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首页> 外文期刊>National Academy Science Letters >Tracking the Yield Sensitivity of Rice-Wheat System to Weather Anomalies
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Tracking the Yield Sensitivity of Rice-Wheat System to Weather Anomalies

机译:跟踪稻麦系统对天气异常的单产敏感性

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摘要

Climate change impacts agriculture, food production and security in view of a region's geography, monsoon dependency and weather extremities. Yield sensitivity to the anomalies of weather is an emerging issue in the perspective of climate smart farming. The present study develops an approach to assess the yield sensitivity of rice-wheat cropping system using a non-parametric Mann-Kendall's test for examining the long-run weather anomalies between 1981 and 2013, followed by regressing yield (stepwise) on weekly weather variables. This approach confirms the long-run weather anomalies and then proceeds to identify the sensitive weeks for climate smart practices, and adaptation strategies for drawing policies. A significant long-run trend has been noticed in the weather variables barring rainfall in rice growing season and evapotranspiration in wheat growing season. Stepwise regression indicated that rice yields are positively influenced by minimum temperature during its growth week 13, evapotranspiration in week 15 and wind speed in week 10; and wheat yields are sensitive to the maximum temperature and evapotranspiration in week 1, as well as sunshine hours in week 6. Volatility in weather variables during ripening in rice and crown root initiation and late tillering in wheat reduces the yield. The interactive effect of significant weather variables on rice and wheat yield indicated its sensitivity due to the weather anomalies in varying magnitudes. This approach is ideal to measure the sensitivity in crop yield owing to anomalies in disaggregated weather variables. Further, it warrants suitable and relevant policy recommendations. In our case, the future research should focus on developing agro-climatic region specific rice and wheat genotypes resilient to climate change, identifying climate smart farming practices as well as adaptation strategies.
机译:鉴于该地区的地理,季风依赖和极端气候,气候变化影响着农业,粮食生产和安全。从气候智能农业的角度来看,对天气异常的产量敏感性是一个新兴问题。本研究开发了一种方法,该方法使用非参数Mann-Kendall检验来评估稻麦种植系统的产量敏感性,以检验1981年至2013年之间的长期天气异常,然后对每周天气变量进行回归(逐步)回归。该方法确认了长期的天气异常,然后继续确定气候智能实践的敏感周数,并制定政策的适应策略。除非稻米生长期出现降雨和小麦生长期蒸发蒸腾,否则天气变量已注意到长期趋势。逐步回归表明,水稻产量在其生长第13周的最低温度,第15周的蒸散和第10周的风速方面均受到积极影响;小麦和小麦的单产对第1周的最高温度和蒸散量以及第6周的日照小时敏感。水稻成熟期间和冠根萌发以及小麦分till晚时的天气变量波动会降低产量。重大天气变量对稻米和小麦产量的交互作用表明了其敏感性,原因是天气异常程度不同。由于分类天气变量异常,这种方法非常适合测量农作物产量的敏感性。此外,它还需要适当和相关的政策建议。在我们的案例中,未来的研究应侧重于发展适应气候变化的农业气候区域特定的水稻和小麦基因型,确定气候智能耕作方法以及适应策略。

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