High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

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cg.authorship.typesCGIAR multi-centreen
cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.affiliationUniversity of Floridaen
cg.contributor.affiliationMinistry of Agriculture, Ethiopiaen
cg.contributor.affiliationInternational Crops Research Institute for the Semi-Arid Tropicsen
cg.contributor.affiliationInternational Livestock Research Instituteen
cg.contributor.affiliationAccelerating Impacts of CGIAR Climate Research for Africaen
cg.contributor.crpClimate Change, Agriculture and Food Security
cg.contributor.donorWorld Banken
cg.coverage.countryEthiopia
cg.coverage.iso3166-alpha2ET
cg.coverage.regionAfrica
cg.coverage.regionSouthern AfricaEastern Africa
cg.creator.identifierKindie Tesfaye: 0000-0002-7201-8053en
cg.creator.identifierRobel Takele Miteku: 0000-0003-0151-2537en
cg.creator.identifierVakhtang Shelia: 0000-0002-9768-7958en
cg.creator.identifieresayas Hayi: 0000-0003-1441-8320en
cg.creator.identifierAddisu Dabale Gonfa: 0000-0002-7990-1308en
cg.creator.identifierPierre C. Sibiry Traore: 0000-0001-8881-4794en
cg.creator.identifierDawit Solomon: 0000-0002-6839-6801en
cg.creator.identifierGerrit Hoogenboom: 0000-0002-1555-0537en
cg.edition100425en
cg.identifier.doihttps://doi.org/10.1016/j.cliser.2023.100425en
cg.isijournalISI Journalen
cg.issn2405-8807en
cg.journalClimate Servicesen
cg.reviewStatusPeer Reviewen
cg.volume32en
dc.contributor.authorTesfaye, Kindieen
dc.contributor.authorTakele, Robelen
dc.contributor.authorShelia, Vakhtangen
dc.contributor.authorLemma, Esayasen
dc.contributor.authorDabale, Addisuen
dc.contributor.authorSibiry Traoré, Pierre C.en
dc.contributor.authorSolomon, Dawiten
dc.contributor.authorHoogenboom, Gerriten
dc.date.accessioned2023-11-23T15:24:50Zen
dc.date.available2023-11-23T15:24:50Zen
dc.identifier.urihttps://hdl.handle.net/10568/134694
dc.titleHigh spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopiaen
dcterms.abstractSeasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademicsen
dcterms.audienceCGIARen
dcterms.audienceDevelopment Practitionersen
dcterms.audienceDonorsen
dcterms.audiencePolicy Makersen
dcterms.audienceScientistsen
dcterms.available2023-12en
dcterms.bibliographicCitationTesfaye K, Takele R, Shelia V, Lemma E, Dabale A, Traore PC, Solomon D, Hoogenboom G. 2023. High Spatial Resolution Seasonal Crop Yield Forecasting for Heterogeneous Maize Environments in the Oromia Regional State, Ethiopia. Climate Services 32:100425.en
dcterms.extent13 p.en
dcterms.issued2023-11en
dcterms.languageen
dcterms.licenseCC-BY-NC-ND-4.0
dcterms.publisherElsevieren
dcterms.subjectcropsen
dcterms.subjectforecastingen
dcterms.subjectspatial dataen
dcterms.subjectmaizeen
dcterms.subjectenvironmenten
dcterms.subjectagricultureen
dcterms.subjectclimate changeen
dcterms.typeJournal Article

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