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.
Root Zone Water Quality Model 2 (RZWQM2) is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. It emphasizes the effects of agricultural management practices on physical, chemical and biological processes. RZWQM2 is a one-dimensional model with a pseudo 2-dimensional drainage flow. Crop simulation options include the generic plant growth model, DSSAT-CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm (PEST) was added to RZWQM2 for objective model calibration and uncertainty analysis.
Data from: Rerouting of carbon flux in a glycogen mutant of cyanobacteria assessed via isotopically non‐stationary 13C metabolic flux analysis
Isotopically non‐stationary 13C metabolic flux analysis (INST‐13C‐MFA) is performed to analyze rerouting of carbon in a glycogen synthase deficient mutant strain (glgA‐I glgA‐II) of the model cyanobacterium Synechococcus sp. PCC 7002.
The Rapid Carbon Assessment (RaCA) was initiated by the USDA-NRCS Soil Science Division in 2010 with the following objectives:
- To develop statistically reliable quantitative estimates of amounts and distribution of carbon stocks for U.S. soils under various land covers and to the extent possible, differing agricultural management.
- To provide data to support model simulations of soil carbon change related to land use change, agricultural management, conservation practices, and climate change.
- To provide a scientifically and statistically defensible inventory of soil carbon stocks for the U.S.
LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture Forest Service and U.S. Department of the Interior, providing landscape scale geo-spatial products to support cross-boundary planning, management, and operations. LANDFIRE is a program that provides over 20 national geo-spatial layers (e.g. vegetation, fuel, disturbance, etc.), databases, and ecological models that are available to the public for the US and insular areas.
Data from: Forest harvest dataset for northern Colorado Rocky Mountains (1984–2015) generated from a Landsat time series and existing forest harvest records
This dataset provides a shapefile containing approximately 3500 polygons with the location, extent, size, and year of clearcut harvest events occurring between 1984 and 2015 in forested areas of northern Colorado.
Data from: Effects of conifer treatments on soil nutrient availability and plant composition in sagebrush steppe
Conifer control in sagebrush steppe of the western United States causes various levels of site disturbance influencing vegetation recovery and resource availability. The data set presented in this article include growing season availability of soil micronutrients and levels of total soil carbon, organic matter, and N spanning a six year period following western juniper (Juniperus occidentalis spp. occidentalis) reduction by mechanical cutting and prescribed fire of western juniper woodlands in southeast Oregon. These data can be useful to further evaluate the impacts of conifer woodland reduction to soil resources in sagebrush steppe plant communities.
The ISCN is a self-chartered, international, collaborative organization composed of scientists who recognize a need for and value in large-scale synthesis of soil carbon science.
Process-based models are increasingly used to study mass and energy fluxes from agro-ecosystems, including nitrous oxide (N2O) emissions from agricultural fields. This data set is the output of three process-based models – DayCent, DNDC, and EPIC – which were used to simulate fluxes of N2O from dairy farm soils. The individual models' output and the ensemble mean output were evaluated against field observations from two agricultural research stations in Arlington, WI and Marshfield, WI. These sites utilize cropping systems and nitrogen fertilizer management strategies common to Midwest dairy farms.