福建省茶叶寒冻害气象指数保险分级设计

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黄川容^1,,
陈家金^1,,,
孙朝锋¹,
吴立¹,
陶红超²,
林辉阳¹
1.福建省气象服务中心　福州　350008
2.福建师范大学地理科学学院　福州　350007
基金项目:福建省科技计划项目(2018Y0002, 2019Y0062, 2021J01462)资助

详细信息

作者简介:黄川容, 主要从事农业气象研究。E-mail: chr_huang@163.com

通讯作者:陈家金, 主要从事农业气象灾害风险研究。E-mail: cjj8284@163.com

中图分类号:F840.66

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出版历程

收稿日期:2021-05-19
录用日期:2021-08-04
网络出版日期:2021-08-27
刊出日期:2021-12-09

Classification design of the meteorological index insurance for cold-frost damage on tea in Fujian Province

HUANG Chuanrong^1,,
CHEN Jiajin^1,,,
SUN Chaofeng¹,
WU Li¹,
TAO Hongchao²,
LIN Huiyang¹
1. Fujian Meteorological Service Center, Fuzhou 350008, China
2. College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
Funds:This study was supported by the Science and Technology Project of Fujian Province (2018Y0002, 2019Y0062, 2021J01462)

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Corresponding author:E-mail: cjj8284@163.com

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摘要
摘要:为了给茶叶保险提供精细化气象指数类产品, 利用福建省历年气象资料、产量资料和灾情资料, 构建茶叶寒冻害保险时段和触发气象指标, 利用数理统计方法计算茶叶不同等级寒冻害产量减产率和出现概率, 进而得出保险纯费率, 基于GIS进行茶叶寒冻害风险评估, 厘定出不同风险区不同触发条件下的区域保险费率; 考虑茶叶寒冻害强度、危害时段和保险历年平均赔付情况, 制定出不同寒冻害等级、不同时段的赔付比例和保险赔偿金, 并验证寒冻害理赔的合理性, 设计出茶叶寒冻害气象指数保险产品。结果表明: 以极端最低气温4 ℃作为茶叶寒冻害指数保险起始触发指标, 并按照间隔1 ℃划分7个理赔等级; 将茶叶寒冻害保险时段(3月1日—5月10日)划分为12个赔付时段。在极端低温4 ℃触发条件下, 北部地区海拔高度<200 m、200~600 m、600~900 m和>900 m的区域平均保险费率分别为1.1%、3.6%、6.1%和8.4%, 南部地区海拔高度<300 m、300~700 m、700~1100 m和>1100 m的区域平均保险费率分别为0.5%、2.0%、3.9%和5.5%; 西北部、东北部、西南部和东南部4个区域的春茶寒冻害平均赔付率分别为78.4%、90.5%、72.7%和36.6%, 全省平均赔付率为69.5%, 基差比均值为?1.65%, 达到保险公司对赔付率和基差比的基本要求。设计的产品可应用于福建春茶寒冻害气象指数保险, 可为茶农转移、分散寒冻害风险提供保险技术支撑。
关键词:茶叶保险/
寒冻害/
气象指数/
理赔产品
Abstract:To provide meteorological index products for tea insurance, the tea cold and frost damage insurance period and trigger meteorological indicators were analyzed to calculate the yield reduction rate, occurrence probability of cold-forest damage, and pure premium rate based on the meteorological data, and tea yield and disaster data of Fujian Province. Then, based on the risk assessment of cold-frost damage in tea plants, the regional premium rates under different trigger conditions and in different risk areas were improved. The insurance compensation ratio and compensation under different levels of cold-frost damage and in different periods were formulated and verified based on the intensity of cold-frost damage, the period of damage, and the average insurance compensation situation. Finally, a weather index insurance product for cold and frost damage to tea was provided. Results showed that the extreme minimum temperature of 4 °C, used as the initial trigger index of the tea cold-frost damage index insurance, as well as and seven grades with 1 °C interval, can fully reflect the historical disaster situation. Under the extremely low temperature of 4 °C, the average insurance premium rates in areas with altitudes below 200 m, 200?600 m, 600?900 m, and above 900 m in the northern region were 1.1%, 3.6%, 6.1%, and 8.4%, respectively; the average insurance premium rates in areas with altitudes below 300 m, 300?700 m, 700?1100 m, and above 1100 m in the southern region were 0.5%, 2.0%, 3.9%, and 5.5%, respectively. The average compensation rate of spring tea cold-forest damage in the northwest, northeast, southwest, and southeast regions was 78.4%, 90.5%, 72.7%, and 36.6%, respectively; the province’s average compensation rate was 69.5%, and the average basis ratio was ?1.65%. The compensation rate and basis ratio met the basic requirements of insurance companies for the compensation ratio and basis ratio. The designed product can be applied to the weather index insurance of cold and frost damage of Fujian spring tea; therefore, this study could provide technical support for tea farmers to transfer and disperse the cold and frost damage risks.
Key words:Tea insurance/
Cold-frost damage/
Meteorological index/
Claims product

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图1研究区域图
Figure1.Study area diagram

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表1福建各气象站点茶叶减产率(y)与极端低温(x)的相关分析
Table1.Correlation analysis between tea yield reduction rate (y) and extreme low temperature (x) in each meteorological station of Fujian Province

站点 Station	回归方程 Regression equation	相关系数R Correlation coefficient	站点 Station	回归方程 Regression equation	相关系数R Correlation coefficient
建瓯 Jian’ou	y=0.196x?1.0703	0.9369^*	屏南 Pingnan	y=0.2857x?1.3616	0.6799^*
建阳 Jianyang	y=0.4148x?2.0525	0.9222^**	寿宁 Shouning	y=0.5954x?2.2314	0.9925^*
延平 Yanping	y=0.9426x?8.7709	0.7813^**	柘荣 Zherong	y=0.5815x?3.0072	0.7955^**
邵武 Shaowu	y=1.1474x?4.5979	0.8495^**	周宁 Zhouning	y=0.1224x?1.5335	0.6913^*
顺昌 Shunchang	y=0.5234x?2.705	0.9333^**	华安 Hua’an	y=0.7035x?6.1541	0.9517^**
沙县 Shaxian	y=0.7524x?4.2887	0.7618^**	南靖 Nanjing	y=0.5125x?5.0897	0.6963^**
永安 Yong’an	y=0.4137x?5.8464	0.9818^**	连城 Liancheng	y=0.6287x?7.9652	0.6797^**
三明 Sanming	y=2.0431x?10.961	0.9335^**	新罗 Xinluo	y=0.7827x?6.9397	0.6948^**
浦城 Pucheng	y=0.326x?2.2703	0.9242^*	上杭 Shanghang	y=0.1854x?1.2996	0.9346^*
松溪 Songxi	y=1.379x?5.8005	0.8042^**	武平 Wuping	y=0.2581x?2.0011	0.9174^**
武夷山 Wuyishan	y=0.5221x?4.7502	0.8318^**	永定 Yongding	y=0.406x?2.9927	0.6253^**
政和 Zhenghe	y=1.3987x?8.1869	0.7592^**	漳平 Zhangping	y=0.488x?3.5118	0.7549^**
建宁 Jianning	y=0.9394x?4.1772	0.8743^**	长汀 Changting	y=0.881x?5.7504	0.7581^**
明溪 Mingxi	y=1.4279x?8.0672	0.6296^**	福清 Fuqing	y=0.2152x?1.8169	0.8424^**
宁化 Ninghua	y=0.6959x?5.0198	0.7548^**	长乐 Changle	y=0.686x?4.8377	0.7547^*
清流 Qingliu	y=0.4065x?2.0969	0.9827^*	莆田 Putian	y=0.5344x?5.3924	0.5259^**
泰宁 Taining	y=0.9808x?7.5285	0.6252^**	仙游 Xianyou	y=0.1304x?4.3521	0.6086^*
将乐 Jiangle	y=2.1656x?9.9491	0.8397^**	安溪 Anxi	y=0.694x?6.4913	0.7588^**
光泽 Guangze	y=0.9303x?5.2426	0.6594^**	南安 Nan’an	y=0.2751x?2.8375	0.7211^*
连江 Lianjiang	y=0.9214x?7.5601	0.6269^**	永春 Yongchun	y=0.3792x?2.6927	0.6965^**
罗源 Luoyuan	y=0.9291x?7.1085	0.7791^**	惠安 Hui’an	y=0.7209x?6.9138	0.7156^**
闽侯 Minhou	y=0.3447x?2.8639	0.6316^**	泉州 Quanzhou	y=0.8262x?8.6577	0.9975^**
闽清 Minqing	y=1.0362x?8.0447	0.5169^**	厦门 Xiamen	y=0.8431x?6.7992	0.6435^**
永泰 Yongtai	y=0.7426x?4.971	0.6588^**	平和 Pinghe	y=0.4475x?3.9084	0.7564^**
福安 Fu’an	y=1.2872x?8.6613	0.8994^**	长泰 Changtai	y=0.6606x?5.7161	0.9818^**
福鼎 Fuding	y=0.4865x?2.8378	0.9776^**	诏安 Zhao’an	y=0.6728x?5.4918	0.9671^**
蕉城 Jiaocheng	y=1.0039x?7.0768	0.7099^**	龙海 Longhai	y=0.1977x?3.6122	0.9985^*
霞浦 Xiapu	y=0.545x?3.1403	0.9769^**	云霄 Yunxiao	y=0.7722x?9.1259	0.9195^**
古田 Gutian	y=0.9078x?5.328	0.6370^**	漳浦 Zhangpu	y=0.4687x?4.6172	0.9991^*
大田 Datian	y=0.7608x?6.191	0.7084^**	漳州 Zhangzhou	y=0.748x?6.5158	0.9268^**
尤溪 Youxi	y=1.7559x?9.4586	0.9172^**	福州 Fuzhou	y=0.0483x?1.5318	0.5424^*
德化 Dehua	y=1.1589x?5.6118	0.8598^**
* 表示相关性达P<0.05显著水平, ** 表示相关性达P<0.01显著水平。* represents significant correlation at P<0.05 level, ** represents significant correlation at P<0.01 level.

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表2福建省各县市茶叶寒冻害保险4 ℃触发条件下的保险费率
Table2.Premium rate of tea cold-frost damage insurance under 4 ℃ trigger condition in each county (city) in Fujian Province %　

北部县市 Northern county	海拔高度 Altitude (m)				南部县市 Southern county	海拔高度 Altitude (m)
北部县市 Northern county	<200	200~600	600~900	>900	南部县市 Southern county	<300	300~700	700~1100	>1100
光泽 Guangze	2.02	6.56	10.64	15.05	连城 Liancheng	1.54	6.49	11.28	15.63
建瓯 Jian’ou	0.24	0.79	1.28	1.81	新罗 Xinluo	0.59	2.47	4.28	5.94
建阳 Jianyang	0.53	1.72	2.78	3.94	上杭 Shanghang	0.10	0.43	0.74	1.03
延平 Yanping	0.93	3.01	4.88	6.90	武平 Wuping	0.28	1.18	2.05	2.84
浦城 Pucheng	0.83	2.69	4.36	6.16	永定 Yongding	0.28	1.17	2.03	2.82
邵武 Shaowu	1.37	4.45	7.21	10.20	漳平 Zhangping	0.29	1.20	2.09	2.90
松溪 Songxi	1.67	5.43	8.80	12.45	长汀 Changting	1.27	5.35	9.29	12.88
顺昌 Shunchang	0.52	1.70	2.76	3.91	华安 Hua’an	0.30	1.28	2.23	3.09
武夷山 Wuyishan	1.56	5.06	8.21	11.62	南靖 Nanjing	0.25	1.07	1.85	2.57
政和 Zhenghe	2.12	6.88	11.16	15.79	福州 Fuzhou	0.37	1.47	2.99	4.13
建宁 Jianning	1.66	5.40	8.75	12.38	福清 Fuqing	0.20	0.77	1.57	2.17
明溪 Mingxi	2.70	8.77	14.22	20.12	长乐 Changle	0.87	3.41	6.95	9.60
宁化 Ninghua	1.86	6.05	9.80	13.87	莆田 Putian	0.74	2.91	5.93	8.18
清流 Qingliu	0.61	1.98	3.21	4.54	仙游 Xianyou	1.09	4.26	8.70	12.01
沙县 Shaxian	0.70	2.29	3.71	5.25	安溪 Anxi	0.71	2.78	5.67	7.82
泰宁 Taining	2.77	9.02	14.62	20.68	惠安 Hui’an	0.17	0.68	1.39	1.92
永安 Yong’an	0.99	3.23	5.24	7.41	泉州 Quanzhou	0.37	1.44	2.94	4.07
将乐 Jiangle	1.71	5.56	9.01	12.75	南安 Nan’an	0.31	1.22	2.50	3.45
三明 Sanming	1.32	4.27	6.93	9.80	永春 Yongchun	0.58	2.26	4.61	6.36
连江 Lianjiang	1.00	3.03	5.47	7.32	厦门 Xiamen	0.14	0.55	1.12	1.54
罗源 Luoyuan	0.96	2.90	5.25	7.02	平和 Pinghe	0.75	2.93	5.98	8.26
闽侯 Minhou	0.24	0.72	1.29	1.73	长泰 Changtai	0.48	1.90	3.87	5.35
闽清 Minqing	1.23	3.74	6.76	9.04	诏安 Zhao’an	0.53	2.07	4.22	5.82
永泰 Yongtai	0.94	2.85	5.14	6.88	龙海 Longhai	0.28	1.09	2.23	3.08
福安 Fu’an	1.23	3.72	6.72	8.99	云霄 Yunxiao	0.57	2.25	4.59	6.34
福鼎 Fuding	0.63	1.90	3.43	4.59	漳浦 Zhangpu	0.29	1.15	2.35	3.25
古田 Gutian	1.23	3.72	6.73	8.99	漳州 Zhangzhou	0.39	1.54	3.14	4.34
蕉城 Jiaocheng	0.43	1.30	2.35	3.14
霞浦 Xiapu	0.37	1.12	2.02	2.70
德化 Dehua	1.21	3.67	6.64	8.88
大田 Datian	1.26	3.81	6.89	9.21
尤溪 Youxi	1.82	5.51	9.96	13.32
柘荣 Zherong	0.81	2.46	4.45	5.95
寿宁 Shouning	0.68	2.07	3.74	5.00
屏南 Pingnan	0.39	1.18	2.14	2.86
周宁 Zhouning	0.42	1.26	2.28	3.05

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表3福建省茶叶寒冻害事件出现日期对应的赔付比例
Table3.Ratios of compensation corresponding to the date of occurrence of the cold-frost damage of tea in Fujian Province %　

极端最低气温(T_d) Extreme minimum temperature (T_d,℃)	出现日期(月-日) Date of occurrence (month-day)
极端最低气温(T_d) Extreme minimum temperature (T_d,℃)	03-01—03-05	03-06—03-10	03-11—03-15	03-16—03-20	03-21—03-25	03-26—03-30
3<T_d≤4	1	1	2	2	3	3
2<T_d≤3	2	2	3	3	4	4
1<T_d≤2	3	3	4	4	5	5
0<T_d≤1	4	4	5	5	6	6
?1<T_d≤0	5	6	7	8	9	10
?2<T_d≤?1	6	7	8	9	10	11
T_d≤?2	7	8	9	10	15	20

极端最低气温 Extreme minimum temperature (T_d, ℃)	03-31—04-04	04-05—04-09	04-10—04-14	04-15—04-19	04-20—04-24	04-25—05-10
3<T_d≤4	4	5	5	7	5	3
2<T_d≤3	5	6	6	8	6	4
1<T_d≤2	6	7	8	10	7	5
0<T_d≤1	7	8	15	20	15	10
?1<T_d≤0	13	15	20	30	20	15
?2<T_d≤?1	15	20	40	60	30	20
T_d≤?2	30	40	50	100	40	25

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表4福建省各区域各县市的茶叶寒冻害气象指数保险平均赔付率
Table4.Average loss rates of meteorological index insurance of tea cold-frost damage in counties/cities of different regions of Fujian Province %　

西北部 Northwest		东北部 Northeast		西南部 Southwest		东南部 Southeast
站点 Station	赔付率 Loss rate	站点 Station	赔付率 Loss rate	站点 Station	赔付率 Loss rate	站点 Station	赔付率 Loss rate
建瓯 Jian’ou	82.10	连江 Lianjiang	70.23	华安 Hua’an	42.70	福清 Fuqing	49.29
建阳 Jianyang	96.56	罗源 Luoyuan	77.34	南靖 Nanjing	33.62	长乐 Changle	64.66
南平 Nanping	44.78	闽侯 Minhou	48.93	连城 Liancheng	85.41	莆田 Putian	38.69
邵武 Shaowu	114.29	闽清 Minqing	82.07	龙岩 Longyan	34.53	仙游 Xianyou	59.89
顺昌 Shunchang	69.51	永泰 Yongtai	108.90	上杭 Shanghang	74.50	安溪 Anxi	41.87
沙县 Shaxian	56.91	福安 Fu’an	85.23	武平 Wuping	105.39	南安 Nan’an	35.51
永安 Yong’an	51.31	福鼎 Fuding	124.68	永定 Yongding	73.59	永春 Yongchun	66.25
三明 Sanming	47.11	蕉城 Jiaocheng	40.25	漳平 Zhangping	68.14	惠安 Hui’an	20.67
浦城 Pucheng	91.43	霞浦 Xiapu	78.91	长汀 Changting	136.29	泉州 Quanzhou	27.56
松溪 Songxi	80.70	古田 Gutian	84.44	—	—	厦门 Xiamen	24.91
武夷山 Wuyishan	77.44	大田 Datian	61.55	—	—	平和 Pinghe	42.93
政和 Zhenghe	63.53	尤溪 Youxi	69.44	—	—	长泰 Changtai	25.97
建宁 Jianning	108.69	德化 Dehua	86.02	—	—	诏安 Zhao’an	20.67
明溪 Mingxi	88.16	屏南 Pingnan	132.93	—	—	龙海 Longhai	23.85
宁化 Ninghua	100.29	寿宁 Shouning	133.59	—	—	云霄 Yunxiao	12.72
清流 Qingliu	76.50	柘荣 Zherong	122.04	—	—	漳浦 Zhangpu	23.32
泰宁 Taining	90.03	周宁 Zhouning	131.62	—	—	漳州 Zhangzhou	18.02
将乐 Jiangle	45.25	—	—	—	—	福州 Fuzhou	62.01
光泽 Guangze	104.49	—	—	—	—	—	—
平均 Average	78.40	平均 Average	90.50	平均 Average	72.70	平均 Average	36.60

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表5福建省各区域茶叶寒冻害平均赔付比例、损失率和保险基差比
Table5.Average compensation ratios, loss ratios and insurance basis ratios of tea cold-frost damage in different regions of Fujian Province %　

区域 Region	赔付比例 Compensation ratio	损失率 Loss rate	基差比 Basis ratio
西北部 Northwest	3.60	3.75	?2.83
东北部 Northeast	3.08	3.08	?1.26
西南部 Southwest	2.48	2.48	?0.50
东南部 Southeast	1.99	2.00	?1.09
福建省 Fujian Province	2.91	2.96	?1.65

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参考文献(27)

[1]	高水练. 不良天气对乌龙茶产业的影响及其补偿措施?以安溪县为例[J]. 亚热带农业研究, 2011, 7(4): 261?265 GAO S L. Impact of bad weather on Oolong tea industry and its compensation measures— Taking Anxi County as a case[J]. Subtropical Agriculture Research, 2011, 7(4): 261?265
[2]	洪晓湘, 陈娇娜, 施宗强. 漳州市茶叶生产的气象条件分析[J]. 福建农业科技, 2009, (6): 75?76 doi: 10.3969/j.issn.0253-2301.2009.06.037 HONG X X, CHEN J N, SHI Z Q. Analysis on meteorological conditions of tea production in Zhangzhou City[J]. Fujian Agricultural Science and Technology, 2009, (6): 75?76 doi: 10.3969/j.issn.0253-2301.2009.06.037
[3]	彭继达. 2018年福建省春茶霜冻害特征分析[J]. 福建热作科技, 2019, 44(1): 30?34 PENG J D. Analysis about the characteristic of spring tea frostbite in Fujian Province in 2018[J]. Fujian Science & Technology of Tropical Crops, 2019, 44(1): 30?34
[4]	杨太明, 刘布春, 孙喜波, 等. 安徽省冬小麦种植保险天气指数设计与应用[J]. 中国农业气象, 2013, 34(2): 229?235 doi: 10.3969/j.issn.1000-6362.2013.02.016 YANG T M, LIU B C, SUN X B, et al. Design and application of the weather indices of winter wheat planting insurance in Anhui Province[J]. Chinese Journal of Agrometeorology, 2013, 34(2): 229?235 doi: 10.3969/j.issn.1000-6362.2013.02.016
[5]	杨太明, 孙喜波, 刘布春, 等. 安徽省水稻高温热害保险天气指数模型设计[J]. 中国农业气象, 2015, 36(2): 220?226 doi: 10.3969/j.issn.1000-6362.2015.02.013 YANG T M, SUN X B, LIU B C, et al. Design on weather indices model for insurance of rice heat damage in Anhui Province[J]. Chinese Journal of Agrometeorology, 2015, 36(2): 220?226 doi: 10.3969/j.issn.1000-6362.2015.02.013
[6]	肖秀珠, 刘君, 张维, 等. 基于天气指数保险的龙岩市烟草冻灾和水灾分析[J]. 福建农业科技, 2013, (3): 43?45 doi: 10.3969/j.issn.0253-2301.2013.03.021 XIAO X Z, LIU J, ZHANG W, et al. Analysis on freezing and flood injury to tobacco in Longyan City based on insurance of meteorological index[J]. Fujian Agricultural Science and Technology, 2013, (3): 43?45 doi: 10.3969/j.issn.0253-2301.2013.03.021
[7]	娄伟平, 吉宗伟, 邱新法, 等. 茶叶霜冻气象指数保险设计[J]. 自然资源学报, 2011, 26(12): 2050?2060 doi: 10.11849/zrzyxb.2011.12.005 LOU W P, JI Z W, QIU X F, et al. Design of weather index insurance contact for tea frost[J]. Journal of Natural Resources, 2011, 26(12): 2050?2060 doi: 10.11849/zrzyxb.2011.12.005
[8]	吴利红, 娄伟平, 姚益平, 等. 水稻农业气象指数保险产品设计?以浙江省为例[J]. 中国农业科学, 2010, 43(23): 4942?4950 doi: 10.3864/j.issn.0578-1752.2010.23.021 WU L H, LOU W P, YAO Y P, et al. Design of products for rice agro-meteorological index insurance: A case in Zhejiang Province[J]. Scientia Agricultura Sinica, 2010, 43(23): 4942?4950 doi: 10.3864/j.issn.0578-1752.2010.23.021
[9]	毛裕定, 吴利红, 苗长明, 等. 浙江省柑桔冻害气象指数保险参考设计[J]. 中国农业气象, 2007, 28(2): 226?230 doi: 10.3969/j.issn.1000-6362.2007.02.027 MAO Y D, WU L H, MIAO C M, et al. A reference design for Citrus freeze damage insurance by using meteorological index in Zhejiang Province[J]. Chinese Journal of Agrometeorology, 2007, 28(2): 226?230 doi: 10.3969/j.issn.1000-6362.2007.02.027
[10]	OZAKI V A, GOODWIN B K, SHIROTA R. Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts[J]. Applied Economics, 2008, 40(9): 1151?1164 doi: 10.1080/00036840600749680
[11]	TURVEY C G, WEERSINK A, CHIANG S H C. Pricing weather insurance with a random strike price: the Ontario ice-wine harvest[J]. American Journal of Agricultural Economics, 2006, 88(3): 696?709 doi: 10.1111/j.1467-8276.2006.00889.x
[12]	MARLETTO V, VENTURA F, FONTANA G, et al. Wheat growth simulation and yield prediction with seasonal forecasts and a numerical model[J]. Agricultural and Forest Meteorology, 2007, 147(1/2): 71?79
[13]	LU Y, RAMIREZ O A, REJESUS R M, et al. Empirically evaluating the flexibility of the Johnson family of distributions: a crop insurance application[J]. Agricultural and Resource Economics Review, 2008, 37(1): 79?91
[14]	娄伟平, 吴利红, 邱新法, 等. 柑桔农业气象灾害风险评估及农业保险产品设计[J]. 自然资源学报, 2009, 24(6): 1030?1040 doi: 10.3321/j.issn:1000-3037.2009.06.010 LOU W P, WU L H, QIU X F, et al. Risk assessment and agricultural insurance design of agrometeorological disasters risk for citrus[J]. Journal of Natural Resources, 2009, 24(6): 1030?1040 doi: 10.3321/j.issn:1000-3037.2009.06.010
[15]	王春乙, 张亚杰, 张京红, 等. 海南省芒果寒害气象指数保险费率厘定及保险合同设计研究[J]. 气象与环境科学, 2016, 39(1): 108?113 WANG C Y, ZHANG Y J, ZHANG J H, et al. Determination of the premium rate based on the weather indices of chilling injury in mangoes and contract design in Hainan Province[J]. Meteorological and Environmental Sciences, 2016, 39(1): 108?113
[16]	张京红, 黄海静, 王春乙, 等. 基于产量风险的海南荔枝寒害保险费率研究[J]. 热带气象学报, 2015, 31(6): 862?868 ZHANG J H, HUANG H J, WANG C Y, et al. Study on insurance rates of litchi chilling injury based on production risk in Hainan[J]. Journal of Tropical Meteorology, 2015, 31(6): 862?868
[17]	王丽红, 杨华, 田志宏, 等. 非参数核密度法厘定玉米区域产量保险费率研究?以河北安国市为例[J]. 中国农业大学学报, 2007, 12(1): 90?94 doi: 10.3321/j.issn:1007-4333.2007.01.019 WANG L H, YANG H, TIAN Z H, et al. Maize GRP rate of premium deciding by nonprarametric kernel density — A case study on Anguo City, Hebei Province[J]. Journal of China Agricultural University, 2007, 12(1): 90?94 doi: 10.3321/j.issn:1007-4333.2007.01.019
[18]	陈新建, 陶建平. 基于风险区划的水稻区域产量保险费率研究[J]. 华中农业大学学报: 社会科学版, 2008, (4): 14?17 CHEN X J, TAO J P. Study on GRP rate of rice production based on risk zoning[J]. Journal of Huazhong Agricultural University: Social Science Edition, 2008, (4): 14?17
[19]	孙擎, 杨再强, 殷剑敏, 等. 江西早稻高温逼熟气象灾害指数保险费率的厘定[J]. 中国农业气象, 2014, 35(5): 561?566 doi: 10.3969/j.issn.1000-6362.2014.05.013 SUN Q, YANG Z Q, YIN J M, et al. Estimation of premium rates of high temperature disaster for early rice in Jiangxi[J]. Chinese Journal of Agrometeorology, 2014, 35(5): 561?566 doi: 10.3969/j.issn.1000-6362.2014.05.013
[20]	刘凯文, 刘可群, 邓爱娟, 等. 基于开花期地域差异的中稻高温热害天气指数保险设计[J]. 中国农业气象, 2017, 38(10): 679?688 doi: 10.3969/j.issn.1000-6362.2017.10.007 LIU K W, LIU K Q, DENG A J, et al. Weather index insurance design of middle-season rice heat damage based on regional difference of flowering stage[J]. Chinese Journal of Agrometeorology, 2017, 38(10): 679?688 doi: 10.3969/j.issn.1000-6362.2017.10.007
[21]	曲思邈, 王冬妮, 郭春明, 等. 玉米干旱天气指数保险产品设计?以吉林省为例[J]. 气象与环境学报, 2018, 34(2): 92?99 doi: 10.3969/j.issn.1673-503X.2018.02.012 QU S M, WANG D N, GUO C M, et al. Insurance product design based on maize drought weather index: A case study in Jilin Province[J]. Journal of Meteorology and Environment, 2018, 34(2): 92?99 doi: 10.3969/j.issn.1673-503X.2018.02.012
[22]	丁烨毅, 杨栋, 陈鑫磊, 等. 杨梅降水气象指数保险产品设计?以慈溪市为例[J]. 浙江农业学报, 2017, 29(12): 2032?2037 doi: 10.3969/j.issn.1004-1524.2017.12.11 DING Y Y, YANG D, CHEN X L, et al. Design of products for precipitation meteorological index insurance of Myrica rubra — A case study of Cixi City[J]. Acta Agriculturae Zhejiangensis, 2017, 29(12): 2032?2037 doi: 10.3969/j.issn.1004-1524.2017.12.11
[23]	王新伟, 杜明哲, 王丽, 等. 河南省花生连阴雨灾害气象指数保险设计[J]. 生态学杂志, 2018, 37(11): 3390?3395 WANG X W, DU M Z, WANG L, et al. Design of the continuous rainfall days index insurance of peanut in Henan Province[J]. Chinese Journal of Ecology, 2018, 37(11): 3390?3395
[24]	李睿涛, 刘京会, 周洪奎, 等. 华北平原冬小麦因旱减产气象指数保险产品研究[J]. 灾害学, 2017, 32(3): 216?221 doi: 10.3969/j.issn.1000-811X.2017.03.036 LI R T, LIU J H, ZHOU H K, et al. The agro-meteorological index insurance products: a case of winter wheat drought in North China Plain[J]. Journal of Catastrophology, 2017, 32(3): 216?221 doi: 10.3969/j.issn.1000-811X.2017.03.036
[25]	曹雯, 武万里, 杨太明, 等. 宁夏枸杞炭疽病害天气指数保险研究[J]. 干旱气象, 2019, 37(5): 857?865 CAO W, WU W L, YANG T M, et al. Study on weather index insurance of Colletotrichum gloeosporioides Penz of China wolfberry in Ningxia[J]. Journal of Arid Meteorology, 2019, 37(5): 857?865
[26]	陶红超, 陈家金, 陈志彪, 等. 基于危险性评估的福建省茶叶寒冻害保险费率厘定[J]. 中国生态农业学报(中英文), 2020, 28(11): 1778?1788 TAO H C, CHEN J J, CHEN Z B, et al. Determination of premium rates for tea cold-frost damage in Fujian Province based on risk assessment[J]. Chinese Journal of Eco-Agriculture, 2020, 28(11): 1778?1788
[27]	李志学, 程猛. 美国和日本的农业保险模式及其对我们的启示[J]. 西安石油大学学报: 社会科学版, 2008, 17(1): 15?19 LI Z X, CHENG M. The model of American and Japanese agricultural insurance and its enlightenment[J]. Journal of Xi’an Shiyou University: Social Science Edition, 2008, 17(1): 15?19