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343 results on '"O'Neal, Matthew"'

4. Exploring the Dynamics of Virulent and Avirulent Aphids: A Case for a 'Within Plant' Refuge

Author

Banerjee, Aniket, Valmorbida, Ivair, O'Neal, Matthew, and Parshad, Rana

Subjects
Mathematics - Dynamical Systems, Quantitative Biology - Populations and Evolution
Abstract

The soybean aphid, Aphis glycines (Hemiptera: Aphididae), is an invasive pest that can cause severe yield loss to soybeans in the northcentral United States. A tactic to counter this pest is the use of aphid-resistant soybean varieties. However, the occurrence of virulent biotypes can alter plant physiology and impair the use of this management strategy. Soybean aphids can alter soybean physiology primarily by two mechanisms, feeding facilitation and the obviation of resistance, favoring subsequent colonization by additional conspecifics. We developed a non-local, differential equation population model, to explore the dynamics of these biological mechanisms on soybean plants co-infested with virulent and avirulent aphids. We then use demographic parameters from laboratory experiments to perform numerical simulations via the model. These simulations successfully mimic various aphid dynamics observed in the field. Our model showed an increase in colonization of virulent aphids increases the likelihood that aphid-resistance is suppressed, subsequently increasing the survival of avirulent aphids, producing an indirect, positive interaction between the biotypes. These results suggest the potential for a "within plant" refuge that could contribute to the sustainable use of aphid resistant soybeans., Comment: 15 pages, 3 figures, 2 tables, Accepted in Journal of Economic Entomology

Published
2021
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6. No evidence hunting bait increases American black bear population growth in Maine, USA.

Author

Morin, Dana J., Vashon, Jennifer H., O'Neal, Matthew, Hostetter, Nathan J., and Flaherty, Elizabeth A.

Subjects
BEAR populations, STABLE isotope analysis, FOOD preferences, NATURAL resources, HAIR analysis, BLACK bear, PRODUCTION increases
Abstract

American black bear (Ursus americanus) populations are increasing throughout much of North America. Use of multiple harvest methods, including hunting over bait, is intended to increase harvest success rates to meet harvest and population management objectives. However, black bear population growth can be influenced by food availability, and some speculate the use of bait may inadvertently increase bear abundance through food resource supplementation. We collected hair from black bears captured from 2005–2019 and a selection of potential food items, including those used as bait from 3 study areas in Maine, USA, with different levels of human influence. We used stable isotope analysis of the black bear hair and food item samples to evaluate the contribution of different identified food groups to the diet of individual bears. We found no evidence that greater contribution of human food resources, including bait, increased the number of cubs per litter for reproductive females or that human foods comprised a greater part of the diet in years when natural food resources were less abundant. Approximately 69% of black bear harvest occurred over bait. We found the probability of reproductive‐age females to be harvested increased with average representation of human foods with high carbon enrichment (including bait) in their diet, but the relationship was weak, likely because of sampling constraints. Additionally, the probability of being harvested was greater in years when natural food resources were scarce. We conclude that bait is not available on the landscape in large enough amounts or for a sufficient amount of time in Maine to substantially influence female reproduction. Our results indicate hunting over bait is an effective tool in a state that is attempting to maintain a stable bear population via harvest, and dispels conjecture that hunting over bait may increase cub production and offset the intention of harvest. [ABSTRACT FROM AUTHOR]

Published
2025
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7. Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference.

Author

Fotouhi, Fateme, Menke, Kevin, Prestholt, Aaron, Gupta, Ashish, Carroll, Matthew E., Yang, Hsin-Jung, Skidmore, Edwin J., O'Neal, Matthew, Merchant, Nirav, Das, Sajal K., Kyveryga, Peter, Ganapathysubramanian, Baskar, Singh, Asheesh K., Singh, Arti, and Sarkar, Soumik

Subjects
COMPUTER vision, DEEP learning, MACHINE learning, KNOWLEDGE transfer, AGRICULTURE
Abstract

Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. Methods: To tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results. Results: The outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI. Discussion: We also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Soybean aphid biotype 1 genome: Insights into the invasive biology and adaptive evolution of a major agricultural pest

Author

Akraiko, Tatsiana, Aschwanden, Andrew, Avalos, Arian, Band, Mark, Bonning, Bryony, Bretaudeau, Anthony, Chiesa, Olga, Chirumamilla, Anitha, Coates, Brad S., Cocuzza, Giuseppe, Cullen, Eileen, Desborough, Peter, Diers, Brian, DiFonzo, Christina, Heimpel, George E., Herman, Theresa, Huanga, Yongping, Knodel, Janet, Ko, Chiun-Cheng, Labrie, Genevieve, Lagos-Kutz, Doris, Lee, Joon-Ho, Lee, Seunghwan, Legeai, Fabrice, Mandrioli, Mauro, Manicardi, Gian Carlo, Mazzoni, Emanuele, Melchiori, Giulia, Micijevic, Ana, Miller, Nick, Nasuddin, Andi, Nault, Brian A., O'Neal, Matthew E., Panini, Michela, Pessino, Massimo, Prischmann-Voldseth, Deirdre, Robertson, Hugh M., Sijun Liu, Song, Hojun, Tilmon, Kelley, Tooker, John, Wu, Kongming, Zhan, Shuai, Giordano, Rosanna, Donthu, Ravi Kiran, Zimin, Aleksey V., Julca Chavez, Irene Consuelo, Gabaldon, Toni, van Munster, Manuella, Hon, Lawrence, Hall, Richard, Badger, Jonathan H., Nguyen, Minh, Flores, Alejandra, Potter, Bruce, Giray, Tugrul, Soto-Adames, Felipe N., Weber, Everett, Marcelino, Jose A.P., Fields, Christopher J., Voegtlin, David J., Hill, Curt B., and Hartman, Glen L.

Published
2020
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15. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

Author

Karp, Daniel S., Chaplin-Kramer, Rebecca, Meehan, Timothy D., Martin, Emily A., DeClerck, Fabrice, Grab, Heather, Gratton, Claudio, Hunt, Lauren, Larsen, Ashley E., Martínez-Salinas, Alejandra, O’Rourke, Megan E., Rusch, Adrien, Poveda, Katja, Jonsson, Mattias, Rosenheim, Jay A., Schellhorn, Nancy A., Tscharntke, Teja, Wratten, Stephen D., Zhang, Wei, Iverson, Aaron L., Adler, Lynn S., Albrecht, Matthias, Alignier, Audrey, Angelella, Gina M., Anjum, Muhammad Zubair, Avelino, Jacques, Batáry, Péter, Baveco, Johannes M., Bianchi, Felix J. J. A., Birkhofer, Klaus, Bohnenblust, Eric W., Bommarco, Riccardo, Brewer, Michael J., Caballero-López, Berta, Carrière, Yves, Carvalheiro, Luísa G., Cayuela, Luis, Centrella, Mary, Ćetković, Aleksandar, Henri, Dominic Charles, Chabert, Ariane, Costamagna, Alejandro C., De la Mora, Aldo, de Kraker, Joop, Desneux, Nicolas, Diehl, Eva, Diekötter, Tim, Dormann, Carsten F., Eckberg, James O., Entling, Martin H., Fiedler, Daniela, Franck, Pierre, van Veen, F. J. Frank, Frank, Thomas, Gagic, Vesna, Garratt, Michael P. D., Getachew, Awraris, Gonthier, David J., Goodell, Peter B., Graziosi, Ignazio, Groves, Russell L., Gurr, Geoff M., Hajian-Forooshani, Zachary, Heimpel, George E., Herrmann, John D., Huseth, Anders S., Inclán, Diego J., Ingrao, Adam J., Iv, Phirun, Jacot, Katja, Johnson, Gregg A., Jones, Laura, Kaiser, Marina, Kaser, Joe M., Keasar, Tamar, Kim, Tania N., Kishinevsky, Miriam, Landis, Douglas A., Lavandero, Blas, Lavigne, Claire, Le Ralec, Anne, Lemessa, Debissa, Letourneau, Deborah K., Liere, Heidi, Lu, Yanhui, Lubin, Yael, Luttermoser, Tim, Maas, Bea, Mace, Kevi, Madeira, Filipe, Mader, Viktoria, Cortesero, Anne Marie, Marini, Lorenzo, Martinez, Eliana, Martinson, Holly M., Menozzi, Philippe, Mitchell, Matthew G. E., Miyashit, Tadashi, Molina, Gonzalo A. R., Molina-Montenegro, Marco A., O’Neal, Matthew E., Opatovsky, Itai, Ortiz-Martinez, Sebaastian, Nash, Michael, Östman, Örjan, Ouin, Annie, Pak, Damie, Paredes, Daniel, Parsa, Soroush, Parry, Hazel, Perez-Alvarez, Ricardo, Perović, David J., Peterson, Julie A., Petit, Sandrine, Philpott, Stacy M., Plantegenest, Manuel, Plećaš, Milan, Pluess, Therese, Pons, Xavier, Potts, Simon G., Pywell, Richard F., Ragsdale, David W., Rand, Tatyana A., Raymond, Lucie, Ricci, Benoît, Sargent, Chris, Sarthou, Jean-Pierre, Saulais, Julia, Schäckermann, Jessica, Schmidt, Nick P., Schneider, Gudrun, Schüepp, Christof, Sivakoff, Frances S., Smith, Henrik G., Whitney, Kaitlin Stack, Stutz, Sonja, Szendrei, Zsofia, Takada, Mayura B., Taki, Hisatomo, Tamburini, Giovanni, Thomson, Linda J., Tricault, Yann, Tsafack, Noelline, Tschumi, Matthias, Valantin-Morison, Muriel, Van Trinh, Mai, van der Werf, Wopke, Vierling, Kerri T., Werling, Ben P., Wickens, Jennifer B., Wickens, Victoria J., Woodcock, Ben A., Wyckhuys, Kris, Xiao, Haijun, Yasuda, Mika, Yoshioka, Akira, and Zou, Yi

Published
2018

18. Perspectives of Extension Agents and Farmers toward Multifunctional Agriculture in the United States Corn Belt

Author

Doudna, John W., O'Neal, Matthew E., Tyndall, John C., and Helmers, Matthew J.

Abstract

We surveyed the perspectives of farmers, crop professionals, and Extension agents and found that they have positive perspectives concerning multifunctional agriculture, including a positive effect of a nearby prairie to cropland productivity. The survey was conducted in central Iowa and included individuals predominantly from Iowa involved in commodity research and production. Our results are preliminary and provide a baseline for further research into the perspectives of change agents in the U.S. Corn Belt. They also provide insight into similarities among key links in the diffusion of innovation chain.

Published
2015

22. Self‐supervised learning improves classification of agriculturally important insect pests in plants

Author

Kar, Soumyashree, primary, Nagasubramanian, Koushik, additional, Elango, Dinakaran, additional, Carroll, Matthew E., additional, Abel, Craig A., additional, Nair, Ajay, additional, Mueller, Daren S., additional, O'Neal, Matthew E., additional, Singh, Asheesh K., additional, Sarkar, Soumik, additional, Ganapathysubramanian, Baskar, additional, and Singh, Arti, additional

Published
2023
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27. Self-supervised agricultural insect pest classification

Author

Kar, Soumyashree, primary, Nagasubramanian, Koushik, additional, Elango, Dinakaran, additional, Nair, Ajay, additional, Mueller, Daren, additional, O’Neal, Matthew, additional, Singh, Asheesh, additional, Sarkar, Soumik, additional, Ganapathysubramanian, Baskar, additional, and Singh, Arti, additional

Published
2022
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30. Effects of seed treatments and storage duration on Myzus persicae(Hemiptera: Aphididae) and amaranth fresh leaf yield

Author

Nampeera, Esther L, O’Neal, Matthew E, Nonnecke, Gail R, Murungi, Lucy K, Abukutsa-Onyango, Mary O, and Wesonga, John M

Abstract

The green peach aphid, Myzus persicae(Sulzer) (Hemiptera: Aphididae), is a key insect pest of amaranth in East Africa. Pest management has been restricted to indiscriminate application of insecticides to foliage. Applying systemic insecticides to seeds has been shown to manage aphid infestations in other crop systems. We evaluated two commercially available seed treatments in East Africa, Apron Star (thiamethoxam 20 g/kg + metalaxyl-M 20 g/kg + difenoconazole 2 g/kg) and Menceron (imidacloprid 233 g/L + pencycuron 50 g/L + thiram 107 g/L) for their efficacy against M. persicaeand impact on fresh leaf yield with two Amaranthusspecies, Amaranthus blitum(2 selections), Amaranthus hybridus(4 selections) and untreated control. Two storage periods (24 h and 3 months) with seed treatments were used. Each amaranth selection was treated individually with Apron and Monceren or untreated, and seeds were planted either 24 h or 3 months after treatment. Significant reduction in live aphids was observed with A. blitumand A. hybridusselections grown with seed treatment, at 6, 8 and 10 d after infestation (DAI) when compared with seeds grown without seed treatment. Untreated seeds of A. hybridus(selection 5) had significantly higher number of live aphids up to 243, greater percentage of damaged leaves and leaf damage score up to 84% and 64% respectively when compared with treated seeds of specific amaranth at 10 DAI. No significant difference was noted between seed treatment and storage time. Amaranth seeds treated with Monceren offered more protection against infestations of Myzus persicaethan amaranth seeds treated with Apron under high tunnel experiments.

Published
2023
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35. Self-supervised agricultural insect pest classification

Author

Kar, Soumyashree, primary, Nagasubramanian, Koushik, additional, Elango, Dinakaran, additional, Nair, Ajay, additional, Mueller, Daren, additional, O’Neal, Matthew, additional, Singh, Asheesh, additional, Sarkar, Soumik, additional, Ganapathysubramanian, Baskar, additional, and Singh, Arti, additional

Published
2021
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37. PREVALENCE AND RISK FACTORS OF ANAPLASMA INFECTIONS IN EASTERN MOOSE (ALCES ALCES AMERICANA) AND WINTER TICKS (DERMACENTOR ALBIPICTUS) IN MAINE, USA

Author

Elliott, James A., primary, Dickson, Carly C., additional, Kantar, Lee, additional, O'Neal, Matthew R., additional, Lichtenwalner, Anne, additional, Bryant, Ann, additional, Jakubas, Walter J., additional, Pekins, Peter J., additional, De Urioste-Stone, Sandra M., additional, and Kamath, Pauline L., additional

Published
2021
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38. Agronomic and seed traits of soybean lines with the Rag1 gene for aphid resistance

Author

Mardorf, Justin L., Fehr, Walter R., and O'Neal, Matthew E.

Subjects
Plant breeding -- Research, Aphididae -- Research, Soybean -- Diseases, Soybean -- Research, Soybean -- Genetic aspects, Plant immunology -- Research, Plant immunology -- Genetic aspects, Agricultural industry, Business
Abstract

Soybean [Glycine max (L.) Merrill] yields can be reduced significantly by infestations of the soybean aphid (Aphis glycines Matsumura). A dominant allele, Rag1, providing resistance to the aphid was identified in PI 548663. The objective of this study was to compare the agronomic and seed traits of 27 lines with the Rag1 allele to those of 27 lines with the rag1 allele that were derived from the same segregating population. The lines were evaluated under aphid-infested conditions at two Iowa locations in 2008 and under aphid-infested and aphid-free conditions at three Iowa locations during 2009. There were significant differences in mean yield between the Rag1 and rag1 lines in all the aphid-infested environments. The difference in yield between the two types reached 47.6% at one location under heavy infestation. Under aphid-free conditions, there was no significant difference in mean yield between the two types. The differences between the two types of lines for maturity, height, lodging, protein content, oil content, and seed weight were either not significant or sufficiently small to make it possible to develop aphid-resistant cultivars with the Rag1 gene that were comparable to susceptible cultivars. doi: 10.2135/cropsci2010.02.0079

Published
2010

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