[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122116-en":3,"doc-seo-122116-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},122116,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Where Can We Grow?Machine Learning to Predict the Future of U.S.Maize - 2024","This poster presents research on utilizing machine learning to predict the future of U.S. maize cultivation, focusing on the factors influencing growth. The study investigates the impact of weather, soil conditions, and terrain on crop yield, aiming to provide insights for optimizing agricultural practices. The research is part of the Three-Minute Thesis (3MT) competition at the University of Arkansas, Fayetteville, and is made available through ScholarWorks@UARK. The work, authored by Harrison Smith, a PhD Candidate in Environmental Dynamics, explores predictive modeling for agricultural sustainability and efficiency. The poster highlights key variables that affect maize production and suggests potential applications of machine learning in addressing future challenges in agriculture. The citation details are provided for further reference, along with information about the open access repository where the poster is archived. The focus is on how machine learning can contribute to understanding and enhancing the future of maize farming in the United States by analyzing environmental data.","2024  \n# Where Can We Grow?Machine Learning to Predict the Future ofU.S.Maize(2024)\n\nHarrison SmithUniversity of Arkansas,Fayetteville  \nFollow this and additional works at:https://scholarworks.uark.edu/mtsturpc  \nPart of the Higher Education Commons  \n# Citation\n\nSmith,H.(2024).Where Can We Grow?Machine Learning to Predict the Future of U.S.Maize(2024).Three-Minute Thesis Research Posters.Retrieved from https://scholarworks.uark.edu/mtsturpc/47  \nThis Poster is brought to you for free and open access by the Three-Minute Thesis at ScholarWorks@UARK.It hasbeen accepted for inclusion in Three-Minute Thesis Research Posters by an authorized administrator ofScholarWorks@UARK.For more information,please contact uarepos@uark.edu.  \nHarrison Smith  \nWhere can we grow?Machine learning to predict the future of U.S.maizeFaculty Advisor:Amanda Ashworth  \n# Where can we grow?\n\nMachine learning to predict the future of U.S.maize  \n——Harrison Smith|PhD Candidate|Environmental Dynamics  \nWeather  \nSoil  \nTerrain","cbCainpyHPrm7apb","https://ap.wps.com/l/cbCainpyHPrm7apb","pdf",1457193,1,3,"English","en",105,"# Where Can We Grow?\n## Machine Learning to Predict the Future of U.S. Maize\n### Factors Influencing Maize Growth\n#### Weather\n#### Soil\n#### Terrain","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"The main goal is to use machine learning to predict the future of U.S. maize cultivation and identify factors that influence its growth.\"},{\"question\":\"Who conducted this research and where?\",\"answer\":\"The research was conducted by Harrison Smith, a PhD Candidate in Environmental Dynamics at the University of Arkansas, Fayetteville.\"},{\"question\":\"What are the key factors considered in predicting maize growth?\",\"answer\":\"The key factors considered are weather, soil conditions, and terrain.\"}]","Where Can We Grow?Machine Learning to Predict the Future of U.S.Maize - 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