,This dataset consists of growth and yield data for each season when upland cotton [Gossympium hirsutum (L.)] was grown for lint and seed at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In the 2000 through 2004, 2008, 2010, 2012, and 2020 seasons, cotton was grown on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field also planted to cotton. The square fields were themselves arranged in a larger square with four fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field were thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Cotton was grown on different combinations of fields in different years. When irrigated, irrigation was by linear move sprinkler system years before 2014, and by both sprinkler and subsurface drip irrigation in 2020. Irrigation protocols described as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigation protocols described as deficit typically involved irrigation at rates established as percentages of full irrigation ranging from 33% to 75% depending on the year.,The growth and yield data typically include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, boll mass (when present), lint mass, seed mass, final yield, and lint quality. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from only manual sampling on replicate plots in each field and lysimeters.,These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on cotton ET, crop coefficients, crop water productivity, and simulation modeling of crop water use, growth, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.,See the README for descriptions of each data file.,

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.

,This dataset consists of agronomic calendars for each growing season (year) when upland cotton [Gossypium hirsutum (L.)] was grown for fiber and seed at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). In 2000, 2001, 2008, 2020, and 2021, cotton was grown on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. In 2002, 2010, and 2012, cotton was grown on two large, precision weighing lysimeters and their surrounding 4.44 ha square fields. In 2003 and 2004, cotton was grown on only one large weighing lysimeter in rotation with sorghum. The four fields were contiguous. The fields were designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW), and were themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Irrigation was by linear move sprinkler system in from 2000 through 2012. In 2020 and 2021, the NE and SE fields were irrigated using subsurface drip irrigation (SDI), while the NW and SW fields were irrigated using a linear move system. Cotton was sometimes grown as a dryland crop, sometimes as a fully irrigated crop, and sometimes as a deficit irrigated crop. Irrigations designated as full were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Irrigations designated as deficit typically involved full irrigation to establish the crop. A crop calendar for each season lists by date the pertinent agronomic and maintenance operations (e.g., planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest). For each season there is one crop calendar for each two lysimeters (NE and SE, and/or NW and SW). These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used for testing, and calibrating models of ET that use satellite and/or weather data.,See the README for descriptions of each data file.,

,This dataset consists of weighing lysimeter data for upland cotton [Gossypium hirsutum (L.)] grown for lint and seed at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL) in 2000 through 2004, 2008, 2010, 2012, 2020, and 2021 on from one to four large, precision weighing lysimeters, each in the center of a 4.44 ha square field similarly cropped. In 2019, cotton was grown on four large, precision weighing lysimeters, each in the center of a 4.4-ha square field. The weighing lysimeters were used to measure mass, which was converted to relative soil water storage with 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Although a quality control process was used, the ET data in this dataset are considered raw data. Advanced algorithms for detection of precipitation, dew and frost were applied in a separate process to determine ET values that are reported in files in a dataset entitled "Evapotranspiration and Water Balance Data for The Bushland, Texas Cotton Datasets". Those files have "water-balance" in their names. Each lysimeter was equipped with a suite of instruments to sense wind speed, air temperature and relative humidity, components of the radiation balance (e.g., net radiation, incoming and reflected shortwave, photosynthetically active radiation (PAR), incoming and reflected longwave, thermal infrared emitted by the plant/soil surface), soil heat flux, soil temperature, and soil volumetric water content at certain depths. Not all properties were always sensed in any one year; and instruments used changed from season to season, which are reasons that subsidiary datasets and data dictionaries for each season are required. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), lack of irrigation (dryland production), agronomic practices, cultivar, and weather. Prior publications have focused on cotton ET, crop coefficients, crop water productivity, and simulation modeling of crop growth, water use, and yield. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by both USDA and university researchers.,,See the README file (README_Bushland_Cotton_Lys.txt) for descriptions of each data file. Descriptions are different for each year because experimental protocols changed yearly.,

,Dr. Kevin Bronson provides a dataset representing the third of three consecutive years of cotton and nitrogen management experimentation in Field 113 of the Maricopa Agricultural Center, Arizona USA. Included is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.,See included README file for operational details and further description of the measured data signals.,Summary - Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2018 cotton season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of high-throughput plant phenotyping. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake was also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).,

For the latest Cotton Chart see the Cotton and Wool Outlook report which can be found on the Cotton and Wool Outlook report page on USDA’s Economic Research Service website.

,Dr. Kevin Bronson provides a dataset representing the second of three consecutive years of cotton and nitrogen management experimentation in Field 113. Included is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.,See included README file for operational details and further description of the measured data signals.,Summary - Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2017 cotton season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of high-throughput plant phenotyping. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake was also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).,

,On 19 June 2014, we conducted a two-tiered search (through that date) on the Web of Science Core Collection, CAB International, MEDLINE, Biological Abstracts, FSTA (Food Science and Technology Abstracts), and Zoological Record databases, using the ISI Web of Science search tool. We located 239,571 unique publications with the search terms: cotton OR Gossypium. A search of these records using the term “cover crop” resulted in 424 publications, composed of refereed articles, conference proceedings, research reports, and bulletins. With examination of these 424 eligible publications, 320 were excluded because they met our exclusion criteria: means for cover crop or no-cover crop treatments were not included, cotton yield or weed growth were not reported, article was a duplicate, article did not contain primary data (review or book), or they were not obtainable using interlibrary loan services (five articles). We did not include intercropping (cover crops grown simultaneously with cotton) studies, nor did we include studies that used weed count as the response variable. For the weed biomass effect size (ES), if an experiment included both weed and weed-free fallow no-cover-crop controls, we used the weed fallow no-cover-crop control in our analysis. If an experiment included herbicides applied over all treatments in season, we excluded the weed biomass ES but included the cotton biomass ES. We identified 104 articles that met our screening criteria (a full citation list and details of primary studies are provided in the supplemental material). Papers spanned 48 yr and were in English and Portuguese languages.,Treatment means and number of replications (sample sizes) were collected for each study. For publications reporting means for more than one no-cover-crop (control) treatment in a nonfactorial experiment, we used the no-cover-crop control that most closely approximated the cover crop treatment. If replications were given as a range, we used the smallest value. For studies that did not report number of replications, we used n = 1 unless LSD or SEs were provided, in which case we used n = 2. If data were provided in graphical form, means were extracted using WebPlotDigitizer (Rogatgi, 2011).,Multiple treatment combinations from one article were treated as independent studies (also referred to as trials or paired observations in the meta-analysis literature) and represented individual units in the meta-analysis. For example, Ashworth et al. (2018) and Li et al. (2013) examined the effects of two cover crop species over 3 yr, resulting in six studies from that article for lint yield ES. Vasilakoglou et al. (2011) studied control of three weed genera by four varieties of one cover crop species, resulting in 12 studies for the weed control ES. Although, the use of multiple studies from one publication has the disadvantage of increasing the dependence among studies that are assumed to be independent (Gurevitch and Hedges, 1999), the greater number of studies maximizes the meta-analysis’ statistical power (Lajeuness and Forbes, 2003). This approach has been used often in agricultural and plant biology meta-analyses (Mayerhofer et al., 2013; McGrath and Lobell, 2013; Ferraretto and Shaver, 2015). Therefore, we derived 1117 studies from 104 articles. As in prior meta-analyses (Ashworth et al., 2018; Mayerhofer et al., 2013), we used the final time point in the meta-analysis for studies that included data for multiple time points in one season. One exception was weed control, as an article used in this meta-analysis reported means that were recorded at three time points during the season (Norsworthy et al., 2010). Considering that each year of an experiment provides varying growing conditions only weakly correlated with other years (repeated measures across years is not needed in our experience), we considered each year as an independent study in the meta-analysis.,

,In 2017, cotton (Gossypium hirsutum L.) leafroll dwarf virus (CLRDV) was first reported in the United States. One CLRDV inoculum source includes the previous year’s cotton stalks, hence destroying cotton stalks could be effective for CLRDV management. However, tillage intensive stalk destruction methods (SDMs) can degrade southeastern soils, but a cover crop may provide short-term benefits and reduce CLRDV incidence. Therefore, we examined three SDMs (Tillage, Pull, Mow) across two cover crop levels [no cover and rye (Secale cereale L.) /clover (Trifolium incarnatum L.) mixture] and two cotton varieties to determine how cotton growth, soil penetration resistance (PR), and two CLRDV incidence sample times (pre-harvest and post-harvest) were affected across six environments during the 2021 and 2022 growing seasons. None of the SDMs affected any factors examined in this experiment, except soil PR and cotton yield. The Pull and Mow SDMs both increased soil PR compared to the Tillage SDM. An 8% yield increase (Pull > Mow) was observed, but the Tillage SDM yield did not differ from Pull or Mow SDMs. The rye/clover mixture also increased soil PR. Although cotton stands were 15% greater with no cover crop, subsequent cotton yield and fiber quality were minimally affected by cover crops. The rye/clover mixture increased post-harvest CLRDV incidence, and cotton yields were equal between cover crops. Pre-harvest CLRDV incidence probability was 0.23, but post-harvest CLRDV incidence probability was 0.71. Continuing to identify and evaluate cultural practices that reduce CLRDV incidence is imperative to prevent negative impacts.,This dataset contains all data and code required to reproduce the analyses, tables, and figures in the associated manuscript. A list of R packages used to create the aforementioned items can be found in the associated manuscript.,