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Improving Parameters of Nonlinear Accumulated Temperature Model for Spring Maize in Northeast China
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摘要: 结合我国东北地区春玉米生长发育的实际情况，以观测年份较多、观测地点较广为原则选取4个春玉米品种，分别为东农248、龙单13、四单19和丹玉13，利用生长发育观测资料和同期气象观测资料，判断4个玉米品种的相对熟型并对沈国权非线性积温模型（简称NLM）进行参数拟合，讨论参数的生物学意义及其与品种熟型的关系，对NLM进行有效改进及验证。结果表明：4个春玉米品种NLM均不存在无效参数，参数K与参数Q存在显著的相关性，说明K可能仅是一个统计参数，没有明确的生物学意义；积温在品种间存在显著差异，全生育期模型参数Q与多年站次平均有效积温或活动积温有较好的相关性，由于不同的积温意味着不同的玉米品种熟型，说明Q与玉米品种的熟型有关，将模型参数Q和K用反映玉米品种熟型的参数（有效积温、活动积温）表示，建立了适用于不同品种的通用积温模型，取得较好的应用效果。Abstract: Heat condition is one of the most crucial factors that affect the growth and development of crops, which is also of great importance for Northeast China located in the middle and high latitudes. Accumulated temperature is the main factor affecting yield formation, and similarly, temperature plays a key role in determining the duration of growing seasons, yield and so on. Effects of temperature on growth and development of crops are investigated extensively and various kinds of accumulated temperature indexes are established. However, these accumulated temperature models are not always stable. Therefore, it is important to improve the indexes or models, making them convenient to calculate with great stability and applicable to different regions, environments and varieties. A relatively stable accumulated temperature model (NLM) proposed by Shen Guoquan is studied which is applicable to spring maize in northeast China. There are numerous maize varieties and significant differences in parameters determined by different varieties when applying NLM. Therefore, it is important to establish a general accumulated temperature model considering varietal attribute to improve the applicability. Four varieties with more observable years and stations are selected, which are Dongnong 248, Longdan 13, Sidan 19 and Danyu 13. The NLM is adopted based on observations of the growth and development of spring maize and meteorological data in-situ in Northeast China. Biological significance of parameters and the relationship between parameters and varieties or mature period are analyzed, and thus NLM is improved effectively and verified.Results show that, there are no invalid parameters in fitting equations of four maize varieties. The parameter P is determined as 0.5 based on the smallest variation coefficient of accumulated temperature. There is a significant correlation between parameter K and parameter Q, indicating that the parameter K may be only a statistical parameter with no clear biological significance. There is a significant difference of accumulated temperature among varieties. The relationship between the parameter Q and the mean value of effective accumulated temperature or active accumulated temperature during the whole growth period is found to be of good correlation, which indicates that Q is related to the mature period types of different maize varieties. Therefore, a general model applicable to different varieties is proposed whose parameters Q and K are represented by effective accumulated temperature or active accumulated temperature. The application capacity of the model has been significantly improved.
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图 2 NLM拟合积温与平均温度的相关性
Fig. 2 Correlations between fitted accumulated temperature by NLM and mean temperature
图 3 出苗-成熟期参数Q与春玉米品种实际积温的关系
Fig. 3 Relationship between Q and actual mean accumulated temperature of spring maize varieties during emergence to maturity
图 4 不同生育期NLM参数Q与lnK相关性
Fig. 4 Correlations between Q and lnK of NLM in different growth stages
图 5 模型计算积温与实际积温比较
Fig. 5 Comparison between modelled accumulated temperature and actual accumulated temperature
图 6 出苗-成熟期参数Q与4个春玉米品种实际积温平均值的关系
Fig. 6 Relationship between Q and actual mean accumulated temperature of 4 spring maize varieties during emergence to maturity
图 7 验证品种模型计算积温与实际积温比较
Fig. 7 Comparison between modelled accumulated temperature and actual accumulated temperature of validation varieties
表 1 不同春玉米品种NLM
Table 1 NLM of different northeast spring maize varieties
生育期 品种 拟合方程 出苗-拔节期 东农248 A(T)=e12.8329(T-B)-0.5(M-T)-1-1.2432 龙单13 A(T)=e16.7168 (T-B)-0.5(M-T)-1-2.6496 四单19 A(T)=e16.8326 (T-B)-0.5(M-T)-1-2.6384 丹玉13 A(T)=e14.1694 (T-B)-0.5(M-T)-1-1.6888 拔节-抽雄期 东农248 A(T)=e13.6580 (T-B)-0.5(M-T)-1-2.1777 龙单13 A(T)=e12.7644 (T-B)-0.5(M-T)-1-1.7177 四单19 A(T)=e11.0222 (T-B)-0.5(M-T)-1-1.0965 丹玉13 A(T)=e10.6121 (T-B)-0.5(M-T)-1-0.8284 抽雄-成熟期 东农248 A(T)=e20.0138 (T-B)-0.5(M-T)-1-3.9681 龙单13 A(T)=e21.8610 (T-B)-0.5(M-T)-1-4.6597 四单19 A(T)=e17.7128 (T-B)-0.5(M-T)-1-3.1065 丹玉13 A(T)=e13.2941 (T-B)-0.5(M-T)-1-1.4421 出苗-成熟期 东农248 A(T)=e14.2904 (T-B)-0.5(M-T)-1-1.3630 龙单13 A(T)=e14.2213 (T-B)-0.5(M-T)-1-1.3216 四单19 A(T)=e13.7376 (T-B)-0.5(M-T)-1-1.1076 丹玉13 A(T)=e13.1114 (T-B)-0.5(M-T)-1-0.8660 注：方程均达到0.001显著性水平。 
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表 2 不同品种拟合积温(单位：℃·d)
Table 2 Modelled accumulated temperature of different varieties(unit:℃·d)
生育期 东农248 龙单13 四单19 丹玉13 出苗-拔节期 305.7±39.6 317.7±66.8 406.0±90.3 337.9±73.8 拔节-抽雄期 115.2±29.6 146.4±26.8 141.4±29.9 183.0±51.4 抽雄-成熟期 252.7±58.0 256.8±61.2 342.4±71.9 424.3±65.5 出苗-成熟期 911.9±85.5 983.6±58.0 1154.5±83.7 1270.9±116.0 注：拟合积温值为平均值±标准差。
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表 3 3种模型决定系数和归一化均方根误差
Table 3 Determination coefficients and normalized root mean square errors of three models
品种 决定系数 归一化均方根误差/% NLM EARM AARM NLM EARM AARM 东农248 0.8685 0.8684 0.8679 3.6621 6.1867 4.9196 龙单13 0.4446 0.4447 0.4445 6.6612 6.8837 6.6700 四单19 0.5786 0.5786 0.5787 6.1211 8.0315 8.5170 丹玉13 0.6116 0.6117 0.6115 7.0394 9.0017 9.0352
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表 4 积温变异系数比较(单位：%)
Table 4 Variation coefficient of accumulated temperature(unit:%)
品种 实际积温 NLM拟合积温 EARM模拟积温 AARM模拟积温 东农248 10.08 9.23 9.29 10.13 龙单13 8.93 5.74 5.52 5.95 四单19 9.42 7.14 6.70 7.33 丹玉13 11.29 8.89 8.88 10.13
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表 5 不同模型参数
Table 5 Parameters of different models
品种 NLM EARM AARM P Q K P Q K P Q K 丹玉6 0.5 1.17 e13.82 0.5 1.10 e13.67 0.5 1.27 e14.10 桦单9 0.5 1.47 e14.59 0.5 1.34 e14.29 0.5 1.43 e14.53
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[1] 唐余学, 郭建平.我国东北地区玉米冷害风险评估.应用气象学报, 2016, 27(3):352-360. doi: 10.11898/1001-7313.20160310 [2] 王永光, 艾婉秀.东北地区≥ 10℃有效积温的分析及预报.中国农业气象, 1997, 18(3):39-44. http://www.doc88.com/p-0791672728066.html [3] 王艳华, 任传友, 韩亚东, 等.东北地区活动积温和极端持续低温的时空分布特征及其对粮食产量的影响.农业环境科学学报, 2011, 30(9):1742-1748. http://d.wanfangdata.com.cn/Periodical_nyhjbh201109008.aspx [4] 闫淑琴.玉米籽粒灌浆速度研究进展.园艺与种苗, 2006, 26(4):285-287. doi: 10.3969/j.issn.2095-0896.2006.04.014 [5] 张宇, 王石立.气候变化对我国小麦发育及产量可能影响的模拟研究.应用气象学报, 2000, 11(3):264-270. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20000341&flag=1 [6] 张家诚, 高素华, 潘亚茹.我国温度变化与冬季采暖气候条件的探讨.应用气象学报, 1992, 3(1):70-75. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19920114&flag=1 [7] 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. doi: 10.11898/1001-7313.20160401 [8] 朱海霞, 李秀芬, 王萍, 等.黑龙江省水稻生长季积温计算方法.应用气象学报, 2017, 28(2):247-256. doi: 10.11898/1001-7313.20170212 [9] 韩永庄.戈配兹模型在生物积温预报中的应用.安徽农学通报, 2013, 19(23):11-12. http://industry.wanfangdata.com.cn/dl/Export/Papers?ids=ahnxtb201323005&type=excel [10] 沈国权.影响作物发育速度的非线性温度模式.气象, 1980, 6(6):9-11. doi: 10.7519/j.issn.1000-0526.1980.06.005 [11] 程德瑜, 邓剑华, 刘文俊.论危害积温及其在双季稻农业气候区划中的应用.安徽农业科学, 1981(3):53-63. http://edu.wanfangdata.com.cn/Periodical/Detail/gxsfdxxb201304024 [12] 沈国权.当量积温及其应用.气象, 1981, 7(7):23-25. doi: 10.7519/j.issn.1000-0526.1981.07.009 [13] 高亮之, 金之庆, 黄耀, 等.水稻计算机模拟模型及其应用之一水稻钟模型—水稻发育动态的计算机模型.中国农业气象, 1989, 10(3):3-10. https://www.cnki.com.cn/qikan-NJQX199904013.html [14] 蓝鸿第.水稻发育速度模式的初步研究.科学通报, 1982, 27(3):180-185. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB198203015.htm [15] 钱拴, 陈晖, 王良宇.全国棉花发育期业务预报方法研究.应用气象学报, 2007, 18(4):539-547. doi: 10.11898/1001-7313.20070415 [16] 张雪芬, 王春乙, 陈东, 等.基于位温的小麦发育期的小网格推算方法.应用气象学报, 2007, 18(6):865-869. doi: 10.11898/1001-7313.200706130 [17] Dejonge K C, Ascough J C, Andales A A, et al.Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation.Agricultural Water Management, 2012, 115(12):92-103. https://www.sciencedirect.com/science/article/pii/S0378377412002272 [18] Ajwde W, Cavan D.Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts.Agricultural & Forest Meteorology, 2007, 146(1-2):38-56. https://www.sciencedirect.com/science/article/pii/S0168192307001402 [19] Bouazzama B, Mailhol J C, Xanthoulis D, et al.Silage maize growth simulation using pilote and CropSyst Model.Irrigation & Drainage, 2013, 62(1):84-96. https://core.ac.uk/display/62121696 [20] 李向岭, 赵明, 李从锋, 等.玉米叶面积系数动态特征及其积温模型的建立.作物学报, 2011, 37(2):321-330. http://d.wanfangdata.com.cn/Periodical_zuowxb201102015.aspx [21] 李迎, 邓忠, 翟国亮, 等.冬小麦作物系数的积温模型.节水灌溉, 2015(4):36-40. http://d.wanfangdata.com.cn/Periodical_jsgg201504011.aspx [22] 赵倩, 郭建平.不同积温模型的稳定性评估—以东北春玉米为例.生态学杂志, 2016, 35(10):2852-2860. http://www.cje.net.cn/CN/abstract/abstract8263.shtml [23] 曹广才, 吴东兵.高寒旱地玉米熟期类型的温度指标和生育阶段.北京农业科学, 1995, 13(1):40-43. http://www.oalib.com/paper/4834891 [24] 曹广才, 吴东兵.植株叶数是北方旱地玉米品种熟期类型的形态指标.北京农业科学, 1996, 14(4):4-7. http://www.cqvip.com/QK/90383X/199604/2214075.html [25] Chirkov Y I.Agrometeorological indices in the development and formation of maize crops.Agr Meteorol, 1965, 2(2):121-126. doi: 10.1016/0002-1571(65)90005-1 [26] 孙孟梅, 姜丽霞, 于荣环, 等.玉米生育期热量指标及其不同品种的栽培北界.黑龙江气象, 1998(3):38-43. http://www.cnki.com.cn/Article/CJFDTOTAL-HLJQ803.003.htm [27] 刘永花.不同熟期玉米品种积温需求定量研究.晋中:山西农业大学, 2014. [28] 方华, 李青松, 郭玉伟, 等.中国玉米品种生育期的研究.河北农业科学, 2010, 14(4):1-5. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hebnykx201004001 [29] 袁彬, 郭建平, 冶明珠, 等.气候变化下东北春玉米品种熟型分布格局及其气候生产潜力.科学通报, 2012, 57(14):1252-1262. https://mall.cnki.net/qikan-KXTB201214010.html [30] Xu Y H, Guo J P, Zhao J F.Scenario analysis on the adaptation of different maize varieties to future climate change in Northeast China.J Meteor Res, 2014, 28(3):469-480. doi: 10.1007/s13351-014-3141-4 [31] 李蕊, 郭建平.东北春玉米积温模型的改进与比较.应用气象学报, 2017, 28(6):678-689. doi: 10.11898/1001-7313.20170604 -
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