{"id":"https://openalex.org/W7204234461","doi":"https://doi.org/10.5281/zenodo.22113117","title":"APPLICATION OF MACHINE LEARNING MODELS IN FORECASTING AGRICULTURAL PRODUCTIVITY IN NIGERIA","display_name":"APPLICATION OF MACHINE LEARNING MODELS IN FORECASTING AGRICULTURAL PRODUCTIVITY IN NIGERIA","publication_year":2026,"publication_date":"2026-08-26","ids":{"openalex":"https://openalex.org/W7204234461","doi":"https://doi.org/10.5281/zenodo.22113117"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.22113117","doi":"https://doi.org/10.5281/zenodo.22113117","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22113117","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.22113117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148181641","display_name":"Mr. Aniekan Amos Thompson","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mr. Aniekan Amos Thompson","raw_affiliation_strings":["Tetfund Centre of Excellence in CIR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tetfund Centre of Excellence in CIR","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148153739","display_name":"Oto-abasi Essien","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4403386464","display_name":"Akwa Ibom State Polytechnic","ror":"https://ror.org/00g7v8090","country_code":"NG","type":"education","lineage":["https://openalex.org/I4403386464"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Oto-abasi Essien","raw_affiliation_strings":["Computer Science, Akwa Ibom State Polytechnic & Tetfund Centre of Excellence in CIR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Akwa Ibom State Polytechnic & Tetfund Centre of Excellence in CIR","institution_ids":["https://openalex.org/I4403386464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007401901","display_name":"Midighe Usoh","orcid":"https://orcid.org/0000-0001-7522-0487","observed_orcids":["https://orcid.org/0000-0001-7522-0487"]},"institutions":[{"id":"https://openalex.org/I4403386464","display_name":"Akwa Ibom State Polytechnic","ror":"https://ror.org/00g7v8090","country_code":"NG","type":"education","lineage":["https://openalex.org/I4403386464"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Midighe Usoh","raw_affiliation_strings":["Computer Science, Akwa Ibom State Polytechnic & Tetfund Centre of Excellence in CIR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science, Akwa Ibom State Polytechnic & Tetfund Centre of Excellence in CIR","institution_ids":["https://openalex.org/I4403386464"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"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":null,"issue":null,"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":0.9663000106811523,"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":0.9663000106811523,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.01899999938905239,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10439","display_name":"Climate change impacts on agriculture","score":0.01269999984651804,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/crop-yield-prediction","display_name":"crop yield prediction","score":0.906000018119812},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.7440000176429749},{"id":"https://openalex.org/keywords/nigeria","display_name":"Nigeria","score":0.5820000171661377},{"id":"https://openalex.org/keywords/food-security","display_name":"food security","score":0.828000009059906},{"id":"https://openalex.org/keywords/yield-gap","display_name":"yield gap","score":0.7540000081062317},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6840000152587891},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.8479999899864197},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.32499998807907104},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.5820000171661377},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.48500001430511475},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.6259999871253967},{"id":"https://openalex.org/keywords/agroecological-zones","display_name":"agro-ecological zones","score":0.859000027179718},{"id":"https://openalex.org/keywords/agricultural-extension","display_name":"agricultural extension","score":0.7450000047683716},{"id":"https://openalex.org/keywords/agricultural-insurance","display_name":"agricultural insurance","score":0.9259999990463257},{"id":"https://openalex.org/keywords/data-infrastructure","display_name":"data infrastructure","score":0.7200000286102295},{"id":"https://openalex.org/keywords/digital-skills","display_name":"digital skills","score":0.49900001287460327},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.7409999966621399}],"concepts":[{"id":"https://openalex.org/C549605437","wikidata":"https://www.wikidata.org/wiki/Q1229911","display_name":"Food security","level":3,"score":0.5467000007629395},{"id":"https://openalex.org/C128383755","wikidata":"https://www.wikidata.org/wiki/Q3816336","display_name":"Agricultural productivity","level":3,"score":0.4652999937534332},{"id":"https://openalex.org/C204983608","wikidata":"https://www.wikidata.org/wiki/Q2111958","display_name":"Productivity","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45680001378059387},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4316999912261963},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.41589999198913574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3970000147819519},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3882000148296356},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36390000581741333},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.3617999851703644},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.34850001335144043},{"id":"https://openalex.org/C1670747","wikidata":"https://www.wikidata.org/wiki/Q396622","display_name":"Agribusiness","level":3,"score":0.34360000491142273},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C156663261","wikidata":"https://www.wikidata.org/wiki/Q396338","display_name":"Agroecology","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2892000079154968},{"id":"https://openalex.org/C129225989","wikidata":"https://www.wikidata.org/wiki/Q932139","display_name":"Cash crop","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C58941895","wikidata":"https://www.wikidata.org/wiki/Q295865","display_name":"Ecosystem services","level":3,"score":0.28369998931884766},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C159009313","wikidata":"https://www.wikidata.org/wiki/Q5283159","display_name":"Distributed lag","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C3987366","wikidata":"https://www.wikidata.org/wiki/Q6658164","display_name":"Livelihood","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.22113117","doi":"https://doi.org/10.5281/zenodo.22113117","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22113117","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},{"id":"doi:10.5281/zenodo.22113118","doi":"https://doi.org/10.5281/zenodo.22113118","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22113118","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.5281/zenodo.22113117","doi":"https://doi.org/10.5281/zenodo.22113117","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22113117","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"score":0.991100013256073,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"},{"score":0.48510000109672546,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"},{"score":0.428600013256073,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4415258248","https://openalex.org/W4390676838","https://openalex.org/W4392653376","https://openalex.org/W7154593549","https://openalex.org/W4408077649","https://openalex.org/W4403020907","https://openalex.org/W4399215214","https://openalex.org/W7132821510","https://openalex.org/W4401777206","https://openalex.org/W7157056418"],"abstract_inverted_index":{"Agricultural":[0],"productivity":[1,71],"remains":[2],"the":[3,13,18,89,98,101,118,194],"backbone":[4],"of":[5,12,21,84,162],"Nigeria's":[6,188,222],"economy,":[7],"employing":[8],"roughly":[9],"a":[10,215],"third":[11],"labour":[14],"force":[15],"and":[16,39,51,77,80,93,112,114,117,136,151,166,184,191,206,210,226,240,251],"underpinning":[17],"food":[19,248],"security":[20,249],"more":[22,81],"than":[23],"220":[24],"million":[25],"people.":[26],"Yet":[27],"yields":[28],"for":[29,217],"staple":[30],"crops":[31],"continue":[32],"to":[33,54,68,196],"lag":[34],"well":[35],"behind":[36],"agronomic":[37],"potential,":[38],"production":[40],"is":[41],"increasingly":[42],"destabilised":[43],"by":[44],"climate":[45],"variability,":[46],"soil":[47,107],"degradation,":[48],"market":[49],"shocks":[50],"weak":[52],"access":[53],"timely":[55],"information.":[56],"This":[57],"article":[58,99,175],"examines":[59],"how":[60],"machine":[61,243],"learning":[62,153,244],"(ML)":[63],"models":[64,154],"can":[65,245],"be":[66],"applied":[67],"forecast":[69],"agricultural":[70,223],"in":[72,169,237,254],"Nigeria,":[73],"offering":[74],"decision-makers,":[75],"farmers":[76],"agribusinesses":[78],"earlier":[79],"reliable":[82],"estimates":[83],"crop":[85],"output.":[86],"Drawing":[87],"on":[88,94],"wider":[90],"scientific":[91],"literature":[92],"representative":[95],"empirical":[96],"patterns,":[97],"reviews":[100],"principal":[102],"data":[103,199,238],"sources":[104],"meteorological":[105],"records,":[106],"surveys,":[108],"satellite":[109],"remote":[110],"sensing,":[111],"household":[113],"farm-level":[115],"surveys":[116],"dominant":[119],"modelling":[120],"families,":[121],"including":[122],"multiple":[123],"linear":[124],"regression,":[125,128],"support":[126],"vector":[127],"random":[129],"forests,":[130],"gradient-boosted":[131],"trees,":[132],"artificial":[133],"neural":[134],"networks":[135],"recurrent":[137],"architectures":[138],"such":[139],"as":[140],"long":[141],"short-term":[142],"memory":[143],"(LSTM)":[144],"networks.":[145],"Comparative":[146],"evidence":[147],"indicates":[148],"that":[149],"ensemble":[150],"deep":[152],"routinely":[155],"outperform":[156],"traditional":[157],"statistical":[158],"approaches,":[159],"with":[160,214,235],"coefficients":[161],"determination":[163],"above":[164],"0.85":[165],"substantial":[167],"reductions":[168],"root":[170],"mean":[171],"squared":[172],"error.":[173],"The":[174,229],"presents":[176],"an":[177],"end-to-end":[178],"forecasting":[179,220],"workflow,":[180],"discusses":[181],"feature":[182],"importance":[183],"yield-gap":[185],"analysis":[186],"across":[187],"agroecological":[189],"zones,":[190],"critically":[192],"appraises":[193],"barriers":[195],"adoption:":[197],"fragmented":[198],"infrastructure,":[200],"limited":[201],"digital":[202],"skills,":[203],"connectivity":[204],"gaps":[205],"concerns":[207],"around":[208],"interpretability":[209],"trust.":[211],"It":[212],"concludes":[213],"roadmap":[216],"embedding":[218],"ML-driven":[219],"into":[221],"extension,":[224],"insurance":[225],"policy":[227],"systems.":[228],"findings":[230],"suggest":[231],"that,":[232],"when":[233],"paired":[234],"investment":[236],"systems":[239],"human":[241],"capacity,":[242],"materially":[246],"strengthen":[247],"planning":[250],"rural":[252],"livelihoods":[253],"Nigeria":[255]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-08-27T00:00:00"}