Regional Crop Yield Estimation Under Climate Change Using the YIELDSTAT Model – a Case Study for the Free State of Thuringia, Germany W

Leibniz Centre for Agricultural Landscape Research

Regional crop yield estimation under climate change using the YIELDSTAT model – a case study for the Free State of Thuringia, Germany W. Mirschel1, R. Wieland1, C. Guddat2, H. Michel2

1) Leibniz-Centre for Agricultural Landscape Research (ZALF), Institute of Landscape Systems Analysis, Eberswalder Str. 84, D-15374 Müncheberg, Germany; [email protected] 2) 2) State Office of Thuringia for Agriculture, Department of plant production and agro-ecology, Apoldaer Str. 4, 07774 Dornburg-Camburg, Germany

Content

1. Introduction and aim 2. Methodology ● Climate data regionalization ● YIELDSTAT - hybrid model of intermediate complexity ● Input map information ● Model GIS coupling

3. Results ● Climate changes 1981/2010 vs. 2021/2050 ● Yield changes 2021-2050 vs. 1981-2010

4. Conclusions

Leibniz Centre for Agricultural Landscape Research

• Climate change is going on with increasing temperatures especially during winter and decreasing precipitation amounts especially during the main growing season of crops. • For developing climate adaptation and mitigation strategies in agriculture on a regional scale better information about long-term effects of climate changes on yields of agricultural crops are necessary. • The usage of well validated crop yield models is the only possibility for assessing the impact of climate change on crop yields in agriculture. In the field of biomass and crop yield modelling different model types exist. The process oriented mechanistic models are very complex, usually over- parameterized and have very special input data demands. Their usage for spatial simulations generally is very limited. • For climate change impact assessments on a spatial scale regional models with an intermediate complexity (REMICs) with realistic input data demands in space and time are favoured. • For the regional assessment of crop yields on arable land in different regions of the Free State of Saxony the statistical oriented hybrid model YIELDSTAT - a REMICs - was developed and spatial validated.

P R O C E S S GERMANY O F 250 x 250 km

Statistic Downscaling D using WETTREG 2010 method O W N S C A L I Zoning of Free State of N Thuringia into 299 „climate G patches“ with a representative weather station for each subzone using the VORONOI method

Matrix for basic natural yield (according Kindler) for: - 56 site types - 16 different agricultural crops - 2 grassland-types (intensive, extensive) Basis: field-specific yield data from about 300 farms during a period of 15 years

Site-specific yield extra charges (+ , - ) Ekorr= f (slope, stoniness, altitude, hydromorphology, soil quality index, climate zone, crop growth temperature, average winter temperature, climatic water balance, …)

Soil tillage effect

ESoilTil = f(soil type, soil tillage method, crop type, pre-crop) Basis: soil tillage experiments

Pre-crop effect

EPre-Crop = f(current crop, pre-crop) Basis: crop rotation experiments

Technological progress

ETrend = f (cropping year,level of plant breeding, level of agro- management) Basis: long-term yield statistics, trend prediction

CO2-effect

ECO2 = f (crop species (C3, C4), cropping year, atmospheric CO2- concentration) Basis: FACE-, open-top and climate chamber experiments

diluvial soils alluvial soils loess soils disintegrated soils StT NYWW NYTR StT NYWW NYTR StT NYWW NYTR StT NYWW NYTR D1a 3.5 3.7 Al1a 6.1 5.6 Lö1a 7.6 7.1 V1a 7.0 6.5 D2a 3.7 4.2 Al1b 5.8 5.3 Lö1b 7.2 6.7 V2a 6.5 6.0 D2b 4.0 4.6 Al1c 5.5 5.0 Lö1c 6.8 6.3 V2c 6.1 5.7 D3a 4.4 4.6 Al2b 5.6 5.1 Lö2c 6.6 6.1 V3a 6.1 5.7 D3b 4.7 4.7 Al2c 5.2 4.8 Lö2d 6.4 5.9 V3b 6.0 5.5 D3c 4.5 4.4 Al3a 6.2 5.7 Lö3a 7.6 7.1 V3c 5.0 4.6 D4a 5.4 5.2 Al3b 5.9 5.4 Lö3c 6.8 6.3 V4a 5.6 5.2 D4b 5.7 5.5 Al3c 5.7 5.3 Lö4b 6.8 6.3 V4b 5.0 4.8 D4c 5.7 5.4 Lö4c 6.3 5.8 V5a 5.9 5.4 D5a 6.0 5.4 Lö5b 6.7 6.2 V5b 5.8 5.5 D5b 6.5 5.7 Lö5c 6.5 6.0 V5c 5.0 5.4 D5c 6.5 5.6 Lö6b 6.4 5.9 V6b 5.5 5.3 D6a 6.2 5.8 Lö6c 6.0 5.5 V7a 5.4 4.9 D6b 6.7 6.2 V7b 5.5 5.1 D6c 6.7 6.2 V7c 4.8 4.7 V8a 5.5 5.5 V9a 4.4 4.9

StT - soil type NY - natural yield (t ha-1) WW - winter wheat TR - triticale

International Summer School / Workshop „Fundamental and Applied Research in Mathematical Ecology and Agro-ecology“, Altai State University and Agrophysical Research Institute St. Petersburg, Barnaul 22-24 July 2012 Methodology (2-3) - YIELDSTAT - hybrid model of intermediate complexity- Leibniz Centre for Agricultural Landscape Research Crop type specific yield extra charges as function of site specific characteristics (here for winter rape)

0; 0  HaNe  9      0.04  KWB; 1  KlZ  14 und KWB   25  2.0; 9  HaNe  14; StT (V1a...V 9a)     0.03 KWB; 14  KlZ  18 und  25  KWB  100 4.0; HaNe  14   0.04  AZ; KlZ  (3...6,15...18) Ekorr (WRa)          0.02 KWB; 18  KlZ  25 und KWB  100  0; 0  HaNe  9   0; sonst     0; sonst  1.0; 9  HaNe  14; StT (V1a...V 9a)    2.0; HaNe  14  

 0; SK  25       1.0; 25  SK  100 ; HaNe  14 2.0; Hy  (S3,G3) und KlZ  (3...6,15...18)      3.0; Hy  (S3,G3) und KWB   15          3.0; Hy  (GS2,GS3,G2) und KlZ  (3...6,15...18)  2.0; SK  100   2.0; Hy  (GS2,GS3,G2,S2) und KWB   15        1.0; Hy  (S2) und KlZ  (3...6,15...18)     1.0; Hy  (G1,S1) und KWB   15  0; SK  25     2.0; Hy  (S1,G1) und KlZ  (3...6,15...18)      0; else    0.5; 25  SK  100 ; HaNe  14 0; else       1.0; SK  100  

2.0; StT  (V1a...V 9a) und 600  HüNN  700 und WaWiT  4.0    4.0; StT  (V1a...V 9a) und HüNN  700 und WaWiT  4.0  + 0.02 KWBJ-A   0; sonst 

StT – site type (V – disintegrated type) SK - stoniness (t/ha) KWB – climatic water balance during vegetation (mm) AZ – soil quality index

KWBJ-A – climatic water balance forJune – August (mm) HüNN - altitude (m) KlZ – mesoscalic climatic zone Hy – hydromorphy class HaNe – slope steepness (%) WaWiT – groth temperature threshold

International Summer School / Workshop „Fundamental and Applied Research in Mathematical Ecology and Agro-ecology“, Altai State University and Agrophysical Research Institute St. Petersburg, Barnaul 22-24 July 2012 Methodology (2-4) - YIELDSTAT - hybrid model of intermediate complexity- Leibniz Centre for Agricultural Landscape Research Yield effects - of previous crops Current crop Previous crop Wheat Rape Potatoes Maize Clover Winter wheat 1.00 1.05 1.05 1.02 1.04 Winter barley 1.00 1.05 0.95 0.95 1.02 Oat 0.91 1.05 0.95 1.02 1.04 Potatoes 0.99 1.05 0.95 0.95 1.06 Sugar beet 1.00 1.08 1.04 0.98 1.05

- of soil tillage

Crop Plough Low tillage No-tillage Cereals Foliage plants Cereals Foliage plants Winter wheat 1.00 1.00 1.05 0.95 1.03 Oat 1.00 0.90 0.98 0.85 0.92 Sugar beet 1.00 1.04 1.04 1.04 1.06

Ytrend (Year)  Tcrop (Year 1990)

Crop yield trend estimation for the Free State of

Thuringia )

-1 3,5 t ha Declining crop yield trends (dt ha-1 a-1) for the Free State of Thuringia, ( 3,0 Germany, for the time period up to 2050 started from the real yield 2,5 Winter barley trends for 1991-2010 2,0 Winter wheat 1,5 Winter rape 1,0

0,5 Silage maize Spring barley 0,0 Progress in agro-technology agro-technology in Progress 1990 2000 2010 2020 2030 2040 2050

Yield effect of rising atmospheric CO2

CO2Eff ; KWB  50   | KWB  50 | FCO2 (FA)  [CO2(KlSz, J )  385]CO2Eff (1 0,186 ; 130  KWB  50  80 1,186*CO2 ; 130  KWB  Eff FCO2(FA) - ffactor of complex impact of CO2 on yield FA - crop type CO2(KlSz, J) – atmospheric CO2-content J – year of simulation KlSz - climate scenario used

CO2Eff - efficiency factor (% per 1 ppm CO2 increase) KWB - climatic water balance (mm) for the FA-dependent vegetation year

Crop CO2Eff Crop CO2Eff Winter wheat 6,218 10-2 Silage maize 1,589 10-2 Winter barley 7,547 10-2 Clover 9,046 10-2 Effectiveness of a atmospheric CO2- Winter rye 6,883 10-2 Alfalfa 7,853 10-2 increase on biomass accumulation of agricultural crops [% (1ppm CO - Sugar beet 3,744 10-2 Grass 4,308 10-2 2 increase)-1] based on results from -2 -2 Winter rape 9,434 10 Clover-grass-mix (70:30) 7,748 10 the „Centre for the Study of Potato 6,162 10-2 Alfalfa-grass-mix (70:30) 6,727 10-2 Carbon Dioxide and Global Change“ (2009) International Summer School / Workshop „Fundamental and Applied Research in Mathematical Ecology and Agro-ecology“, Altai State University and Agrophysical Research Institute St. Petersburg, Barnaul 22-24 July 2012 Methodology (2-6) - YIELDSTAT - hybrid model of intermediate complexity- Leibniz Centre for Agricultural Landscape Research Yield losses by adverse weather situations during harvest time 0; NiTage  miNiTage   0; Ni  miNi EV    A  B* NiTage   Ni;miNi  Ni  maNi  C  D*      maNi; Ni  maNi EV -yield loss (dt ha-1); NiTage -number of days with precipitation > 0 mm during harvest periode; miNiTage -long-term average of number of days with precipitation during harvest period; Ni∑ -precipitation sum during harvest period (mm); miNi∑ -long-term average of precipitation sum during harvest period (mm); maNi∑ -max. precipitation sum during harvest period (mm); A, B, C, D -statistical parameters

Crop type dependent parameter values for the yield loss algorithm International Summer School / Workshop „Fundamental and Applied Research in Mathematical Ecology and Agro-ecology“, Altai State University and Agrophysical Research Institute St. Petersburg, Barnaul 22-24 July 2012 Methodology (3-1) - Input map information- Leibniz Centre for Agricultural Landscape Research

Soil quality index Site type for arable land

Height above sea level Type of hydromorphy

Slopeness Mesoscale climatic zoning

Climate patches zoning according the Thuringian weather station network

Maps Spatial Analysis and Modeling Tool Soil index Altitude Data base (Weather/Climate, Parameter, Management) Hydromorphy

Soil type Climate zoning

Model

Hybrid model Szenario simulations for Silage maize YIELDSTAT Crop yield Irrigation water demand

Distribution of Distribution of annual mean tem- annual precipitation perature within the within the Free State Free State of of Thuringia up to Thuringia up to 2100 1981-2010 2100

2021-2050

2071-2100

Changes in mean temperature and precipitation up to 2100 in the Free State of Thuringia for spring, summer, autumn, winter and the whole year

Winter rape Silage maize

Distribution of crop yield changes within Thuringia 1981-2010 vs. 2021-2050 International Summer School / Workshop „Fundamental and Applied Research in Mathematical Ecology and Agro-ecology“, Altai State University and Agrophysical Research Institute St. Petersburg, Barnaul 22-24 July 2012 Results (2-2) - Yield changes 2021-2050 vs. 1981-2010- Leibniz Centre for Agricultural Landscape Research

Crop yield changes 2021-2050 vs. 1981-2010 (%, regionalized for the Free State of Thuringia)

Winter wheat Spring barley

Winter rape Silage maize

International Summer School / Workshop „Fundamental and Applied Research in Mathematical Ecology and Agro-ecology“, Altai State University and Agrophysical Research Institute St. Petersburg, Barnaul 22-24 July 2012 Results (2-3) - Yield changes 2021-2050 vs. 1981-2010- Leibniz Centre for Agricultural Landscape Research Crop yield changes 2021-2050 vs. 1981-2010 taking into account different progress levels in plant breeding and agro-management

Variant 1 - without progress Variant 2 – very optimistic progress Variant 3 – semi-optimistic progress Variant 4 – pessimistic declining progress

Comparison of crop yield deviation and range between min. and max. crop yields within 30 years for 1981-2010 and 2021-2050

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 Because of more different and more extrem years in future up to 2050 the crop yield deviation and the crop yield range between the worst and the best year will increase, i.e. the probability for stabile cropping conditions will decrease !

Regional crop yield simulations show that the effects of rising temperatures, changing distributions of precipitation within the vegetation years and rising atmospheric CO2 on future agricultural yields is less than the effect of future progress in agro-technology and plant breeding. The impacts of climate change on agriculture may be positive or negative depending on the variability of weather conditions, site quality, land use and agro-management. The simulated impacts of regional climate change on yields in Southeast Germany in the near and medium future are relatively small. There are winners (winter crops) and losers (summer crops). The best way for adaptation of agriculture to climate change is: ● a good mix of different cropping systems ● various management options ● a wide range of agricultural crops ● environmental sound irrigation technologies ● conservation of a high soil fertility level ● highly productive and stress tolerant crop varieties ● new agricultural technologies (strip cropping, precision agriculture, energy plantations …) Interactive simulations und integrated impact assessment of agricultural adaptation strategies to climate change beginning with the regional crop yield estimation are very important prerequisites to support farmers and other stakeholders to find out cost effective adaptation strategies to climate change.

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