Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models.
The USLE_1981-4 project data (Universal Soil Loss Equation) was collected from of (9) sites at (4) locations. A Swanson rotating boom simulator with (30) V-Jet 80100 nozzles applied rainfall at two different intensities, 60 or 130 mm/hour depending on how many nozzles were turned on. Specially designed flumes used with the FW-1 automatic water level recorder were used to obtain continuous runoff flow measurements. The sites in this data set followed a standardized rainfall simulator protocol which future studies by multiple investigators would continue to use. The data set contains rainfall simulator hydrologic and erosion data as well as vegetation and ground data collected in spring and fall from 1981 to 1984.
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 2012 USDA Plant Hardiness Zone Map (PHZM) is the standard by which gardeners and growers can determine which plants are most likely to thrive at a location. The map is based on the average annual minimum winter temperature, divided into 10-degree F zones. For the first time, the map is available as an interactive GIS-based map, for which a broadband Internet connection is recommended, and as static images for those with slower Internet access. Users may also simply type in a ZIP Code and find the hardiness zone for that area. No posters of the USDA Plant Hardiness Zone Map have been printed. But state, regional, and national images of the map can be downloaded and printed in a variety of sizes and resolutions.
To assess the magnitude of greenhouse gas (GHG) fluxes, nutrient runoff and leaching from dairy barnyards and to characterize factors controlling these fluxes, nine barnyards were built at the U.S. Dairy Forage Research Center Farm in Prairie du Sac, WI (latitude 43.33N, longitude 89.71W). The barnyards were designed to simulate outdoor cattle-holding areas on commercial dairy farms in Wisconsin. Each barnyard was approximately 7m x 7m; areas of barnyards 1-9 were 51.91, 47.29, 50.97, 46.32, 45.64, 46.30, 48.93, 48.78, 46.73 square meters, respectively. Factors investigated included three different surface materials (bark, sand, soil) and timing of cattle corralling. Each barnyard included a gravity drainage system that allowed leachate to be pumped out and analyzed. Each soil-covered barnyard also included a system to intercept runoff at the perimeter and drain to a pumping port, similar to the leachate systems.
The LDMI experiment (Low-Disturbance Manure Incorporation) was designed to evaluate nutrient losses with conventional and improved liquid dairy manure management practices in a corn silage (Zea mays) / rye cover-crop (Secale cereale) system. The improved manure management treatments were designed to incorporate manure while maintaining crop residue for erosion control. Field observations included greenhouse gas (GHG) fluxes from soil, soil nutrient concentrations, crop growth and harvest biomass and nutrient content, as well as monitoring of soil physical and chemical properties. Observations from LDMI have been used for parameterization and validation of computer simulation models of GHG emissions from dairy farms (Gaillard et al., submitted). The LDMI experiment was performed as part of the Dairy CAP.