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The Applicability of Mechanism Phenology Models to Simulating Apple Flowering Date in Shaanxi Province
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摘要: 以陕西苹果花期为研究对象，针对4个机理性物候模型——顺序模型（SM）、平行模型（PM）、深度休息模型（DRM）和热时模型（TTM），基于各果区代表站的花期数据及同期气象数据订正模型参数，利用内部检验和交叉验证（留一验证）方法，评价模型在模拟花期上的适用性。结果表明：内部检验时各站点的最适模型不同，总体上，SM和TTM均方根误差略低（3.30 d）；交叉验证时模型表现相当，各模型平均的均方根误差为4.52 d，略优于内部检验。使用单站外推和求平均后外推将TTM参数应用至果区内其他站，这两种方法的均方根误差均优于国外同类研究（10.0 d），其中单站外推的均方根误差（5.90 d）又高于求平均后外推（7.21 d）。综合考虑模型的复杂性与模拟精度，推荐使用TTM并分果区模拟陕西苹果花期。Abstract: China's apple growing area and production rank first in the world, and thus apple is one of important economical crops in China. Meteorological disaster occurring in apple critical phenology stage is one of the main disasters impacting yield and quality, especially in the dominant planting provinces such as Shaanxi. Accurate forecasting on flowering date in Shaanxi can provide scientific support for taking applicable defensive management and improving the ability to resist meteorological disasters, and therefore benefit to apple yield and quality. Taking apple flowering stage as an example, the applicability of 4 typical phenology models is evaluated, including Sequential Model (SM), Parallel Model (PM), Deepening Rest Model (DRM), and Thermal Time Model (TTM). There are 4 apple planting divisions in Shaanxi Province. In each division, there are two phenology observation sites. Internal validation and cross validation (Leave One Out Cross Validation) of 4 models are done using sites with longer observations, while shorter record sites are used to evaluate the effect of model extrapolation application. In 4 divisions, 4 sites performing internal validation and cross validation are Xunyi, Luochuan, Liquan and Baishui, respectively, while four sites to conduct extrapolation application are Changwu, Baota, Fengxiang and Tongchuan, respectively. Model performance is assessed according to the root mean square error (RMSE) of modelled flowering date. Internal validation results show that optimal models are different in different sites and generally TTM and SM give similar accuracy (3.30 d). Cross validation also verifies and there is no particularly prominent model. The average RMSE for all four models is 4.52 d. TTM is then extrapolatively applied to other sites with two methods (extrapolation based on values in a single site, and extrapolation based on average values of 4 sites). The accuracy of both methods is higher than that of similar studies abroad (10.0 d), while the accuracy of extrapolation based on values in a single site (5.90 d) is higher than that of extrapolation based on average values of 4 sites (7.21 d). Considering the complexity and simulation accuracy, TTM is recommended to be used to simulate the flowering period in each apple planting division in Shaanxi Province.
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图 2 4个校正站开花期观测值和内部检验的模拟值
Fig. 2 Observed flowering date at 4 parameter-calibration sites and values given by 4 models in internal validation
图 3 4个校正站开花期的观测值与交叉验证的模拟值
Fig. 3 Observed flowering date at 4 parameter-calibration sites and values given by 4 models in cross validation
图 4 TTM参数在4个外推站应用的模拟结果
(a)单站外推，(b)参数求平均值后外推至4个站
Fig. 4 Simulated flowering date applied by TTM at 4 parameter-validation sites
(a)based on values of a single site, (b)based on average values of 4 sites
表 1 各站的观测年份和年数
Table 1 Years and number of observations at each site
所属果区 站名 观测年份 观测年数 关中果区 礼泉 1973—1974，1976，1980，1982—1983，1989，1992，1998，2000—2001，2003—2004，2007—2014 21 凤翔 2001—2009 9 渭北西部果区 旬邑 1994—2014 21 长武 1995—2002 8 渭北东部果区 白水 2001—2009，2011—2014 13 铜川 2001—2005，2007—2009 8 延安果区 洛川 1998—2014 17 宝塔 2001—2003，2005，2007，2009 6 
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表 2 SM，PM和DRM的参数寻优空间
Table 2 Parametric optimization spaces of SM, PM and DRM
参数 最小值 最大值 Tb/℃ -20 -5 Topt/℃ 2.0 6.9 Tmax/℃ 7 11 Rc 3 20 a 0.1 3.0 b 5 15 c -5 10 Rf 10 40
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表 3 4个模型在校正站的内部检验结果
Table 3 Internal validation results of 4 models at 4 parameter-calibration sites
站点 统计量 SM PM DRM TTM 旬邑 均方根误差/d 4.02 4.43 4.75 3.63 R2 0.438*** 0.446*** 0.402* 0.412** 洛川 均方根误差/d 2.8 3.48 2.78 3.07 R2 0.618*** 0.474** 0.611*** 0.536*** 礼泉 均方根误差/d 2.86 4.27 3.17 2.88 R2 0.834*** 0.698*** 0.802*** 0.836*** 白水 均方根误差/d 3.5 4.63 3.78 3.67 R2 0.638*** 0.369* 0.556*** 0.531*** 注：*，**和***分别表示达到0.05, 0.01和0.001显著性水平。
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表 4 4个模型在校正站的交叉验证结果
Table 4 Cross validation results of 4 models at 4 parameter-calibration sites
站名 统计量 SM PM DRM TTM 旬邑 均方根误差/d 5.36 4.75 6.65 6.41 R2 0.273*** 0.335** 0.247* 0.193* 洛川 均方根误差/d 4.2 4.09 3.48 3.79 R2 0.241* 0.259* 0.406** 0.365** 礼泉 均方根误差/d 3.82 3.73 3.74 3.53 R2 0.718*** 0.728*** 0.735*** 0.761*** 白水 均方根误差/d 6.34 5.24 4.21 5.19 R2 0.32* 0.43* 0.48** 0.32* 注：*，**和***分别表示达到0.05, 0.01和0.001显著性水平。
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表 5 TTM内部检验时4个校正站的参数取值、热驱动阈值F*和准确率
Table 5 Parameter values, thermal threshold(F*) and simulation accuracy in internal validation of TTM at 4 parameter-calibration sites
站点 下限气温/℃ 静止期始期 热驱动阈值F* 内部验证准确率/% 旬邑 2.5 03-11 206.0 61.9 洛川 2.0 02-24 317.5 94.1 礼泉 2.9 02-13 287.7 81.0 白水 2.7 03-09 218.3 69.2
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表 6 TTM参数在4个外推站应用时的均方根误差(单位：d)
Table 6 Root mean square errors of simulated flowering date applied by TTM at 4 parameter-validation sites(unit:d)
站点 单站外推 求平均后外推 长武 6.24 9.80 宝塔 5.08 5.69 凤翔 5.34 4.51 铜川 5.34 7.62
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