基于MODIS GPP产品的冬小麦保险费率厘定方法研究

doi:10.12082/dqxxkx.2020.190207

Abstract

Abstract:

Crop insurance is an important measure to reduce farmers' economic loss caused by disasters and to guarantee farmers' basic income of agricultural production. Traditionally, the rate-making of crop insurance is usually based on statistical data collected at administrative level without considering the differences in risk between farms within each administrative unit. Hence, how to obtain the pure premium rate of crop insurance for land parcels within each administrative unit is critical in precision crop insurance. In this study, we calculated the pure premium rate of winter wheat at the farm level in Zhoukou city, Henan, China, based on remote sensing data and insurance actuarial model. We first extracted the GPP data in the growing season from 2005 to 2015 using Moderate Resolution Imaging Spectroradiometer(MODIS) GPP product(MOD17A2) and the administrative boundary of Zhoukou city. We then generated the ratios of winter wheat area in the pre-winter periods from 2004 to 2014 in Zhoukou city at 1 km spatial resolution using Landsat TM/ETM/OLI data. The guaranteed GPP was estimated based on the Bühlmann-Straub reliability model and the real GPP estimates. Second, we calculated the GPP loss rate using the guaranteed GPP and real GPP estimates. Finally, we used the empirical rate method to set the pure insurance premium rate for winter wheat in each land parcel. Our results showed a map of winter wheat pure premium insurance rate at fine scale for Zhoukou city. Compared to the insurance rate calculated using administrative-level statistical data, the insurance rates calculated using remote sensing data were more precision. The fine-scale pure insurance rate map provides an important reference for farm-level winter wheat insurance pricing, making the pricing of crop insurance more reasonable. Our study highlights the integration of remote sensing in determination of crop insurance rates and improvement on the spatial fineness of crop insurance, which could further promote the commercial development of precision crop insurance. Government can hence formulate appropriate crop insurance policies for different farmers, while insurance companies can charge premiums reasonably.

Key words: crop, insurance, remote sensing, pure premium rate, winter wheat, Zhoukou, GPP, spatial fineness

WANG Weijia, WANG Wen, YANG Xi, ZHAO Yanyun. Pure Premium Rate-making of Winter Wheat Insurance based on MODIS GPP[J].Journal of Geo-information Science, 2020, 22(7): 1578-1587.DOI:10.12082/dqxxkx.2020.190207

Figures/Tables 8

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Tab. 1

Tab. 2

References 25

[1]	Orcutt D M, Nilsen E T. Physiology of plants under stress: Soil and biotic factors[M]. New York: John Wiley & Sons, 2000.
[2]	Mahul O, Stutley C J. Government support to agricultural insurance: Challenges and options for developing countries[M]. Washington: The World Bank, 2010.
[3]	Wang E, Yu Y, Little B B, et al. Crop insurance premium design based on survival analysis mode[J]. Agriculture and Agricultural Science Procedia, 2010,1:67-75.
[4]	Erik O D. The effects of premium subsidies on demand for crop insurance[R]. United States Department of Agriculture, Economic Research Service, 2014: 33.
[5]	Zhu Y, Goodwin B K, Ghosh S K. Modeling yield risk under technological change: Dynamic yield distributions and the US crop insurance program[J]. Journal of Agricultural and Resource Economics, 2011,36(1):192-210.
[6]	Zhang Y, Dukic V, Guszcza J. A Bayesian non‐linear model for forecasting insurance loss payments[J]. Journal of the Royal Statistical Society: Series A (Statistics in Society), 2012,175(2):637-656.
[7]	Marr A, Winkel A, van Asseldonk M, et al. Adoption and impact of index-insurance and credit for smallholder farmers in developing countries: A systematic review[J]. Agricultural Finance Review, 2016,76(1):94-118.
[8]	Tadesse M A, Shiferaw B A, Erenstein O. Weather index insurance for managing drought risk in smallholder agriculture: lessons and policy implications for sub-Saharan Africa[J]. Agricultural and Food Economics, 2015,3(1):26.
[9]	Piet L, Bougherara D. The impact of farmers' risk preferences on the design of an individual yield crop insurance[J]. Paris: Institute National dela recherche Agronomique, 2016,16(3):1-18.
[10]	周县华. 我国种植业保险风险区划与分级费率定价研究——以吉林省玉米种植保险为例[J]. 保险研究, 2018(2):72-84.
	[ Zhou X H. A study on the risk zoning and class rate determination in crop insurance in China: Taking corn insurance in Jilin province as an example[J]. Insurance Studies, 2018(2):72-84. ]
[11]	Rao K N. Index based crop insurance[J]. Agriculture and Agricultural Science Procedia, 2010,1:193-203.
[12]	Bolton D K, Friedl M A. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics[J]. Agricultural and Forest Meteorology, 2013,173:74-84.
[13]	Becker Reshef I, Vermote E, Lindeman M, et al. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data[J]. Remote Sensing of Environment, 2010,114(6):1312-1323.
[14]	Lobell D B. The use of satellite data for crop yield gap analysis[J]. Field Crops Research, 2013,143:56-64.
[15]	Kouadio L, Duveiller G, Djaby B, et al. Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,18:111-118.
[16]	周口统计局. 周口统计年鉴[M]. 北京: 中国统计出版社,2004-2015.
	[ Bureau, Zhoukou Statistics. Zhoukou statistical yearbook. 2004-2015[M]. Beijing: China Statistics Press. ]
[17]	Johnson D M. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,52:65-81.
[18]	河南统计局. 河南统计年鉴[M]. 北京: 中国统计出版社,2004-2015.
	[ Bureau Henan Statistics. Henan statistical yearbook. 2004-2015[M]. Beijing: China Statistics Press. ]
[19]	李中元, 吴炳方, 张淼等. 利用物候差异与面向对象决策树提取油菜种植面积[J]. 地球信息科学学报, 2019,21(5):720-730.
	[ Li Z Y, Wu B F, Zhang M, et al. Identifying rapeseed planting area using an object-oriented method and crop phenology[J]. Journal of Geo-information Science, 2019,21(5):720-730. ]
[20]	张超, 万庆, 张继权等. 基于格网数据的洪水灾害风险评估方法——以日本新川洪灾为例[J]. 地球信息科学, 2003,5(4):69-73.
	[ Zhang C, Wan Q, Zhang J Q, et al. The method of flood disaster risk evaluation based upon data of grid square[J]. Geo-information Science, 2003,5(4):69-73. ]
[21]	Turner D P, Ritts W D, Cohen W B, et al. Scaling gross primary production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation[J]. Remote Sensing of Environment, 2003,88(3):256-270.
[22]	Heinsch F A, Reeves M, Votava P, et al. GPP and NPP (MOD17A2/A3) Products NASA MODIS Land Algorithm[J]. MOD17 User's Guide, 2003:1-57.
[23]	Klugman S A, Panjer H H, Willmot G E. Loss models: from data to decisions[M]. New York: John Wiley & Sons, 2012.
[24]	Pinquet J. Experience rating in nonlife insurance[M]. New York: Handbook of Insurance, Springer, 2013:471-485.
[25]	Harri A, Coble K H, Ker A P, et al. Relaxing heteroscedasticity assumptions in area-yield crop insurance rating[J]. American Journal of Agricultural Economics, 2011,93(3):707-717.

		Landsat7 ETM分类结果/像元		用户精度/%
		冬小麦	其他
GF-1分类结果/像元	冬小麦	395	10	95.7
	其他	18	167	94.3

县名	基于遥感数据	基于统计数据
川汇	3.27	0.76
郸城	1.74	1.62
扶沟	1.67	3.61
淮阳	2.19	3.46
鹿邑	2.57	4.17
商水	2.48	1.15
沈丘	2.32	0.13
太康	1.69	0.84
西华	2.03	0.39
项城	2.16	0.58
平均	2.41	1.67

Pure Premium Rate-making of Winter Wheat Insurance based on MODIS GPP

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 25

Related Articles 15

Metrics

Comments

Recommended 0

[1]	SHUAI Yanmin, MA Xianwei, QU Ge, SHAO Congying, LIU Tao, LIU Shoumin, HUANG Huabing, GU Lingxiao, LATIPA Tuerhanjiang, LIANG Ji, LI Ling. Cascade Extraction of Impervious Surface Information based on the Signature of Temporal Spectrum [J]. Journal of Geo-information Science, 2021, 23(1): 171-186.
[2]	WANG Zhihua, YANG Xiaomei, ZHOU Chenghu. Geographic Knowledge Graph for Remote Sensing Big Data [J]. Journal of Geo-information Science, 2021, 23(1): 16-28.
[3]	WANG Yanjie, WANG Juanle, WEI Haishuo, ALTANSUKH Ochir, DAVAADORJ Davaasuren, SONOMDAGVA Chonokhuu. Study on Estimation Method of Mongolia Grassland Production based on Sparse Samples [J]. Journal of Geo-information Science, 2020, 22(9): 1814-1822.
[4]	CHEN Hui, LI Qing, WANG Zhongting, MA Pengfei, LI Ying, ZHAO Aimei. Retrieval of Aerosol Optical Depth Using FY3D MERSI2 Data [J]. Journal of Geo-information Science, 2020, 22(9): 1887-1896.
[5]	PU Dongchuan, WANG Guizhou, ZHANG Zhaoming, NIU Xuefeng, HE Guojin, LONG Tengfei, YIN Ranyu, JIANG Wei, SUN Jiayue. Urban Area Extraction based on Independent Component Analysis and Random Forest Algorithm [J]. Journal of Geo-information Science, 2020, 22(8): 1597-1606.
[6]	LI Wan, NIU Lu, CHEN Hong, WU Hua. Robust Downscaling Method of Land Surface Temperature by Using Random Forest Algorithm [J]. Journal of Geo-information Science, 2020, 22(8): 1666-1678.
[7]	Aynigar· yalkun, ALI Mamtimin, LIU Suhong, YANG Fan, HE Qing, LIU Yongqiang. Remote Sensing Estimation of Surface Broadband Emissivity over the Deserts in Xinjiang [J]. Journal of Geo-information Science, 2020, 22(8): 1743-1751.
[8]	WU Mingquan, WANG Biaocai, NIU Zheng, HUANG Wenjiang. Theoretical Framework and Research Progress of Big Earth Data Technology in Projects Construction [J]. Journal of Geo-information Science, 2020, 22(7): 1408-1423.
[9]	GONG Wei, LI Li, LIU Qinhuo, XIN Xiaozhou, PENG Zhiqing, WU Mingqun, NIU Zheng, TIAN Haifeng. Monitoring and Analyzing Ecosystem Impact on Hydropower Projects by Remote Sensing in the Belt and Road Region [J]. Journal of Geo-information Science, 2020, 22(7): 1424-1436.
[10]	ZHU Hui, ZHANG Qingling, ZHANG Shan. Spatial and Temporal Characteristics of Socio-Economic Development in Central Asia based on A Series of Nighttime Light Images from 1992 to 2017 [J]. Journal of Geo-information Science, 2020, 22(7): 1449-1462.
[11]	YE Huping, LIAO Xiaohan, HE Xianqiang, YUE Huanyin. Remote Sensing Monitoring and Variation Analysis of Marine Ecological Environment in Coastal Waters of Sri Lanka [J]. Journal of Geo-information Science, 2020, 22(7): 1463-1475.
[12]	GAO Wangwang, FENG Jianzhong, BAI Linyan, YANG Jianhua, GUO Leifeng, LI Hualin, CUI Mengrui. Spatiotemporal Dynamics and Tracing of Aerosol over Hainan Island [J]. Journal of Geo-information Science, 2020, 22(7): 1532-1543.
[13]	WANG Yongquan, WANG Chisheng, WANG Lehan, SHE Jiani, LI Qingquan. Preliminary Application of Night Light Remote Sensing based on Passenger Aircraft in Hong Kong Economic Activity Zone Changes Identification [J]. Journal of Geo-information Science, 2020, 22(5): 1153-1160.
[14]	LIAN Licong, WAN Zhiwei, JU Min, JIA Yulian, HONG Yijun, JIANG Meixin, ZENG Fenghai. Reconstruction of the Cropland Area and Its Spatial Distribution Pattern at Dongting Lake District of Hunan Province in the Middle of the Republic of China [J]. Journal of Geo-information Science, 2020, 22(5): 989-996.
[15]	ZHANG Hongyan, ZHOU Chenghu, LV Guonian, WU Zhifeng, LU Feng, WANG Jinfeng, YUE Tianxiang, LUO Jiancheng, GE Yong, QIN Chengzhi. The Connotation and Inheritance of Geo-information Tupu [J]. Journal of Geo-information Science, 2020, 22(4): 653-661.