Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review

Rice–wheat (RW) systems are critical to food security and livelihoods of rural and urban poor in south Asia and China, and to regional economies in southeast Australia. The sustainability of RW systems in south Asia is, however, threatened by yield stagnation or decline, and declining partial factor productivity, soil organic C and water availability. Crop models potentially offer a means to readily explore management options to increase yield, and to determine trade-off between yield, resource-use efficiency and environmental outcomes. This paper reviews the performance of CERES-Rice and CERES-Wheat in Asia and Australia in relation to their potential application towards increasing resource use efficiency and yield of RW systems.

The performance of the models was evaluated using simulated and observed data on anthesis and maturity dates, in-season LAI and growth, final grain yield and its components, and soil water and N balances from published studies across Asia and Australia, and then by computing the statistical parameters for the major characters. Over the four data sets examined for anthesis and six for maturity dates, CERES-Rice predicted those dates fairly well (normalised RMSE = 4–5%; D-index = 0.94–0.95), but over the 11 sets for grain and 4 for biomass yield, the predictions were more variable (normalised RMSE = 23% for both; D-index 0.90 and 0.76, for grain and biomass, respectively). Model performance was poorer under conditions of low N, water deficit, and low temperatures during the reproductive stages. Over the three data sets examined, CERES-Wheat predicted the anthesis and maturity dates quite well (normalised RMSE = 4–5%; D-index = 0.94–0.99), and over eight sets for grain and two sets for biomass yield the model predicted them also reasonably well (RMSE = 13–16%; D-index = 0.86–0.97). Only one study evaluated the DSSAT RW sequence model with fairly satisfactory predictions of rice and wheat yields over 20 years with adequate N, but not the long-term change in soil organic C and N. Predictions of in-season LAI and crop growth, and soil and water processes were quite limited to investigate the robustness of model processes.

Application of models to evaluate options to increase water and N use efficiency requires the ability to perform well at the margin where deficit stress begins. While both models generally perform satisfactorily under water and N non-limiting conditions, the little evidence available suggests that they do not perform well under resource-limiting situations. We recommend that the models’ key processes under the water and N limiting conditions be further evaluated urgently. The DSSAT sequence model also needs to be further evaluated against observations for a range of locations and management using data from long-term experiments in RW systems.     

基于高光谱响应与模拟模型的冬小麦变量追氮研究

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.     

Contribution of cultivar,fertilizer and weather to yield variation of winter wheat over three decades: A case study in the North China Plain

Long-term field measured yield data provides good opportunity to assess the impacts of climate and management on crop production. This study used the yield results from a long-term field experiment (1979–2012) at Luancheng Experimental Station in the central part of the North China Plain (NCP) to analyze the seasonal yield variation of winter wheat (Triticum aestivum L.) under the condition of sufficient water supply. The yield change of winter wheat over the last 33 growing seasons was divided into three time periods: the 1980s, the 1990s, and the years of 2001–2012. The grain yield of winter wheat during the 1980s was relative stable. During the 1990s, the annual yield of this crop was continuously increased by 193 kg/ha/year (P < 0.01). While for the past 12 years, yield of winter wheat was maintained at relative higher level, but with larger seasonal yield variation than that back in 1980s. CERES-Wheat model was calibrated and was used to verify the effects of management practices on grain yield. Seven scenarios were simulated with and without improvements in management. The simulated results show that the yield of winter wheat was decreased by 5.3% during 1990s and by 9.2% during the recent 12 seasons, compared with that during 1980s, under the scenario that the yield of winter wheat was solely affected by weather. Seasonal yield variation caused by weather factors was around −39% to 20%, indicating the great effects of weather on yearly yield variation. Yield improvement by cultivars was around 24.7% during 1990s and 52.0% during the recent 12 seasons compared with that during 1980s. The yield improvement by the increase in soil fertility and chemical fertilizer input was 7.4% and 6.8% during the two periods, respectively. The initial higher soil fertility and chemical fertilizer input might be the reasons that the responses of crop production to the further increase in chemical fertilizer were small during the simulation period. Correlation analysis of the grain yield from the field measured data with weather factors showed that sunshine hours and diurnal temperature difference (DTR) were positively, and relative humidity was negatively related to grain yield of winter wheat. The climatic change trends in this area showed that the DTR and sunshine hours were declining. This type of climatic change trend might further negatively affect winter wheat production in the future.     

Designing crop technology for a future climate: An example using response surface methodology and the CERES-Wheat model

Future crop production will be adapted to climate change by implementing alternative management practices and developing new genotypes that are adapted to future climatic conditions. It is difficult to predict what new agronomic technologies will be necessary for crop production under future climatic conditions. The purpose of this work was to develop an approach useful in identifying crop technologies for future climatic conditions. As an example of the approach, we used response surface methodology (RSM) in connection with the CERES-Wheat model and the HADCM2 climate simulation model to identify optimal configurations of plant traits and management practices that maximize yield of winter wheat in high CO₂ environments. The simulations were conducted for three Nebraska locations differing in altitude and rainfall (Lincoln, Dickens and Alliance), which were considered representative of winter wheat growing areas in the central Great Plains. At all locations, the identified optimal winter wheat cultivar under high CO₂ conditions had a larger number of tillers, larger kernel size, fewer days to flower, grew faster and had more kernels m⁻² than the check cultivar under normal CO₂ conditions. In addition, optimal sowing dates were later and optimal plant densities were smaller than under normal conditions. We concluded that RSM used in conjunction with crop and climate simulation models was useful in understanding the complex relationship between wheat genotypes, climate and management practices.     

基于CERES-Wheat模型的沧州地区冬小麦需水量分析

作物生长模拟模型的应用为农田水资源分析和水分管理措施的优化提供了新的方法手段。本研究以CERES-Wheat模型为基础,通过情景分析的方法,分析了华北平原沧州地区冬小麦1981—2014年产量、大田蒸散量(ET)、作物蒸腾(EP)、土壤蒸发(ES)、水分生产率(WP)的年际变化特征,并建立了ET与WP定量化关系模型,利用该模型计算出了WP达到最大值的经济蒸散量为435 mm;利用ET多年平均值与多年平均降雨量差值计算出了T0(不灌溉)、T1(拔节期灌水75 mm)、T2(拔节期、扬花期各灌水75 mm)、T3(起身期、孕穗期和扬花期各灌水75 mm)4个水分处理不同产量目标下冬小麦多年平均的需水量分别为189 mm、264 mm、298 mm和319 mm,对应的平均产量分别为4 144 kg?hm?2、7 293 kg?hm?2、7 301 kg?hm?2和8 245 kg?hm?2;采取地膜覆盖等栽培管理措施扣除土壤蒸发,T0、T1、T2、T3水分处理可分别节水80 mm、71 mm、71 mm和70 mm。在此情况下,利用EP多年平均值与多年平均降雨量之间的差值计算出不同水分处理下冬小麦多年平均的需水量分别为109 mm(T0)、193 mm(T1)、227 mm(T2)和249 mm(T3)。以上研究结果可为沧州地区冬小麦水分定量化管理措施的制定提供参考。     

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

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 m2 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.     

气候变化对沧州地区冬小麦产量潜力的影响

本研究利用DSSAT V4.5模型模拟近50年来沧州地区冬小麦光温生产潜力、气候生产潜力及其在实际水肥条件下可获得产量的变化情况,分析沧州地区近50年的气候变化特征及气候变化对冬小麦产量潜力的影响,旨在为分析气候变化对黄淮海平原冬小麦生产系统的影响提供方法和思路。结果表明:1)近50年来沧州地区冬小麦生育期内平均温度呈极显著上升趋势,平均日照时数和太阳辐射呈减少趋势,平均降雨量呈不显著减少趋势,但降雨分布发生了季节性转移。2)冬小麦的光温生产潜力近50年呈减少趋势,平均每年减少17.94kg/hm2;气候生产潜力和可获得产量均呈增长趋势,平均每年分别增长31.02和3.62kg/hm2;实际产量呈极显著增长趋势,平均每年增长100.85kg/hm2。3)光温生产潜力和气候生产潜力之间的产量差呈不显著减少趋势,气候生产潜力和可获得产量之间的产量差呈不显著增长趋势,可获得产量和实际产量之间的产量差呈极显著减少趋势。4)气候因素的变化对冬小麦的生长和产量产生了明显影响,其中温度增加对冬小麦生长影响较为复杂,不同时期温度变化对冬小麦生长影响不同,总体呈负面影响;平均日照时数和太阳辐射的减少是冬小麦产量潜力降低的主要原因;平均降雨量的变化对冬小麦生长有利,气候变化的综合影响使冬小麦的生育期极显著缩短,开花期和成熟期极显著提前。因此,气候变化对沧州地区冬小麦的生产和产量潜力影响具有一定的复杂性,可以通过优化水肥管理措施来抵消气候变化产生的负效应。     

基于CERES-Wheat和遥感数据的土壤水分供给量反演

以关中平原为研究区域,以冬小麦为研究对象,基于2007—2008年TM遥感数据反演的和CERES-Wheat模型模拟的生物量和叶面积指数,将由离散积分思想计算的日均分摊系数和由冬小麦各生育阶段生长特点划分的分段蒸腾系数引入土壤水分平衡方程,建立土壤水分供给量反演模型。利用该模型进行研究区域2007—2008年冬小麦全生育期的蒸散量和土壤水分供给量的单点和区域尺度的定量反演。选取拥有多时相遥感数据的2000—2001年进行模型验证,结果表明,在充分获取降水、灌溉信息和多时相遥感数据的条件下,土壤水分供给量的反演结果准确度较高。区域尺度的土壤水分供给量呈现出西高东低和北高南低的分布特征,自西北部向东南部逐渐递减,与关中平原冬小麦受水分胁迫程度的区域性变化趋势基本一致,表明应用模型反演冬小麦全生育期的土壤水分供给量是可行的。     

未来气候情景下黄淮海平原不同灌溉制度的产量补偿效应模拟

利用黄淮海平原典型农业气象站点生育期和产量资料对CERES-Wheat模型遗传参数进行调试,结合HadGEM2-ES气候模式在RCP8.5（辐射强迫上升至8.5W·m?2的典型浓度目标）情景下的预估结果,模拟分析未来不同时段（近期：2010-2039年、中期：2040-2069年、远期：2070-2099年）雨养条件下冬小麦的减产率（与潜在产量相比）,并比较不同灌溉制度下冬小麦减产率与雨养条件下减产率的差值即灌溉制度的产量补偿效应。结果表明,经参数调试后的CERES-Wheat模型能较为准确地模拟冬小麦生长发育过程。与潜在产量相比,雨养条件对冬小麦造成的减产率在未来近、中、远期分别为47.3%、53.5%和50.9%,黄淮海平原北部Ⅰ-Ⅳ区的潜在减产率高于区域平均水平,而南部Ⅵ区的减产率仅为7.4%（近期）、12.8%（中期）和9.7%（远期）。多种灌溉策略模拟结果表明,不同生育期灌溉对冬小麦产量的补偿效应（指减产率的减少量）差异较大,北部亚区的补偿效应高于南部亚区。拔节水的补偿效应最强,对未来不同时段冬小麦产量的补偿效应为16.3～18.6个百分点;灌浆水次之,补偿效应为5.1～6.1个百分点;而越冬水补偿效应仅为0.4～0.6个百分点。两水灌溉条件下,拔节水+灌浆水的补偿效应为23.6～25.1个百分点,能够挽回雨养损失的50%左右。因此,在未来水分亏缺加剧的背景下,应注重保障拔节期的需水,灌两水情况下重点保障拔节和灌浆阶段需水。