[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122058-en":3,"doc-seo-122058-105":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},122058,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluation of Ensemble Machine Learning for Geospatial Prediction of Soil Iron in Croatia","Soil fertility underpins agricultural productivity, and iron (Fe) is a key micronutrient required for healthy crop development. This study evaluates ensemble machine-learning approaches for geospatial prediction of soil Fe in Croatia using 686 soil samples. Three individual models—extreme gradient boosting (XGB), support vector machine (SVM), and Cubist—are compared with their ensemble. The ensemble achieves the best performance (R2=0.578, RMSE=0.837, MAE=0.550). Clay content is identified as the most influential predictor, followed by sand content, pH, and bioclimatic variables.","Evaluation of Ensemble Machine Learning for Geospatial Prediction of Soil Iron in Croatia  \nEvaluacija kombinacije strojnog učenja za geoprostorno predviđanje sadržaja željeza u tlu u Hrvatskoj  \nRadočaj, D., Tuno, N., Mulahusić, A., Jurišić, M.  \nPoljoprivreda / Agriculture  \nISSN: 1848-8080 (Online)  \nISSN: 1330-7142 (Print)  \n[https://doi.org/10.18047/poljo.29.2.7](https://doi.org/10.18047/poljo.29.2.7)  \nFakultet agrobiotehničkih znanosti Osijek, Poljoprivredni institut Osijek  \nFaculty of Agrobiotechnical Sciences Osijek, Agricultural Institute Osijek  \nISSN 1330-7142  \nUDK=631.416.3:591.513-047.44 [https://doi.org/10.18047/poljo.29.2.7](https://doi.org/10.18047/poljo.29.2.7)  \nEVALUATION OF ENSEMBLE MACHINE LEARNING FOR GEOSPATIAL PREDICTION OF SOIL IRON IN CROATIA  \nRadočaj, D. (1), Tuno, N. (2), Mulahusić, A. (2), Jurišić, M. (1)  \nOriginal scientific paper Izvorni znanstveni rad  \nSUMMARY  \nSoil fertility is pivotal for agricultural productivity, and iron (Fe) is a critical micronutrient essential for a successful crop development. This study investigates a potential of ensemble machine-learning methods in geospatial prediction of soil Fein Croatia. Using a dataset of 686 soil samples, three individual machine-learning methods, including the extreme gradient boosting (XGB), support vector machine (SVM), and Cubist, as well as their ensemble, were evaluated for the soil Fe prediction. The ensemble method outperformed the individual models, exhibiting a higher prediction accuracy expressed by the coefficient of determination (R2 = 0.578), with a lower root-mean-square error (RMSE = 0.837) and the mean absolute error (MAE = 0.550). The soil clay content emerged as the most influential predictor, followed by the sand content, pH values, and select bioclimatic variables. This study’s results demonstrate the effectiveness of ensemble machine learning in an accurate prediction of soil Fe content and contribute to an informed decision-making in sustainable agricultural land-use planning and management. By including the complementary machine-learning methods into an ensemble with the representative environmental covariates, a geospatial prediction aids to a reliable comprehension of soil properties and their spatial variability.  \nKeywords: soil samples, extreme gradient boosting, support vector machine, cubist, land-use planning  \nINTRODUCTION  \nMicronutrients are essential for the insurance of an adequate crop development, and soil fertility is a key factor to the determination of an agricultural production. Since it is a crucial component of the enzymes involved in photosynthesis, respiration, and nitrogen fixation, iron (Fe), one of these micronutrients, has a special relevance, because it is engaged in crucial physiological processes within plants (Mondal and Bose, 2019) . Although both excessive and inadequate Fe concentrations in the soil can have a negative impact on a crop growth, the availability and distribution of Fe in the soil can have a substantial influence on how it is absorbed by the crops. A Fe toxicity can cause a root damage, nutritional imbalances, and decreased nutrient absorption efficiency, whereas a Fe shortage can cause chlorosis, stunted growth, and decreased yield (Zaid et al., 2020) its threshold value in plants increases by diverse anthropogenic and natural sources, which results in the  \ninhibition of plant growth and development. This inhibition is due to excess Fe availability, in the soil environment, leading to direct or indirect Fe toxicity. This toxicity, as well as the opposite, i. e., iron deficiency, results in disturbance of basic plant metabolism due to disruption in the rate of uptake and translocation of other essential and beneficial mineral nutrient elements. Since other key nutrients and excess Fe compete, in root rhizosphere(s. Traditionally, a difficult and timeconsuming field collection and laboratory analysis of soil samples has been conducted to determine the soil Fe concent","cbCaimdg0W0cfiv1","https://ap.wps.com/l/cbCaimdg0W0cfiv1","pdf",780410,1,10,"English","en",105,"# Summary\n# Introduction\n## Importance of micronutrients and iron in crops\n## Geospatial soil prediction and digital soil mapping\n## Role of machine learning and ensemble learning","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To evaluate whether ensemble machine-learning methods can improve geospatial prediction of soil iron content in Croatia.\"},{\"question\":\"Which machine-learning models are compared in the evaluation?\",\"answer\":\"The study compares three individual models (XGB, SVM, Cubist) and an ensemble built from these methods.\"},{\"question\":\"How does the ensemble method perform compared with individual models?\",\"answer\":\"The ensemble outperforms the individual models, with higher predictive accuracy (R2=0.578) and lower RMSE (0.837) and MAE (0.550).\"}]","Evaluation of Ensemble Machine Learning for Geospatial Prediction of Soil Iron in Croatia | PDF",1785808605,25,{"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},"evaluation-of-ensemble-machine-learning-for-geospatial-prediction-of-soil-iron-in-croatia","",{"@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/evaluation-of-ensemble-machine-learning-for-geospatial-prediction-of-soil-iron-in-croatia/122058/",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},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To evaluate whether ensemble machine-learning methods can improve geospatial prediction of soil iron content in Croatia.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning models are compared in the evaluation?",{"text":80,"@type":76},"The study compares three individual models (XGB, SVM, Cubist) and an ensemble built from these methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the ensemble method perform compared with individual models?",{"text":84,"@type":76},"The ensemble outperforms the individual models, with higher predictive accuracy (R2=0.578) and lower RMSE (0.837) and MAE (0.550).","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]