冬小麦物候期对土壤水分胁迫的响应机制与模拟

摘要

作物模型在农业生产管理和决策中发挥着重要作用，而物候期模拟是作物模型正确模拟作物生长发育和产量形成过程的基础。作物模型模拟物候发育的常用算法一般是基于积温的计算，同时也考虑光周期和春化作用的影响，但是水分胁迫对物候发育的次级影响却较少被考虑在内。该研究以连续2季（2013－2014和2014－2015）的遮雨棚下土柱试验和连续3季（2012－2013、2013－2014和2014－2015）的遮雨棚下大田试验数据和前人研究成果为基础提出了冬小麦物候期对水分胁迫的响应机制理论假设，并以土壤相对有效含水率为水分胁迫指标校正冬小麦物候期水分胁迫响应函数。该研究以2014－2015生长季土柱试验各处理试验数据来建立冬小麦物候期水分胁迫响应函数，确定发育加速点A、发育减速点D和发育停止点S所对应的相对有效含水率值分别为0.30、0.10和0。结果发现拔节期和开花期模拟值和观测值之间的均方根误差（root mean square error，RMSE）分别为0.8和1.7 d，绝对相对误差（absolute relative error，ARE）分别低于0.68%和2.09%。然后用2013－2014生长季土柱试验各处理数据进行验证，结果发现拔节期和开花期模拟值和观测值之间的RMSE分别约为0.9和1.1 d，ARE分别在1.37%和1.68%以下。最后再用3年独立大田试验数据对上述修正后的冬小麦物候期算法进行验证，结果发现开花期和成熟期的模拟值与观测值之间的RMSE分别约为2.4和2.0 d，ARE分别低于4.21%和2.67%；与DSSAT-CERES-Wheat模型的模拟结果进行比较，发现修正算法能反映出水分胁迫对冬小麦物候期造成的差异（有提前也有推迟），而DSSAT-CERES-Wheat模型无法体现这种差异，且开花期和成熟期的模拟值与观测值之间的RMSE分别约为4.0和5.5 d，误差最大分别为8和6 d。这表明校正后的冬小麦物候期算法模拟精度得到了较大提高，能在一定程度上描述和量化水分胁迫对冬小麦物候期的影响机制，可用来模拟不同水分胁迫条件下不同品种冬小麦的物候期。%Crop growth simulation models are important in agricultural planning and management. The simulation of crop phenology is the basis of correct simulation of growth and development processes in crop models. Calculation of accumulative thermal time is a common way of simulating crop phonological development in crop models, while the effects of photoperiod and vernalization are also considered. However, the response of crop phenology to water stress is rarely quantified and often neglected. The main objective of this study was to explore and quantify the mechanism of phenology response of winter wheat (Triticumaestivum L.) to water stress. Experiments were conducted in plastic columns under a rainout shelter for winter wheat growing under water stresses at different growth stages in two growing seasons (from October 2013 to June 2014 and from October 2014 to June 2015). Another independent field experiment was conducted under a rainout shelter for winter wheat under water stresses at different growth stages in three continuous growing seasons (2012-2013, 2013-2014, and 2014-2015). In this study, relative water availability (Aw) was chosen as water stress index. WhenAwwas below a certain value of A (defined as critical point of accelerating development), crop began to hasten development, while there was no effect on crop phenological development above A. WhenAwwas below a certain value of S(defined as critical point of ceasing development), crop development stopped. Thus, it was reasonable to propose that there existed a certain value of D (defined as critical point of decelerating development) between points A and S. Thus,Awwould hasten crop development between A and D and delay development between D and S. WhenAw did not affect crop phenological development, the value of water modification factor (WMF) was set as 1; when accelerating crop development, WMF was greater than 1; and when decelerating development, WMF was smaller than 1. Then, modified physiological day (MPD) was computed through multiplying WMF with physiological day (PD). The values of MPD were used to quantify the phenology response of winter wheat to soil water stress. The soil column experimental data of 2014-2015 growing season were used to calibrate the phenology water stress response function. The estimated values of relative water availability of points A, D, and S were 0.30, 0.10 and 0, respectively. The root mean square error (RMSE) between simulated and observed jointing and flowering dates were 0.8 and 1.7 d. The values of absolute relative error (ARE) were below 0.68% and 2.09%, respectively. When verifying the phenology water stress response function with the data of 2013-2014 soil column experiment, the RMSE between simulated and observed jointing and flowering dates were 0.9 and 1.1 d and ARE were less than 1.37% and 1.68%, respectively. When verifying the modified phenology algorithm with the data of independent field experiment of three growing seasons, the RMSE between simulated and observed flowering and maturity dates were 2.4 and 2.0 d and ARE were less than 4.21% and 2.67%, respectively. Compared with the simulation results of CERES-Wheat model in the DSSAT, it showed that the modified algorithm was able to reflect the influences of water stress on winter phenology while CERES-Wheat model showed no difference among different treatments in the same year. The RMSE between CERES-Wheat simulated and observed flowering and maturity dates were 4.0 and 5.5 d and the maximum error were 8 and 6 d, respectively. The results of calibration and verification showed that the phenology water stress response function developed in this study could be used to accurately simulate the variations in phenological dates of different winter wheat varieties caused by different scenarios of soil water stress. This response function needs to be evaluated further in more field experiments and then be embedded in current popular crop models, such as CERES-Wheat in the DSSAT model, to improve their simulation accuracy of phenology under water stress conditions. Consequently, modified crop models are supposed to have a better accuracy and applicability in arid and semi-arid areas.