Browsing by Subject "Integrated Pest Management"

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    Advancing Remote Sensing For Soybean Aphid (Hemiptera: Aphididae) Management In Soybean
    (2019-07) Marston, Zachary
    Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is the most economically important insect pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in the north-central United States. Current management recommendations for soybean aphid include frequent scouting of soybean fields and application of foliar insecticides when soybean aphid populations exceed an economic threshold of 250 aphids per plant. The scouting process for soybean aphid is time consuming, expensive, and also fails to thoroughly assess populations across the entire field. Because of these drawbacks, 84% of soybean farmers want to reduce scouting efforts. In 2015, it was determined that soybean aphid-induced stress had a significant effect on red-edge and near-infrared (NIR) reflectance of soybean canopies, offering the potential to use remote sensing for soybean aphid scouting. Utilizing remote sensing for soybean aphid scouting may decrease human effort, increase spatial coverage, and ultimately increase the adoption of recommended management practices. However, it was unknown whether soybean aphid-induced stress could be detected from aerial platforms, whether these reflectance data of aphid-induced stress could be classified into treatment groups, and how confounding factors might affect classification results. My first chapter determined that soybean aphid-induced stress could be detected from an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. Findings indicated that NIR reflectance decreased as aphid populations increased in both caged and open-field experiments. Chapter 2 evaluated ground-based hyperspectral samples and determined that soybean reflectance samples that were above the economic threshold of 250 aphids per plant could be classified with over 86% accuracy using linear support vector machine classification. Chapter 3 further evaluated ground-based hyperspectral samples in the presence of the confounding disease, soybean sudden death syndrome (SDS) caused by the fungal pathogen Fusarium virguliforme O’Donnell and T. Aoki (Hypocraeles: Nectriaceae). Findings indicated that when using linear support vector machines, it was difficult to differentiate between healthy and diseased samples; however, including the diseased group in the classification model decreased false positives for soybean aphid-induced stress. Overall, these findings advance the use of remote sensing for soybean aphid management and provide the first documentation of spectral classification of soybean aphid into threshold-based groups.
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    Economic Aspects of Crop Pest Management and Monarch Conservation
    (2020-08) Thakur, Tiesta
    The dramatic rise in prophylactic chemical treatments for pest and weed control in the past two decades have raised many environmental concerns. Although cost effective, such treatments which include neonicotinoids and herbicide tolerant (HT) crops contaminate soil and water and cause wildlife habitat loss. In my thesis, I explore the economics of eco-friendly practices of the farmers ranging from bio-diversity conservation to adapting integrated pest management. In the first chapter, I survey Midwestern farmers to estimate their willingness to grow milkweed on their non-cropland for Monarch butterfly conservation for various remuneration rates. I also approximate intrinsic motivation of farmers from their actual conservation data using reverse regression, distance discriminant analysis and control functions to test for motivation crowding out. Findings indicate motivation crowding out at modest levels of compensation. Alternatively, high remuneration crowds in farmers motivation to conserve Monarchs. The second chapter estimate soybean farmers' value of information provided by alternative configurations of a monitoring network for soybean rust (Phakopsora pachyrhizi). It shows that a network of 400 sentinel plots can maximize the expected profit of soybean farmers provided more plots are placed in the Corn Belt where the risk of soybean rust infection is lower, but where much more soybean is produced in contrast to the current spatial arrangement where sentinel plots are disproportionately placed in the Southern US. The last chapter examines the economic suitability of Unmanned Aerial Vehicles (UAVs) for scouting soybean aphids (Aphis glycines Matsumura) based on a plant-level spatiotemporal bioeconomic model of infestation. Findings indicate that the optimal profit from UAV based scouting is equivalent to that from manual scouting. But its greater tendency to detect false positives can also trigger frequent unnecessary treatments and dramatically reduce farmers' profits. Yet, UAV's commercial viability depends more on reducing its operating cost than improving its precision, once it has a tally threshold of 250 soybean aphids per plant.