Sediment and nutrient loss from agricultural landscapes contributes to water quality impairment and has the potential to impact crop yield. Best management practices (BMPs) such as riparian buffers have been designed to combat these issues; however, concentrated flow paths (CFPs) reduce their effectiveness and are often overlooked in agricultural fields. Conventional management of CFPs is to fill and grade them, however this provides only a short term solution leading to their reformation and increased sediment loss. The objectives of this project were: i) to determine if the filling of CFPs influence crop growth (yield and biomass), ii) determine a distance at which crop growth is no longer influenced by CFPs, iii) assess the impact that topography and CFPs have on crop growth, iv) analyze water quality in surface runoff leaving crop fields via CFPs, and v) develop an economic analysis for CFP’s influence on crop returns. Six small agricultural catchments, CFPs, and topographic positions (i.e., depositional, backslope, and shoulder) were delineated using ArcGIS and LiDAR data. In each catchment, six 4 m2 plots were established along CFPs where crop biomass and crop yield were measured. Additionally, six plots with no influence from CFPs were established as reference plots. Surface water quality was assessed by taking edge-of-field grab samples at the CFP outlet during significant rain events (i.e., precipitation exceeding 2.5 cm). Water samples were analyzed for total suspended solids (TSS), total phosphorus (TP), dissolved reactive phosphorus (DRP), ammonium-nitrogen (NH4+-N), and nitrate-N (NO3- -N). Through this study it was shown that CFPs served as a conduit for transporting nutrient and sediment laden runoff to receiving waters, that increasing/decreasing distance from CFPs had an impact on crop yields, and that there was no crop yield advantage from the filling of CFPs vs. leaving them unfilled. Median values for NO3-N (1.85 mg L-1) and TSS (140 mg L-1) in the Fill catchments were higher than the No-Fill catchments (0.77 mg L-1 and 35.5 mg L-1, respectively), while DRP and TP concentrations were higher in the No-Fill catchments (1.31 mg L-1 and 2.37 mg L-1, respectively) compared to the Fill catchments (0.91 mg L-1 and 1.83 mg L-1, respectively) over the growing season. Crop biomass and yield results between the depositional and backslope positions were similar regardless of treatment, but were lower than the reference plots and shoulder position. Results from the economic analysis on the cost of farming in/near CFPs indicated that the economic return was greatly dependent on precipitation. CFPs are generally concave positions on the landscapes and have been eroded to a clayey subsoil, both resulting in greater water accumulation and retention than elsewhere in the field. During wetter years, an economic loss was incurred nearest to the CFP and during drier years, sites nearest to CFPs saw positive returns. Farmers and land managers may consider implementing stabilization measures, such as grassed waterways, in CFPs since crop yields are typically lower in wetter years, there’s increased cost to maintain these areas, and accelerated sediment loss can exacerbate the crop yield losses and impact on water quality.

A geographic information system (GIS) was used to create landform measurements and maps for elevation, slope, aspect, landform index, relative phenologic change, and slope position for 3 topographic quadrangles in Virginia. A set of known observation points of the Northern dusky flying squirrel (Glaucomys sabrinus) was used to build 3 models to delineate sites with landform characteristics equivalent to those known points. All models were built using squirrel observation points from 2 topographic quadrangles. The first model, called "exclusionary", excluded those pixels with landform characteristics different from the known squirrel pixels based on histogram analyses. Logistic regression was used to create the other 2 models. Each model resulted in an image of pixels considered equivalent to the known squirrel pixels. Each model excluded approximately 65% of the Highland study area, but the exclusionary model excluded the fewest known squirrel pixels (12.62%). Both logistic regression models excluded approximately 10% more known squirrel pixels than the exclusionary approach. The models were tested in the area of a third quadrangle with points known to be occupied by squirrels. After the model was applied to the third topographic quadrangle, the exclusionary model excluded the least amount of full-area pixels (79.30%) and only 14.81% of the known squirrel pixels. The second logistic regression excluded 81.16 % of the full area and no known squirrel pixels. All models proved useful in quickly delineating pixels equivalent to areas where wildlife were known to occur. / Master of Science

Limited knowledge exists of the terrain variables that have an influence on gopher tortoise (Gopherus polyphemus) burrow locations. Previous studies suggest that terrain features may play a role in preference of burrow location. LiDAR- (Light Detection and Ranging) derived terrain features can be evaluated through GIS (Geographic Information System) analysis at a fine spatial scale. LiDAR data acquired at 0.5 meter post spacing over three locations on Camp Shelby Joint Forces Training Center, MS were used to develop DTMs (Digital Terrain Models) for use in burrow site characterization. Terrain variables (e.g. elevation, slope, aspect) were developed from the LiDAR DTM in ArcGIS. Burrows and randomly allocated non-burrow points were used in logistic regression analysis to model the relationship between burrow occurrence and terrain features. Four models correctly classified more than 83% of the burrow locations. The R2 were 34.83%, 49.31%, 28.09%, and 31.51%.

Non-forested ecosystems dominate high elevation sites in coastal British Columbia, yet there has never been a comprehensive classification or mapping of all high-elevation community types. The objective of this study is to collate and expand upon previous classifications, and thereby to increase our understanding of the habitats and composition of these plant communities.

This report expands and clarifies previous classifications of non-forested plant communities from upper subalpine and alpine locations in coastal British Columbia. A total of 80 plots (releves) sampled specifically for this study were added to 202 releves from published and unpublished studies conducted since 1963. We used tabular and multivariate methods to synthesize and classify plant communities according to the Braun-Blanquet approach. Plant communities were classified into 37 vegetation units (associations or subassociations) which served as the basis of the resulting hierarchical classification. We describe the habitat and species composition of these vegetation units and their relationship to units recogized elsewhere in the Pacific Northwest. We then present eight generalized habitat types which we propose as the basic units for future ecosystem mapping. Each of these habitat types includes a predictable mosaic of vegetation units whose pattern occurs at too fine a scale to map individually.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.