Comparison of the applicability of phenological models in major maize production areas in China
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摘要: 发育模式是预测作物发育对气候变化响应的主要工具。模式在变暖环境下的适用性对作物产量评估有重要影响。本研究比较了机器学习算法(多层感知器神经网络, MLP)以及3个机理发育模式[MAIS (maize simulation)、Beta和耦合响应与适应机制的模式(RAM)]在变暖环境下的适用性。选择东北春玉米和华北夏玉米具有20年以上物候观测的站点, 使用发育期资料及同期的逐日气象数据, 按生长季均温异常度, 将观测年份划分为冷、暖年。冷年数据用于率定模式参数, 暖年数据用于验证变暖环境下模式的表现。根据冷暖年表现的差异评价模式在变暖情景下的适用性。评价指标包括归一化均方根误差(NRMSE)、平均偏差(MBE)和模拟误差随发育阶段均温的趋势。结果表明: 在冷年校正时, MLP的模拟精度明显高于机理发育模式, 机理发育模式中RAM表现最优, 其次是Beta和MAIS。MAIS和Beta模拟值略早于观测值, 而RAM和MLP普遍迟于观测值。机理发育模式的误差随发育阶段均温有显著趋势的站点比例明显低于MLP, 3个机理发育模式中, Beta有显著趋势的站点比例最小, 其次是RAM和MAIS。在变暖情景下进行验证, Beta模拟精度表现最好, 其次是MLP和RAM, MAIS表现较差。3个机理发育模式模拟值早于观测值, 而MLP迟于观测值。除MLP以外的模式模拟误差随发育阶段均温有显著趋势的站点比例较冷年均增大。4个模式中Beta有显著趋势的站点比例最小, 其次是MLP、MAIS和RAM。整体来看, 没有一个模式在校正和验证阶段均全面占优, MLP虽然冷年校正时最优, 但暖年表现较差, 而机理发育模式虽然冷年校正时不是最优, 但暖年验证时表现较好。本研究认为, 不同模式有不同的适用场景, 关注准确反演历史气候变化对发育期影响时, 建议采用MLP, 而关注准确预测未来气候变化对发育期影响时, 应以机理发育模式为主。Abstract: Crop growth simulation models are the primary instrument used for predicting crop developmental responses to climate change. The understanding of the applicability of phenological models is critical for measuring how climate change affects crop yields, particularly under warm climate conditions. Machine learning algorithm (MLP), maize simulation model (MAIS), response and adaption model (RAM), and Beta model (Beta) in warm climate were compared in this study. Based on phenological observation data of over 20 years for spring maize in Northeast China and summer maize in North China from agricultural meteorological station records and daily weather data, we separated the data into two categories: cold and warm years. Data were obtained by calculating the abnormality based on the mean temperature during the maize growing season. Cold years data were used to calibrate the models, and warm years data were used to validate them and subsequently evaluate their performance. The normalized root mean square error (NRMSE), mean bias error (MBE), and systematic bias of the simulation error against the average temperature of the growth period were used to evaluate the deviation between the simulated and observed maize phenology of the four models. The results showed that MLP performed much better than the three mechanistic phenology models during cold years. RAM outperformed the other mechanistic phenology models, followed by Beta and MAIS models. Our results indicated that the dates of MAIS and Beta were earlier than the observations, whereas the simulations of RAM and MLP were later than the observations. The proportion of sites with a significant trend of simulation error against the average temperature of the growth period for the RAM, MAIS, and Beta models was lower than that for MLP. Compared to that for MAIS and RAM, the proportion of sites with a significant trend for Beta was the smallest. In warm years, Beta performed better than the other models, followed by MLP, RAM, and MAIS. The simulations of the three mechanistic phenology models were earlier than the observations, but the simulations of MLP were later than the observations. Beta showed the smallest proportion of sites with a significant trend, followed by MLP, MAIS, and RAM. Overall, the models did not benefit from both calibration and validation. MLP performed well during calibration in cold years, but poorly in warm years. The overall performance of the mechanistic phenology models was worse than that of MLP in cold years, but they performed better in warm years. Different models are appropriate in various contexts. The MLP can be recommended to precisely reverse the impact of historical climate change on growth period. However, mechanistic models should be used to precisely predict the impact of future climate change on growth period.
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Keywords:
- Phenology /
- Mechanistic phenology model /
- Machine learning /
- Climate warming /
- Maize
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图 1 研究区域和农业气象站点的分布
Figure 1. Study area and location of agrometeorological observation sites
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图 2 不同发育模式冷年模拟发育期的归一化均方根误差(a: 春玉米营养生长阶段; b: 春玉米生殖生长阶段; c: 夏玉米营养生长阶段; d: 夏玉米生殖生长阶段)
箱体实线从上至下分别为上边缘、上四分位数、中位数、下四分位数、下边缘, 方框中的空心方块是平均值。空心圆表示异常值, 下、上边缘的“$ \times $”分别是第1个和第99个百分位数。From top to bottom, solid lines in boxplots represent the maximum, the 1st quantile, the median, the 3rd quantile and the minimum, and open square in the box is the mean. The open circles represent the outliers, “$ \times $” symbols below and above the whiskers are the 1st and 99th percentiles, respectively.
Figure 2. Normalized root mean square error of simulated maize growth period of different phenological models in cold years. a: vegetative growth period (VGP) of spring maize; b: reproductive growth period (RGP) of spring maize; c: VGP of summer maize; d: RGP of summer maize.
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图 3 不同发育模式冷年模拟发育期的平均偏差(a: 春玉米营养生长阶段; b: 春玉米生殖生长阶段; c: 夏玉米营养生长阶段; d: 夏玉米生殖生长阶段)
箱体实线从上至下分别为上边缘、上四分位数、中位数、下四分位数、下边缘, 方框中的空心方块是平均值。空心圆表示异常值, 下、上边缘的“$ \times $”分别是第1个和第99个百分位数。From top to bottom, solid lines in boxplots represent the maximum, the 1st quantile, the median, the 3rd quantile and the minimum, and open square in the box is the mean. The open circles represent the outliers, “$ \times $” symbols below and above the whiskers are the 1st and 99th percentiles, respectively.
Figure 3. Mean bias error of simulated growth period of different phenological models in cold years. a: vegetative growth period (VGP) of spring maize; b: reproductive growth period (RGP) of spring maize; c: VGP of summer maize; d: RGP of summer maize.
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图 4 不同发育模式在冷年模拟时的模拟误差随发育阶段均温的趋势[a: 春玉米营养生长阶段; b: 春玉米生殖生长阶段; c: 夏玉米营养生长阶段; d: 夏玉米生殖生长阶段。实心点为站点趋势显著(P<0.05)]
Figure 4. Trend of simulation error against growth period average temperature of different phenological models in cold years. a: vegetative growth period (VGP) of spring maize; b: reproductive growth period (RGP) of spring maize; c: VGP of summer maize; d: RGP of summer maize. Solid point indicate the trends are statistically significant at P<0.05 level.
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图 5 不同发育模式暖年模拟发育期的归一化均方根误差(a: 春玉米营养生长阶段; b: 春玉米生殖生长阶段; c: 夏玉米营养生长阶段; d: 夏玉米生殖生长阶段)
箱体实线从上至下分别为上边缘、上四分位数、中位数、下四分位数、下边缘, 方框中的空心方块是平均值。空心圆表示异常值, 下、上边缘的“$ \times $”分别是第1个和第99个百分位数。From top to bottom, solid lines in boxplots represent the maximum, the 1st quantile, the median, the 3rd quantile and the minimum, and open square in the box is the mean. The open circles represent the outliers, “$ \times $” symbols below and above the whiskers are the 1st and 99th percentiles, respectively.
Figure 5. Normalized root mean square error of simulated growth period of different phenological models in warm years. a: vegetative growth period (VGP) of spring maize; b: reproductive growth period (RGP) of spring maize; c: VGP of summer maize; d: RGP of summer maize.
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图 6 不同发育模式暖年模拟发育期的平均偏差(a: 春玉米营养生长阶段; b: 春玉米生殖生长阶段; c: 夏玉米营养生长阶段; d: 夏玉米生殖生长阶段)
箱体实线从上至下分别为上边缘、上四分位数、中位数、下四分位数、下边缘, 方框中的空心方块是平均值。空心圆表示异常值, 下、上边缘的“$ \times $”分别是第1个和第99个百分位数。From top to bottom, solid lines in boxplots represent the maximum, the 1st quantile, the median, the 3rd quantile and the minimum, and open square in the box is the mean. The open circles represent the outliers, “$ \times $” symbols below and above the whiskers are the 1st and 99th percentiles, respectively.
Figure 6. Mean bias error of simulated growth period of different phenological models in warm years. a: vegetative growth period (VGP) of spring maize; b: reproductive growth period (RGP) of spring maize; c: VGP of summer maize; d: RGP of summer maize.
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图 7 不同发育模式暖年模拟时的模拟误差随发育阶段均温的趋势。a: 春玉米营养生长阶段; b: 春玉米生殖生长阶段; c: 夏玉米营养生长阶段; d: 夏玉米生殖生长阶段。实心点为站点趋势显著(P<0.05)。
Figure 7. Trend of simulation error against growth period average temperature of different phenological models in warm years. a: vegetative growth period (VGP) of spring maize; b: reproductive growth period (RGP) of spring maize; c: VGP of summer maize; d: RGP of summer maize. Solid point indicate the trends are statistically significant at P<0.05 level.
表 1 发育模式参数/超参数取值范围
Table 1 Parameter / hyperparameter value range of phenological models
模式 Model 阶段 Period 参数/超参数名称 Parameter / hyperparameter 取值范围 Range of value 步长 Loop step MAIS VGP, RGP 热时 Thermal time (GTI) 300~1000 10 Beta VGP, RGP 基本发育系数 Basic duration coefficient (K) 3~5 0.25 增温促进系数 Thermal sensitivity coefficient (P) 0.01~3 0.1 VGP 感光性系数 Photoperiod sensitivity coefficient (G ) 0~0.5 0.02 MLP VGP, RGP 隐藏层大小 Hidden layer size (hidden_layer_sizes) 25~125 25 最大迭代次数 Maximum iteration (max_iter) 300~1500 50 随机种子 Random seed (random_state) 0~9 1 VGP: 营养生长阶段; RGP: 生殖生长阶段。VGP: vegetative growth period; RGP: reproductive growth period. 
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