Improvement and Comparison of the Accumulated Temperature Model of Northeast Spring Maize

Li Rui; Guo Jianping

doi:10.11898/1001-7313.20170604

Vol.28, NO.6, 2017

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Li Rui, Guo Jianping. Improvement and comparison of the accumulated temperature model of northeast spring maize. J Appl Meteor Sci, 2017, 28(6): 678-689. DOI: 10.11898/1001-7313.20170604.

Citation:

Li Rui, Guo Jianping. Improvement and comparison of the accumulated temperature model of northeast spring maize. J Appl Meteor Sci, 2017, 28(6): 678-689. DOI: 10.11898/1001-7313.20170604.

Li Rui, Guo Jianping. Improvement and comparison of the accumulated temperature model of northeast spring maize. J Appl Meteor Sci, 2017, 28(6): 678-689. DOI: 10.11898/1001-7313.20170604.

Citation:

Li Rui, Guo Jianping. Improvement and comparison of the accumulated temperature model of northeast spring maize. J Appl Meteor Sci, 2017, 28(6): 678-689. DOI: 10.11898/1001-7313.20170604.

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Improvement and Comparison of the Accumulated Temperature Model of Northeast Spring Maize

DOI: 10.11898/1001-7313.20170604

Li Rui¹,
Guo Jianping^{1,2
,
,}

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Collaborative Innovation Center of Meteorological Disaster Forecast, Early-Warning and Assessment, Nanjing University of Information Science & Technology, Nanjing 210044

Received Date: 2017-07-14
Rev Recd Date: 2017-09-19
Publish Date: 2017-11-30

Abstract

Abstract

Spring maize in Northeast China plays a more and more important role in the national maize production. The gradually increase in acreage, the per unit area yield and total yield of spring maize have markedly improved since 1980s. Accumulated temperature is one of indexes which are commonly used in agricultural meteorological research and operation service. It's also used in crop model and regional thermal resource analysis which can reflect differences in demand of heat resources between different crops and varieties. And it can also be used to evaluate the suitability of heat conditions in a certain area for crop growth and development to avoid blindness of crops introduction. But in fact, the stability of accumulated temperature is relative, and it fluctuates with differences of crop varieties, locations, years and growth periods. It results in the limited application of the accumulated temperature index. Besides environmental conditions, the instability of accumulated temperature is also affected by different calculation methods. In general, accumulated temperature models are divided into two categories, including linear model and nonlinear model. Therefore, how to choose and revise the existing model for stabilizing the calculation value of accumulated temperature and making it fit well with the actual situation is of great significance for agricultural production and meteorological service.Based on observations of spring maize and meteorological data in Northeast China, the spring maize Sidan19 is taken as an example. The nonlinear accumulated temperature model proposed by Shen Guoquan with good stability is adopted to fit, and the influence of parameter selection on the stability of accumulated temperature is analyzed. The quadratic function of mean temperature to the liner model is revised and analyzed, and the nonlinear model is compared. Results show that the stability of accumulated temperature is related to the parameter P, more stable with smaller P. However, accumulated temperature calculated by the nonlinear model shows inter-annual and inter-regional differences. The main cause for the instability is different temperature strength and its less correlated with other meteorological factors. For each growth period, fitted curves between accumulated temperature and mean temperature are quadratic. The fitting effect of the accumulated temperature calculated by the revised linear model is better than that of Shen Guoquan nonlinear model. Moreover, the stability doesn't appear to be much different between two methods. Thus, the revision of linear model considering the mean temperature for spring maize in Northeast China is feasible, which can help revising agro-meteorological indexes and improving agriculture service capacity.
- spring maize;
- accumulated temperature model;
- stability;
- revision model

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References(31)

Figures and Tables

Fig. 1 Correlations between accumulated temperature by nonlinear model and mean temperature at different stations

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Fig. 2 Comparison between accumulated temperature obtained by nonlinear model and linear model

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Fig. 3 Relationship between accumulated temperature by nonlinear model and meteorological factors during emergence to maturity

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Fig. 4 Quadratic correlation between accumulated temperature by nonlinear model and mean temperature(mixed stations)

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Fig. 5 Results obtained by temperature revision model(mixed stations)

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Table 1 Temperatures of three fundamental points of spring maize in growing seasons(unit:℃)

生育期	最适温度	下限温度	上限温度
出苗-拔节期	24.0	12.0	35.0
拔节-抽雄期	28.0	16.0	35.0
抽雄-成熟期	24.0	15.0	35.0
出苗-成熟期	25.0	12.0	35.0

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Table 2 Fitting parameters of nonlinear model and variation coefficient(Tailai Station)

生育期	P	Q	K	线性转换决定系数	积温变异系数/%
出苗-拔节期	0.5	1.0123	e^12.6868	0.70	9.38
	0.6	1.1621	e^13.3011	0.73	9.40
	0.7	1.3120	e^13.9153	0.75	9.42
	0.8	1.4619	e^14.5296	0.77	9.44
	0.9	1.6117	e^15.1439	0.79	9.46
	1.0	1.7616	e^15.7581	0.81	9.48
拔节-抽雄期	0.5	0.8693	e^10.2277	0.87	20.97
	0.6	1.0041	e^10.7549	0.88	21.21
	0.7	1.1390	e^11.2821	0.89	21.46
	0.8	1.2738	e^11.8093	0.90	21.73
	0.9	1.4086	e^12.3365	0.90	22.01
	1.0	1.5434	e^12.8637	0.91	22.30
抽雄-成熟期	0.5	4.2089	e^20.7425	0.91	14.47
	0.6	4.5281	e^21.7664	0.92	14.54
	0.7	4.8472	e^22.7903	0.92	14.60
	0.8	5.1663	e^23.8143	0.93	14.67
	0.9	5.4855	e^24.8382	0.93	14.74
	1.0	5.8046	e^25.8621	0.94	14.81
出苗-成熟期	0.5	1.1779	e^13.9831	0.97	6.62
	0.6	1.3366	e^14.6211	0.98	6.63
	0.7	1.4953	e^15.2591	0.98	6.63
	0.8	1.6539	e^15.8971	0.98	6.64
	0.9	1.8126	e^16.5351	0.98	6.64
	1.0	1.9713	e^17.1731	0.98	6.65
注：方程均达到0.001显著性水平。

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Table 3 Nonlinear model fitted for each station and mixed ones

生育期	站点	拟合方程
出苗-拔节期	泰来	A(T)=e^12.6868(T-B)^-0.5(M-T)^-1-1.0123
	哈尔滨	A(T)=e^14.6129(T-B)^-0.5M-T^-1-1.8809*
	青冈	A(T)=e^14.4623(T-B)^-0.5M-T^-1-1.7370**
	混合站点	A(T)=e^16.8326(T-B)^-0.5M-T^-1-2.6384
拔节-抽雄期	泰来	A(T)=e^10.2277(T-B)^-0.5M-T^-1-0.8693
	哈尔滨	A(T)=e^11.5074(T-B)^-0.5M-T^-1-1.2226
	青冈	A(T)=e^16.3605(T-B)^-0.5M-T^-1-3.2350
	混合站点	A(T)=e^11.0222(T-B)^-0.5M-T^-1-1.0965
抽雄-成熟期	泰来	A(T)=e^20.7425(T-B)^-0.5M-T^-1-4.2089
	哈尔滨	A(T)=e^14.1598(T-B)^-0.5M-T^-1-1.7701
	青冈	A(T)=e^26.7917(T-B)^-0.5M-T^-1-6.3742
	混合站点	A(T)=e^17.7128(T-B)^-0.5M-T^-1-3.1065
出苗-成熟期	泰来	A(T)=e^13.9831(T-B)^-0.5M-T^-1-1.1779
	哈尔滨	A(T)=e^12.3094(T-B)^-0.5M-T^-1-0.5786
	青冈	A(T)=e^12.7876(T-B)^-0.5M-T^-1-0.7696***
	混合站点	A(T)=e^13.7376(T-B)^-0.5M-T^-1-1.1076
注：表示达到0.004显著性水平，表示达到0.01显著性水平，**表示达到0.029显著性水平，其余均达到0.001显著性水平。

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Table 4 Accumulated temperature of NLM at different stations(unit:℃·d)

生育期	泰来	哈尔滨	青冈
出苗-拔节期	540.2±53.1	314.2±30.5	400.9±37.6
拔节-抽雄期	116.1±25.50	169.6±34.8	138.1±42.6
抽雄-成熟期	355.9±54.0	382.3±39.9	255.5±41.8
出苗-成熟期	1253.0±87.0	1147.7±42.54	1036.1±26.33
注：表中数值为积温平均值±标准差，各生育期NLM积温地区间差异均达到0.01显著性水平。

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Table 5 Comparison between variation coefficient of accumulated temperature obtained by nonlinear and linear models(unit:%)

站点	出苗-拔节期		拔节-抽雄期		抽雄-成熟期		出苗-成熟期
站点	NLM	LM	NLM	LM	NLM	LM	NLM	LM
泰来	9.38	13.03	20.97	21.82	14.47	14.64	6.62	6.79
哈尔滨	9.35	17.51	19.75	24.29	9.91	15.61	3.57	6.26
青冈	8.76	12.47	28.88	29.19	15.30	18.58	2.38	5.12
混合站点	21.91	27.70	20.82	30.37	20.67	21.76	7.14	9.42

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Table 6 R² and normalized root mean square error of accumulated temperature obtained by nonlinear model and temperature revision model

站点	生育期	决定系数		归一化均方根误差
站点	生育期	NLM	TRM	NLM	TRM
泰来	出苗-拔节期	0.4149^*	0.5713^**	10.0224	8.5341
	拔节-抽雄期	0.7513^**	0.7746^**	11.0674	10.3617
	抽雄-成熟期	0.8014^**	0.8553^**	6.6636	5.5704
	出苗-成熟期	0.9366^**	0.9406^**	1.7100	1.6534
哈尔滨	出苗-拔节期	0.2549	0.3139^*	15.1518	14.5037
	拔节-抽雄期	0.5065^**	0.5265^**	17.2740	16.7126
	抽雄-成熟期	0.4084^*	0.4139^*	12.0380	11.9491
	出苗-成熟期	0.3735^*	0.4050^*	4.9659	4.8311
青冈	出苗-拔节期	0.4540	0.4963	9.2304	8.8518
	拔节-抽雄期	0.9574^**	0.9621^**	6.0310	5.6866
	抽雄-成熟期	0.7024^**	0.7065^**	10.1577	10.0677
	出苗-成熟期	0.1740	0.7359^**	4.6613	2.6322
混合站点	出苗-拔节期	0.5096^**	0.5427^**	19.5490	18.7301
	拔节-抽雄期	0.2628^**	0.3897^**	26.6452	23.7275
	抽雄-成熟期	0.6340^**	0.7285^**	13.5658	11.3389
	出苗-成熟期	0.5786^**	0.5782^**	6.1211	6.1213
注：表示达到0.05的显著性水平；*表示达到0.01的显著性水平。

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Table 7 Comparison between variation coefficient of accumulated temperature obtained by nonlinear model and temperature revision model(unit: %)

站点	出苗-拔节期		拔节-抽雄期		抽雄-成熟期		出苗-成熟期
站点	NLM	TRM	NLM	TRM	NLM	TRM	NLM	TRM
泰来	9.38	9.85	20.97	19.20	14.47	13.54	6.62	6.58
哈尔滨	9.35	9.81	19.75	17.63	9.91	10.04	3.57	3.99
青冈	8.76	8.79	28.88	28.62	15.30	15.62	2.38	4.39
混合站点	21.91	20.40	20.82	18.96	20.67	18.57	7.14	7.17

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References

[1]	郭建平.气候变化对中国农业生产的影响研究进展.应用气象学报, 2015, 26(1):1-11. doi: 10.11898/1001-7313.20150101
[2]	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
[3]	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
[4]	刘实, 王勇, 缪启龙, 等.近50年黑龙江省地区热量资源变化特征.应用气象学报, 2010, 21(3):266-278. doi: 10.11898/1001-7313.20100302
[5]	西涅里席柯夫B B. 普通农业气象学. 北京农业大学译. 北京: 高等教育出版社, 1959: 84-95.
[6]	王宏燕, 彭驰, 侯中田.降水和地积温对有机肥腐解的动态分析.黑龙江省农业大学学报, 1996, 27(1):20-25.
[7]	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
[8]	刘少军, 周广胜, 房世波.1961-2010年中国橡胶寒害的时空分布特征.生态学杂志, 2015, 34(5):1282-1288. http://d.wanfangdata.com.cn/Periodical/stxzz201505015
[9]	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
[10]	Birch C J, Hammer G L, Rickert K G.Temperature and photoperiod sensitivity of development in five cultivars of maize (Zea mays L) from emergence to tassel initiation.Field Crops Research, 1998, 55(1-2):93-107. doi: 10.1016/S0378-4290(97)00062-2
[11]	Tollenaar M.Duration of the grain-filling period in maize is not affected by photoperiod and incident PPFD during the vegetative phase.Field Crops Research, 1999, 62(1):15-21. doi: 10.1016/S0378-4290(98)00170-1
[12]	徐铭志, 任国玉.近40年中国气候生长期的变化.应用气象学报, 2004, 15(3):306-312. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040339&flag=1
[13]	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
[14]	全国杂交水稻气象科研协作组.杂交水稻制种花期相遇的积温稳定性研究.气象, 1981, 7(1):21-24. doi: 10.7519/j.issn.1000-0526.1981.01.012
[15]	马玉平, 张黎, 孙琳丽, 等.持续性温强和土壤水分对玉米发育进程的影响及其模拟.中国农学通报, 2015, 31(3):186-193. http://d.wanfangdata.com.cn/Periodical/zgnxtb201503031
[16]	朱伯伦.积温的不稳定性及其订正.贵州农业科学, 1985(1):63-64. http://d.wanfangdata.com.cn/Periodical/jxqxkj200503008
[17]	孙小龙, 闫伟兄, 武荣盛, 等.基于气候适宜度建立河套灌区玉米生育期模拟模型.中国农业气象, 2014, 35(1):62-67. http://d.wanfangdata.com.cn/Periodical/zgnyqx201401009
[18]	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
[19]	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. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=zgny198903001&dbname=CJFD&dbcode=CJFQ
[20]	殷新佑.水稻发育温度效应的非线性模型及其应用.作物学报, 1994, 20(6):692-700. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=xbzw199406007&dbname=CJFD&dbcode=CJFQ
[21]	詹习武.作物发育速度与温度关系的数学模式研究.南京气象学院学报, 1988, 11(1):15-24. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=njqx198801001&dbname=CJFD&dbcode=CJFQ
[22]	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://d.wanfangdata.com.cn/Periodical/ahnxtb201323005
[23]	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
[24]	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://d.wanfangdata.com.cn/Periodical/stxzz201610037
[25]	王宗明, 张柏, 张树清, 等.松嫩平原农业气候生产潜力及自然资源利用率研究.中国农业气象, 2005, 26(1):1-6. http://d.wanfangdata.com.cn/Periodical/zgnyqx200501001
[26]	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. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=kxtb201214010&dbname=CJFD&dbcode=CJFQ
[27]	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
[28]	潘永地, 粟志钢.有关水稻生育期生长模拟综述.浙江农业科学, 2011, 1(2):434-438. http://d.wanfangdata.com.cn/Periodical/zjnykx201102075
[29]	白彩云. 中国东北地区玉米种植的气候适应性研究. 石河子: 石河子大学, 2010. http://cdmd.cnki.com.cn/Article/CDMD-10759-1011058298.htm
[30]	张吉旺, 董树亭, 王空军, 等.大田增温对夏玉米光合特性的影响.应用生态学报, 2008, 19(1):81-86. http://d.wanfangdata.com.cn/Periodical/yystxb200801014
[31]	李超, 李文峰, 钱晔.热带区域冬玉米物候发育的模拟与模型检验.中国农学通报, 2015, 31(24):53-58. doi: 10.11924/j.issn.1000-6850.casb15010202