The authors constructed a simplified model of spring wheat (Triticum aestivum L.) carbon assimilation and dry matter accumulation (DMA) process which consisted of two independent variables, day length (L) and total...The authors constructed a simplified model of spring wheat (Triticum aestivum L.) carbon assimilation and dry matter accumulation (DMA) process which consisted of two independent variables, day length (L) and total daily radiation (TDR). Leaf water potential (Ψ) was incorporated into the simplified growth model based on the assumption that both light use efficiency (α) and CO 2 conductance of assimilation (g c) were depressed by water limitation. Finally,Ψ was estimated from a regression equation in which the independent variables were relative soil water content in the upper 80 cm (θ R,80 ), ambient temperature (T a), vapor pressure deficit (VPD), the cumulative leaf water potential below thresholds of -1.5 MPa (Ψ c,1.5 ). Some applications in research program of field experiment of atmosphere_land surface processes in Heihe River region were tested. The simulated data agreed well with the data observed at Linze oasis in 1989 for various levels of water supply and at Zhangye oasis in 1992 in the field. The analysis and simulation using the model demonstrated that the simplified growth model could describe very well the DMA process of spring wheat with and without water limitation in the region of HEIFE (Heihe field experiment).展开更多
The yellowed-leaf rate is one of the important variables in simulation models for thegrowth of spring wheat. Based on the field experiments (1985-1988), the evolution of yellowed-leafrate of spring wheat is analyzed. ...The yellowed-leaf rate is one of the important variables in simulation models for thegrowth of spring wheat. Based on the field experiments (1985-1988), the evolution of yellowed-leafrate of spring wheat is analyzed. The functional relationship between the yellowing process of greenleaves and the development stages of spring wheat is established. Based on modelling and correctingfor the yellowing proass of green leaves affected by temperature and moisture, the synthetic modelfor simulating the dynaniical evolution of yellowed-leaf rate is constructed. The numerical experi-inents show that the result of the modelling is satisfactory.展开更多
In the model developed in this paper, taking the characters and requirements of meteorological services into account, some conventional meteorological observations which are easy to be obtained have been ch.osen, and ...In the model developed in this paper, taking the characters and requirements of meteorological services into account, some conventional meteorological observations which are easy to be obtained have been ch.osen, and mathematical equations describing micro-growth processes of crops have been established on the basis of the field experiments, laboratorial analysis and computer's modelling tests with time interval of ten-days for several years (1987-1989), in accordance with the known biological and physical rules and corresponding reference literatures. It is a preliminary simplified simulation model of spring wheat growth in optimal water and nutrient conditions. The field experiments show that simulation results of this simplified model are satisfactory. The potential operational application and theoretical sense are significant in the meteorological forecast of yield and in the assessment of influences of climatic change on agriculture.展开更多
Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production.The agricultural sector plays a significant role in the economy of Upper Midwestern states in th...Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production.The agricultural sector plays a significant role in the economy of Upper Midwestern states in the USA,especially that of North Dakota(ND).Spring wheat contributes most of the wheat production in ND,which is a major producer of wheat in the USA.This study focuses on assessing possible impacts of three climate variables on spring wheat yield in ND by building a regression model.Eighty-five years of field data were collected and the trend of average minimum temperature along with average maximum temperature,average precipitation,and spring wheat yield was analyzed using Mann-Kendall test.The study area was divided into 9 divisions based on physical locations.The minimum temperature plays an important role in the region as it impacts the physiological development of the crops.Increasing trend was noticed for 6 divisions for average minimum temperature and average precipitation during growing season.Northeast and Southeast division showed the strongest increasing trend for average minimum temperature and average precipitation,respectively.East-central division had the most decreasing trend for average maximum temperature.A significant relationship was established between spring wheat yield and climatic parameters as the p-value is lower than 0.05 level which rejects the null hypothesis.The regression model was tested for forecasting accuracy.The percentage deviation of error for the model is approximately±30%in most of the years.展开更多
Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for sce...Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for scenario analysis of cropping system models has been increasing. The capability of CropSyst, a cropping system simulation model, to simulate spring wheat growth of a widely grown spring cultivar, 'Longmai 19', in the black soil zone in northeast China under different water and nitrogen regimes was evaluated. Field data collected from a rotation experiment of three growing seasons (1992-1994) were used to calibrate and validate the model. The model was run for 3 years by providing initial conditions at the beginning of the rotation without reinitializing the model in later years in the rotation sequence. Crop input parameters were set based on measured data or taken from CropSyst manual. A few cultivar-specific parameters were adjusted within a reasonable range of fluctuation. The results demonstrated the robustness of CropSyst for simulating evapotranspiration, aboveground biomass, and grain yield of 'Longmai 19' spring wheat with the root mean square errors being 7%, 13% and 13% of the observed means for evapotranspiration (ET), grain yield and aboveground biomass, respectively. Although CropSyst was able to simulate spring production reasonably well, further evaluation and improvement of the model with a more detailed field database was desirable for agricultural systems in northeast China.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
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
基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensembl...基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES),构建了适合旱区春小麦的SWAP-IES同化模拟系统,并利用2019—2020年田间观测试验数据,评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明,相较于无同化情景,在吸收6次土壤水分观测数据后,模型对土壤水分模拟的R^(2)从0.48提升到0.87。同化LAI时,各水分胁迫处理下LAI的模拟精度均最高,R^(2)从无同化的0.35~0.62提升到0.76~0.96。同化LAI+SW时,各处理对生物量模拟的精度均最高,R^(2)从无同化的0.40~0.67提升到0.73~0.96。轻度水分胁迫处理(T4~T5)下,仅同化LAI即可达到较好的估产效果,相对误差为4.05%~9.17%,而在中度或重度水分胁迫处理(T1~T3)下,准确的产量估算需同时吸收LAI和SW,相对误差为3.87%~8.38%。开花期和拔节期的观测数据对提高SWAP-IES系统估产精度的作用最大,同时吸收开花期和拔节期LAI+SW观测数据时估产的R^(2)可从无同化的0.45提高到0.79。说明所构建的SWAP-IES同化模拟系统,在融入开花期和拔节期等关键生育期的观测数据后能有效模拟不同水分处理下春小麦生长和产量形成过程,可为田块尺度下旱区春小麦精准监测提供技术参考。展开更多
文摘The authors constructed a simplified model of spring wheat (Triticum aestivum L.) carbon assimilation and dry matter accumulation (DMA) process which consisted of two independent variables, day length (L) and total daily radiation (TDR). Leaf water potential (Ψ) was incorporated into the simplified growth model based on the assumption that both light use efficiency (α) and CO 2 conductance of assimilation (g c) were depressed by water limitation. Finally,Ψ was estimated from a regression equation in which the independent variables were relative soil water content in the upper 80 cm (θ R,80 ), ambient temperature (T a), vapor pressure deficit (VPD), the cumulative leaf water potential below thresholds of -1.5 MPa (Ψ c,1.5 ). Some applications in research program of field experiment of atmosphere_land surface processes in Heihe River region were tested. The simulated data agreed well with the data observed at Linze oasis in 1989 for various levels of water supply and at Zhangye oasis in 1992 in the field. The analysis and simulation using the model demonstrated that the simplified growth model could describe very well the DMA process of spring wheat with and without water limitation in the region of HEIFE (Heihe field experiment).
文摘The yellowed-leaf rate is one of the important variables in simulation models for thegrowth of spring wheat. Based on the field experiments (1985-1988), the evolution of yellowed-leafrate of spring wheat is analyzed. The functional relationship between the yellowing process of greenleaves and the development stages of spring wheat is established. Based on modelling and correctingfor the yellowing proass of green leaves affected by temperature and moisture, the synthetic modelfor simulating the dynaniical evolution of yellowed-leaf rate is constructed. The numerical experi-inents show that the result of the modelling is satisfactory.
文摘In the model developed in this paper, taking the characters and requirements of meteorological services into account, some conventional meteorological observations which are easy to be obtained have been ch.osen, and mathematical equations describing micro-growth processes of crops have been established on the basis of the field experiments, laboratorial analysis and computer's modelling tests with time interval of ten-days for several years (1987-1989), in accordance with the known biological and physical rules and corresponding reference literatures. It is a preliminary simplified simulation model of spring wheat growth in optimal water and nutrient conditions. The field experiments show that simulation results of this simplified model are satisfactory. The potential operational application and theoretical sense are significant in the meteorological forecast of yield and in the assessment of influences of climatic change on agriculture.
文摘Understanding the impacts of climate change in agriculture is important to ensure optimal and continuous crop production.The agricultural sector plays a significant role in the economy of Upper Midwestern states in the USA,especially that of North Dakota(ND).Spring wheat contributes most of the wheat production in ND,which is a major producer of wheat in the USA.This study focuses on assessing possible impacts of three climate variables on spring wheat yield in ND by building a regression model.Eighty-five years of field data were collected and the trend of average minimum temperature along with average maximum temperature,average precipitation,and spring wheat yield was analyzed using Mann-Kendall test.The study area was divided into 9 divisions based on physical locations.The minimum temperature plays an important role in the region as it impacts the physiological development of the crops.Increasing trend was noticed for 6 divisions for average minimum temperature and average precipitation during growing season.Northeast and Southeast division showed the strongest increasing trend for average minimum temperature and average precipitation,respectively.East-central division had the most decreasing trend for average maximum temperature.A significant relationship was established between spring wheat yield and climatic parameters as the p-value is lower than 0.05 level which rejects the null hypothesis.The regression model was tested for forecasting accuracy.The percentage deviation of error for the model is approximately±30%in most of the years.
基金Project supported by the National Natural Science Foundation of China (No. 40401003)the Knowledge Innovation Program of the Chinese Academy of Sciences (No. KZCX3-SW-356)the Key Laboratory of Ecological Restoration and Ecosystem Management of Jilin Province (No. DS2004-03)
文摘Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for scenario analysis of cropping system models has been increasing. The capability of CropSyst, a cropping system simulation model, to simulate spring wheat growth of a widely grown spring cultivar, 'Longmai 19', in the black soil zone in northeast China under different water and nitrogen regimes was evaluated. Field data collected from a rotation experiment of three growing seasons (1992-1994) were used to calibrate and validate the model. The model was run for 3 years by providing initial conditions at the beginning of the rotation without reinitializing the model in later years in the rotation sequence. Crop input parameters were set based on measured data or taken from CropSyst manual. A few cultivar-specific parameters were adjusted within a reasonable range of fluctuation. The results demonstrated the robustness of CropSyst for simulating evapotranspiration, aboveground biomass, and grain yield of 'Longmai 19' spring wheat with the root mean square errors being 7%, 13% and 13% of the observed means for evapotranspiration (ET), grain yield and aboveground biomass, respectively. Although CropSyst was able to simulate spring production reasonably well, further evaluation and improvement of the model with a more detailed field database was desirable for agricultural systems in northeast China.
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。
文摘基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES),构建了适合旱区春小麦的SWAP-IES同化模拟系统,并利用2019—2020年田间观测试验数据,评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明,相较于无同化情景,在吸收6次土壤水分观测数据后,模型对土壤水分模拟的R^(2)从0.48提升到0.87。同化LAI时,各水分胁迫处理下LAI的模拟精度均最高,R^(2)从无同化的0.35~0.62提升到0.76~0.96。同化LAI+SW时,各处理对生物量模拟的精度均最高,R^(2)从无同化的0.40~0.67提升到0.73~0.96。轻度水分胁迫处理(T4~T5)下,仅同化LAI即可达到较好的估产效果,相对误差为4.05%~9.17%,而在中度或重度水分胁迫处理(T1~T3)下,准确的产量估算需同时吸收LAI和SW,相对误差为3.87%~8.38%。开花期和拔节期的观测数据对提高SWAP-IES系统估产精度的作用最大,同时吸收开花期和拔节期LAI+SW观测数据时估产的R^(2)可从无同化的0.45提高到0.79。说明所构建的SWAP-IES同化模拟系统,在融入开花期和拔节期等关键生育期的观测数据后能有效模拟不同水分处理下春小麦生长和产量形成过程,可为田块尺度下旱区春小麦精准监测提供技术参考。
