Evaluation of Best Management Practices to Reduce Nutrients Runoff in Watersheds in Arkansas

There are many non point sources (NPS) of pollution issues across the state of Arkansas. Each region of the state has different concerns. Many watersheds have been included in the Arkansas\u27s 2008 303(d) list for NPS impairments with sediment and nutrients being the primary causes of concern. This research hypothesized that there are no cost or net returns risks when adopting best management practices (BMPs) to control nutrients runoff and that selection, timing, placement and cost have no impact on the implementation of BMPs. Using two priority watersheds, the L\u27Anguille River and the Lincoln Lake, as examples, the environmental benefits and the cost-effectiveness of several BMPs were compared to representative systems that producers currently use. Current systems were rice and soybeans production under various tillage, buffers and nutrient management practices. Also analyzed were alternative pasture management systems, buffers and poultry litter applications for bermudagrass production. For each system, total phosphorous (TP) loss estimates were linked with production costs, BMP costs and risk premiums within a watershed to create an environmental-economic model. The model was used to analyze the impact of BMPs in reducing nutrient runoff while minimizing the producers\u27 exposure to additional risk. To accomplish this goal, two mathematical techniques were used: stochastic dominance and genetic algorithm. Findings showed that BMPs have the potential for reducing nutrient pollutant losses from agricultural land areas. However, ranking BMPs solely in terms of their effectiveness to reduce nutrient runoff can lead to cost-prohibited recommendations. Since producer\u27s risk aversion level matters, for producers to adopt any of the BMPs analyzed in this study, they would have to receive a risk premium. This is true for both row crop and forage producers. Still, there are some BMPs that can reduce nutrient runoff, maintain agricultural production and improve water quality without affecting producers\u27 cost or net returns dramatically. Consequently, decision makers need to weight trade-offs between nutrient runoff reduction and net cost increase when selecting BMPs. Cost-savings from selecting BMPs become evident when critical factors for reducing TP runoff are analyzed using an environmental-economic model

Evaluating the Least Cost Selection of Agricultural Management Practices in the Five-Mile Creek Area of Fort Cobb Watershed

One of the main causes of water quality impairment in the United States is human induced Non-Point Source (NPS) pollution through intensive agriculture. The Fort Cobb Reservoir (FCR) watershed located in southwestern Oklahoma, United States is a rural agricultural catchment with known issues of NPS pollution including suspended solids, siltation, nutrients, and pesticides. The FCR watershed with an area of 813 km2 includes one major lake fed by four tributaries. Despite efforts and research to improve water quality in the FCR watershed through the implementation of varieties of Best Management Practices (BMPs) for decades, there are still problems of sediment and phosphorous loads in this catchment, which demonstrates the need for research. Since the cost of implementing some BMPs can be expensive, the cost effective selection and location of BMPs can aid in increasing both the efficiency of public funds and the total income of farmers. The major goal of this study was to identify optimal selection and location of livestock-crop-BMPs including crop types, production methods, and agricultural management practices that could further reduce sediment and phosphorous loss from the agricultural fields in Five-Mile Creek (FMC) sub-watershed of FCR watershed at the least-cost to producers and the public in both the dry and irrigated areas with consideration of existing BMPs. For this, a hydrological model of the study area was developed using the Soil and Water Assessment Tool (SWAT). The model was calibrated and validated satisfactorily for streamflow, crop yield, sediment, and phosphorous. The verified model was used to simulate 22 crop-BMP combinations over the 1989-2016 period. A Linear Programming (LP) model was used to determine the crop-BMP choice that would maximize income and minimize public cost while abating sediment and phosphorous under two different scenarios: market solution (maximize revenue with no constraints on sediment and phosphorous production) and tax solution (discourage sediment and phosphorous production through incentive programs).The model was capable of providing precise information for stakeholders to prioritize ecologically sound and economically feasible BMPs that are capable of mitigating human induced impacts at the watershed scale based on soil texture, land slope and dryland and irrigated areas.Biosystems and Agricultural Engineerin

Balancing Ecological and Economic Objectives in Land Use and Management: Modeling to Identify Sustainable Spatial Patterns.

Human-driven land-use/cover (LULC) changes threaten the integrity of ecosystems in many ways. To evaluate possible impacts of future changes in LULC on ecosystem services and support more sustainable environmental management, it is essential to understand how land-use patterns affect both ecological and economic outcomes, and how alternative spatial land-use and -management strategies may improve sustainability in land-use systems. I developed and tested a spatial simulation approach that can help improve our understanding of how human-driven landscape conditions at the watershed scale might reshape impacts on both water quality and economic performance in a Lake Erie watershed under a changing climate. The dissertation is organized into three chapters. The first chapter describes a study in which I evaluated sensitivity of a stochastic land-change model (LCM) to pixel versus polygonal land unit derived from parcel maps. Performance of pixel- and polygon-based simulations suggest that using polygonal unit is helpful with generating more realistic landscape patterns, but at the cost of spatial allocation accuracy. For the second chapter, I developed the first integrated modeling approach that compares the relative economic efficiency of alternative spatial land-use and -management strategies for addressing non-point source (NPS) nutrient pollution. Using the Soil Water Assessment Tool (SWAT) and data on crop costs and prices, I evaluated joint impacts on nutrient reduction and economic returns for optimized patterns of land-use changes (LUCs) versus conservation practices (CPs) at the field scale. Simulated results showed relying on CPs alone might not be sufficient to restore water quality in Lake Erie, and a combination strategy including both LUCs and CPs would be necessary and more efficient. Finally, I examined sensitivity of optimized spatial patterns of land-use and -management (CPs) approaches to climate change. I found optimal land-use and -management placement can be quite sensitive to change in climatic conditions. CP targeting was found to be more robust to climate change than land-use change, but integration of both strategies would be necessary to achieve high DRP reduction (>65%) targets. Results from this study highlight the need for future spatial optimization studies to consider adaptive capacity of conservation actions under a changing climate.PhDNatural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133330/1/xuhui_1.pd

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

A Multi-objective Bi-level Optimisation model for Agricultural Policy in Scotland

Agricultural policy analysis can be visualised as a multiple objective hierarchical optimisation problem whereby sequential non-cooperative interactions between the policy makers and the farmers take place. The objectives and choices of policy makers will almost always diverge from the objectives and choices of farmers. Policy makers exercise authority over some, but not all, of the variables in the total system whereas other variables affecting their multiple goals are under the direct control of myriad farmers who operate according to their own utility maximising motives. In order to advance their own objectives, the policy makers unilaterally and pre-emptively set the policy measures to influence the farmers. The farmers execute their decisions after, and in view of, the policies and make their production decisions that observe their goals best. Ultimately, the payoffs to both the policy makers and the farmers depend not only on the actions of the former, but also on the reactions of the latter. Such problems are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This thesis shows how multi-objective genetic algorithms (MOGA) in conjunction with mathematical programming (MP) can be used for solving this type of problems. A MP model is developed to capture the production choices of farmers. The model is based on positive mathematical programming and its objective function parameters are estimated using the method of generalised maximum entropy. The model is nested in and controlled by a MOGA which captures the process of multi-objective optimisation of policy decisions. The approach is illustrated using a case study taken from the Scottish agricultural systems, where several socio-economic and environmental objectives for policy making are considered. Four types of policy instruments are examined: the current single payment scheme, a multi-payment scheme based on land use, an input taxation and a regulatory scheme. For a selection of scenarios alternative Pareto-optimal solutions are discovered and tradeoffs between the policy objectives are presented along with their associated production patterns. The performance of the modelling tool developed suggests that it is well suited to dealing with real-world policy issues. It offers considerable possibilities for exploring tradeoffs between non-commensurable and conflicting objectives relevant to sustainable development of Scottish agriculture

Multidisciplinary optimisation of an Unmanned Aerial Vehicle with a fuel cell powered energy system

ALF/ENGAER 139425-J Bernardo Miguel Teixeira Alves. Examination Committee: Chairperson: COR/ENGAER Luís António Monteiro Pessanha; Supervisors: Prof. André Calado Marta, MAJ/ENGAER Luís Filipe da Silva Félix; Member of the Committee: Prof. Pedro Vieira GamboaPara explorar a utilização de células de combustível a hidrogénio como alternativa viável aos combustíveis nocivos em veículos aéreos não-tripulados, um conceito de UAV de classe I foi desenvolvido no Centro de Investigação da Força Aérea (CIAFA). Este trabalho foca-se nos estudos trade-off realizados durante a sua conceção e na subsequente otimização. Primeiro, uma abordagem de otimização multi-objetivo foi utilizada com o auxílio do algoritmo genético NSGA-II para balancear dois objetivos em conflito: peso reduzido; e elevada autonomia. Conclui-se que é possível voar mais de três horas com um peso máximo à descolagem de 21,6 kg, uma célula de hidrogénio de 800 W e 148 g de hidrogénio. Uma configuração mais pesada com maior potência nominal e mais combustível foi descartada devido a um constragimento na envergadura. Posteriormente, com um conceito que satisfaz os requisitos impostos, uma abordagem multi-disciplinar (MDO) foi utilizada para maximizar a autonomia. O software utilizado foi o OpenAeroStruct, método dos elementos finitos (FEM) e o método da malha de vórtices (VLM) para modelar superfícies sustentadoras. Inicialmente, uma condição de cruzeiro e de carga foram utilizadas com torção geométrica da asa como variável de projeto. Posteriormente, maior complexidade foi introduzida atrav´es da utilização de afilamento, corda e envergadura. Finalmente, uma terceira condição de voo foi introduzida com o intuito de garantir o requisito de perda. Com a utilização de MDO foi possível aumentar a autonomia em 21% satisfazendo todos os requisitos. Este trabalho marca um passo importante no desenvolvimento de um futuro protótipo no Centro de Investigação.To explore the use of hydrogen fuel cells as a feasible alternative to pollutant fuels on Unmanned Aerial Vehicles (UAVs), a class I concept was designed at the Portuguese Air Force Research Centre. This work focuses on the trade-off studies performed during its design and on the optimisation that followed. First, a multi-objective optimisation approach was used with the aid of the Algorithm NSGAII to balance between two conflicting objectives: low weight and high endurance. It was found that it is possible to fly for more than 3 hours with a Maximum Take-off Weight of 21.6 kg, an 800 W fuel cell and 148 g of hydrogen. A heavier configuration with more power and fuel was discarded due to a wingspan constraint. Later, after the concept satisfied the project requirements, Multi-Disciplinary Design Optimisation (MDO) was performed to achieve the maximum endurance possible. The software used was OpenAeroStruct, low fidelity Finite Element Analysis (FEA) and Vortex Lattice Method (VLM) to model lifting surfaces. Initially, a cruise and a load flight point were used with wing geometric twist only as design variable. After, more complexity was added by introducing taper, wing chord and span. Finally, a third flight point was introduced to ensure the stall requirements were satisfied. The use of MDO allowed a 21% increase in endurance with a smaller wing area. Other improvements could not be achieved without violation of the constraints. This work marks an important milestone in the development of a future prototype at the Research Centre.N/