1. Introduction

Winter wheat is one of the most important food crops in the North China Plain (NCP) (Mueller et al. 2012).Approximately one-third of the total arable land in the NCP is currently used for winter wheat cultivation (CAYN 2013). In the NCP, the growth and yield of winter wheat is mainly determined by water availability from precipitation and irrigation. The average precipitation during the winter wheat growing season (October–June) in this area is approximately 117 mm, which is less than the water requirements for achieving the maximum grain yield and water use efficiency (WUE) for winter wheat. Irrigation is thus critical for maintaining high wheat production (Chen et al. 2015). Traditional high-yielding irrigation strategies are usually based on the full water requirements at different growth stages in winter wheat. Previous studies have reported three to five or more irrigation strategies to maintain wheat yield (Zhang et al. 2005). Through studies over the course of many years, cultivation systems with water-saving,fertilizer-saving, high-yielding and simplified management have been developed for winter wheat, and the WUE has improved by 20% compared to the traditional high-yielding technology (Wang et al. 2006).

Underground water is the major source of irrigation water in the NCP. Large-scale irrigation in this region began in the 1970s, and since the adoption of this practice, the extraction of groundwater increasingly exceeds depletion of groundwater table. As a result, groundwater levels are continuously decreasing (Fang et al. 2010; Piao et al. 2010).Although more efficient irrigation technologies have been introduced over the past 40 years, these developments have not slowed the depletion of underground water (Ma et al. 2015). Studies have shown that the groundwater table in the NCP is declining at a rate of 0.8 m yr–1, and this has led to a serious underground water crisis in the area (Chen et al. 2003; Han et al. 2016). On the other hand, precipitation in NCP has exhibited a long-term trend of decline (Piao et al. 2010). The groundwater resource crisis has led to an urgent need for a reduction of irrigation to maintain agricultural sustainability in the area (Fang et al.2010). In the future, irrigation strategies will emphasize maximizing the productivity per unit of water used rather than the productivity per unit of crop area (Fereres and Soriano 2007).

Reduction of the overall application of irrigation water to conserve water resources is possible through deficit irrigation (Guo et al. 2014). Deficit irrigation is defined as the application of irrigation water in volumes less than the full evapotranspiration (ET) requirement of the crop(Geerts and Raes 2009). Irrigation only at certain stages of crop growth is a strategy that has been widely utilized in areas where water resources are limited (Kang 2004). In the NCP, deficit irrigation can be applied to wheat crop at specific growth stages to mitigate the adverse effects of water stress on plants (Zhang et al. 2013). For example,Kang (2002) reported that irrigation below the full potential ET requirement did not necessarily reduce the winter wheat yield. Determining the critical growth stages for applying the limited water available for irrigation will be crucial for reducing the impact of water shortage on wheat grain yield while maintaining economic returns. Precipitation is also a critical factor affecting wheat production in the NCP because it directly affects irrigation requirements and water balance in wheat (Sun et al. 2010).

明廷对亦失哈巡视奴儿干并输送人员物资非常重视，但罢松花江造船直接影响了亦失哈巡视奴儿干等地，也给奴儿干都司辖内其他地区之间的联系带来了不便。于是，“造舟役复兴，中官阮尧民、都指挥刘清等董之”。［7］

The CERES-Wheat model is a cropping system model that is a component of the Decision Support System for Agrotechnology Transfer, popularly known as DSSAT (Jones et al. 2003; Hoogenboom et al. 2004). High performance has been reported for CERES-Wheat in simulating wheat growth and yield in response to environmental factors and management under a wide range of soil and climatic conditions (Arora et al. 2007; He et al. 2013; Ji et al. 2014;Mavromatis 2014; Yu et al. 2014; Dokoohaki 2015; Ahmed et al. 2016; Attia et al. 2016). The objectives of this study were to: (1) calibrate the CERES-Wheat model to accurately predict the winter wheat grain and biomass yield, harvest index (HI), and WUE responses to different irrigation scheduling practices using long-term weather datasets available for NCP; (2) simulate the development and growth of winter wheat under different irrigation treatments using historical weather data from 33 years (1981–2014) with crop seasons that were classified into three types according to seasonal precipitation; and (3) discuss the possibility of further reducing irrigation by optimizing the irrigation strategies for sustainable groundwater management.

2. Materials and methods

2.1. Model description

In this study, we used CERES-Wheat, a component of the DSSAT Cropping System Model v4.6 (Hoogenboom et al. 2015) that can simulate the growth and development of wheat using a daily time-step model. Since its initial development and evaluation (Ritchie and Otter 1985), the model has been documented extensively. It has been widely used to simulate the effects of weather, genotype,soil properties, and management factors on wheat growth and development and water and nitrogen dynamics (Arora et al. 2007; He et al. 2013; Attia et al. 2016). The crop growth model divides phasic development into nine growth stages from pre-sowing to harvest in relation to thermal time.The DSSAT-Wheat model calculates plant phenological development based on the calibration of three varietyspecific coefficients (P1V, P1D, and P5), while three other specific coefficients (G1, G2, and G3) control grain yield.The phyllochron interval is controlled by PHINT (Jones et al. 2003).

Daily soil water balance is modeled based on rainfall,irrigation, transpiration, soil evaporation, in filtration,drainage, and surface run-off. Each layer has a characteristic lower limit (LL), a drained upper limit (DUL), and a saturated water content (SAT). The model utilizes the LL, DUL,and SAT to estimate water flow using a simple cascading approach. Runoff from rainfall is estimated based on the USDA Natural Resources Conservation Service curve number method by Jones et al. (2013), and the remaining water in filtrates into the soil pro file. Drainage is estimated following the slowest draining layer of the soil pro file. Plant transpiration (EP) is calculated from the distribution of roots and available water in the soil layers. Actual soil evaporation(ES) is independently estimated using the two-stage soil evaporation model (Ritchie 1972).

新媒体时代，语言资源深层结构正在被重组，如何保持方言活力与生命力，要依靠新媒体进行深挖掘、重塑造和再传播，注重方言文化传播的精准性和个性化。因此，笔者提出创建方言传播新范式（图2）。

2.2. Experimental datasets

Similar to the trend for variation in grain yield, different rainfall amounts among years and irrigation treatments significantly affected the simulated biomass yield over the long-term simulation period(Table 6). Biomass increased with the increase in irrigation (Fig. 4).The rainfed treatment had the lowest biomass yield at 7.15 Mg ha–1, while the biomass yield was 17.68 Mg ha–1 of T7 when the crop season precipitation was below 100 mm. In those crop seasons,T1 resulted in almost two times the biomass yield of T0. The rainfed biomass yield was significantly increased to 9.55 Mg ha–1 at crop seasons with precipitation was between 100 and 140 mm, while was added to 78% on account of T1, the biomass yield improved to 18.45 Mg ha–1 when the crop was irrigated four times (T7). When the crop season precipitation was above 140 mm, the biomass yield ranged from 11.23 Mg ha–1 for T0 to 18.55 Mg ha–1 for T7. There was an obvious difference between biomass yield and grain yield in response to irrigation treatment. The biomass of the three-irrigation(T5, T6) and four-irrigation (T7) treatments were significantly higher than for T3.

All the experiments mentioned above were conducted with randomized complete block design with three or four replicates. The wheat cultivar Shijiazhuang 8 was planted with 15.6 cm row spacing using a seed rate of 300 kg ha–1 for all experiments. All treatments received 75 mm of irrigationprior to sowing to ensure uniform emergence. Irrigation was applied through gated pipes and measured with propellertype meters in all experiments. Fertilizer was applied at planting at a rate of 157.5 kg N ha–1. Weeds, diseases and pests were controlled following the local agronomic recommendations. Additional details of cultural practices and crop management are reported in Wang et al. (2006).

Data from four studies were used for calibrating,while data from the remaining four studies were used for evaluating the model. Experiments 1 through 4 were conducted in the 2004–2008 growing seasons (Zhang 2009), and these data were used for model calibration. In these studies, the researchers investigated winter wheat growth and yield responses to three irrigation treatments(rainfed, one irrigation and two irrigations) applied at different plant developmental stages in plot areas of 56 m2. The details of the irrigation treatments for the four experiments are shown in Table 2 and more details about the cultural practices in the experiments can be found in Zhang (2009).The data included measurements of grain yield, biomass yield, grain weight, grains per spike, and HI. The data from Experiments 5 to 8 were used for model evaluation and were obtained from previous studies by Wu (2005), Liu(2010) and Zhang (2009); these studies focus on the effect of irrigation strategies ranging from rainfed to four-irrigation treatments on winter wheat grain yield, yield component,biomass yield and WUE in the 2003–2004, 2007–2008,and 2008–2009 growing seasons, respectively. The plot area in these studies ranged from 30 to 150 m2. The details of the irrigation treatments for these four experiments are shown in Table 2.

根据中国互联网协会在2017年中国互联网大会上发布的《2017年中国社交电商和微商行业发展报告》中对社交电商的定义，将其定义基于人际关系网络，利用互联网社交工具，从事商品或服务销售的经营行为，是新型电子商务的重要表现形式之一。相对与传统电商而言，社交电商有着显著的特征。

Table 1 Soil physical and chemical properties in the experimental area

– indicates no data.

Depth (cm) Soil texture Total nitrogen content (%)Bulk density(g cm–3) pH Clay (%) Silt (%) Sand (%)Organic matter(mg kg–1)0–20 8.8 79.1 12.1 0.06 1.04 1.45 8.1 20–40 11.8 78.1 10.1 0.05 0.85 1.41 8.2 40–60 10.8 81.1 8.1 0.04 0.69 1.42 8.2 60–80 15.2 75.9 8.9 0.04 0.66 1.43 8.3 80–100 19.0 76.3 4.7 0.04 0.64 1.41 8.3 100–120 15.7 80.7 3.6 0.01 0.17 1.40 8.1 120–140 16.7 81.2 2.1 0.01 0.13 1.38 8.0 140–160 23.2 76.3 0.5 – 0.10 1.37 8.2 160–180 24.8 71.3 3.9 – 0.04 1.45 8.3 180–200 25.6 69.2 5.2 – 0.03 1.43 8.1

Table 2 Details of the irrigation treatments used for calibration and evaluation of the CERES-Wheat model

1) Data from these experiments were used for calibration of the CERES-Wheat model. 2) Data from these experiments were used for evaluation of the CERES-Wheat model.

Experiment Season Irrigation time and amount (mm) Sowing date (d/mon/yr)Exp11) 2004–2005 Three treatments: rainfed, one irrigation of 75 mm at jointing stage, two irrigations of 75 mm each at the jointing stage and anthesis stage 15/10/2004 Exp21) 2005–2006 Same as 2004–2005 16/10/2005 Exp31) 2006–2007 Same as 2004–2005 15/10/2006 Exp41) 2007–2008 Three treatments: rainfed, one irrigation of 75 mm at the jointing stage,two irrigations of 75 mm each at the jointing stage and grain- filling stage 14/10/2007 Exp52) 2007–2008 Four treatments: three of four the same as in Exp1, and a treatment of three irrigations of 75 mm each at the jointing stage, booting stage and anthesis stage 19/10/2007 Exp62) 2007–2008 Three treatments: rainfed, one irrigation of 75 mm at the jointing stage,and three irrigations, as follows: 90 mm at the upstanding stage, 80 mm at the booting stage and 90 mm at 20 days after anthesis 14/10/2007 Exp72) 2008–2009 Four treatments: rainfed, one irrigation of 70 mm at the jointing stage,two irrigations (65 mm at the jointing stage and 110 mm at anthesis stage), and three irrigations (75 mm at upstanding stage, 90 mm at the booting stage and 100 mm at anthesis stage)14/10/2008 Exp82) 2003–2004 Four treatments: three of four the same as in Exp1, and a treatment of four irrigations of 75 mm each at the re-growing stage, the booting stage,the anthesis stage and the grain- filling stage 17/10/2003

2.3. Model calibration and evaluation

The CERES-Wheat model requires daily weather data,crop management data, soil pro file data, and genotype coefficients as basic input. Daily precipitation, sunshine hours, and maximum and minimum air temperatures were obtained from the Wuqiao County Bureau of Meteorology,Hebei Province, China. We used GLUE method (He et al.2010) and trial and error for calibration. The CERESWheat model was calibrated using field experimental data obtained from the 2004 to 2008 cropping seasons (Zhang 2009). To calibrate the phenology and crop growth of the specified wheat variety, three types of coefficients were de fined: cultivar, ecotype, and species coefficients. The phenological development parameters related to anthesis and physiological maturity date (i.e., P1V, P1D, P5 and PHINT) were calibrated first. The crop growth parameters(G1, G2 and G3) were then con firmed based on field data. Meanwhile, the ecotype and species coefficient parameters were also adjusted to the precise model. After calibration, the model was used for evaluation by comparing field observations and simulated data. In this study, the observed and simulated grain yield, biomass yields, grain weight, grains per spike, and HI were compared. The calibrated genetic coefficients for the winter wheat cultivar Shijiazhuang 8 in the CERES-Wheat model are shown in Table 3.

2.4. Long-term scenario analyses using historical meteorological data

The D-index values range from 0 to 1. A D-index value of 1 indicates perfect agreement between the observed and simulated data. A D-index value less than 0.50 suggests greater diversity and inconsistency in the model predictions. D-index values closer to 0.0 indicate that the model predictions are equal to the average of the observed data, which indicates no agreement between the observed and simulated values (Willmott 1981, 1982).

The NRMSE provides a measure of the relative difference(percentage of the mean observed value, O) between the simulated (Si) versus observed (Oi) values. The simulation was identified as excellent when the NRMSE was less than 10%, good when the NRMSE was greater than 10% and less than 20%, fair when the NRMSE was greater than 20% and less than 30%, and poor when the NRMSE was greater than 30% (Bannayan and Hoogenboom 2009; Dettori et al. 2011).

由图11可见，直流正极接地故障发生时，故障极直流电压降至零，非故障极电压升高至极间电压，即直流零电位参考点发生偏移；但直流极间电压保持不变。同时导致三端换流器发生交流电压偏置、出现中性点故障电流。故障电流经故障线路两端向故障点流入，故障电流的大小与接地电阻值成反比。

Table 3 Genetic coefficients for the winter wheat cultivar Shijiazhuang 8 in the CERES-Wheat model

Crop file Parameter Description Calibrated value Genotype P1V Days required for vernalization under optimum vernalizing temperature (day) 33.23 P1D Photoperiod response (%) 115 P5 Grain filling phase duration (°C day) 573.5 G1 Kernel number per unit canopy weight at anthesis (no. g–1) 22.66 G2 Standard kernel size under optimum conditions (mg) 34.20 G3 Standard non-stressed mature tiller dry weight (including grain) (g) 2.415 PHINT Interval between successive leaf tip appearance (°C day) 105 Ecotype P1 Duration of phase end juvenile to terminal spikelet (PVTU) 316 P2 Duration of phase terminal spikelet to end leaf growth (TU) 225 P3 Duration of phase end leaf growth to end spike growth (TU) 150 P4 Duration of phase end spike growth to end grain fill lag (TU) 200 SLAS specific leaf area, standard first leaf (cm2 g–1) 240 TDFAC Tiller death factor (%) 20 Species WFPU Water stress factor, photosynthesis, upper (fr) 0.5 TRGFW Temperature response, grain filling, dry weight (°C)Tbase Base temperature, below which increase in grain weight is zero (°C) 3 Topt1 The 1st optimum temperature, at which increase in grain weight is most rapid (°C) 20 Topt2 The 2nd optimum temperature, the highest temperature at which increase in grain weight is still at its maximum (°C)25 Tmax Maximum temperature, at which increase in grain weight is zero (°C) 40

Fig. 1 Long-term precipitation at the experimental site from 1981 to 2014 during the winter wheat growing season from October 10 of the current year to June 15 of the following year.There were 11 seasons with precipitation ＜100 mm, 12 seasons with precipitation between 100 and 140 mm, and 10 seasons with precipitation ＞140 mm.

2.5. Statistical analysis

The root mean square error (RMSE) and normalized RMSE(NRMSE) were calculated to evaluate the simulation error for the different parameters between the observed values from field experiments and the simulated values from the CERES-Wheat model.

Climate data, including daily minimum and maximum temperature, solar radiation and daily precipitation from 1981–2014, were obtained from the Wuqiao County Bureau of Meteorology. Eight irrigation treatments, representing rainfed (T0); one irrigation of 75 mm at the jointing stage(T1); one irrigation of 75 mm at anthesis stage (T2); two irrigations of 75 mm each at the jointing stage and anthesis stage (T3); two irrigations of 75 mm each at the jointing stage and grain- filling stage (T4); three irrigations of 75 mm each at the upstanding stage, booting stage and 20 days after anthesis (T5); three irrigations of 75 mm each at the upstanding stage, booting stage and anthesis stage (T6);and four irrigations of 75 mm each at the upstanding stage,booting stage, anthesis stage and grain- filling stage (T7),were used for long-term simulations. These treatments were similar to those described in the experiments used for calibration and evaluation using CERES-Wheat model(Table 2). The scenario analyses were defined by flood irrigation, and 75 mm irrigation was applied before wheat crop planting for all irrigation treatments. Therefore, the initial soil water content pro file was considered to be near field capacity. Rates of 157 kg ha–1 nitrogen and 70 kg ha–1 phosphorus were applied at planting, which was set to October 17 in all years. Other cultivation practices like those described by Zhang (2009) were used in the model.

The coefficient of residual mass (CRM) indicates whether the model predictions tend to over- or underestimate observed data. A negative CRM value indicates a tendency of the model toward overestimation, while a positive CRM value indicates a tendency of the model toward underestimation (Loague and Green 1991).

宝剑递出去的那一瞬间，奇怪的事情发生了——对方不避不挡,只是留给了岳无影一个平静的微笑，微笑中充满了对生的眷恋和对死亡的坦然接受。

The development and growth of winter wheat under different irrigation treatments was simulated using the CERES-Wheat model over the 33 crop seasons, and the results were classified into three types according to seasonal precipitation.The three-year types include seasons with precipitation below 100 mm, between 100 and 140 mm, and above 140 mm. There were 11 seasons with precipitation ＜100 mm,12 seasons with precipitation between 100 and 140 mm, and 10 seasons with precipitation ＞140 mm during the 33 crop seasons. Long-term precipitation in the winter wheat crop season at the experimental site is shown in Fig. 1.

3. Results

3.1. Model calibration and evaluation

Overall, the calibrated model accurately simulated phenology (anthesis day, maturity day), grain and biomass yields, yield components, and HI as indicated by the goodness of fit statistics (Table 4).

The simulated and observed values for anthesis, maturity dates, grain and biomass yields, product number, final shoot number, thousand-grain weight (TGW), and HI after calibration based on data from Experiments 1–4 are presented in Table 4. The results indicate a very close match between the simulated and observed anthesis (mean values:204 vs. 204 days after planting) and physiological maturity dates (mean values: 235 vs. 236 days after planting).There was excellent agreement between the observed and simulated grain yields (mean value: 7 327 vs. 7 261 kg ha–1). The calibrated model explained 80% of the variation(Fig. 2-A) with a NRMSE＜10% and a D-index value of 0.92. The simulated biomass yields also closely matched the observed values with a NRMSE＜10% and a D-index value of 0.93 (Fig. 2-B). The performance of the calibrated model regarding simulation of product number, final shoot number, and TGW was considered good according to the statistic results.

根据GB50201—2014《防洪标准》，35kV以上的高压和超高压输变电设施，其电压分为3个防护等级，其中800kV特高压输配电设施防护等级为Ⅰ级，其防洪标准为100年一遇［2-3］。

The independent field studies (Experiments 5–8) were used for further evaluation of the performance of the calibrated model. The observed grain yield averaged 7 145 kg ha–1 and ranged from 3 723 to 8 502 kg ha–1 with a NRMSE of 10.37%, and 73% of yield variation could be explained by the calibrated model (Table 5; Fig. 2-C). The treatments with three irrigations in Experiments 5 and 6 and the treatment with four irrigations in Experiment 7 overestimated the yield, were considered to be the major sources of error in the model simulation accuracy. Biomass yield was predicted well by the calibrated model (Fig. 2-D),producing a mean value of 15 657 kg ha–1 compared to the observed value of 15 117 kg ha–1. The NRMSE was 9.81%, with r2 =0.83. The product number was adequately predicted in all treatments with r2=0.91 and NRMSE=5.77%.However, TGW was not accurately estimated by the model(r2=0.2, D-index=0.57) with a value of NRMSE=12.58%.The final shoot number was also not well predicted, with similar values (Table 5). The stability and accuracy of the calibrated model was con firmed by the above evaluation.The calibrated model can be used to simulate grain and biomass yields of winter wheat in response to irrigation management in the NCP.

1.5 评估方法 观察比较患者在临床药师干预前，干预后3个月，6个月贫血的临床疗效，并记录干预期间药物不良反应发生情况。并对患者的达标率进行计算比较。此外，研究组患者采用问卷调查方式评价患者对维持性血液透析贫血相关知识的掌握程度，患者临床药师监护前和临床药师监护6个月后以相同试题进行测试并给予评分，为避免人为因素对结果的干扰，临床药师亲自监督患者完成答题。每份试卷10题，每题1分，满分10分，其中＜6分为不合格，＞6分为合格。

3.2. Scenario analyses using long-term weather data

Grain and biomass yields Using the calibrated and validated CERES-Wheat model, the effects of different yearly rainfall and irrigation amounts on grain and biomass yields were simulated for the NCP based on historical weather data from 1981 to 2014 (Table 6). Simulated grain yield from different rainfall years was more affected by irrigation when the growing season precipitation was below 100 mm (Fig. 3). Compared to rainfed, the treatments T1,T3, T4, T5, T6, and T7 significantly improved wheat grain yield. The average T0 yield was 4.06 Mg ha–1, while T6 increased grain yield to 7.87 Mg ha–1 during seasons when precipitation was below 100 mm. The grain yield in T3 was almost the same as in T5, T6 and T7. We also found that T1 produced higher yield than T2, but yield was the same as T4. This result con firmed that the timing of irrigation has a strong impact on grain yield under deficit irrigation conditions. When the precipitation was between 100 and 140 mm for the winter wheat growing season, the rainfed wheat grain yield increased to 5.25 Mg ha–1 (Table 6). T1, T3, T4, T5,T6, and T7 increased the wheat grain yield significantly compared to T0. These results showed that T1 attained 89% of maximum grain yield of T6 and T7 strategies, while T3 attained 96% of the highest grain yield. When precipitation in the winter wheat crop growing season was above 140 mm, grain yield varied from 5.91 Mg ha–1 of T0 to 8.44 Mg ha–1 of T6. Overall,the long-term scenario analyses based on 33 years of weather data demonstrated that deficit irrigation at the jointing stage and anthesis stage (T3) produced yield that was not significantly different from the three irrigations at T6, which was the treatment that produced the highest yield of all the irrigation strategies. A single irrigation at T1 significantly improved the grain yield compared to the rainfed treatment, and the yield was similar to that of the T3 treatment, especially when precipitation was higher.

Table 4 Calibration results from the CERES-Wheat model for winter wheat cultivar Shijiazhuang 8 using experimental data from the 2004–2008 crop seasons (Zhang 2009)1)

1) DAP, days after planting; Sim, simulated value; Obs, observed value. 2) NRMSE, normalized root mean square error; D-index, index of agreement; E, Nash-Sutcliffe coefficient.

Item2)1 000-grain weight (g)Sim Obs Sim Obs Sim Obs Sim Obs Sim Obs Sim Obs Sim Obs Mean 204 204 235 236 7 327 7 261 15 131 15 723 26.0 26.9 742 771 37.8 38.3 SE 1.36 1.03 2.34 1.56 1 232 961 3 626 2 618 2.89 2.93 87 117 1.9 2.0 Min. 202 202 232 234 5 007 6 015 10 144 11 859 19.4 19.1 628 587 35.6 35.0 Max. 205 205 238 239 8 968 8 837 21 465 21 040 29.8 29.9 878 949 42.5 41.0 r2 0.00 0.01 0.80 0.88 0.82 0.61 0.33 NRMSE (%) 0.81 1.13 7.52 9.81 5.50 9.90 4.69 CRM 0.00 0.00 –0.00 0.04 0.03 0.04 0.01 D-index 0.42 0.46 0.92 0.93 0.93 0.85 0.75 E–1.83 –2.16 –0.65 0.62 –0.72 0.54 –0.13 Anthesis(DAP)Maturity(DAP)Grain yield(kg ha–1)Biomass yield(kg ha–1)Product number(no. group–1)Final shoot number (no. m–2)

Fig. 2 Results of the model calibration for observed vs. simulated grain yield (A) and observed vs. simulated biomass yield (C),and the model evaluation results for grain yield (B) and biomass yield (D) from the CERES-Wheat model. The line represents the 1:1 relationship.

Table 5 Results of the evaluation of the CERES-Wheat model for winter wheat cultivar Shijiazhuang 8 using experimental data from the 2007–2009 (Liu 2010), 2007–2008 (Zhang 2009), and 2003–2004 (Wu 2005) crop seasons1)

1) DAP, days after planting; Sim, simulated value; Obs, observed value. 2) NRMSE, normalized root mean square error; D-index, index of agreement; E, Nash-Sutcliffe coefficient.

Item2)1 000-grain weight (g)Sim Obs Sim Obs Sim Obs Sim Obs Sim Obs Sim Obs Sim Obs Mean 203 204 235 235 7 377 7 145 15 657 15 117 25.6 26.0 740 711 38.8 38.8 SE 2.71 4.49 2.45 3.72 1 439 1 439 4 061 3 539 3.96 2.83 74.4 85.5 1.5 4.4 Min. 200 198 232 230 4 052 3 723 9 598 9 210 14.9 17.7 631 596 36.8 33.3 Max. 206 209 238 241 8 740 8 502 21 231 19 560 29.8 28.9 793 872 43.7 48.3 r2 0.78 0.91 0.73 0.83 0.91 0.30 0.20 NRMSE (%) 1.25 0.67 10.37 11.26 5.77 11.20 12.58 CRM 0.00 0.00 –0.03 –0.04 0.02 –0.04 0.07 D-index 0.86 0.93 0.92 0.94 0.95 0.70 0.57 E 0.65 0.81 0.65 0.75 0.70 0.07 –0.12 Anthesis(DAP)Maturity(DAP)Grain yield(kg ha–1)Biomass yield(kg ha–1)Product number(no. group–1)Final shoot number (no. m–2)

?

To calibrate and evaluate the CERES-Wheat model, data from eight field studies conducted in NCP were used.These field experiments were conducted at the Wuqiao Experimental Station of the China Agricultural University in Hebei Province, China (37°41´N, 116°37´E, elevation 20 m above sea level, groundwater table 6–9 m). The soil series at this location is Calcaric Fluvisol with sandy clay loam texture (FAO 1990). The physiochemical characterization of the soil pro file in this region is given in Table 1. The study area is characterized by a summer monsoon climate with average annual rainfall of approximately 545 mm(1981–2014). The growing season for winter wheat is from mid-October to early June of the following year, and the annual rainfall is approximately 117 mm.

Fig. 3 Simulated grain yield using the CERES-Wheat model as affected by irrigation treatments, using long-term weather data from 1981 to 2014 in the North China Plain. T0, rainfed; T1, a single irrigation at the jointing stage; T2, a single irrigation at the flowering stage; T3, two irrigations at the jointing and anthesis stages; T4, two irrigations at the jointing and grain- filling stages;T5, three irrigations at the upstanding stage, booting stage and 20 days after anthesis; T6, three irrigations at the upstanding stage, booting stage and anthesis stage; T7, four irrigations at the upstanding stage, booting stage, anthesis stage and grain- filling stage. Crop seasons were classified into three rainfall/year categories according to the amount of precipitation:＜100 mm (11 seasons), 100–140 mm (12 seasons), and ＞140 mm(10 seasons).

Fig. 4 Simulated biomass yield using the CERES-Wheat model as affected by irrigation treatments, using long-term weather data from 1981 to 2014 in the North China Plain. T0, rainfed;T1, a single irrigation at the jointing stage; T2, a single irrigation at the flowering stage; T3, two irrigations at the jointing and anthesis stages; T4, two irrigations at the jointing and grainfilling stages; T5, three irrigations at the upstanding stage,booting stage and 20 days after anthesis; T6, three irrigations at the upstanding stage, booting stage and anthesis stage;T7, four irrigations at the upstanding stage, booting stage,anthesis stage and grain- filling stage. Crop seasons were classified into three rainfall/year categories according to the amount of precipitation: ＜100 mm (11 seasons), 100–140 mm(12 seasons), and ＞140 mm (10 seasons).

Harvest index The effect of irrigation strategy on HI in wheat crop in semi-arid regions has been broadly con firmed throughout the world (Eck 1988; Zhang et al. 2008). This effect is more obvious in the NCP, where the water deficit is very serious. The HI of different irrigation treatments was averaged over the long-term simulation period based on the rainfall year category, and the values are presented in Table 6. When the precipitation was below 100 mm for the winter wheat growing season, HI ranged from 0.39 for T2 and T4 to 0.48 for T1 and T3. A similar trend of irrigation strategy effect was found in the other two precipitation crop seasons with precipitation between 100 and 140 mm and above 140 mm (Fig. 5). This result is consistent with previous studies of NCP irrigation systems (Zhang 2008;Dong 2011) and in the arid and semi-arid region of Northwest China (Kang 2002).

Fig. 5 Simulated harvest index using the CERES-Wheat model as affected by irrigation treatments, using long-term weather data from 1981 to 2014 in the North China Plain. T0, rainfed;T1, a single irrigation at the jointing stage; T2, a single irrigation at the flowering stage; T3, two irrigations at the jointing and anthesis stages; T4, two irrigations at the jointing and grainfilling stages; T5, three irrigations at the upstanding stage,booting stage and 20 days after anthesis; T6, three irrigations at the upstanding stage, booting stage and anthesis stage; T7, four irrigations at the upstanding stage, booting stage, anthesis stage and grain- filling stage. Crop seasons were classified into three rainfall/year categories according to the amount of precipitation:＜100 mm (11 seasons), 100–140 mm (12 seasons), and＞140 mm (10 seasons).

Water use efficiency The water use efficiency (WUE) was calculated as the ratio of grain yield to seasonal ET and showed significant differences in the response to irrigation treatments (Table 6). There was an obvious difference between the WUE and grain yield in response to irrigation strategy. Unlike the grain yield, the WUE did not always increase with higher irrigation. Generally, the WUE of T1 and T3 was greatly improved compared to the other irrigation treatments (Fig. 6). When the crop season precipitation was below 100 mm, the average rainfed WUE was 12.52 kg ha–1 mm–1, whereas the highest WUE was 18.26 kg ha–1 mm–1 of T3. The WUE of T1 was near that of T3, and the WUE in these two treatments was higher than in the other irrigation treatments with 100% probability (Fig. 7-A). Compared to the rainfed treatment, the treatment T2 resulted in the lowest WUE of 11.31 kg ha–1 mm–1. When the crop season precipitation was between 100 and 140 mm (Fig. 7-B), the WUE ranged from 13.09 kg ha–1 mm–1 of T2 to 17.88 kg ha–1 mm–1 of T3. With precipitation ＞140 mm, the WUE ranged from 13.92 kg ha–1 mm–1 for the treatment T2 to 17.70 kg ha–1 mm–1 for T1, and this value showed little difference from the other rainfall year categories. In addition, there was no noticeable difference between the WUE in T3 compared to T1 or of the WUE in T4 compared to T5, T6 and T7 (Figs. 6 and 7-C). The improvement in WUE compared to T0 varied from 12.52 to 15.31% among the three rainfall year categories, and the WUE was most affected by this variable.

4. Discussion

4.1. Yield and water use efficiency as winter wheat responses to irrigation strategies

Fig. 7 Cumulative probability distribution of the water use efficiency under different irrigation treatments using a historical weather dataset from 1981 to 2014 in the North China Plain. A, amount of precipitation below 100 mm (11 seasons). B, amount of precipitation between 100 and 140 mm (12 seasons). C, amount of precipitation above 140 mm (10 seasons). T0, rainfed; T1, a single irrigation at the jointing stage;T2, a single irrigation at the flowering stage; T3, two irrigations at the jointing and anthesis stages; T4, two irrigations at the jointing and grainfilling stages; T5, three irrigations at the upstanding stage, booting stage and 20 days after anthesis; T6, three irrigations at the upstanding stage, booting stage and anthesis stage; T7, four irrigations at the upstanding stage, booting stage, anthesis stage and grain- filling stage.

Irrigation is one of the most important cultivation management measures to ensure winter wheat high-yield production in regions such as the NCP and mainly depends on the water supply from underground. However, declined underground water level in this area is a major concern for producers and policy makers. For ensuring crop irrigation, deep wells below 100 m deep or more are needed in the NCP. This situation has caused the underground water table to decline rapidly. Three- and four-irrigation treatments were the traditional irrigation practices for high-yielding wheat in the NCP (Li et al. 2005). In recent years, one- or two-irrigation strategies were recommended for this area, especially in Hebei Province and surrounding areas, and are being accepted by farmers and applied to winter wheat production due to a higher WUE and higher grain yield. The results of our study indicated that the average yield increase was 2.39 Mg ha–1 from T0 to T1 and 0.59 Mg ha–1 from T1 to T3, and the yield increase with increased irrigation was non-significant. The maximum average yield increase occurred from the rainfed treatment to the single irrigation at the jointing stage (T1).

Fig. 6 Simulated water use efficiency (WUE; kg ha–1 mm–1)using the CERES-Wheat model as affected by irrigation treatments, using long-term weather data from 1981 to 2014 in the North China Plain. T0, rainfed; T1, a single irrigation at the jointing stage; T2, a single irrigation at the flowering stage; T3, two irrigations at the jointing and anthesis stages;T4, two irrigations at the jointing and grain- filling stages; T5,three irrigations at the upstanding stage, booting stage and 20 days after anthesis; T6, three irrigations at the upstanding stage, booting stage and anthesis stage; T7, four irrigations at the upstanding stage, booting stage, anthesis stage and grainfilling stage. Crop seasons were classified into three rainfall/year categories according to the amount of precipitation: ＜100 mm (11 seasons), 100–140 mm (12 seasons), and ＞140 mm(10 seasons).

Chen et al. (2015) and Chennafiet al. (2006) indicated that the impact of water deficit on wheat yield and WUE differs among the growth stages of wheat crop, and the most sensitive stage depends on the area. Adequate water supply during sensitive stages is critical for growth and yield formation. Li et al. (2005) concluded that in the NCP,the jointing to booting stage was the growth period most sensitive to a soil water deficit with respect to wheat growth,and con firmed that irrigating at the jointing stage increased the number of ears per unit area and grain number per ear.This result is similar to those of our study. A single irrigation at the jointing stage can maintain sustainable positive wheat production in the NCP while protecting underground water resources from further depletion (Lan and Zhou 1995; Zhang et al. 1998). On the other hand, although a negative effect on grain yield is usually found under water deficit, crops have complex mechanisms of response to water stress (Chaves and Oliveira 2004; Cattivelli et al.2008). Moderate water stress at certain growth stages can stimulate the redistribution of photosynthates to grain and improve the HI. Zhang et al. (2008) reported a moderate water deficit at the grain- filling stage increased the mobilization of assimilates stored in vegetative organs to grains, and this resulted in higher grain yield, HI and WUE. Hocking (1994) found that water deficit at the grain- filling stage increases the redistribution of dry matter accumulated before anthesis from vegetative organs to reproductive organs. In this region, annual precipitation is concentrated during the summer months. The higher grain yield in T1 may have been due to the moderate water deficit that occurred around the grain- filling stage, which stimulated the redistribution of photosynthates to grain and improved the HI. Furthermore, when exposed to soil drying, winter wheat crop develops a deeper root system and modi fies its canopy structure when grown in waterlimited environments. This is one of the positive effects of a moderate water deficit. The irrigation strategy of T1, which limited irrigation to before the jointing stage to maintain the soil drying environment, promoted the development of a deep root system. And the deep root system allows the plants to use water from a greater depth, which is stored from precipitation in the previous summer season (Xue et al. 2003). Thus, where there is water deficit in the NCP,T3 is the best irrigation strategy for winter wheat production for higher grain yield, and T1 may be used as an alternative irrigation scheme for further water savings.

4.2. The effects of different rainfall year categories on winter wheat production

The WUE also varied among the different rainfall categories per year (i.e., growing season). Chen et al. (2014) found that seasonal precipitation obviously affected winter wheat yield and that the effect of precipitation significantly declined when the crops were well irrigated. Our results showed that grain yield varied with rainfall year category. The average yield of T3 varied from 7.85 to 8.26 Mg ha–1 for the three rainfall categories and had the lowest range of yield values among those different categories. The average yield of T1 varied from 7.01 to 7.91 Mg ha–1 for the three rainfall categories, and the variation in yield was lower than in the rainfed treatment. Meanwhile, the highest WUE was achieved with one irrigation at the jointing stage for most of the year. The WUE declined significantly with increased irrigation. The mean values of WUE in this study conform with those of a previous study on irrigated winter wheat crop(Zhang et al. 2008) and indicated that one irrigation at the jointing stage (T1) or two irrigations at the joint and anthesis stage (T3) may be adequate for winter wheat production in the NCP. Considering the water resource crisis in this area, a substantial decrease in water consumption for winter wheat production is needed to meet the underground water challenges facing the NCP in the next few decades. A single irrigation at the jointing stage is possibly the most suitable irrigation strategy for winter wheat production.

徐锦霞[17]认为无障碍网络课程是一种新的网络课程的视角，最终实现无论何人何时何地都能学习的无障碍网络教学模式。

5. Conclusion

The CERES-Wheat model was very well simulated winter wheat phenology, grain and biomass yields, and WUE responses to irrigation management in the NCP after genetic coefficients calibrated, based on the data got from field experiments. Scenario analyses indicated that simulated grain yield based on different growing season rainfall categories was more affected by irrigation when the growing season precipitation was below 100 mm. Two irrigations(75 mm each at the jointing stage and anthesis stage)resulted in the highest grain yield and WUE. Meanwhile,simulated grain yield based on two irrigations (one each at the jointing and anthesis stages) was not obviously impacted by the different rainfall levels. For the other irrigation treatments, grain yield was improved when rainfall increased. A single irrigation at the jointing stage may thus be an alternative irrigation scheme for further water savings.

Acknowledgements

This work was funded by the Special Fund for Agro-scientific Research in the Public Interest of China (201203031,201303133) and the National Natural Science Foundation of China (31071367).

References

Ahmed M, Akram M N, Asim M, Aslam M, Hassan F, Higgins S, Claudio O S, Hoogenboom G. 2016. Calibration and validation of APSIM-Wheat and CERES-Wheat for spring wheat under rainfed conditions: Models evaluation and application. Computers & Electronics in Agriculture, 123,384–401.

Arora V K, Singh H, Singh B. 2007. Analyzing wheat productivity responses to climatic, irrigation and fertilizer-nitrogen regimes in a semi-arid sub-tropical environment using the CERES-Wheat model. Agricultural Water Management,94, 22–30.

Attia A, Rajan N, Xue Q W, Nair S, Ibrahim A, Hays D. 2016.Application of DSSAT-CERES-Wheat model to simulate winter wheat response to irrigation management in the Texas High Plains. Agricultural Water Management, 165,50–60.

Bannayan M, Hoogenboom G. 2009. Using pattern recognition for estimating cultivarco efficients of a crop simulation model. Field Crops Research, 111, 290–302.

Cattivelli L, Rizza F, Badeck F W, Mazzucotelli E, Mastrangelo A M, Francia E, Mare C, Tondelli A, Stanca A M. 2008.Drought tolerance improvement in crop plants: An integrated view from breeding to genomics. Field Crops Research,105, 1–14.

CAYN (China Agricultural Yearbook Newsroom). 2013. China Agriculture Yearbook 2013. Jindun Publishing House,Beijing. (in Chinese)

Chaves M M, Oliveira M M. 2004. Mechanisms underlying plant resilience to water deficits: Prospects for water-saving agriculture. Journal Experimental Botany, 55, 2365–2384.

Chen J Y, Tang C Y, Shen Y J, Sakura Y, Kondoh A, Shimada J. 2003. Use of water balance calculation and tritium to examine the dropdown of groundwater table in the piedmont of the North China Plain (NCP). Environmental Geology,44, 564–571.

Chen R, Cheng W H, Cui J, Liao J, Fan H, Zheng Z, Ma F Y.2015. Lateral spacing spacing in drip-irrigated wheat: The effects on soil moisture, yield, and water use efficiency.Field Crops Research, 179, 52–62.

Chen S Y, Sun H Y, Shao L W, Zhang X Y. 2014. Performance of winter wheat under different irrigation regimes associated with weather conditions in the North China Plain. Australian Journal of Crop Science, 8, 550–557.

Chenna fiH, Aidaoui A, Bouzerzour H, Saci A. 2006. Yield response of durum wheat (Triticum durum Desf.) cultivar Waha to de ficit irrigation under semi-arid growth conditions.Asian Journal of Plant Sciences, 5, 854–860.

Dettori M, Cesaraccio C, Motroni A, Spano D, Duce P. 2011.Using CERES-Wheat to simulate durum wheat production and phenology in Southern Sardinia, Italy. Field Crops Research, 120, 179–188.

Dokoohaki H, Gheysari M, Mehnatkesh A, Ayoubi S. 2015.Applying the CSM-CERES-Wheat model for rainfed wheat with specified soil characteristic in undulating area in Iran.Archives of Agronomy and Soil Science, 61, 1231–1245.

Dong B D, Shi L, Shi C H, Qiao Y Z, Liu M Y, Zhang Z B. 2011.Grain yield and water use efficiency of two types of winter wheat cultivars under different water regimes. Agricultural Water Management, 99, 103–110.

Eck H V. 1988. Winter wheat response to nitrogen and irrigation.Agronomy Journal, 80, 902–908.

Fang Q X, Ma L, Green T R, Yu Q, Wang T D, Ahuja L R. 2010.Water resources and water use efficiency in the North China Plain: Current status and agronomic management options.Agricultural Water Management, 97, 1102–1116.

FAO. 1990. FAO-UNESCO Soil Map of the World. vol. VIII.UNESCO, Paris.

Fereres E, Soriano M A. 2007. Deficit irrigation for reducing agricultural water use. Journal of Experimental Botany,58, 147–159.

Geerts S, Raes D. 2009. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management, 96, 1275–1284.

Guo Z, Zhang Y, Zhao J, Shi Y, Yu Z. 2014. Nitrogen use by winter wheat and changes in soil nitrate nitrogen levels with supplemental irrigation based on measurement of moisture content in various soil layers. Field Crops Research, 164,117–125.

Han S J, Tian F Q, Liu Y, Duan X H. 2016. Socio-hydrologic perspectives of the co-evolution of humans and groundwater in Cangzhou, North China Plain. Hydrology & Earth System Sciences Discussions, 21, 1–17.

He J, Cai H, Bai J. 2013. Irrigation scheduling based on CERESWheat model for spring wheat production in the Minqin Oasis in Northwest China. Agricultural Water Management,128, 19–31.

He J, Jones J W, Graham W D, Dukes M D. 2010. Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agricultural Systems, 103, 256–264.

Hocking P J. 1994. Dry-matter production, mineral nutrient concentrations, and nutrient distribution and redistribution in irrigated spring wheat. Journal of Plant Nutrition, 17,1289–1308.

Hoogenboom G, Jones J W, Porter G H, Wilkens P W, Boote K J, Batchelor W D, Hunt L A, Tsuji G Y. 2004. DSSAT v.4.0,vol. 1, Overview. ICASA, University of Hawaii, Honolulu,USA.

Hoogenboom G, Jones J W, Wilkens P W, Porter C H, Boote K J, Hunt L A, Singh U, Lizaso J I, White J W, Uryasev O, Ogoshi R, Koo J, Shelia V, Tsuji G Y. 2015. Decision Support System for Agrotechnology Transfer (DSSAT). ver.4.6. DSSAT Foundation, Prosser, Washington.

Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T. 2003. The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.

Ji J, Cai H, He J, Wang H. 2014. Performance evaluation of CERES-Wheat model in Guanzhong Plain of Northwest China. Agricultural Water Management, 144, 1–10.

Kang S. 2002. Effects on yield and water use efficiency of winter wheat on the Loess Plateau of China. Agricultural Water Management, 55, 203–216.

Kang S. 2004. Controlled alternate partial root-zone irrigation:Its physiological consequences and impact on water use efficiency. Journal of Experimental Botany, 55, 2437–2446.

Lan L, Zhou D. 1995. Wheat Production for High Yield and Water-Saving. Beijing Agricultural University Publishers,China. (in Chinese)

Li J M, Inanaga S, Li Z H, Eneji A E. 2005. Optimizing irrigation scheduling for winter wheat in the North China Plain.Agricultural Water Management, 76, 8–23.

Liu K. 2010. Effects of water-saving cultivation on utilization of water and nitrogen for winter wheat and following summer maize in North China Plain. MSc thesis, China Agricultural University, Beijing. (in Chinese)

Loague K, Green R W. 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology, 7, 51–73.

Ma Y, Feng S Y, Song X. 2015. Evaluation of optimal irrigation scheduling and groundwater recharge at representative sites in the North China Plain with SWAP model and field experiments. Computers & Electronics in Agriculture, 116,125–136.

Mavromatis T. 2014. Pre-season prediction of regional rainfed wheat yield in Northern Greece with CERES-Wheat.Theoretical Applied Climatology, 117, 653–665.

Mueller N D, Gerber J Q, Johnston M, Ray D K, Ramankutty N, Foley J A. 2012. Closing yield gaps through nutrient and water management. Nature, 490, 254–257.

Wang Z M, Wang P, Li X H, Li J M, Lu L Q. 2006. Principle and technology of water-saving fertilizer-saving high-yielding and simple cultivation in winter wheat. Review of China Agricultural Science and Technology, 8, 38–44. (in Chinese)

Piao S L, Ciais P, Huang Y, Shen Z H, Peng S S, Li J S, Zhou L P, Liu H Y, Ma Y C, Ding Y H, Friedlingstein P, Liu C Z,Tan K, Yu Y Q, Zhang T Y, Fang J Y. 2010. The impacts of climate change on water resources and agriculture in China. Nature, 467, 43–51.

Ritchie J T. 1972. Model for predicting evaporation from a row crop with incomplete cover. Water Resources Research,8, 1204–1213.

Ritchie J T, Otter S. 1985. Description and performance of CERES-Wheat: A user-oriented wheat yield model. In: Willis W O, ed., ARS Wheat Yield Project. National Technical Information Service, Spring field, VA. pp. 159–175.

Sun H Y, Shen S, Yu Q, Flerchinger G N, Zhang Y Q. 2010.Effect of precipitation change on water balance and WUE of the winter wheat-summer maize rotation in the North China Plain. Agricultural Water Management, 97, 1139–1145.

Willmott C J. 1981. On the validation of models. Physical Geography, 2, 184–194.

Willmott C J. 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorogical Society,63, 1309–1369.

Wu Y C. 2005. Improvement of nitrogen fertilizer use efficiency by water-saving cultivation in winter wheat-summer maize production system in northern China. Ph D thesis, China Agricultural University, Beijing. (in Chinese)

Xue Q, Zhu Z, Musick J T, Stewart B A, Dusek D A. 2003.Root growth and water uptake in winter wheat under de ficit irrigation. Plant & Soil, 257, 151–161.

Yu Q, Li L H, Luo Q Y, Eamus D, Xu S H, Chen C, Wang E L,Liu J D, Nielsen D C. 2014. Year patterns of climate impact on wheat yields. International Journal of Climatology, 34,518–528.

Zadoks J C, Chang T T, Konzak C F. 1974. A decimal code for the growth stages of cereals. Weed Research, 14, 415–421.

Zhang J, Sui X, Li B, Su B L, Li J M, Zhou D X. 1998. An improved water-use efficiency for winter wheat grown under reduced irrigation. Field Crops Research, 59, 91–98.

Zhang S Q. 2009. Mechanism of yield formation and characteristics of population in winter wheat under the water-saving and high-yielding cultivation condition. Ph D thesis, China Agricultural University, Beijing. (in Chinese)

Zhang X, Chen S, Sun H, Pei D, Wang Y. 2008. Dry matter,harvest index, grain yield and water use efficiency as affected by water supply in winter wheat. Irrigation Science,27, 1–10.

Zhang X Y, Chen S Y, Liu M Y, Pei D, Sun H Y. 2005. Improved water use efficiency associated with cultivars and agronomic management in the North China Plain. Agronomy Journal,97, 783–790.

Zhang X Y, Wang Y Z, Sun H Y, Chen S Y, Shao L W.2013. Optimizing the yield of winter wheat by regulating water consumption during vegetative and reproductive stages under limited water supply. Irrigation Science, 31,1103–1112.