长江流域资源与环境 >> 2020, Vol. 29 >> Issue (9): 2035-2046.doi: 10.11870/cjlyzyyhj202009014

• 农业发展 • 上一篇    下一篇

基于DNDC模型的双季稻体系氨挥发损失研究

夏文建1#, 王淳2#, 张丽芳1, 张文学1,冀建华1, 陈金1, 刘增兵1,刘光荣1*   

  1. (1. 江西省农业科学院土壤肥料与资源环境研究所/农业农村部长江中下游作物生理生态与耕作重点实验室,
    国家红壤改良工程技术研究中心, 江西 南昌 330200;2. 山东农业大学资源与环境学院/
    土肥资源高效利用国家工程实验室, 山东 泰安 271018)
  • 出版日期:2020-09-20 发布日期:2020-09-30

Suitability of DNDC Model to Simulate Ammonia Volatilization for Double Rice Cropping System

XIA Wen-jian 1,WANG Chun 2, ZHANG Li-fang 1, ZHANG Wen-xue 1, JI Jian-hua 1, CHEN Jin 1, LIU Zeng-bin 1, LIU Guang-rong 1   

  1. (1 Institute of Soil Fertilizer and Resource Environment, Jiangxi Academy of Agricultural Sciences; Key Laboratory
    of Crop Ecophysiology and Farming System for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs,
    National Engineering and Technology Research Center for Red Soil Improvement, Nanchang 330200, China; 2 College of Resources and
    Environment, Shandong Agricultural University,National Engineering Laboratory for Efficient Utilization of
     Soil and Fertilizer Resources, Taian 271018, China)
  • Online:2020-09-20 Published:2020-09-30

摘要: 为了探索长江流域双季稻体系氮肥施用对氨挥发损失的影响,评价Denitrification Decomposition (DNDC)模型对产量和氨挥发拟合的适应性,设计了早稻和晚稻不同氮肥用量田间试验,采用密闭室间歇通气法原位观测氨挥发排放通量,利用DNDC模型进行模拟分析,并运用模拟结果探讨了水稻产量和氨挥发损失与施氮量之间的关系。结果显示,模型能较好模拟双季稻体系水稻产量和氨挥发,早稻、晚稻和双季稻产量模拟值与观测值的相关系数分别为0.994、0.928、0.979,早稻、晚稻和整个双季稻生育期氨挥发模拟值与观测值的相关系数分别为0.994、0.998和0.997,均达到极显著水平。DNDC模型能较好预测因施肥引起的氨挥发排放峰,但在氨挥发通量和排放总量的定量上还需要进一步改进。敏感性指数分析表明,气温是影响作物产量的关键因素,氮肥用量和气温是影响氨挥发的主要因素。DNDC模型在模拟双季稻体系籽粒产量上具有较高的可信度,DNDC模拟和田间观测数据计算的最高产量施氮量分别是420和417 kg·hm-2。稻田氨挥发损失量与施氮量之间满足二次函数和线性关系,二次函数能更好描述两者之间的关系。为使DNDC更准确的进行估算和应用,有必要获取更翔实的环境资料以减少输入数据的不确定性。

Abstract: The objective of this study was to evaluate the validity and suitability of denitrification-decomposition model(DNDC)to estimate grain yield and ammonia volatilization in double rice cropping system. The different nitrogen application levels experiments were conducted under double rice cropping system, located in the middle and lower reaches of the Yangtze Plain. A continuous airflow enclosure (CAE) method was used to estimate NH3 emission, and DNDC model was used to simulate grain yield and ammonia volatilization. The uncertainty and sensitivity analysis was conducted to assess the variability in the model outputs and input parameters of climate conditions, soil properties and farming management etc. The results showed that the DNDC simulated grain yield and ammonia volatilization fitted well with the field observed data. The model can capture the peak of ammonia volatilization caused by fertilizer N application. The correlation coefficients between simulated and observed grain yields of early rice, late rice and double rice cropping system were 0.994, 0.928 and 0.979, respectively. The correlation coefficients between simulated and observed values of ammonia volatilization in early rice, late rice and double rice growth period were 0.994, 0.998 and 0.997, respectively. According to the sensitivity test on the DNDC model, grain yield was significantly affected by temperature, and ammonia volatilization was increased with the nitrogen application rate and temperature. The relationship between grain yield and nitrogen application amount was curvilinear. The nitrogen fertilizer requirement of theoretical maximum yield calculated from DNDC simulation data and field observation data were 420 and 417 kg·hm-2, respectively. Soil ammonia volatilization increased with nitrogen application rate following a quadratic function equation, rather than a linear equation. A higher reliability was found between DNDC simulated value and field measured results about grain yields than ammonia volatilization. However, there is a certain deviation between the simulated and measured values of ammonia flux and cumulative ammonia volatilization, ongoing modification and calibration still need in order to improve the model's performance. Environmental data such as meteorology, soil, water, etc., as more detailed, accurate, and informative as possible, is necessary to reduce the uncertainty of input data, before DNDC application.

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