{"id":"https://openalex.org/W7130850124","doi":"https://doi.org/10.5281/zenodo.18720479","title":"Dynamic Cotton Yield Prediction Using Machine Learning and Statistical Modeling: Application to the Climatic Conditions of Chad","display_name":"Dynamic Cotton Yield Prediction Using Machine Learning and Statistical Modeling: Application to the Climatic Conditions of Chad","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7130850124","doi":"https://doi.org/10.5281/zenodo.18720479"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.18720479","doi":"https://doi.org/10.5281/zenodo.18720479","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18720479","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":"JournalArticle"},"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.18720479","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069425992","display_name":"ALI Ouchar Cherif","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ALI Ouchar Cherif","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002510935","display_name":"Octave Ringar","orcid":"https://orcid.org/0009-0004-3147-3190","observed_orcids":["https://orcid.org/0009-0004-3147-3190"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"OCTAVE Ringar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092579328","display_name":"Abakar Mahamat Ahmat","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ABAKAR Mahamat Ahmat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126559361","display_name":"FARIKOU Ousman","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"FARIKOU Ousman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10439","display_name":"Climate change impacts on agriculture","score":0.9585000276565552,"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"}},"topics":[{"id":"https://openalex.org/T10439","display_name":"Climate change impacts on agriculture","score":0.9585000276565552,"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/T10616","display_name":"Smart Agriculture and AI","score":0.03180000185966492,"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":0.00559999980032444,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cotton-yield-prediction","display_name":"cotton yield prediction","score":0.8500000238418579},{"id":"https://openalex.org/keywords/crop-yield-prediction","display_name":"crop yield prediction","score":0.9900000095367432},{"id":"https://openalex.org/keywords/climate-variability","display_name":"climate variability","score":0.8429999947547913},{"id":"https://openalex.org/keywords/climatic-time-series","display_name":"climatic time series","score":0.7110000252723694},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"partial least squares regression","score":0.9440000057220459},{"id":"https://openalex.org/keywords/principal-component-regression","display_name":"principal component regression","score":0.9589999914169312},{"id":"https://openalex.org/keywords/multiple-linear-regression","display_name":"multiple linear regression","score":0.9980000257492065},{"id":"https://openalex.org/keywords/multicollinearity","display_name":"multicollinearity","score":0.6859999895095825},{"id":"https://openalex.org/keywords/decision-support-system","display_name":"decision support system","score":0.9300000071525574},{"id":"https://openalex.org/keywords/climate-resilient-agriculture","display_name":"climate-resilient agriculture","score":0.968999981880188},{"id":"https://openalex.org/keywords/chad","display_name":"Chad","score":0.8790000081062317}],"concepts":[{"id":"https://openalex.org/C106192678","wikidata":"https://www.wikidata.org/wiki/Q1419761","display_name":"Collinearity","level":2,"score":0.678600013256073},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5372999906539917},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.5188999772071838},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.4862000048160553},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4724999964237213},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.46619999408721924},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.42820000648498535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4165000021457672},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C189285262","wikidata":"https://www.wikidata.org/wiki/Q1332350","display_name":"Multicollinearity","level":3,"score":0.32089999318122864},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.31119999289512634},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3091000020503998},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.29829999804496765},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C54924851","wikidata":"https://www.wikidata.org/wiki/Q2751054","display_name":"Sustainable agriculture","level":3,"score":0.2838999927043915},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C170964787","wikidata":"https://www.wikidata.org/wiki/Q7611170","display_name":"Stepwise regression","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.2549999952316284},{"id":"https://openalex.org/C66204764","wikidata":"https://www.wikidata.org/wiki/Q219416","display_name":"Sustainability","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.18720479","doi":"https://doi.org/10.5281/zenodo.18720479","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18720479","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":"JournalArticle"},{"id":"doi:10.5281/zenodo.18720480","doi":"https://doi.org/10.5281/zenodo.18720480","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18720480","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":"JournalArticle"}],"best_oa_location":{"id":"doi:10.5281/zenodo.18720479","doi":"https://doi.org/10.5281/zenodo.18720479","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18720479","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":"JournalArticle"},"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.9208999872207642}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7130852888","https://openalex.org/W4399990402","https://openalex.org/W2990374280","https://openalex.org/W7204126196","https://openalex.org/W4282971355","https://openalex.org/W3117566954","https://openalex.org/W4414795436","https://openalex.org/W2892538429","https://openalex.org/W4407864952","https://openalex.org/W4406382340"],"abstract_inverted_index":{"Crop":[0],"yield":[1,43],"forecasting":[2,44],"represents":[3],"a":[4,23,57,165],"major":[5],"strategic":[6],"challenge":[7],"for":[8,61,174],"developing":[9],"countries,where":[10],"agriculture":[11,178],"remains":[12],"highly":[13],"dependent":[14],"on":[15,69],"climatic":[16,79,153],"conditions":[17],"[3,24].":[18],"In":[19],"Chad,cotton":[20],"cultivation":[21],"constitutes":[22],"key":[24],"pillar":[25],"of":[26,64,109,121,171],"the":[27,62,83,105,134,143],"national":[28],"economy;":[29],"however,":[30],"itsproductivity":[31],"is":[32,89,112,162],"strongly":[33],"affected":[34],"by":[35,48,149],"interannual":[36],"climate":[37],"variability":[38],"[25,26].":[39],"The":[40,137],"absenceof":[41],"reliable":[42],"systems":[45],"limits":[46],"decision-making":[47],"farmers,":[49],"financialinstitutions,":[50],"and":[51,99,124,155,176],"public":[52],"policymakers":[53],"[23].This":[54],"paper":[55],"proposes":[56],"comprehensive":[58],"methodological":[59,130],"framework":[60],"dynamicprediction":[63],"cotton":[65],"crop":[66],"yield,":[67],"based":[68],"supervised":[70],"statistical":[71,116],"models":[72],"related":[73],"tomachine":[74],"learning,":[75],"applied":[76],"to":[77],"long-term":[78],"time":[80,158],"series":[81],"covering":[82],"period":[84],"1985\u20132025.":[85],"A":[86],"comparative":[87],"analysis":[88],"conducted":[90],"between":[91],"Multiple":[92],"Linear":[93],"Regression":[94,97,103],"(MLR),Principal":[95],"Component":[96],"(PCR),":[98],"Partial":[100],"Least":[101],"Squares":[102],"(PLS)in":[104],"Logone":[106],"Occidental":[107],"region":[108],"Chad.Model":[110],"performance":[111],"evaluated":[113],"using":[114],"standard":[115],"indicators":[117],"(RMSE,":[118],"MAE,nRMSE,":[119],"coefficient":[120,126],"determination":[122],"R\u00b2,":[123],"correlation":[125],"r),":[127],"in":[128,133,147],"accordancewith":[129],"recommendations":[131],"established":[132],"literature":[135],"[27,28].":[136],"resultsshow":[138],"that":[139],"PLS":[140],"consistently":[141],"outperforms":[142],"other":[144],"models,":[145],"particularly":[146],"contextscharacterized":[148],"strong":[150],"collinearity":[151],"among":[152],"variables":[154],"over":[156],"long":[157],"series.The":[159],"optimal":[160],"model":[161],"integrated":[163],"into":[164],"decision":[166],"support":[167],"system,":[168],"highlighting":[169],"thepotential":[170],"data-driven":[172],"approaches":[173],"sustainable":[175],"climate-resilient":[177],"inSub-Saharan":[179],"Africa.":[180]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-22T00:00:00"}
