@article{fdi:010082718,
  title = {{P}erformance of dry and wet spells combined with remote sensing indicators for crop yield prediction in {S}enegal},
  author = {{F}all, {C}. {M}. {N}. and {L}avaysse, {C}hristophe and {K}erdiles, {H}. and {D}ram{\'e}, {M}. {S}. and {R}oudier, {P}. and {G}aye, {A}. {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}tudying 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. {T}he 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 {S}enegal using multiple linear regression. {U}sing dry and wet spells with traditional indicators i.e. proxies of crop biomass and cumulated rainfall, hereafter called remote sensing indicators ({NDVI}, {SPI}3, {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. {W}e 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. {A}t the spatial scale, the strongest performing combinations include the dry spell indicators {DSC}20 and {DS}xl in the peanut basin. {W}hile in the south of the country, the combination of wet period indicators {WS}1 or {WSC}5 with the {RG} is fairly reliable. {F}ocussing on {T}hies, 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. {R}egarding the timing of prediction, millet yield can be forecast as early as {J}uly with an accuracy of 40% of the mean yield but the best forecast is obtained in early {S}eptember, at the peak of crop development (accuracy of 100 kg/ha i.e. 20% of the mean yield). {A}lthough, the estimated yields show biases over some years identified as extremely deficient or in oversupply in terms of agricultural yields.},
  keywords = {{D}ry/{W}et spells ; {R}emote sensing ; {C}rop yields ; {M}ultiple linear regression ; model ; {SENEGAL}},
  booktitle = {},
  journal = {{C}limate {R}isk {M}anagement},
  volume = {33},
  numero = {},
  pages = {100331 [27 p.]},
  ISSN = {2212-0963},
  year = {2021},
  DOI = {10.1016/j.crm.2021.100331},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082718},
}