A Deep Factor Model for Crop Yield Forecasting and Insurance Ratemaking

REFERENCES

• Annan, F., J. Tack, A. Harri, and K. Coble. 2014. Spatial pattern of yield distributions: Implications for crop insurance. American Journal of Agricultural Economics 96 (1):253–68.
   Web of Science ®Google Scholar
• Baldi, P. 2012. Autoencoders, unsupervised learning, and deep architectures. In Proceedings of ICML workshop on unsupervised and transfer learning, eds. I. Guyon, G. Dror, V. Lemaire, G. Taylor, and D. Silver, 37–49. Bellevue, WA: PMLR.
   Google Scholar
• Barriguinha, A., M. de Castro Neto, and A. Gil. 2021. Vineyard yield estimation, prediction, and forecasting: A systematic literature review. Agronomy 11 (9):1789.
   Google Scholar
• Bottou, L., and O. Bousquet. 2008. The tradeoffs of large scale learning. In Advances in neural information processing systems, ed. J. Platt, D. Koller, Y. Singer, and S. Roweis, 161–68. New York: Curran Associates.
   Google Scholar
• Caruana, R., S. Lawrence, and C. L. Giles. 2001. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. In Advances in neural information processing systems, eds. T. Leen, T. Dietterich and V. Tresp, 402–8. Massachusetts, USA: MIT Press.
   Google Scholar
• Cassman, K. 1999. Ecological intensification of cereal production system: Yield potential, soil quality, and precision agriculture. Proceedings of the National Academy of Sciences of the United States of America 96 (11):5952–59. 10.1073/pnas.96.11.5952
   PubMed Web of Science ®Google Scholar
• Coble, K. H., M. F. Miller, R. M. Rejesus, R. Boyles, T. O. Knight, and B. K. Goodwin. 2011. Methodology analysis for weighting of historical experience. Washington, DC: USDA Risk Management Agency.
   Google Scholar
• Dietterich, T. G. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings 1, eds. K. Josef and R. Fabio, 1–15. Berlin and Heidelberg: Springer.
   Google Scholar
• Du, X., D. A. Hennessy, H. Feng, and G. Arora. 2018. Land resilience and tail dependence among crop yield distributions. American Journal of Agricultural Economics 100 (3):809–28.
   Web of Science ®Google Scholar
• Gallagher, P. 1986. U. S. corn yield capacity and probability: Estimation and forecasting with none-symmetric distributions. North Central Journal of Agriculture Economics 8:109–22.
   Google Scholar
• Ghahari, A., N. K. Newlands, V. Lyubchich, and Y. R. Gel. 2019. Deep learning at the interface of agricultural insurance risk and spatio-temporal uncertainty in weather extremes. North American Actuarial Journal 23 (4):535–50.
   Web of Science ®Google Scholar
• Gomes, C., Z. Jin, and H. Yang. 2021. Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance 88 (3):591–624.
   Web of Science ®Google Scholar
• Gu, S., B. Kelly, and D. Xiu. 2020. Empirical asset pricing via machine learning. Review of Financial Studies 33 (5):2223–73.
   Web of Science ®Google Scholar
• Hao, M., A. S. Macdonald, P. Tapadar, and R. G. Thomas. 2018. Insurance loss coverage and demand elasticities. Insurance: Mathematics and Economics 79 (1):15–25.
   Google Scholar
• Harri, A., K. H. Coble, A. P. Ker, and B. J. Goodwin. 2011. Relaxing heteroscedasticity assumptions in area-yield crop insurance rating. American Journal of Agricultural Economics 93 (3):707–17.
   Web of Science ®Google Scholar
• Hastie, T., R. Tibshirani, J. Friedman, and J. Franklin. 2005. The elements of statistical learning: Data mining, inference and prediction. The Mathematical Intelligencer 27 (2):83–85.
   Google Scholar
• Hoerl, A. E., and R. W. Kennard. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12 (1):55–67.
   Web of Science ®Google Scholar
• Intergovernmental Panel on Climate Change. 2021. Climate change 2021: The physical science basis. In Contribution of Working Group I to the sixth assessment report of the Intergovernmental Panel on Climate Change, eds. V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. L. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, 2391. Cambridge, UK and New York, NY, USA: Cambridge University Press.
   Google Scholar
• Ker, A. P., and B. K. Goodwin. 2000. Nonparametric estimation of crop insurance rates revisited. American Journal of Agricultural Economics 83 (2):463–78.
   Google Scholar
• Ker, A. P., and P. McGowan. 2000. Weather-based adverse selection and the U.S. Crop Insurance Program: The private insurance company perspective. Journal of Agricultural and Resource Economics 25 (2):386–410.
   Web of Science ®Google Scholar
• Ker, A. P., T. N. Tolhurst, and Y. Liu. 2016. Bayesian estimation of possibly similar yield densities: Implications for rating crop insurance contracts. American Journal of Agricultural Economics 98 (2):360–82.
   Web of Science ®Google Scholar
• Khor, J. F., L. Ling, Z. Yusop, W. L. Tan, J. L. Ling, and E. Z. X. Soo. 2021. Impact of El Niño on oil palm yield in Malaysia. Agronomy 11 (11):2189.
   Google Scholar
• Kleibera, W., A. E. Rafterya, and T. Gneitinga. 2011. Geostatistical model averaging for locally calibrated probabilistic quantitative precipitation forecasting. Journal of the American Statistical Association 106 (496):1291–303.
   Web of Science ®Google Scholar
• Kramer, M. A. 1991. Nonlinear principal component analysis using auto-associative neural networks. AIChE Journal 37 (2):233–43.
   Web of Science ®Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM 60 (6):84–90.
   Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86 (11):2278–324.
   Web of Science ®Google Scholar
• Li, Y., K. Guan, G. D. Schnitkey, E. DeLucia, and B. Peng. 2019. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global Change Biology 25 (7):2325–37.
   PubMed Web of Science ®Google Scholar
• Liu, Y., and A. P. Ker. 2021. Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies. Journal of Risk and Insurance 88 (1):231–57.
   Web of Science ®Google Scholar
• Lobell, D. B., and M. B. Burke. 2010. On the use of statistical models to predict crop yield responses to climate change. Agriculture and Forest Meteorology 150 (11):1443–52.
   Web of Science ®Google Scholar
• Lobell, D. B., W. Schlenker, and J. Costa-Roberts. 2011. Climate trends and global crop production since 1980. Science 333 (6042):616–20.
   PubMed Web of Science ®Google Scholar
• Masters, T. 1993. Practical neural network recipes in C++. San Diego, CA: Academic Press Professional.
   Google Scholar
• Midtiby, H. S., and E. Pastucha. 2022. Pumpkin yield estimation using images from a UAV. Agronomy 12 (4):964.
   Google Scholar
• Miranda, M. J. 1991. Area yield crop insurance reconsidered. American Journal of Agricultural Economics 73:233–42.
   Web of Science ®Google Scholar
• Nyéki, A., C. Kerepesi, B. Daróczy, A. Benczúur, G. Milics, J. Nagy, E. Harsányi, A. Kovács, and M. Neményi. 2021. Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods. Precision Agriculture 22 (5):1397–415.
   Web of Science ®Google Scholar
• Nyéki, A., and M. Neményi. 2022. Crop yield prediction in precision agriculture. Agronomy 12 (10):2460.
   Google Scholar
• Ozaki, V. A., B. K. Goodwin, and R. Shirota. 2008. Parametric and nonparametric statistical modelling of crop yield: Implications for pricing crop insurance contracts. Applied Economics 40 (9):1151–64.
   Web of Science ®Google Scholar
• Park, E., B. W. Brorsen, and A. Harri. 2019. Using Bayesian kriging for spatial smoothing in crop insurance rating. American Journal of Agricultural Economics 101 (1):330–51.
   Web of Science ®Google Scholar
• Perla, F., R. Richman, S. Scognamiglio, and M. V. Wüthrich. 2021. Time-series forecasting of mortality rates using deep learning. Scandinavian Actuarial Journal 2021 (7):572–98.
   Web of Science ®Google Scholar
• Piekutowska, M., G. Niedbała, T. Piskier, T. Lenartowicz, K. Pilarski, T. Wojciechowski, A. A. Pilarska, and A. Czechowska-Kosacka. 2021. The application of multiple linear regression and artificial neural network models for yield prediction of very early potato cultivars before harvest. Agronomy 11 (5):885.
   Google Scholar
• Porth, L., J. Pai, and M. Boyd. 2015. A portfolio optimization approach using combinatorics with a genetic algorithm for developing a reinsurance model. Journal of Risk and Insurance 82 (3):687–713.
   Web of Science ®Google Scholar
• Porth, L., and K. S. Tan. 2015. Agricultural insurance. More room to Grow. The Actuary Magazine 12 (2):1–3.
   Google Scholar
• Porth, L., K. S. Tan, and W. Zhu. 2019. A relational data matching model for enhancing individual loss experience: An example from crop insurance. North American Actuarial Journal 23 (4):551–72.
   Web of Science ®Google Scholar
• Porth, L., W. Zhu, and K. S. Tan. 2014. A credibility-based Erlang mixture model for pricing crop reinsurance. Agricultural Finance Review 74 (2):162–187.
   Google Scholar
• Prechelt, L. 1998. Automatic early stopping using cross validation: Quantifying the criteria. Neural Networks 11 (4):761–67.
   PubMed Web of Science ®Google Scholar
• Ramirez, O. A., and J. S. Shonkwiler. 2017. A probabilistic model of the crop insurance purchase decision. Journal of Agricultural and Resource Economics 42 (1):10–26.
   Web of Science ®Google Scholar
• Ramsey, A. F. 2020. Probability distributions of crop yields: A Bayesian spatial quantile regression approach. American Journal of Agricultural Economics 102 (1):220–39.
   Web of Science ®Google Scholar
• Richman, R. 2021. AI in actuarial science—A review of recent advances–part 1. Annals of Actuarial Science 15 (2):207–29.
   Google Scholar
• Richman, R., and M. V. Wüthrich. 2021. A neural network extension of the Lee-Carter model to multiple populations. Annals of Actuarial Science 15 (2):346–66.
   Web of Science ®Google Scholar
• Rosch, S. 2021. Federal crop insurance: A primer. Washington, DC: Congressional Research Service.
   Google Scholar
• Rowell, D., and L. B. Connelly. 2012. A history of the term “moral hazard.” Journal of Risk and Insurance 79 (4):1051–75.
   Web of Science ®Google Scholar
• Schlenker, W., and M. J. Roberts. 2009. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences 106 (37):15594–98.
   PubMed Web of Science ®Google Scholar
• Sherrick, B., F. Zanini, G. Schnitkey, and S. Irwin. 2004. Crop insurance valuation under alternative yield distributions. American Journal of Agricultural Economics 86 (2):406–19.
   Web of Science ®Google Scholar
• Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15 (1):1929–58.
   Google Scholar
• Suyker, A. E., and S. B. Verma. 2008. Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem. Agricultural and Forest Meteorology 148 (3):417–27.
   Web of Science ®Google Scholar
• Swiss Re. 2021. More risk: The changing nature of P&C insurance opportunities to 2040. https://www.swissre.com/institute/research/sigma-research/sigma-2021-04.html.
   Google Scholar
• Thompson, L. M. 1988. Effects of changes in climate and weather variability on the yields of corn and soybeans. Journal of Production Agriculture 1 (1):20–27.
   Google Scholar
• Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58 (1):267–88.
   Web of Science ®Google Scholar
• Ulfa, F., T. G. Orton, Y. P. Dang, and N. W. Menzies. 2022. Developing and testing remote-sensing indices to represent within-field variation of wheat yields: Assessment of the variation explained by simple models. Agronomy 12 (2):384.
   Google Scholar
• U.S. Department of Agriculture. 2022. 2023 Standard reinsurance agreement. https://www.rma.usda.gov/-/media/RMA/Regulations/Appendix-2023/23sra.ashx?la=en.
   Google Scholar
• USDA. 2022. Iowa’s Rank in United States Agriculture. United States Department of Agriculture National Agricultural Statistics Service. https://www.nass.usda.gov/Statistics_by_State/Iowa/Publications/Rankings/IA-2022-Rankings.pdf.
   Google Scholar
• van Klompenburg, T., A. Kassahun, and C. Catal. 2020. Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture 177:105709.
   Web of Science ®Google Scholar
• Wang, H. H., and H. Zhang. 2003. On the possibility of a private crop insurance market: A spatial statistics approach. Journal of Risk and Insurance 70 (1):111–24.
   Web of Science ®Google Scholar
• Woodard, J. D. 2014. Impacts of weather and time horizon selection on crop insurance ratemaking: A conditional distribution approach. North American Actuarial Journal 18 (2):279–93.
   Google Scholar
• Woodard, J. D., and P. Garcia. 2008. Weather derivatives, spatial aggregation, and systemic risk: Implications for reinsurance hedging. Journal of Agricultural and Resource Economics 33 (1):34–51.
   Web of Science ®Google Scholar
• Woodard, J. D., G. D. Schnitkey, B. J. Sherrick, N. Lozano-Gracia, and L. Anselin. 2012. A spatial econometric analysis of loss experience in the U.S. Crop Insurance Program. Journal of Risk and Insurance 79 (1):261–86.
   Web of Science ®Google Scholar
• Woodard, J. D., A. Shee, and A. Mude. 2016. A spatial econometric approach to designing and rating scalable index insurance in the presence of missing data. The Geneva Papers on Risk and Insurance - Issues and Practice 41 (2):259–79.
   Web of Science ®Google Scholar
• Woodard, J. D., and B. J. Sherrick. 2011. Estimation of mixture models using cross-validation optimization: Implications for crop yield distribution modeling. American Journal of Agricultural Economics 93 (4):968–82.
   Web of Science ®Google Scholar
• Zhang, Y. Y. 2017. A density-ratio model of crop yield distributions. American Journal of Agricultural Economics 99 (5):1327–43.
   Web of Science ®Google Scholar
• Zhou, Y.-T., and R. Chellappa. 1988. Computation of optical flow using a neural network. IEEE 1988 International Conference on Neural Networks, San Diego, CA, USA, 1988, 71–78.
   Google Scholar
• Zhu, W., K. S. Tan, and L. Porth. 2019. Agricultural insurance ratemaking: Development of a new premium principle. North American Actuarial Journal 23 (4):512–34.
   Web of Science ®Google Scholar
• Zhu, W., K. S. Tan, L. Porth, and C.-W. Wang. 2018. Spatial dependence and aggregation in weather risk hedging: A Lévy subordinated hierarchical Archimedean copulas (LSHAC) approach. ASTIN Bulletin 48 (2):779–815.
   Web of Science ®Google Scholar
• Zou, H., and T. Hastie. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2):301–20.
   Web of Science ®Google Scholar