[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118867-en":3,"doc-seo-118867-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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":29},118867,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Applications for Agricultural Economics - Doctoral Dissertation","The dissertation applies machine learning to answer research questions in agricultural economics through three related but independent essays. It uses data-driven methods to study how COVID-19 and media coverage influenced specialty crop shipping-point prices and demand dynamics. It also develops insights into how attributes of a newly developed apple variety can be leveraged in an advertising campaign to derive demand, including brand sentiment and effective market segments. Forecasting experiments compare neural networks with ARIMA in event-study settings.","MACHINE LEARNING APPLICATIONS FOR AGRICULTURAL ECONOMICS  \nBy  \nKENNEDY ODONGO  \nA dissertation submitted in partial fulfillment of  \nthe requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nWASHINGTON STATE UNIVERSITY  \nSchool of Economic Sciences  \nMAY 2023  \n© Copyright by KENNEDY ODONGO, 2023  \nAll Rights Reserved  \n© Copyright by KENNEDY ODONGO, 2023 All Rights Reserved  \nTo the Faculty of Washington State University:  \nThe members of the Committee appointed to examine the dissertation of KENNEDY ODONGO find it satisfactory and recommend that it be accepted.  \nJill J. McCluskey, Ph.D., Chair  \nRon C. Mittelhammer, Ph.D.  \nJia Yan, Ph.D.  \nACKNOWLEDGMENT  \nGrowing up in rural Kenya, I always wanted to be a professor. I remember when my uncle Tom came to my school for Parents’ Day, and I said I wanted to teach at the university. It did not bother me what it would take to do it because, despite being poor and orphaned, I knew I had the brains and the drive to do it. Looking back, I never anticipated how difficult this journey would be and how much help I would need to actualize my dreams. With this document, those dreams come true. I never intended to come to the U.S. to study. My only plan was to get on an aircraft anywhere. At 24 years, on August 16th, 2016, I was on a flight to the US to pursue a master's degree in economics. Six years later, I have two master's degrees and a Ph.D. in Economics.  \nI would like to thank my dissertation committee; Dr. Jill McCluskey for allowing me to experiment with new methods and data types and for teaching me the rigors of research, Dr. Ron Mittelhammer for teaching me econometrics beyond class, and Dr. Jia Yan for his input into all the machine learning techniques I have used in parts of my dissertation. I feel very blessed to have worked with you, and I hope that in the future, we can work together on more research projects. I would also like to acknowledge the Economic Research Service, Greg Astill and the Mercer School of Economic Sciences Fellowship for research funding and providing access to data sets.  \nOn my way to this Ph.D., I have had a lot of help from my family, friends, and well-wishers. I could not have done it without all of you. I am very thankful for all the scholarships I have received, all the advice, and most importantly, the prayers that make me who I am today. To my uncle Tom, I say thank you very much. I am a son of many mothers and fathers, too many to mention here. To all of you who have motivated me, nurtured me, and took care of me, I say thank you very much. To my fiancé, Gorety, thank you for everything. You have been a time, and-tested warrior. Always in my corner, praying, listening, and advising me.  \nFinally, I would like to thank God for good health, protection, and the ability he has given me to pursue education to the fullest.  \nMACHINE LEARNING APPLICATIONS FOR AGRICULTURAL ECONOMICS  \nAbstract  \nby Kennedy Odongo, Ph.D.  \nWashington State University  \nMay 2023  \nChair: Jill J. McCluskey  \nThis dissertation utilizes machine learning to answer questions in agricultural economics in three related but independent essays. Machine learning and data science are increasingly being adopted in interdisciplinary work providing complimentary analytical methods and data tools for economics research. I use machine learning to investigate how COVID-19 and the resulting media coverage affected specialty crop markets dynamics and to develop insights into how attributes of a new apple variety can be utilized in an advertising campaign to derive demand.  \nThe first paper of my dissertation investigates how COVID-19 and related social and traditional media coverage affected shipping point prices of specialty crops. I use Twitter data to estimate how the prevalence COVID-19 topics affect crop demand. The results show that crops that are usually consumed as food away from home (FAFH) were the most affected by COVID-19 relative to crops usually consumed as food at ho","cbCairNlOYTY04Vo","https://ap.wps.com/l/cbCairNlOYTY04Vo","pdf",1461219,1,126,"English","en",105,"# Acknowledgment\n# Abstract\n# List of Tables\n# List of Figures\n# Chapter 1: Introduction\n## 1.1 Machine learning and agricultural economics\n# Chapter 2\n# Chapter 3\n# Chapter 4","[{\"question\":\"What is the overall purpose of the dissertation?\",\"answer\":\"It uses machine learning to address questions in agricultural economics across three related but independent essays.\"},{\"question\":\"How does the dissertation examine the effect of COVID-19?\",\"answer\":\"It studies how COVID-19 and related social and traditional media coverage affected specialty crop shipping-point prices and demand, using Twitter data to estimate topic prevalence effects.\"},{\"question\":\"Which forecasting methods are compared, and what is the key outcome?\",\"answer\":\"The dissertation compares two time series forecasting techniques in an event-study context and finds that a neural network outperforms ARIMA on error metrics such as mean absolute error.\"}]","Machine Learning Applications for Agricultural Economics - Doctoral Dissertation | PDF",1785720700,318,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-applications-for-agricultural-economics-doctoral-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-applications-for-agricultural-economics-doctoral-dissertation/118867/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the overall purpose of the dissertation?","Question",{"text":75,"@type":76},"It uses machine learning to address questions in agricultural economics across three related but independent essays.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation examine the effect of COVID-19?",{"text":80,"@type":76},"It studies how COVID-19 and related social and traditional media coverage affected specialty crop shipping-point prices and demand, using Twitter data to estimate topic prevalence effects.",{"name":82,"@type":73,"acceptedAnswer":83},"Which forecasting methods are compared, and what is the key outcome?",{"text":84,"@type":76},"The dissertation compares two time series forecasting techniques in an event-study context and finds that a neural network outperforms ARIMA on error metrics such as mean absolute error.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]