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Marginal Agricultural Land Identification In The Lower Mississippi Alluvial Valley, Prakash Tiwari
Marginal Agricultural Land Identification In The Lower Mississippi Alluvial Valley, Prakash Tiwari
Theses and Dissertations
This study identified marginal agricultural lands in the Lower Mississippi Alluvial Valley using crop yield predicting models. The Random Forest Regression (RFR) and Multiple Linear Regression (MLR) models were trained and validated using county-level crop yield data, climate data, soil properties, and Normalized Difference Vegetation Index (NDVI). The RFR model outperformed MLR model in estimating soybean and corn yields, with an index of agreement (d) of 0.98 and 0.96, Nash-Sutcliffe model efficiency (NSE) of 0.88 and 0.93, and root mean square error (RMSE) of 9.34% and 5.84%, respectively. Marginal agricultural lands were estimated to 26,366 hectares using cost and sales …
Sustainable Sidedress Nitrogen Applications For Early Corn And Cotton Crops Using Small Unmanned Aerial Systems, James Nolan Parker
Sustainable Sidedress Nitrogen Applications For Early Corn And Cotton Crops Using Small Unmanned Aerial Systems, James Nolan Parker
Theses and Dissertations
Nitrogen run-off from agriculture have been linked to human health problems on a global level. Large-scale conventional producers struggle to redefine themselves as sustainable because reducing nitrogen (N) inputs without justification or validation may lead to severe profit losses. Small unmanned aerial systems (sUAS) sensing may allow for decreased N runoff. Failure to address this problem will exacerbate already excessive N runoff into the Mississippi River and beyond. The purpose of this study was to reduce fertilizer N input using sUAS technology to assess crop canopy needs. In 2020 and 2021, variable rate nitrogen (VRN) side-dress N application maps were …
Utilization Of Various Methods And A Landsat Ndvi/Google Earth Engine Product For Classifying Irrigated Land Cover, Andrew Nemecek
Utilization Of Various Methods And A Landsat Ndvi/Google Earth Engine Product For Classifying Irrigated Land Cover, Andrew Nemecek
Graduate Student Theses, Dissertations, & Professional Papers
Methods for classifying irrigated land cover are often complex and not quickly reproducible. Further, moderate resolution time-series datasets have been consistently utilized to produce irrigated land cover products over the past decade, and the body of irrigation classification literature contains no examples of subclassification of irrigated land cover by irrigation method. Creation of geospatial irrigated land cover products with higher resolution datasets could improve reliability, and subclassification of irrigation by method could provide better information for hydrologists and climatologists attempting to model the role of irrigation in the surface-ground water cycle and the water-energy balance. This study summarizes a simple, …
Sustainable Intensification Of Agriculture: Opportunities And Challenges For Food Security And Agrarian Adaptation To Environmental Change In Bangladesh, Aaron Michael Shew
Sustainable Intensification Of Agriculture: Opportunities And Challenges For Food Security And Agrarian Adaptation To Environmental Change In Bangladesh, Aaron Michael Shew
Graduate Theses and Dissertations
This dissertation investigates three unique aspects of sustainable agricultural intensification (SAI) in the context of Bangladeshi rice production. The first article presents a qualitative analysis of SAI and farmer surveys in the embanked polder region of coastal Bangladesh. The second article investigates the global food security and environmental impacts of already adopted High Yielding Variety (HYV) rice and double-cropped rice systems in Bangladesh using a spatial partial equilibrium trade model and a Life Cycle Assessment (LCA). The final article demonstrates a remote sensing methodology for monitoring dry season rice production at 30 m resolution in Bangladesh using a harmonic time …