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CPM - Cotton Production Model
,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.
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GOSSYM
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,GOSSYM is a dynamic, process-level simulation model of cotton growth and yield. GOSSYM essentially is a materials balance model which keeps track of carbon and nitrogen in the plant and water and nitrogen in the soil root zone. GOSSYM predicts the response of the field crop to variations in the environment and to cultural inputs. Specifically, the model responds to weather inputs of daily total solar radiation, maximum and minimum air temperatures, daily total wind run, and rainfall and/or irrigation amount. The model also responds to cultural inputs such as preplant and withinseason applications of nitrogen fertilizer, row spacing and within row plant density as they affect total plant population, and cultivation practices.,
Growth and Yield Data for the Bushland, Texas, Cotton Datasets
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,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.,
Agronomic Calendars for the Bushland, Texas Cotton Datasets
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,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.,
Season-Average Price Forecasts
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This data product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). #### Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.) This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page. #### Spreadsheet Models For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for: 1. the national-level season-average price received by farmers and 2. the implied counter-cyclical payment rate. Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.
Modeled conterminous United States Crop Cover datasets for 2000 - 2013
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Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.
Modeled conterminous United States Crop Cover datasets for 2011
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Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.
Modeled conterminous United States Crop Cover datasets for 2010
공공데이터포털
Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.
Modeled conterminous United States Crop Cover datasets for 2003
공공데이터포털
Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.
Data and code from: Cotton stalk management and a cover crop produce minimal effects on cotton leafroll dwarf virus
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,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.,
Modeled conterminous United States Crop Cover datasets for 2007
공공데이터포털
Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008-2013. In this investigation we wanted to expand the temporal coverage of the NASS CDL archive back to 2000 by creating yearly NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million crop sample records to train a classification tree algorithm and to develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000-2013 at 250 meter spatial resolution. The CCM and the maps for years 2008-2013 were assessed for accuracy relative to downscaled NASS CDLs to 250 meter. The CCM performed well against a withheld test dataset with a prediction accuracy of over 90 percent. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains. However, the model did show a bias toward the “Other” crop cover class which caused frequent misclassifications of pixels around the periphery of large crop cover patches and of pixels that form small, sparsely dispersed crop cover patches.
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