基于APSIM模型的降水和温度变化对秸秆覆盖冬小麦产量的影响

    Response of winter wheat yield under straw mulching in dryland to precipitation and temperature using APSIM model

    • 摘要: 为探究秸秆覆盖处理下降水和温度变化对冬小麦产量的影响,基于秸秆覆盖长期定位试验观测数据和1999—2022年的逐日气候数据,运用APSIM(agricultural production systems simulator)模型模拟分析了未来降水(逐日降水±20%、±10%、0)和温度(逐日温度0 ℃、+1 ℃、+2 ℃、+3 ℃、+4 ℃)变化对冬小麦产量的影响,并对小麦产量变异性和可持续性进行了分析。秸秆覆盖田间试验设计高量覆盖(HSM,9000 kg/hm2)、低量覆盖(LSM,4500 kg/hm2)和不覆盖对照(CK)3个处理。模拟结果表明:1)APSIM模型对3种秸秆覆盖处理冬小麦产量和生物量的模拟精度较高,决定系数R2在0.75~0.92之间,归一化均方根误差在11.07%~14.65%之间,模型一致性指标在0.84~0.91之间;2)降水和温度变化对冬小麦产量均有显著影响。当温度不变时,降水增加会提高小麦产量,处理间的增产效应为HSM>LSM>CK;而当降水不变时,温度升高会导致产量下降,减产效应为LSM>HSM>CK;降水和温度协同作用下同样会导致小麦减产,处理间的减产效应为CK>LSM>HSM。3)与其他气候情景模拟结果相比,降水减少20%和增温2~3 ℃情景下冬小麦产量具有最大的变异系数和最小的可持续指数,作物生产风险较高。4)与CK和LSM相比,HSM处理在不同的气候变化情景下平均具有最高的产量和可持续性指数以及最低的变异系数。因此,未来气候变化背景下,采用高量覆盖管理措施更有利于黄土高原地区冬小麦生产。

       

      Abstract: Wheat production is controlled by precipitation and temperature as well as management practices. However, how wheat yield responds to straw mulching under different climate change scenarios are not well known. Crop models can effectively analyze the impact of different climate conditions and field management practices on crop growth and yield. In this study, based on the observation data from a long-term field experiment that conducted in the Loess Plateau and the on-site daily climatic records from 1999 to 2022, we explored the response of winter wheat to straw mulching under different climate scenarios using the APSIM(agricultural production systems simulator)model. Three treatments as wheat straw mulching at high rate of 9000 kg/hm2 (HSM), low rate of 4500 kg/hm2 (LSM), and no mulching control (CK) were included in the field experiment. Data from field observations of crop growth and soil properties were used to calibrate and validate the APSIM model, ensuring accurate simulation of the conditions. Five levels of precipitation changes (Daily precipitation ±20%, ±10%, and 0) and five levels of temperature changes (Daily temperature 0 ℃, +1 ℃, +2 ℃, +3 ℃, and +4 ℃) were interacted to established a set of climate change scenarios in APSIM model. The variation coefficient and sustainability index of winter wheat yield were also calculated with the modeling data. The simulation results showed that the APSIM model is powerful simulating the grain yield and aboveground biomass accurately with the determination coefficients varied between 0.75-0.92, the normalized root mean square errors varied between 11.07%-14.65%, and the consistency index D varied between 0.84-0.91, respectively. Both precipitation and temperature changes had significant effects on winter wheat yield, and precipitation was more dominant influence than air temperature. When the temperature was constant, winter wheat yield increased with increasing precipitation, with the yield enhancement effect ranked as HSM>LSM>CK among treatments. However, when the precipitation was constant, winter wheat yield decreased with increasing temperature, with the reduction effect ranked as LSM>HSM>CK. Wheat yield also decreased under the interacted scenarios of precipitation and temperature, with the yield reduction effect ranked as CK>LSM>HSM among treatments. Across all climate change scenarios, winter wheat yield was greater in HSM than in LSM and CK. The sustainability index of winter wheat yield was also higher and the variation coefficient of winter wheat yield was lower in HSM than in LSM and CK. Compared with those under other climate change scenarios, wheat yield had a larger variation coefficient and lower sustainability index under the scenario of 20% decrease in precipitation and 2-3 ℃ increase in temperature, indicating a high risk in wheat production. In conclusion, winter wheat production in the Loess Plateau region can be improved by adopting high straw mulching in the context of future climate change. The results of the study provide a theoretical basis for future production and management of winter wheat on the Loess Plateau. In future modeling study, more climate change factors should be included to reduce the uncertainties and provide more comprehensive predictions for wheat production.

       

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