Transcript PrésentationA0_iCropM2016x

CROP YIELDS, SOIL NITROGEN AND SOIL ORGANIC CARBON
CONTENT CHANGES UNDER CLIMATE CHANGE
Dumont B.1,2,* - Basso B.2 - Shcherbak I.2 - Asseng S.3- Bassu S.4 - Biernath C.5 - Boote K.6 – Cammarano D.7 –
de Sanctis G.8 - Durand J.-L.9 - Ewert F.10- Gayler S.11 - Grace P.12 – Grant R.13 – Kent J.14 - Martre P.15 –
Nendel C.16 - Paustian K.14 - Priesack E.5 - Ripoche D.17 – Ruane A.18 –
Thorburn P. 19 - Hatfield J.20 - Jones J.21 - Rosenzweig C.18
Introduction
It has been demonstrated that model ensemble is an efficient way to reduce the uncertainty associated with climate change impact on crop growth. (Asseng et al., 2015).
Using this approach, wheat and maize grain yields response to temperature increase were simulated by Asseng et al., 2015 and Bassu et al., 2014 using annually reinitialized soil conditions (soil water and nitrogen). However, Basso et al., 2015, showed that, when running models in a continuous mode, yield results differed from the
annual reinitialized runs. In this study, we present the results of continuous model runs of the AgMIP wheat- and maize-pilot under temperature and CO2 changes and
different management practices.
Material & Methods
Crop model ensemble
Table 2: Factorial analysis
Simulation protocol
Five maize models and seven wheat models
involved respectively in the maize- and wheat-pilot
initiatives
of
the
Agricultural
Model
Intercomparison and Improvement Project
(AgMIP) were run in the Soils and Crop Rotations
initiative (Table 1).
Results were averaged using the median of model
simulations (Martre et al., 2015).
Table 1: Models run in the AgMIP Soils & Crop Rotation pilot
Model (Version)
Maize
APSIM (V7.3)
APSIM-NWheat (V1.55)
DayCent
Ecosys
MONICA (V1.0)
SALUS (V1.0)
STICS (V8.1)
Expert-N (V3.0.10) – SPASS (2.0)
x
x
x
x
x
Wheat
x
x
x
x
x
x
x
The same factorials temperature, CO2, rain and
nitrogen fertilization levels as the one defined in the
original AgMIP wheat- and maize-pilots were also
simulated (Asseng et al., 2015; Bassu et al., 2014).
The same sites (4 locations for each crops) and the
calibrated versions of the models were used in this
initiative (Table 2).
Additionally, models were also run under
conventional tillage and
no-till management
practices.
Finally, modelers were also asked to run their model
under annually pre-season reinitialized soil
conditions and under continuous running mode.
Under continuous running mode, wheat-fallow and
maize-fallow rotations were simulated (Table 2).
Initial soil organic carbon (SOC) content were
provided for each specific site (Table 3).
Factors
Factor levels
Maize Wheat
Site
Temperature [°C]
CO2 [ppm]
Rain [%]
N fertilization [%]
Tillage
Running mode
4 sites
Baseline, -3, +3, +6, +9
360, 450, 540, 630, 720
100, 70
100, 50, 150
Conv. tillage, No-till
Reinit., Cont.
x
x
x
x
x
x
x
x
x
x
x
x
As each site had a different initial SOC content,
simulated SOC data were expressed each year (y) as
a percentage of the initial value according to :
𝑆𝑂𝐶𝑦 − 𝑆𝑂𝐶1980
∆𝑆𝑂𝐶 % =
𝑆𝑂𝐶1980
Table 3: Initial SOC content
Maize
BR
FR
TZ
US
SOC [%]
1.1
0.9
1.4
2.4
Wheat
AR
AU
IN
NL
SOC [%]
2.7
0.6
0.4
2.4
Results
Figure 1: Model ensemble of the relative SOC evolution for two wheat
sites (AR, NL) and two maize sites (BR, US)
 All individual models globally agreed
in the direction of the changes for
yield, soil N-NO3- and SOC content
under the different temperature
treatments;
 Soil N-NO3- was found to increase,
while yields and SOC were found to
decrease with temperature increases
(Fig. 1 and 2);
 Model ensemble highlighted that
SOC decreases were higher were
initial SOC content was higher (Fig. 1);
 Yields were overall higher when
CO2
increased,
whatever
the
temperature treatment (Fig. 2);
 Soil N-NO3- was lower under higher
CO2 concentration (Fig. 2), but
increased with temperature under
higher CO2 treatments.
Figure 2: Impact of CO2 concentration on yield (A-B) and soil N-NO3- (C-D)
content under different temperature (-3 to +9°C) treatments.
Conclusions
Continuous running mode of crop models allow to better understand the interactions within the soil-plant-atmosphere continuum, in conjunction with variable soil and crop
management practices. Ensemble of continuous modelling will provide critical insights when used to identify adaptation and mitigation strategies to climate change.
Acknowledgements
This work was supported by the AgMIP (http://www.agmip.org/) project.
References
Asseng, S., et al., 2015. Nature Climate Change 5: 143-147.
Bassu, S., et al., 2014. Global Change Biology 20: 2301-2320.
Basso, B. et al., 2015. Plos One 10(6): e0127333.
Martre, P. et al., 2015 Global Change Biology 21: 911-925
Authors affiliation
1 Department Terra & AgroBioChem, Gembloux Agro-Bio Tech, ULg-GxABT, Gembloux, Belgium; 2 Department of Geological Sciences,
Michigan State University, MI, USA; 3 Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA; 4
INRA-AgroParisTech, Thiverval-Grignon, France; 5 Institute of Biochemical Plant Pathology, Helmholtz Zentrum München—German
Research Center for Environmental Health, Neuherberg, Germany; 6 Department of Agronomy, University of Florida, Gainesville, FL,
USA; 7 The James Hutton Institute, Invergowrie, Scotland, UK; 8 European Commission - Joint Research Center, Ispra, Italy; 9 INRAURP3F, Lusignan, France; 10 Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, Germany; 11
Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; 12 Institute for Future Environments,
Queensland University of Technology, Brisbane, Queensland, Australia; 13 Earth Sciences, University of Alberta, Edmonton, AB,
Canada; 14 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA; 15 INRA, UMR759 LEPSE,
Montpellier, France; 16 Institute of Landscape Systems Analysis, ZALF, Leibniz-Centre for Agricultural Landscape Research,
Muencheberg, Germany; 17 INRA-AGROCLIM, Avignon, France; 18 Climate Impacts Group, NASA Goddard Institute for Space
Studies, New York, NY, USA; 19 CSIRO Ecosystem Sciences, Dutton Park, Queensland, Australia; 20 USDA-ARS National Soil Tilth
Laboratory for Agriculture and the Environment, Ames, IA, USA; 21 Department of Agricultural & Biological Engineering, University of
Florida, Gainesville, FL, USA; * Passage des Déportés, 2, 5030 Gembloux, Belgium. @: [email protected]