[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117457-en":3,"doc-seo-117457-105":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},117457,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Exploring the Complementarity Between Traditional Econometric Methods and Machine Learning - An Application to Adoption and Disadoption of Conservation Practices","Study examines how traditional econometric methods and machine learning complement each other when analyzing adoption and disadoption of cover crops, a key agricultural conservation practice. Using a longitudinal panel survey of Iowa farmers, the research compares logistic regression with a Random Forest model to identify drivers of both initial conservation choices and later discontinuation. Results show logistic regression supports economic-theory interpretability, while Random Forest improves prediction and captures complex, non-linear effects. SHAP highlights adoption scale, past behavior, and environmental factors; larger prior acreage and consistent histories reduce disadoption, while cost-share long-run effects appear limited.","Exploring the Complementarity Between Traditional Econometric Methods and Machine Learning – An Application to Adoption and Disadoption of Conservation Practices  \nZhushan Du, Hongli Feng, and J. Arbuckle  \nWorking Paper 25-WP 668  \nFebruary 2025  \nCenter for Agricultural and Rural Development  \nIowa State University  \nAmes, Iowa 50011-1070  \n[www.card.iastate.edu](www.card.iastate.edu)  \nAn updated version of this working paper is forthcoming in Applied Economics  \nZhushan Du is PhD Student, Department of Economics, Iowa State University, Ames, Iowa, 50011. [E-mail: zhushand@iastate.edu](E-mail: zhushand@iastate.edu).  \nHongli Feng is Assistant Professor, Department of Economics, Iowa State University, Ames, Iowa, [50011. E-mail: hfeng@iastate.edu](50011. E-mail: hfeng@iastate.edu).  \nJ. Arbuckle is Professor, Department of Sociology and Criminal Justice, Iowa State University, Ames, Iowa, [50011. E-mail: arbuckle@iastate.edu](50011. E-mail: arbuckle@iastate.edu).  \nThis publication is available online on [the CARD website: www.card.iastate.edu. Permission](the CARD website: www.card.iastate.edu. Permission) is granted to reproduce this information with appropriate attribution to the author and the Center for Agricultural and Rural Development, Iowa State University, Ames, Iowa 50011-1070.  \nFor questions or comments about the contents of this paper, please contact Rabail Chandio, [rchandio@iastate.edu](rchandio@iastate.edu).  \nIowa State University does not discriminate on the basis of race, color, age, ethnicity, religion, national origin, pregnancy, sexual orientation, gender identity, genetic information, sex, marital status, disability, or status asa U.S. veteran. Inquiries regarding non-discrimination policies may be directed to Office of Equal Opportunity, 3410 Beardshear Hall, 515 Morrill Road, Ames, Iowa 50011, Tel. (515) 294-7612, Hotline: (515) 294-1222, email [eooffice@iastate.edu](eooffice@iastate.edu).  \nExploring the Complementarity Between Traditional Econometric Methods and Machine Learning – An Application to Adoption and Disadoption of Conservation Practices  \nZhushan Du, Hongli Feng, J. Arbuckle  \nAn updated version of this working paper is forthcoming in Applied Economics  \nAuthor name and affiliations  \nZhushan Du, [zhushand@iastate.edu](zhushand@iastate.edu), Department of Economics, Iowa State University, United States  \nHongli Feng, [hfeng@iastate.edu](hfeng@iastate.edu), Department of Economics and Center for Agricultural and Rural Development, Iowa State University, United States  \nJ. Gordon Arbuckle, [arbuckle@iastate.edu](arbuckle@iastate.edu), Department of Sociology and Criminal Justice, Iowa State University, United States  \nAbstract:  \nThis study explores the potential complementarity between traditional econometric methods and machine learning in analyzing the adoption and disadoption of a key conservation practice in agriculture, cover crops. While the adoption of conservation practices has been widely examined, the literature on their disadoption is limited. Using a unique longitudinal panel survey of Iowa farmers, we compare logistic regression models with a Random Forest algorithm to examine factors driving conservation adoption and disadoption. Our findings show that while traditional logistic regression models offer interpretability grounded in economic theory, Random Forest provides superior predictive power and reveals complex, non-linear relationships among key factors such as past adoption behavior, cost-share participation, and farmer perceptions. SHAP (SHapley Additive exPlanations) analysis identifies adoption scale, past adoption behavior, and environmental factors as primary drivers of disadoption. Farmers with larger previous cover crop acreage and consistent adoption history are significantly less likely to disadopt, while the longterm impact of cost-share programs on continued use appears limited. By combining machine learning’s predictive power with the interpretability of ","cbCaigQD4t40zEyi","https://ap.wps.com/l/cbCaigQD4t40zEyi","pdf",1407699,1,52,"English","en",105,"# Abstract\n# Introduction\n## Post-initial adoption and disadoption\n## Limits of traditional econometric approaches\n## Role of machine learning models","[{\"question\":\"What conservation practice and data does the study focus on?\",\"answer\":\"The study focuses on cover crops and uses a unique longitudinal panel survey of Iowa farmers to track adoption and disadoption over time.\"},{\"question\":\"How do the authors compare econometric methods with machine learning?\",\"answer\":\"They compare logistic regression models with a Random Forest algorithm to assess factors driving both adoption and disadoption decisions, evaluating interpretability and predictive performance.\"},{\"question\":\"What does the SHAP analysis identify as key drivers of disadoption?\",\"answer\":\"SHAP analysis indicates adoption scale, past adoption behavior, and environmental factors as primary drivers of disadoption, with consistent prior adoption reducing the likelihood of discontinuation.\"}]","Exploring the Complementarity Between Traditional Econometric Methods and Machine Learning - 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