Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: a remote sensing approach. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Moussa Kourouma, Jean; Eze, Emmanuel; Negash, Emnet; Phiri, Darius; Vinya, Royd; Girma, Atkilt; Zenebe, Amanuel
• Is Part Of: Geomatics, natural hazards & risk. Volume 12:Issue 1(2021)
• Page Start: 2880
• Page End: 2903
• Publication Date: 2021-01-01
• Subjects: Drought -- Normalized Difference Vegetation Index (NDVI) -- Vegetation Condition Index (VCI) -- NDVI anomaly -- crop yield; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2021.1976849
• Languages: English
• ISSNs: 1947-5705

Abstract

Abstract: This study aims at characterizing agricultural drought in Ethiopia and understanding the effects of drought on crop yield. Monthly, seasonal and annual Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) values were calculated using MODIS (MOD13Q1) from the year 2003 to 2017. The relationships between NDVI, VCI, and crop yield were examined to predict the possibility of drought impacts on crop productivity. We found that VCI and NDVI data provides consistent and spatially explicit information for operational drought monitoring in Ethiopia. Results also indicated that the most extreme agricultural drought in recent years occurred in 2003, 2004, 2008, 2009, and 2015. These findings also show that mild to severe droughts have a great chance of occurrence in Ethiopia. However, only severe drought has significant impacts on crops. The food crops yield data used in this study include cereals, legumes, and tubers. It was observed that cereals such as ( Zea mays ), teff ( Eragrostis tef ), haricot beans ( Phaseolus vulgaris ) are more sensitive to agricultural drought when compared to the tubers such as sweet potato ( Ipomoea batatas ) and taro ( Colocasia esculenta ). Thus, drought preparedness programs need to pay more attention to the cultivation of these crops under severe drought conditions. Highlights: NDVI and VCI patterns easily discriminate cereals and legumes when compared to tuber crops. 45% and 43% yield variability of respectively teff and maize is explained by the NDVI patterns. The studied crops (Teff, Maize, Sweet potato and Taro) are less discriminable to seasonal VCI variation. Drought preparedness strategies should encourage farmers to cultivate tubers instead of cereals. The PMARE value for Taro and sweet potato exceeded the model acceptable range.