Reducing meteorological basis risk in a         semi-arid agricultural regionTHU 2.3: DLDD and climate change (ID 344)Pres...
Kazakhstan: study region4/12/2013            DUSYS/IED/AFEE   2
Crop insurancesIndemnity-based        Damage / insured yieldIndex-based            (weather) index              e.g.      ...
Basis risk                                                          Which indices are suitable                           ...
Why Quantile Regression?   Precipitation limiting  constant        parameter assumptions?   Extreme events (lower tails...
Phenological phasesFreitag, 12. April 2013   DUSYS/IED/AFEE   6
Summary & Discussion Index-based insurance for extreme events Basis risk major challenge Dependency & index improvement...
Appendix: RegressionFreitag, 12. April 2013   DUSYS/IED/AFEE   8
Appendix: Optimization Time period variable each year, depending ext. conditions        Plant adapts vegetative / genera...
Appendix: Risk reductionTable 1: Relative risk reduction due to the cumulative                Table 2: Relative risk reduc...
Appendix: Indices    4/12/2013       DUSYS/IED/AFEE   11
Appendix: Weather indices & Optimization Time period constant for all years Variable for different farms and indices Mo...
Appendix: Study region Table 1 Summary statistics of the 47 farm data            Number Average yield Min.      Max.      ...
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Sarah CONRADT "Reducing meteorological basis risk in a semi-arid agricultural region"

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  • Also a warm welcome from my side. I will talk today Agriculture production in a semi-arid regionMy short talk today deals with agriculture in a arid region: namellyKazakshstan.
  • K. represents one the most important wheat producers in the world and regularly faces extreme droughts that lead to substantial yield losses. (semi-) arid regions, systemic weatherevents, such as droughts, dry windsHow to stabilize income of farmers?
  • 2 basic groups: Damage-based indemnity insurance is crop insurance in which the insurance claim is calculated by measuring the percentage damage in the field soon after the damage occurs. With index insurance products, payments are based on an independent measure highly correlated with farm-level yieldrealizations of a specific weather parameter Here the indemnity is based on realizations of a specific weather parameter measured over a prespecified period of time at a particular weather station. Weather index based: explain: indemnification payments are triggered by a specific pattern of an index and not by actual yield or in field assessment. Strong dependency b/W yield and index is necessary. Certain advantages: Asym. Info reduced: moral hazard (The term defines a situation where behaviour of one party change in a detremental way after buying insurance) and adverse selection. Lower transaction costs, covariate risk exposure.
  • Basis risk: is the mismatch between the index realization actual yield realization. Thus you get a payment even if you had a good year with high yields or the inverse where you had very low yields but you get no indemnity payment.  inefficientCopulas: describedependence b/w random variables; marginal distributions and dependency by copulaNon linear dependency structures can be modelled, Copulas: regression analysis based on or assumes linear correlation (multivariate normal distribution  fine); tail dependency  downside risk; marginal distributions to form joint distribution.
  • 90%confintSkewed yield distribution (median)Calculate the standard errors or conf. intervalls, I used a bootstrap approach (Monte Carlo method where size n samples are taken from observed data with replacement)Different: - Conditional mean function (conditional mean of a response variable!) vscondquantilefctDifferent assumptions on error termsLRM: error identically independently and normally distributed with mean zero, unknown variance sigma2: Homoscedasticity: conditional Var(Y|x) is a constant sigma2 for all values of covariatesConditional mean of y given x E[y|x], i.e. average of y values corresponding to a fixed value of covariate x (how location of conditional distr. behaves by utilizing mean of distrib. to represent central tendency)HERE: average yield given weather conditionsQR: minimize average weighted distance, with weighting depending on wheather points are above/below qMonotone equivariance: (Q(h(y)|x) = h Q[y|x] not the case for LRM
  • Critical periods in that region for that crop
  • Both: - continuous response variable, response variable is linear in unknown parametersDifferent: - Conditional mean function (conditional mean of a response variable!) vscondquantilefctDifferent assumptions on error termsLRM: error identically independently and normally distributed with mean zero, unknown variance sigma2: Homoscedasticity: conditional Var(Y|x) is a constant sigma2 for all values of covariatesConditional mean of y given x E[y|x], i.e. average of y values corresponding to a fixed value of covariate x (how location of conditional distr. behaves by utilizing mean of distrib. to represent central tendency)HERE: average yield given weather conditionsQR: minimize average weighted distance, with weighting depending on wheather points are above/below qMonotone equivariance: (Q(h(y)|x) = h Q[y|x] not the case for LRM
  • Weather index based: explain: indemnification payments are triggered by a specific pattern of an index and not by actual yield or in field assessment. Strong dependency b/W yield and index is necessary. Certain advantages. Asym. Info reduced: moral hazard (The term defines a situation where behaviour of one party change in a detremental way after buying insurance) and adverse selectionK. represents one the most important wheat producers in the world and regularly faces extreme droughts that lead to substantial yield losses. (semi-) arid regions, systemic weatherevents, such as droughts, dry windsDetrending remove deterministic component; comparable; remove component which influenced yield level over timeCopulas: regression analysis based on or assumes linear correlation (multivariate normal distribution  fine); tail dependency  downside risk; marginal distributions to form joint distribution.Priors: posterior distribution (prior info used, combine with observed data)
  • Sarah CONRADT "Reducing meteorological basis risk in a semi-arid agricultural region"

    1. 1. Reducing meteorological basis risk in a semi-arid agricultural regionTHU 2.3: DLDD and climate change (ID 344)Presented by: Sarah Conradt R. Bokusheva
    2. 2. Kazakhstan: study region4/12/2013 DUSYS/IED/AFEE 2
    3. 3. Crop insurancesIndemnity-based Damage / insured yieldIndex-based (weather) index e.g. Indemnity payment Rainfall [mm]4/12/2013 DUSYS/IED/AFEE 3
    4. 4. Basis risk Which indices are suitable for (semi-) arid regions? How? Precipitation Ordinary Least Squares Temperature Quantile Regression Soil moisture Time period? …4/12/2013 DUSYS/IED/AFEE 4
    5. 5. Why Quantile Regression?  Precipitation limiting  constant parameter assumptions?  Extreme events (lower tails) ? yield Rainfall4/10/2013 DUSYS/IED/AFEE 5
    6. 6. Phenological phasesFreitag, 12. April 2013 DUSYS/IED/AFEE 6
    7. 7. Summary & Discussion Index-based insurance for extreme events Basis risk major challenge Dependency & index improvement conradts@ethz.chFreitag, 12. April 2013 DUSYS/IED/AFEE 7
    8. 8. Appendix: RegressionFreitag, 12. April 2013 DUSYS/IED/AFEE 8
    9. 9. Appendix: Optimization Time period variable each year, depending ext. conditions  Plant adapts vegetative / generative growth  How to determine ‘trigger’? Teff4/12/2013 DUSYS/IED/AFEE 9
    10. 10. Appendix: Risk reductionTable 1: Relative risk reduction due to the cumulative Table 2: Relative risk reduction ofrainfall index, EU and ES estimates (*) ES using 6 indices of 1 county (**) EU ES ES OLS QR OLS QR OLS QR C1 0.053 0.080 0.288 0.461 SI 0.263 0.389 C2 0.073 0.143 0.105 0.199 W100 0.241 0.328 C3 0.079 0.128 0.072 0.135 DCDS 0.305 0.492 C4 0.026 0.052 0.131 0.207 GDD 0.190 0.239 C5 0.034 0.090 0.167 0.287 VCI 0.251 0.349 Total 0.053 0.099 0.152 0.258 TCI 0.252 0.436(*) CR: cumulative rainfall, OLS: Ordinary Least Squares, QR: (**) OLS: Ordinary Least Squares, QR:Quantile Regression, EU: Expected Utility, ES: Expected Quantile Regression, ES: ExpectedShortfall, C1-C5: 5 different counties. Shortfall.4/12/2013 DUSYS/IED/AFEE 10
    11. 11. Appendix: Indices 4/12/2013 DUSYS/IED/AFEE 11
    12. 12. Appendix: Weather indices & Optimization Time period constant for all years Variable for different farms and indices More ‘advanced’ model? (external weather conditions)Freitag, 12. April 2013 DUSYS/IED/AFEE 12
    13. 13. Appendix: Study region Table 1 Summary statistics of the 47 farm data Number Average yield Min. Max. Average Min. Max. CV sown Rayon of 1980-2010 yield [0.1 yield [0.1 CV yield sown area sown area sown area area Farms [0.1 t /ha] t /ha] t /ha] [ha] [ha] [ha] R1 12 8.9 0.2 24.0 0.44 13’599 805 24’700 0.43 R2 11 8.8 0.8 21.0 0.43 16’900 800 34’073 0.41 R3 7 8.3 1.2 19.3 0.42 15’316 500 30’750 0.49 R4 10 10.7 0.9 25.6 0.47 14’720 1155 10’940 0.44 R5 7 9.2 0.3 22.1 0.43 19’666 2000 82’850 0.65 CV: coefficient of variation. Source: Data from the regional statistical offices of Kazakhstan. Table 1 Summary statistics: weather indices (1980-2010) Average Average Number of years Rayon Min. CP Max. CP CV CP Min. SI Max. SI CV SI CP SI where SI < 0.7 R1 140.9 94 215 0.26 0.72 0.26 1.38 0.38 14 R2 132.7 33 234 0.34 0.71 0.14 1.57 0.47 19 R3 126.5 46 267 0.38 0.65 0.22 1.74 0.55 20 R4 163.5 83 269 0.35 0.87 0.30 1.93 0.50 13 R5 147.7 70 297 0.39 0.75 0.22 1.82 0.48 16 CV: coefficient of variation, CP: cumulative precipitation [mm], SI: Selyaninov index. Source: Data from the National Hydro-Meteorological Agency of Kazakhstan.4/12/2013 DUSYS/IED/AFEE 13

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