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

Simple item page
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeClimate Resilienceen_US
cg.coverage.countrySenegalen_US
cg.coverage.iso3166-alpha2SNen_US
cg.creator.identifierShweta Panjwani: 0000-0002-5558-5830en_US
cg.creator.identifierMahesh Jampani: 0000-0002-8925-719Xen_US
cg.creator.identifierGiriraj Amarnath: 0000-0002-7390-9800en_US
cg.identifier.iwmilibraryH053408en_US
cg.identifier.projectIWMI - C-0008en_US
cg.identifier.urlhttps://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1658877en_US
cg.reviewStatusPeer Reviewen_US
dc.contributor.authorPanjwani, Shwetaen_US
dc.contributor.authorJampani, Maheshen_US
dc.contributor.authorAmarnath, Girirajen_US
dc.contributor.authorSambou, Mame Henriette Astouen_US
dc.date.accessioned2025-01-14T05:20:54Zen_US
dc.date.available2025-01-14T05:20:54Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/168944en_US
dc.titleEvaluating climate change impacts and seasonal dynamics in Senegal to predict crop yields and develop early warning signalsen_US
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_US
dcterms.accessRightsOpen Accessen_US
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_US
dcterms.extent1p.en_US
dcterms.issued2024-12-13en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.subjectclimate change impactsen_US
dcterms.subjectyield forecastingen_US
dcterms.subjectcrop yielden_US
dcterms.subjectseasonal variationen_US
dcterms.subjectclimate variabilityen_US
dcterms.subjectearly warning systemsen_US
dcterms.typeAbstracten_US

Files

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

IWMI Conference Chapters or Papers
CGIAR Initiative on Climate Resilience
Water Data Science for Action (WDSA)