[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120842-en":3,"doc-seo-120842-105":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},120842,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","CLIMATE CHANGE IMPACT ON AGRICULTURAL LAND SUITABILITY: A MACHINE LEARNING-BASED EURASIA CASE STUDY - A PREPRINT","Improving food security and reducing hunger are central to the UN’s sustainable development goals, yet climate change is reshaping agricultural land suitability and may trigger severe food shortages and social or political conflicts. This preprint presents an interpretable machine learning framework for Central Eurasia, predicting risks of substantial suitability degradation and irrigation-pattern shifts under multiple carbon emissions scenarios. Using feature-importance analysis with CMIP6 projections, the approach quantifies climate and terrain drivers and supports policy decisions such as targeting additional water and fertilizers.","arXiv :2310 . 15912v1 [ cs .LG] 24 Oct 2023  \nCLIMATE CHANGE IMPACT ON AGRICULTURAL LAND SUITABILITY: A MACHINE LEARNING-BASED EURASIA CASE STUDY  \nA PREPRINT  \nValeriy Shevchenko, Daria Taniushkina, Aleksander Lukashevich, Aleksandr Bulkin, Roland Grinis, Kirill Kovalev, Veronika Narozhnaia, Nazar Sotiriadi, Alexander Krenke, Yury Maximov  \nOctober 25, 2023  \nABSTRACT  \nThe United Nations has identified improving food security and reducing hunger as essential components ofits sustainable development goals. As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition, with numerous fatalities reported. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages and subsequent social and political conflicts. To address this pressing issue, we have developed a machine learning-based approach to predict the risk of substantial land suitability degradation and changes in irrigation patterns. Our study focuses on Central Eurasia, a region burdened with economic and social challenges.  \nThis study represents a pioneering effort in utilizing machine learning methods to assess the impact of climate change on agricultural land suitability under various carbon emissions scenarios.  \nThrough comprehensive feature importance analysis, we unveil specific climate and terrain characteristics that exert influence on land suitability. Our approach achieves remarkable accuracy, offering policymakers invaluable insights to facilitate informed decisions aimed at averting a humanitarian crisis, including strategies such as the provision of additional water and fertilizers. This research underscores the tremendous potential of machine learning in addressing global challenges, with a particular emphasis on mitigating hunger and malnutrition.  \nKeywords Climate Change · Machine Learning · Food Security  \n1 Introduction  \nThe impact of global climate change permeates various spheres of human activity, exerting a significant influence on global pandemics, food security, and political and social stability. With Earth’s land surface facing alarming temperature increases and humidity decreases, the croplands and pastures that cover nearly 40% of the planet’s surface are under threat [1] . Numerous studies suggest that global food demand may surge by approximately 110% by 2050 [2–5] . Additionally, the rising average temperature, snow-water equivalent, and carbon dioxide (CO2 ) concentration pose challenges to the optimal conditions required for crop growth [6–8] .  \nThis study examines the ramifications of climate change on agricultural and population sustainability under various Shared Socioeconomic Pathways (SSP) [9] based on different climate conditions. The Coupled Model Intercomparison Project (CMIP) offers a coarse-grained assessment of critical climate indicators, including mean temperature, humidity, and atmospheric pressure. We utilize three robust CMIP6 models (CMCC-ESM2, CNRM-CM6-1, and MRIESM2-0) to evaluate three SSP scenarios [10–12]: sustainable green energy (SSP1-2.6), business-as-usual (SSP2-4.5), and increased reliance on fossil fuels (SSP5-8.5) . We also incorporate the Global Food Security-support Analysis Data at a Nominal 1 km (GFSAD) [13] to study cropland watering methods.  \nOur study addresses several key questions:  \n1. How does the distribution of croplands change due to climate change under different SSP scenarios?  \n2. Which areas face significant food security risks?  \n3. What are the primary factors influencing cropland suitability?  \nWe employ recurrent neural networks based on Long Short-Term Memory cells (LSTM,[14]) to accurately classify agricultural land into four distinct classes: irrigation major, irrigation minor, rainfed, and minor cropland (noncropland) based on prevailing climate conditions. Our findings suggest a significant expansion of land suitable for agriculture by the year 2050, particularly in the category of irrigation","cbCaiqW0BB8v0MoG","https://ap.wps.com/l/cbCaiqW0BB8v0MoG","pdf",2687813,1,13,"English","en",105,"# Abstract\n# Introduction\n## Climate change and food security context\n## Study setup: SSP scenarios and datasets\n## Method: LSTM classification of land classes\n## Key research questions and outputs\n# Related Work","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets how climate change affects agricultural land suitability, including risks of suitability degradation and changes in irrigation patterns, which can undermine food security.\"},{\"question\":\"Which scenarios and models are used to evaluate climate impacts?\",\"answer\":\"It evaluates three SSP scenarios using CMIP6 climate projections from three models (CMCC-ESM2, CNRM-CM6-1, MRIESM2-0) and includes GFSAD for cropland watering information.\"},{\"question\":\"How does the machine learning model classify agricultural land?\",\"answer\":\"The approach uses recurrent neural networks with LSTM cells to classify land into four classes: irrigation major, irrigation minor, rainfed, and minor cropland (noncropland) based on prevailing climate conditions.\"}]","CLIMATE CHANGE IMPACT ON AGRICULTURAL LAND SUITABILITY: A MACHINE LEARNING-BASED EURASIA CASE STUDY - A PREPRINT | PDF",1785732303,33,{"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},"climate-change-impact-on-agricultural-land-suitability-a-machine-learning-based-eurasia-case-study-a-preprint","",{"@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/climate-change-impact-on-agricultural-land-suitability-a-machine-learning-based-eurasia-case-study-a-preprint/120842/",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","2026-08-04","2026-08-03",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 problem does the study address?","Question",{"text":76,"@type":77},"The study targets how climate change affects agricultural land suitability, including risks of suitability degradation and changes in irrigation patterns, which can undermine food security.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which scenarios and models are used to evaluate climate impacts?",{"text":81,"@type":77},"It evaluates three SSP scenarios using CMIP6 climate projections from three models (CMCC-ESM2, CNRM-CM6-1, MRIESM2-0) and includes GFSAD for cropland watering information.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the machine learning model classify agricultural land?",{"text":85,"@type":77},"The approach uses recurrent neural networks with LSTM cells to classify land into four classes: irrigation major, irrigation minor, rainfed, and minor cropland (noncropland) based on prevailing climate conditions.","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},[94,98,102,106,111,116,121,124,129,132,136],{"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":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]