Climate-informed agronomic advisories for maize in Colombia: Progress report for the Excellence in Agronomy (EiA) initiative Latin America Use Case

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cg.authorship.typesCGIAR single centre
cg.contributor.affiliationBioversity International
cg.contributor.affiliationInternational Center for Tropical Agriculture
cg.contributor.donorCGIAR Trust Fund
cg.contributor.initiativeExcellence in Agronomy
cg.coverage.countryColombia
cg.coverage.iso3166-alpha2CO
cg.coverage.regionAmericas
cg.coverage.regionSouth America
cg.coverage.regionLatin America and the Caribbean
cg.creator.identifierMaría Victoria Díaz López: 0000-0002-0919-4679
cg.creator.identifierOscar Hernan Estrada Vargas: 0009-0000-9536-5317
cg.creator.identifierLizeth Llanos-Herrera: 0000-0003-3540-7348
cg.creator.identifierJulian Ramirez-Villegas: 0000-0002-8044-583X
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.alliancebiovciatCLIMATE CHANGE ADAPTATION
cg.subject.alliancebiovciatMODELING
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.impactAreaPoverty reduction, livelihoods and jobs
cg.subject.sdgSDG 1 - No poverty
cg.subject.sdgSDG 2 - Zero hunger
cg.subject.sdgSDG 5 - Gender equality
cg.subject.sdgSDG 6 - Clean water and sanitation
cg.subject.sdgSDG 8 - Decent work and economic growth
cg.subject.sdgSDG 10 - Reduced inequalities
cg.subject.sdgSDG 12 - Responsible consumption and production
cg.subject.sdgSDG 13 - Climate action
cg.subject.sdgSDG 15 - Life on land
cg.subject.sdgSDG 17 - Partnerships for the goals
dc.contributor.authorDiaz, Maria Victoria
dc.contributor.authorEstrada, Oscar
dc.contributor.authorLlanos, Lizeth
dc.contributor.authorRamírez Villegas, Julián Armando
dc.date.accessioned2024-01-09T09:27:26Zen
dc.date.available2024-01-09T09:27:26Zen
dc.identifier.urihttps://hdl.handle.net/10568/137380
dc.titleClimate-informed agronomic advisories for maize in Colombia: Progress report for the Excellence in Agronomy (EiA) initiative Latin America Use Caseen
dcterms.abstractDecision making in agriculture has been based on general (blanket) recommendations made by technicians, the farmer's own knowledge or local practices that are adopted as customary for generations. Recognizing the need to generate information to help make site-specific decisions based on traditional agronomic research, this study uses Machine Learning (ML) models and a Global Harmony Search (GHS) methodology to find an optimal solution to the combination of practices that a farmer could implement according to his soil and climate conditions specific to his land. The dataset used included 748 observations, and 45 explanatory variables, with the only response variable being maize yield, and covered the period 2013–2019. The ML models used, namely, Random Forest (R2=0.64) and CatBoost (R2=0.68) showed relatively high performance. The most important variables in both models were related to climate. We highlight in particular the importance of the rainfall during the various stages of the growing cycle, as well as the frequency of rainfall events with more than 10 millimeters. The GHS approach showed that producing agronomic recommendations based on a climate forecast can help maintain yield levels. Future work should focus on adding new farmer field observations, retraining the ML models, and exploring the importance of independent variable interaction. These steps will help develop more robust recommendations for maize farmers in Colombia.en
dcterms.accessRightsOpen Access
dcterms.audienceScientists
dcterms.bibliographicCitationDiaz, M.V.; Estrada, O.; Llanos, L.; Ramirez-Villegas, J. (2023) Climate-informed agronomic advisories for maize in Colombia: Progress report for the Excellence in Agronomy (EiA) initiative Latin America Use Case. 14 p.en
dcterms.extent14 p.
dcterms.issued2023-12-21
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.subjectclimate change adaptationen
dcterms.subjectdata analysisen
dcterms.subjectmodellingen
dcterms.subjectforecastingen
dcterms.typeReport

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