Master\u27s Project: Community Engagement at UVM\u27s Horticulture Research and Education Center

The Friends of the Horticulture Farm is a non-profit organization “dedicated to protecting, enhancing, and promoting the significant plant collections and natural areas of the UVM Horticulture Research and Education Center in South Burlington, Vermont for education, research, and public enrichment”. My project seeks to help The Friends of the Horticulture Farm determine how to revitalize their relationship with the community of the greater Burlington area. My primary goal has been to provide direction and recommendations that will help to increase community engagement and meet the educational potential of the Horticulture Farm. To achieve this goal, I have met with community members, visited other horticultural facilities, and interviewed people involved with outdoor educational programming. My final assessment makes recommendations in four arenas of community engagement: service learning, community service, workshops and classes, and self-guided adventures. I have compiled my findings and recommendations in a document to serve as a resource for The Friends of the Horticulture Farm as they work to grow and rejuvenate the community role of the Horticulture Farm

Master of Science

thesisIncreasing evidence of ecosystem damage caused by burning fossil fuels has sparked an interest in finding new methods of storing and utilizing energy that does not contribute to release of carbon dioxide. There is untapped potential for storing cold energy within the ground in climates with extreme winters and summers. This stored energy can be employed as a method of cooling buildings if effective heat transfer to and from the storage media and the building is obtained. A thermosiphon array is considered a means of enhancing that heat transfer. A test site has been studied at the University of Utah Sill Center consisting of two concentric thermosiphon arrays. It was designed to operate such that its performance is indistinguishable from traditional cooling systems. The research objective of this paper is to document the initial stages of the installation and operation, as it has spanned many years. The data collected from the thermosiphons is presented as a baseline for performance of the system. Ground temperature data is analyzed by finding the thermal diffusivity of the soil at the site. The scope of this work includes implementing the data acquisition system, ensuring the thermosiphon array was operational and leak proof and presenting preliminary performance data as a background for future work. Results include the preliminary performance data for two charged thermosiphons as well as ground temperature data. The thermal diffusivity of the ground was calculated using iterative methods to match the actual temperature distribution found in the ground from February to April 2015 to a mathematical model. The mild temperatures occurring late in the winter of 2015 stifled the freezing in the ground near the thermosiphons but still allowed detailed measurements of the thermal response to various atmospheric temperatures. It was also discovered that the thermosiphons were not receiving sufficient refrigerant, and that the addition of a flow meter and a pump would be beneficial. However, this project is to be continued for seasons to come in an effort develop a cost-effective method to reduce carbon dioxide emissions from cooling of commercial and residential buildings

Synthesis and characterization of Mercury vapor Coordination Species Using 1,3-Benzenedioethanethiolate (BDET)

Mercury is a major pollutant in the air today. Some possible chelators that have been used are British anti-Lewisite (BAL) 2,3-dimercaptopropanol, dimercaptosuccinic acid (DMSA). diinercaptopropanesulfonic acid (DMPS). and 1,3-benzenediamidoethanethiolate (BDE\u27I1. All contain two thiol groups, which \u27capture\u27 the heavy mercury element. Due to their structure, they all differ in how well they bind to the mercury atom. BDET is the most recent and efficient chelate used. Statistics show that coal-fired power plants are the nation\u27s largest mercury polluter. Steps have been taken to lower the emission of this toxic metal by the Clean Air Act. However, recently The Bush Administration\u27s Air Pollution Plan undermines the act. It delays the expectations and plans of the Clean Air Act by 10 years [ 1]

The Harvest

There once was a tiny speck of the universe called World

Node harvest

When choosing a suitable technique for regression and classification with multivariate predictor variables, one is often faced with a tradeoff between interpretability and high predictive accuracy. To give a classical example, classification and regression trees are easy to understand and interpret. Tree ensembles like Random Forests provide usually more accurate predictions. Yet tree ensembles are also more difficult to analyze than single trees and are often criticized, perhaps unfairly, as `black box' predictors. Node harvest is trying to reconcile the two aims of interpretability and predictive accuracy by combining positive aspects of trees and tree ensembles. Results are very sparse and interpretable and predictive accuracy is extremely competitive, especially for low signal-to-noise data. The procedure is simple: an initial set of a few thousand nodes is generated randomly. If a new observation falls into just a single node, its prediction is the mean response of all training observation within this node, identical to a tree-like prediction. A new observation falls typically into several nodes and its prediction is then the weighted average of the mean responses across all these nodes. The only role of node harvest is to `pick' the right nodes from the initial large ensemble of nodes by choosing node weights, which amounts in the proposed algorithm to a quadratic programming problem with linear inequality constraints. The solution is sparse in the sense that only very few nodes are selected with a nonzero weight. This sparsity is not explicitly enforced. Maybe surprisingly, it is not necessary to select a tuning parameter for optimal predictive accuracy. Node harvest can handle mixed data and missing values and is shown to be simple to interpret and competitive in predictive accuracy on a variety of data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS367 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Post-harvest technology change in cassava processing: a choice paradigm

Open Access Article; Available online: 27 Jan 2020This study employed a choice model to examine the factors influencing the choice of post-harvest technologies in cassava starch processing, using a sample of five hundred and seventy (570) processors in the forest and guinea savanna zones of Nigeria. In addition, the profitability of various post-harvest technologies in the study area was assessed using the budgetary technique while the impact of improved post-harvest technology on processors’ revenue and output was analysed using the average treatment effect model. Sex of the processor, processing experience, income, and cost of post-harvest technology, the capacity of post-harvest technology and access to credit amongst others significantly influence the choice of post-harvest technologies. Although the use of improved post-harvest technology comes with a high cost, the net income from its use was higher than the other types of post-harvest technologies, suggesting that the use of improved techniques was more beneficial and profitable. In addition, using improved post-harvest technology had a positive and significant effect on output and income. These findings shows that investment in improved post-harvest technologies by cassava starch processors and other stakeholders would increase income, thus, improving welfare

Hop Harvest Timing

In the Northeast, hop harvest generally begins in mid-August and continues through mid-September. Harvest date is primarily dependent on the hop variety but weather can delay or hasten maturation and impact when harvest will occur. In addition to weather, various pests, such as spider mites and downy mildew, can similarly impact harvest timing. The time at which you harvest hops can affect the various qualities of your finished product. Alpha and beta acid content peaks before many essential oils have fully developed. Delaying harvest can provide time for these oils to develop but increases the amount of time the hops are left vulnerable to disease and fall rains which can result in degradation of resins

Forest Soil Carbon and Nitrogen Cycles under Biomass Harvest: Stability, Transient Response, and Feedback

Biomass harvest generates an imbalance in forest carbon (C) and nitrogen (N) cycles and the nonlinear biogeochemical responses may have long-term consequences for soil fertility and sustainable management. We analyze these dynamics and characterize the impact of biomass harvest and N fertilization on soil biogeochemistry and ecosystem yield with an ecosystem model of intermediate complexity that couples plant and soil C and N cycles. Two harvest schemes are modeled: continuous harvest at low intensity and periodic clear-cut harvest. Continuously-harvested systems sustain N harvest at steady-state under net mineralization conditions, which depends on the C:N ratio and respiration rate of decomposers. Further, linear stability analysis reveals steady-state harvest regimes are associated with stable foci, indicating oscillations in C and N pools that decay with time after harvest. Modeled ecosystems under periodic clear-cut harvest operate in a limit-cycle with net mineralization on average. However, when N limitation is strong, soil C–N cycling switches between net immobilization and net mineralization through time. The model predicts an optimal rotation length associated with a maximum sustainable yield (MSY) and minimum external N losses. Through non-linear plant–soil feedbacks triggered by harvest, strong N limitation promotes short periods of immobilization and mineral N retention, which alter the relation between MSY and N losses. Rotational systems use N more efficiently than continuous systems with equivalent biomass yield as immobilization protects mineral N from leaching losses. These results highlight dynamic soil C–N cycle responses to harvest strategy that influence a range of functional characteristics, including N retention, leaching, and biomass yield