Cotton rose to 64.25 USd/Lbs on October 18, 2025, up 0.73% from the previous day. Over the past month, Cotton's price has fallen 0.76%, and is down 11.25% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cotton - values, historical data, forecasts and news - updated on October of 2025.

Dropping Ogallala aquifer levels and changing commodity prices and energy costs make irrigation management an important but uncertain issue to west Texas cotton producers. For example, is deficit or full irrigation more profitable under the current lint price and pumping cost conditions? Also, what is the best way to divide production into dryland and irrigated acreage with limited well capacity? To help producers answer these questions this web application estimates the effects of irrigation on the profitability of center pivot cotton production on the Southern High Plains. It's main purpose is to show the impact of irrigation on yield and the related effects on both profits per acre and profits over a center pivot area with combined dryland and irrigated production. Resources in this dataset:Resource Title: Cotton Irrigation Tool. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=486&modecode=30-96-05-00 download page

Global Cotton Prices - Historical chart and current data through 2025.

This data product contains data on U.S. cotton and wool supply, demand, and prices, as well as U.S. cotton and textile trade data, maintained by the Economic Research Service to support related commodity market analysis and research.

About this dataset

Context

The dataset tabulates the Cotton Plant population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Cotton Plant across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Cotton Plant was 499, a 1.58% decrease year-by-year from 2022. Previously, in 2022, Cotton Plant population was 507, a decline of 1.17% compared to a population of 513 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cotton Plant decreased by 452. In this period, the peak population was 951 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Cotton Plant is shown in this column.
• Year on Year Change: This column displays the change in Cotton Plant population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cotton Plant Population by Year. You can refer the same here

Cotton root rot is a century-old cotton disease that now can be effectively controlled with Topguard Terra fungicide. Because this disease tends to occur in the same general areas within fields in recurring years, site-specific application of the fungicide only to infested areas can be as effective as and considerably more economical than uniform application. The overall objective of this research was to demonstrate how site-specific fungicide application could be implemented based on historical remote sensing imagery and using variable-rate technology. Procedures were developed for creating binary prescription maps from historical airborne and high-resolution satellite imagery. Two different variable-rate liquid control systems were adapted to two existing cotton planters, respectively, for site-specific fungicide application at planting. One system was used for site-specific application on multiple fields in 2015 and 2016 near Edroy, Texas, and the other system was used on multiple fields in both years near San Angelo, Texas. Airborne multispectral imagery taken during the two growing seasons was used to monitor the performance of the site-specific treatments. Results based on prescription maps derived from historical airborne and satellite imagery of two fields in 2015 and one field in 2016 are reported in this article. Two years of field experiments showed that the prescription maps and the variable-rate systems performed well and that site-specific fungicide treatments effectively controlled cotton root rot. Reduction in fungicide use was 41%, 43%, and 63% for the three fields, respectively. The methodologies and results of this research will provide cotton growers, crop consultants, and agricultural dealers with practical guidelines for implementing site-specific fungicide application using historical imagery and variable-rate technology for effective management of cotton root rot. Resources in this dataset: Resource Title: A ground picture of cotton root rot File Name: IMG_0124.JPG Resource Description: A cotton root rot-infested area in a cotton field near Edroy, TX. Resource Title: An aerial image of a cotton field File Name: Color-infrared image of a field.jpg Resource Description: Aerial color-infrared (CIR) image of a cotton field infested with cotton root rot. Resource Title: As-applied fungicide application data File Name: Jim Ermis-Farm 1-Field 11 Fungicide Application.csv Resource Description: As-applied fungicide application rates for variable rate application of Topguard to a cotton field infested with cotton rot

Agricultural Prices: Average Weekly Prices: Producer: Cotton Seed: 15 Kg: Ceará data was reported at 23.580 BRL in 19 Jun 2015. This stayed constant from the previous number of 23.580 BRL for 12 Jun 2015. Agricultural Prices: Average Weekly Prices: Producer: Cotton Seed: 15 Kg: Ceará data is updated daily, averaging 25.430 BRL from Jan 2014 (Median) to 19 Jun 2015, with 72 observations. The data reached an all-time high of 26.000 BRL in 15 Aug 2014 and a record low of 23.580 BRL in 19 Jun 2015. Agricultural Prices: Average Weekly Prices: Producer: Cotton Seed: 15 Kg: Ceará data remains active status in CEIC and is reported by National Supply Company. The data is categorized under Brazil Premium Database’s Prices – Table BR.PA140: Agricultural Prices: CONAB: Average Weekly Prices: Producer: Cotton Seed.

This product summarizes fertilizer consumption in the United States by plant nutrient and major fertilizer products—as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients—for 1960 through the latest year for which statistics are available. The share of planted crop acreage receiving fertilizer, and fertilizer applications per receiving acre (by nutrient), are presented for major producing States for corn, cotton, soybeans, and wheat (data on nutrient consumption by crop start in 1964). Fertilizer farm prices and indices of wholesale fertilizer prices are also available.

Here are a few use cases for this project:

1. Precision Agriculture: This model can be used by agribusiness or farmers to automate the detection of weeds in cotton fields. Once identified, specific eradication measures can be implemented, reducing the time and cost of manual weed inspection and removal.

2. Crop Health Management Software: Application developers can use this model to create solutions that monitor crop health in real-time to identify and address weed infestations.

3. Educational Resources for Agronomy or Botany: The model could be used in educational setups to help students or researchers learn and distinguish between different types of weeds specifically seen in cotton fields.

4. Development of Autonomous Farming Equipment: Companies designing AI-driven farming equipment could use this model to enhance the 'weed detection' capability of their devices, enabling autonomous weeding operations.

5. Pesticide Management: The model can be used to identify specific weed types in cotton fields to apply appropriate and targeted pesticides, thus reducing overall pesticide usage and improving environmental sustainability.

Here are a few use cases for this project:

1. Precision Agriculture: The model can be integrated into drones, robots, or IoT devices in the field to identify and map the spread of various weed types within the cotton field. This will allow farmers to tackle the problem area by area, reducing the overall use of herbicides and cost, and increase the cotton yield.

2. Weed Management Research: Botanists and agricultural researchers can use the model to study the growth patterns and behavior of specific weed types such as Blackpig Weed and Nut Grass in cotton farms. The insight gained can be used to develop improved weed control strategies.

3. Agriculture Education Tool: It can be used in an e-learning platform for students studying agriculture, botany or related fields. Through this, students can learn to identify different types of weeds in cotton farms in a more interactive manner.

4. Development of Herbicides: Agrichemical companies can leverage the model to test the effectiveness of their developed herbicides. The system can identify the type of weed and measure its prevalence before and after spraying the herbicide, providing quantitative data on herbicide performance.

5. Sustainable Farming Advisory: The model can be used by sustainable farming consultants or advisors to provide a more informed suggestion in managing weed infestation in organic cotton farming, where the use of chemical herbicides is limited or completely prohibited.

The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to its reliance on custom machine-vision classifier running on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.Methods:Each image was hand-classed into one of four classes {cotton, empty-tray, plastic, hand-intrusion}. In the original dataset there were over 6000 hand-classed images. A few were removed as it was unclear from visual inspection as to which class the image should belong to and was classed as non-determinant, "ND". The ND images were omitted from this dataset. So this data provides a high quality training or validation image dataset. Images were collected at three commercial cotton gins in the 2023 cotton ginning season with one gin in mid-west, one in south and one in W. Texas; all in the U.S.A.

The global Multiple-Rows Cotton Picker market is poised for significant expansion, projected to reach a valuation of approximately USD 750 million by 2025. This robust growth is fueled by a Compound Annual Growth Rate (CAGR) of around 6.5% during the forecast period of 2025-2033. The increasing demand for advanced agricultural machinery that enhances efficiency and reduces labor costs in cotton cultivation is a primary driver. Furthermore, the growing adoption of precision agriculture techniques and the need for higher yields are propelling the market forward. Key segments like the 5-8 rows cotton pickers are gaining traction due to their superior capacity and operational efficiency, making them a preferred choice for large-scale farming operations. The application sector, particularly the farm segment, will continue to dominate, driven by the direct ownership and operational needs of large agricultural enterprises. The market is characterized by a competitive landscape featuring established players like Deere Company, CNH Industrial America LLC, and emerging innovators. These companies are investing in research and development to introduce more technologically advanced, fuel-efficient, and automated cotton picking solutions. However, certain restraints, such as the high initial investment cost of these machines and the availability of manual labor in some developing regions, could temper the pace of adoption. Geographically, Asia Pacific, led by China and India, is expected to be a significant growth engine, owing to its vast cotton cultivation area and increasing mechanization efforts. North America and Europe will also maintain substantial market shares, driven by advanced farming practices and a continuous focus on operational optimization. The trend towards rental services is also emerging, offering flexibility for smaller farmers. This comprehensive report delves into the global Multiple-Rows Cotton Picker market, offering in-depth analysis and actionable insights. With a focus on the evolving agricultural machinery landscape, this report provides a detailed examination of production volumes, market dynamics, technological advancements, and regional trends. The report leverages a significant dataset, estimating world multiple-rows cotton picker production in the tens of millions of units.

The Price Discovery is a web based tool that allows users to view pricing information for the following crops covered by the Common Crop Insurance and the Area Risk Protection policies: barley, canola (including rapeseed), corn, cotton, grain sorghum, rice, soybeans, sunflowers, and wheat, and coverage prices, rates and actual ending values for the Livestock Risk Protection program, and expected and actual gross margin information for the Livestock Gross Margin program.

Russia Avg Producer Price: OKPD2: Textile: Yarn: Cotton data was reported at 172,708.290 RUB/Ton in Jan 2019. This records a decrease from the previous number of 181,215.350 RUB/Ton for Dec 2018. Russia Avg Producer Price: OKPD2: Textile: Yarn: Cotton data is updated monthly, averaging 170,079.550 RUB/Ton from Jan 2017 (Median) to Jan 2019, with 25 observations. The data reached an all-time high of 225,885.700 RUB/Ton in May 2018 and a record low of 126,748.810 RUB/Ton in Jul 2017. Russia Avg Producer Price: OKPD2: Textile: Yarn: Cotton data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PB004: Average Producer Price: Textile.

About this dataset

Context

The dataset tabulates the Cotton township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Cotton township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Cotton township was 454, a 0% decrease year-by-year from 2022. Previously, in 2022, Cotton township population was 454, an increase of 0.22% compared to a population of 453 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cotton township decreased by 63. In this period, the peak population was 517 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Cotton township is shown in this column.
• Year on Year Change: This column displays the change in Cotton township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cotton township Population by Year. You can refer the same here

Indonesia Average Rural Consumer Price: Cotton Sarong data was reported at 64,396.000 IDR/Piece in Dec 2018. This records an increase from the previous number of 64,342.000 IDR/Piece for Nov 2018. Indonesia Average Rural Consumer Price: Cotton Sarong data is updated monthly, averaging 44,239.000 IDR/Piece from Jan 2008 (Median) to Dec 2018, with 132 observations. The data reached an all-time high of 64,467.000 IDR/Piece in Oct 2018 and a record low of 20,151.000 IDR/Piece in Jan 2008. Indonesia Average Rural Consumer Price: Cotton Sarong data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Prices – Table ID.PE107: Average Rural Consumer Price: By Province: Housing Product: Cotton Sarong.

The spread of cotton leaf curl disease in China, India and Pakistan is a recent phenomenon. Analysis of available sequence data determined that there is a substantial diversity of cotton-infecting geminiviruses in Pakistan. Phylogenetic analyses indicated that recombination between two major groups of viruses, cotton leaf curl Multan virus (CLCuMuV) and cotton leaf curl Kokhran virus (CLCuKoV), led to the emergence of several new viruses. Recombination detection programs and phylogenetic analyses showed that CLCuMuV and CLCuKoV are highly recombinant viruses. Indeed, CLCuKoV appeared to be a major donor virus for the coat protein (CP) gene, while CLCuMuV donated the Rep gene in the majority of recombination events. Using recombination free nucleotide datasets the substitution rates for CP and Rep genes were determined. We inferred similar nucleotide substitution rates for the CLCuMuV-Rep gene (4.96X10-4) and CLCuKoV-CP gene (2.706X10-4), whereas relatively higher substitution rates were observed for CLCuMuV-CP and CLCuKoV-Rep genes. The combination of sequences with equal and relatively low substitution rates, seemed to result in the emergence of viral isolates that caused epidemics in Pakistan and India. Our findings also suggest that CLCuMuV is spreading at an alarming rate, which can potentially be a threat to cotton production in the Indian subcontinent.

Indonesia Average Rural Consumer Price: Cotton Sarong: West Java data was reported at 60,556.000 IDR/Piece in Dec 2018. This stayed constant from the previous number of 60,556.000 IDR/Piece for Nov 2018. Indonesia Average Rural Consumer Price: Cotton Sarong: West Java data is updated monthly, averaging 44,535.500 IDR/Piece from Jan 2008 (Median) to Dec 2018, with 132 observations. The data reached an all-time high of 60,556.000 IDR/Piece in Dec 2018 and a record low of 14,614.000 IDR/Piece in Jan 2008. Indonesia Average Rural Consumer Price: Cotton Sarong: West Java data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Prices – Table ID.PE107: Average Rural Consumer Price: By Province: Housing Product: Cotton Sarong.

About this dataset

Context

The dataset tabulates the data for the Cotton Plant, AR population pyramid, which represents the Cotton Plant population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Cotton Plant, AR, is 27.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Cotton Plant, AR, is 62.2.
• Total dependency ratio for Cotton Plant, AR is 89.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Cotton Plant, AR is 1.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Cotton Plant population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Cotton Plant for the selected age group is shown in the following column.
• Population (Female): The female population in the Cotton Plant for the selected age group is shown in the following column.
• Total Population: The total population of the Cotton Plant for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cotton Plant Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Cotton Valley population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Cotton Valley across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Cotton Valley was 751, a 1.05% decrease year-by-year from 2022. Previously, in 2022, Cotton Valley population was 759, a decline of 1.04% compared to a population of 767 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cotton Valley decreased by 408. In this period, the peak population was 1,159 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Cotton Valley is shown in this column.
• Year on Year Change: This column displays the change in Cotton Valley population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cotton Valley Population by Year. You can refer the same here