This statistic shows the total cotton yield per harvested acre in the U.S. from 2001 to 2024. In 2024, the total cotton yield per harvested acre amounted to approximately 836 pounds. This constitutes a decrease of 63 pounds from 2023.

United States Crop Production: Cotton: Yield Per Acre data was reported at 905.000 Bale in 2017. This records an increase from the previous number of 867.000 Bale for 2016. United States Crop Production: Cotton: Yield Per Acre data is updated yearly, averaging 619.000 Bale from Dec 1963 (Median) to 2017, with 55 observations. The data reached an all-time high of 905.000 Bale in 2017 and a record low of 404.000 Bale in 1980. United States Crop Production: Cotton: Yield Per Acre data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s USA – Table US.B068: Agriculture Crop Production.

The yield of cotton in Brazil was forecast to continuously increase between 2024 and 2033 by in total 0.1 tonnes per hectare (+5.4 percent). After the ninth consecutive increasing year, the yield is estimated to reach 1.94 tonnes per hectare and therefore a new peak in 2033. Notably, the yield of cotton was continuously increasing over the past years.Find more statistics on other topics about Brazil with key insights such as production volume of soybean and harvested area of soybean.

United States Crop Production: Cotton: Upland: Yield Per Acre data was reported at 895.000 Bale in 2017. This records an increase from the previous number of 855.000 Bale for 2016. United States Crop Production: Cotton: Upland: Yield Per Acre data is updated yearly, averaging 683.000 Bale from Dec 1978 (Median) to 2017, with 40 observations. The data reached an all-time high of 895.000 Bale in 2017 and a record low of 402.000 Bale in 1980. United States Crop Production: Cotton: Upland: Yield Per Acre data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s USA – Table US.B068: Agriculture Crop Production.

Cotton is an important crop and commodity produced in the Indian subcontinent. At the end of fiscal year 2024, the yield of cotton produced in the country was estimated to be around 436 kilograms per hectare, a decrease from the previous fiscal year.  Cotton production  India is the world's leading cotton producing country. Cotton cultivation has witnessed major technological advancement with the introduction of hybrids for commercial use in late 1960s. Additionally, due to shorter cultivation duration of many superior cotton hybrids, crop rotation with wheat has been practiced in many northern states. The area for cotton cultivation across the country was nearly 13 million hectares in 2018.  Importance of cotton in economy   Cotton plays an important role in in the agricultural and industrial economy of the country. For instance, the export value of cotton and cotton products was over 783 billion Indian rupees in fiscal year 2019. As an economically viable crop, cotton, has gained much attention from the government and has benefited from various schemes and programs.

United States Crop Production: Cotton: American Pima: Yield Per Acre data was reported at 1,341.000 Bale in 2017. This records a decrease from the previous number of 1,454.000 Bale for 2016. United States Crop Production: Cotton: American Pima: Yield Per Acre data is updated yearly, averaging 1,028.000 Bale from Dec 1978 (Median) to 2017, with 40 observations. The data reached an all-time high of 1,581.000 Bale in 2012 and a record low of 531.000 Bale in 1979. United States Crop Production: Cotton: American Pima: Yield Per Acre data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s USA – Table US.B068: Agriculture Crop Production.

This statistic shows the cotton yield in China in selected years from 2000 to 2024. In 2024, China's cotton yield amounted to ******* kilograms per hectare.

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

Annual cotton production in the United States grew from just a few thousand tons at the turn of the 19th century, to fluctuating between 1.6 million and 4.3 million tons throughout most of the 20th century. The amount of space used to produce cotton also grew from three to almost 18 million hectares of land between 1866 and the 1920s, before dropping to around four or five million hectares between the 1960s and 1980s. Despite this drop in land usage, advancements in agricultural technology meant that output remained relatively constant in the 20th century, meaning that output per hectare actually increased significantly.

The mechanical cotton gin's invention in 1793 revolutionized the U.S. cotton industry, which grew exponentially in the early 19th century. Cotton was the U.S.' primary export in these years, and its production was driven by slave labor in the southern states (particularly South Carolina). For the first time, output exceeded one million tons in 1859, and again in 1861, however, the disruption of the American Civil War caused cotton output to drop by over 93 percent in the next three years, to just 68 thousand tons by 1864. Production resumed upon its previous trajectory following the war's end, and many of the former-slaves forced to work on cotton plantations continued to work in the cotton industry, but as sharecroppers who worked the land in exchange for a share of the harvest, as well as housing and facilities (this was similar to tenant farming, although sharecroppers received a smaller share of the crop and had fewer legal protections).

United States Long Term Projections: Upland Cotton: Yield (Pounds per Harvested Acre) data was reported at 886.000 lb/Acre in 2034. This records an increase from the previous number of 882.000 lb/Acre for 2033. United States Long Term Projections: Upland Cotton: Yield (Pounds per Harvested Acre) data is updated yearly, averaging 870.000 lb/Acre from Dec 2022 (Median) to 2034, with 13 observations. The data reached an all-time high of 942.000 lb/Acre in 2022 and a record low of 778.054 lb/Acre in 2024. United States Long Term Projections: Upland Cotton: Yield (Pounds per Harvested Acre) data remains active status in CEIC and is reported by U.S. Department of Agriculture. The data is categorized under Global Database’s United States – Table US.RI012: Agricultural Projections: Upland Cotton.

This statistic shows the world's leading cotton producing countries in crop year 2022/2023. In that year, cotton production in China amounted to around 6.7 million metric tons.

Cotton production

Cotton is a natural plant fiber which grows around the seed of the cotton plant. Fibers are used in the textile industry, where they are the starting point of the production chain. First, the cotton fiber is obtained from the cotton plant and then spun into yarn. From there, the cotton yarn is woven or knitted into fabric.

The use of cotton has a long tradition in the clothing industry due to its desirable characteristics. Cloths made of this fiber are moisture-absorbent, have a good drape and are known for their long durability. Consumers continue to purchase large amounts of cotton products as they prefer cotton’s light and comfortable qualities. Products made out of cotton range from highly absorbent bath towels over bed linens to basic clothes such as t-shirts, underwear or socks.

The top cotton producing countries include China, India and the United States respectively. Within the United States, the Southern states traditionally harvest the largest quantities of cotton. This region was formerly known as the ‘Cotton Belt’, where cotton was the predominant cash crop from the 18th to the 20th century. Due to soil depletion and social and economic changes, cotton production has declined and acres in this region are now mainly used for crops such as corn, soybeans and wheat.

Myanmar Crop Yield: HA: Cotton: Long Staple: per Viss data was reported at 546.000 Unit/Acre in 2017. This records an increase from the previous number of 540.000 Unit/Acre for 2016. Myanmar Crop Yield: HA: Cotton: Long Staple: per Viss data is updated yearly, averaging 197.000 Unit/Acre from Mar 1986 (Median) to 2017, with 24 observations. The data reached an all-time high of 546.000 Unit/Acre in 2017 and a record low of 147.000 Unit/Acre in 2001. Myanmar Crop Yield: HA: Cotton: Long Staple: per Viss data remains active status in CEIC and is reported by Central Statistical Organization. The data is categorized under Global Database’s Myanmar – Table MM.RI005: Agriculture: Crop Yield.

Agricultural Yield: Cotton: Andhra Pradesh data was reported at 353.000 kg/ha in 2025. This records an increase from the previous number of 297.000 kg/ha for 2024. Agricultural Yield: Cotton: Andhra Pradesh data is updated yearly, averaging 409.000 kg/ha from Mar 2001 (Median) to 2025, with 25 observations. The data reached an all-time high of 649.000 kg/ha in 2020 and a record low of 230.000 kg/ha in 2003. Agricultural Yield: Cotton: Andhra Pradesh data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIC023: Yield of Non Foodgrains in Major States: Cotton.

This statistic shows the leading ten U.S. states with the highest cotton production in 2024. In that year, Texas was estimated to produce about *** million bales of cotton. The United States in total were ranked as third leading cotton producing country worldwide in 2023/2024. Cotton production The production of cotton is one of the United States’ most important industries. The country was the world’s third largest cotton producer after China and India, as well as the leading global cotton exporter in 2022/2023. In terms of cotton production by state, U.S. cotton is mostly grown and harvested in the Southern states, also known as the “Cotton Belt” where cotton plantations were established in the 1800s. More than *** billion U.S. dollars’ worth of cotton was produced in Texas in 2022. Upland cotton is the nation’s most commonly cultivated variety of cotton. American Pima, a finer and more expensive cotton fiber, is mainly cultivated in Arizona and California. In 2023, the United States allocated some ***** million acres of land for planting cotton. Approximately ***** million bales of cotton were produced that year.Cotton is typically used in the production of textiles. Hence Bangladesh, a leader in textile manufacturing, was the largest cotton importer in 2022/2023.

农作物产量：棉花：Upland：Yield Per Acre在12-01-2017达895.000包，相较于12-01-2016的855.000包有所增长。农作物产量：棉花：Upland：Yield Per Acre数据按年更新，12-01-1978至12-01-2017期间平均值为683.000包，共40份观测结果。该数据的历史最高值出现于12-01-2017，达895.000包，而历史最低值则出现于12-01-1980，为402.000包。CEIC提供的农作物产量：棉花：Upland：Yield Per Acre数据处于定期更新的状态，数据来源于National Agricultural Statistics Service，数据归类于Global Database的美国 – 表 US.B068：农作物生产。

Two datasets in the EOS-WEBSTER US County Data Collection provide county-level data for crop acreage, production and yield statistics. Crop data for 22 different field crops were acquired from the National Agricultural Statistical Service (NASS) for 1972 through 1998. One dataset provides data for individual varieties/types of each crop while the second dataset provides summary data by crop only. Data can be subset by irrigated and non-irrigated areas. Sucrose content, where applicable, is also included.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

 US County Data Datasets:

 1) Agricultural Management
 2) Crop Data (NASS Crop data)
 3) Crop Summary (NASS Crop data)
 4) Geography and Population
 5) Land Use
 6) Livestock Populations
 7) Soil Properties

United States Crop Production: Cotton: Area Harvested data was reported at 11,100.400 Acre th in 2017. This records an increase from the previous number of 9,507.800 Acre th for 2016. United States Crop Production: Cotton: Area Harvested data is updated yearly, averaging 11,470.900 Acre th from Dec 1963 (Median) to 2017, with 55 observations. The data reached an all-time high of 16,006.700 Acre th in 1995 and a record low of 7,367.500 Acre th in 1983. United States Crop Production: Cotton: Area Harvested data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s USA – Table US.B068: Agriculture Crop Production.

United States Crop Production: Cotton: Upland: Area Planted data was reported at 13,275.000 Acre th in 2018. This records an increase from the previous number of 12,360.000 Acre th for 2017. United States Crop Production: Cotton: Upland: Area Planted data is updated yearly, averaging 12,668.300 Acre th from Dec 1983 (Median) to 2018, with 36 observations. The data reached an all-time high of 16,716.800 Acre th in 1995 and a record low of 7,883.300 Acre th in 1983. United States Crop Production: Cotton: Upland: Area Planted data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s USA – Table US.B068: Agriculture Crop Production.

Cotton Picking Robot Market Outlook

According to our latest research, the global cotton picking robot market size in 2024 stands at USD 1.34 billion, reflecting a robust adoption rate across key agricultural economies. The market is projected to expand at a CAGR of 16.7% from 2025 to 2033, reaching an estimated USD 4.37 billion by the end of the forecast period. This accelerated growth is primarily driven by the acute labor shortages in agriculture, rising demand for higher operational efficiency, and the increasing integration of advanced technologies such as machine vision and artificial intelligence in farm machinery.

One of the most significant growth factors for the cotton picking robot market is the persistent scarcity of manual labor in the agricultural sector, especially in developed and rapidly industrializing countries. Traditional cotton harvesting is labor-intensive and time-consuming, leading to higher operational costs and inconsistent yields. As rural populations migrate to urban centers and the average age of the agricultural workforce increases, farmers face mounting challenges in maintaining productivity. The deployment of autonomous and semi-autonomous cotton picking robots offers a technologically advanced solution, enabling large-scale farms to sustain output levels while reducing dependency on manual labor. The integration of robotics in cotton harvesting not only addresses labor shortages but also enhances efficiency, consistency, and precision in the picking process.

Another key driver of the cotton picking robot market is the increasing emphasis on sustainable agricultural practices and the need to maximize yield per acre. Cotton is a major cash crop in several regions, and fluctuations in yield can significantly impact both local economies and global supply chains. By adopting machine vision, AI and machine learning, and GPS/GNSS technologies, cotton picking robots can optimize harvesting routes, minimize crop damage, and ensure selective picking of mature cotton bolls. This not only boosts yield quality but also reduces post-harvest losses, contributing to greater profitability for farmers. Furthermore, the adoption of electric and hybrid power sources in these robots aligns with the global shift towards eco-friendly and energy-efficient agricultural machinery, further propelling market growth.

Government initiatives and policy support play a vital role in accelerating the adoption of cotton picking robots, particularly in emerging economies where agriculture remains a backbone of the economy. Subsidies, tax incentives, and grants for the adoption of precision agriculture technologies are encouraging both large-scale and small-to-medium farms to invest in robotic solutions. Additionally, collaborations between research institutes, agricultural universities, and technology providers are fostering innovation and facilitating the transfer of cutting-edge solutions to end-users. This ecosystem of support, combined with rising awareness about the benefits of automation, is expected to sustain the upward trajectory of the cotton picking robot market over the forecast period.

Regionally, the Asia Pacific currently leads the global cotton picking robot market, accounting for a significant share due to the presence of major cotton-producing countries such as China and India. North America and Europe are also witnessing substantial adoption, driven by advanced technological infrastructure and a strong focus on precision agriculture. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by ongoing modernization efforts and increasing investments in agricultural automation. The regional dynamics are further shaped by varying degrees of labor availability, farm sizes, and regulatory frameworks, influencing the pace and scale of robot adoption across different geographies.

Product Type Analysis

The product type segment of the cotton picking ro

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from India were selected based on the following criterion: (a) Corn growers in Davanagere, Belgaum, Warangal, Kurnool (all = districts) Location: Davanegere, Belgaum, Warangal, Kurnool
Average adapter of innovation
Mechanized tillage operation due labour shortage
Keeps animals for milk
Corn forage is used for animal feeding
Keep update on commercial market trend Secondary source of Agriculture income is dairy Relies on high fertilizer use. (Farmers who use >2 bags of urea and 1 bag of DAP per acre is considered as High fertilizer use growers) Low use of crop protection products (aim for growers who don't use CPP, if not possible, low use --> UPDATED: maximum of 2 sprays!) Traditional way of weed control (bullock drawn harrow followed by ridging)

(b) Cotton growers in WC & South
Location: Yavatmal , Akola, Aurangabad, Jalgaon, Warangal , Kurnool , Karimnagar (= all districts)
Commercial, normally traditional practices but a few always looks for new products. (Use hybrids and are interested in new products which deliver higher yields, with less disease and pests.)
Very particular about quality seed.
High expectation of profit from farming.
Good investment on inputs for getting maximum returns.
Some irrigation available but not sufficient, Manual operations.
Social and seeks knowledge from other fellow farmers and retailers. Western regions: I take all decisions in terms of cotton production by myself, without consulting fellow farmers, retailers, agronomists or sales representatives (based on answers of RF) Use generic / branded chemistry Dependent on retailers to fund his crop protection chemicals
Prefer Cotton hybrid which give good re flushing
Rotation with Bengalgram

(c) Rice growers in North & East Location: Karnal, Ludhiana, Sri Muktsar Sahib, Patiala, Allahabad,Gorakhpur, Barabanki (North & East)
Commercial ,Average adapter of innovation.
Medium input cost. (Spend 300 - 500 Rs on fertilizers, About 400-500 Rs on CP products can be considered as moderate or medium input cost.)
Mechanized tillage operations due to shortage of labour.
Good use of CP products. (Use products of leading MNCs; new chemistry/new products etc) Very particular about quality seeds.
Always look forward to new technologies that would reduce costs or increase profits.
High expectation of profits from farming.
Good investment on input for getting maximum returns.
Not aware about soil fertility issues.
Use generic chemicals
Dependent on commission agent for his recurring expenses or retailer to fund his inputs. = ALL BACKGROUND INFO
May or may not own a tractor.
High involvement of retailer/ commission agent on his decision of CP inputs Rice wheat rotation.

(d) Rice growers in East Location: Ranchi, Raipur (= west), Burdwan, Midnapore , Bhagalpur . (= East)
Late adapter of innovation . --> UPDATED: Western region (Raipur): BF is not late adapter of innovation (based on answer of RF) Usage of hybrid Rice or traditional varieties . (Either Open Pollinated Varieties or certified hybrids is fine. )
Moderate usage of CP products . (The spend on CP products is relatively lower i.e. less number of sprays or lower dose of recommended CP products. ) = ALL BACKGROUND INFO
Lack of resources ( irrigation, finance ) ,less educated ,traditional (= background info),low financial status .
Primarily dependent on farm for food and income. --> RF in Raipur (western region) says to not depend on his farm for income but BF will be recruited based on the original screening criteria above
not aware about soil fertility . --> UPDATED: in western region: BF are aware about soil fertility (based on answer of RF) --> UPDATED: Eastern region (Jharkhand & Bihar): BF are aware about soil fertility (based on answer of RF)
Depends on fertilizer for enhancing productivity.
Usage of generic chemistry. May or may not own tractor. High involvement of retailer on his decision of CP inputs . --> RF in Raipur (western region) says to take all decisions himself but BF will be recruited based on the original screening criteria above
Migrated farmers adopt technology . = ALL BACKGROUND INFO
Traditional cultivation practice. (This generally means OPV, little fertilizers and little chemicals.) = ALL BACKGROUND INFO
Conversion happening from OP to hybrid seeds in rainfed areas. = ALL BACKGROUND INFO

(e) Tomato growers location: Nasik, Pune, Ahmednagar, Belgaum, Vadodara, Jaipur.
Early adapter of innovation.
Mechanized tillage operations due to labour shortage.
Very particular about quality Seeds.
Always look forward to new CP technologies to increase profit
Good crop knowledge & Use advance chemistry ( Farmers who use newly launched, high performance CP products from leading MNCs can be considered as "Advance" or new chemistry products.). --> UPDATED: in Western regions: only have a little bit of knowledge about this and use only a little bit (based on answers of RF)
Use of SYT tomato seeds & CP products. (only for RF, BF can use SYT products but not necessarily) = ALL BACKGROUND INFO, is asked in screening but nobody is screened out (!)
Keep updates on commercial market trend .
Irrigated farms
Has milch animals. --> UPDATED: in Western regions, not all should have livestock (based on answer of RF)
Brand loyalty
Commercially very active.
Knows market prices in leading cities.
Has relationship with market forces.
Keeps in touch with other progressive farmers, good retailers and company professionals.

(f) Soybean growers location: Ratlam, Dhar, Hoshangabad, Washim Follow traditional cultivation practices . (Usually the use of farm-saved seeds and varieties, do not use adequate fertilizers, follow traditional interculture practices etc.)
Limited technical knowledge.
Many use farm saved seed.
Mechanized tillage and spraying operation.
Use of tractor for sowing and threshing operations.
Low investment on input in comparison with actual requirement.
Farmers are members of co-operative society in some areas. = ALL BACKGROUND INFO
Soyabean wheat rotation
Some involvement of retailer/commission agent on his decision of CP inputs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a.