[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128391-en":3,"doc-seo-128391-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},128391,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Predictive Machine Learning Models for Assessing the Effects of Land Use and Climate Change on Food Affordability in the UK","This research analyzes the trilateral relationship among land use patterns, climate change, and consumer food affordability in the UK using data collected from FAO covering 1961–2022. While agriculture contributes only a small share of UK GDP, it supports economic stability and a resilient, sustainable planet. The study applies AI and machine learning to extract trends and dependencies, using correlation analysis and multiple classification and prediction models. Support vector regression achieves the strongest R-squared (0.96). Decision tree and random forest reach 0.8 accuracy for forest growth region patterns, indicating agriculture expansion does not reduce affordability and informing policy directions.","Latest updates: h􀀍ps://dl.acm.org/doi/10.1145/3743158.3783856  \nRESEARCH-ARTICLE  \nPredictive Machine Learning Models for Assessing the Effects of Land Use and Climate Change on Food Affordability in the UK  \nRAZIA SULTHANA ABDUL KAREEM, University of Greenwich, London, U. K.  \nOpen Access Support provided by:  \nUniversity of Greenwich  \nPDF Download 3743158.3783856.pdf 12 February 2026 Total Citations: 0  \nTotal Downloads: 27  \nPublished: 14 September 2025  \nCitation in BibTeX format  \nICICA 2025: The 14th International Conference on Information Communication and Applications September 14-16, 2025  \nOxford, United Kingdom  \nICICA '25: Proceedings of the 14th International Conference on Information Communication and Applications (September 2025) h􀀤ps://doi.org/10 . 1145/3743158 .3783856  \nISBN: 9798400721151  \n.  \nPredictive Machine Learning Models for Assessing the Effects of Land Use and Climate Change on Food Affordability in the UK  \nRazia Sulthana Abdul Kareem  \nSchool of Computing and Mathematical Sciences, Faculty of Engineering and Sciences  \nUniversity of Greenwich  \nLondon, United Kingdom  \n[razia.sulthana@gre.ac.uk](razia.sulthana@gre.ac.uk)  \nAbstract  \nThis research analyses the complex trilateral relationship, land use pattern, climate change and consumer affordability of food products in the UK based on the data set collected from Food Agriculture and Organisation (FAO) from 1961 to 2022 . Though agriculture contributes minimally to the UK’s GDP, it plays a very major role in economic stability and in building resilient and sustainable planet. Artificial intelligence is a critical tool that helps in understanding, forecasting and predicting patterns on the complex multidimensional data. This paper aims to apply AI techniques on the data to understand the patterns and dependencies. Initially, the data extracted from the FAO is analysed to understand the trends and the relationship between the attributes is identified using correlation matrix. Several hypotheses are framed, and classification and prediction machine learning algorithms are applied on them. Trend analysis reveals that a decrease in carbon dioxide emission is caused by expansion in the forest land with a very steady high increase in the cost of buying a healthy diet in the UK. Several machine learning models are applied on land use and climate emissions and the support vector regressor shows the highest performance with an R-squared value of 0.96 . Furthermore, classification models are applied to get relation between the high and low forest growth regions where the decision tree and the random forest achieved the highest accuracy of 0.8 . This research provides valuable insight into the fact that increasing the agriculture land does not reduce the affordability to buy healthy food. Hence, to economically stabilize, the UK should come up with different policies and measures to provide affordable healthy food to people and not just by increasing the agriculture land it can be achieved.  \nCCS Concepts  \n• Computing methodologies → Machine learning algorithms.  \nKeywords  \nAgriculture, Climate Change, CO2 Emissions, Consumer Affordability, United Kingdom (UK), Machine Learning, Sustainable Development Goals (SDGs)  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. ICICA 2025, Oxford, United Kingdom  \n© 2025 Copyright held by the owner/author(s) .  \nACM ISBN /25/09  \n[https://doi.org/10.1145/3743158.3783856](https://doi.org/10.1145/3743158.3783856)  \nACM Reference Format:  \nRazia Sulthana Abdul Kareem. 2025. Predictive Machine Learning Models for Assessing the Effects of Land Use and Climate Change on Food Affordability in the UK. In The 14th International Conference on Information Communication and Applications (ICICA 2025), September 14–16, 2025, Oxford, United Kingdom. ACM, New York, NY, USA, 8 pages. [https:](https:)//[doi.org/10.1145/3743158.3783856](doi.org/10.1145/3743158.3783856)  \n1 Introduction  \nAgriculture ","cbCaimUiUZGS6fNi","https://ap.wps.com/l/cbCaimUiUZGS6fNi","pdf",1558761,1,9,"English","en",105,"# Abstract\n## Data source and study goal\n## Methods: correlation analysis and ML models\n## Key findings and model performance\n## Implications for policy and affordability\n# Introduction\n## Role of agriculture in the UK economy\n## Challenges in modern agricultural practices\n## AI applications in agriculture and prediction","[{\"question\":\"What data period and source does the study use to analyze food affordability in the UK?\",\"answer\":\"The study uses FAO data from 1961 to 2022 to examine how land use patterns and climate change relate to UK consumer food affordability.\"},{\"question\":\"Which machine learning models are applied, and what performance does the study report?\",\"answer\":\"The research applies classification and prediction algorithms, with support vector regressor achieving the highest R-squared value of 0.96. For classification of high vs. low forest growth regions, decision tree and random forest reach the highest accuracy of 0.8.\"},{\"question\":\"What main conclusion does the paper draw about agriculture expansion and affordability of healthy food?\",\"answer\":\"The findings indicate that increasing agriculture land does not reduce the affordability of buying healthy food in the UK, supporting the need for policies and measures that improve affordable access rather than relying only on land expansion.\"}]","Predictive Machine Learning Models for Assessing the Effects of Land Use and Climate Change on Food Affordability in the UK | PDF",1785947248,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predictive-machine-learning-models-for-assessing-the-effects-of-land-use-and-climate-change-on-food-affordability-in-the-uk","",{"@graph":36,"@context":86},[37,54,69],{"@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/predictive-machine-learning-models-for-assessing-the-effects-of-land-use-and-climate-change-on-food-affordability-in-the-uk/128391/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What data period and source does the study use to analyze food affordability in the UK?","Question",{"text":76,"@type":77},"The study uses FAO data from 1961 to 2022 to examine how land use patterns and climate change relate to UK consumer food affordability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are applied, and what performance does the study report?",{"text":81,"@type":77},"The research applies classification and prediction algorithms, with support vector regressor achieving the highest R-squared value of 0.96. For classification of high vs. low forest growth regions, decision tree and random forest reach the highest accuracy of 0.8.",{"name":83,"@type":74,"acceptedAnswer":84},"What main conclusion does the paper draw about agriculture expansion and affordability of healthy food?",{"text":85,"@type":77},"The findings indicate that increasing agriculture land does not reduce the affordability of buying healthy food in the UK, supporting the need for policies and measures that improve affordable access rather than relying only on land expansion.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]