[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126094-en":3,"doc-seo-126094-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126094,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Prediction of weaning weight of grazing beef by machine learning","Objective: develop and validate models using variables available at calving to predict the weaning weight (WW) of grazing beef calves. Design/Methodology/Approach: WW was modeled with machine learning (ML) algorithms and ordinary least squares (OLS), considering three variable-availability scenarios; best fit was evaluated using coefficient of determination (r²), mean squared error, and bias. Results: ML outperformed OLS in all scenarios, with r² ranging from 0.67 to 0.78. Implications: performance reflects a specific database; regional or national modeling should be assessed, and scenario B with fewer variables provides acceptable WW prediction.","The World's Largest Open Access Agricultural &Applied Economics Digital Library  \nThis document is discoverable and free to researchers across theglobe due to the work of AgEcon Search.  \nHelp ensure our sustainability.  \nGive to AgEcon Search  \nAgEcon Searchhttp://ageconsearch.umn.eduaesearch@umn.edu  \nPapers downloaded from AgEcon Search may be used for non-commercial purposes and personal study only.No other use,including posting to another Internet site,is permitted without permission from the copyrightowner(not AgEcon Search),or as allowed under the provisions of Fair Use,U.S.Copyright Act,Title 17U.S.C.  \nNo endorsement of AgEcon Search or its fundraising activities by the author(s)of the following work or theiremployer(s)is intended orimplied.  \n# Prediction of weaning weight of grazing beef bymachine learning\n\nGuevara-Escobar,Aurelio';CervantesJiménez,Mónica¹;Lemus-Ramírez,Vicente²;  \nKunio-Yabuta-Osorio,Adolfo²;García-Muniz,José G.³  \nUnivesidad Autónoma de Querétaro,Facultad de Ciencias Naturales,Av.de las Ciencias sn,JurquillaSantiago de Querétaro,Querétaro,México,C.P.76230.  \n²Universidad Nacional Autónoma de Mexico,Centro de Ensenanza,InvestigaciónyExtensión en ProducciónAnimal en Altiplano CEIEPAA,Facultad de Medicina Veterinaria y Zootecnia,Tequisquiapan,Querétaro,México,C.P.76790.  \n3Universidad Autónoma Chapingo,Departamento deZootecnia,Posgrado en Producción Animal,CarreteraMexico-Texcoco km 38.5,Chapingo,Estado de Mexico,Mexico,C.P.56230.*Correspondence:monica.cervantes@uaq.mx  \n## ABSTRACT\n\nCitation:Guevara-Escobar,A.,Cervantes-Jiménez,M.,Lemus-Ramírez,V.,Kunio-Yabuta,Osorio,  \nObjective:To develop and validate models using the variables available at calving to predict the weaningweight(WW)of grazing beef calves.  \nDesign/Methodology/Approach:The WW was modelled using machine learning (ML)algorithms andordinary least squares(OLS).The model included three variable availability scenarios and the best fit wasidentified using the coefficient of determination(r²),the mean squared error,and the bias.  \nA.,&Gacía-Muniz,J.G.(2022).Prediction of weaning weight ofgrazing beefby machine learning.AgroProdudividad.https//doi.org10.32854/agrop.v14i6.2172  \nResults:ML algorithms achieved a better fit than OLS in all scenarios.ML had a 0.70,0.67,and 0.78 r²when the following modelling variables were available:B)dam age at calving and parity,calf sex and weight,weaning age,and calving date;I)in addition to the previous variables,dams'weight at calving,type of calving,calf and cow racial purity;and A)in addition to the all the previous variables,type of service,cow and sire tagsand sire breed.  \nAcademic Editors:Jorge CadenaIiiguez and Libia Iris Trejo Tellez  \nStudy Limitations/Implications:The ML and OLS models were representative of a specific database.Modelling based on regional or national data should be studied.Using the lowest number of variables in thisstudy,MLin scenario B provided an acceptable fitting for the prediction modelling of the WW of grazing beefcalves.  \nReceived:November 16,2021.  \nAccepted:May 23,2022.  \nPublished on-line:July 05,2022.  \nAgro Productizidad,156.June.2022.Pp:83-92.  \nFindings/Conclusions:ML performed better than OLS,without causing an overfitting,based on thesuitability of the WWpredictions regarding a database that was not used to train the model.  \nThis work is licensed under a  \nCreative Commons Attribution-Non-Keywords:Alfalfa,artificial intelligence,regression.Commercial 4.0 International license.C①图  \n## INTRODUCTION\n\nBeef production will amount to 75 Mt in 2030(a 5.8%increase),in response to thegrowing demand for animal products(OECD/FAO,2021).Although grazing animalproduction pollutes the environment (Steinfeld et al.,2006)and there are proposals forhuman feeding based on meatless meals,beef production has a significant socioculturalaspect and it is also an option in arid ecosystems where foraging of the primary productionis the main strategy.In the short term,improving the effic","cbCaieIahgzdXDZn","https://ap.wps.com/l/cbCaieIahgzdXDZn","pdf",890558,7,1,11,"English","en",105,"# ABSTRACT\n# INTRODUCTION","[{\"question\":\"What is the objective of the study on grazing beef weaning weight?\",\"answer\":\"To develop and validate prediction models that use only variables available at calving to estimate weaning weight (WW) of grazing beef calves.\"},{\"question\":\"Which modeling approaches are compared in the research?\",\"answer\":\"The study compares machine learning (ML) algorithms with ordinary least squares (OLS), using three scenarios based on which variables are available.\"},{\"question\":\"How do the results of ML compare with OLS?\",\"answer\":\"ML achieves a better fit than OLS across all scenarios, improving prediction accuracy without causing overfitting according to the suitability of WW predictions on data not used for training.\"}]","Prediction of weaning weight of grazing beef by machine learning | 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