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.

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

Abstract copyright UK Data Service and data collection copyright owner.The collection of data in these studies is part of a larger study on the decline of the Scottish cotton industry. The primary aim of the project was to answer questions regarding the causality and chronology of decline in the cotton industry. Since economic historians have viewed the decline of the Scottish cotton industry as a consequence of poor productivity it was decided to explore the relationship between capital and labour and in particular, the gender structure of the cotton mill. To examine links between household and workplace. To establish the home backgrounds of females in spinning and weaving as a means of : establishing their respective status; establishing links between fathers in skilled occupations and propensity to strike among women in the cotton industry. Main Topics: This dataset contains three files, one for each census. It was derived from the dataset of census information on Paisley and Bridgeton by extracting records of heads of households from the original dataset and adding a number of derived fields which condensed household information to one record per household. Additional fields contain information on: total number in household; total number occupied; total working in textiles; total working in cotton; total number these occupied in other industries; number working in textiles related to each other; total number of boarders; total number of boarders working in textiles. One-stage stratified or systematic random sample

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes cotton production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Cotton ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BalesSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes cotton production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Cotton ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BalesSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users.For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers.This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Abstract copyright UK Data Service and data collection copyright owner.The collection of data in these studies is part of a larger study on the decline of the Scottish cotton industry. The primary aim of the project was to answer questions regarding the causality and chronology of decline in the cotton industry. Since economic historians have viewed the decline of the Scottish cotton industry as a consequence of poor productivity it was decided to explore the relationship between capital and labour and in particular, the gender structure of the cotton mill. To examine the extent of employer-owned housing in Paisley as part of a wider analysis of paternalism. Main Topics: Paternalism; employer's ownership of properties; occupations of residents. This dataset is derived from the Valuation Rolls of Paisley with additional census information on households. The dataset consists of one file which lists all the residential properties owned by thread manufacturers for the decennial years 1871-1911 and includes limited information about the occupants. This file also contains data extracted from the Valuation Rolls for the decennial years 1871 and 1891 which is linked to census information in order to establish a link between occupations and tenancy of a property. No information recorded Compilation or synthesis of existing material

A new process-based cotton model, CPM, has been developed to simulate the growth and development of upland cotton (Gossypium hirsutum L.) throughout the growing season with minimal data input. CPM predicts final cotton yield for any combination of soil, weather, cultivar and sequence of management actions. Over the last 30 years, the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) has conducted a wide range of research on cotton, including work to develop a series of "production models" designed to serve as decision aids to cotton producers. In 1996, ARS decided to develop a new "second generation" Cotton Production Model (CPM) that would retain the best features of the earlier versions in a new, more versatile, and more user friendly framework. The development process was completed to the stage of beta-testing, when the need to redirect limited resources to other priorities caused ARS to decide not to complete the validation process. ARS believes that CPM, while only partially validated, has the potential to make useful contributions to American cotton producers when completed. For these reasons, ARS decided to make the model available for further development and commercialization. The Cotton Production Model (CPM) was developed with a modular structure using an object-oriented programming language, C++. The model draws upon the latest scientific knowledge available, and is intended to be used with a wide variety of cotton types across the entire US Cotton Belt. CPM is written in C++ using a new modular structure that allows flexibility and adaptability. This object-oriented structure should allow modules to be incorporated into process-based models of other crop species (see Acock, B. and V. R. Reddy. 1977. Designing an object-oriented structure for crop models. Ecological Modeling 94: 33-44). In addition to being modular and generic, CPM has other advantages over earlier models. Compared to previous cotton models, CPM is more robust, more user-friendly, more easily maintained, and more easily updated with future advances in science. The algorithms that simulate crop growth are derived in part from the best of each of the previous models, and they incorporate new physiological information as well. A new feature of CPM is that it incorporates 2DSOIL, an excellent up-to-date soil and root process model (see Timlin, D. J., Y. Pachepsky, and B. Acock. 1996. A design for a modular, generic soil simulator to interface with plant models. Agronomy Journal 88:162-169 ). 2DSOIL tracks water movement through the soil-plant-atmosphere continuum with hourly time-steps. It also incorporates a new model of plant water relations that responds realistically to water stress. CPM has updated treatments of carbon and nitrogen stresses compared to previous models, and it is designed for easy addition of responses to phosphorus and potassium. Because the growth of each leaf, inter-node and fruit is simulated separately, CPM should be easily linked to pest or disease models. CPM has the potential to be useful as a decision aid for cotton farmers and crop production consultants. If fully developed, it would be a valuable tool to optimize management inputs such as irrigation, fertilization, plant growth regulators, and defoliant application prior to harvest. In its current version, however, CPM has not yet been fully validated to be useful as a decision aid. The released version of CPM should be considered an advanced model suitable for research purposes. ARS does not endorse its use for any other purpose at this time. Of particular importance to a decision aid model is the user interface. The interface under which CPM has been developed and tested is one that was earlier developed for the soybean model, GLYCIM, and has been documented elsewhere (Acock, B., Pachepsky, Y. A., Mironenko, E. V., Whisler, F. D., and Reddy, V. R. 1999. GUICS: A Generic User Interface for On-Farm Crop Simulations. Agronomy Journal. 91:657-665). CPM is part of the current release of GUICS.

The MegaWeeds dataset consists of seven existing datasets:

• WeedCrop dataset; Sudars, K., Jasko, J., Namatevs, I., Ozola, L., & Badaukis, N. (2020). Dataset of annotated food crops and weed images for robotic computer vision control. Data in Brief, 31, 105833. https://doi.org/https://doi.org/10.1016/j.dib.2020.105833

• Chicory dataset; Gallo, I., Rehman, A. U., Dehkord, R. H., Landro, N., La Grassa, R., & Boschetti, M. (2022). Weed detection by UAV 416a Image Dataset. https://universe.roboflow.com/chicory-crop-weeds-5m7vo/weed-detection-by-uav-416a/dataset/1

• Sesame dataset; Utsav, P., Raviraj, P., & Rayja, M. (2020). crop and weed detection data with bounding boxes. https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes

• Sugar beet dataset; Wangyongkun. (2020). sugarbeetsAndweeds. https://www.kaggle.com/datasets/wangyongkun/sugarbeetsandweeds

• Weed-Detection-v2; Tandon, K. (2021, June). Weed_Detection_v2. https://www.kaggle.com/datasets/kushagratandon12/weed-detection-v2

• Maize dataset; Correa, J. M. L., D. Andújar, M. Todeschini, J. Karouta, JM Begochea, & Ribeiro A. (2021). WeedMaize. Zenodo. https://doi.org/10.5281/ZENODO.5106795

• CottonWeedDet12 dataset; Dang, F., Chen, D., Lu, Y., & Li, Z. (2023). YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture, 205, 107655. https://doi.org/https://doi.org/10.1016/j.compag.2023.107655

All the datasets contain open-field images from crops and weeds with annotations. The annotation files were converted to text files so it can be used in the YOLO model. All the datasets were combined into one big dataset with in total 19,317 images. The dataset is split into a training and validation set.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of Texas' farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry.The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishingThis layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively.AlmondsAnimal TotalsBarleyCattleChickensCornCottonCrop TotalsGovt ProgramsGrainGrapesHayHogsLaborMachinery TotalsRiceSorghumSoybeanTractorsTrucksTurkeysWheatWinter Wheat

LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2018, http://doi.org/10.5194/gmd-2017-145). We here present an extended version of the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). Additional features of this version, including agricultural trees as a new cultivation type in LPJmL4, are described and used in Jans et al. (2020, HESS) The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. The model data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2018 part I). Additionally, these results include agricultural trees (olives, non-citrus orchards, and cotton) implemented as a new cultivation type into LPJmL4. Standard results are evaluated in Schaphoff et al. (2018 part II). Results of cotton as a newly implemented agricultural tree are evaluated in Jans et al. (2020), HESSD. The data collection includes some key output variables made with the model setup described by Jans et al. (2020, HESS). Overall, data sets are resulting from 40 different simulations, where we combined 5 different GCMs (GFDL, HadGEM, IPSL, MIROC, NorESM) with 4 different RCPs (2p6, 4p5, 6p0, 8p5) without and with CO2 fertilization, respectively. The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for crop yields, absorbed photosynthetically active radiation and the water fluxes (irrigation, transpiration, evaporation,interception, blue and green evapotranspiration). Crop yields, and water fluxes are given for each crop functional type (CFT), respectively. Monthly data are provided for one carbon flux (net primary production) and the water fluxes transpiration, evaporation, interception, and runoff. The data are provided in one binary file for each variable and simulation. An overview of all variables and information on how data are stored within the binary files are given in the file inventory.

This dataset contains basic information about soft commodities (mostly agricultural) production, supply and distribution.

The most interesting part of this dataset is that it contains not only the latest numbers but also all the revisions USDA released for a specific Country/Commodity/Attribute/Year combination. This reflects their point of view on how large events impacted these production, supply and distribution numbers.

Questions you may answer with this dataset: - How much Coffee Brazil exported in an yearly basis between 1990 until 2021? - How the USDA changed their estimations for India 2020 Cotton production across 2019-2021? - Did Corn yearly consumption raised in the U.S. between 1990 and 1999? And between 2000 and 2010? These questions are answered with code examples, check them out

To make things interesting, try to cross this data with weather forecasts and large geopolitic events (sanctions, embargos, etc.) and specific consumption trends (e.g. Corn to make biofuel)

This dataset is about books. It has 6 rows and is filtered where the book subjects is Textile industry-United States. It features 9 columns including author, publication date, language, and book publisher.

This dataset is about books. It has 6 rows and is filtered where the book subjects is Textile industry-Developing countries. It features 9 columns including author, publication date, language, and book publisher.

This dataset is about book series. It has 1 row and is filtered where the books is Development of power in the textile industry from 1700 -1930. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset shows the long-run projections (US Upland Cotton) for the US agricultural sector to 2033 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.

India Agricultural Production: Major Crops: Target: Cotton data was reported at 355.000 Ton mn in 2019. This records an increase from the previous number of 35.500 Ton mn for 2018. India Agricultural Production: Major Crops: Target: Cotton data is updated yearly, averaging 24.000 Ton mn from Mar 1998 (Median) to 2019, with 22 observations. The data reached an all-time high of 355.000 Ton mn in 2019 and a record low of 14.500 Ton mn in 2002. India Agricultural Production: Major Crops: Target: Cotton data remains active status in CEIC and is reported by Department of Agriculture and Cooperation. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIB002: Agricultural Production: Targets & Achievement of Major Crops.

This dataset is about books. It has 1 row and is filtered where the book is The Indian textile and clothing industry : an economic analysis. It features 7 columns including author, publication date, language, and book publisher.

Abstract copyright UK Data Service and data collection copyright owner. The aims of this project were to make available to scholars and other researchers information on average weekly age-and sex-specific earnings and employment shares (i.e. proportions of the total industry labour force employed) of British cotton textile workers in each year between 1833 and 1906, and to provide details on how those estimates were prepared. Main Topics: This collection comprises data on estimates of the average weekly earnings, and proportions of the industry labour force employed, by age and sex in every year between 1833 and 1906. R code and data are also provided so that interested users can use them to reproduce (but also to re-run the calculations making different assumptions) the results published in The Economic History Review (see the 'External Note' section below) and generate the data provided to History Data Service. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

Abstract This research aimed to investigate the effect of treated wastewater and doses of nitrogen on the quality of the herbaceous cotton fiber, variety BRS 187 8H. The experimental design was in randomized blocks with factorial scheme [(2 x 5) + 2], in which the factors were: two types of water, 5 doses of nitrogen (0, 80, 160, 240 and 320 kg ha-1 of N), basal application with phosphorus and potassium, and two absolute controls (supply water and wastewater). The studied variables were: percentage of the sample with impurities; amount of particles, interpreted as impurities; fiber length; uniformity; index of short fibers and resistance; rupture elongation micronaire index; maturity; reflectance; yellowness degree; and fiability index. It was verified that The treated wastewater contributed positively and cotton fibers under this treatment presented larger length, uniformity, rupture elongation and had a smaller index of short fibers.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Industrial cutting of textile materials. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.