAGR of an estimated 1.2% over the next five years to $78.5 billion in 2029.

After two years of decline, the Nepalese sugar market increased by 2.1% to $97M in 2024. In general, consumption posted a modest increase. Sugar consumption peaked at $190M in 2018; however, from 2019 to 2024, consumption remained at a lower figure.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the Sugar Index Sweeten Your Portfolio?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Overview
This study uses a case study approach to demonstrate the potential to estimate farm shares and price spreads in Australia using a relatively simple methodology developed by the United States Department of Agriculture Economic Research Service. In this instance, the methodology has been applied to Australian sugar price data.

Key Points
• The study demonstrates that data is available that allows an analysis of farm share and price spread for raw sugar exports and refined sugar sold at retail outlets. • The analysis shows that trends in farm shares of retail and export prices were relatively flat between 1984-85 and 2014-15. So too were trends in farm-to-retail and farm-to-export price spreads. • If it is assumed that the emergence of market power beyond the farm gate is likely to be reflected in changes in trends in farm share and price spread, then these results suggest that there has been no obvious change in market power within the sugar industry over this period.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

DJ Commodity Sugar Index: Analysts Predict Further Volatility.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dangote Sugar Refinery PLC reported 239.32 in PE Price to Earnings for its fiscal quarter ending in September of 2023. Data for Dangote Sugar Refinery PLC | DANGSUGA - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Dangote Sugar Refinery PLC reported NGN421.14B in Current Assets for its fiscal quarter ending in March of 2025. Data for Dangote Sugar Refinery PLC | DANGSUGA - Current Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

DJ Commodity Sugar index projects moderate gains.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data