Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa

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cg.authorship.typesCGIAR multi-centreen_US
cg.contributor.affiliationInternational Institute of Tropical Agricultureen_US
cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren_US
cg.contributor.affiliationInternational Centre of Insect Physiology and Ecologyen_US
cg.contributor.crpMaizeen_US
cg.contributor.crpPolicies, Institutions, and Marketsen_US
cg.contributor.crpGrain Legumesen_US
cg.contributor.donorUnited States Agency for International Developmenten_US
cg.coverage.countryMozambiqueen_US
cg.coverage.countrySouth Africaen_US
cg.coverage.iso3166-alpha2MZen_US
cg.coverage.iso3166-alpha2ZAen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionSouthern Africaen_US
cg.coverage.regionEastern Africaen_US
cg.creator.identifierJulius Manda: 0000-0002-9599-5906en_US
cg.creator.identifierChristian Thierfelder: 0000-0002-6306-7670en_US
cg.creator.identifierMateete Bekunda: 0000-0001-7297-9383en_US
cg.creator.identifierIrmgard Hoeschle-Zeledon: 0000-0002-2530-6554en_US
cg.identifier.iitathemeBIOMETRICSen_US
cg.identifier.iitathemeNATURAL RESOURCE MANAGEMENTen_US
cg.identifier.iitathemePLANT PRODUCTION & HEALTHen_US
cg.identifier.iitathemeSOCIAL SCIENCE & AGRICUSINESSen_US
cg.identifier.urlhttps://ieeexplore.ieee.org/document/9530335en_US
cg.isbn978-1-7281-6561-5en_US
cg.placeShenzhen, Chinaen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.actionAreaSystems Transformationen_US
cg.subject.iitaCROP SYSTEMSen_US
cg.subject.iitaMAIZEen_US
cg.subject.impactAreaClimate adaptation and mitigationen_US
dc.contributor.authorMuthoni, Francis K.en_US
dc.contributor.authorThierfelder, Christian L.en_US
dc.contributor.authorMudereri, B.T.en_US
dc.contributor.authorManda, J.en_US
dc.contributor.authorBekunda, Mateete A.en_US
dc.contributor.authorHoeschle-Zeledon, Irmgarden_US
dc.date.accessioned2022-06-17T08:40:48Zen_US
dc.date.available2022-06-17T08:40:48Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/119867en_US
dc.titleMachine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africaen_US
dcterms.abstractAdoption of CA in smallholder farmers in Africa is (s)low partly due to poor spatial targeting. Mapping the crop yield from different CA systems across space and time can reveal their spatial recommendation domains. Integration of machine learning (ML) and free remotely sensed big data have opened huge opportunities for data-driven insights into complex problems in agriculture. The objective of this study was to estimate the spatial-temporal variations of maize grain yields from 13-year multi-location on-farm trials implemented across four countries in southern Africa. The agronomic data from the long-term CA trials is used together with gridded biophysical and socio-economic variables. A spatially explicit random forest (RF) algorithm was developed. Spatial variation of yield advantage or loss from CA practices was compared with conventional tillage practices (CP) during seasons with above and below-normal precipitation. The out-of-bag accuracy of the RF model was R 2 = 0.63 and RMSE = 1.2 t ha -1 . The variable importance analysis showed that the altitude, precipitation, temperature, and soil physical and nutrients conditions variables explained most of the variation in maize grain yield. Maps were generated to identify the locations where CA had a yield advantage over CP during seasons with below and above-average precipitation. The CA showed yield gains of up-to 1 t ha -1 during the season with drought compared to CP. In contrast, the CA returned yield losses of similar magnitude during the season with above-normal precipitation, except in Mozambique. The maps on yield advantage will support the spatial targeting of CA to suitable biophysical and socioeconomic contexts. Results demonstrates that multi-source remotely sensed data, coupled with advanced and efficient machine learning algorithms can provides accurate, cost-effective, and timely platform for predicting the optimal locations for the upscaling sustainable agricultural technologies.en_US
dcterms.accessRightsLimited Accessen_US
dcterms.audienceScientistsen_US
dcterms.bibliographicCitationMuthoni, F.K., Thierfelder, C., Mudereri, B.T., Manda, J., Bekunda, M. & Hoeschle-Zeledon, I. (2021). Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa. 9th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 26-29 July 2021, Shenzhen, China: IEEE, (p. 1-5).en_US
dcterms.extent1-5en_US
dcterms.issued2021-07en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.publisherInstitute of Electrical and Electronics Engineersen_US
dcterms.subjectdataen_US
dcterms.subjectclimate variabilityen_US
dcterms.subjectconservation agricultureen_US
dcterms.subjectmachine learningen_US
dcterms.subjectforesten_US
dcterms.subjectremote sensingen_US
dcterms.typeConference Paperen_US

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