Integrated assessment of drought and adaptation scenario impacts on crop production in Austria

Transcription

1 ntegrated assessment of drought and adaptation scenario impacts on crop production in Austria Hermine Mitter 1, Erwin Schmid 1, Uwe A. Schneider 2 1 University of Natural Resources and Life Sciences, Vienna (BOKU) Department of Economics and Social Sciences nstitute for Sustainable Economic Development 2 University of Hamburg, Center for Earth System Research and Sustainability, Research Unit Sustainability and Global Change 6 th AgMP Global Workshop Montpellier, 28 th June 2016

2 Drought information Drought information systems have been developed at various scales, with different spatial and temporal resolutions, and focus on meteorological, agronomic and hydrological indicators. Economic valuation of drought information is of interest. Considering uncertainty in drought information plays an important role for economic valuation. (Kusunose and Mahmood 2016, Agr Systems 146, )

3 Research objectives Spatially explicit, integrated drought assessment exemplified on Austrian cropland for the next decades identifying optimal crop management portfolios for adapting to drought scenarios calculating the value of drought scenario information for farmers adaptation considering uncertainties in drought scenarios assessing the effect of farmers risk aversion on the composition of crop management portfolios and the value of drought scenario information

4 Spatially explicit integrated assessment framework Statistical climate model O 3 drought scenarios Typical crop rotation O Crop rotation model nput O Output Topography Soil type EPC Bio-physical process model Bio-physical domain 3 intensities, rain-fed 2 intensities, irrigated O Crop yields 1 km grid, Legend Model/ Calculation Commodity prices Variable production costs Annual capital costs for irrig Policy premiums Crop gross margin calculation Portfolio optimization model O Optimal crop management portfolios Computation of value of drought scen information Socioeconomic domain 4 risk aversion parameter values 1 km grid,

5 Drought scenarios until 2040 Average annual precipitation sums on cropland in the drought scenarios S1 (baseline), S2 (moderate drought), and S3 (severe drought) 30 bootstrapped re-allocations for each drought scenario to capture uncertainty (Strauss et al., 2013, Am J ClimChange 2, 1-11) Baseline (S1) Moderate drought (S2) Severe drought (S3)

6 Portfolio optimization model Non-linear mean-standard deviation model (similar to E-V model; Markowitz, 1952, J Financ 7, 77 91). Seeks to find optimal crop management portfolios per cropland pixel by drought scenario and 4 levels of farmers risk aversion (θ) from risk neutral to highly risk averse. Maximizes the weighted sum of expected gross margins discounted by the standard deviation using a risk aversion parameter value (θ) (Freund, 1956, Econometrica 24, ).

7 Computing the value of drought scenario information for adaptation Optimal crop management portfolios in the baseline scenario (S1) are evaluated in the moderate (S2) and severe drought scenarios (S3), respectively for each risk aversion parameter value (θ) and considering 30 bootstraped re-allocations for each drought scenario. Calculation of differences in average gross margins (ΔMeanGM) and standard deviations (ΔStdGM) between optimal management portfolios in S2/S3, and optimal management portfolios in S1 evaluated in S2/S3. Value of drought scenario information = ΔMeanGM - θ*δstdgm

8 Optimal crop management portfolios Average annual gross margins by levels of risk aversion and drought scenarios, considering adaptation Baseline (S1) Moderate drought (S2) Severe drought (S3) 4 risk aversion levels

9 Adaptation measures in crop managment portfolios With increasing risk aversion: low fertilization intensity: on 8-24% of the total cropland moderate or high irrigation intensity: on 6-47% of the total cropland diversification With more severe drought scenario: moderate or high irrigation intensity: on 37-47% of the total cropland in the severe drought scenario S3 total irrigation water input baseline scenario (S1): mil. m³ severe drought scenario (S3): mil. m³

10 Value of drought scenario information in /ha/a Severe vs. moderate drought RAP Area-weighted national average Moderate drought (S2) Severe drought (S3) θ=0.0 Risk neutral θ=1.0 Low RA θ=2.0 Moderate RA θ=2.5 High RA 33 99

11 Value of drought scenario information in /ha/a Regional heterogeneities Moderate drought (S2) Severe drought (S3) θ=0.0 Risk neutral θ=2.5 High risk aversion

12 Conclusions Key determinants of value of drought scenario information: initial state of knowledge quality of additional information, i.e. extent to which it reduces uncertainty available drought adaptation measures and their expected outcomes risk preference of decision-maker Value of drought scenario information = 0 crop management portfolios are robust, i.e. no portfolio changes with or without dry weather conditions. mainly in the western, humid parts of Austria. Value of drought scenario information increases moderately with risk aversion. increases with more extreme drought scenarios. is highest in the eastern, semi-arid parts of Austria. Assumptions: Risk aversion remains similar under changing drought conditions (CARA). Full information, high adaptive capacity and flexibility in farm management.

13 University of Natural Resources and Life Sciences, Vienna (BOKU) nstitute for Sustainable Economic Development Doctoral School of Sustainable Development University of Hamburg, Center for Earth System Research and Sustainability, Research Unit Sustainability and Global Change Thank you! Hermine Mitter, Erwin Schmid, Uwe A. Schneider