world markets. In the context of climate change and higher
occurrence of extreme events, farmers’ exposure to yield,
price, and income variability will increase. In this scenario,
Italy is one of the EU countries whit higher public intervention in the field of risk management in agriculture.
Nowadays, public support, however, has been characterized by a substantial failure if we consider that less of
20% of the overall production in Italy is insured. We showed
that QR improves the yield-index representation, especially
in the lower tails of the distribution and hence increases the
risk-reducing properties of index-based insurances. This
finding contributes to an improved contract design, which is
essential as imperfect hedging seriously challenges the
acceptance, implementation, and up-scaling of index-based
insurance tool.
In this view, paying attention to new tools to manage
farmer revenue risk would represent a fundamental task for
Italian Agriculture Ministry; index-based contract would
catch a large share of agricultural production considering
lower costs of the insurance premium and his simple
mechanism. Moreover, would identify a solution of asymmetric information problem that notoriously plagued crop
insurance viability.
References
Alderman, H., & Haque, T. (2007). Insurance against covariate shocks:
The role of index-based insurance in social protection in
low-income countries of Africa. World Bank Working Paper
No. 95.
Antle, J. (1987). Econometric estimation of producers’ risk attitudes.
American Journal of Agricultural Economics, 69(3), 509–522.
Arias, O. V., Garrido, A., Villeta, M., & Tarquis, A. M. (2018).
Homogenisation of a soil properties map by principal component
analysis to define index agricultural insurance policies. Geoderma,
311, 149–158.
Barnett, B. J., & Mahul, O. (2007). Weather index insurance for
agriculture and rural areas in lower-income countries. American
Journal of Agricultural Economics, 89(5), 1241–1247.
Barnett, T. P., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D.,
Das, T., Bala, G., Wood, A. W., Nozawa, T., Mirin, A. A., Cayan,
D. R., & Dettinger, M. D. (2008). Human-induced changes in the
hydrology of the Western United States. Science. 319(5866), 1080–
1083.
Berg, E., & Schmitz, B. (2008). Weather-based instruments in the
context of whole-farm risk management. Agricultural Finance
Review, 68(1), 119–133.
Birthal, P., Roy, D., & Negi, D. (2015). Assessing the impact of crop
diversification on farm poverty in India. World Development, 72
(10), 1016.
Bouyé, E., & Salmon, M. (2013). Dynamic copula quantile regressions
and tail area dynamic dependence in Forex markets. In Copulae and
multivariate probability distributions in finance (pp. 125–154).
Routledge.
Chowdhury, S. P., Uhl, J., Grosch, R., Alqueres, S., Pittroff, S., Dietel,
K., Schmitt-Kopplin, P., Borriss, R., & Hartmann, A. (2016).
(Chowdhury et al 2015).
Clover, A. T., & Nieuwoudt, W. L. (2003). An economic evaluation of
area yield insurance for small-scale cane growers. Development
Southern Africa, 20, 293–305.
Dalhaus, T., Musshoff, O., & Finger, R. (2018). Phenology information
contributes to reduce temporal basis risk in agricultural weather
index insurance. Scientific reports, 8(1), 46.
Faccia, A. (2012). Analisi dei dati RICA finalizzati all’approfondimento del tema della gestione del rischio in agricoltura. Misurazione delle performance finanziarie e patrimoniali delle aziende
agrarie e relativa definizione di un modello di rating.
Finger, D., Wüest, A., & Bossard, P. (2013). Effects of oligotrophication on primary production in peri-alpine lakes. Water Resources
Research, 49, 4700–4710.
Glauber, J. W. (2004). Crop insurance reconsidered. American Journal
of Agricultural Economics, 86(5), 1179–1195.
Groom, M. J., Gray, E. M., & Patricia A. T. (2008). Biofuels and
biodiversity: principles for creating better policies for biofuel
production. Conservation Biology. 22(3), 602–609.
Hazell, P. B. (1992). The appropriate role of agricultural insurance in
developing countries. Journal of International Development, 4(6),
567–581.
Hubert, L. J., Golledge, R. G., & Costanzo, C. M. (2010) Generalized
procedures for evaluating spatial autocorrelation. Geographical
Analysis.
Kapphan, I., Calanca, P., & Holzkämper, A. (2012). Climate change,
weather insurance design and hedging effectiveness. The Geneva
Papers on Risk and Insurance—Issues and Practice.
Khan, M., & Watts, R. L. (2009). Estimation and empirical properties
of a firm-year measure of accounting conservatism. Journal of
Accounting and Economics, 48(2-3), 132–150.
Koenker, R. W., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
Kumbhakar, S., & Tveterås, R. (2003). Risk preferences, production
risk and firm heterogeneity. Scandinavian Journal of Economics.
105(2), 275–293.
Lobell, D. B., & Burke, M. (Eds.). (2010). Climate change and food
security. Adapting Agriculture to a Warmer World.
Mahul, O., & Stutley, C. J. (2010). Government support to agricultural
insurance: challenges and options for developing countries. The
World Bank.
Moschini, G., & Hennessy, D. (1999). Uncertainty, risk aversion and
risk management for agricultural producers. Economic Staff Paper
Series. 315.
Mullins, J., Zivin, J. G., Cattaneo, A., Paolantonio, A., & Cavatassi, R.
(2018). The adoption of climate smart agriculture: The role of
information and insurance under climate change. In Climate Smart
Agriculture (pp. 353–383). Cham: Springer.
Olalekan, A., & Adeyinka, S. (2013). Capital adequacy and banks’
profitability: an empirical evidence from Nigeria. American International Journal of Contemporary Research. 3(10).
Piearce, T. G. (1984). Earthworm populations in soils disturbed by
trampling. Biological Conservation, 29, 241–252.
Počuča, M., Petrović, Z., & Mrkšić, D. (2018). Insurance in agriculture.
Economics of Agriculture, 60(1), 163–177.
Poudel, M. P., Chen, S. E., & Huang, W. C. (2018). Pricing of rainfall
index insurance for rice and wheat in Nepal.
Schlenker, W., & Roberts, M. (2006). Nonlinear effects of weather on
crop yields: Implications for climate change. Review of Agricultural
Economics. 28.
Sidibé, Y., Foudi, S., Pascual, U., & Termansen, M. (2018). Adaptation
to climate change in rainfed agriculture in the global south: soil
biodiversity as natural insurance. Ecological Economics, 146, 588–
596.
Swinton, S., & King, R. (1991). Evaluating robust regression
techniques for detrending crop yield data with nonnormal errors.
Assessing Crop Yield and Risk: A New Method …
59
occurrence of extreme events, farmers’ exposure to yield,
price, and income variability will increase. In this scenario,
Italy is one of the EU countries whit higher public intervention in the field of risk management in agriculture.
Nowadays, public support, however, has been characterized by a substantial failure if we consider that less of
20% of the overall production in Italy is insured. We showed
that QR improves the yield-index representation, especially
in the lower tails of the distribution and hence increases the
risk-reducing properties of index-based insurances. This
finding contributes to an improved contract design, which is
essential as imperfect hedging seriously challenges the
acceptance, implementation, and up-scaling of index-based
insurance tool.
In this view, paying attention to new tools to manage
farmer revenue risk would represent a fundamental task for
Italian Agriculture Ministry; index-based contract would
catch a large share of agricultural production considering
lower costs of the insurance premium and his simple
mechanism. Moreover, would identify a solution of asymmetric information problem that notoriously plagued crop
insurance viability.
References
Alderman, H., & Haque, T. (2007). Insurance against covariate shocks:
The role of index-based insurance in social protection in
low-income countries of Africa. World Bank Working Paper
No. 95.
Antle, J. (1987). Econometric estimation of producers’ risk attitudes.
American Journal of Agricultural Economics, 69(3), 509–522.
Arias, O. V., Garrido, A., Villeta, M., & Tarquis, A. M. (2018).
Homogenisation of a soil properties map by principal component
analysis to define index agricultural insurance policies. Geoderma,
311, 149–158.
Barnett, B. J., & Mahul, O. (2007). Weather index insurance for
agriculture and rural areas in lower-income countries. American
Journal of Agricultural Economics, 89(5), 1241–1247.
Barnett, T. P., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D.,
Das, T., Bala, G., Wood, A. W., Nozawa, T., Mirin, A. A., Cayan,
D. R., & Dettinger, M. D. (2008). Human-induced changes in the
hydrology of the Western United States. Science. 319(5866), 1080–
1083.
Berg, E., & Schmitz, B. (2008). Weather-based instruments in the
context of whole-farm risk management. Agricultural Finance
Review, 68(1), 119–133.
Birthal, P., Roy, D., & Negi, D. (2015). Assessing the impact of crop
diversification on farm poverty in India. World Development, 72
(10), 1016.
Bouyé, E., & Salmon, M. (2013). Dynamic copula quantile regressions
and tail area dynamic dependence in Forex markets. In Copulae and
multivariate probability distributions in finance (pp. 125–154).
Routledge.
Chowdhury, S. P., Uhl, J., Grosch, R., Alqueres, S., Pittroff, S., Dietel,
K., Schmitt-Kopplin, P., Borriss, R., & Hartmann, A. (2016).
(Chowdhury et al 2015).
Clover, A. T., & Nieuwoudt, W. L. (2003). An economic evaluation of
area yield insurance for small-scale cane growers. Development
Southern Africa, 20, 293–305.
Dalhaus, T., Musshoff, O., & Finger, R. (2018). Phenology information
contributes to reduce temporal basis risk in agricultural weather
index insurance. Scientific reports, 8(1), 46.
Faccia, A. (2012). Analisi dei dati RICA finalizzati all’approfondimento del tema della gestione del rischio in agricoltura. Misurazione delle performance finanziarie e patrimoniali delle aziende
agrarie e relativa definizione di un modello di rating.
Finger, D., Wüest, A., & Bossard, P. (2013). Effects of oligotrophication on primary production in peri-alpine lakes. Water Resources
Research, 49, 4700–4710.
Glauber, J. W. (2004). Crop insurance reconsidered. American Journal
of Agricultural Economics, 86(5), 1179–1195.
Groom, M. J., Gray, E. M., & Patricia A. T. (2008). Biofuels and
biodiversity: principles for creating better policies for biofuel
production. Conservation Biology. 22(3), 602–609.
Hazell, P. B. (1992). The appropriate role of agricultural insurance in
developing countries. Journal of International Development, 4(6),
567–581.
Hubert, L. J., Golledge, R. G., & Costanzo, C. M. (2010) Generalized
procedures for evaluating spatial autocorrelation. Geographical
Analysis.
Kapphan, I., Calanca, P., & Holzkämper, A. (2012). Climate change,
weather insurance design and hedging effectiveness. The Geneva
Papers on Risk and Insurance—Issues and Practice.
Khan, M., & Watts, R. L. (2009). Estimation and empirical properties
of a firm-year measure of accounting conservatism. Journal of
Accounting and Economics, 48(2-3), 132–150.
Koenker, R. W., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
Kumbhakar, S., & Tveterås, R. (2003). Risk preferences, production
risk and firm heterogeneity. Scandinavian Journal of Economics.
105(2), 275–293.
Lobell, D. B., & Burke, M. (Eds.). (2010). Climate change and food
security. Adapting Agriculture to a Warmer World.
Mahul, O., & Stutley, C. J. (2010). Government support to agricultural
insurance: challenges and options for developing countries. The
World Bank.
Moschini, G., & Hennessy, D. (1999). Uncertainty, risk aversion and
risk management for agricultural producers. Economic Staff Paper
Series. 315.
Mullins, J., Zivin, J. G., Cattaneo, A., Paolantonio, A., & Cavatassi, R.
(2018). The adoption of climate smart agriculture: The role of
information and insurance under climate change. In Climate Smart
Agriculture (pp. 353–383). Cham: Springer.
Olalekan, A., & Adeyinka, S. (2013). Capital adequacy and banks’
profitability: an empirical evidence from Nigeria. American International Journal of Contemporary Research. 3(10).
Piearce, T. G. (1984). Earthworm populations in soils disturbed by
trampling. Biological Conservation, 29, 241–252.
Počuča, M., Petrović, Z., & Mrkšić, D. (2018). Insurance in agriculture.
Economics of Agriculture, 60(1), 163–177.
Poudel, M. P., Chen, S. E., & Huang, W. C. (2018). Pricing of rainfall
index insurance for rice and wheat in Nepal.
Schlenker, W., & Roberts, M. (2006). Nonlinear effects of weather on
crop yields: Implications for climate change. Review of Agricultural
Economics. 28.
Sidibé, Y., Foudi, S., Pascual, U., & Termansen, M. (2018). Adaptation
to climate change in rainfed agriculture in the global south: soil
biodiversity as natural insurance. Ecological Economics, 146, 588–
596.
Swinton, S., & King, R. (1991). Evaluating robust regression
techniques for detrending crop yield data with nonnormal errors.
Assessing Crop Yield and Risk: A New Method …
59