Browsing by Author "David Crouse, Committee Member"

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Estimating CSM-CERES-Maize Genetic Coefficients and Soil Parameters and Evaluating Model Response to Varying Nitrogen Management Strategies under North Carolina Conditions
(2008-08-08) Yang, Zhengyu; Gail Wilkerson, Committee Chair; Daryl Bowman, Committee Member; Ron Heiniger, Committee Member; David Crouse, Committee Member
Evaluation of Realistic Yield Expectations in the North Carolina Piedmont and Coastal Plain
(2004-11-09) Lohman, Mindy; Deanna Osmond, Committee Co-Chair; Jeffrey White, Committee Co-Chair; David Crouse, Committee Member; Randy Weisz, Committee Member
Realistic Yield Expectations (RYE) have been developed in North Carolina to assist in site-specific farming decisions that will improve N-use efficiency and reduce N contamination of ground- and surface water, especially in the Neuse River Basin. This study was conducted to determine whether correlations exist between soil chemical properties, actual yields, soil map units, zones, and RYEs. Soil surveys of the fields were completed at an approximate scale of 1:3500 in 2002 (remapped soil map units) and compared to existing county soil surveys (original soil map units). Samples from equilateral triangle grid soil sampling were analyzed and used to map the spatial distribution of soil pH, soil P, and soil K and lime requirement. Interpolated maps were created to display the spatial distribution of the investigated soil chemical properties. To represent zones (transition zone or map unit interior), 20 m buffers centered on map unit boundaries were created in order to investigate these highly unique and variable areas. Soybean (Glycine max [L.] Merr) and wheat (Triticum aestivum L.) yield data was collected for two site-years in one Piedmont field, while wheat and corn (Zea mays L.) were sampled for one site-year in the two remaining Piedmont fields. Soybean and wheat yield data was collected for three site-years in two Coastal Plain fields. Interpolated nutrient maps showed visual correlations between soil map units and soil K values in the Coastal Plain, but no other relationships between soil chemical properties and soil map units or zones were visually apparent for either location. Yield maps showed visual relationships with soil map units in the Coastal Plain but not in the Piedmont. Various statistical models were utilized to analyze the data and the spatial covariance model was determined to be more efficient than the independent and identically distributed fixed effects model in capturing a significant proportion of the variability for tested soil chemical properties and crop yield in both locations. Remapped and original soil map units were also studied to determine their effectiveness in capturing the variability of soil chemical properties and crop yield. The remapped soil map units were more effective than the original soil map units in capturing this variability in most cases. Soil K was highly significant among the remapped soil map units in the Field 7 in the Piedmont where r2=0.82. In all locations, other investigated parameters also displayed significance, but none as highly significant as soil K in Field 7. RYEs were found to be greater than actual yields for all crops harvested in both locations.
Evaluation of the Phosporous Loss Assessment Tool (PLAT) and Revised Universal Soil Loss Equation (RUSLE) using Geospatial Information
(2007-04-24) Yuan, Xu; John Havlin, Committee Chair; Rory Maguire, Committee Member; David Crouse, Committee Member; Montserrat Fuentes, Committee Member
Excessive agricultural phosphorus (P) has been a major contributor to non-point source pollution. North Carolina developed the Phosphorus Loss Assessment Tool (PLAT) to evaluate the potential P loss from agricultural fields to waterbodies via four components. Our overall goal was to evaluate the potential of using spatial data to estimate P loss without physically visiting fields since many PLAT required parameters occur in spatial formats. The objective of the first study was to assess the possibility of spatial implementation of PLAT and to compare the effect of scale on the PLAT numerical results and the associated categorical rankings. Since an important input parameter, the average annual soil loss determined by the Revised Universal Soil Loss Equation, is not directly available from field measurement, our objective in the second study was to assess the potential of obtaining RUSLE estimates, specifically the topography factor LS, through Digital Elevation Model data in a Geographic Information System environment. In the first study, two methods of whole field average (WFA) and grid average (GA) were used to compare the difference in modeling P loss at different scales. The same list of PLAT required parameters were prepared from soil test reports and spatial database at the coarse scale of whole agriculture field and the fine scale of 0.4-ha grid. Soil tolerance value was used to temporarily replace the soil loss data. In the second study, a widely used Arc Macro Language (AML) program for estimating RUSLE topographic factor LS was evaluated through two approaches of whole field (WF) and representative profile (RP) analysis on a North Carolina landscape. Watershed delineation technique was adopted to select the representative profiles based on the references of slope distributions and field subdivisions from NRCS water quality specialists. Results from the first study indicated that soluble and particulate P loss, which occupied 59.3% and 26.3% of the total P loss through WFA method, and 56.1% and 39.0% through GA method, were the major pathways. Leaching P loss from PLAT was negligible. Particulate P loss was sensitive to scale as verified by the 12.7% increase of proportion in total P loss. The difference of particulate P loss through two methods was significant (p < 0.05), but no difference of soluble P loss and P source effect was found on a 95% confidence level. The overall P loss potential through two methods exhibited no significant difference due to the neutralization effect of individual pathways. Results from the second study showed that the AML program alone was not suitable for calculating RUSLE topographic factor on a North Carolina landscape because of the significant underestimation (˜35% and ˜20% through WF and RP approach, respectively). The concept of representative profile indeed improved the estimation accuracy (˜15%), however, the linearity of the fitted line between field measured LS and GIS-aided LS estimate was not satisfactory. An adjustment factor was proposed rectifying the RUSLE-based AML program in order to approximate field measurements. This study demonstrated the potential of implementing PLAT model and the soil loss equation using spatial parameters derived from database instead of visiting the fields. The scale of modeling in estimating particulate P loss and RUSLE topographic factor LS was important and the adjustment factor was necessary to adapt the AML program application. The accuracy of model performance needed to be improved before claiming that GIS-aided PLAT modeling will provide a complete replacement for the field measurement.