Our Common Future Under Climate Change

International Scientific Conference 7-10 JULY 2015 Paris, France

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Wednesday 8 July - 15:00-16:30 UPMC Jussieu - Amphi 25

2223 - Modeling Our Agricultural Future

Parallel Session

Lead Convener(s): A. Ruane (NASA Goddard Institute for Space Studies, New York, NY, United States of America)

Convener(s): J. Jones (University of Florida, Gainesville, United States of America)

15:00

The Agricultural Model Intercomparison and Improvement Project: Transdisciplinary and Multi-scale Agricultural Projections of Climate Change Impacts

C. Rosenzweig (NASA Goddard Institute for Space Studies, New York, United States of America)

Abstract details
The Agricultural Model Intercomparison and Improvement Project: Transdisciplinary and Multi-scale Agricultural Projections of Climate Change Impacts

C. Rosenzweig (1)
(1) NASA Goddard Institute for Space Studies, New York, United States of America

Abstract content

The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. Currently, AgMIP has over 700 participants from more than 45 countries contributing their expertise to over 30 projects and activities. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries.

 

Since 2010, AgMIP has engaged stakeholders and researchers to assess climate impacts on food security and plan for a more resilient future. AgMIP has built a cutting-edge assessment framework on both global and regional scales, which links climate, crops, livestock, and economics to help decision-makers better understand how climate change will reverberate through complex agricultural systems and markets.

 

AgMIP initiatives include regional integrated assessments, global economic assessments and global crop modeling activities, data and tools to facilitate multi-model and multi-discipline assessments, and cross-cutting themes to help interpret agricultural model results for decision-making. Results from these initiatives contributed to the Intergovernmental Panel on Climate Change Fifth Assessment Report, provide important context for national and regional stakeholders interpreting climate change risks, further state-of-the-art global food security assessments and agricultural models, and deliver key inputs, such as commodity prices, into regional integrated assessments.

 

AgMIP is now planning a coordinated global and regional assessment of future food securtiy under changing climate.

15:20

Projecting grassland sensitivity to climate change from an ensemble of models

J.-F. Soussana (Inra, Paris, France), F. Ehrhardt (Inra, Paris, France), R. Conant (NREL, Colorado State University, , Fort Collins, CO, United States of America), M. Harrison (Tasmanian institute of Agriculture, Burnie, Australia), M. Lieffering (AgResearch Grasslands, Palmerston North, New Zealand), G. Bellocchi (Inra, Clermont-Ferrand, France), A. Moore (CSIRO, Canberra, Australia), S. Rolinski (Potsdam Institute for Climate Impact Research, Potsdam, Germany), V. Snow (AgResearch, Christchurch, New Zealand), L. Wu (Rothamsted Research, Rothamsted, United Kingdom), A. Ruane (NASA Goddard Institute for Space Studies, New York, NY, United States of America)

Abstract details
Projecting grassland sensitivity to climate change from an ensemble of models

JF. Soussana (1) ; F. Ehrhardt (1) ; R. Conant (2) ; M. Harrison (3) ; M. Lieffering (4) ; G. Bellocchi (5) ; A. Moore (6) ; S. Rolinski (7) ; V. Snow (8) ; L. Wu (9) ; A. Ruane (10)
(1) Inra, Paris, France; (2) NREL, Colorado State University, , Fort Collins, CO, United States of America; (3) Tasmanian institute of Agriculture, Burnie, Australia; (4) AgResearch Grasslands, Palmerston North, New Zealand; (5) Inra, Grassland ecosystem research, Clermont-Ferrand, France; (6) CSIRO, Canberra, Australia; (7) Potsdam Institute for Climate Impact Research, Potsdam, Germany; (8) AgResearch, Christchurch, New Zealand; (9) Rothamsted Research, Rothamsted, United Kingdom; (10) NASA Goddard Institute for Space Studies, Climate Impacts Group, New York, NY, United States of America

Abstract content

The grassland biome covers about one-quarter of the earth’s land area and contributes to the livelihoods of ca. 800 million people. Increased aridity and persistent droughts are projected in the twenty-first century for most of Africa, southern Europe and the Middle East, most of the Americas, Australia and South East Asia. A number of these regions have a large fraction of their land use covered by grasslands and rangelands. Grasslands are the ecosystems that respond most rapidly to precipitation variability. However, global projections of climate change impacts on grasslands are still lacking in the scientific literature. Within AgMIP, based on the C3MP protocol initially developed for crops, we have explored the sensitivity of temperate grasslands to climate change drivers with an ensemble of models. Site calibrated models are used to provide projections under probabilistic climate change scenarios, which are defined by a combination of air temperature, precipitation and atmospheric CO2 changes resulting in 99 runs for each model times site combination. This design provides a test of grassland production, GHG (N2O and CH4) emissions and soil carbon sensitivity to climate change drivers. This integrated approach has been tested for 12 grassland simulation models applied to 19 sites over three continents. We show here that a single polynomial emulator can be fitted with high significance to the results of all models and sites, when these are expressed as relative changes from the optimal combination of climate drivers. This polynomial emulator shows that elevated atmospheric CO2 expands the thermal and hydric range which allows for the development of temperate grasslands. Moreover, we calculate the climatic response surface of GHG emissions per unit grassland production and we show that this surface varies with elevated CO2. From these results we provide first estimates of the impacts of climate change on temperate grasslands based on a range of climate scenarios.

15:40

Impacts and implications of global and regional climate change for agriculture

C. Müller (Potsdam Institute for Climate Impact Research, Potsdam, Germany), J. Elliott (University of Chicago and Argonne National Laboratory Computation Institute, Chicago, United States of America)

Abstract details
Impacts and implications of global and regional climate change for agriculture

C. Müller (1) ; J. Elliott (2)
(1) Potsdam Institute for Climate Impact Research, Potsdam, Germany; (2) University of Chicago and Argonne National Laboratory Computation Institute, Chicago, United States of America

Abstract content

Agriculture faces severe challenges from increasing demand for agricultural food and non-food products, the need to decrease its environmental burden and to build resilience against climate change impacts. While impacts and adaptation measures need to be assessed and understood at local scales, much of their cross-interaction and societal implications can only be understood at regional to global scale analyses. The Agricultural Model Intercomparison and Improvement Project (AgMIP) and the Intersectoral Impact Model Intercomparison (ISI-MIP) have conducted comprehensive global-scale assessments of climate change impacts on agricultural productivity and related sectors (e.g. water, markets) that allows for understanding the scope of the climate challenge for agriculture and food security, including an assessment of associated uncertainties. Climate change under the RCP8.5 emission scenario has the potential to reduce global crop production of the 4 major crops by 24 to 43%, which may be amended by positive effects of carbon dioxide fertilization to losses of 8 to 24%. Climate-driven reductions in availability of irrigation water could lead to a loss of 20-60Mha of irrigated cropland. Associated adaptation responses in land-use patterns, trade and consumption are able to compensate for climate-driven impacts but lead to higher food prices (20% on average). Challenges that need to be addressed at the global scale are the increasing disparity between high- and low productivity countries, which often reflects the disparity in development. Also, increasing variability under climate change is a robust finding across the board of scenarios and will require adequate measures to avoid devastating effects, especially for the poor.

16:00

FACCE MACSUR: Modelling Agriculture with Climate Change for Food Security

M. Köchy (Thünen Institute, Braunschweig, Germany), M. Banse (Thünen Institute, Braunschweig, Germany), R. Tiffin (University of Reading, Reading, United Kingdom), F. Ewert (University of Bonn, Bonn, Germany), N. Scollan (Aberystwyth University, Aberystwyth, United Kingdom), F. Brouwer (Wageningen UR, LEI, The Hague, Netherlands), R. Rötter (Luke, Natural Resources Institute Finland, Mikkeli, Finland), A. Bannink (Wageningen UR, Wageningen, Netherlands), F. Sinabell (WIFO - Austrian Institute of Economic Research, Vienna, Austria)

Abstract details
FACCE MACSUR: Modelling Agriculture with Climate Change for Food Security

M. Köchy (1) ; M. Banse (1) ; R. Tiffin (2) ; F. Ewert (3) ; N. Scollan (4) ; F. Brouwer (5) ; R. Rötter (6) ; A. Bannink (7) ; F. Sinabell (8)
(1) Thünen Institute, Market analysis, Braunschweig, Germany; (2) University of Reading, Centre for food security, school of agriculture, policy and development, Reading, United Kingdom; (3) University of Bonn, Institute of crop science and resource conservation, Bonn, Germany; (4) Aberystwyth University, Institute of biological, environmental and rural sciences, Aberystwyth, United Kingdom; (5) Wageningen UR, LEI, The Hague, Netherlands; (6) Luke, Natural Resources Institute Finland, Mikkeli, Finland; (7) Wageningen UR, Animal sciences group, Wageningen, Netherlands; (8) WIFO - Austrian Institute of Economic Research, Vienna, Austria

Abstract content

FACCE MACSUR (http://macsur.eu) is a network of currently 270 scientists from 18 European and associated countries for improving the European capacity of modelling the effects of climate change and socio-economic changes on agriculture. This concerns crop and grassland production, livestock production, farm management related to adaptation and mitigation measures, and development of price relations on national to global markets. The emphasis is on the linking of models and data across scientific disciplines. We will present an overview of achievements in the network. Collaborative efforts in the network include the advancement of modelling methodologies, agreement on common modelling scenarios for joint evaluation, comparison of model performance, development of new research projects, organization of training courses and workshops, and interactions with decision-makers, farmers, and other stakeholders. MACSUR collaborates internationally with AgMIP and MACSUR members are engaged in many other international projects and networks.

In the field of crop modelling, MACSUR has set-up and performed a comprehensive, unique model comparison study on simulating crop rotations using long term trial data from various locations in Europe and looking at various output variables; also an inventory has been made on the available crop models and modelled cropping systems for Europe by the MACSUR CropM partners. MACSUR developed extensive databases on important ongoing and future modelling studies in Europe and it also embarked on developing a centralized system for data storage, distribution and visualization of model results. The knowledge hub systematically analysed scaling methods with focus of scaling up weather and soil information for regional and (supra-) national climate change (CC) impact assessments and related uncertainties for a range of crop models. A large ensemble of 26 crop models has been used for a systematic climate sensitivity analysis based on impact response surface. New CC scenario data was developed for selected locations and regional case studies in Europe and use was made of agroclimatic indicator approaches to indicate shifts in (multiple) risks to wheat production in the EU. Five PhD courses have been organized, dealing with various issues of generating data and applying modelling techniques for assessing CC impacts and adaptations to CC.

In the field of modelling of permanent grasslands, livestock and farms, the main focus across these diverse disciplines was to bring together specialists on a common subject. MACSUR established a performance comparison across several prominent models. Modelling of livestock productivity focused on the impacts of changing climatic conditions on dairy cow health, mortality and milk quantity and quality, and provided contributions to regional case study research. Datasets were identified relating to animal health and disease, and gaps in knowledge were explored at a broad level.

In socio-economic modelling, MACSUR focused on the soft-linking of crop production models to economic models at national and global levels and on comparisons of projections of crop price changes considering global trends in populations, politics, and climate.

Regional case studies constitute opportunities for linking models with less spatial heterogeneity and a longer tradition of model linkage across scientific disciplines. They also allow studying practical effects of the impacts of climate change and discussing them with stakeholders. Our case studies in Finland, Austria, and Italy suggest that a simple climate envelope approach (moving production zones of crops northward) neglects important interactions with soils (water holding capacity) and effects on landscape function/ecosystem services and rural livelihoods.

In the next two years, MACSUR will improve modelling the impacts of weather extremes and consider variations in farm management, cross- and multi-scale issues, uncertainty and error propagation. Exploration of techniques to improve the characterisation (e.g. quality) of feed sources in farm-scale models will also be addressed. Understanding the reasons for the difference between optimal and realised grassland and crop yields and finding solutions for linking these economic models at national to global scales remains a challenge for the next years. Furthermore, we will include more regional case studies and intensify our interactions with stakeholders.

16:15

How accurately do crop models simulate the impact of CO2 atmospheric concentration on maize yield and water use ?

K. Delusca (INRA, Lusignan, France), J.-L. Durand (INRA, Lusignan, France), K. Boote (University of Florida, Gainesville, United States of America), J. Lizaso (ETSIA UPM, Madrid, Spain), R. Manderscheid (Johann Heindrich von Thunen Institute, Braunschweig, Germany), H. J. Weigel (Johann Heindrich von Thunen Institute, Braunschweig, Germany), A. Ruane (NASA Goddard Institute for Space Studies, New York city, United States of America), C. Rosenzweig (NASA Goddard Institute for Space Studies, New York city, United States of America), J. Jones (University of Florida, Gainesville, United States of America)

Abstract details
How accurately do crop models simulate the impact of CO2 atmospheric concentration on maize yield and water use ?

JL. Durand (1) ; K. Delusca (2) ; K. Boote (3) ; J. Lizaso (4) ; R. Manderscheid (5) ; C. Rosenzweig (6) ; J. Jones (3) ; HJ. Weigel (5) ; A. Ruane (6)
(1) INRA, Environnement et Agronomie, Lusignan, France; (2) INRA, Lusignan, France; (3) University of Florida, Gainesville, United States of America; (4) ETSIA UPM, Madrid, Spain; (5) Johann Heindrich von Thunen Institute, Braunschweig, Germany; (6) NASA Goddard Institute for Space Studies, New York city, United States of America

Abstract content

Authors (continued): S. Anapalli (6); L. Ahuja (7); B. Basso (7); C. Baron (8); P. Bertuzzi (9); D. Ripoche (9); C. Biernath (10); E. Priesak (10); D. Derynge (11); F. Ewert (12); T. Gaiser (12); S. Gayler (12); F. Heilein (12); KC. Kersebaum (13); SH. Kim (14); C. Müller (15); C. Nendel (16); J. Ramirez (17); F. Tao (18); D. Timlin (19); K. Waha (20); T. Twine (21); E. Wang (22); H. Webber (23); Z. Zhao (4); R. Rötter (25); A. Srivastava (23); S. Seidel (26).

6 USDA-ARS, Colorado State University, Colorado, USA, 7 Department of Geological Sciences, Michigan State University, Michigan, USA, 8 CIRAD, UMR TETIS, Montpellier, France, 9 INRA, Avignon, France, 10 Institute für Bodenökologie, Helmholtz Zentrum München, Neuherberg, Germany, 11 Tyndall Centre for Climate Change research and School of Environmental Sciences, University of East Anglia, Norwich, UK, 12 Water & Earth System Science (WESS) Competence Cluster, c/o University of Tübingen, Tübingen, Germany, 13 Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research, Muencheberg, Germany, 14 School of Environmental and Forest Sciences, University of Washington, Seattle, USA, 15 Potsdam Institute for Climate Impact Research, Potsdam, Germany, 16 Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research, Muencheberg, Germany, 17 School of Earth and Environment, University of Leeds, Leeds, UK, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Cali, Colombia International Center for Tropical Agriculture (CIAT), Cali, Colombia, 18 Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China, 19 Crop Systems and Global Change Laboratory, USDA/ARS, Beltsville, USA, 20 Indian Agricultural Research Institute, Centre for Environment Science and Climate Resilient Agriculture, New Delhi, India, 21 Department of Soil, Water, & Climate, University of Minnesota, Minnesota, USA, 22 CSIRO, Land and Water, Black Mountain, Australia, 23 Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany, 24 China Agricultural University, Beijing, China, 25 Natural Resources Institute, Luke, Finland. 26 Technishe Universität Dresden, Germany.

Methods and Results

Given the incertitudes on the climate change impacts on C4 crops, projections of regional maize production remain speculative. Assessment of the impacts of atmospheric CO2 concentration ([CO2]) on crop  yield and resources uses using mechanistic models becomes increasingly important. Free Air [CO2] Enrichment (FACE) studies offer data to test and improve model quality. The objective of this work by the AgMIP Maize group was (i) to test multiple maize models for [CO2] responses against data gathered from a FACE study under two water regimes carried out in Germany during 2007 and 2008, and (ii) to pave the way to potential model modifications so as to improve their simulations of crop responses to [CO2]. The Experiment combined two [CO2] levels with two watering regimes. Yield, leaf area, soil water content and [CO2] levels were recorded both years, 2008 only exhibiting significant water deficit. After a preliminary calibration based on non limiting water conditions and under ambient [CO2] treatments of both years, a blind simulation was undertaken for the other treatments: High [CO2] (550 ppm) 2007 and 2008, both watering regimes, and DRY AMBIENT 2007 and 2008. Secondly, with full growth and yield data along with soil moisture data of all treatments, improvements of simulation results were attempted. Changes made to the models have been documented and submitted for further analysis. The results revealed: minimal [CO2] impacts with low variations among « uncalibrated » models except for the dry season of 2008 where the observed drought impact was simulated by the majority of models; most models caught but underestimated the CO2 impact on crop water status, leaf area, grain number and yield; the CO2 effect on transpiration was generally properly simulated, transpiration per leaf area decreasing but green leaf area duration increasing at 550 ppm [CO2]. As more data from FACE experiments become available, it will be highly desirable to replicate this exercise in order to come up with more robust conclusions on these responses and to improve model response to CO2.