作物模型为人们认识旱区农业生境过程并对其进行调控提供了一种有效的工具。为了探讨小麦生长模拟模型DSSAT-CERES-Wheat能否准确模拟水分胁迫条件下旱区冬小麦的生长发育和产量形成过程,同时确定参数估计和模型验证的最优方案,该研究...作物模型为人们认识旱区农业生境过程并对其进行调控提供了一种有效的工具。为了探讨小麦生长模拟模型DSSAT-CERES-Wheat能否准确模拟水分胁迫条件下旱区冬小麦的生长发育和产量形成过程,同时确定参数估计和模型验证的最优方案,该研究进行了连续两季（2012.10-2013.06和2013.10-2014.06）的冬小麦分段受旱田间试验。试验将冬小麦整个生育期划分为越冬、返青、拔节、抽穗和灌浆5个主要生长阶段,每相邻两个生长阶段连续受旱,形成4个不同的受旱时段水平（D1-D4）,根据小麦生育期的需水量,设置灌水定额分别为40和80 mm 2个水平（I1和I2）,共形成8个处理,每处理3次重复,在遮雨棚内采用裂区试验布置,此外在旁边设置1个各生育期全灌水的对照处理。文中设置了5套不同的参数估计和验证方案,利用DSSAT-GLUE参数估计模块得到不同的参数估计结果。通过对比分析冬小麦物候期、单粒质量、生物量、产量、以及土壤水分含量的模拟值和实测值之间的差异,以确定利用DSSAT-CERES-Wheat模型模拟旱区冬小麦生境过程的精度。结果表明,参数P1V（最适温度条件下通过春化阶段所需天数）和G3（成熟期非水分胁迫下单株茎穂标准干质量）具有较强的变异性,变异系数分别为19.07%和16.34%,受基因型-环境互作的影响较大,而其他参数的变异性则较弱,变异系数均小于10%;DSSAT-GLUE参数估计工具具有较好的收敛性,不同参数估计方案所得的参数值具有一定的一致性;不同的参数估计方案所得的模型输出结果有较大差异,其中参数估计方案1（利用两季试验中的充分灌溉处理CK数据进行参数估计,其他不同阶段受旱处理数据进行验证）的模型校正和验证精度最高,其中模型校正的绝对相对误差（absolute relative error,ARE）和相对均方根误差（relative root mean squared error,RRMSE）分别为4.89%和5.18%。在冬小麦抽穗期和灌浆期受旱时,DSSAT-CERES-Wheat模型可以较好地模拟小麦的生长发育过程以及土壤水分的动态变化,但是在越冬期和返青期受旱时,模拟结果相对较差,并且随着受旱时段提前和受旱程度的加重,模拟精度将变得更低。此外,该模型无法模拟由不同水分胁迫造成的冬小麦物候期差异,需要对模型进行相应的改进。交叉验证表明DSSAT-CERES-Wheat模型模拟该研究中不同水分胁迫条件下冬小麦生长和产量的总体性误差在15%~18%左右。总之,DSSAT-CERES-Wheat模型在模拟旱区冬小麦生境过程时存在着一定的局限性,若要更广泛地将该模型应用在中国干旱半干旱地区的冬小麦生产管理和研究,有必要对冬小麦营养生长阶段前期的水分胁迫响应机制和模拟方法进行进一步的深入研究。展开更多
Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate...Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.展开更多
To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irr...To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency（WUE） responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications（full irrigation） using historical weather data from crop seasons over 33 years（1981–2014）. The data were classified into three types according to seasonal precipitation： 〈100 mm, 100–140 mm, and 〉140 mm. Our results showed that the grain and biomass yield, harvest index（HI） and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage（T3） resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage（T1） improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.展开更多
Accurately estimating the wheat yield potential under climate changes is essential for assessing food production capacity. However, studies based on crop modeling and imperfect management experiment data frequently un...Accurately estimating the wheat yield potential under climate changes is essential for assessing food production capacity. However, studies based on crop modeling and imperfect management experiment data frequently underestimate the wheat yield potential. In this study, we evaluated wheat yield potential based on the CERES-Wheat model and a well-managed 10-year(2008–2017) field study in the North China Plain(NCP), and further identified the critical climate and management yield-limiting factors for improving wheat yield potential and closing the wheat yield gap. Our results revealed that wheat yield potential averaged 10.8 t ha–1in the recent decade. The low growing degree days(GDD) in the pre-winter growing season(592℃ d) and solar radiation in the whole growth season(3,036 MJ m–2) are the most critical climatic factors limiting wheat yield potential in the current production system. Nonetheless, wheat yield potential in the NCP is projected to decline during 2040–2059 by 1.8 and 5.1% under the representative concentration pathway(RCP) 4.5 and RCP8.5 scenarios, respectively, without considering the elevated CO_(2) concentration. However, the positive influence of CO_(2) fertilization will be sufficient to offset these negative impacts from climatic warming and solar dimming, ultimately leading to an enhancement in wheat yield potential during 2040–2059 by 7.5 and 9.8% compared to the baseline under RCP4.5 and RCP8.5, respectively. To improve the wheat yield potential, we recommend selecting an appropriate planting date(5 October) and planting density(400 plants m–2) that align with light and temperature conditions during the wheat growing season. In addition, optimizing the timing and rate of water application(three times, 270 mm) and fertilizer use(based on inseason root zone nitrogen management) is crucial for closing the wheat yield gap. This study underscores the importance of adopting multiple management practices that account for complex climate–crop–soil interconnections to enhance the wheat yield based on a long-term field experiment under the changing climate.展开更多
为给小麦变量施氮提供依据,利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数OSAVI(Optimization of soil-adjusted vegetation index)与CERES-Wheat模型相结合进行变量施肥管理(变量区),以相邻地...为给小麦变量施氮提供依据,利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数OSAVI(Optimization of soil-adjusted vegetation index)与CERES-Wheat模型相结合进行变量施肥管理(变量区),以相邻地块常规非变量(均一)施肥区(对照区)为对照,对变量追氮模型的可行性进行探讨,并对变量追肥处理的实际效果进行分析。结果表明,CERES-Wheat模型能较好地反映冬小麦的生长状况,在冬小麦产量预测中,目标产量与实测产量具有良好的一致性。变量施肥区的产量、籽粒蛋白质含量及经济效益均优于对照区,同时变量施肥区的籽粒产量和蛋白质含量的空间变异较对照均有所降低,说明基于高光谱响应与模拟模型的冬小麦变量追氮技术具有一定的理论意义和实用价值。展开更多
文摘作物模型为人们认识旱区农业生境过程并对其进行调控提供了一种有效的工具。为了探讨小麦生长模拟模型DSSAT-CERES-Wheat能否准确模拟水分胁迫条件下旱区冬小麦的生长发育和产量形成过程,同时确定参数估计和模型验证的最优方案,该研究进行了连续两季（2012.10-2013.06和2013.10-2014.06）的冬小麦分段受旱田间试验。试验将冬小麦整个生育期划分为越冬、返青、拔节、抽穗和灌浆5个主要生长阶段,每相邻两个生长阶段连续受旱,形成4个不同的受旱时段水平（D1-D4）,根据小麦生育期的需水量,设置灌水定额分别为40和80 mm 2个水平（I1和I2）,共形成8个处理,每处理3次重复,在遮雨棚内采用裂区试验布置,此外在旁边设置1个各生育期全灌水的对照处理。文中设置了5套不同的参数估计和验证方案,利用DSSAT-GLUE参数估计模块得到不同的参数估计结果。通过对比分析冬小麦物候期、单粒质量、生物量、产量、以及土壤水分含量的模拟值和实测值之间的差异,以确定利用DSSAT-CERES-Wheat模型模拟旱区冬小麦生境过程的精度。结果表明,参数P1V（最适温度条件下通过春化阶段所需天数）和G3（成熟期非水分胁迫下单株茎穂标准干质量）具有较强的变异性,变异系数分别为19.07%和16.34%,受基因型-环境互作的影响较大,而其他参数的变异性则较弱,变异系数均小于10%;DSSAT-GLUE参数估计工具具有较好的收敛性,不同参数估计方案所得的参数值具有一定的一致性;不同的参数估计方案所得的模型输出结果有较大差异,其中参数估计方案1（利用两季试验中的充分灌溉处理CK数据进行参数估计,其他不同阶段受旱处理数据进行验证）的模型校正和验证精度最高,其中模型校正的绝对相对误差（absolute relative error,ARE）和相对均方根误差（relative root mean squared error,RRMSE）分别为4.89%和5.18%。在冬小麦抽穗期和灌浆期受旱时,DSSAT-CERES-Wheat模型可以较好地模拟小麦的生长发育过程以及土壤水分的动态变化,但是在越冬期和返青期受旱时,模拟结果相对较差,并且随着受旱时段提前和受旱程度的加重,模拟精度将变得更低。此外,该模型无法模拟由不同水分胁迫造成的冬小麦物候期差异,需要对模型进行相应的改进。交叉验证表明DSSAT-CERES-Wheat模型模拟该研究中不同水分胁迫条件下冬小麦生长和产量的总体性误差在15%~18%左右。总之,DSSAT-CERES-Wheat模型在模拟旱区冬小麦生境过程时存在着一定的局限性,若要更广泛地将该模型应用在中国干旱半干旱地区的冬小麦生产管理和研究,有必要对冬小麦营养生长阶段前期的水分胁迫响应机制和模拟方法进行进一步的深入研究。
基金supported by the National Key Research and Development Program of China (2018YFD020040103)the National Key Research and Development Program of Shanxi Province, China (201803D221005-2)。
文摘Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.
基金funded by the Special Fund for Agro-scientific Research in the Public Interest of China (201203031,201303133)the National Natural Science Foundation of China (31071367)
文摘To improve efficiency in the use of water resources in water-limited environments such as the North China Plain（NCP）, where winter wheat is a major and groundwater-consuming crop, the application of water-saving irrigation strategies must be considered as a method for the sustainable development of water resources. The initial objective of this study was to evaluate and validate the ability of the CERES-Wheat model simulation to predict the winter wheat grain yield, biomass yield and water use efficiency（WUE） responses to different irrigation management methods in the NCP. The results from evaluation and validation analyses were compared to observed data from 8 field experiments, and the results indicated that the model can accurately predict these parameters. The modified CERES-Wheat model was then used to simulate the development and growth of winter wheat under different irrigation treatments ranging from rainfed to four irrigation applications（full irrigation） using historical weather data from crop seasons over 33 years（1981–2014）. The data were classified into three types according to seasonal precipitation： 〈100 mm, 100–140 mm, and 〉140 mm. Our results showed that the grain and biomass yield, harvest index（HI） and WUE responses to irrigation management were influenced by precipitation among years, whereby yield increased with higher precipitation. Scenario simulation analysis also showed that two irrigation applications of 75 mm each at the jointing stage and anthesis stage（T3） resulted in the highest grain yield and WUE among the irrigation treatments. Meanwhile, productivity in this treatment remained stable through different precipitation levels among years. One irrigation at the jointing stage（T1） improved grain yield compared to the rainfed treatment and resulted in yield values near those of T3, especially when precipitation was higher. These results indicate that T3 is the most suitable irrigation strategy under variable precipitation regimes for stable yield of winter wheat with maximum water savings in the NCP. The application of one irrigation at the jointing stage may also serve as an alternative irrigation strategy for further reducing irrigation for sustainable water resources management in this area.
基金supported by the National Key Research and Development Program of China (2023YFD2302801)the Central Public-interest Scientific Institution Basal Research Fund of Chinese Academy of Agricultural Sciences (JBYW-AII-2024-38)+1 种基金the Fundamental Research Funds for the Central UniversitiesChina Scholarship Council。
文摘Accurately estimating the wheat yield potential under climate changes is essential for assessing food production capacity. However, studies based on crop modeling and imperfect management experiment data frequently underestimate the wheat yield potential. In this study, we evaluated wheat yield potential based on the CERES-Wheat model and a well-managed 10-year(2008–2017) field study in the North China Plain(NCP), and further identified the critical climate and management yield-limiting factors for improving wheat yield potential and closing the wheat yield gap. Our results revealed that wheat yield potential averaged 10.8 t ha–1in the recent decade. The low growing degree days(GDD) in the pre-winter growing season(592℃ d) and solar radiation in the whole growth season(3,036 MJ m–2) are the most critical climatic factors limiting wheat yield potential in the current production system. Nonetheless, wheat yield potential in the NCP is projected to decline during 2040–2059 by 1.8 and 5.1% under the representative concentration pathway(RCP) 4.5 and RCP8.5 scenarios, respectively, without considering the elevated CO_(2) concentration. However, the positive influence of CO_(2) fertilization will be sufficient to offset these negative impacts from climatic warming and solar dimming, ultimately leading to an enhancement in wheat yield potential during 2040–2059 by 7.5 and 9.8% compared to the baseline under RCP4.5 and RCP8.5, respectively. To improve the wheat yield potential, we recommend selecting an appropriate planting date(5 October) and planting density(400 plants m–2) that align with light and temperature conditions during the wheat growing season. In addition, optimizing the timing and rate of water application(three times, 270 mm) and fertilizer use(based on inseason root zone nitrogen management) is crucial for closing the wheat yield gap. This study underscores the importance of adopting multiple management practices that account for complex climate–crop–soil interconnections to enhance the wheat yield based on a long-term field experiment under the changing climate.
文摘为给小麦变量施氮提供依据,利用冬小麦起身期和拔节期冠层光谱数据,选用反映冬小麦长势信息的优化土壤调节植被指数OSAVI(Optimization of soil-adjusted vegetation index)与CERES-Wheat模型相结合进行变量施肥管理(变量区),以相邻地块常规非变量(均一)施肥区(对照区)为对照,对变量追氮模型的可行性进行探讨,并对变量追肥处理的实际效果进行分析。结果表明,CERES-Wheat模型能较好地反映冬小麦的生长状况,在冬小麦产量预测中,目标产量与实测产量具有良好的一致性。变量施肥区的产量、籽粒蛋白质含量及经济效益均优于对照区,同时变量施肥区的籽粒产量和蛋白质含量的空间变异较对照均有所降低,说明基于高光谱响应与模拟模型的冬小麦变量追氮技术具有一定的理论意义和实用价值。
