This study was conducted on two Mississippi watersheds. The SWAT model was applied to the Upper Pearl River Watershed (UPRW) to evaluate flow, sediment, nutrients, and fecal coliform bacteria (FCB) transport. The model was further applied to evaluate crop and sediment yields from three tillage systems (Conventional, Reduce 1, and Reduce 2) of the Big Sunflower River Watershed (BSRW). In the UPRW, flow and sediment simulations showed good to very good model performances (for flow R2 up to 0.76 and NSE up to 0.75; and for sediment R2 up to 0.72 and NSE up to 0.54). Both total nitrogen (TN) and total phosphorous (TP) simulations showed fair to good model performances (R2 up to 0.71 and NSE up to 0.63 for TN; R2 up to 0.70 and NSE up to 0.59 for TP). The FCB simulation showed good model performance (R2 up to 0.59 and NSE up to 0.58). In the BSRW, crop simulations showed good to very good model performances (for corn yield R2 up to 0.5 and NSE up to 0.9; and for soybean yield R2 and NSE up to 0.6). Furthermore, modeling outputs of the BSRW explained 64% of the water table fluctuations in the Mississippi alluvial aquifer. The future climates of the UPRW and the BSRW were evaluated for three emission scenarios (A1B, A2, and B1) from the Intergovernmental Panel on Climate Change (IPCC) with the help of the general circulation model, CCSM3. Simulations predict future sediment yields will increase as much as 25% in the UPRW. Both TN and TP yields will also be elevated as much as 7.3% and 14.3% respectively in future climates of the UPRW. Four best management practices (BMPs) were applied to the current and future climates in the UPRW and results showed that BMPs were able to reduce 51% of flow, 55% of sediment, 44% of TN, and 88% of TP in the baseline climate. Moreover, the effectiveness of TN removal will increase in future climates, while the effectiveness of TP removal will remain unchanged. The effects of climate variability on corn and soybean yield were insignificant in the BSRW.

To effectively analyze and design a flexible supply chain (FSC), a variety of variables need to be considered. This research presents a framework, an extension of Chan et al. (2009) that identifies a more extensive yet salient set of variables for designing FSCs. This framework provides a basis for using simulation to better understand, and to better design, FSCs. Conceptual simulation models are developed to represent general flexible supply chains in terms of using design and system variables. The proposed conceptual model incorporates many elements from the framework considering a wide variety of variables to demonstrate the approach for building a FSC model. This research provides a general FSC simulation model, built in FlexSim, that implements many variables from the framework and aspects of the conceptual framework. Variability plays an important role in FSC model. Two key supply-chain performance measures are lead time and variability in lead time. One way that has been proposed to improve both measures is to increase supplier flexibility. Through simulation this research provides a means to assess the effects of various manufacturing and logistics flexibility-related variables on lead time and its variability. This research includes effect of several experiments that consider the effect of supplier flexibility level, proportion of process time that is production and transportation time, and level of variability in process time on lead time. The triangular distribution is used often in simulation when process data are not available. Thus, the triangular distribution is used in the FSC simulation model. This research provides a means to effectively consider alternative values of the parameters of the triangular distribution during experimentation. The method facilitates specification of both moment and location parameters.

Previous studies have shown significant warming through the 1990s in the West Antarctic Ice Sheet (WAIS); but the records used in those studies end in early 2000, preventing trend analysis into the latest decade. Fourteen new snowpits and firn cores were collected in 2010 and 2011, which have been combined with previous cores to extend the isotopic records over WAIS. Significance of these isotopic patterns across WAIS was determined and is used to re-evaluate the warming of the West Antarctic interior over recent decades. We find that isotopic records longer than 50 years are needed to assess climate trends due to decadal variability. When assessed over periods greater than 50 years, there is a statistically significant warming trend over central WAIS. However, the isotopes in the 2000s are anomalously low in the isotopic records, which challenge the recent suggestion that the warming trend is accelerating. We attribute the isotopic low over the most recent decade to the coupling effect of anomalously low temperatures over central WAIS and associated increase in sea ice in the adjacent seas. This work strongly indicates that decadal variability and likely climate trends are both driven, at least in part, by atmospheric variability in the tropics as well as at high latitudes.

Site-specific irrigation decisions require information about variations in soil moisture within the rooting depth actively being used by the crop. Producers have been using soil moisture sensors to make irrigation decisions, and it has been shown that soil moisture sensors can reduce water usage without reducing yields. There are still unanswered questions on improving efficiency with soil moisture sensors based on density and location of sensors within a field. This three-year study uses sensors to evaluate the spatio-temporal variability of soil moisture across an 18-ha production field in a corn/soybean rotation. The IDW results show that when uniform irrigation applications are made to the field, fewer sensors that are placed in better locations throughout the field can be as useful as a densely gridded array of sensors. Although, if variable rate irrigation (VRI) is being used, a dense array could be used the first season to fine tune management zones.

Precision forestry uses information collecting techniques to detect within stand variability and inform sub-stand management treatments, such as stand thinning guidelines. This study uses LiDAR to assess individual-tree stem volumes in Pinus taeda L. plantations in the southeast US. Currently, starting rows in commercial row thinning operations are arbitrarily selected, but the study used LiDAR collected stem volume data to inform starting row selection. Three study sites were measured to provide evidence of between-row volume variability. The primary study site was set up in an alternative treatment design. Two treatments were tested: a fourth row removal scenario which removed the most volume of the four possible scenarios versus a fourth row removal which targeted the least amount of volume removed. Between-row volume variability was shown in all study sites and LiDAR data accurately assessed volume in the primary study site. The primary site saw the two blocks homogenized by their thinning treatments, demonstrating the ability to increase or decrease residual volumes using targeted row selection . Targeted row removal retained more volume and larger trees and may lead to higher harvest yields and shorter rotations. Timber managers across the globe are increasingly using remote sensing to inventory stands, thus LiDAR-informed volume acquisition may be an additional application to increase the efficiency and productivity of forests. / Master of Science / Loblolly pine (Pinus taeda L.) is the most important commercial timber species in the southeastern US. Commonly grown in plantations and planted in rows, these forest tracts often receive at least one "commercial thinning" (i.e. profit yielding thinning) between planting and harvesting. This thinning typically removes every fourth row as well as undesirable individuals in residual rows, thus providing residual trees more space and resources to grow. Due to the high costs of manually inventorying these stands, row removal selection is arbitrary and may fail to fully address stem volume variability among rows. LiDAR (light detection and ranging) is a technology that creates 3D models from sent and returned light signals. This technology was aerially employed to inventory a P. taeda plantation in the Virginia Piedmont and models were developed to measure stem volumes from collected data. These stem volumes were consolidated into row volumes and were used to inform row selection during commercial row thinning operations. Targeted volume removal showed the ability of LiDAR application to alter residual stand volumes. Through low volume removal, residual volumes were increased. Additionally, more large trees were retained. Large trees are especially important as they have a competitive advantage in growth response post-thinning. These results have the potential to increase harvest yields and therefore pine plantation productivity and efficiency.

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