[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126106-en":3,"doc-seo-126106-105":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},126106,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Integrating machine learning with agroecosystem modelling - Current state and future challenges","Machine learning (ML), especially deep learning (DL), is increasingly adopted in agroecosystem modelling because it can accelerate computationally intensive tasks. A PRISMA-based review compiles modelling studies that use ML and organizes integration strategies into process-based modelling (PBM) and data-driven modelling (DDM). The article contrasts PBMs’ ability to represent complex biophysical and biogeochemical processes with their analytical and computational limitations. It explains how ML addresses these gaps through model replacement, hybrid PBM-ML systems, and meta-modelling while focusing on interpretability, data needs, validation, and scalability to support sustainable agricultural decisions.","European Journal of Agronomy 168 (2025) 127610  \nContents lists available at ScienceDirect European Journal of Agronomy  \njournal [homepage:](homepage: www.elsevier.com/locate/eja)[ www.elsevier.com/locate/eja](homepage: www.elsevier.com/locate/eja)  \n| Integrating machine learning with agroecosystem modelling: Current state and future challenges\u003Cbr>Meshach Ojo Aderelea , Amit Kumar Srivastava b,c, Klaus Butterbach-Bahla,d , Jaber Rahimia,d,* \u003Cbr>a Pioneer Center Land-CRAFT, Department of Agroecology, Aarhus University, Aarhus, Denmark b Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany c Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany\u003Cbr>d Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Garmisch-Partenkirchen, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Process-based models (PBM) Data-driven models(DDM) Machine learning (ML) Agroecosystem |  | Machine learning (ML), especially deep learning (DL), is gaining popularity in the agroecosystem modelling community due to its ability to improve the efficiency of computationally intensive tasks. By reviewing previous modelling studies using the PRISMA technique, we present several examples of ML applications in this domain. The potential of using such models is highligthed. The different types of integration and model-building methods are categorized into process-based modelling (PBMs) and data-driven modelling (DDMs), which simulate different aspects of agroecosystem dynamics. While PBMs excel at capturing complex biophysical and biogeochemical processes, they are computationally intensive and may not always be solvable using analytical methods. To address these challenges, machine learning (ML) techniques, including deep learning (DL), are increasingly being integrated into agroecosystem modelling. This integration involves replacing PBMs with data-driven models, using hybrid models that combine PBMs and ML, or constructing simplified versions of PBMs through meta-modelling. ML-based meta-models offer computational efficiency and can capture intricate patterns and non-linear relationships in complex agricultural systems. However, challenges such as interpretability and data requirements remain. This review highlights the importance of addressing gaps and challenges to fully realize the potential of ML to identify the most promising ways of field management in promoting sustainable agricultural systems. It also highlights specific considerations such as data requirements, interpretability, model validation, and scalability for the successful integration of ML with PBMs in agriculture and the transformative potential of combining ML with PBMs, particularly in extending simulations from field to global scales and streamlining data collection processes through advanced sensor technologies based on their applications. |\n\n1. Introduction  \nAgroecosystem models play a critical role in agriculture, offering valuable insights into crop yield, soil N transport and transformation, soil-heat transfer, soil-water movement, soil nitrogen (N) transport and transformation, soil organic matter (SOM) turnover, and greenhouse gas (GHG) emissions that can be used to optimize crop management practices and conduct environmental impact assessments (Li et al., 2000; Parton et al., 1998). These models are typically categorized into two main types: the process-based modelling (PBMs) approach and the data-driven modelling (DDMs) approach.  \nPBMs (such as DSSAT (Jones et al., 2003), APSIM (Holzworth et al.,  \n2014), and LandscapeDNDC (Haas et al., 2013)), simulate various aspects of plant growth and ecosystem dynamics by incorporating mathematical equations that represent the complex interactions between weather, topography, soil properties, management practices, genetics, pests and diseases, and a range of other factors (Yin and Van Laar, ","cbCaidXk8Cp6ylpH","https://ap.wps.com/l/cbCaidXk8Cp6ylpH","pdf",3010239,5,1,11,"English","en",105,"# Abstract\n# Introduction\n## Role of agroecosystem models\n## Two modelling paradigms: PBM and DDM\n# Machine learning integration directions","[{\"question\":\"Why is machine learning, particularly deep learning, becoming popular in agroecosystem modelling?\",\"answer\":\"Machine learning can improve the efficiency of computationally intensive tasks. This supports faster analysis while working within complex agroecosystem dynamics.\"},{\"question\":\"How are integration approaches categorized in the review?\",\"answer\":\"The review categorizes integration into process-based modelling (PBM) and data-driven modelling (DDM). It further describes replacing PBMs with data-driven models, using hybrid PBM-ML systems, and building simplified PBMs via meta-modelling.\"},{\"question\":\"What advantages and remaining challenges are highlighted for ML-based meta-models?\",\"answer\":\"ML-based meta-models provide computational efficiency and can capture nonlinear relationships and intricate patterns. Challenges remain in interpretability and data requirements, alongside validation and scalability considerations.\"}]","Integrating machine learning with agroecosystem modelling - Current state and future challenges | PDF",1785903192,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"integrating-machine-learning-with-agroecosystem-modelling-current-state-and-future-challenges","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/integrating-machine-learning-with-agroecosystem-modelling-current-state-and-future-challenges/126106/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is machine learning, particularly deep learning, becoming popular in agroecosystem modelling?","Question",{"text":77,"@type":78},"Machine learning can improve the efficiency of computationally intensive tasks. This supports faster analysis while working within complex agroecosystem dynamics.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How are integration approaches categorized in the review?",{"text":82,"@type":78},"The review categorizes integration into process-based modelling (PBM) and data-driven modelling (DDM). It further describes replacing PBMs with data-driven models, using hybrid PBM-ML systems, and building simplified PBMs via meta-modelling.",{"name":84,"@type":75,"acceptedAnswer":85},"What advantages and remaining challenges are highlighted for ML-based meta-models?",{"text":86,"@type":78},"ML-based meta-models provide computational efficiency and can capture nonlinear relationships and intricate patterns. Challenges remain in interpretability and data requirements, alongside validation and scalability considerations.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]