气候变化领域集成服务门户(CCPortal): Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal

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DOI	10.1016/j.crm.2021.100331
	Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal
	Fall C.M.N.; Lavaysse C.; Kerdiles H.; Dramé M.S.; Roudier P.; Gaye A.T.
发表日期	2021
ISSN	2212-0963
卷号	33
英文摘要	Studying the relationship between potential high-impact precipitation and crop yields can help us understand the impact of the intensification of the hydrological cycle on agricultural production. The objective of this study is to analyse the contribution of intra seasonal rainfall indicators, namely dry and wet spells, for predicting millet yields at regional scale in Senegal using multiple linear regression. Using dry and wet spells with traditional indicators i.e. proxies of crop biomass and cumulated rainfall, hereafter called remote sensing indicators (NDVI, SPI3, WSI and RG), we analysed the ability of dry and wet spells alone or combined with these remote sensing indicators to provide intraseasonal forecasts covering the period 1991–2010. We analysed all 12 regions producing millet and found that results vary strongly between regions and also during the season, as a function of the dekad of prediction. At the spatial scale, the strongest performing combinations include the dry spell indicators DSC20 and DSxl in the peanut basin. While in the south of the country, the combination of wet period indicators WS1 or WSC5 with the RG is fairly reliable. Focussing on Thies, our best region in the groundnut basin, we showed that dry and wet spells indicators can explain up to 80% of yield variations, alone or in combination with remote sensing indicators. Regarding the timing of prediction, millet yield can be forecast as early as July with an accuracy of 40% of the mean yield but the best forecast is obtained in early September, at the peak of crop development (accuracy of 100 kg/ha i.e. 20% of the mean yield). Although, the estimated yields show biases over some years identified as extremely deficient or in oversupply in terms of agricultural yields. © 2021 The Author(s)
英文关键词	Crop yields; Dry/Wet spells; Multiple linear regression model; Remote sensing
语种	英语
来源期刊	Climate Risk Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/256149
作者单位	Laboratoire de Physique de l'Atmosphère et de l'Océan–Simeon Fongang, ESP, Univ. Cheikh Anta Diop, Fann, Dakar, Senegal; Institut des Géosciences de l'Environnement IGE, Univ. Grenoble Alpes, IRD, CNRS, Grenoble INP, Grenoble, 38000, France; European Commission Monitoring Agricultural Resources, Institute for Environment and Sustainabilty Ispra (VA), Italy; Innovation, Research and Knowledge Department, Agence Française de Développement, Paris, France
推荐引用方式 GB/T 7714	Fall C.M.N.,Lavaysse C.,Kerdiles H.,et al. Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal[J],2021,33.
APA	Fall C.M.N.,Lavaysse C.,Kerdiles H.,Dramé M.S.,Roudier P.,&Gaye A.T..(2021).Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal.Climate Risk Management,33.
MLA	Fall C.M.N.,et al."Performance of dry and wet spells combined with remote sensing indicators for crop yield prediction in Senegal".Climate Risk Management 33(2021).