{"id":"https://openalex.org/W7204629307","doi":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","title":"Machine Learning-Based Crop Yield Prediction Using FAO Global Agricultural Data: A Comparative Study of Linear Regression, Random Forest, and XGBoost","display_name":"Machine Learning-Based Crop Yield Prediction Using FAO Global Agricultural Data: A Comparative Study of Linear Regression, Random Forest, and XGBoost","publication_year":2026,"publication_date":"2026-08-28","ids":{"openalex":"https://openalex.org/W7204629307","doi":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667"},"language":"en","primary_location":{"id":"doi:10.36948/ijfmr.2026.v08i04.86667","doi":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","is_oa":true,"landing_page_url":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","pdf_url":"https://www.ijfmr.com/papers/2026/4/86667.pdf","source":{"id":"https://openalex.org/S4210207214","display_name":"International Journal For Multidisciplinary Research","issn_l":"2582-2160","issn":["2582-2160"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal For Multidisciplinary Research","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W7204629307"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.ijfmr.com/papers/2026/4/86667.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148590393","display_name":"Abdulhafedh Alsarfi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I196652580","display_name":"Swami Ramanand Teerth Marathwada University","ror":"https://ror.org/03nevd013","country_code":"IN","type":"education","lineage":["https://openalex.org/I196652580"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abdulhafedh Alsarfi","raw_affiliation_strings":["School of Computational Sciences , Swami Ramanand Teerth Marathwada University , Nanded 431606 , Maharashtra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computational Sciences , Swami Ramanand Teerth Marathwada University , Nanded 431606 , Maharashtra , India","institution_ids":["https://openalex.org/I196652580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082945344","display_name":"Archana Harsing Sable","orcid":"https://orcid.org/0000-0002-4542-163X","observed_orcids":["https://orcid.org/0000-0002-4542-163X"]},"institutions":[{"id":"https://openalex.org/I196652580","display_name":"Swami Ramanand Teerth Marathwada University","ror":"https://ror.org/03nevd013","country_code":"IN","type":"education","lineage":["https://openalex.org/I196652580"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Archana Sable","raw_affiliation_strings":["School of Computational Sciences , Swami Ramanand Teerth Marathwada University , Nanded 431606 , Maharashtra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computational Sciences , Swami Ramanand Teerth Marathwada University , Nanded 431606 , Maharashtra , India","institution_ids":["https://openalex.org/I196652580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056178502","display_name":"Aymen M. Al-Hejri","orcid":"https://orcid.org/0000-0002-3405-5363","observed_orcids":["https://orcid.org/0000-0002-3405-5363"]},"institutions":[{"id":"https://openalex.org/I196652580","display_name":"Swami Ramanand Teerth Marathwada University","ror":"https://ror.org/03nevd013","country_code":"IN","type":"education","lineage":["https://openalex.org/I196652580"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aymen Al-Hejri","raw_affiliation_strings":["School of Computational Sciences , Swami Ramanand Teerth Marathwada University , Nanded 431606 , Maharashtra , India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computational Sciences , Swami Ramanand Teerth Marathwada University , Nanded 431606 , Maharashtra , India","institution_ids":["https://openalex.org/I196652580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I196652580"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10439","display_name":"Climate change impacts on agriculture","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/1102","display_name":"Agronomy and Crop Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crop-yield-prediction","display_name":"crop yield prediction","score":1.0},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7730000019073486},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9980000257492065},{"id":"https://openalex.org/keywords/linear-regression","display_name":"linear regression","score":0.9959999918937683},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.8460000157356262},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.8080000281333923},{"id":"https://openalex.org/keywords/global-agriculture","display_name":"world agriculture","score":0.5220000147819519},{"id":"https://openalex.org/keywords/food-security","display_name":"food security","score":0.7910000085830688},{"id":"https://openalex.org/keywords/crop-species","display_name":"crop types","score":0.45500001311302185}],"concepts":[{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.6305999755859375},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6283000111579895},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.6082000136375427},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5450999736785889},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.544700026512146},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C549605437","wikidata":"https://www.wikidata.org/wiki/Q1229911","display_name":"Food security","level":3,"score":0.4641000032424927},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.36329999566078186},{"id":"https://openalex.org/C137580998","wikidata":"https://www.wikidata.org/wiki/Q235352","display_name":"Crop","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3197000026702881},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.3109000027179718},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C128383755","wikidata":"https://www.wikidata.org/wiki/Q3816336","display_name":"Agricultural productivity","level":3,"score":0.2818000018596649},{"id":"https://openalex.org/C54924851","wikidata":"https://www.wikidata.org/wiki/Q2751054","display_name":"Sustainable agriculture","level":3,"score":0.27959999442100525},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.36948/ijfmr.2026.v08i04.86667","doi":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","is_oa":true,"landing_page_url":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","pdf_url":"https://www.ijfmr.com/papers/2026/4/86667.pdf","source":{"id":"https://openalex.org/S4210207214","display_name":"International Journal For Multidisciplinary Research","issn_l":"2582-2160","issn":["2582-2160"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal For Multidisciplinary Research","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.36948/ijfmr.2026.v08i04.86667","doi":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","is_oa":true,"landing_page_url":"https://doi.org/10.36948/ijfmr.2026.v08i04.86667","pdf_url":"https://www.ijfmr.com/papers/2026/4/86667.pdf","source":{"id":"https://openalex.org/S4210207214","display_name":"International Journal For Multidisciplinary Research","issn_l":"2582-2160","issn":["2582-2160"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal For Multidisciplinary Research","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":1.0}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204629307.pdf","grobid_xml":"https://content.openalex.org/works/W7204629307.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W4403976848","https://openalex.org/W4411371159"],"related_works":["https://openalex.org/W7162762913","https://openalex.org/W2416782259","https://openalex.org/W7118001878","https://openalex.org/W7169803730","https://openalex.org/W4403157089","https://openalex.org/W7169765647","https://openalex.org/W7131307519","https://openalex.org/W4406829696","https://openalex.org/W4405939290","https://openalex.org/W7153743379"],"abstract_inverted_index":{"Accurate":[0],"crop":[1,33,55],"yield":[2,34,205],"prediction":[3],"is":[4],"essential":[5],"for":[6,31,201],"food":[7],"security":[8],"planning,":[9],"agricultural":[10,43,167,204],"policy-making,":[11],"and":[12,19,29,39,50,57,76,83,113,134,186],"sustainable":[13],"farming":[14],"management.":[15],"This":[16],"study":[17],"develops":[18],"compares":[20],"three":[21],"machine":[22],"learning":[23,194],"models":[24,80],"Linear":[25,148],"Regression,":[26],"Random":[27,119,197],"Forest,":[28,198],"XGBoost":[30,139],"predicting":[32],"using":[35,89,103],"the":[36,59,114,122,162,178],"FAO":[37],"(Food":[38],"Agriculture":[40],"Organization)":[41],"global":[42,166,208],"dataset.":[44],"The":[45],"dataset":[46],"spans":[47],"212":[48],"countries":[49],"regions,":[51],"covers":[52],"10":[53],"major":[54],"types,":[56],"encompasses":[58],"period":[60],"1961\u20132016,":[61],"comprising":[62],"56,717":[63],"records.":[64],"Following":[65],"a":[66],"systematic":[67],"preprocessing":[68],"pipeline":[69],"including":[70],"missing":[71],"value":[72],"verification,":[73],"duplicate":[74],"removal,":[75],"label":[77],"encoding,":[78],"all":[79],"were":[81],"trained":[82],"evaluated":[84],"under":[85],"identical":[86],"experimental":[87],"conditions":[88],"an":[90],"80:20":[91],"train-test":[92],"split":[93],"(45,373":[94],"training":[95],"/":[96],"11,344":[97],"testing":[98],"records).":[99],"Performance":[100],"was":[101],"assessed":[102],"Mean":[104,109],"Absolute":[105],"Error":[106,111],"(MAE),":[107],"Root":[108],"Squared":[110],"(RMSE),":[112],"coefficient":[115],"of":[116,165],"determination":[117],"(R\u00b2).":[118],"Forest":[120],"achieved":[121],"best":[123],"predictive":[124],"performance":[125,142],"with":[126,143,153],"R\u00b2":[127,144,154],"=":[128,131,136,145,155],"0.9586,":[129],"MAE":[130],"6,724.19":[132],"hg/ha,":[133],"RMSE":[135],"13,829.60":[137],"hg/ha.":[138],"demonstrated":[140],"competitive":[141],"0.8115,":[146],"while":[147],"Regression":[149],"showed":[150],"limited":[151],"effectiveness":[152],"0.0608,":[156],"confirming":[157],"its":[158],"inability":[159],"to":[160],"model":[161],"nonlinear":[163],"dynamics":[164],"data.":[168],"Feature":[169],"importance":[170],"analysis":[171],"identified":[172],"Item":[173],"Code":[174],"(crop":[175],"type)":[176],"as":[177],"most":[179],"influential":[180],"predictor,":[181],"followed":[182],"by":[183],"geographic":[184],"region":[185],"temporal":[187],"factors.":[188],"These":[189],"findings":[190],"confirm":[191],"that":[192],"ensemble":[193],"methods,":[195],"particularly":[196],"are":[199],"well-suited":[200],"modelling":[202],"complex":[203],"patterns":[206],"at":[207],"scale.":[209]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-08-30T00:00:00"}
