不同耕作方式下气候条件对小麦条锈病的影响——基于面板数据极大似然分位回归模型的分析
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摘要
【目的】明确不同耕作方式和不同生育阶段气候条件对小麦条锈病的影响。【方法】根据成都平原2007—2016年3种耕作方式(周年旋耕无秸秆还田、麦免稻旋和麦稻双免)下小麦条锈病发病率的面板数据,利用面板数据固定效应模型和极大似然分位回归模型,分析在不同耕作方式下不同生育阶段气候条件对条锈病的影响关系。【结果】固定效应结果表明:周年旋耕无秸秆还田对条锈病发病率的正面影响略低于麦免稻旋和麦稻双免。分位回归动态表明:在幼苗期,日平均温度越高,雨日数、雾日数越多,越容易促使病菌孢子的萌发;在分蘖拔节期,日平均气温和平均湿度的正面影响程度是不同的,随着温度和湿度的不断增大,对条锈病扩散的促进作用逐渐增强;在孕穗抽穗期,日平均气温越高,越有利于条锈病垂直扩展,越是湿润的环境越有利于条锈病发病,造成反复侵染。【结论】3种耕作方式对小麦条锈病的影响不显著,日平均气温和平均湿度对小麦条锈病的影响非常显著。
【Objective】 The effect of different tillage methods and different growth stages of climatic conditions on wheat stripe rust.【Method】Analysis the influence of climatic conditions on the stripe rust under different farming methods at different stages of birth,using fixed effect panel data model and the maximum likelihood quantile regression model,according to panel data on the incidence of wheat stripe rust under different farming methods(CK、WZRR、WZRZ) from 2007 to 2016 in Chengdu plain.【Result】The results of fixation effect showed that the positive effect of CK on the incidence of stripe rust was slightly lower than WZRR and WZRZ. The results of quantile regression showed that the higher the daily average temperature,the more rain days,fog days,the easier it is to promote the germination of spores in the seedling stage. At tillering jointing stage,the positive effects of daily mean temperature and mean humidity were different. With the increasing of temperature and humidity,the effect of promoting the spread of stripe rust is increasing gradually. At heading booting stage,the higher the daily average temperature,the more conducive to the vertical expansion of stripe rust,the more humid environment is more conducive to the occurrence of stripe rust,causing repeated infection.【Conclusion】The effects of the three tillage on wheat stripe rust were not significant,daily average temperature and average humidity were significantly.
【Objective】 The effect of different tillage methods and different growth stages of climatic conditions on wheat stripe rust.【Method】Analysis the influence of climatic conditions on the stripe rust under different farming methods at different stages of birth,using fixed effect panel data model and the maximum likelihood quantile regression model,according to panel data on the incidence of wheat stripe rust under different farming methods(CK、WZRR、WZRZ) from 2007 to 2016 in Chengdu plain.【Result】The results of fixation effect showed that the positive effect of CK on the incidence of stripe rust was slightly lower than WZRR and WZRZ. The results of quantile regression showed that the higher the daily average temperature,the more rain days,fog days,the easier it is to promote the germination of spores in the seedling stage. At tillering jointing stage,the positive effects of daily mean temperature and mean humidity were different. With the increasing of temperature and humidity,the effect of promoting the spread of stripe rust is increasing gradually. At heading booting stage,the higher the daily average temperature,the more conducive to the vertical expansion of stripe rust,the more humid environment is more conducive to the occurrence of stripe rust,causing repeated infection.【Conclusion】The effects of the three tillage on wheat stripe rust were not significant,daily average temperature and average humidity were significantly.
引文
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[11]景小平,唐卷,陆见光,等.不同耕作方式下气候条件对冬小麦籽粒产量的影响:基于面板分位数回归模型的分析[J].西南农业学报,2017,30(1):53-57.
[12]YEH C,WANG K,SUEN Y.A quantile framework for analysing the links between inflation uncertainty and inflation dynamics across countries[J].Applied Economics,2011,43(20):2593-2602.
[2]肖志强,尚学军,李宗明,等.陇南山区小麦条锈病流行对气候偏暖的响应及对策研究[J].气象科学,2007,27(1):140-146.
[3]张旭东,尹东,万信,等.气象条件对甘肃冬小麦条锈病流行的影响研究[J].中国农业气象,2003,24(4):26-28.
[4]刘伟昌,陈怀亮,张雪芬,等.河南省小麦条锈病发生面积与降水量的关系研究[J].气象与环境科学,2007,30(3):24-27.
[5]王金来.气象因素对小麦条锈病发生规律的影响及综防对策[J].安徽农学通报,2010,16(18):118-119.
[6]TSIONAS E.Bayesian quantile inference[J].Journal of Statistics Computation and Simulation,2003,73(9):659-674.
[7]YU K,VAN-KERM P,ZHANG J.Bayesian quantile regression:an application to the wage distribution in 1990s[J].Britain Sankhya,2005,67(2):359-377.
[8]田茂再.高等分层分位回归建模理论[M].北京:科学出版社,2015:193-207.
[9]刘焕鹏,严太华.面板数据分位数回归模型研究综述[J].统计与决策,2014(17):82-84.
[10]曾惠芳,朱慧明,李素芳,等.基于MH算法的极大似然分位自回归模型[J].湖南大学学报(自然科学版),2010,37(2):88-92.
[11]景小平,唐卷,陆见光,等.不同耕作方式下气候条件对冬小麦籽粒产量的影响:基于面板分位数回归模型的分析[J].西南农业学报,2017,30(1):53-57.
[12]YEH C,WANG K,SUEN Y.A quantile framework for analysing the links between inflation uncertainty and inflation dynamics across countries[J].Applied Economics,2011,43(20):2593-2602.