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四川盆地东南部杂交中稻开花期高温伤害的风险预测

Risk Prediction of the Damage of High Temperature at Flowering Stage on Middle-season Hybrid Rice in Southeastern of Sichuan Basin
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摘要 准确预测不同区域杂交中稻开花期与自然极端高温发生期相遇的概率,有利于制定当地水稻生产避险高产稳产技术。以四川省推广的22个杂交中稻新品种为材料,在四川盆地东南部不同生态点开展品种生态适应性试验,研究了基于经度、纬度和海拔高度的杂交中稻开花期受自然极端高温伤害风险的预测方法。结果表明,杂交中稻齐穗后第5天日序与经度呈显著负相关,与海拔高度呈极显著正相关,与纬度相关性不显著。建立的基于经度(x_(1))和海拔(x_(3))预测水稻齐穗后第5天日序的回归模型,F值为13.25^(**)~13.56^(**),决定程度高达0.8688~0.8715。该模型经多个品种连续2年在6个生态点的验证,实测值与预测值1∶1回归模型的决定系数高达0.8362~0.8641,实测值与预测值之间的均方根差(RMSE)值为0.83%~1.18%,预测值与实测值之间具有较好的一致性。将本研究建立的齐穗期与地理位置关系模型与作者等先期建立的基于地理位置(纬度:x_(2)、海拔:x_(3))预测≥35℃最早发生期预测模型相结合,探明了不同地理位置杂交中稻开花期受极端高温伤害的机率。利用地理位置信息可准确预测杂交中稻开花期受极端高温伤害的风险程度,具有较好的生产适用性。 Accurately predicting the probability of encountering extreme high temperature during flowering period of hybrid mid-season rice in different regions, which conducive to the formulate of high-yield and stable-yield production technology for local rice production. In 2018 and 2019, using 22 new mid-season hybrid rice varieties promoted in Sichuan province as materials, the ecological adaptability test of varieties was carried out in different ecological sites in southeast of Sichuan Basin, and the prediction method of the risk of natural high temperature damage to the hybrid mid-season rice during flowering period based on longitude, latitude and altitude was studied. The result showed that:(1)the sequence of the 5 th day after full heading was negatively correlated with longitude while positively correlated with altitude, yet not significantly correlated with latitude. A regression model based on longitude(x1)and elevation(x3)was established to predict the sequence of rice on the 5 th day after full heading, in which the F value was 13.25**~13.56**, and the coefficient of determination is 0.8688~0.8715.(2) The model was validated on several varieties at 6 ecological sites for two consecutive years. The determination coefficient of 1:1 regression model between measured value and predicted value was0.8362~0.8641, and the RMSE between the measured value and the predicted value was 0.83%~1.18%. There is a good performance in consistency between the predicted value and the measured value.(3)Combining the relational model between full heading date and geographical position established in this study with the previous model based on geographical position(latitude: x2, elevation: x3)to predict the earliest occurrence of ≥35℃ established by the author, etc., the research has studied the rate of damage by extreme high temperature in flowering stage of mid-season hybrid rice in different geographical locations. Information of geographical location is useful to achieve accurate prediction of the risk of extreme high temperature injury at flowering stage of mid-season hybrid rice, which has high applicability in production.
作者 徐富贤 袁驰 王学春 韩冬 廖爽 张志勇 陈琨 曾世清 谢戎 周兴兵 曾正明 张林 杨波 蒋鹏 XU Fuxian;YUAN Chi;WANG Xuechun;HAN Dong;LIAO Shuang;ZHANG Zhiyong;CHEN Kun;ZENG Shiqing;XIE Rong;ZHOU Xingbing;ZENG Zhengming;ZHANG Lin;YANG Bo;JIANG Peng(Rice and Sorghum Research Institute,Sichuan Academy of Agricultural Sciences/Key Laboratory of Southwest Rice Biology and Genetic Breeding,Ministry of Agriculture and Rural Affairs/Crop Ecophysiology and Cultivation Key Laboratory of Sichuan Province,Deyang,Sichuan 618000,China;Neijiang Academy of Agricultural Sciences,Neijiang,Sichuan 641000,China;Southwest University of Science and Technology,Mianyang,Sichuan 621010,China;Yibin Academy of Agricultural Sciences,Yibin,Sichuan 644000,China;Mianyang Academy of Agricultural Sciences,Mianyang,Sichuan 621023,China;Institute of Biological and Nuclear Technology,Sichuan Academy of Agricultural Sciences,Chengdu 610066,China;Soil and Fertilizer Research Institute,Sichuan Academy of Agricultural Sciences,Chengdu 610066,China;Fushun County Agricultural Technology Station,Fushun,Sichuan 643200,China)
出处 《中国稻米》 北大核心 2021年第3期83-88,共6页 China Rice
基金 国家现代农业产业技术体系(CARS-01-25) 国家粮食丰产科技工程(2018YFD0301200)。
关键词 四川盆地 杂交中稻 开花期 高温伤害 风险预测 Sichuan Basin mid-season hybrid rice flowering stage high temperature damage risk prediction
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