Vulnerability of crop yields to climate conditions in Ukraine. Friedrich J. Koch, Anne Jungandreas Florian Schierhorn Daniel Müller

Transcription

1 Vulnerability of crop yields to climate conditions in Ukraine Friedrich J. Koch, Anne Jungandreas Florian Schierhorn Daniel Müller

2 Comparing Wheat Yields in Europe Source: FAOSTAT

3 35-Year Climate Trend 700 Precipitation [mm / a] 900 Potential Evapotranspiration [mm / a] Precipitation Potential Evapotranspiration Temperature Normalized Solar Radiation [MJ / m2 / a] Temperature Mean [ C] Temperature Normalized Radiation Temperature Mean Source: ENSEMBLES,

4 IPCC Climate Predictions for Central Europe Source: IPCC Fifth Assessment Report 4

5 Contribution to the Global Function Overall aim: Understanding the impact of climate change on yields of major crops in Ukraine Pre Study: - Vulnerability of crop yields to (actual) climate conditions IN COMPARISON to other impacting variables Problem: - Getting different data types synchronized (statistical data [yields] to continous data [climate] - Having enough data on a suitable and comparable resolution 5

6 Panel Data on Farm Level Total Content: Panel data dimension: Companies 8 years annual data ( ) Data on: 30 food crops 4 fodder crops Livestock Data about (e.g): Production area Outputs (physical, economical) Costs (split) Subsidies Company information Data used or this Analysis Farm/company Address: - For spatial data allocation Data on winter wheat: - Harvested area - Production in tonnes - Money spent on fertilizer Corresponding in ca. 10,000 full observations used out of 11 Mio. Total data tuples Source: Ukrainian Agribusiness Club 6

7 From Panel Data to Map Geocoding to obtain coordinates and allocate the farms (address based) Agricultural Land derived from Global LandCover2000 (GLC) satellite data Representing 90% agr. land relative to UKRSTAT 70% when cleaned Allocation of GLC agricultural land to farms based on their harvested area using a rotation principle (each round 1 pixel per farm) to overcome a negative effect from clustering 7

8 Winter Wheat 2012 in Ukraine derived from panel data

9 Winter Wheat 2012 in Ukraine derived from panel data

10 The Random Forest Decision Tree: An ensemble training method using many decision trees a Forest It give each tree a Randomly selected split point for attributes Wikipedia 2015 Used for: Regression, Classification, Variable Importance 10

11 Variables Considered Climate Variables (from daily) Precipitation Temperature Normalized Solar Radiation Evapotranspiration Standardized Precipitation Index Depended Variable: Winter Wheat Yield Single Variable: Fertilizer Cost Climate Data Aggregation - Phonological Stages 1st vegetation period (Autumn- before dormancy) (0-1a) 2nd vegetation period (Spring) (1c) Flowering (2) Yield Formation - (3) Ripening - (4) 11

12 Model Overall Output Variance explained by Model: 74% Sequence of Variabel Importance: - Fertilizer - Evapotranspiration - Solar Radiation - Precipitation - Drought Index SPI Drought has suprinsingly little importance explaining the yields might be different with model regionalization Variable Importance Measure Variable %IncMSE fert.costs 58.8 YieldForm_sum_ET 34.7 Veg2_sum_ET 25.7 Ripening_sum_ET 25.2 Ripening_sum_SRT 19.4 Veg1_sum_SRT 15.4 Veg2_sum_SRT 14.4 Veg1_sum_ET 14.4 YieldForm_sum_Precip 14.4 Veg2_sum_Precip 14.2 Veg2_DI 13.6 Ripening_DI 11.2 Ripening_sum_Precip 10.7 Veg1_sum_Precip 9.8 YieldForm_sum_SRT 9.7 Flowering_sum_ET 9.2 Flowering_sum_SRT 9.1 Veg1_DI 8.0 Flowering_sum_Precip 7.3 Flowering_DI 6.9 YieldForm_DI

13 Represented Yield Range The model represents the full range of measued yields while it is worse at the upper and lower end in correspondeance to the lesser data points at the extrems 13

14 Variable partial dependency 14

15 Conclusion Random Forest is a good method to analyze climate vulnerability of crop yields Fertilizer application has significant impact on winter wheat yields probably being one of the key factors limiting the productivity Climate is a significant and important source impacting on yields Random Forest can help to understand optimal variable range 15

16 Further intention and expectation Using higher resolution of climate data Splitting the model in regional models Using the models to forecast climate Change Impact Implement additional drought indices 16

17 Thank you for your attention Thank you for your attention! 17

18 Variable Correlation Single variable has low power to explain yields High correlation between Evapotranspiration and Solar Radiation (driven by compilation method) High correlation between precipitation and Drought Index (driven by compilation method) 18