PROCAD

Link para a página do projeto

1. Versão final do projeto

1. Slides primeira Reunião (São Pedro, 29/07/2008)

1. UFMG
2. USP
3. ESALQ
4. UFLA
5. LEG

• Apresentação Ramiro
• Ações
  1. lista de emails procad_l@leg.ufpr.br – todos inscritos no WIKI, grupo PROCAD, são membros desta lista

Previsão sazonal de variáveis climáticas e/ou produtividade agrícola

• Plain, M.B., Minasny, B., McBratney, A. B. and Vervoort, R.W. (2008) Spatially explicit seasonal forecasting using fuzzy spatiotemporal clustering of long-term daily rainfall and temperature data. Hydrology and Earth System Sciences Discussions, 5, 1159–1189.

Modelagem espaço-temporal da produtividade agrícola

• Paper 1

• Ozaki, V.A. (2008) Pricing farm-level agricultural insurance: a Bayesian approach. Empirical Economics.

• Ozaki, V.A., Ghosh, S.K., Goodwin, B.K. and Shirota, R. (2008) Spatio-Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts. American Journal of Agricultural Economics.

• Goodwin, B.K. and Piggott, N.E. (2006) Spatio-Temporal Modeling of Asian Citrus Canker Risks: Implications for Insurance and Indemnification Fund Models.

• Chen, S.L. and Miranda, M.J. (2007) Modeling Texas dryland cotton yields, with application to crop insurance actuarial rating.

Detecção de conglomerados de risco espaciais e/ou temporais

• Popp, M., Rudstrom, M. and Manning, P. (2005) Spatial Yield Risk Across Region, Crop, and Aggregation Method. Canadian Journal of Agricultural Economics, 53, 103–115.

• Rudstrom, M., Popp, M., Manning, P. and Gbur, E. (2002) Data Aggregation Issues for Crop Yield Risk Analysis. Canadian Journal of Agricultural Economics, 50, 185–200.

Teoria de valores extremos aplicada à construção de mapas de risco extremo

• Casson, E. and Coles, S. (1999). Spatial regression models for extremes. Extremes, 1:4, 449–468.

• Gilleland, E., Nychka, D. and Schneider, U. (2004) Spatial models for the distribution of extremes. In Uncertainty and Variability in Ecological Inference, Forecasting and Decision Making. An Introduction to Modern Statistical Computation, Summer Institute at the Center of Global Change, Duke University, Durham.

• Cooley, D., Nychka, D. and Naveau, P. (2007) Bayesian Spatial Modeling of Extreme Precipitation Return Levels. Journal of the American Statistical Association.

• Beguería, S. and Vicente-Serrano, S.M. (2006) Mapping the hazard of extreme rainfall by peaks over threshold extreme value analysis and spatial regression techniques. Journal of Applied Meteorology, 45, 108-124.

Integração entre geotecnologias e seguro agrícola

• Macedo, M.A. e Rudorff, B.F.T. (2003) Geotecnologias no seguro agrícola do milho safrinha. Agricultura São Paulo, 50, 43-52.

• Lobell, D.B., Ortiz-Monasterio, J.I. and Falcon, W.P. (2007) Yield uncertainty at the field scale evaluated with multi-year satellite data. Agricultural Systems, 92, 76–90.

• Foody, G.M. (2003) Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI–rainfall relationship. Remote Sensing of Environment, 88, 283–293.

• Paris, J.F. (?) Applications of Remote Sensing to Agribusiness. Spatial Information, Visualization & Analysis Resources Center.

Modelos para Volatilidade na Precificação de Seguros no Mercado Agrícola

• Andersen, T.G., Bollerslev, T., Christo®ersen, P.F., and Diebold, F.X. (2006) Volatility and Correlation Forecasting. In: G. Elliot, C.W.J. Granger, and Allan Timmermann (eds.), Handbook of Economic Forecasting Vol. 1. Amsterdam: North-Holland, 778{878.

Estratégias de seguro para o setor de biocombustíveis (produção de etanol)

• Paulson, N.D., Babcock, B.A., Hart, C.E. and Hayes, D.J. (2004) Insuring Uncertainty in Value-Added Agriculture: Ethanol Production. Working paper 04-WP 360 Center for Agricultural and Rural Development. Iowa State University.

Modelagem e precificação de derivativos climáticos

• Odening, M, Musshoff, O. and Xu, W. (2007) Analysis of Rainfall Derivatives Using Daily Precipitation Models: Opportunities and Pitfalls. Agricultural Finance Review, 67, 135–156.

• Cao, M., Li, A. and Wei, J. (2004) Precipitation Modeling and Contract Valuation: A Frontier in Weather Derivatives. The Journal of Alternative Investments.

• Carmona, R. and Diko, P. (2005) Pricing precipitation based derivatives. International Journal of Theoretical and Applied Finance, 8, 959–988.

• Martin, S.W., Barnett, B.J. and Coble, K.H. (2001) Developing and Pricing Precipitation Insurance. Journal of Agricultural and Resource Economics, 26, 261–274.

• Campbell, S.D. and Diebold, F.X. (2005). Weather Forecasting for Weather Derivatives. Journal of the American Statistical Association, 100, 6-16.

• Brix, A., Jewson, S. and Ziehmann, C. (2002) Weather Derivative Modelling and Valuation: A Statistical Perspective. In Dischel, R.S., ed. Climate Risk and the Weather Market. London, Risk Books, pp. 127-150.

• Mahul, O. (2001) Optimal Insurance Against Climatic Experience. American Journal of Agricultural Economics, 83, 593-604.

• Turvey, C.G. (2001) Weather Derivatives for Specific Event Risks in Agriculture. Review of Agricultural Economics, 23, 333-351.

Linque externo