Brazil Agricultural Production: Average Yield: Cotton data was reported at 4,385.000 kg/ha in 2023. This records an increase from the previous number of 3,840.000 kg/ha for 2022. Brazil Agricultural Production: Average Yield: Cotton data is updated yearly, averaging 2,903.682 kg/ha from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 4,385.000 kg/ha in 2023 and a record low of 493.216 kg/ha in 1981. Brazil Agricultural Production: Average Yield: Cotton data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIC001: Agricultural Yield.

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.

Brazil Agricultural Average Yield: Cotton data was reported at 4,129.000 kg/ha in Jun 2019. This records a decrease from the previous number of 4,130.000 kg/ha for May 2019. Brazil Agricultural Average Yield: Cotton data is updated monthly, averaging 3,557.500 kg/ha from Mar 1998 (Median) to Jun 2019, with 256 observations. The data reached an all-time high of 4,295.000 kg/ha in Nov 2018 and a record low of 1,413.000 kg/ha in Sep 1998. Brazil Agricultural Average Yield: Cotton data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Agriculture Sector – Table BR.RIC001: Agricultural Yield.

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 *** pounds. This constitutes a decrease of ** pounds from 2023.

Brazil Agricultural Production: Average Yield: Cotton: Arboreal data was reported at 219.000 kg/ha in 2013. This records a decrease from the previous number of 600.000 kg/ha for 2012. Brazil Agricultural Production: Average Yield: Cotton: Arboreal data is updated yearly, averaging 161.500 kg/ha from Dec 1974 (Median) to 2013, with 40 observations. The data reached an all-time high of 712.000 kg/ha in 2011 and a record low of 48.965 kg/ha in 1983. Brazil Agricultural Production: Average Yield: Cotton: Arboreal data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.RIC001: Agricultural Yield.

The yield of cotton in Brazil amounted to *** tons in 2025. Between 1990 and 2025, the yield rose by **** tons, though the increase followed an uneven trajectory rather than a consistent upward trend. The yield will steadily rise by *** tons over the period from 2025 to 2034, reflecting a clear upward trend.

CN: Farm Crops: Cotton: Yield Per Hectare (YH) data was reported at 2,229.000 kg/ha in 2025. This records an increase from the previous number of 2,171.588 kg/ha for 2024. CN: Farm Crops: Cotton: Yield Per Hectare (YH) data is updated yearly, averaging 876.336 kg/ha from Dec 1949 (Median) to 2025, with 77 observations. The data reached an all-time high of 2,229.000 kg/ha in 2025 and a record low of 160.469 kg/ha in 1949. CN: Farm Crops: Cotton: Yield Per Hectare (YH) data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield Per Hectare: Farm Crops: Cotton: By Region.

CN: Farm Crops: Cotton: YH: Jiangsu data was reported at 1,526.300 kg/ha in 2025. This records an increase from the previous number of 1,469.030 kg/ha for 2024. CN: Farm Crops: Cotton: YH: Jiangsu data is updated yearly, averaging 810.000 kg/ha from Dec 1949 (Median) to 2025, with 77 observations. The data reached an all-time high of 1,526.300 kg/ha in 2025 and a record low of 112.500 kg/ha in 1949. CN: Farm Crops: Cotton: YH: Jiangsu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield Per Hectare: Farm Crops: Cotton: By Region.

This dataset contains agricultural data for 1,000,000 samples aimed at predicting crop yield (in tons per hectare) based on various factors. The dataset can be used for regression tasks in machine learning, especially for predicting crop productivity.

• Region: The geographical region where the crop is grown (North, East, South, West).
• Soil_Type: The type of soil in which the crop is planted (Clay, Sandy, Loam, Silt, Peaty, Chalky).
• Crop: The type of crop grown (Wheat, Rice, Maize, Barley, Soybean, Cotton).
• Rainfall_mm: The amount of rainfall received in millimeters during the crop growth period.
• Temperature_Celsius: The average temperature during the crop growth period, measured in degrees Celsius.
• Fertilizer_Used: Indicates whether fertilizer was applied (True = Yes, False = No).
• Irrigation_Used: Indicates whether irrigation was used during the crop growth period (True = Yes, False = No).
• Weather_Condition: The predominant weather condition during the growing season (Sunny, Rainy, Cloudy).
• Days_to_Harvest: The number of days taken for the crop to be harvested after planting.
• Yield_tons_per_hectare: The total crop yield produced, measured in tons per hectare.

CN: Farm Crops: Cotton: YH: Chongqing data was reported at 659.500 kg/ha in 2013. This records an increase from the previous number of 636.364 kg/ha for 2012. CN: Farm Crops: Cotton: YH: Chongqing data is updated yearly, averaging 608.974 kg/ha from Dec 1997 (Median) to 2013, with 17 observations. The data reached an all-time high of 726.087 kg/ha in 2005 and a record low of 421.000 kg/ha in 2000. CN: Farm Crops: Cotton: YH: Chongqing data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield Per Hectare: Farm Crops: Cotton: By Region.

CN: Farm Crops: Cotton: YH: Yunnan data was reported at 483.333 kg/ha in 2020. This records a decrease from the previous number of 500.000 kg/ha for 2019. CN: Farm Crops: Cotton: YH: Yunnan data is updated yearly, averaging 283.800 kg/ha from Dec 1949 (Median) to 2020, with 71 observations. The data reached an all-time high of 2,340.625 kg/ha in 2014 and a record low of 97.500 kg/ha in 1950. CN: Farm Crops: Cotton: YH: Yunnan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield Per Hectare: Farm Crops: Cotton: By Region.

Link to GitHub repository: https://github.com/jmalonso55/polihalita

Link to analysis code and results: https://github.com/jmalonso55/polihalita/blob/main/codigo_polihalita.md

About

This repository contains the data and R code for the statistical analysis and results visualization of a study aimed at evaluating the effects of different potassium fertilizer sources and application methods on cotton cultivation in Western Bahia.

The study was published as an article in Revista Ceres on June 9, 2025, and can be accessed at https://doi.org/10.71252/2177-34912025720018 for further details on the results and their interpretation.

Teixeira, P. C., Alonso, J. M., Furia, L., & Carvalho, M. D. C. S. (2025). Sources and Application Methods of Potassium Fertilizer for Cotton Cultivation in Cerrado Soil of Western Bahia. Revista Ceres, 72, e72018.

The experiment was conducted at Novo Milênio Farm in Luís Eduardo Magalhães, Bahia, Brazil (12°19'10.1" S; 45°54'18.8" W), on a plot with flat relief. The region's climate is classified as Aw (tropical with a dry winter) according to Köppen, with a mean annual temperature of 24ºC and an average annual precipitation of 1,200 mm. The study focused on cotton on the 2018/19 season, following soybean as the previous crop during the 2017/18 growing season under a no-tillage system.

Six fertilization treatments were evaluated, applying K₂O at 160 kg ha⁻¹ through different methods: pre-planting application of muriate of potash (MOP), polyhalite (Poly4), or a 50/50 blend of MOP/Poly4 (Blend), as well as post-planting (top-dressing) application of MOP and Poly4. A control treatment without K application was also included. The MOP used contained 60% K₂O, while Poly4 provided 14% K₂O, 19% S, 17% Ca, and 6% Mg.

The experiment followed a completely randomized block design with four replications. Each plot consisted of five six-meter-long cotton rows (0.76 m spacing), totaling 22.8 m². The sampling area included the two central planting rows, using the central 3 m section of each row.

On February 18, 2019, during the flowering stage, approximately 20 leaves with petioles from the fifth position from the apex were collected per plot. The samples were dried in an oven at 65°C for 72 hours, ground, and analyzed for N, P, K, Ca, Mg, S, B, Fe, Cu, Mn, and Zn levels.

Harvesting occurred from June 29 to July 1, 2019. Thirty cotton bolls were collected from the middle third of plants in each plot to assess fiber quality. The remaining bolls within the plot sampling area were also collected, combined with the initial sample, and used to estimate cotton yield. The harvested material was weighed and transported to the Bahia Cotton Producers Association (ABAPA) laboratory in Luís Eduardo Magalhães. There, fibers were separated from the seeds, and fiber quality was analyzed using a High-Volume Instrument (HVI) machine. This system measures fiber bundle strength and provides simultaneous testing for multiple fiber properties, including micronaire (Mic), fiber length (Len), short fiber index (SFI), uniformity (Uni), strength (Str), and yellowing grade (+b). Productivity was evaluated based on fiber and seed yield, measured within each plot and extrapolated to kg ha⁻¹.

Data were tested for homogeneity of variances using Bartlett’s test and for normality of residuals using the Shapiro-Wilk test (p < 0.05). If necessary, the Yeo-Johnson transformation was applied to meet ANOVA assumptions. Analysis of variance (ANOVA) was performed using the easyanova package (Arnhold, 2013) in R (R Core Team, 2024). When the F-test indicated significant differences between treatments (p < 0.10), the Scott-Knott test was used for multiple comparisons (p < 0.10). Some results were also visualized graphically using the ggplot2 package (Wickham, 2016) in R.

🇮🇳 인도

Table S5. Mean squares of the ANOVA of 15 agronomic traits measurements in 6 environments. “*”, “**” indicate significance at the probability levels of P 

CN: Farm Crops: Cotton: YH: Shaanxi data was reported at 1,789.590 kg/ha in 2024. This records an increase from the previous number of 1,617.250 kg/ha for 2023. CN: Farm Crops: Cotton: YH: Shaanxi data is updated yearly, averaging 765.000 kg/ha from Dec 1949 (Median) to 2024, with 76 observations. The data reached an all-time high of 1,789.590 kg/ha in 2024 and a record low of 157.500 kg/ha in 1983. CN: Farm Crops: Cotton: YH: Shaanxi data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RIB: Yield Per Hectare: Farm Crops: Cotton: By Region.

This dataset is a collection of 810 images of cotton leaf surfaces acquired with a hand-held microscope to develop deep learning models for leaf hairiness and assist Cotton breeders in their variety selection efforts. These images were collected from 27 genotypes grown across 2 seasons (2021-2022 and 2022-2023), one site (Australian Cotton Research Institute, -30.21, 149.60, Narrabri, NSW, Australia) and two growth conditions (Field [both years] and Glasshouse [2021-2022]). Genotypes have been anonymized to protect germplasm Intellectual Property.

This dataset is being released together with our HairNet2 paper (Farazi et al 2024). See below for links to related Datasets and Publications.

Note: if you intend to use this dataset in conjunction with CotLeaf-1, then use the CotLeaf-1 Json file attached to this collection. Lineage: Genotype Selection: A total of 27 Gossypium hirsutum Cotton genotypes were selected based on their known leaf hairiness. Genotypes were anonymised to protect germplasm intellectual property. For details refer to our HairNet2 paper.

Field experiments Plants of the 27 genotypes were established in the summer growing seasons of 2021-22 and 2022-23 at the ACRI. Seeds of each genotype were planted on the 23rd of October 2021 and the 19th of November 2022, at planting density of 10 - 12 plants m-2 in rows spaced at 1 m. Each genotype was grown in a single 13 m row. The study region is semi-arid, characterised by mild winters, hot summers and summer-dominant rainfall patterns. The region has an annual average precipitation of 646 mm. The soil of the site is a uniform grey cracking clay (USDA soil taxonomy: Typic Haplustert; Australian soil taxonomy: Grey Vertosol). Plant available soil water to 1.2 m at the site is between 160 and 180 mm. The soil at ACRI is generally 60 to 65 per cent clay fraction, of low drainage rate, pH range of 8.0 to 8.8, and low in organic matter and nitrogen. Nitrogen was applied as urea approximately 12 weeks before planting at a rate of 240 kg N ha-1. Experiments were planted following an 11-month fallow period which was preceded by a winter wheat crop. Management for all field experiments followed current high-input commercial practices: fully irrigated conditions with careful weed and insect control. Plants were furrow irrigated every 10 to 14 d (approximately 1 ML ha-1 applied at each irrigation) from December through to March, according to crop requirements. Each experiment was managed according to its individual requirements for irrigation and pest control, with all plots receiving the same management regime.

Glasshouse experiment Plants were grown in temperature-controlled glasshouses at the ACRI. About 15 seeds of each genotype were sown in 8 L plastic pots filled with soil on the 7th of November 2021. The soil was obtained from cotton fields at ACRI (see above). To improve the nutrient status of the potting mix 10 g of MULTIgro® (Incitec Pivot Fertilizers, Melbourne, Australia) basal fertiliser was dissolved into the soil before planting. MULTIgro® contains the nutrients N, P, K, S, and Ca at 13.1, 4.5, 7.2, 15.4, and 2.4 percent, respectively. A 10 mm layer of sand was added to the surface of the pots to reduce surface evaporation and assist in seedling emergence. Once emerged seedlings had reached the three-leaf stage, pots were thinned to three plants per pot. Plants were grown at 18 °C night and 32°C during the day, under natural light conditions.

Leaf selection and imaging During the 2021-22 season, leaf samples from these plants were collected on the 10th of January 2022 for the field experiment (at 11 weeks), and 11th of January 2022 for the glasshouse experiment (at 9 weeks). During season 2022-23, leaf samples from these plants were collected on the 23rd of January 2023 for the field experiment (at 9 weeks). For all these, Leaf 3 was harvested from 10 plants per genotype, placed in a paper bag and imaged the same day using the same protocol and equipment as in. Only one image per leaf was collected, along the central midvein. The abaxial side of leaves were imaged at a magnification of about 31x with a portable AM73915 Dino-lite Edge 3.0 (AnMo Electronics Corporation, Taiwan) microscope equipped with a RK-04F folding manual stage (AnMo Electronics Corporation, Taiwan) and connected to a digital tablet running DinoCapture 2.0 (AnMo Electronics Corporation, Taiwan). The exact angle of the mid-vein in each image was not fixed. However, either end of the mid-vein was always cut by the left and right borders of the field of view, and never by the top and bottom ones.

This dataset is a collection of 13,597 images of cotton leaf surfaces acquired with a hand-held microscope to develop deep learning models to classify images based on leaf hairiness and assist Cotton breeders in their variety selection efforts.

These images were collected from 27 genotypes grown across 2 seasons (2019-2020 and 2020-2021), 2 sites (Australian Cotton Research Institute, -30.21, 149.60, Narrabri, NSW, Australia and CSIRO Black Mountain Laboratories, -35.27, 149.11, Canberra, Australian Capital Territory, Australia) and two growth conditions (Field and Glasshouse). Genotypes have been anonymized to protect germplasm Intellectual Property.

Note 1: This dataset was released with our HairNet paper (Rolland et al 2022, see link below). At the time of publishing Rolland, V., Farazi, M.R., Conaty, W.C. et al. HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance. Plant Methods 18, 8 (2022). https://doi.org/10.1186/s13007-021-00820-8, this dataset was called 'Cotton leaf surface image dataset to build deep learning models for leaf hairiness trait (2019-2021)'. It has since being renamed 'CotLeaf-1: Cotton Leaf Surface Images dataset 2019-21'.

Note 2: if you intend to use this dataset in conjunction with CotLeaf-2, CotLeaf-X or AnnCoT datasets, then use the CotLeaf-1 Json file attached to the CotLeaf-2 collection (see link below).

See below for related Datasets and Publications.

Lineage: Genotype Selection: A total of 27 Gossypium hirsutum Cotton genotypes were selected based on their known leaf hairiness. Genotypes were anonymised to protect germplasm intellectual property. Various combinations of these genotypes were grown at two different Australian sites (Narrabri, New South Wales & Canberra, Australian Capital Territory), in the field or controlled glasshouse environment, and over two years (2019-2020 and 2020-2021). For details refer to Rolland et al 2021 (link attached to this submission).

Field experiments - Narrabri Seed of selected genotype were planted on Oct. 21 2019 and Nov. 6 2020, at planting density of 10 - 12 plants m-2 in rows spaced at 1 m. Each genotype was grown in a single 13 m row. The soil of the site is a uniform grey cracking clay. Nitrogen was applied as anhydrous ammonia approximately 12 weeks before planting at a rate of 200 kg N ha-1. Plants were furrow irrigated every 10 to 14 d (approximately 1 ML ha-1 applied at each irrigation) from December through to March, according to crop requirements. Each experiment was managed according to its individual requirements for irrigation and pest control, with all plots receiving the same management regime.

Glasshouse experiments - Narrabri Plants were grown in temperature-controlled glasshouses. About 15 seeds of each genotype were sown in 8 L plastic pots filled with soil on Sept. 6 2019 and Nov. 2 2020, respectively. The soil was obtained from cotton fields as above. To improve the nutrient status of the potting mix 10 g of MULTIgro® basal fertiliser was dissolved into the soil before planting. A 10 mm layer of sand was added to the surface of the pots to reduce surface evaporation and assist in seedling emergence. Once emerged seedlings had reached the three-leaf stage, pots were thinned down to two plants per pot. Plants were grown at 18 °C night and 32 °C during the day, under natural light conditions.

Glasshouse experiment - Canberra Plants were grown in temperature-controlled glasshouses. Eight seeds of selected genotypes were sown in 5 L plastic pots filled with potting mix on Nov. 30 2020. The pots were filled with a 60:40 compost:perlite soil mix. Osmocote® Exact Standard 3-4M was sprinkled on the top layer of soil before flowering. Two weeks after sowing, pots were thinned down to two plants per pot. Plants were grown at 18 °C night and 28 °C during the day, under natural light conditions.

Leaf selection and harvesting: Leaves were numbered in ascending number from the tip of the main stem, with the first fully opened leaf called leaf one. Leaves 3 and 4 from ten individual plants were harvested by cutting their petiole in a proximal position. Harvested leaves were placed in paper bags and imaged within the same day. In the 2019-2020 glasshouse experiment, a few plants died or had a missing leaf, in which case there may be genotypes for which leaves 3 and 4 were harvested from less than 10 plants.

Leaf imaging: Single leaves were imaged at a magnification of about 31x with a portable AM73915 Dino-lite Edge 3.0 microscope equipped with a RK-04F folding manual stage and connected to a digital tablet running DinoCapture 2.0. Images were captured on the abaxial side of the leaf, along the 3 central mid-veins. An average of 3 to 5 images were captured in a proximal to distal fashion along each one of the 3 mid-veins, yielding a total of about 9 to 15 images per leaf. The exact angle of the mid-vein in each image was not fixed. However, either end of the mid-vein was always cut by the left and right borders of the field of view, and never by the top and bottom ones.

Abstract NDLH 2051-1 has a mean seed cotton yield potential of 1590 kg ha-1 and has been singled out for commercial cultivation in the south and central zones of India by the Regional Agricultural Research Station, Nandyal, India. This cultivar represents an essential contribution to sustainable cotton production in India.

This dataset simulates real-world smart farming operations powered by IoT sensors and satellite data. It captures environmental and operational variables that affect crop yield across 500 farms located in regions like India, the USA, and Africa.

Designed to reflect modern agritech systems, the data is ideal for: - Predictive modeling using ML/AI - Time-series analysis - Sensor-based optimization - Environmental data visualizations - Crop health analytics

🧠 Ideal For

• Supervised ML models (regression, classification)
• Yield prediction and optimization
• Agricultural decision support systems
• Smart irrigation strategy analysis
• Data visualization of regional farm efficiency

📌 Columns Description

📫 Let's Collaborate!

If you build a notebook, model, or dashboard using this dataset — feel free to tag me or leave a comment. Happy growing! 🌱🚜

This raster dataset depicts the average number of hectares per land-area of a gridcell for cotton crops. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.