Evaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signals

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cg.contributor.affiliationInternational Water Management Instituteen
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.iwmilibraryH053408
cg.identifier.projectIWMI - C-0008
cg.identifier.urlhttps://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1658877en
cg.reviewStatusPeer Reviewen
dc.contributor.authorPanjwani, Shwetaen
dc.contributor.authorJampani, Maheshen
dc.contributor.authorAmarnath, Girirajen
dc.contributor.authorSambou, Mame Henriette Astouen
dc.date.accessioned2025-01-14T05:20:54Zen
dc.date.available2025-01-14T05:20:54Zen
dc.identifier.urihttps://hdl.handle.net/10568/168944
dc.titleEvaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signalsen
dcterms.abstractFood security has become a critical issue in Senegal due to agricultural losses from climate-related risks and the growing population. In recent years, several studies have reported crop yield losses as a result of seasonal climate variability and extreme events, but crop-wise in-depth analysis is lacking. In this context, we performed district-wise statistical and spatial extent analysis for major growing crops using earth observation and agronomic data from the government to estimate crop-wise correlation. Further, regression analysis was performed for major crops, such as maize and groundnut, using satellite-based climate and vegetation data and observed yield data over a 12-year period. Our results suggested that maize and groundnut crops are mainly distributed in all agroecological zones except the Niayes zone and Senegal River valley in terms of cultivated area and harvested crop yield. We found that seasonal rainfall, particularly from May to September, is highly correlated with the yield, and a 10-20% decrease in seasonal rainfall can lead to crop losses. Additionally, the impact of seasonal rainfall may differ across districts due to climate variability, the onset of monsoon, and cropping seasons. We used the best-fit combinations of rainfall and NDVI and machine-learning models to predict crop yield for the upcoming season for major crop growing districts, with an accuracy (R2) ranging from 0.6 to 0.8 and a one-month lag to the harvest period. The overall goal is to integrate the predictive modeling results into early warning systems such as CGIAR AWARE, which could enhance Senegal's agricultural resilience to climate change and inform decision-makers to take early action.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationPanjwani, Shweta; Jampani, Mahesh; Amarnath, Giriraj; Sambou, Mame Henriette Astou. 2024. Evaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signals [Abstract only]. Paper presented at the American Geophysical Union Annual Meeting 2024 (AGU24) on What’s Next for Science, Washington, DC, USA, 9-13 December 2024. 1p.en
dcterms.extent1p.en
dcterms.issued2024-12-13
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.subjectclimate change impactsen
dcterms.subjectyield forecastingen
dcterms.subjectcrop yielden
dcterms.subjectseasonal variationen
dcterms.subjectclimate variabilityen
dcterms.subjectearly warning systemsen
dcterms.typeAbstract

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