为有效识别基于APSIM模型籽粒生长参数中春小麦产量敏感性参数,快速并准确的估算当地模型参数。使用甘肃省定西市安定区凤翔镇安家沟村1971—2018年的气象数据和2000—2018年旱地春小麦大田试验数据,并利用EFAST方法对进行了5个增温梯...为有效识别基于APSIM模型籽粒生长参数中春小麦产量敏感性参数,快速并准确的估算当地模型参数。使用甘肃省定西市安定区凤翔镇安家沟村1971—2018年的气象数据和2000—2018年旱地春小麦大田试验数据,并利用EFAST方法对进行了5个增温梯度(0℃、0.5℃、1.0℃、1.5℃和2.0℃)下32个模型参数进行敏感性分析。粒子群算法对各个增温条件下均敏感的参数进行优化验证。结果表明:不同温度变化梯度下,对旱地春小麦产量影响最大的籽粒生长模型参数有9个,分别为消光系数、每克茎籽粒数量、穗粒数、单株最大籽粒质量、灌浆到成熟积温、出苗到拔节积温、株高、最大比叶面积和光合叶片老化的水分胁迫斜率。并且对产量敏感性强度有着显著的差异,其中消光系数和每克茎籽粒数量是对春小麦产量影响最大的参数,其他参数在不同温度下对春小麦产量的敏感性顺序存在差异。利用粒子群算法针对这9个参数进行优化,相较于优化前,优化后的春小麦产量、开花期和灌浆期籽粒干物质的均方根误差、归一化均方根误差和模型有效性指数均得到了显著改善,参数优化后开花期、灌浆期、成熟期产量的均方根误差平均值分别由13.50 kg hm-2减小到5.99 kg hm-2、183.17 kg hm-2减小到69.44 kg hm-2、141.69 kg hm-2减小到48.51 kg hm-2,归一化均方根误差平均值分别由4.94%减小到2.19%、10.92%减小到4.65%、8.39%减小到2.87%,模型有效性指数平均值分别由0.894提高到0.979、0.893提高到0.981、0.898提高到0.988。优化后的参数有效地提高了模型的预测精度。此研究为APSIM模型在当地应用和模型参数校准提供了科学依据。展开更多
【目的】开展气候变化对冬小麦生长发育和产量影响研究,为未来气候变化下小麦可持续生产提供依据。【方法】利用2017—2020年度控制气室中不同温度和[CO_(2)]处理下的良星99(晚熟品种)和中科2011(早熟品种)冬小麦生长发育、土壤及气象数...【目的】开展气候变化对冬小麦生长发育和产量影响研究,为未来气候变化下小麦可持续生产提供依据。【方法】利用2017—2020年度控制气室中不同温度和[CO_(2)]处理下的良星99(晚熟品种)和中科2011(早熟品种)冬小麦生长发育、土壤及气象数据,对APSIM(agricultural production systems simulator)模型进行校验,然后,以1986—2005年为基准年份,利用校验好的APSIM模型对未来不同气候条件下(RCP 4.5和RCP 8.5)的冬小麦产量、产量构成和生育期进行模拟,分析气候变化和极端高温对不同熟性冬小麦品种生产潜力的影响。【结果】APSIM模型能较好地模拟不同温度和[CO_(2)]处理下2个品种冬小麦的生育期、产量和生物量,模拟值与实测值的R2均高于0.614,nRMSE均低于10.6%,叶面积指数(LAI)的模拟效果相对较差。从长期模拟结果来看,不同气候条件下,2个品种小麦的播种-拔节天数均较基准年份缩短,且早熟品种小麦播种-拔节缩短的天数小于晚熟品种,2个品种拔节-成熟天数均无明显变化。相较基准年份,未来RCP条件下,2个品种小麦的实际产量和潜在产量均增加,且2100s时段RCP 8.5条件下的产量和潜在产量最高,早熟品种的产量和潜在产量相较晚熟品种增产更明显。与基准年份相比,未来RCP条件下,生育前期2个品种小麦的LAI均升高,但早熟品种LAI变化更明显,生育后期晚熟品种的LAI明显降低,而早熟品种的LAI无明显差异;未来RCP条件下,2个品种的地上部生物量均增加,早熟品种生物量增长更明显。不同RCP条件下,极端高温对2个品种冬小麦产量和千粒重均有一定的负面影响,且开花期极端高温对千粒重的影响最大。与正常年份相比,极端高温年份晚熟品种2100s时段RCP 8.5条件下的千粒重和产量明显降低,籽粒数也略有降低。与正常年份相比,未来RCP条件下,极端高温均明显降低了早熟品种的千粒重,但籽粒数略有升高,因此,极端高温年份早熟品种产量降低不明显。【结论】冬小麦早熟品种更能适应未来气候变化,而选育适宜的小麦品种是应对未来气候变化的有效措施之一。展开更多
Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential ch...Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from ?-24% to -94% depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.展开更多
干旱是影响华北地区冬小麦产量的主要农业气象灾害之一,作物生长模型是评估干旱对作物产量影响主要方法之一,但作物生长模型对极端天气气候条件下(如干旱)作物产量模拟效果仍存在不确定性。为提高作物模型在干旱条件下对作物产量模拟的...干旱是影响华北地区冬小麦产量的主要农业气象灾害之一,作物生长模型是评估干旱对作物产量影响主要方法之一,但作物生长模型对极端天气气候条件下(如干旱)作物产量模拟效果仍存在不确定性。为提高作物模型在干旱条件下对作物产量模拟的精准性,该研究利用调参验证后的农业生产系统模型(agricultural production systems simulator,APSIM),通过查阅与华北地区冬小麦相关的186篇大田试验文献获得1 876对观测数据,以作物水分亏缺指数为干旱指标,评估APSIM模型在冬小麦拔节-开花和开花-成熟阶段干旱对产量影响的模拟效果,提出APSIM在拔节-开花和开花-成熟阶段干旱对小麦产量影响的修正系数。基于历史气候条件、SSP245和SSP585未来气候情景资料,分析了冬小麦拔节-开花和开花-成熟阶段干旱时空分布特征,并采用修正系数校正后的APSIM模型评估华北地区冬小麦拔节-开花和开花-成熟阶段不同等级干旱对其产量的影响。结果表明,APSIM模型低估了拔节-开花阶段干旱对冬小麦产量影响程度,轻旱、中旱和重旱校正系数分别为0.85、0.91和0.85;APSIM模型可准确模拟开花-成熟阶段轻旱和中旱对冬小麦产量影响,但高估了重旱对冬小麦产量影响,重旱校正系数为1.33。历史和未来气候情景下,拔节-开花和开花-成熟阶段干旱导致冬小麦减产率均呈由北到南依次递减的空间分布特征,且开花-成熟阶段干旱对冬小麦负面影响高于拔节-开花阶段。未来气候情景下冬小麦拔节-开花和开花-成熟阶段不同等级干旱导致的冬小麦减产率均低于历史气候条件。未来干旱对华北冬小麦产量的负面影响程度有所缓解。研究为有效评估干旱对冬小麦影响提供方法支撑。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
为明确水氮调控对春小麦(Triticum aestivum)产量的影响,研究自然降水条件下小麦产量达到最佳所需含氮量,将田间试验数据与农业生产系统模拟模型(Agricultural Production System Simulator,APSIM)结合,分别设定降水和施氮梯度为春小麦...为明确水氮调控对春小麦(Triticum aestivum)产量的影响,研究自然降水条件下小麦产量达到最佳所需含氮量,将田间试验数据与农业生产系统模拟模型(Agricultural Production System Simulator,APSIM)结合,分别设定降水和施氮梯度为春小麦的产量变化的影响因素,并对模型予以检验。APSIM模型对春小麦产量模拟精度较高,决定系数(R2)、归一化均方根误差(NRMSE)、模型一致性指标(D)分别为0.95、27.36%和0.91。随降水量或施氮量的增加,整体产量表现出先增后减趋势;其次,在所有组合中降水增加到20%,施氮量为157.5 kg·hm^–2的处理较好,平均产量最高达到5 406.64 kg·hm^–2;最后,根据产量变化曲面拟合方程得出产量最佳方案:当自然降水增加55%,施氮量增加到257.25 kg·ha^–1,产量达到最佳,为5 988 kg·hm^–2。展开更多
文摘为有效识别基于APSIM模型籽粒生长参数中春小麦产量敏感性参数,快速并准确的估算当地模型参数。使用甘肃省定西市安定区凤翔镇安家沟村1971—2018年的气象数据和2000—2018年旱地春小麦大田试验数据,并利用EFAST方法对进行了5个增温梯度(0℃、0.5℃、1.0℃、1.5℃和2.0℃)下32个模型参数进行敏感性分析。粒子群算法对各个增温条件下均敏感的参数进行优化验证。结果表明:不同温度变化梯度下,对旱地春小麦产量影响最大的籽粒生长模型参数有9个,分别为消光系数、每克茎籽粒数量、穗粒数、单株最大籽粒质量、灌浆到成熟积温、出苗到拔节积温、株高、最大比叶面积和光合叶片老化的水分胁迫斜率。并且对产量敏感性强度有着显著的差异,其中消光系数和每克茎籽粒数量是对春小麦产量影响最大的参数,其他参数在不同温度下对春小麦产量的敏感性顺序存在差异。利用粒子群算法针对这9个参数进行优化,相较于优化前,优化后的春小麦产量、开花期和灌浆期籽粒干物质的均方根误差、归一化均方根误差和模型有效性指数均得到了显著改善,参数优化后开花期、灌浆期、成熟期产量的均方根误差平均值分别由13.50 kg hm-2减小到5.99 kg hm-2、183.17 kg hm-2减小到69.44 kg hm-2、141.69 kg hm-2减小到48.51 kg hm-2,归一化均方根误差平均值分别由4.94%减小到2.19%、10.92%减小到4.65%、8.39%减小到2.87%,模型有效性指数平均值分别由0.894提高到0.979、0.893提高到0.981、0.898提高到0.988。优化后的参数有效地提高了模型的预测精度。此研究为APSIM模型在当地应用和模型参数校准提供了科学依据。
文摘【目的】开展气候变化对冬小麦生长发育和产量影响研究,为未来气候变化下小麦可持续生产提供依据。【方法】利用2017—2020年度控制气室中不同温度和[CO_(2)]处理下的良星99(晚熟品种)和中科2011(早熟品种)冬小麦生长发育、土壤及气象数据,对APSIM(agricultural production systems simulator)模型进行校验,然后,以1986—2005年为基准年份,利用校验好的APSIM模型对未来不同气候条件下(RCP 4.5和RCP 8.5)的冬小麦产量、产量构成和生育期进行模拟,分析气候变化和极端高温对不同熟性冬小麦品种生产潜力的影响。【结果】APSIM模型能较好地模拟不同温度和[CO_(2)]处理下2个品种冬小麦的生育期、产量和生物量,模拟值与实测值的R2均高于0.614,nRMSE均低于10.6%,叶面积指数(LAI)的模拟效果相对较差。从长期模拟结果来看,不同气候条件下,2个品种小麦的播种-拔节天数均较基准年份缩短,且早熟品种小麦播种-拔节缩短的天数小于晚熟品种,2个品种拔节-成熟天数均无明显变化。相较基准年份,未来RCP条件下,2个品种小麦的实际产量和潜在产量均增加,且2100s时段RCP 8.5条件下的产量和潜在产量最高,早熟品种的产量和潜在产量相较晚熟品种增产更明显。与基准年份相比,未来RCP条件下,生育前期2个品种小麦的LAI均升高,但早熟品种LAI变化更明显,生育后期晚熟品种的LAI明显降低,而早熟品种的LAI无明显差异;未来RCP条件下,2个品种的地上部生物量均增加,早熟品种生物量增长更明显。不同RCP条件下,极端高温对2个品种冬小麦产量和千粒重均有一定的负面影响,且开花期极端高温对千粒重的影响最大。与正常年份相比,极端高温年份晚熟品种2100s时段RCP 8.5条件下的千粒重和产量明显降低,籽粒数也略有降低。与正常年份相比,未来RCP条件下,极端高温均明显降低了早熟品种的千粒重,但籽粒数略有升高,因此,极端高温年份早熟品种产量降低不明显。【结论】冬小麦早熟品种更能适应未来气候变化,而选育适宜的小麦品种是应对未来气候变化的有效措施之一。
文摘Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from ?-24% to -94% depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.
文摘干旱是影响华北地区冬小麦产量的主要农业气象灾害之一,作物生长模型是评估干旱对作物产量影响主要方法之一,但作物生长模型对极端天气气候条件下(如干旱)作物产量模拟效果仍存在不确定性。为提高作物模型在干旱条件下对作物产量模拟的精准性,该研究利用调参验证后的农业生产系统模型(agricultural production systems simulator,APSIM),通过查阅与华北地区冬小麦相关的186篇大田试验文献获得1 876对观测数据,以作物水分亏缺指数为干旱指标,评估APSIM模型在冬小麦拔节-开花和开花-成熟阶段干旱对产量影响的模拟效果,提出APSIM在拔节-开花和开花-成熟阶段干旱对小麦产量影响的修正系数。基于历史气候条件、SSP245和SSP585未来气候情景资料,分析了冬小麦拔节-开花和开花-成熟阶段干旱时空分布特征,并采用修正系数校正后的APSIM模型评估华北地区冬小麦拔节-开花和开花-成熟阶段不同等级干旱对其产量的影响。结果表明,APSIM模型低估了拔节-开花阶段干旱对冬小麦产量影响程度,轻旱、中旱和重旱校正系数分别为0.85、0.91和0.85;APSIM模型可准确模拟开花-成熟阶段轻旱和中旱对冬小麦产量影响,但高估了重旱对冬小麦产量影响,重旱校正系数为1.33。历史和未来气候情景下,拔节-开花和开花-成熟阶段干旱导致冬小麦减产率均呈由北到南依次递减的空间分布特征,且开花-成熟阶段干旱对冬小麦负面影响高于拔节-开花阶段。未来气候情景下冬小麦拔节-开花和开花-成熟阶段不同等级干旱导致的冬小麦减产率均低于历史气候条件。未来干旱对华北冬小麦产量的负面影响程度有所缓解。研究为有效评估干旱对冬小麦影响提供方法支撑。
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.
