ISSN (Online): 2321-3418
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Economics and Management
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Mitigation of Short-Term Climate Risks and Uncertainties through Insurance and Self-Protection: Theory and Application

DOI: 10.18535/ijsrm/v12i04.em14· Pages: 6239-6277· Vol. 12, No. 04, (2024)· Published: April 1, 2024
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Abstract

This paper investigates the interplay between short-term insurance and self-protection strategies in mitigating weather-related risks and uncertainties amidst rising global temperatures. We explore the decision-making process behind these strategies, focus- ing on whether the choice between insurance and self-protection depends on the type of stochastic loss encountered, distinguishing between risk and uncertainty.

Existing research highlights the context-specific nature of the relationship be- tween insurance and self-protection. While a significant portion of the literature has concentrated on understanding long-term dynamics, the sudden occurrence of weather-related events requires a closer examination of short-term decision-making processes. This paper contributes by providing a theoretical framework for analyzing how weather stochastics influence producers’ decisions regarding insurance and self- protection in the short term.

Simulation outcomes reveal distinct responses of farmers to risk and uncertainty. Under risk, farmers without irrigation systems tend to increase their reliance on crop insurance as precipitation risks heighten, while those with irrigation systems adopt a nuanced approach, adjusting their insurance purchases based on the severity of precip- itation risks. This suggests that irrigation serves as both a substitute and complement to crop insurance, depending on the level of risk. Conversely, under uncertainty, farm- ers exhibit a general trend of decreased crop insurance purchases regardless of their self-protection measures. Addressing uncertainty within agricultural loss mitigation frameworks is crucial for safeguarding against potential food insecurity and increasing investment to mitigate climate-related disasters.

Policy implications underscore the need to consider producers’ level self-protection and the type of stochastics faced in climate policy design. Additionally, reducing un- certainty in weather forecasts is imperative to mitigate farmers’ vulnerability and promote agricultural resilience.

Keywords

self-protectioninsuranceexpected utilityrobust optimizationshortterm risk vs

References

  1. Abdellaoui, M., & Wakker, P. P. (2005). The likelihood method for decision under uncertainty. Theory and Decision, 58 . doi: 10.1007/s11238-005-8320-4DOI ↗Google Scholar ↗
  2. Aimin, H. (2010). Uncertainty, risk aversion and risk management in agriculture.Google Scholar ↗
  3. Agriculture and Agricultural Science Procedia, 1 . doi: 10.1016/j.aaspro.2010DOI ↗Google Scholar ↗
  4. .09.018Google Scholar ↗
  5. Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: Critique des postulats et axiomes de l’ecole americaine. Econometrica, 21 . doi: 10.2307/ 1907921Google Scholar ↗
  6. Aref, S., & Wander, M. M. (1997). Long-term trends of corn yield and soil organic matter in different crop sequences and soil fertility treatments on the morrow plots. Advances in Agronomy, 62 . doi: 10.1016/S0065-2113(08)60568-4DOI ↗Google Scholar ↗
  7. Baillon, A., Bleichrodt, H., Keskin, U., & ... (2013). Learning under ambiguity: An experiment using initial public offerings on a stock market.Google Scholar ↗
  8. Barberis, N. C. (2013). Thirty years of prospect theory in economics: A review and assessment (Vol. 27). doi: 10.1257/jep.27.1.173DOI ↗Google Scholar ↗
  9. Bard, S. K., & Barry, P. J. (2001). Assessing farmers’ attitudes toward risk using the ”closing-in” method. Journal of Agricultural and Resource Economics, 26 .Google Scholar ↗
  10. Batchelor, W. D., Basso, B., & Paz, J. O. (2002). Examples of strategies to analyze spatial and temporal yield variability using crop models. In (Vol. 18). doi: 10.1016/S1161-0301(02)00101-6DOI ↗Google Scholar ↗
  11. Ben-Tal, A., & Hochman, E. (1985). Approximation of expected returns and op- timal decisions under uncertainty using mean and mean absolute deviation. Zeitschrift fu¨r Operations Research, 29 . doi: 10.1007/BF01918761DOI ↗Google Scholar ↗
  12. Ben-Tal, A., & Teboulle, M. (1986). Expected utility, penalty functions, and duality in stochastic nonlinear programming. Management Science, 32 . doi: 10.1287/ mnsc.32.11.1445Google Scholar ↗
  13. Bert, F. E., Laciana, C. E., Podest´a, G. P., Satorre, E. H., & Men´endez, A. N. (2007). Sensitivity of ceres-maize simulated yields to uncertainty in soil properties and daily solar radiation. Agricultural Systems, 94 . doi: 10.1016/j.agsy.2006.08DOI ↗Google Scholar ↗
  14. .003Google Scholar ↗
  15. Boyer, C. N., Larson, J. A., Roberts, R. K., McClure, A. T., Tyler, D. D., & Zhou,Google Scholar ↗
  16. V. (2013). Stochastic corn yield response functions to nitrogen for corn after corn, corn after cotton, and corn after soybeans. Journal of Agricultural and Applied Economics, 45 . doi: 10.1017/s1074070800005198DOI ↗Google Scholar ↗
  17. Bullock, D. G., & Bullock, D. S. (1994). Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: A comparison. Agronomy Journal,Google Scholar ↗
  18. doi: 10.2134/agronj1994.00021962008600010033xDOI ↗Google Scholar ↗
  19. Cabas, J. H., Leiva, A. J., & Weersink, A. (2008). Modeling exit and entry of farmers in a crop insurance program. In (Vol. 37). doi: 10.1017/S1068280500002173DOI ↗Google Scholar ↗
  20. Cameron, T. A., & Quiggin, J. (1994). Estimation using contingent valuation data from a dichotomous choice with follow-up questionnaire. Journal of Environ- mental Economics and Management, 27 . doi: 10.1006/jeem.1994.1035DOI ↗Google Scholar ↗
  21. Cao, R., Carpentier, A., & Gohin, A. (2011). Measuring farmers’ risk aversion: the unknown properties of the value function. 2011 International Congress, . . . .Google Scholar ↗
  22. Cerrato, M. E., & Blackmer, A. M. (1990). Comparison of models for describing; corn yield response to nitrogen fertilizer. Agronomy Journal, 82 . doi: 10.2134/ agronj1990.00021962008200010030xGoogle Scholar ↗
  23. Chambers, R. G., Chung, Y., & Fa¨re, R. (1996). Benefit and distance functions.Google Scholar ↗
  24. Journal of Economic Theory, 70 . doi: 10.1006/jeth.1996.0096DOI ↗Google Scholar ↗
  25. Chung, Y. H., F¨are, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51 . doi: 10.1006/jema.1997.0146DOI ↗Google Scholar ↗
  26. Coble, K. H., Knight, T. O., Patrick, G. F., & Baquet, A. E. (2002). Understanding the economic factors influencing farm policy preferences. Review of Agricultural Economics, 24 . doi: 10.1111/1467-9353.00021DOI ↗Google Scholar ↗
  27. Dalhaus, T., Barnett, B. J., & Finger, R. (2020). Behavioral weather insurance: Applying cumulative prospect theory to agricultural insurance design under narrow framing. PLoS ONE , 15 . doi: 10.1371/journal.pone.0232267DOI ↗Google Scholar ↗
  28. Dinar, A., & Yaron, D. (1992). Adoption and abandonment of irrigation technologies.Google Scholar ↗
  29. Agricultural Economics, 6 . doi: 10.1016/0169-5150(92)90008-MDOI ↗Google Scholar ↗
  30. Dogan, E., Copur, O., Kahraman, A., Kirnak, H., & Guldur, M. E. (2011). Sup- plemental irrigation effect on canola yield components under semiarid climatic conditions. Agricultural Water Management, 98 . doi: 10.1016/j.agwat.2011.04DOI ↗Google Scholar ↗
  31. .006Google Scholar ↗
  32. Dowling, J. A., Rinaldi, K. Z., Ruggles, T. H., Davis, S. J., Yuan, M., Tong, F., . . . Caldeira, K. (2020). Role of long-duration energy storage in variable renewable electricity systems. Joule, 4 . doi: 10.1016/j.joule.2020.07.007DOI ↗Google Scholar ↗
  33. Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. Quarterly Journal of Economics, 75 . doi: 10.2307/1884324DOI ↗Google Scholar ↗
  34. Eurosif. (2018, 6). Eurosif 2018 sri study. Retrieved from https://Google Scholar ↗
  35. www.eurosif.org/wp-content/uploads/2022/06/Eurosif-Report-JuneGoogle Scholar ↗
  36. -22-SFDR-Policy-Recommendations.pdfGoogle Scholar ↗
  37. Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: a survey. Economic Development Cultural Change,Google Scholar ↗
  38. doi: 10.1086/451461DOI ↗Google Scholar ↗
  39. Fern´andez, C., Koop, G., & Steel, M. F. (2002). Multiple-output production with undesirable outputs: An application to nitrogen surplus in agriculture. Journal of the American Statistical Association, 97 . doi: 10.1198/016214502760046989DOI ↗Google Scholar ↗
  40. Fersund, F. R. (2009). Good modelling of bad outputs: Pollution and multiple-output production. International Review of Environmental and Resource Economics,Google Scholar ↗
  41. doi: 10.1561/101.00000021DOI ↗Google Scholar ↗
  42. Flaten, O., Lien, G., Koesling, M., Valle, P. S., & Ebbesvik, M. (2005). Compar- ing risk perceptions and risk management in organic and conventional dairy farming: Empirical results from norway. Livestock Production Science, 95 . doi: 10.1016/j.livprodsci.2004.10.014DOI ↗Google Scholar ↗
  43. Foster, A. D., & Rosenzweig, M. R. (1995). Learning by doing and learning from others: human capital and technical change in agriculture. Journal of Political Economy, 103 . doi: 10.1086/601447DOI ↗Google Scholar ↗
  44. Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adop- tion. Annual Review of Economics, 2 . doi: 10.1146/annurev.economics.102308DOI ↗Google Scholar ↗
  45. .124433Google Scholar ↗
  46. Foudi, S., & Erdlenbruch, K. (2012). The role of irrigation in farmers’ risk manage- ment strategies in france (Vol. 39). doi: 10.1093/erae/jbr024DOI ↗Google Scholar ↗
  47. Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk.Google Scholar ↗
  48. Journal of Political Economy, 56 . doi: 10.1086/256692DOI ↗Google Scholar ↗
  49. Frittelli, M., & Gianin, E. R. (2002). Putting order in risk measures. Journal of Banking and Finance, 26 . doi: 10.1016/S0378-4266(02)00270-4DOI ↗Google Scholar ↗
  50. Fuentes-Arderiu, X., & Dot-Bach, D. (2009). Measurement uncertainty in manual differential leukocyte counting. Clinical Chemistry and Laboratory Medicine,Google Scholar ↗
  51. doi: 10.1515/CCLM.2009.014DOI ↗Google Scholar ↗
  52. Fa¨re, R., Grosskopf, S., Noh, D. W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126 . doi: 10.1016/j.jeconom.2004.05.010DOI ↗Google Scholar ↗
  53. Fa¨re, R., Grosskopf, S., & Weber, W. L. (2006). Shadow prices and pollution costs inGoogle Scholar ↗
  54. u.s. agriculture. Ecological Economics, 56 . doi: 10.1016/j.ecolecon.2004.12.022 Fo¨llmer, H., & Schied, A. (2002). Convex measures of risk and trading constraints.DOI ↗Google Scholar ↗
  55. Finance and Stochastics, 6 . doi: 10.1007/s007800200072DOI ↗Google Scholar ↗
  56. Goodwin, B. K. (1993). An empirical analysis of the demand for multiple peril crop insurance. American Journal of Agricultural Economics, 75 . doi: 10.2307/ 1242927Google Scholar ↗
  57. Han, X., Zhang, G., Xie, Y., Yin, J., Zhou, H., Yang, Y., . . . Bai, W. (2019).Google Scholar ↗
  58. Weather index insurance for wind energy. Global Energy Interconnection, 2 . doi: 10.1016/j.gloei.2020.01.008DOI ↗Google Scholar ↗
  59. Hasenkamp, G. (1976). A study of multiple-output production functions. klein’s rail- road study revisited. Journal of Econometrics, 4 . doi: 10.1016/0304-4076(76) 90036-1DOI ↗Google Scholar ↗
  60. Hayhoe, K., Wuebbles, D., Easterling, D., Fahey, D., Doherty, S., Kossin, J., . . . Wehner, M. (2018). Our changing climate. in impacts, risks, and adaptation in the united states: Fourth national climate assessment, volume ii. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assess- ment, Volume II , II .Google Scholar ↗
  61. Heath, C., & Tversky, A. (1991). Preference and belief: Ambiguity and competence in choice under uncertainty. Journal of Risk and Uncertainty, 4 . doi: 10.1007/ BF00057884Google Scholar ↗
  62. Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15 . doi: 10.1111/j.0956-7976.2004.00715.xDOI ↗Google Scholar ↗
  63. Huettel, S. A., Stowe, C. J., Gordon, E. M., Warner, B. T., & Platt, M. L. (2006). Neural signatures of economic preferences for risk and ambiguity. Neuron, 49 . doi: 10.1016/j.neuron.2006.01.024DOI ↗Google Scholar ↗
  64. Ipcc. (2013). Working group i contribution to the ipcc fifth assessment report, climate change 2013: The physical science basis. Ipcc, AR5 .Google Scholar ↗
  65. Ipcc. (2022). Ar6 synthesis report outline: Climate change 2022. Re-Google Scholar ↗
  66. trieved from https://www.ipcc.ch/site/assets/uploads/2021/12/IPCCGoogle Scholar ↗
  67. -52 decisions-adopted-by-the-Panel.pdfGoogle Scholar ↗
  68. Iyer, P., Bozzola, M., Hirsch, S., Meraner, M., & Finger, R. (2020). Measuring farmer risk preferences in europe: A systematic review. Journal of Agricultural Economics, 71 . doi: 10.1111/1477-9552.12325DOI ↗Google Scholar ↗
  69. Kahn, B. E., & Sarin, R. K. (1988). Modeling ambiguity in decisions under uncer- tainty. Journal of Consumer Research, 15 . doi: 10.1086/209163DOI ↗Google Scholar ↗
  70. Kahneman, D., & Tversky, A. (1979). Kahneman tversky (1979) - prospect theory - an analysis of decision under risk.pdf (Vol. 47).Google Scholar ↗
  71. Kessler, R. (2021, 3). Texas wind farms face billion-dollar losses from blackouts in ’illegal wealth transfer’. Retrieved from https://www.windaction.org/posts/ 52234Google Scholar ↗
  72. Kilka, M., & Weber, M. (2001). What determines the shape of the probability weighting function under uncertainty? Management Science, 47 . doi: 10.1287/ mnsc.47.12.1712.10239Google Scholar ↗
  73. Knight, F. H. (1921). Risk uncertainty and profit knight (Vol. 36).Google Scholar ↗
  74. Kooperman, Chen, Hoffman, Koven, Lindsay, Pritchard, . . . Randerson (2018). Forest response to rising co2 drives zonally asymmetric rainfall change over tropical land. Nature Climate Change. doi: https://doi.org/10.1038/s41558-018-0144-7DOI ↗Google Scholar ↗
  75. Koundouri, P., Nauges, C., & Tzouvelekas, V. (2006). Technology adoption under pro- duction uncertainty: Theory and application to irrigation technology. American Journal of Agricultural Economics, 88 . doi: 10.1111/j.1467-8276.2006.00886.xDOI ↗Google Scholar ↗
  76. Kumbhakar, S. C. (2002). Specification and estimation of production risk, risk pref- erences and technical efficiency. American Journal of Agricultural Economics,Google Scholar ↗
  77. doi: 10.1111/1467-8276.00239DOI ↗Google Scholar ↗
  78. Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic frontier analysis. doi: 10.1017/cbo9781139174411DOI ↗Google Scholar ↗
  79. Kweilin Ellingrud, B. Q., Alex Kimura, & Ralph, J. (2022). Five steps to improve in- novation in the insurance industry. McKinsey & Co. Retrieved from https:// www.mckinsey.com/industries/financial-services/our-insights/Google Scholar ↗
  80. five-steps-to-improve-innovation-in-the-insurance-industryGoogle Scholar ↗
  81. Laeven, R. J., & Stadje, M. (2014). Robust portfolio choice and indifference valuation.Google Scholar ↗
  82. Mathematics of Operations Research, 39 . doi: 10.1287/moor.2014.0646DOI ↗Google Scholar ↗
  83. Lee, D. (2005). Agricultural sustainability and technology adoption: Issues and policies for developing countries. American Journal of Agricultural Economics,Google Scholar ↗
  84. doi: 10.1111/j.1467-8276.2005.00826.xDOI ↗Google Scholar ↗
  85. Lempert, R., Popper, S., & Bankes, S. (2019). Shaping the next one hundred years: New methods for quantitative, long-term policy analysis. doi: 10.7249/mr1626DOI ↗Google Scholar ↗
  86. Levy, I., Snell, J., Nelson, A. J., Rustichini, A., & Glimcher, P. W. (2010). Neu- ral representation of subjective value under risk and ambiguity. Journal of Neurophysiology, 103 . doi: 10.1152/jn.00853.2009DOI ↗Google Scholar ↗
  87. Link, J., Graeff, S., Batchelor, W. D., & Claupein, W. (2006). Evaluating the economic and environmental impact of environmental compensation payment policy under uniform and variable-rate nitrogen management. Agricultural Sys- tems, 91 . doi: 10.1016/j.agsy.2006.02.003DOI ↗Google Scholar ↗
  88. Llewelyn, R. V., & Featherstone, A. M. (1997). A comparison of crop production functions using simulated data for irrigated corn in western kansas. Agricultural Systems, 54 . doi: 10.1016/S0308-521X(96)00080-7DOI ↗Google Scholar ↗
  89. Lyu, K., & Barr´e, T. J. (2017). Risk aversion in crop insurance program purchase decisions evidence from maize production areas in china. China Agricultural Economic Review, 9 . doi: 10.1108/CAER-04-2015-0036DOI ↗Google Scholar ↗
  90. Maharjan, B., Das, S., Nielsen, R., & Hergert, G. W. (2021). Maize yields from manure and mineral fertilizers in the 100-year-old knorr–holden plot. Agronomy Journal, 113 . doi: 10.1002/agj2.20713DOI ↗Google Scholar ↗
  91. Mahul, O. (2002). Hedging in futures and options markets with basis risk (Vol. 22).Google Scholar ↗
  92. doi: 10.1002/fut.2207DOI ↗Google Scholar ↗
  93. Miao, Y., Mulla, D. J., Batchelor, W. D., Paz, J. O., Robert, P. C., & Wiebers,Google Scholar ↗
  94. M. (2006). Evaluating management zone optimal nitrogen rates with a crop growth model. Agronomy Journal, 98 . doi: 10.2134/agronj2005.0153DOI ↗Google Scholar ↗
  95. Murty, S., Russell, R. R., & Levkoff, S. B. (2012). On modeling pollution-generating technologies. Journal of Environmental Economics and Management, 64 . doi: 10.1016/j.jeem.2012.02.005DOI ↗Google Scholar ↗
  96. of Sciences, N. A., Council, N. R., of Mathematical, A., & Sciences, P. (1979). Carbon dioxide and climate: a scientific assessment. Re- trieved from https://nap.nationalacademies.org/catalog/12181/carbonGoogle Scholar ↗
  97. -dioxide-and-climate-a-scientific-assessmentGoogle Scholar ↗
  98. Paz, J. O., Batchelor, W. D., Babcock, B. A., Colvin, T. S., Logsdon, S. D., Kaspar,Google Scholar ↗
  99. T. C., & Karlen, D. L. (1999). Model-based technique to determine variable rate nitrogen for corn. Agricultural Systems, 61 . doi: 10.1016/S0308-521X(99) 00035-9DOI ↗Google Scholar ↗
  100. Piet, L., & Bougherara, D. (2016, 3). The impact of farmers’ risk preferences on the design of an individual yield crop insurance. WORKING PAPER SMART, INARE UMR SMART .Google Scholar ↗
  101. Platt, M. L., & Huettel, S. A. (2008). Risky business: The neuroeconomics of decision making under uncertainty (Vol. 11). doi: 10.1038/nn2062DOI ↗Google Scholar ↗
  102. Pope, R. D. (1982). Expected profit, price change, and risk aversion. American Journal of Agricultural Economics, 64 . doi: 10.2307/1240655DOI ↗Google Scholar ↗
  103. Program, U. G. C. R. (2018). Climate science special report: Fourth national climate assessment, volume i (Vol. 1). doi: 10.7930/J0J964J6DOI ↗Google Scholar ↗
  104. Puntel, L. A., Sawyer, J. E., Barker, D. W., Dietzel, R., Poffenbarger, H., Castellano,Google Scholar ↗
  105. M. J., . . . Archontoulis, S. V. (2016). Modeling long-term corn yield response to nitrogen rate and crop rotation. Frontiers in Plant Science, 7 . doi: 10.3389/ fpls.2016.01630Google Scholar ↗
  106. Raiffa, H. (1993). Decision analysis: introductory lectures on choices under un- certainty. 1968. M.D. computing : computers in medical practice, 10 . doi: 10.2307/2987280DOI ↗Google Scholar ↗
  107. Ruszczy’ski, A. (2006, 8). Stochastic programming. John Wiley Sons, Inc. doi: 10.1002/0471667196.ess3225DOI ↗Google Scholar ↗
  108. Schahczenski, J. (2021, 9). Crop insurance rules challenge organic and sustainable farming practices. Retrieved from https://sustainableagriculture.net/ blog/crop-insurance-rules-challenge-organic-and-sustainableGoogle Scholar ↗
  109. -farming-practicesGoogle Scholar ↗
  110. Schnitkey, G., Batts, R., Swanson, K., Paulson, N., & Zulauf, C. (2021). Crop insurance tools. Farmdoc.Google Scholar ↗
  111. Schultz, W., Preuschoff, K., Camerer, C., Hsu, M., Fiorillo, C. D., Tobler, P. N., & Bossaerts, P. (2008). Review. explicit neural signals reflecting reward uncer- tainty (Vol. 363). doi: 10.1098/rstb.2008.0152DOI ↗Google Scholar ↗
  112. Scofield, C. (1927). Irrigated crop rotations in western nebraska. United States Department of Agriculture, Technical Bulletin, 02 .Google Scholar ↗
  113. Shapiro, A., Tekaya, W., Soares, M. P., & Costa, J. P. D. (2013). Worst-case- expectation approach to optimization under uncertainty. Operations Research,Google Scholar ↗
  114. doi: 10.1287/opre.2013.1229DOI ↗Google Scholar ↗
  115. Shephard, R. W. (1970). Theory of cost and production functions. doi: 10.2307/ 2230285Google Scholar ↗
  116. Sherrick, B. J., Zanini, F. C., Schnitkey, G. D., & Irwin, S. H. (2004). Crop in- surance valuation under alternative yield distributions. American Journal of Agricultural Economics, 86 . doi: 10.1111/j.0092-5853.2004.00587.xDOI ↗Google Scholar ↗
  117. Steiger, R., Damm, A., Prettenthaler, F., & Pro¨bstl-Haider, U. (2021). Climate change and winter outdoor activities in austria. Journal of Outdoor Recreation and Tourism, 34 . doi: 10.1016/j.jort.2020.100330DOI ↗Google Scholar ↗
  118. Strupczewski, G. (2019). What characterizes farmers who purchase crop insurance in poland? Problems of Agricultural Economics, 1 . doi: 10.30858/zer/103596DOI ↗Google Scholar ↗
  119. Sulewski, P., & K-loczko-Gajewska, A. (2014). Farmers’ risk perception, risk aversion and strategies to cope with production risk: An empirical study from poland. Studies in Agricultural Economics, 116 . doi: 10.7896/j.1414DOI ↗Google Scholar ↗
  120. Thorp, K. R., DeJonge, K. C., Kaleita, A. L., Batchelor, W. D., & Paz, J. O. (2008). Methodology for the use of dssat models for precision agriculture de- cision support. Computers and Electronics in Agriculture, 64 . doi: 10.1016/ j.compag.2008.05.022Google Scholar ↗
  121. Ullah, R., Shivakoti, G. P., & Ali, G. (2015). Factors effecting farmers’ risk attitude and risk perceptions: The case of khyber pakhtunkhwa, pakistan. International Journal of Disaster Risk Reduction, 13 . doi: 10.1016/j.ijdrr.2015.05.005DOI ↗Google Scholar ↗
  122. Vajda, S., Luce, R. D., & Raiffa, H. (1958). Games and decisions: Introduction and critical survey. Journal of the Royal Statistical Society. Series A (General),Google Scholar ↗
  123. doi: 10.2307/2342906DOI ↗Google Scholar ↗
  124. Valone.T. (2021). Linear global temperature correlation to carbon dioxide level, sea level, and innovative solutions to a projected 6°c warming by 2100. Journal of Geoscience and Environment Protection. Retrieved from https://www.scirpGoogle Scholar ↗
  125. .org/journal/paperinformation.aspx?paperid=107789Google Scholar ↗
  126. Vollmer, E., Hermann, D., & Mußhoff, O. (2017). Is the risk attitude measured with the holt and laury task reflected in farmers’ production risk? European Review of Agricultural Economics, 44 . doi: 10.1093/erae/jbx004DOI ↗Google Scholar ↗
  127. Weaver, R. (1977). The theory and measurement of provisional agricultural production decisions .Google Scholar ↗
  128. Weber, E. U. (1994). From subjective probabilities to decision weights: The effect of asymmetric loss functions on the evaluation of uncertain outcomes and events. Psychological Bulletin, 115 . doi: 10.1037//0033-2909.115.2.228DOI ↗Google Scholar ↗
  129. Yaari, M. E. (1987). The dual theory of choice under risk. Econometrica, 55 . doi:Google Scholar ↗
  130. 2307/1911158Google Scholar ↗
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