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Crop insurance model to consolidate academia-industry cooperation: a case study over Assam, India

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Abstract

Agriculture is one of the deciding factors of Indian economy, contributing almost 17% of the total GDP. Every year, crops are lost due to natural disasters. This academic research may provide a solution for a long-standing problem in the industry. Crop insurance is one of the most effective ways to not only compensate loss, but also to increase poor farmers’ resilience. Remote sensing has huge potential in the crop insurance market; it can be exploited for vulnerability mapping, damage assessment, risk mapping, and various other aspects. The purpose of this study was to present a method for evaluating crop vulnerability over an area using remote sensing and Geographic Information System (GIS), followed by an assessment of crops damaged due to flood. For application purposes, a crop risk map was prepared from a GIS model for the determination of crop insurance parameters. The study area selected (i.e., the Morigaon and Nagaon districts of Assam) is very much flood-prone. The districts have almost 50% agricultural land of the total land cover, thus making the crops very vulnerable to recurrent flooding. For this study, assessment of damage to crops due to flood was performed for a full year, followed by crop risk map generation from the GIS model. The results revealed that 345 km2 of land was inundated by flood in August 2016. Due to the flooding, 1435.08 km2 of agricultural land bearing crops was damaged at different levels. The crop risk map depicts 103.33 km2 of cropland at high risk due to flood.

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Notes

  1. Data from Maps of India https://www.mapsofindia.com/top-ten/india-crops/rice.html Accessed on 25 Dec 2018.

  2. Data from Pradhan Mantri Fasal Bima Yojana official websitehttps://pmfby.gov.in/stateWiseDataPage Accessed on 25 Dec 2018.

References

  1. Arjun, K. M. (2013). Indian agriculture- status, importance and role in Indian economy. International Journal of Agriculture and Food Science Technology.,4(4), 343–346.

    Google Scholar 

  2. World Bank. (2015a). Agricultural land (% of land area), World Development Indicators, The World Bank Group. https://data.worldbank.org/indicator/AG.LND.AGRI.ZS?locations=IN&view=chart. Accessed 25 Dec 2018.

  3. World Bank. (2015b) Agricultural land (km2), World Development Indicators, The World Bank Group. https://data.worldbank.org/indicator/AG.LND.AGRI.K2?locations=IN&view=chart. Accessed 25 Dec 2018.

  4. Madhusudhan, L. (2015). Agriculture role on Indian economy. Business and Economics Journal,6, 176. https://doi.org/10.4172/2151-6219.1000176.

    Article  Google Scholar 

  5. Hazell, P., Pomareda Benel, C. F., Valdés, A. (1986). Crop insurance for agricultural development. No. IICA-E20 P784 CDP-1418. IFPRI, Washington, DC (EUA) IICA, San José (Costa Rica).

  6. NITI Aayog task force on use of technologies for agriculture insurance, NRSC, ISRO, 25th Oct 2016.

  7. De Leeuw, J., Vrieling, A., Shee, A., Atzberger, C., Hadgu, K., Biradar, C., et al. (2014). The potential and uptake of remote sensing in insurance: A review. Remote Sensing,6(11), 10888–10912. https://doi.org/10.3390/rs61110888.

    Article  Google Scholar 

  8. Haq, M., Akhtar, M., Muhammad, S., Paras, S., & Rahmatullah, J. (2012). Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan. The Egyptian Journal of Remote Sensing and Space Science,15(2), 135–141. https://doi.org/10.1016/j.ejrs.2012.07.002.

    Article  Google Scholar 

  9. Okamoto, K., Yamakawa, S., & Kawashima, H. (1998). Estimation of flood damage to rice production in North Korea in 1995. International Journal of Remote Sensing,19(2), 365–371. https://doi.org/10.1080/014311698216332.

    Article  Google Scholar 

  10. Okamoto, K., & Fukuhara, M. (1996). Estimation of paddy field area using the area ratio of categories in each mixel of landsat TM. International Journal of Remote Sensing,17(9), 1735–1749. https://doi.org/10.1080/01431169608948736.

    Article  Google Scholar 

  11. Yamagata, Y., & Akiyama, T. (1988). Flood damage analysis using multitemporal landsat thematic mapper data. International Journal of Remote Sensing,9(3), 503–514. https://doi.org/10.1080/01431168808954871.

    Article  Google Scholar 

  12. Bhuyan S. (1998). The agricultural sector in Assam: Its importance. https://assam.org/node/2371. Accessed 25 Dec 2018.

  13. Pantaleoni, E., Engel, B. A., & Johannsen, C. J. (2007). Identifying agricultural flood damage using landsat imagery. Precision Agriculture,8(1–2), 27–36. https://doi.org/10.1007/s11119-006-9026-5.

    Article  Google Scholar 

  14. Census of India. (2011). Planning Commission. Government of India.

  15. Jürgens, C. (1993). Use of satellite remote sensing for an operational procedure to control permanent fallow land of the EEC-temporary set-aside arable land programme. In European communities agriculture series EUR15143, Brussels.

  16. Jürgens, C. (1997). The Modi ed Normalized DiVerence Vegetation Index (mNDVI)—a newindex in determining frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing,18, 3583–3594.

    Article  Google Scholar 

  17. Jürgens, C., & Fander, M. (1993). Soil erosion assessment and simulation by means ofSGEOS and ancillary digital data. International Journal of Remote Sensing,14, 2847–2855.

    Article  Google Scholar 

  18. Silleos, N., Perakis, K., & Petsanis, G. (2002). Assessment of crop damage using space remote sensing and GIS. International Journal of Remote Sensing,23(3), 417–427. https://doi.org/10.1080/01431160110040026.

    Article  Google Scholar 

  19. Ji, Luyan, XiuruiGeng, Kang Sun, Zhao, Yongchao, & Gong, Peng. (2015). Target detection method for water mapping using landsat 8 OLI/TIRS imagery. Water,7(12), 794–817. https://doi.org/10.3390/w7020794.

    Article  Google Scholar 

  20. Acharya, Tri, Anoj, S., & Dong, L. (2018). Evaluation of water indices for surface water extraction in a landsat 8 scene of Nepal. Sensors,18(8), 2580. https://doi.org/10.3390/s18082580.

    Article  Google Scholar 

  21. Sarp, Gulcan, & Ozcelik, Mehmet. (2017). Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science,11(3), 381–391. https://doi.org/10.1016/j.jtusci.2016.04.005.

    Article  Google Scholar 

  22. McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing,17(7), 1425–1432. https://doi.org/10.1080/01431169608948714.

    Article  Google Scholar 

  23. Feyisa, Gudina L., Meilby, Henrik, Rasmus, F., & Simon, R. P. (2014). Automated water extraction index: A new technique for surface water mapping using landsat imagery. Remote Sensing of Environment,140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029.

    Article  Google Scholar 

  24. Bentley, M. L., Mote, T. L., & Thebpanya, P. (2002). Using Landsat to identify thunderstorm damage in agricultural regions. Bulletin of the American Meteorological Society,83(3), 363–376. https://doi.org/10.1175/1520-0477-83.3.363.

    Article  Google Scholar 

  25. BehrangManesh, M., Khosravi, H., Heydari, E. A., et al. (2019). TheorApplClimatol. https://doi.org/10.1007/s00704-019-02878-w.

    Article  Google Scholar 

  26. Xu, Hao-jie, Wang, Xin-ping, Zhao, Chuan-yan, & Yang, Xue-mei. (1981). Diverse responses of vegetation growth to meteorological drought across climate zones and land biomes in Northern China from 1981 to 2014. Agricultural and Forest Meteorology,262(2018), 1–13. https://doi.org/10.1016/j.agrformet.2018.06.027.

    Article  Google Scholar 

  27. Alamdarloo, E. H., Manesh, M. B., & Khosravi, H. (2018). Probability assessment of vegetation vulnerability to drought based on remote sensing data. Environmental monitoring and assessment,190(12), 702. https://doi.org/10.1007/s10661-018-7089-1.

    Article  Google Scholar 

  28. Tran, H. T., Campbell, J. B., Tran, T. D., & Tran, H. T. (2017). Monitoring drought vulnerability using multispectral indices observed from sequential remote sensing (Case Study: Tuy Phong, Binh Thuan, Vietnam). GIScience & Remote Sensing,54(2), 167–184. https://doi.org/10.1080/15481603.2017.1287838.

    Article  Google Scholar 

  29. Ogashawara, I., Curtarelli, M. P., & Ferreira, C. M. (2013). The use of optical remote sensing for mapping flooded areas. International Journal of Engineering Research and Applications,3(5), 1956–1960.

    Google Scholar 

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Correspondence to A. C. Pandey.

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Banerjee, S., Pandey, A.C. Crop insurance model to consolidate academia-industry cooperation: a case study over Assam, India. Spat. Inf. Res. 27, 719–731 (2019). https://doi.org/10.1007/s41324-019-00291-z

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