This data product summarizes the extent of adoption of herbicide-tolerant (HT), insect-resistant (Bt), and those with both traits ("stacked") genetically engineered (GE) crops in the United States. Data cover GE varieties of corn, cotton, and soybeans over the 2000-2013 period, for the U.S.

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.

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.

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.

CottonGen has an instance of the JBrowse genome browser for viewing genome data. A list of the genomes available in CottonGen can be accessed by clicking the JBrowse link in the Tools menu. Whole Genomes 2019-03: Gossypium barbadense AD2 Hai-7124 genome ZJU v1.1_a1 2018-12: Gossypium barbadense AD2 3-79 genome HAU v2_a1 2015-12: Gossypium barbadense AD2 3-79 genome HAU v1_a1 2019-03: Gossypium hirsutum AD1 genome ZJU-Improved v2.1_a1 2018-12: Gossypium hirsutum AD1 genome HAU v1_a1 2017-09: Gossypium hirsutum AD1 genome TX-JGI v1.1_a1 2015-04: Gossypium hirsutum AD1 genome NAU-NBI v1.1_a1.1 2015-04: Gossypium hirsutum AD1 genome CGP-BGI v1_a1 2018-05: Gossypium arboreum A2 genome CRI-Updated v1_a1 2014-05: Gossypium arboreum A2 genome CGP-BGI v2_a1 2012-12: Gossypium raimondii D5 genome JGI v2_a2.1 2012-08: Gossypium raimondii D5 genome CGP-BGI v1_a1 Chloroplast Genomes Gossypium arboreum chloroplast Gossypium barbadense chloroplast Gossypium hirsutum chloroplast Gossypium raimondii chloroplast Please watch the JBrowse tutorial for more details about how to navigate and use JBrowse.

This is digital research data corresponding to a published manuscript, Cover cropping history affects cotton boll distribution, lint yields, and fiber quality, in Crop Science, Vol. 63 p. 1209–1220. There has been limited introduction of new cover crop species into cotton (Gossypium hirsutum L.) production within the last 30 years. Mounting evidence shows that traditional cover cropping species may be detrimental to cotton production, either by depleting soil fertility with crop removal, immobilizing minerals from high carbon residue, or excessive quantity of residue remaining at planting. The objective of this study was to determine the effects of growing a novel cover crop species, carinata (Brassica carinata A. Braun), as a winter annual cover crop for cotton rotation in the southeastern Coastal Plain. Over a 2-year period, carinata, winter wheat (Triticum aestivum L.), and fallow covers were maintained over winter months, then rotated into cotton. Each year, seedcotton and lint yields were collected, along with subsamples for ginning and subsequent fiber quality analyses. Additionally, end-of-season plant mapping was conducted on plants from 1-m of row per plot to determine cover crop effects on boll formation, retention, and distribution, as well as canopy architecture.

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

The Germplasm Resources Information Network (GRIN) is an online portal for information about agricultural genetic resources that are managed by the Agricultural Research Service of USDA, along with U.S. partnering organizations. The content includes general information about ARS animal, microbial and plant germplasm collections, most notably the U.S. National Plant Germplasm System (NPGS). The NPGS curates more than 600,000 active accessions of living plant material at 20 genebank locations around the U.S., and makes small quantities available globally to plant breeders and other professional scientists. GRIN also documents activities of Crop Germplasm Committees (CGC) that support the NPGS. The CGCs are comprised of public and private sector subject matter experts for a given crop (there are currently 44 CGCs) who voluntarily provide input on technical and operational matters to the NPGS. The site includes two searchable datasets: the ARS Rhizobium collection and Plant Variety Protection Certificates. The Rhizobium collection is living bacteria that nodulate the roots of leguminous plants symbiotically to provide nitrogen fixation. Samples are available to research scientists globally upon request. The Plant Variety Protection (PVP) Certificates are issued by the Agricultural Marketing Service (AMS) of USDA to provide intellectual property protection to registered new varieties of plants that are propagated by seed or tubers. The GRIN site allows queries of PVPs by certificate number, name of the crop, variety name, or certificate holder, all using data provided by the AMS.

The Germplasm Resources Information Network (GRIN) is an online portal for information about agricultural genetic resources that are managed by the Agricultural Research Service of USDA, along with U.S. partnering organizations. The content includes general information about ARS animal, microbial and plant germplasm collections, most notably the U.S. National Plant Germplasm System (NPGS). The NPGS curates more than 600,000 active accessions of living plant material at 20 genebank locations around the U.S., and makes small quantities available globally to plant breeders and other professional scientists. GRIN also documents activities of Crop Germplasm Committees (CGC) that support the NPGS. The CGCs are comprised of public and private sector subject matter experts for a given crop (there are currently 44 CGCs) who voluntarily provide input on technical and operational matters to the NPGS. The site includes two searchable datasets: the ARS Rhizobium collection and Plant Variety Protection Certificates. The Rhizobium collection is living bacteria that nodulate the roots of leguminous plants symbiotically to provide nitrogen fixation. Samples are available to research scientists globally upon request. The Plant Variety Protection (PVP) Certificates are issued by the Agricultural Marketing Service (AMS) of USDA to provide intellectual property protection to registered new varieties of plants that are propagated by seed or tubers. The GRIN site allows queries of PVPs by certificate number, name of the crop, variety name, or certificate holder, all using data provided by the AMS.

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.

Irrigator Pro is an expert system designed to provide irrigation scheduling recommendations based on scientific data resulting in conservation minded irrigation management. The success of Irrigator Pro for Peanuts created interest in other groups. A collaborative effort between the NPRL, Cotton Commission, University of Georgia, and the Peanut Foundation was established to create comparable models for cotton and corn. Irrigator Pro is an irrigation scheduling tool for peanuts, corn, and cotton developed by the USDA Agricultural Research Service National Peanut Research Lab. Irrigator Pro is an expert system designed to provide recommendations based on scientific data resulting in conservation-minded irrigation management while maintaining high yields. The Flint River Soil and Water Conservation District, with funding from USDA NRCS, partnered with the Peanut Lab and University of Georgia to develop a smartphone app and cloud-based platform for Irrigator Pro. The new version has been in beta testing for the last two crop seasons with a full launch planned for 2019. Irrigator Pro is a trusted tool by farmers, crop consultants, Extension agents, and researchers across the Southeast. The original version is a desktop software that requires manual reading of soil moisture sensors in the field and manual data entry. The new smartphone app and cloud platform have automated the data collection process, integrating remote upload of soil moisture and temperature data with the Irrigator Pro model through the app and cloud platform.

Irrigator Pro is an expert system designed to provide irrigation scheduling recommendations based on scientific data resulting in conservation minded irrigation management. The success of Irrigator Pro for Peanuts created interest in other groups. A collaborative effort between the NPRL, Cotton Commission, University of Georgia, and the Peanut Foundation was established to create comparable models for cotton and corn. Irrigator Pro is an irrigation scheduling tool for peanuts, corn, and cotton developed by the USDA Agricultural Research Service National Peanut Research Lab. Irrigator Pro is an expert system designed to provide recommendations based on scientific data resulting in conservation-minded irrigation management while maintaining high yields. The Flint River Soil and Water Conservation District, with funding from USDA NRCS, partnered with the Peanut Lab and University of Georgia to develop a smartphone app and cloud-based platform for Irrigator Pro. The new version has been in beta testing for the last two crop seasons with a full launch planned for 2019. Irrigator Pro is a trusted tool by farmers, crop consultants, Extension agents, and researchers across the Southeast. The original version is a desktop software that requires manual reading of soil moisture sensors in the field and manual data entry. The new smartphone app and cloud platform have automated the data collection process, integrating remote upload of soil moisture and temperature data with the Irrigator Pro model through the app and cloud platform. The mobile version of the app can be downloaded at https://irrigatorpro.org

The National Cotton Variety Test is an on-going standardized cotton research database originating in 1960 and covers the entire US Cotton Belt.

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

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

Soil Dynamics Research for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Auburn, Alabama This study provides data on soil C and N dynamics and subsequent trace gas emissions at the landscape scale. Evaluates effects of landscape and soil management on 1) methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) fluxes, 2) soil carbon (C) and nitrogen (N) mineralization and 3) cover crop decomposition and mineralization. Gas fluxes, C and N mineralization, and cover crop decomposition were determined on a 9-ha field at the E.V. Smith Research Center near Shorter, in AL. Consists of six replications of agroecosystem management [(corn (Zea mays L.)-cotton (Gossypium hirsutum L). rotation] that traverse the landscape. Soil managements included CsT, conventional tillage (CT), conservation tillage with dairy manure (CsTM), and conventional tillage with dairy manure (CTM) treatments. The soil management treatments were within summit, sideslope and the drainageway landscape positions. The drainageway landscape position emitted 46, 251, 59, and 185 mg CH4-C ha-1 h-1 from CT, CTM, CsT and CsTM treatments, respectively. The summit position was a CH4 consumer with CT and CsT treatments. Significant soil management treatment differences in N2O-N flux were observed only within the drainageway landscape position. Averaged across seasons, CT and CsT emitted similar N2O-N in the drainageway. Within the drainageway, dairy manure decreased N2O-N emission on CT treatments. Carbon dioxide emission in winter 2005 from CsT treatments (averaged across landscape positions) was 1304 g ha-1 h-1 CO2-C compared to 227 g ha-1 h-1 CO2-C from CT treatments. CsT and CsTM treatments increased soil organic C and total soil N after six years. This resulted in higher C and N mineralization on soils from CsT and CsTM treatments, with no differences between landscape positions. Potential C mineralization was similar for crimson clover, spring forage rape and white lupin amended soil while black oat amended soil immobilized N. Buried cover crops decomposed and mineralized faster than surface applied materials, with no differences in cover crop decomposition and mineralization k across landscape positions. Overall, landscape variability had minimal effect on C and N dynamics and cover crop decomposition compared to soil management effects. Conservation tillage, dairy manure applications, and cover crops showed potential to sequester soil organic C and increase total soil N in these systems.The study site is located at the Alabama Agricultural Experiment Station’s E.V. Smith Research Center, near Shorter. Four management treatments were established in late summer of2000 on a corn and cotton rotation that has both crops present each year. The management systems included a conventional tillage system (chisel- followed by disc-plow) with (CT+M) and without (CT) manure, and a conservation tillage system (non- inversion tillage) that incorporated the use of winter cover crops with (NT+M) and without manure (NT). A mixture of rye (Secale cereale L.) with black oat (Avena strigosa Schreb.), and a mixture of crimson clover (Trifolium incarnatum L.) with white lupin (Lupinus albus L.) and fodder radish (Raphanus sativus L.) were typically used as winter cover before cotton (Gossypium hirsutum L.) and corn (Zea mays L.), respectively. Four strips with an average length of 800 ft were established across the landscape to represent the four management systems for each crop per each replication. Each strip was further divided into cells to simplify sampling and field measurements. A total of six replications were established on the 22 ac field. Maximum slope is 8% and 9 soil map units are contained within this landscape. Prior research work at the same field site delineated four distinct zones using a digital elevation map, electrical conductivity survey, and traditional soil mapping techniques. For this study, three of these zones were selected and recognized as summit, backslope, and accumulation zones in the landscape. Two cells per management and zone were selected to conduct soil physical properties characterization (Fig. 1). Soil properties studied included total soil C by dry combustion at three depths, water infiltration with a mini-disk infiltrometer (Decagon Devices Inc., Pullman, WA)1, and water stable aggregates (Nimmo and Perkings, 2002). Data were analyzed with the MIXED model procedure in SAS (SAS Institute Inc., Cary, NC). Management system, landscape position, depth, and their interactions were considered as fixed effects.

This study examined average annual changes in soil erosion from rainfall and wind forces, and trends in soil organic carbon (SOC). The diversity of geo-climatic land bases and potential feedstocks within the United States Central Great Plains (CGP) requires sustainable production that provides optimal resource utilization while maintaining or enhancing localized soil and environmental quality as much as possible. This study examined average annual changes in soil erosion from rainfall and wind forces and trends in soil organic carbon (SOC) as a function of commodity and/or bioenergy-based crop rotations, yield variations, and different field management practices, including residue removal across all land capability class (LCC) I-VIII soils in select areas of the CGP. Soil erosion and SOC (proxied by a soil conditioning index, or SCI) were analyzed on individual soil map unit components using the Revised Universal Soil Loss Equation, Version 2 (RUSLE2) and Wind Erosion Prediction System (WEPS) models.

GRIN-Global is an ongoing international collaborative project to develop shared and open-source applications that help manage plant germplasm collections. The software was jointly developed by the Agricultural Research Service of USDA, Global Crop Diversity Trust, and Bioversity International, with the first version released in December 2011. The ARS has used GRIN-Global to manage its plant germplasm collections, the U.S. National Plant Germplasm System, since November 2015. GRIN-Global is an extension of Germplasm Resources Information Network (GRIN) information management system, which was first developed by ARS beginning in the mid-1980s. GRIN-Global is comprised of a suite of computer applications that are used internally by genebank staff to curate collections, as well as a public website through which scientists can query the database and request samples of germplasm through a shopping cart process.