,GPFARM (Great Plains Framework for Agricultural Resource Management) is a simulation model computer application. It incorporates state of the art knowledge in agronomy, animal science, economics, weed science and risk management into a user-friendly, decision support tool. Producers, agricultural consultants, action agencies and scientists can utilize GPFARM to test alternative management strategies that may in turn lead to sustainable agriculture, a reduction in pollution, or maximum economic return. GPFARM Express contains default projects to allow users to quickly set up their operations.,GPFARM Decision Support System (DSS) Objective: Develop a resource management decision support system (DSS) that is capable of simulating and analyzing 10-50 year farm/ranch production plans with respect to water, nutrient, and pest management along with their associated economic and environmental risks.,GPFARM DSS Benefits: GPFARM integrates state of the art agricultural science knowledge with associated economic and environmental analysis into a whole-enterprise evaluation. Results from the DSS provide agricultural consultants, producers, and action agencies with information for making management decisions that promote sustainable agriculture.,GPFARM provides feedback concerning the most effective technology and assists in determining areas requiring further research and development. This is an evolutionary process that ties research and technology transfer closely together.,GPFARM serves to bring scientists from different disciplines together with producers and consultants to solve complex problems in agriculture. Products within GPFARM:,

,This dataset contains water balance data for each year when alfalfa was grown 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). Alfalfa was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field in 1996 through 1999. Irrigation was by linear move sprinkler system. Irrigations 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. The weighing lysimeters were used to measure relative soil water storage to 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. Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The water balance data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost fall, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected. The ET data should be considered to be the best values offered in these datasets. Even though ET data are also presented in the "lysimeter" datasets, the values herein are the result of a more rigorous quality control process. Dew and frost accumulation varies from year to year and seasonally within a year, and it is affected by lysimeter surface condition [bare soil, tillage condition, residue amount and orientation (flat or standing), etc.]. Particularly during winter and depending on humidity and cloud cover, dew and frost accumulation sometimes accounts for an appreciable percentage of total daily ET. These datasets originate from research aimed at determining crop water use (ET), reference "tall crop" 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 alfalfa ET, crop coefficients, crop water productivity reference "tall crop" ET, alternative methods of estimating reference ET from weather data. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield.,,

,2D finite element water, solute, and heat mover model for plant models.,Most crops are grown in rows and this introduces spatial variability in soil processes with respect to the row. However, this variability can be exploited to reduce chemical transport to groundwater or improve management of irrigation water. Unless a model can account for variability perpendicular to crop rows as well as vertically into the soil profile it will not be able to fully evaluate all possible management practices that can be used to make agriculture more efficient and less harmful to the environment. To address this concern we developed 2DSOIL, the first comprehensive, modular, two-dimensional soil simulator that can simulate the major physical, chemical and biological processes in soil. Fully implemented, principles of modular modeling facilitate the addition and replacement of modules, as well as the reuse of existing code. The modularity of 2DSOIL has been designed to make it easy to modify the model and to make it easy to incorporate into plant models. 2DSOIL was used to simulate the effect of several water and nitrogen management practices and was incorporated into ARS potato and cotton models, into the Root Zone Water Quality Model, and into the USGS Modular Modeling System.,

,SHOOTGRO emphasizes the development and growth of the shoot apex of small-grain cereals such as winter and spring wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). To better incorporate the variability typical in the field, up to six cohorts, or age classes, of plants are followed using a daily time step.,Assessing the influence of nitrogen and water availability on development and growth of individual organs of winter wheat (Triticum aestivum L.) is critical in evaluating the response of wheat to environmental conditions. We constructed a simulation model (SHOOTGRO 2.0) of shoot vegetative development and growth from planting to early boot by adding nitrogen and water balances and response functions for seedling emergence, tiller and leaf appearance, leaf and internode growth, and leaf and tiller senescence to the existing wheat development and growth model, SHOOTGRO 1.0. Model inputs include daily maximum and minimum air temperature, rainfall, daily photosynthetically active radiation, soil characteristics necessary to compute soil N and water balances, and several factors describing the cultivar and soil conditions at planting. The model provides information on development and growth characteristics of up to six cohorts of plants within the canopy (cohort groupings are based on time of emergence). The cohort structure allows SHOOTGRO 2.0 to provide output on the frequency of occurrence of plants with specific features (tillers and leaves) within the canopy. The model was constructed so that only water availability limited seedling emergence. Resource availability (nitrogen and water) does not influence time of leaf appearance. Leaf and internode growth, and leaf and tiller senescence processes are limited by the interaction of N and water availability. Tiller appearance is influenced by the correspondence to: W.W. Wilhelm, USDA-ARS, Department of Agronomy, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0934, USA. 0304-3800/93/$06.00 0 1993 - Elsevier Science Publishers B.V. All rights reserved 184 W.W. WILHELM ET AL. interaction of N, radiation and water availability. Predicted and observed dates of emergence and appearance of the first tiller had correlation coefficients of 0.98 and 0.93, respectively. However, these events were, on average, predicted 3.2 and 5.2 days later than observed. SHOOTGRO 2.0 generally under-predicted the number of culms per unit land area, partially because the simulation is limited to a maximum of 16 culms/plant. Model output shows that the simulation is sensitive to N and water inputs. The model provides a tool for predicting vegetative development and growth of the winter wheat with individual culms identified and followed from emergence through boot. SHOOTGRO 2.0 can be used in evaluating alternative crop management strategies.,,

,PhenologyMMS is a simulation model that outlines and quantifies the developmental sequence of different crops under varying levels of water deficits, provides developmental information relevant to each crop, and is intended to be used either independently or inserted into existing crop growth models.,,

This data bundle contains some of the inputs, all of the processing instructions and all outputs from two VisTrails/SAHM workflows, one creating a global habitat suitability model for buffelgrass and another creating a habitat suitability model for buffelgrass in Saguaro National Park, AZ. The bundle documentation files are: 1) '_archive_bundle_metadata.xml' (this file) which contains FGDC metadata describing the archive bundle. 2) 'modelSelectionCV_MakeAbsPntsFilt100_2.csv' containing the field data used as inputs for the Saguaro National Park model. 2) '_archive_raster_inputs.csv' a list of the raster inputs that were used to generate these model results. These are not included in the archive bundle due to size constraints but are identified in this file as well as the metadata document. 3) '_archive_workflow_Final_model_output.vt' where 'Final_model_output' is the name of the history node for the global model and 'SNP final model' is the name of the history node for the Saguaro National Park model being archived in this bundle. Contains a VisTrails/SAHM workflow document which contains the workflow that was used to create these outputs. 4) '_archive_workflow_Final_model_output.vt' where Final_model_output is the name of the history node being archive in this bundle. Contains a VisTrails/SAHM workflow document which contains the workflow that was used to create these outputs. The remaining data files are the spatial and diagnostic model outputs.

These data were compiled as a supplement to a previously published journal article (Bradford et al., 2019), that employed a ecosystem water balance model to characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America. Also, these data are associated with a published USGS data release (Bradford and Schlaepfer, 2019). The objectives of our study were to (1) characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America, (2) evaluate the impact of these changes on estimation of resilience and resistance among a representative set of climate scenarios. These data represent geographic patterns in simulated soil temperature and soil moisture conditions and underlying variables based on SOILWAT2 simulations under climate conditions representing historical (current) time period (1980-2010) and two future projected time periods (2020-2050, d40yrs) and (2070-2100, d90yrs) for two representative concentration pathways (RCP4.5, RCP8.5) as medians across simulation runs based on output from each of the available downscaled global circulation models that participated in CMIP5 (RCP4.5, 37 GCMs; RCP8.5, 35 GCMs; Maurer et al. 2007). Additional information about the SOILWAT2 simulation experiments can be found in Bradford et al. 2019. These data were created in 2018, 2019, and 2021 for the area of the sagebrush region in the western North America. These data were created by a collaborative research project between the U.S. Geological Survey, Marshall University and Yale University. These data can be used with the high-resolution matching as defined by Renne et al. (in prep.), and within the scope of Bradford et al. 2019. These data may also be used to evaluate the potential impact of changing climate conditions on geographic patterns in simulated soil temperature and soil moisture conditions.

These data were compiled as a supplement to a previously published journal article (Bradford et al., 2019), that employed a ecosystem water balance model to characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America. Also, these data are associated with a published USGS data release (Bradford and Schlaepfer, 2019). The objectives of our study were to (1) characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America, (2) evaluate the impact of these changes on estimation of resilience and resistance among a representative set of climate scenarios. These data represent geographic patterns in simulated soil temperature and soil moisture conditions and underlying variables based on SOILWAT2 simulations under climate conditions representing historical (current) time period (1980-2010) and two future projected time periods (2020-2050, d40yrs) and (2070-2100, d90yrs) for two representative concentration pathways (RCP4.5, RCP8.5) as medians across simulation runs based on output from each of the available downscaled global circulation models that participated in CMIP5 (RCP4.5, 37 GCMs; RCP8.5, 35 GCMs; Maurer et al. 2007). Additional information about the SOILWAT2 simulation experiments can be found in Bradford et al. 2019. These data were created in 2018, 2019, and 2021 for the area of the sagebrush region in the western North America. These data were created by a collaborative research project between the U.S. Geological Survey, Marshall University and Yale University. These data can be used with the high-resolution matching as defined by Renne et al. (in prep.), and within the scope of Bradford et al. 2019. These data may also be used to evaluate the potential impact of changing climate conditions on geographic patterns in simulated soil temperature and soil moisture conditions.