Multi-objective methods for crop insurance premiums: framework proposal and a case study in sugarcane
Resumo
The increase in extreme climate events due to climate change has resulted in crop losses, quality losses, environmental and social impacts in agricultural areas. Insurance against extreme events is a vital tool adaptation to deal with the impacts of those hazards. However, few works consider the optimization of different dimensions related to this tool. This work proposes a framework to use multi-objective optimization models to better design and evaluate crop insurance premiums and conducts a case study for sugarcane premiums at São Paulo state in 2010. The framework can be adopted for different crops, objectives, and models. The case study showed that around 20% of the policies evaluated were efficient solutions from the farmer's point of view.
Palavras-chave:
Insurance premiums
Referências
Ball, A. (2012). Review of data management lifecycle models. University of Bath.
Bowers, N., Gerber, H., Hickman, J., Jones, D., and Nesbitt, C. (1997). Actuarial Mathematics. Society of Actuaries. DOI: https://doi.org/10.1080/00029890.1986.11971867
Da Fonseca, V. G., Fonseca, C. M., and Hall, A. O. (2001). Inferential performance assessment of stochastic optimisers and the attainment function. In International Conference on Evolutionary Multi-Criterion Optimization, pages 213–225. Springer. DOI: https://doi.org/10.1007/3-540-44719-9_15
De Brito, M. M. and Evers, M. (2016). Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Natural Hazards and Earth System Sciences, 16(4):1019–1033. DOI: https://doi.org/10.5194/nhess-16-1019-2016
Dunnett, A., Shirsath, P., Aggarwal, P., Thornton, P., Joshi, P. K., Pal, B. D., KhatriChhetri, A., and Ghosh, J. (2018). Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions. Ecological modelling, 381:23–35. DOI: https://doi.org/10.1016/j.ecolmodel.2018.04.008
Fadhliani, Z., Luckstead, J., and Wailes, E. J. (2019). The impacts of multiperil crop insurance on indonesian rice farmers and production. Agricultural Economics, 50(1):15–26. DOI: https://doi.org/10.1111/agec.12462
Formetta, G. and Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57:101920. DOI: https://doi.org/10.1016/j.gloenvcha.2019.05.004
Groot, J. C., Oomen, G. J., and Rossing, W. A. (2012). Multi-objective optimization and design of farming systems. Agricultural Systems, 110:63–77. DOI: https://doi.org/10.1016/j.agsy.2012.03.012
Hajkowicz, S. and Collins, K. (2007). A review of multiple criteria analysis for water resource planning and management. Water resources management, 21(9):1553–1566. DOI: https://doi.org/10.1007/s11269-006-9112-5
IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Technical report, Cambridge University Press.
Jiang, S., Zhang, H., Cong, W., Liang, Z., Ren, Q., Wang, C., Zhang, F., and Jiao, X. (2020). Multi-objective optimization of smallholder apple production: Lessons from the bohai bay region. Sustainability, 12(16):6496. DOI: https://doi.org/10.3390/su12166496
Kim, W., Iizumi, T., and Nishimori, M. (2019). Global patterns of crop production losses associated with droughts from 1983 to 2009. Journal of Applied Meteorology and Climatology, 58(6):1233 – 1244. DOI: https://doi.org/10.1175/JAMC-D-18-0174.1
Klein, T., Holzkämper, A., Calanca, P., Seppelt, R., and Fuhrer, J. (2013). Adapting agricultural land management to climate change: a regional multi-objective optimization approach. Landscape ecology, 28(10):2029–2047. DOI: https://doi.org/10.1007/s10980-013-9939-0
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444. DOI: https://doi.org/10.1038/nature14539
López-Ibáñez, M., Paquete, L., and Stützle, T. (2010). Exploratory analysis of stochastic local search algorithms in biobjective optimization. In Bartz-Beielstein, T., Chiarandini, M., Paquete, L., and Preuss, M., editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany. DOI: https://10.1007/978-3-642-02538-9_9
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A. (2010). Review article "Assessment of economic flood damage”. Natural Hazards and Earth System Sciences, 10(8):1697–1724. DOI: https://doi.org/10.5194/nhess-10-1697-2010
Mohor, G. S. and Mendiondo, E. M. (2017). Economic indicators of hydrologic drought insurance under water demand and climate change scenarios in a brazilian context. Ecological Economics, 140:66–78. DOI: https://doi.org/10.1016/j.ecolecon.2017.04.014
Navarro, F. A. R., Gesualdo, G. C., Ferreira, R. G., Rápalo, L. M. C., Benso, M. R., de Macedo, M. B., and Mendiondo, E. M. (2021). A novel multistage risk management applied to water-related disaster using diversity of measures: A theoretical approach. Ecohydrology & Hydrobiology. DOI: https://doi.org/10.1016/j.ecohyd.2021.07.004
Seifert-Dähnn, I. (2018). Insurance engagement in flood risk reduction – examples from household and business insurance in developed countries. Natural Hazards and Earth System Sciences, 18(9):2409–2429. DOI: https://doi.org/10.5194/nhess-18-2409-2018
SISSER (2021). Apólices de seguros agrícolas - 2006 a 2016. Available at: https://dados.gov.br/dataset/sisser3. Accessed on: 01.09.2021.
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A. (2013). A framework for global river flood risk assessments. Hydrology and Earth System Sciences, 17(5):1871–1892. DOI: https://doi.org/10.5194/hess-17-1871-2013
Bowers, N., Gerber, H., Hickman, J., Jones, D., and Nesbitt, C. (1997). Actuarial Mathematics. Society of Actuaries. DOI: https://doi.org/10.1080/00029890.1986.11971867
Da Fonseca, V. G., Fonseca, C. M., and Hall, A. O. (2001). Inferential performance assessment of stochastic optimisers and the attainment function. In International Conference on Evolutionary Multi-Criterion Optimization, pages 213–225. Springer. DOI: https://doi.org/10.1007/3-540-44719-9_15
De Brito, M. M. and Evers, M. (2016). Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Natural Hazards and Earth System Sciences, 16(4):1019–1033. DOI: https://doi.org/10.5194/nhess-16-1019-2016
Dunnett, A., Shirsath, P., Aggarwal, P., Thornton, P., Joshi, P. K., Pal, B. D., KhatriChhetri, A., and Ghosh, J. (2018). Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions. Ecological modelling, 381:23–35. DOI: https://doi.org/10.1016/j.ecolmodel.2018.04.008
Fadhliani, Z., Luckstead, J., and Wailes, E. J. (2019). The impacts of multiperil crop insurance on indonesian rice farmers and production. Agricultural Economics, 50(1):15–26. DOI: https://doi.org/10.1111/agec.12462
Formetta, G. and Feyen, L. (2019). Empirical evidence of declining global vulnerability to climate-related hazards. Global Environmental Change, 57:101920. DOI: https://doi.org/10.1016/j.gloenvcha.2019.05.004
Groot, J. C., Oomen, G. J., and Rossing, W. A. (2012). Multi-objective optimization and design of farming systems. Agricultural Systems, 110:63–77. DOI: https://doi.org/10.1016/j.agsy.2012.03.012
Hajkowicz, S. and Collins, K. (2007). A review of multiple criteria analysis for water resource planning and management. Water resources management, 21(9):1553–1566. DOI: https://doi.org/10.1007/s11269-006-9112-5
IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Technical report, Cambridge University Press.
Jiang, S., Zhang, H., Cong, W., Liang, Z., Ren, Q., Wang, C., Zhang, F., and Jiao, X. (2020). Multi-objective optimization of smallholder apple production: Lessons from the bohai bay region. Sustainability, 12(16):6496. DOI: https://doi.org/10.3390/su12166496
Kim, W., Iizumi, T., and Nishimori, M. (2019). Global patterns of crop production losses associated with droughts from 1983 to 2009. Journal of Applied Meteorology and Climatology, 58(6):1233 – 1244. DOI: https://doi.org/10.1175/JAMC-D-18-0174.1
Klein, T., Holzkämper, A., Calanca, P., Seppelt, R., and Fuhrer, J. (2013). Adapting agricultural land management to climate change: a regional multi-objective optimization approach. Landscape ecology, 28(10):2029–2047. DOI: https://doi.org/10.1007/s10980-013-9939-0
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444. DOI: https://doi.org/10.1038/nature14539
López-Ibáñez, M., Paquete, L., and Stützle, T. (2010). Exploratory analysis of stochastic local search algorithms in biobjective optimization. In Bartz-Beielstein, T., Chiarandini, M., Paquete, L., and Preuss, M., editors, Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin, Germany. DOI: https://10.1007/978-3-642-02538-9_9
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A. (2010). Review article "Assessment of economic flood damage”. Natural Hazards and Earth System Sciences, 10(8):1697–1724. DOI: https://doi.org/10.5194/nhess-10-1697-2010
Mohor, G. S. and Mendiondo, E. M. (2017). Economic indicators of hydrologic drought insurance under water demand and climate change scenarios in a brazilian context. Ecological Economics, 140:66–78. DOI: https://doi.org/10.1016/j.ecolecon.2017.04.014
Navarro, F. A. R., Gesualdo, G. C., Ferreira, R. G., Rápalo, L. M. C., Benso, M. R., de Macedo, M. B., and Mendiondo, E. M. (2021). A novel multistage risk management applied to water-related disaster using diversity of measures: A theoretical approach. Ecohydrology & Hydrobiology. DOI: https://doi.org/10.1016/j.ecohyd.2021.07.004
Seifert-Dähnn, I. (2018). Insurance engagement in flood risk reduction – examples from household and business insurance in developed countries. Natural Hazards and Earth System Sciences, 18(9):2409–2429. DOI: https://doi.org/10.5194/nhess-18-2409-2018
SISSER (2021). Apólices de seguros agrícolas - 2006 a 2016. Available at: https://dados.gov.br/dataset/sisser3. Accessed on: 01.09.2021.
Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A. (2013). A framework for global river flood risk assessments. Hydrology and Earth System Sciences, 17(5):1871–1892. DOI: https://doi.org/10.5194/hess-17-1871-2013
Publicado
10/11/2021
Como Citar
SILVA, Roberto F.; BENSO, Marcos R.; GESUALDO, Gabriela C.; MENDIONDO, Eduardo M.; SARAIVA, Antônio M.; MARQUES, Patrícia A. A.; DELBEM, Alexandre C. B..
Multi-objective methods for crop insurance premiums: framework proposal and a case study in sugarcane. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 225-233.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2021.18394.
