[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119171-en":3,"doc-seo-119171-105":30,"detail-sidebar-cat-0-en-105":95},{"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},119171,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Driven Imputations to Filling Agricultural Data Gaps","This paper addresses missing crop yield data in large-scale agricultural surveys, where crop-cutting is the most accurate measurement method but is often constrained by budget and implementation capacity. It applies multiple imputation techniques supported by machine learning models to predict missing yield values, using Mali survey data that contains both crop-cut and self-reported yield information. The analysis examines multiple crops, identifies the role of key predictors, and evaluates validity conditions. Results indicate accurate ML-based imputations for specific contexts, while cross-survey extrapolations are less reliable.","Public Disclosure Authorized Public Disclosure Authorized  \nPolicy Research Working Paper 10964  \nYielding Insights  \nMachine Learning-Driven Imputations to Filling Agricultural Data Gaps  \nIsmaël Yacoubou Djima  \nMarco Tiberti  \nTalip Kilic  \nDevelopment Economics Development Data Group  \nNovember 2024  \nPolicy Research Working Paper 10964  \nAbstract  \nThis paper addresses the challenge of missing crop yield data in large-scale agricultural surveys, where crop-cutting, the most accurate method for yield measurement, is often limited due to cost constraints. Multiple imputation techniques, supported by machine learning models are used to predict missing yield data. This method is validated using survey data from Mali, which includes both crop-cut and self-reported yield information. The analysis covers several crops, providing insights into the importance of different predictors, including farmer-reported yields and geo-spatial  \nvariables, and the conditions under which the approach is valid. The findings show that machine learning-based imputations can provide accurate yield estimates, especially for crops with low intercropping rates and higher commercialization. However, survey-to-survey imputations are less accurate than within-survey imputations, suggesting limitations in extrapolating data across different survey rounds. The study contributes valuable insights into improving cost-efficiency in agricultural surveys and the potential of imputation methods.  \nThis paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at [http://www.worldbank.org/prwp. The authors may](http://www.worldbank.org/prwp. The authors may)  \nbe contacted at [iyacouboudjima@worldbank.org](iyacouboudjima@worldbank.org); [mtiberti@orldbank.org](mtiberti@orldbank.org); [and tkilic@worldbank.org](and tkilic@worldbank.org).  \nThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.  \nProduced by the Research Support Team  \nYielding Insights: Machine Learning-Driven Imputations to Filling  \nAgricultural Data Gaps ∗  \nIsma􀁿el Yacoubou Djima† Marco Tibertiy Talip Kilicy  \nJEL Codes: C53; C55; C83; Q12 .  \nKey words: Smallholder farming, Agricultural Crop Yields Measurements, Machine learning, Missing Data, Multiple Imputation, Household Surveys  \n∗ The authors would like to thank (i) Ksenia Abanokova, the participants of The Ninth International Conference on Agricultural Statistics (ICAS IX) for their comments, (ii) the Statistics Unit of the Ministry of Agriculture in Mali (CPS/SDR) for the successful implementation of the agricultural surveys that this study leverages, (iii) Giulia Ponzini who co-lead the technical support to CPS/SDR for the implementation and the data curation of the surveys data used for this study. Weare grateful for the funding from the Mali Mission of the United States Agency for International Development (USAID) for the implementation of the LSMS{Integrated Surveys on Agriculture (LSMS-ISA)-supported surveys in Mali. This paper was produced with the 􀀌nancial support from the World Bank LSMS Program (worldbank.org/lsms) and the 50x2030 Initiative to Close the Agricultural Data Ga","cbCaiixGZfWNyJuw","https://ap.wps.com/l/cbCaiixGZfWNyJuw","pdf",630851,1,52,"English","en",105,"# Abstract\n# Introduction\n## Crop yield measurement challenges\n## Reported vs crop-cut yields\n## Motivation and approach overview","[{\"question\":\"What problem does the paper address in agricultural surveys?\",\"answer\":\"It targets missing crop yield data in large-scale surveys, where the most accurate measurement method (crop-cutting) is often unavailable for all observations due to cost constraints.\"},{\"question\":\"How does the paper predict missing yield values?\",\"answer\":\"It uses multiple imputation methods supported by machine learning models to estimate missing plot-level yield data.\"},{\"question\":\"What does the validation data include?\",\"answer\":\"The study validates the approach using survey data from Mali that contains both crop-cut and self-reported yield information.\"},{\"question\":\"Are imputations equally accurate across different survey settings?\",\"answer\":\"No. Within-survey imputations are more accurate than survey-to-survey imputations, highlighting limitations when extrapolating across different survey rounds.\"}]","Machine Learning-Driven Imputations to Filling Agricultural Data Gaps | 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problem does the paper address in agricultural surveys?","Question",{"text":75,"@type":76},"It targets missing crop yield data in large-scale surveys, where the most accurate measurement method (crop-cutting) is often unavailable for all observations due to cost constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper predict missing yield values?",{"text":80,"@type":76},"It uses multiple imputation methods supported by machine learning models to estimate missing plot-level yield data.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the validation data include?",{"text":84,"@type":76},"The study validates the approach using survey data from Mali that contains both crop-cut and self-reported yield information.",{"name":86,"@type":73,"acceptedAnswer":87},"Are imputations equally accurate across different survey settings?",{"text":88,"@type":76},"No. Within-survey imputations are more accurate than survey-to-survey imputations, highlighting limitations 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