The MACSUR and AgMIP projects › ...• Ideotype design. UREP Clermon-Ferrand (G. Bellocchi) PaSim...

The MACSUR and AgMIP projects

Pierre Martre 10 March 2016

.02Séminaire « Recherche et adaptation au changement climatique »

10 et 11 mars 2016

P. Martre / MACSUR- AgMIP

Project started 1 June 2012 for 3 years

MACSUR2 starts 1 July 2015 for 2 years


10 et 11 mars 2016


Vision

A network of ~ 200 scientists

• with an expertise to develop shared methodologies

• to assess the impact of climate change on European agriculture for food security with models

Its mission is to advance science on the modeling of agriculture under climate change to improve food security through interdisciplinary European collaboration


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Project structure


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Activities

• Model inter-comparison & improvement • Uncertainty and risk assessment • Capacity Building • Regional case studies • Impact assessment for Europe • Overall scenario development• Variability and extreme climatic events • Identifying sustainable opportunities to reduce yield gaps in Europe• Feeding livestock • Impact on ecosystem services and rural development • GHG mitigation from agriculture


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Agronomie Grignon(D. Makowski)

• Meta-model• Uncertainty (Bayesian approach)

AgroClim Avignon(M. Launay, D. Ripoche)STICS

• Model intercomparison (crop rotation)

EMMAH Avignon(F. Ruget, S. Buis)STICS

• Model intercomparison• Mdoel sensitivity to climate

factors• Data management• Region-specific adaptation

option

INRA contribution

LEPSE Montpellier(P. Martre)SiriusQuality

• Ideotype designUREP Clermon-Ferrand(G. Bellocchi)PaSim

• CC Vulnerability• Participatory model evaluation

AGIR Toulouse(J.E. Bergez, H. Ranayl, J. Constantin, S. Savary, D. Wallach)RECORD-STICS

• Upscaling methods• Training• Uncertainty (parametrization)


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Crop model climatesensitivity

a. median b. uncertainty

-2 0 2 4 6 8

-40

-20

020

40

(a) Finland, Mbaseline = 5155 kg/ha

Temperature change (°C)

Pre

cipi

tatio

n ch

ange

(%

)

-40 -30

-30 -20

-10

0

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

%

-2 0 2 4 6 8

-40

-20

020

40

(b) Finland, IQRbaseline = 1277 kg/ha


Pre

cipi

tatio

n ch

ange

(%

)

100 120

120

140

140

160

160

180

180

60

80

100

120

140

160

180

200

220

240

%

-2 0 2 4 6 8

-40

-20

020

40

(c) Germany, Mbaseline = 7995 kg/ha


Pre

cipi

tatio

n ch

ange

(%

)

-50 -40 -30 -20 -10

0

-2 0 2 4 6 8

-40

-20

020

40

(d) Germany, IQRbaseline = 1341 kg/ha


Pre

cipi

tatio

n ch

ange

(%

)

100 120

120

140 160

160

180

180

200

200

220

-2 0 2 4 6 8

-40

-20

020

40

(e) Spain, Mbaseline = 4005 kg/ha

Temperature change (°C)P

reci

pita

tion

chan

ge (

%)

-50 -40 -30

-20 -10

0 10

20

30

-2 0 2 4 6 8

-40

-20

020

40

(f) Spain, IQRbaseline = 2165 kg/ha


Pre

cipi

tatio

n ch

ange

(%

)

60

80

80

100

100

120


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Goals

• To improve the characterization of risk of hunger and world food security due to climate change,

• To enhance adaptation capacity in both developing and developedcountries.

Led by Cynthia Rosenzweig, James W. Jones, Jerry Hatfield & John Antle

www.agmip.org


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AgMIP Objectives

• Improve scientific and adaptive capacity of major agricultural regions in developing and developed world

• Collaborate with regional experts in agronomy, economics, and climate to build strong basis for applied simulations addressing key regional questions

• Develop framework to identify and prioritize regional adaptation strategies

• Incorporate crop and agricultural trade model improvements in coordinated assessment of future climate conditions

• Include multiple models, scenarios, locations, crops and participants to explore uncertainty and the impact of methodological choices

• Understand roles of land use and mitigation

• Link to key on-going efforts (CGIAR/ESSP, CCAFS, Global Futures, MOSAICC, IPCC AR5, MACSUR…)


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AgMIP elements and linkages

Rosenzweig C, et al. (2013) Agric. For. Meteorol.

INRA


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Agronomie Grignon(D. Makowski)

• Meta-model• Uncertainty (Bayesian approach)

AgroClim Avignon(D. Ripoche) STICSWheat and maize projectIT (data management)

EMMAH Avignon(F. Ruget, S. Buis)STICS

• ET (maize) project• Rice project• C3MP

INRA contribution

LEPSE Montpellier(P. Martre)SiriusQuality

• Wheat Project

URP3F Lusignan(J.L. Durand)

• Maize project

AGIR Toulouse(H. Raynal, J. Constantin, D. Wallach)RECORD-STICS

• Upscaling methods• Training• Uncertainty (parametrization)

DS Environnement(J.S Soussana)

• Pasture and Livestock project


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The AgMIP Crop Pilot Studies


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Objectifs des projets pilotes par espèce

• Constituer une communauté scientifique internationale pour confronter les approches.

• Comparer les résultats.• Avec un objectif commun: réponse à l’eau, CO2, température, et azote.• Dans le cadre de protocoles de simulation identiques avec le plus grand nombre de modèles possibles.

• Explorer les incertitudes : d’où viennent les différences ?• Ordre de grandeur.• Dépendance des lieux, des cultures.• Rôle des types de modèles.• Entrées ? Equations ? Paramètres ? Données ?

• Constituer des outils faits d’ensembles de modèles.• Combien ? • Comment les utiliser ? (moyennes ? Méta-modèles ?)

• Améliorer les modèles.• Nouvelles équations (ex: réponses à la T).• Informatique pour les relier entre eux (format des entrées sorties).• Mieux identifier les paramètres.


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Crop model intercomparison pilot studies

• Wheat (27 models), Maize (18), and Rice (14) Pilots underway• Pilots under development for sugarcane, millet/sorghum, soybean, groundnut, and potato


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1st run: “low information” Daily weather data Soil texture Soil water and N initial conditions Crop management (sowing date, water and N) Cultivar (qualitative phenology information) Observed phenology (anthesis and maturity)

2nd run: “ful information”Measured in-season crop responses (biomass, LAI, N, …)Measured in-season soil soil water N contents

CALIBRATION PHASE OF THE MODEL

RECOMMANDATION TO LIMIT CALIBRATION TO CULTIVAR PARAMETERS

3rd run: sensitivity analysisAir temperature: -3°C, 0°C, +3°C, +6°C, and + 9°CAtmospheric [CO2] : 360, 540, 720 ppm +/- 50 % of N Fertilizer +/- 20 % of available soil water +/- 10 days of sowing date

Simulation for 30 years of current climate and A2 scenario for mid-century

4th run: sensitivity to various CGMs cenarios

A2 scenario for mid-century downscaled from 16 CGMs

A shared protocol


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The largest ensembles of crop models to date


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Multi-model yield response to CO2and T changes

-120 -80 -40 0 40 80 120

0

0

0+3

+3

+3

+6

+6

+6

Temperature change (oC) Atmospheric CO2 concentration (ppm)

360

540

720

0+3+6

Simulated yield change (%)

Australia Asseng et al. 2013 Nature Clim Change


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Global impact of temperature on wheat

6% decline in global wheat production for each degree in global warming


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ACE-ACMO Harmoniozed data


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30 citations

27 citations

77 citations

252 citations

59 citations


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Expert Working Group on crop modeling

Chair: S. AssengCo-chair: P. Martre, F. Ewert, S. Chapman


10 et 11 mars 2016


The MACSUR and AgMIP projects › ...• Ideotype design. UREP Clermon-Ferrand (G. Bellocchi) PaSim...

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