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Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
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Full-Text Articles in Plant Sciences
Development And Assessment Of A Groundwater Sustainability Index In Climatically Diverse Groundwater Irrigated Regions In Nebraska, Maria A. Mulet Jalil
Development And Assessment Of A Groundwater Sustainability Index In Climatically Diverse Groundwater Irrigated Regions In Nebraska, Maria A. Mulet Jalil
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
The aim of this research was to evaluate the impact of regional change in ET on groundwater level changes and the assessment and development of a groundwater sustainability index for climatically diverse regions across Nebraska during 2000-2014. Irrigation in the selected regions is predominantly supplied by groundwater. The hypothesis is that groundwater use can become sustainable if the regional evapotranspiration (ET) is managed so that it equals the ET of vegetation that is native to the region. Site locations were Box Butte, Chase, Dundy, Holt LNNRD and York Counties and 3 ecosystems were evaluated: native vegetation, dryland and irrigated cropping …
Using A Vnir Spectral Library To Model Soil Carbon And Total Nitrogen Content, Nuwan K. Wijewardane
Using A Vnir Spectral Library To Model Soil Carbon And Total Nitrogen Content, Nuwan K. Wijewardane
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
n-situ soil sensor systems based on visible and near infrared spectroscopy is not yet been effectively used due to inadequate studies to utilize legacy spectral libraries under the field conditions. The performance of such systems is significantly affected by spectral discrepancies created by sample intactness and library differences. In this study, four objectives were devised to obtain directives to address these issues. The first objective was to calibrate and evaluate VNIR models statistically and computationally (i.e. computing resource requirement), using four modeling techniques namely: Partial least squares regression (PLS), Artificial neural networks (ANN), Random forests (RF) and Support vector regression …