The results of pot and pond trials are as follows.On equal amount of nitrogen(N)applied,the wheat nitrate reduction activity(NRA)and photosynthesis strength and other phy-siological properties are stronger and yields ...The results of pot and pond trials are as follows.On equal amount of nitrogen(N)applied,the wheat nitrate reduction activity(NRA)and photosynthesis strength and other phy-siological properties are stronger and yields are higher at normal soil moisture than those atdrought.At the same soil moisture,in a certain range of N applied,these properties of wheat in-crease with the increasing of N applied,but they descend when the amount of N applied exceedsa certain range.展开更多
Field trials with a set of 108 doubled haploid lines(DHs) derived from a cross between the Chinese winter wheat cvs.CA9613 and H1488 were run at Beijing(China).Phenotypic data were recorded for major agronomic yield t...Field trials with a set of 108 doubled haploid lines(DHs) derived from a cross between the Chinese winter wheat cvs.CA9613 and H1488 were run at Beijing(China).Phenotypic data were recorded for major agronomic yield traits,i.e.grain weight per ear,grain number per ear and thousand grain weight(Tgw) in two field trials at Beijing.Based on the phenotypic data and a genetic map comprising 168 SSR markers,an analysis of quantitative trait loci(QTL) was carried out for yield and yield parameters using the composite interval mapping(CIM) approach.A total of 14 QTL were detected for these traits across two environments.Four of these QTL located on chromosomes 1A and 2B,respectively,exhibited pleiotropic effects.Loci showing pleiotropic effects will be very useful for understanding the homeologous relationships of QTL and designing an appropriate marker-assisted selection programme by multi-trait selection in order to accumulate("pyramide") favorable alleles at different loci.展开更多
作物生长模型是评估作物生产、资源利用及气候变化影响等的有效工具,准确地确定作物模型参数是应用模型的关键。WheatSM(Wheat Growth and Development Simulation Model)模型已在作物生产优化管理上得到一定的应用,并取得较好的效果,...作物生长模型是评估作物生产、资源利用及气候变化影响等的有效工具,准确地确定作物模型参数是应用模型的关键。WheatSM(Wheat Growth and Development Simulation Model)模型已在作物生产优化管理上得到一定的应用,并取得较好的效果,但由于该模型参数较多,模型参数调试复杂。为了快速、准确地确定WheatSM模型参数,简化该模型的调参工作,促进其在农业气象领域中广泛应用,本研究在国内外作物模型参数自动调节方法的基础上,基于PEST(Parameter Estimation)方法构建了WheatSM模型参数的自动调节耦合系统,并对WheatSM模型的发育期和产量参数进行了自动寻优。选择北京上庄作为代表性试验点,以试验点的气象数据、土壤数据和2014~2016年冬小麦不同播期试验数据为基础,应用PEST参数自动优化方法和试错法分别对小麦生长模型WheatSM发育期参数和产量参数进行调试,并将优化结果和试错法的模拟结果进行比较。研究结果表明,基于PEST方法的模型参数调节精准度较高,模拟发育期的误差不大于7天,模拟产量的误差不大于228.63kg·hm^(-2)。同时,与试错法相比,PEST方法具有耗时少、可同时批量处理数据、更高效快捷等优点,使用该自动调参系统可减少参数率定的工作量,节省模型的操作时间,简化工作的复杂度和获得较高的模拟精度。该研究为WheatSM模型参数的自动优化提供一种便捷方法,为提高作物模型参数调试的效率和准确性提供了理论参考和指导。展开更多
文摘The results of pot and pond trials are as follows.On equal amount of nitrogen(N)applied,the wheat nitrate reduction activity(NRA)and photosynthesis strength and other phy-siological properties are stronger and yields are higher at normal soil moisture than those atdrought.At the same soil moisture,in a certain range of N applied,these properties of wheat in-crease with the increasing of N applied,but they descend when the amount of N applied exceedsa certain range.
文摘Field trials with a set of 108 doubled haploid lines(DHs) derived from a cross between the Chinese winter wheat cvs.CA9613 and H1488 were run at Beijing(China).Phenotypic data were recorded for major agronomic yield traits,i.e.grain weight per ear,grain number per ear and thousand grain weight(Tgw) in two field trials at Beijing.Based on the phenotypic data and a genetic map comprising 168 SSR markers,an analysis of quantitative trait loci(QTL) was carried out for yield and yield parameters using the composite interval mapping(CIM) approach.A total of 14 QTL were detected for these traits across two environments.Four of these QTL located on chromosomes 1A and 2B,respectively,exhibited pleiotropic effects.Loci showing pleiotropic effects will be very useful for understanding the homeologous relationships of QTL and designing an appropriate marker-assisted selection programme by multi-trait selection in order to accumulate("pyramide") favorable alleles at different loci.
文摘作物生长模型是评估作物生产、资源利用及气候变化影响等的有效工具,准确地确定作物模型参数是应用模型的关键。WheatSM(Wheat Growth and Development Simulation Model)模型已在作物生产优化管理上得到一定的应用,并取得较好的效果,但由于该模型参数较多,模型参数调试复杂。为了快速、准确地确定WheatSM模型参数,简化该模型的调参工作,促进其在农业气象领域中广泛应用,本研究在国内外作物模型参数自动调节方法的基础上,基于PEST(Parameter Estimation)方法构建了WheatSM模型参数的自动调节耦合系统,并对WheatSM模型的发育期和产量参数进行了自动寻优。选择北京上庄作为代表性试验点,以试验点的气象数据、土壤数据和2014~2016年冬小麦不同播期试验数据为基础,应用PEST参数自动优化方法和试错法分别对小麦生长模型WheatSM发育期参数和产量参数进行调试,并将优化结果和试错法的模拟结果进行比较。研究结果表明,基于PEST方法的模型参数调节精准度较高,模拟发育期的误差不大于7天,模拟产量的误差不大于228.63kg·hm^(-2)。同时,与试错法相比,PEST方法具有耗时少、可同时批量处理数据、更高效快捷等优点,使用该自动调参系统可减少参数率定的工作量,节省模型的操作时间,简化工作的复杂度和获得较高的模拟精度。该研究为WheatSM模型参数的自动优化提供一种便捷方法,为提高作物模型参数调试的效率和准确性提供了理论参考和指导。
