籽粒蛋白质积累过程的准确模拟对黄土丘陵区旱地小麦优质生产的有效调控有重要意义。利用甘肃省定西市安定区凤翔镇安家沟村2016-2017年大田试验数据及定西市安定区1971-2017年气象资料,建立基于APSIM(agricultural production systems ...籽粒蛋白质积累过程的准确模拟对黄土丘陵区旱地小麦优质生产的有效调控有重要意义。利用甘肃省定西市安定区凤翔镇安家沟村2016-2017年大田试验数据及定西市安定区1971-2017年气象资料,建立基于APSIM(agricultural production systems simulator)的旱地小麦籽粒蛋白质含量模型,采用相关性分析方法检验,并定量分析了耕作方式(传统耕作、传统耕作+秸秆覆盖、免耕及免耕+秸秆覆盖)和播期(正常播期、早播、晚播)对小麦籽粒蛋白质含量的影响。结果表明:3个播期处理和4种耕作方式下,产量和籽粒蛋白质含量模拟值和观测值之间的均方根误差(RMSE)分别为66.4~121.9 kg·hm-2和0.2%~1.1%;归一化均方根误差(NRMSE)分别为1.23%~9.66%和1.31%~9.94%,模型模拟精度较高。播期对旱地小麦籽粒蛋白质含量的影响显著,正常播期的蛋白质含量最高,晚播明显降低了蛋白质含量。4种耕作方式的小麦产量与籽粒蛋白质含量均呈开口向下的二次曲线关系,随着蛋白质含量的升高,产量呈先增加后减少的态势,经过秸秆覆盖的耕作方式(传统耕作+秸秆覆盖和免耕+秸秆覆盖)比不覆盖的耕作方式(传统耕作和免耕)更利于小麦籽粒蛋白质含量的提高。展开更多
Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders.Nevertheless,there exists inherent uncertainty in such cases.General Circulation Models(GCMs)...Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders.Nevertheless,there exists inherent uncertainty in such cases.General Circulation Models(GCMs)and future climate change scenarios(different Representative Concentration Pathways(RCPs)in different future time periods)are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield.This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments(Shiraz,Hamedan,Sanandaj,Kermanshah and Khorramabad)in eastern and southern Iran.These five representative locations can be categorized into three climate classes:arid cold(Shiraz),semi-arid cold(Hamedan and Sanandaj)and semi-arid cool(Kermanshah and Khorramabad).Accordingly,the downscaled daily outputs of 29 GCMs under two RCPs(RCP4.5 and RCP8.5)in the near future(2030s),middle future(2050s)and far future(2080s)were used as inputs for the Agricultural Production Systems sIMulator(APSIM)-wheat model.Analysis of variance(ANOVA)was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield.Years from 1980 to 2009 were regarded as the baseline period.The projection results indicated that wheat grain yield was expected to increase by 12.30%,17.10%,and 17.70%in the near future(2030s),middle future(2050s)and far future(2080s),respectively.The increases differed under different RCPs in different future time periods,ranging from 11.70%(under RCP4.5 in the 2030s)to 20.20%(under RCP8.5 in the 2080s)by averaging all GCMs and locations,implying that future wheat grain yield depended largely upon the rising CO2 concentrations.ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations,followed by scenarios,GCMs,and their interactions.Specifically,at the semi-arid climate locations(Hamedan,Sanandaj,Kermanshah and Khorramabad),most of the variations arose from the scenarios(77.25%),while at the arid climate location(Shiraz),GCMs(54.00%)accounted for the greatest variation.Overall,the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions,particularly in dryland environments characterized by large fluctuations in rainfall and temperature.Moreover,the current research suggested some GCMs(e.g.,the IPSL-CM5B-LR,CCSM4,and BNU-ESM)that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.展开更多
以APSIM甘蔗模型(APSIM-Sugar)为例,简要介绍了由澳大利亚农业生产系统研究协作组(APSRU)开发的农业生产系统模拟模型(Agricultural Production System si Mulator,APSIM)中作物、土壤等核心模块的基本过程原理、参数确定、模型验证等...以APSIM甘蔗模型(APSIM-Sugar)为例,简要介绍了由澳大利亚农业生产系统研究协作组(APSRU)开发的农业生产系统模拟模型(Agricultural Production System si Mulator,APSIM)中作物、土壤等核心模块的基本过程原理、参数确定、模型验证等模块化设计与应用研究进展,为学习了解和使用APSIM模型开展农田土壤-作物系统模拟研究提供参考。展开更多
以采集的北京市海淀区东北旺试验站大量的田间实测数据为基础,对农业生产系统模型(Agricultural Production Systems Simulator,APSIM)进行详细的参数灵敏度分析与标定,结果显示:标定模拟的土壤剖面体积含水量动态的精度较高,平均相对...以采集的北京市海淀区东北旺试验站大量的田间实测数据为基础,对农业生产系统模型(Agricultural Production Systems Simulator,APSIM)进行详细的参数灵敏度分析与标定,结果显示:标定模拟的土壤剖面体积含水量动态的精度较高,平均相对误差为13.41%;标定模拟的土壤剖面硝态氮浓度动态的精度较低,平均的相对误差为29.69%;标定模拟的冬小麦叶面积指数、生物量和产量的平均相对误差分别为36.49%、24.18%和32.31%;标定模拟的夏玉米叶面积指数、生物量和产量的平均相对误差分别为31.76%、35.84%和26.44%。以上标定模拟的精度就田间实际情况而言,总体是可以接受的。经过详细的参数标定后的APSIM模型将为深入探讨不同农田管理措施下水氮利用效率的长期变化提供可靠的定量分析手段。展开更多
文摘籽粒蛋白质积累过程的准确模拟对黄土丘陵区旱地小麦优质生产的有效调控有重要意义。利用甘肃省定西市安定区凤翔镇安家沟村2016-2017年大田试验数据及定西市安定区1971-2017年气象资料,建立基于APSIM(agricultural production systems simulator)的旱地小麦籽粒蛋白质含量模型,采用相关性分析方法检验,并定量分析了耕作方式(传统耕作、传统耕作+秸秆覆盖、免耕及免耕+秸秆覆盖)和播期(正常播期、早播、晚播)对小麦籽粒蛋白质含量的影响。结果表明:3个播期处理和4种耕作方式下,产量和籽粒蛋白质含量模拟值和观测值之间的均方根误差(RMSE)分别为66.4~121.9 kg·hm-2和0.2%~1.1%;归一化均方根误差(NRMSE)分别为1.23%~9.66%和1.31%~9.94%,模型模拟精度较高。播期对旱地小麦籽粒蛋白质含量的影响显著,正常播期的蛋白质含量最高,晚播明显降低了蛋白质含量。4种耕作方式的小麦产量与籽粒蛋白质含量均呈开口向下的二次曲线关系,随着蛋白质含量的升高,产量呈先增加后减少的态势,经过秸秆覆盖的耕作方式(传统耕作+秸秆覆盖和免耕+秸秆覆盖)比不覆盖的耕作方式(传统耕作和免耕)更利于小麦籽粒蛋白质含量的提高。
基金funded by the Deputy of Research Affairs, Lorestan University, Iran (Contract No. 1400-6-02-518-1402)
文摘Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders.Nevertheless,there exists inherent uncertainty in such cases.General Circulation Models(GCMs)and future climate change scenarios(different Representative Concentration Pathways(RCPs)in different future time periods)are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield.This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments(Shiraz,Hamedan,Sanandaj,Kermanshah and Khorramabad)in eastern and southern Iran.These five representative locations can be categorized into three climate classes:arid cold(Shiraz),semi-arid cold(Hamedan and Sanandaj)and semi-arid cool(Kermanshah and Khorramabad).Accordingly,the downscaled daily outputs of 29 GCMs under two RCPs(RCP4.5 and RCP8.5)in the near future(2030s),middle future(2050s)and far future(2080s)were used as inputs for the Agricultural Production Systems sIMulator(APSIM)-wheat model.Analysis of variance(ANOVA)was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield.Years from 1980 to 2009 were regarded as the baseline period.The projection results indicated that wheat grain yield was expected to increase by 12.30%,17.10%,and 17.70%in the near future(2030s),middle future(2050s)and far future(2080s),respectively.The increases differed under different RCPs in different future time periods,ranging from 11.70%(under RCP4.5 in the 2030s)to 20.20%(under RCP8.5 in the 2080s)by averaging all GCMs and locations,implying that future wheat grain yield depended largely upon the rising CO2 concentrations.ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations,followed by scenarios,GCMs,and their interactions.Specifically,at the semi-arid climate locations(Hamedan,Sanandaj,Kermanshah and Khorramabad),most of the variations arose from the scenarios(77.25%),while at the arid climate location(Shiraz),GCMs(54.00%)accounted for the greatest variation.Overall,the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions,particularly in dryland environments characterized by large fluctuations in rainfall and temperature.Moreover,the current research suggested some GCMs(e.g.,the IPSL-CM5B-LR,CCSM4,and BNU-ESM)that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.
文摘以APSIM甘蔗模型(APSIM-Sugar)为例,简要介绍了由澳大利亚农业生产系统研究协作组(APSRU)开发的农业生产系统模拟模型(Agricultural Production System si Mulator,APSIM)中作物、土壤等核心模块的基本过程原理、参数确定、模型验证等模块化设计与应用研究进展,为学习了解和使用APSIM模型开展农田土壤-作物系统模拟研究提供参考。
文摘以采集的北京市海淀区东北旺试验站大量的田间实测数据为基础,对农业生产系统模型(Agricultural Production Systems Simulator,APSIM)进行详细的参数灵敏度分析与标定,结果显示:标定模拟的土壤剖面体积含水量动态的精度较高,平均相对误差为13.41%;标定模拟的土壤剖面硝态氮浓度动态的精度较低,平均的相对误差为29.69%;标定模拟的冬小麦叶面积指数、生物量和产量的平均相对误差分别为36.49%、24.18%和32.31%;标定模拟的夏玉米叶面积指数、生物量和产量的平均相对误差分别为31.76%、35.84%和26.44%。以上标定模拟的精度就田间实际情况而言,总体是可以接受的。经过详细的参数标定后的APSIM模型将为深入探讨不同农田管理措施下水氮利用效率的长期变化提供可靠的定量分析手段。
