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.

研究潜在的高影响降水与作物产量之间的关系可以帮助我们了解水文循环的强化对农业生产的影响。本研究的目的是分析季节性降雨指标（即旱季和雨季）对使用多元线性回归预测塞内加尔区域尺度小米产量的贡献。使用干湿期与传统指标，即作物生物量和累积降雨量的代理，以下称为遥感指标（NDVI、SPI3、WSI 和 RG），我们分析了干湿期单独或结合这些遥感指标的能力提供涵盖 1991-2010 年期间的季节性内预测。我们分析了所有 12 个生产小米的地区，发现结果在地区之间和季节之间差异很大，作为预测的函数。在空间尺度上，表现最强的组合包括花生盆地中的干旱期指标 DSC20 和 DSxl。而在该国南部，湿润期指标 WS1 或 WSC5 与 RG 的组合相当可靠。以我们在花生盆地中最好的地区 Thies 为重点，我们表明干湿期指标可以单独或与遥感指标结合解释高达 80% 的产量变化。关于预测时间，早在 7 月份就可以预测小米产量，准确度为平均产量的 40%，但最好的预测是在 9 月初，即作物发育的高峰期（准确度为 100 公斤/公顷，即 平均产量的 20%）。尽管如此，估计产量在某些年份显示出偏差，被确定为农业产量极度不足或供过于求。