Climate change impacts on yields and incomes : a EU15 modelling appraisal

Transcription

1 Climate change impacts on yields and incomes : a EU15 modelling appraisal D. Leclere* 1,2, P-A. Jayet 2, N. De Noblet 1 1 LSCE/IPSL, UMR CEA/CNRS/UVSQ, CEA Orme des Merisiers, Gif/Yvette, FRANCE 2 UMR Economie Publique, INRA/AgroParisTech, Thiverval-Grignon, FRANCE * corresponding author: david.leclere@lsce.ipsl.fr Motivations - (1/3) Atmosphere Land Ocean Climate system mean state & variability (T, SW & LW rad, pr, [trace gases], ) biophysical /biogeochemical exchanges Land cover change Management practices IPCC AR4, 2007 Land Europe : Forest <-> agricultural land conversions : weak (e.g. Rounsevell et al AgEcoSys&Envt 2006) Continental GHG balance : croplands = annulates biospherical C sink (e.g. Schulze et al NatureGeo 2009) croplands = major contributor to non CO2 GHGs emissions Biospherical processes not well quantified

2 Motivations - (2/3) Atmosphere Land Ocean Climate system biophysical /biogeochemical exchanges mean state & variability (T, SW & LW rad, pr, [trace gases], ) Land cover change Management practices Resources Farmers Inputs Outputs Other economical sectors Agrosystems market interactions (prices) Motivations - (2/3) Atmosphere Land Ocean Climate system biophysical /biogeochemical exchanges mean state & variability (T, SW & LW rad, pr, [trace gases], ) Land cover change Management practices Resources Farmers Inputs Outputs Other economical sectors (Infra/supra) national policies Agrosystems Institutions market interactions (prices) externalities (costs/benefits) regulation (taxes&subsidies/ norms)

3 Motivations - (3/3) CC impacts on European agricultural supply : beyond the yields, which adaptations possible? towards which agricultural systems? Farmers Inputs Outputs Agrosystems Challinor et al. JEB 2009, Tubiello et al. PNAS 2007, DaMattaa et al FAO 2009, SEAMLESS FP6 To go further, need for : - progress on crop knowledge - downscaling (pedo-climatic heterogeinity & economical drivers) - progress on adaptation representation (climate & economical drivers) - uncertainty quantification Modelling framework - (1/7) Representing an explicit sensitivity of EU agricultural supply to: economic / pedologic / climatic variability

4 Modelling framework - (2/7) AROPAj EU 15 agricultural supply-side model (e.g. De Caraet al 2005) For each region : typology of economic agents farm-types producers Modelling framework - (2/7) AROPAj EU 15 agricultural supply-side model (e.g. De Caraet al 2005) Each economic agent k ~ MIL math program : x k activities, g k gross margin, A k cost, z k resource

5 Modelling framework - (3/7) Representing an explicit sensitivity of EU agricultural supply to: economic / pedologic / climatic variability Modelling framework (4/7) ArTiX Linking regionally yields to management Simulating agent-crop specific production functions : (Godard et al 2008, AgSystems) for each crop of each agent : Yield (N) = B (B-A). e -τ. N Agent typology + Soil database + Management scenarios + Climate scenarios + STICS Simulations 1/τ = N input sensitivity B= non N-limited yield A = N-limited yield

6 Modelling framework (5/7) ArTiX Soil-Management scenarii : irrigation or not (fixed for crop-agent cases) [FADN Statistics : total area] + [priority order between crops] x 5 soils available / FADN region [European soil data base v1.0] + [ predotransfert rules INFOSOL] x 2 preceding crops : [winter wheat or peas] Linking regionally yields to management (Godard et al 2008, AgSystems) x either (3 cultivars x 1 sowing date) or (1 cultivar x 3 sowing date) [STICS model cultivars differing by precocity group)] = 5 x 2 x 3 = 30 soil-itk options (per crop per agent) Modelling framework - (6/7) ArTiX Regionalised soil-climate effect on crop yields (Godard et al 2008, AgSystems) New production functions : new climate (climate change) same/new management (adaptation) Crop C, region R, climate 3 Best suitable ITKs Yield soil-itk options 1 2 with present day observations / assumptions (yield, prices) Yield N-input Production functions N-input

7 Modelling framework - (7/7) Δ(Economic drivers) Agr. supply, land value, Prices, norms, taxes AROPAj EU Agricultural supply (farms) Management options Soils ARTIX DB & Crop model Production fonctions Surface climate Δ(Climate) Preliminary results - (1/3) Relevance of enhanced integration of spatial economic, soil and climate variability Assessing the vulnerability of EU15 agricultural supply to: - a mitigation policy - a climate change ceteris paribus no price feedbacks, no change in ITK, unchanged utilized agricultural area (UAA),, slight variation of the livestock (inside [-15%,+15%])

8 Preliminary results - (1/16) Introducing a mitigation scenario Generating production functions («CTL» climate data) Implement a tax on GHG emissions in AROPAj Emissions are calculated with as : GHG emissions = activities x IPCC emission factors (IPCC guidelines) Direct emissions -CH 4 and N 2 O- are subject to a tax («1st-best tax» based on CO2eq) Possible actions at the farm level: Change yield & fertilisation rate per crop Change crop area shares Change livestock & feeding Mineral fertilizer v.s. manure application Marketed production v.s. on-fam consumption Preliminary results - (2/16) Assessing climate change impact (without agronomical adaptation) Generating production functions («CTL» climate data) Regenerating production functions (SRES A , same management) A2 (2071_2100) - CTL (1976_2005) Winter Spring Data from RCA3 RGCM, driven by ECHAM5 AOGCM. Summer T change [ C] Autumn (Kjellström et al, Tellus, 2010)

9 Preliminary results - (3/16) Assessing climate change impact (without agronomical adaptation) Yield impact - light overestimation (current) - 98% current area covered Present Clim A2 B1 Preliminary results - (3/16) Assessing climate change impact (without agronomical adaptation) Yield impact - light overestimation (current) - 98% current area covered Present Clim A2 B1 - good agreement (current) - 50% current area covered Present Clim A2 B1

10 Preliminary results - (4/16) Assessing climate change impact (without agronomical adaptation) Yield impact CTL A2H2 CTL A2H2 Preliminary results - (5/16) Indicators: Gross margin impact on farmer incomes Production change feedbacks from supply-demand equil. GHG emissions change on envir. policy Land Use Change feedbacks on climate system Analysed with Ag2000 CAP at 3 resolutions: EU15 national level regional level For 3 climate scenarios: CTL («present») A2 hor. H2 B1 hor. H2 (H2=« ») C «economic» A1 A C B1 B2 «environmental» global regional C C

11 Preliminary results - (6/16) Gross margin 30% variation of the agricultural net gross margin CTL tax 40 A2H2 B1H2 % CTL 20% 10% 0% Key features EU15 level, CC ~ % -10% -20% -30% BE DK DE GR ES FR UK IE IT LU NL AT PT FI SW EU15 But : - Strong rational to regional EU-MS - Can outweigh the tax impact locally Preliminary results - (7/16) Gross margin (including tax recept) 30% variation of the agricultural net gross margin, tax recept included CTL tax 40 A2H2 B1H2 % CTL 20% 10% 0% Key features EU15 level, tax impact ~ - 1 % -10% -20% if tax returned to farmers -30% BE DK DE GR ES FR UK IE IT EU-MS LU NL AT PT FI SW EU15 if not returned : ~ -12% [6, national D. Leclère et al. LSCE/UMR Economie Publique ACCAE conference 20-22/10/

12 Preliminary results - (8/16) Gross margin [ (B1) CTL ] / CTL in % Key features : - Spatial patterns different from South-North gradient Preliminary results - (9/16) Gross margin [ (A2) CTL ] / CTL in % Key features : - Coherence between CC Scenarios? - Intensity differences

13 Preliminary results - (10/16) Production change / soft wheat area 80% variation of the agricultural area dedicated to soft wheat CTL tax 40 A2H2 B1H2 % CTL 60% 40% 20% 0% -20% -40% -60% -80% Quite weak EU15 (& FR, UK) Getting more lower resolution -100% BE DK DE GR ES FR UK IE IT EU-MS LU NL AT PT FI SW EU15 CC >> tax Preliminary results - (11/16) Production change / soft wheat marketed production 200% variation of the marketed soft wheat production CTL tax 40 A2H2 B1H2 150% 100% Possibly EU15 (B1) % CTL 50% 0% -50% -100% Getting much more lower resolution -150% BE DK DE GR ES FR UK IE IT LU NL AT PT FI SW EU15 CC >> tax EU-MS D. Leclère et al. LSCE/UMR Economie Publique ACCAE conference 20-22/10/

14 Preliminary results - (12/16) Production change / soft wheat on-farm consumption 200% variation of the on-farm soft wheat consumption CTL tax 40 A2H2 B1H2 >450% 150% 100% Possibly EU15 (B1) % CTL 50% 0% -50% -100% Getting much more lower resolution -150% BE DK DE GR ES FR UK IE IT LU NL AT PT FI SW EU15 CC >> tax EU-MS D. Leclère et al. LSCE/UMR Economie Publique ACCAE conference 20-22/10/ Preliminary results - (13/16) Production change / soft wheat total production variation of the total soft wheat production 250% CTL tax 40 A2H2 B1H2 EU soft wheat farm use 200% 150% marketed SW on-farm SW % CTL 100% 50% 0% -50% -100% -150% BE DK DE GR ES FR UK IE IT LU NL AT PT FI SW EU15 production (Mt) CTL CTL tax 40 A2H2 B1H2 EU-MS D. Leclère et al. LSCE/UMR Economie Publique ACCAE conference 20-22/10/

15 Preliminary results - (14/16) GHG emissions / N2O + CH4 x GWPs 10% variation of the direct (non CO2) GHG emissions CTL tax 40 A2H2 B1H2 % CTL 5% 0% -5% -10% -15% Quite significant impact Tax >> CC -20% -25% -30% BE DK DE GR ES FR UK IE IT LU NL AT PT FI SW EU15 CC: A2 > B1 & reverse EU EU-MS Quite national D. Leclère et al. LSCE/UMR Economie Publique ACCAE conference 20-22/10/ Preliminary results - (15/16) Diagnosing management adaptation : yield build-up analysis : yield ~ (mean grain weight) x (mean grain number/m²) mean grain weight ~ nutrient & water limitation during grain filling sensitivity to thermic & hydric stresses Use of STICS simulations to diagnose: yield build-up sensitivity to climate and management practices crop cycle length & positionment sowing date & cultivar hydric stress irrigation PROPOSE NEW MANAGEMENT (Work in progress)

16 Preliminary results - (16/16) Diagnosing management adaptation : yield build-up analysis : MAIZE CTL A Yield Cyle length (sowing to harvest) Cycle length shortening (temperature accumulation driven) Italia Grain filling during hottest days of year Low grain weight ADAPTATION : anticipate sowing date and/or use shorter cycle cultivar CONCLUSION (1/2) We introduced a regional sensitivity of an economic agricultural supply-side model to soil and climate distribution the key point of the resolution scale Downscaling to that level may be important for both : assessing vulnerability at a regional level, assessing EU relocation of the agricultural supply. Impact on farmers incomes: tax effect EU15 level, but CC can be higher (positive or national level. Impact on activities : production can change EU15 level -> potential price feedbacks land-use (crop cover & management) changes regionally -> potential feedbacks on climate Impact on GHG emissions quite strong with tax significant & highly differentiated between EU-MS

17 CONCLUSION (2/2) need for improving consistency of the analysis Crops not selected by the build-up of «dose - response» functions Others plant production (fodder & grasslands) & CC Livestock & CC (FP7-AnimalChange, ANR-Validate) need for economic completion Price feedbacks required regarding the supply impact Middle-run re-allocation of the land (agric./forest/others) (ANR-ORACLE) WORK IN PROGRESS (DL s PhD thesis) Vulnerability and resilience under CC : -> regions and farm types ranking? Impacts of temporal climate variability change? D. Leclère et al. LSCE/UMR Economie Publique ACCAE conference 20-22/10/