Leveraging crop yield forecasts using satellite information for early warning in Senegal

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cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.affiliationUniversity of Abomey-Calavi, Beninen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeClimate Resilience
cg.coverage.countrySenegal
cg.coverage.iso3166-alpha2SN
cg.creator.identifierShweta Panjwani: 0000-0002-5558-5830
cg.creator.identifierMahesh Jampani: 0000-0002-8925-719X
cg.creator.identifierGiriraj Amarnath: 0000-0002-7390-9800
cg.identifier.dataurlhttps://chc.ucsb.edu/data/chirpsen
cg.identifier.dataurlhttps://lpdaac.usgs.gov/products/mod13q1v061/en
cg.identifier.doihttps://doi.org/10.1016/j.csag.2024.100024en
cg.identifier.iwmilibraryH053264
cg.identifier.projectIWMI - C-0008
cg.issn2950-4090en
cg.issue2en
cg.journalClimate Smart Agricultureen
cg.reviewStatusPeer Reviewen
cg.volume1en
dc.contributor.authorPanjwani, Shwetaen
dc.contributor.authorJampani, Maheshen
dc.contributor.authorSambou, Mame H. A.en
dc.contributor.authorAmarnath, Girirajen
dc.date.accessioned2024-11-29T05:23:55Zen
dc.date.available2024-11-29T05:23:55Zen
dc.identifier.urihttps://hdl.handle.net/10568/162863
dc.titleLeveraging crop yield forecasts using satellite information for early warning in Senegalen
dcterms.abstractAgricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 % decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (Rsquared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.en
dcterms.accessRightsOpen Access
dcterms.available2024-10-26
dcterms.bibliographicCitationPanjwani, Shweta; Jampani, Mahesh; Sambou, Mame H. A.; Amarnath, Giriraj. 2024. Leveraging crop yield forecasts using satellite information for early warning in Senegal. Climate Smart Agriculture, 1(2):100024. [doi: https://doi.org/10.1016/j.csag.2024.100024]en
dcterms.extent100024.en
dcterms.issued2024-11
dcterms.languageen
dcterms.licenseCC-BY-NC-ND-4.0
dcterms.publisherElsevieren
dcterms.subjectcrop yielden
dcterms.subjectyield forecastingen
dcterms.subjectearly warning systemsen
dcterms.subjectclimate changeen
dcterms.subjectfood securityen
dcterms.subjectcrop productionen
dcterms.subjectmaizeen
dcterms.subjectgroundnutsen
dcterms.subjectsatellite observationen
dcterms.subjectnormalized difference vegetation indexen
dcterms.subjectrainfallen
dcterms.subjectspatial distributionen
dcterms.subjectdecision makingen
dcterms.subjectstrategiesen
dcterms.typeJournal Article

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