Browsing by Author "Dr. Jeffrey G. White, Committee Co-Chair"

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    Characterization of Soil Spatial Variability for Site-Specific Management Using Soil Electrical Conductivity and Other Remotely Sensed Data
    (2006-03-30) Bang, Jisu; Dr. Randall Weisz, Committee Chair; Dr. Jeffrey G. White, Committee Co-Chair; Dr. Marcia L. Gumpertz, Committee Member; Dr. Deana Osmond, Committee Member
    Field-scale characterization of soil spatial variability using remote sensing technology has a great potential for achieving the successful implementation of site-specific management (SSM). The objectives of this study were to: (i) examine the spatial relationships between apparent soil electrical conductivity (ECa) and soil chemical and physical properties to determine if ECa could be useful to characterize soil properties related to crop productivity in the Coastal Plain and Piedmont of North Carolina; (ii) evaluate the effects of in-situ soil moisture variation on ECa mapping as a basis for characterization of soil spatial variability and as a data layer in cluster analysis as a means of delineating sampling zones; (iii) evaluate clustering approaches using different variable sets for management zone delineation to characterize spatial variability in soil nutrient levels and crop yields. Field studies were conducted in two physiographic regions of North Carolina, the Piedmont and Coastal Plain. Spatial measurements of ECa via electromagnetic induction (EMI) were compared with soil chemical parameters (extractable P, K, and micronutrients; pH, cation exchange capacity (CEC), humic matter or soil organic matter; and physical parameters (percentage sand, silt, and clay; and plant-available water (PAW) content; bulk density; cone index; saturated hydraulic conductivity [Ksat] in one of the coastal plain fields) using correlation analysis across fields. We also collected ECa measurements in one coastal plain study field on four days with significantly different naturally occurring soil moisture conditions measured in five increments to 0.75 m using profiling time-domain reflectometry probes to evaluate the temporal variability of ECa associated with changes in in situ soil moisture content. Nonhierarchical k-means cluster analysis using sensor-based field attributes including vertical ECa, near-infrared (NIR) radiance of bare-soil from an aerial color infrared (CIR) image, elevation, slope, and their combinations was performed to delineate management zones. The strengths and signs of the correlations between ECa and measured soil properties varied among fields. Few strong direct correlations were found between ECa and the soil chemical and physical properties studied (r2 < 0.50), but correlations improved considerably when zone mean ECa and zone means of selected soil properties among ECa zones were compared. The results suggested that field-scale ECa survey is not able to directly predict soil nutrient levels at any specific location, but could delimit distinct zones of soil condition among which soil nutrient levels differ, providing an effective basis for soil sampling on a zone basis. The strengths of the correlations of ECa with measured soil properties varied depending on soil moisture conditions. In general, the strongest correlations were observed when ECa was measured under relatively dry conditions. The results suggest that the spatial and temporal ECa variability measured under different soil moisture conditions could be a critical factor when evaluating the ability of ECa to predict soil chemical and physical characteristics important to soil and crop productivity and management. The relationships of sensor-based field attributes as clustering variables to group soil test parameters and crop yields varied among fields. Most of the clustering data combinations effectively captured the within-zone variability of soil test parameters and crop yield, but the use of different variables for cluster analysis resulted in different results in terms of capturing soil test and yield variability by management zones. The results indicate that zones created by cluster analysis could provide a way to group and manage spatial variability of soil nutrients within fields; however, appropriate selection of clustering variables for each individual field could be critical to implement cluster analysis for developing management zones.
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    Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography
    (2005-04-19) Sripada, Ravi Prakash; Dr. Ronnie W. Heiniger, Committee Co-Chair; Dr. Jeffrey G. White, Committee Co-Chair; Dr. David A. Crouse, Committee Member; Dr. Randy Weisz, Committee Member; Dr. John L. Havlin, Committee Chair
    Fast, accurate methods to determine in-season corn (Zea mays L.) nitrogen (N) requirements are needed to provide more precise and economical management and potentially decrease groundwater N contamination. The objectives of this study were to: (i) determine if there is a response to in-season N applied to corn at (V7: NV7) and pre-tassel (VT: NVT) under irrigated and non-irrigated conditions; (ii) develop a methodology for predicting in-season N requirement for corn at the V7 and VT stages using aerial color infrared (CIR) photography; (iii) validate the RGDVI-based remote sensing technique for determining in-season N requirements for corn at VT growth stage and to test the robustness of the model across years; (iv) examine the response of corn agronomic parameters (biomass, plant N concentration, and total N uptake) and spectral parameters (near-infrared [NIR], red [R], and green [G]) from CIR measured at the V7 and VT growth stages to changing environments (year), irrigation, and N applied at planting (NPL); and (v) determine the relationships between corn agronomic parameters and spectral parameters that influence the prediction of optimum NV7 and NVT rates. Field studies were conducted for four years over a wide range of soil conditions and water regimes in the North Carolina Coastal Plain. A two-way factorial experimental design was implemented as a split-plot in randomized complete blocks with NPL as the main plot factor and NV7 or NVT as the sub-plot factor. Corn agronomic parameters were measured and aerial CIR photographs were obtained for each site at V7 or VT prior to N application. Significant grain yield responses to NPL and NV7, and NVT were observed. Spectral radiation of corn measured using the Green Difference Vegetation Index (GDVI) relative to high-N reference strips using a linear-plateau model was the best predictor of optimum NVT (R2 = 0.67). Optimum N rates at V7 (NV7) ranged from 0 to 207 kg N ha-1 with a mean of 67 kg N ha-1. Very weak correlations were observed between optimum rates of NV7 and band combinations with significant correlations for relative G, RGDVI, and relative difference vegetation index (RDVI). In the VT validation study, the difference between predicted and observed optimum NVT rates ranged from -30 to 90 kg N ha-1. Overall, the remote sensing technique was successful (r2 = 0.85) in predicting optimum NVT rates despite the inherent constraints of predicting yield potential in any particular year. Although the model tended to over-predict optimum N rates, it was able to capture changes in optimum N rates across the range of conditions tested. Corn spectral parameters measured at V7 and VT also varied with year and NPL. G and NIR were significantly correlated with biomass and total N uptake. Relative indices using G and NIR were related to plant N concentration. The spectral index RGDVI showed consistently significant relationships with corn agronomic parameters measured at VT when analyzed across irrigated and non-irrigated experiments. Lack of adequate N prior to VT resulted in a loss of yield potential that was irreversible, that is, not regained by N additions at VT. Thus adequate N applied earlier in the season is necessary to maintain yield potential through VT. By assessing corn N requirements late in the season during the period of maximum N uptake and applying fertilizer appropriately, application of large amounts of N early in the season when corn uptake is low and leaching potential high might be avoided, and thus minimize groundwater pollution. There exists a potential to improve in-season estimates of N requirements earlier in the season by investigating further into the use of high resolution images.
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    Using Apparent Electrical Conductivity (ECa) via Electromagnetic Induction (EMI) to Characterize Soils and the Stratigraphy for Wetland Restoration
    (2007-10-03) Davis, Karen Melissa; Dr. Rod Huffman, Committee Member; Dr. Jeffrey G. White, Committee Co-Chair; Dr. Michael J. Vepraskas, Committee Co-Chair