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首页> 外文期刊>Archives of Agronomy and Soil Science >Monitoring of meteorological drought and its impact on rice (Oryza sativa L.) productivity in Odisha using standardized precipitation index
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Monitoring of meteorological drought and its impact on rice (Oryza sativa L.) productivity in Odisha using standardized precipitation index

机译:使用标准化降水指数监测奥里萨邦的气象干旱及其对水稻(Oryza sativa L.)生产力的影响

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The dynamics of meteorological drought and its relationship with block level rice productivity over the Eastern Indian state of Odisha was assessed during the wet season (June to November) using the standardized precipitation index (SPI). The time series of rainfall data (1983-2008) from 168 rain gauge stations was used to derive the 1- and 3-month SPI of different wet season months. The 1- and 3-month SPI data were interpolated to map spatial patterns of meteorological drought and its severity, and the maps of a drought (2008) and normal (2007) year were discussed in detail. Further, the time series of SPI was exploited to assess the drought risk in Odisha. Correlation analysis of 1- and 3-month SPI with rice productivity index (RPI) showed that the SPI of 1-month time scale particularly in July (r = 0.49) and October (r = 0.33) had significantly stronger relationship with RPI than any of the 3-month SPI individually during wet season. The cumulative degree of severity of drought could be better explained by 3-month SPI map when drought events are well spread in the preceding months. Regression models were developed using 1- and 3-month SPI for forecasting rice productivity of blocks with varying proportion of rainfed area in Odisha. Model developed based on 1-month SPI accounted for 27% yield variability in rice and could be used for forecasting rice productivity
机译:使用标准降水指数(SPI)在雨季(6月至11月)评估了印度东部奥里萨邦州气象干旱的动态及其与稻米产量的关系。利用168个雨量计站的降雨数据的时间序列(1983-2008)得出了不同湿季月份的1个月和3个月的SPI。内插了1个月和3个月的SPI数据,以绘制气象干旱及其严重程度的空间图,并详细讨论了干旱(2008年)和正常(2007年)的地图。此外,利用SPI的时间序列来评估奥里萨邦的干旱风险。 1月和3个月SPI与稻米生产力指数(RPI)的相关性分析表明,特别是7月(r = 0.49)和10月(r = 0.33)的1个月时间尺度的SPI与RPI的关系要强得多。雨季中三个​​月的SPI的百分比。当干旱事件在前几个月中广泛传播时,可以通过3个月的SPI图更好地解释干旱的严重程度的累积程度。使用1个月和3个月SPI开发了回归模型,以预测奥里萨邦不同雨养面积比例的块稻的水稻生产力。基于1个月SPI开发的模型占水稻产量变化的27%,可用于预测水稻生产力

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