The HPRCC has an established partnership with the North Central Climate Science Center (NC CSC) and has enjoyed collaborating on regional projects since its inception. Housed at Colorado State University in Fort Collins, the NC CSC is one of eight such centers that were established in 2010 within the U.S. Department of the Interior. The mission of the Climate Science Centers is to help meet the changing needs of land and resource managers across the U.S. (For more information on the Climate Science Centers, please visit: https://www.doi.gov/csc/about.) The NC CSC collaborates with a consortium of nine institutions that provide expertise in climate science and sectors impacted by climate. The University of Nebraska-Lincoln, where HPRCC is housed, is a member of this consortium. Read More:   http://hprcc.unl.edu/hprccquarterly/HPRCCQuarterly-Fall2015.pdf

This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 75.5%. A series of post-modeling steps brought the final number of land cover classes to 28.

This landcover raster was generated through a Random Forest predictive model developed in R using a combination of image-derived and ancillary variables, and field-derived training points grouped into 18 classes. Overall accuracy, generated internally through bootstrapping, was 72.7%. A series of post-modeling steps brought the final number of land cover classes to 28.

This study had two objectives: first, to generate a landcover map for the Charles M. Russell Wildlife Refuge (CMR) emphasizing the distribution of land cover types in relation to greater sage grouse ( Centrocercus urophasianus) habitat needs, and second, to provide data that would allow a determination of whether results were better with SPOT imagery or Landsat 8 imagery. SPOT imagery is provided at a 10m pixel resolution, while Landsat 8 is at 30m. Results from this classification will allow managers to determine which resolution provides the accuracy needed for habitat planning and management.

This code was used in a simulated decision analysis project designed to evaluate the value of different kinds of information with regard to making optimal investments in invasive plant control programs. The code was developed in the R programming environment. The file "sim_code.R" contains the initialization of the parameters and analysis; the file "pop_sim.ccp" is a C++ program that executes the actual simulation and returns the results to R. We developed a hypothetical scenario in which a manager is tasked with control of invasive plants on 100 management units each 100 ha in size. 90 of these units were assumed to be under private management and 10 were assumed to be conservation units (i.e. under public management). For this problem we assumed that there were only two target species to control: leafy spurge (Euphorbia esula) and yellow toadflax (Linaria vulgaris). These two species were identified of examples of species that are controlled within the LCC geography. They are also species for which there is some literature to help parameterize a model. We should note that this model is gross generalization and was not intended to provide insight into invasive species biology. Because this model is stochastic, we measured this objective as the number of years out of 50 that at least 50% of the units were uninfested. We arbitrarily chose the target of 50%, which could have been any value. However, we felt that because these species are so difficult to control, target of 50% was fairly attainable. The dynamics of the model assume that management units can either be infested with one of the species, both of them or none at all. We further assumed that once a unit was infested, the species immediately achieved some average density and began producing propagules. These propagules had to disperse some arbitrary distance before they could infest another unit. Once those seeds landed in an uninfested unit, there was some probability they would lead to an infestation. To describe infestation dynamics, we developed a model that was composed of eight states to describe the management units. The model simulates the dynamics of infestation and control and then produces an Expected Value of Information analysis that shows the improvement in performance given resolution of each uncertain parameter in the model.

Training points collected in the field between 2012 and 2013 were grouped into 18 classes: Forested Burn (66), Foothill Woodland Steppe Transition (73), Greasewood Flat (73), Greasewood Steppe (239), Greasewood Sage Steppe (277), Great Plains Badlands (166), Great Plains Riparian (255), Low Density Sage Steppe (776), Medium Density Sage Steppe (783), Mixed Grass Prairie (555), Mixed Grass Prairie Burned (278), Ponderosa Pine Woodland and Shrubland (512), Riparian Floodplain (223), Semi-Desert Grassland (103), Sparsely Vegetated Mixed Shrub (252), Silver Sage Flat (70) , Silver Sage Steppe (64), and Water (246). When insufficient field data were available for a class, we augmented it through photointerpretation of 15 cm aerial imagery, using expert knowledge and field experience to guide us. The final dataset had 5,011 training points.

An increase in land conversion from grassland to cropland in the United States has attracted attention in recent years. According to Claassen et al. (2011a), grassland to cropland conversion is concentrated in the Northern Plains, including Kansas, Nebraska, North Dakota and South Dakota, which encompasses only 18% of U.S. rangeland but accounted for 57 percent of U.S. rangeland to cropland conversion during the study period of 1997 to 2007. Focusing on land cover data in the Western Corn Belt, Wright and Wimberly (2013) also pointed out that grassland conversion was mostly concentrated in the Dakotas, east of the Missouri River and between 2006 and 2011.

Abstract (from http://econpapers.repec.org/paper/agsaaea16/235895.htm): We evaluate the regional-level agricultural impacts of climate change in the Northern Great Plains. We first estimate a non-linear yield-weather relationship for all major commodities in the area: corn, soybeans, spring wheat and alfalfa. We separately identify benevolent and harmful temperature thresholds for each commodity, and control for severe-to-extreme dry/wet conditions in our yield models. Analyzing all major commodities in a region extends the existing literature beyond just one crop, most typically corn yields. Alfalfa is particularly interesting since it is a legume-crop that is substitutable with grasses as animal feed and rotated with other row-crops for nitrogen-fixation of soils. Our model includes trend-weather and soil-weather interaction terms that extend the existing yield-weather models in the literature. Results suggest that temporal adaptations have not mitigated the negative impacts of weather stressors in the past, and that the spatial soil profile only weakly influences weather impacts on crop yields. We estimate yield-weather elasticities and find that historical weather patterns in the region have benefited corn and soybeans (spring wheat) the most (least). We expand our analysis to formally evaluate the role of short-run weather fluctuations in determining land-use decisions. We utilize decomposed crop yield estimates due to trend and weather in order to model crop acreage shares. Our preliminary results suggest that short-run weather fluctuations are an important factor for decisions on soybeans and spring wheat shares, however only yield trends drive corn shares.