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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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[1] 王树桐, 王亚南, 曹克强.近年我国重要苹果病害发生概况及研究进展.植物保护, 2018, 44(5):13-25. http://d.old.wanfangdata.com.cn/Periodical/zwbh201805003 [2] 屈振江, 周广胜.中国产区苹果越冬冻害的风险评估.自然资源学报, 2017, 32(5):829-840. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zrzyxb201705010 [3] 王景红, 刘璐, 高峰, 等.陕西富士系苹果花期霜冻灾害气象指标的修订.中国农业气象, 2015, 36(1):50-56. doi: 10.3969/j.issn.1000-6362.2015.01.007 [4] 屈振江, 周广胜, 魏钦平.苹果花期冻害气象指标和风险评估.应用气象学报, 2016, 27(4):385-395. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160401&flag=1 [5] 刘璐, 郭兆夏, 柴芋, 等.陕西省苹果花期冻害风险评估.干旱地区农业研究, 2009, 27(5):251-255. http://d.old.wanfangdata.com.cn/Periodical/ghdqnyyj200905045 [6] 王景红, 张勇, 刘璐.基于多尺度标准化降水指数的陕西苹果主产区气象干旱分析.气象, 2013, 39(12):656-662. http://d.old.wanfangdata.com.cn/Periodical/qx201312015 [7] 王景红, 柏秦凤, 梁轶, 等.陕西苹果干旱指数研究及基于县域单元的苹果干旱风险分布.气象科技, 2014, 42(3):516-523. doi: 10.3969/j.issn.1671-6345.2014.03.025 [8] 田俊, 霍治国.江西省早稻雨洗花灾害指标构建与灾损评估.应用气象学报, 2018, 29(6):657-666. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180602&flag=1 [9] 田俊, 崔海建.江西省双季早稻灌浆乳熟期高温热害影响评估.中国农业气象, 2015, 36(1):67-73. doi: 10.3969/j.issn.1000-6362.2015.01.009 [10] 范雨娴, 霍治国, 杨宏毅, 等.湖南省油菜春季涝溃过程灾变判别指标.应用气象学报, 2018, 29(2):141-153. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180202&flag=1 [11] 郭春明, 任景全, 曹铁华, 等.春玉米穗分化期低温对产量构成因素的影响.应用气象学报, 2018, 29(4):505-512. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180411&flag=1 [12] 杨爱萍, 杜筱玲, 王保生, 等.江西省多气象要素的柑橘冻害指标.应用气象学报, 2013, 24(2):248-256. doi: 10.3969/j.issn.1001-7313.2013.02.013 [13] 李星敏, 柏秦凤, 朱琳.气候变化对陕西苹果生长适宜性影响.应用气象学报, 2011, 22(2):241-248. doi: 10.3969/j.issn.1001-7313.2011.02.013 [14] Wittich K P, Liedtke M.Shifts in plant phenology:A look at the sensitivity of seasonal phenophases to temperature in Germany.Int J Climatol, 2015, 35(13):3991-4000. doi: 10.1002/joc.4262 [15] 宋富强, 张一平.动态物候模型发展及其在全球变化研究中的应用.生态学杂志, 2007, 26(1):115-120. doi: 10.3321/j.issn:1000-4890.2007.01.022 [16] Hänninen H.Modelling bud dormancy release in trees from cool and temperate regions.Acta Forestalia Fennica, 1990, 213:1-47. https://www.researchgate.net/publication/28361839_Modelling_bud_dormancy_release_in_trees_from_cool_and_temperate_regions [17] Kramer K.Selecting a model to predict the onset of growth of Fagus-Sylvatica.J Appl Ecol, 1994, 31:172-181. doi: 10.2307/2404609 [18] Chuine I.A unified model for budburst of trees.J Theor Biol, 2000, 207:337-347. doi: 10.1006/jtbi.2000.2178 [19] 李荣平, 周广胜, 阎巧玲.植物物候模型研究.中国农业气象, 2005, 26(4):210-214. doi: 10.3969/j.issn.1000-6362.2005.04.002 [20] 韩小梅, 申双和.物候模型研究进展.生态学杂志, 2008, 27(1):89-95. doi: 10.3969/j.issn.1005-376X.2008.01.040 [21] Cannell M G, Smith R I.Thermaltime, chilling days and prediction of budburst in Picea sitchensis.J Appl Ecol, 1983, 20(3):951-963. doi: 10.2307/2403139 [22] Hanninen H, Tanino K K.Tree seasonality in a warming climate.Trends in Plant Science, 2011, 16(8):412-416. doi: 10.1016/j.tplants.2011.05.001 [23] Cesaraccio C, Spano D, Snyder R L, et al.Chilling and forcing model to predict bud-burst of crop and forest species.Agric For Meteorol, 2004, 126(126):1-13. https://www.sciencedirect.com/science/article/pii/S0168192304000632 [24] Chen X, Hu B, Yu R, et al.Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China.Global Change Biol, 2005, 11(7):1118-1130. doi: 10.1111/j.1365-2486.2005.00974.x [25] Darbyshire R, Farrera I, Martinezluscher J, et al.A global evaluation of apple flowering phenology models for climate adaptation.Agric For Meteorol, 2017(240/241):67-77. https://www.sciencedirect.com/science/article/pii/S0168192317301284 [26] 蒲金涌, 姚小英, 姚晓红, 等.气候变暖对甘肃黄土高原苹果物候期及生长的影响.中国农业气象, 2008, 29(2):181-183. doi: 10.3969/j.issn.1000-6362.2008.02.016 [27] 孟秀美, 李世奎, 佟万福, 等.苹果花期预报方法的研究.园艺学报, 1983, 10(3):159-164. http://d.old.wanfangdata.com.cn/Periodical/zgnyqx201004025 [28] 毛明策, 刘敏茹, 姜创业, 等.苹果初花期与温度的关系研究.中国农业气象, 2005, 26(2):123-124. doi: 10.3969/j.issn.1000-6362.2005.02.013 [29] 李美荣, 杜继稳, 李星敏, 等.陕西果区苹果始花期预测模型.中国农业气象, 2009, 30(3):417-420. doi: 10.3969/j.issn.1000-6362.2009.03.025 [30] 柏秦凤, 王景红, 屈振江, 等.陕西苹果花期预测模型研究.中国农学通报, 2013, 29(19):164-169. doi: 10.11924/j.issn.1000-6850.2012-4088 [31] 张艳艳, 赵玮, 高庆先, 等.气候变化背景下陇东塬区"红富士"苹果始花期研究.果树学报, 2017, 34(4):427-434. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gskx201704004 [32] 屈振江, 郑小华, 刘璐, 等.陕西不同生态区苹果品质差异及与气象因子的关系.气象, 2017, 43(7):872-878. http://d.old.wanfangdata.com.cn/Periodical/qx201707011 [33] 屈振江, 尚小宁, 王景红, 等.黄土高原两种树形苹果园花期温度垂直变化特征及预测.应用生态学报, 2015, 26(11):3405-3412. http://d.old.wanfangdata.com.cn/Periodical/yystxb201511021 [34] 毛明策, 刘敏茹, 姜创业, 等.苹果初花期与温度的关系研究.中国农业气象, 2005, 26(2):123-124. doi: 10.3969/j.issn.1000-6362.2005.02.013 [35] 鲍小娟.陕西省气候变化对经济作物物候的影响——以苹果、梨、棉花为例.西安: 陕西师范大学, 2012. [36] Kobayashi K D, Fuchigami L H.Modelling temperature effects in breaking rest in Red-osier Dogwood (Cornus sericea L.).Annual of Botany, 1983, 52:205-215. doi: 10.1093/oxfordjournals.aob.a086566 [37] 王芳, 邬定荣, 王春乙, 等.两种参数化方案下冬小麦品种对发育期的影响.应用气象学报, 2017, 28(4):493-503. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20170410&flag=1 [38] Akbari R, Ziarati K.A multilevel evolutionary algorithm for optimizing numerical functions.International Journal of Industrial Engineering Computations, 2011, 2(2):419-430. doi: 10.5267/j.ijiec.2010.03.002 [39] 张爱英, 王焕炯, 戴君虎, 等.物候模型在北京观赏植物开花期预测中的适用性.应用气象学报, 2014, 25(4):483-492. doi: 10.3969/j.issn.1001-7313.2014.04.012 [40] 杨月, 刘兵, 刘小军, 等.小麦生育期模拟模型的比较研究.南京农业大学学报, 2014, 37(1):6-14. http://d.old.wanfangdata.com.cn/Periodical/njnydxxb201401002 [41] 刘志娟, 杨晓光, 王静, 等.APSIM玉米模型在东北地区的适应性.作物学报, 2012, 38(4):740-746. http://d.old.wanfangdata.com.cn/Periodical/zuowxb201204021 [42] 莫志鸿, 冯利平, 邹海平, 等.水稻模型ORYZA2000在湖南双季稻区的验证与适应性评价.生态学报, 2011, 31(16):4628-4637. http://d.old.wanfangdata.com.cn/Periodical/stxb201116016 -
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