Confronting input, parameter, structural, and measurement uncertainty in multi-site multiple-response watershed modeling using Bayesian inferences

2012 Fall.Includes bibliographical references.Simulation modeling is arguably one of the most powerful scientific tools available to address questions, assess alternatives, and support decision making for environmental management. Watershed models are used to describe and understand hydrologic and water quality responses of land and water systems under prevailing and projected conditions. Since the promulgation of the Clean Water Act of 1972 in the United States, models are increasingly used to evaluate potential impacts of mitigation strategies and support policy instruments for pollution control such as the Total Maximum Daily Load (TMDL) program. Generation, fate, and transport of water and contaminants within watershed systems comprise a highly complex network of interactions. It is difficult, if not impossible, to capture all important processes within a modeling framework. Although critical natural processes and management actions can be resolved at varying spatial and temporal scales, simulation models will always remain an approximation of the real system. As a result, the use of models with limited knowledge of the system and model structure is fraught with uncertainty. Wresting environmental decisions from model applications must consider factors that could conspire against credible model outcomes. The main goal of this study is to develop a novel Bayesian-based computational framework for characterization and incorporation of uncertainties from forcing inputs, model parameters, model structures, and measured responses in the parameter estimation process for multisite multiple-response watershed modeling. Specifically, the following objectives are defined: (i) to evaluate the effectiveness and efficiency of different computational strategies in sampling the model parameter space; (ii) to examine the role of measured responses at various locations in the stream network as well as intra-watershed processes in enhancing the model performance credibility; (iii) to facilitate combining predictions from competing model structures; and (iv) to develop a statistically rigorous procedure for incorporation of errors from input, parameter, structural and measurement sources in the parameter estimation process. The proposed framework was applied for simulating streamflow and total nitrogen at multiple locations within a 248 square kilometer watershed in the Midwestern United States using the Soil and Water Assessment Tool (SWAT). Results underlined the importance of simultaneous treatment of all sources of uncertainty for parameter estimation. In particular, it became evident that incorporation of input uncertainties was critical for determination of model structure for runoff generation and also representation of intra-watershed processes such as denitrification rate and dominant pathways for transport of nitrate within the system. The computational framework developed in this study can be implemented to establish credibility for modeling watershed processes. More importantly, the framework can reveal how collection of data from different responses at different locations within a watershed system of interest would enhance the predictive capability of watershed models by reducing input, parametric, structural, and measurement uncertainties

Feature Papers in Horticulturae

Several of the 17 papers in this volume represent diverse strategies for improving sustainability in crop production systems. The maintenance of soil quality and the reclamation of marginal soils, improving tolerance to saline irrigation water, biodegradable alternatives to black plastic mulch, use of natural plant extracts against bacterial disease, and development of cultivars resistant to herbivorous arthropods address urgent priorities in sustainable systems. Two papers examine the driving forces and effects of adopting innovative agricultural technologies in food value chains in underdeveloped regions of the world, and identification of new Asian vegetable crop species for European environments and markets. Three papers reported on managing fruit set and ripening in important fruit crop species like orange, apple, and plum. Postharvest techniques to reduce disease and maintain fruit nutraceutical content were reported in separate papers. Classification techniques, conservation and utilization of unique plant species, and in vitro propagation techniques of species with potential horticultural value were described in four papers

MP753: The Role of Interfering Plants in Regenerating Hardwood Stands of Northeastern North America

An annotated bibliography for American beech (Fagus grandifolia Ehrh.), striped maple (Acer pensylvanicum L.), hobblebush (Viburnum alnifolium Marsh.), hayscented fern (Dennstaedtia punctilobula L.), New York fern (Thelypteris noveborecensis L.), bracken fern (Pteridium aquilinum (L.) Kuhn), raspberries (Rubus spp.), and pin cherry (Prunus pensylvanica L.f.). While accessible literature includes many references to these species, the information remains scattered. No one has previously consolidated the separate reports for easy reference, nor summarized the findings relative to interference with tree regeneration. This annotated bibliography serves that purpose.https://digitalcommons.library.umaine.edu/aes_miscpubs/1023/thumbnail.jp

In search of a better fly trap: chemical and visual ecology of Drosophila suzukii

Drosophila suzukii is an invasive species of concern to fruit growers throughout temperate regions worldwide. Unlike most Drosophila species, D. suzukii has an enlarged and heavily sclerotized ovipositor that allows female flies to lay eggs in fruits before they are fully ripened and, in most cases, before fruits are harvestable. Initial efforts at mitigating damage have relied on chemical pesticides to reduce D. suzukii populations in crop areas; however, on-going research efforts have focused on more environmentally sustainable integrated pest management alternatives. This thesis investigates aspects of D. suzukii behaviour and physiology that promoted its successful global invasion. Chapter one discusses the role of behavioural and physiological plasticity in giving D. suzukii an ecological edge during introduction and successful invasion. Chapter two investigates D. suzukii host selection behaviour and preference among commercial fruits and novel native fruits in a boreal environment. I investigated the fruit characters thought to play a role in host choice, including fruit sweetness (brix), fruit acidity (pH), and fruit firmness (penetration force [gfmm2]). Based on D. suzukii behaviour observed in field settings, the investigation was expanded to include the role of fruit and foliage colour in host selection. Additionally, we beta-tested a citizen science initiative to identify native fruit species at risk and to confirm the range limits of D. suzukii in Atlantic Canada. Chapter three further explores colour preference and use of colour by D. suzukii as attraction cues, first as cues to differentiate among fruits of different ripeness stages, and second as visual targets for potential use in monitoring traps. Chapter four investigates D. suzukii physiological sensitivity and behavioural activity to odorants associated with fruits and foliage, and odorants known to be important to other Drosophila species. An iterative process of laboratory and field trials was used to test individual odorant compounds and odorant blends in combination with results of colour preference testing to improve trapping efficacy. Given the behavioural and physiological plasticity of D. suzukii, trials were conducted among different fruit crops and growing environments. Chapter five synthesizes lessons learned about D. suzukii behaviour and preferences to make recommendations for effective monitoring traps for blueberry and raspberry crop systems

Salmonella and tomatoes

Outbreak information linking fresh tomato fruit to illnesses is reviewed in this chapter. While tomato fruit appear to support substantial proliferation of certain serovars of Salmonella enterica, detection of this pathogen in tomato plants prior to harvest is rare, and reports of Salmonella existence in tomato fruit still attached to field-grown plants are virtually non-existent. The bacterium is sensitive to UV and can be outcompeted by the native phytomicrobiota, which may explain its absence in field-grown crops. However, the persistence of certain serovars in fields and ponds of certain production areas is noted. Together with evidence of bacteria becoming internalized in tomato fruit during crop development likely through natural apertures, the presence of S. enterica in and around production fields suggests that an unusual weather event could lead to Salmonella contamination of fruit prior to harvest. The bacterium appears physiologically adaptive toward proliferation in tomato fruit. Once inside tomatoes, Salmonella is capable of sensing the availability of nutrients and physiological state of the fruit and differentially regulates specific genes. However, because Salmonella is an efficient nutrient scavenger, removal of multiple metabolic and regulatory genes was required to reduce its fitness within the fruit. Plants do not appear to recognize human enterics as pathogens, and their defenses treat them as endophytes

Salmonella and tomatoes

Outbreak information linking fresh tomato fruit to illnesses is reviewed in this chapter. While tomato fruit appear to support substantial proliferation of certain serovars of Salmonella enterica, detection of this pathogen in tomato plants prior to harvest is rare, and reports of Salmonella existence in tomato fruit still attached to field-grown plants are virtually non-existent. The bacterium is sensitive to UV and can be outcompeted by the native phytomicrobiota, which may explain its absence in field-grown crops. However, the persistence of certain serovars in fields and ponds of certain production areas is noted. Together with evidence of bacteria becoming internalized in tomato fruit during crop development likely through natural apertures, the presence of S. enterica in and around production fields suggests that an unusual weather event could lead to Salmonella contamination of fruit prior to harvest. The bacterium appears physiologically adaptive toward proliferation in tomato fruit. Once inside tomatoes, Salmonella is capable of sensing the availability of nutrients and physiological state of the fruit and differentially regulates specific genes. However, because Salmonella is an efficient nutrient scavenger, removal of multiple metabolic and regulatory genes was required to reduce its fitness within the fruit. Plants do not appear to recognize human enterics as pathogens, and their defenses treat them as endophytes

DEEP LEARNING FOR IMAGE RESTORATION AND ROBOTIC VISION

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It\u27s also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is crucial to robots especially the autonomous ones. However, it is challenging to have a precise mathematical model to describe the aforementioned problems. Compared with traditional approach, learning-based approach has an edge since it does not require any model to describe the problem. Moreover, learning-based approach now has the best-in-class performance on most of the vision problems such as image dehazing, super-resolution, and image recognition. In this dissertation, we address the problem of image restoration and robotic vision with deep learning. These two problems are highly related with each other from a unique network architecture perspective: It is essential to select appropriate networks when dealing with different problems. Specifically, we solve the problems of single image dehazing, High Efficiency Video Coding (HEVC) loop filtering and super-resolution, and computer vision for an autonomous robot. Our technical contributions are threefold: First, we propose to reformulate haze as a signal-dependent noise which allows us to uncover it by learning a structural residual. Based on our novel reformulation, we solve dehazing with recursive deep residual network and generative adversarial network which emphasizes on objective and perceptual quality, respectively. Second, we replace traditional filters in HEVC with a Convolutional Neural Network (CNN) filter. We show that our CNN filter could achieve 7% BD-rate saving when compared with traditional filters such as bilateral and deblocking filter. We also propose to incorporate a multi-scale CNN super-resolution module into HEVC. Such post-processing module could improve visual quality under extremely low bandwidth. Third, a transfer learning technique is implemented to support vision and autonomous decision making of a precision pollination robot. Good experimental results are reported with real-world data

Annual Report: 2006

I submit herewith the annual report from the Agricultural and Forestry Experiment Station, School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, for the period ending December 31, 2006. This is done in accordance with an act of Congress, approved March 2, 1887, entitled, “An act to establish agricultural experiment stations, in connection with the agricultural college established in the several states under the provisions of an act approved July 2, 1862, and under the acts supplementary thereto,” and also of the act of the Alaska Territorial Legislature, approved March 12, 1935, accepting the provisions of the act of Congress. The research reports are organized according to our strategic plan, which focuses on high-latitude soils, high-latitude agriculture, natural resources use and allocation, ecosystems management, and geographic information. These areas cross department and unit lines, linking them and unifying the research. We have also included in our financial statement information on the special grants we receive. These special grants allow us to provide research and outreach that is targeted toward economic development in Alaska. Research conducted by our graduate and undergraduate students plays an important role in these grants and the impact they make on Alaska. Carol E. Lewis, Dean and DirectorFinancial statement -- Grants -- Students -- Research reports: Geographic Information, High-Latitude Agriculture, High-Latitude Soils, Management of Ecosystems, Natural Resources Use and Allocatio