[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123805-en":3,"doc-seo-123805-105":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},123805,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Imputation of missing microclimate data of coffee-pine agroforestry with machine learning","Research analyzes imputation approaches to address missing microclimate data in coffee-pine agroforestry land in UB Forest. Using big data and machine learning, the study compares interpolation, shifted interpolation, K-nearest neighbors (KNN), and linear regression across multiple time frames: -6 hours, daily, weekly, and monthly. Model quality is evaluated with MAE, MSE, RMSE, and MAPE. Results show linear regression consistently delivers the lowest error values in every time frame, indicating strong accuracy under microclimate variability. Findings support reliable environmental data imputation to improve microclimate modeling and support sustainable ecosystem management decisions.","Imputation of missing microclimate data of coffee-pine agroforestry with machine learning  \nHeru Nurwarsito a, 1,*, Didik Suprayogo b,2, Setyawan P. Sakti c,3, Cahyo Prayogo d,4,  \nNovanto Yudistira d,5, Muhammad Rifqi Fauzi d,6, Simon Oakley e,7, Wayan Firdaus Mahmudy d,8  \na Faculty of Agriculture, University of Brawijaya, Malang, Indonesia b Faculty of Agriculture, University of Brawijaya, Malang, Indonesia  \nc Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang, Indonesia d Faculty of Computer Science, University of Brawijaya, Malang, Indonesia  \ne Lancaster Environment Centre, UK Centre for Ecology & Hydrology, United Kingdom  \n[1](1 heru@ub.ac.id)[ heru@ub.ac.id](1 heru@ub.ac.id); [2](2 suprayogo@ub.ac.id)[ suprayogo@ub.ac.id](2 suprayogo@ub.ac.id); [3](3 sakti@ub.ac.id)[ sakti@ub.ac.id](3 sakti@ub.ac.id); [4](4 c.prayogo@ub.ac.id)[ c.prayogo@ub.ac.id](4 c.prayogo@ub.ac.id); [5](5 yudistira@ub.ac.id)[ yudistira@ub.ac.id](5 yudistira@ub.ac.id); [6](6 mrifqifauzi@student.ub.ac.id)[ mrifqifauzi@student.ub.ac.id](6 mrifqifauzi@student.ub.ac.id); [7](7 soak@ceh.ac.uk)[ soak@ceh.ac.uk](7 soak@ceh.ac.uk); [8](8 wayanfm@ub.ac.id)[ wayanfm@ub.ac.id](8 wayanfm@ub.ac.id)  \n* corresponding author  \nARTICLE INFO ABSTRACT  \n\n| Article history\u003Cbr>Received November 5, 2023\u003Cbr>Revised December 18, 2023\u003Cbr>Accepted December 27, 2023\u003Cbr>Available online December 29, 2023\u003Cbr>Keywords\u003Cbr>Microclimate data Interpolation Shifted interpolation\u003Cbr>K-nearest neighbors (KNN) Linear regression | \u003Cbr>This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffeepine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, KNearest Neighbors (KNN), and Linear Regression methods across multiple time frames -6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.\u003Cbr>\u003Cbr>This is an open access article under the CC–BY-SA license\u003Cbr> |\n| --- | --- |\n| 1. Introduction\u003Cbr>Micro-climate data plays a crucial role in agroforestry management, particularly in coffee-pine ecosystems. Tropical agroforestry systems, such as those involving coffee and Faidherbia albida trees, aim to mitigate extreme temperatures, highlighting the significance of micro-climate data in designing resilient and climate-smart farming systems [1] . Additionally, the role ofagroforestry systems as a climate change mitigation strategy has been emphasized, further underlining the importance of micro-climate data in such ecosystems [2] . Furthermore, the agroforestry system of coffee cultivation in pine forests has been recognized for its essential role in providing ecosystem services, including habitat for wildlife, carbon storage, and sequestration [3] . The use of UAVs for vegetation monitoring in agroforestry applica","cbCaitDqwHeLeWYR","https://ap.wps.com/l/cbCaitDqwHeLeWYR","pdf",1046535,1,22,"English","en",105,"# Introduction\n## Importance of microclimate data in agroforestry systems\n## Challenges of missing microclimate measurements\n## Motivation for imputation using machine learning","[{\"question\":\"Which imputation methods are compared for missing microclimate data?\",\"answer\":\"The study compares interpolation, shifted interpolation, K-nearest neighbors (KNN), and linear regression.\"},{\"question\":\"How is performance evaluated across time frames?\",\"answer\":\"Performance is measured using MAE, MSE, RMSE, and MAPE for -6 hours, daily, weekly, and monthly time frames.\"},{\"question\":\"Which method shows the best overall performance?\",\"answer\":\"Linear regression outperforms the other methods across all evaluated time frames, producing the lowest error rates.\"}]","Imputation of missing microclimate data of coffee-pine agroforestry with machine learning | PDF",1785818643,55,{"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},"imputation-of-missing-microclimate-data-of-coffee-pine-agroforestry-with-machine-learning","",{"@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/imputation-of-missing-microclimate-data-of-coffee-pine-agroforestry-with-machine-learning/123805/",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","2026-08-04",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},"Which imputation methods are compared for missing microclimate data?","Question",{"text":75,"@type":76},"The study compares interpolation, shifted interpolation, K-nearest neighbors (KNN), and linear regression.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is performance evaluated across time frames?",{"text":80,"@type":76},"Performance is measured using MAE, MSE, RMSE, and MAPE for -6 hours, daily, weekly, and monthly time frames.",{"name":82,"@type":73,"acceptedAnswer":83},"Which method shows the best overall performance?",{"text":84,"@type":76},"Linear regression outperforms the other methods across all evaluated time frames, producing the lowest error rates.","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"]