T. A. M. Pugh1,* – C. Müller2 – J. Elliott3 – A. Arneth1
1 Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany. thomas.pugh@kit.edu
2 Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
3 University of Chicago and Argonne National Laboratory Computation Institute, Chicago, IL, 60637 Introduction
The projected increase in global population suggests that, among a range of measures, a large increase in food production will likely be necessary to achieve food security (Alexandratos and Bruinsma 2012). A great deal of effort has been focused on the so-called “yield gap”, the difference between actual and maximum-attainable yields (Mueller et al., 2012). The closing of this yield gap would bring about massive increases in production. Intensification actions such as irrigation, fertilisation, and better farming practices can bring the actual yield closer to the potential yield, although such actions may not be practical everywhere. Yet climate change greatly complicates this picture;
crops are sensitive to their growing environment, and it is therefore inevitable that climate change will impact upon potential crop yields, changing the target for which intensification measures are aiming, and meaning that significant intensification may be required just to hold actual yields constant. Global crop models give some insight into such changes, but huge uncertainties in their process representations currently means that even the direction of future change remains uncertain (Rosenzweig et al., 2014).
Materials and Methods
We demonstrate a complementary data-driven approach, based on observations of current maximum-attainable yield and climate analogues (Williams et al., 2007; Koven 2013), to assess the vulnerability of yields of the three major cereal crops, wheat, maize and rice, to climate change. Present-day analogues of future climate are defined based on outputs from five CMIP5 GCMs (Hempel et al., 2013), and combined with information on current maximum-attainable yield (Mueller et al., 2012) to derive global, spatially-explicit, changes in crop yield potential over the 21st century. The results are compared and contrasted against output from an ensemble of global gridded crop models (Rosenzweig et al., 2014).
Results and Discussion
We find that huge swathes of current cropland show strong reductions in their potential yields of major cereal crops by the mid 21st century (Table 1), indicating a large vulnerability of crop production in these areas to climate change, and greatly reducing the capacity for intensification of yields. These reductions are predominately
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in tropical or arid areas, and include current high-productivity areas like the North American corn-belt. Conversely, however, we also find large areas where potential yields increase substantially under climate change. These areas are most prominent in the northern temperate zone, and include areas not currently under cropland.
Analogues for mid.-latitude croplands are typically drawn from regions 1000km or more closer to the equator, and even from tropical regions, suggesting that present-day investment in more southerly, or even tropical, climates may pay dividends in temperate regions in the future. Our approach is independent of the crop modelling methodologies previously used for future yield projections, however we find our results to be consistent with those from an ensemble of process-based global crop models, providing an important additional constraint on projections of future yield under climate change.
Table 1. Perentage of current global harvested area in areas showing reductions in attainable yield for at least 4 out of 5 GCM climates.
Crop 2041-2060 2081-2099
Maize 47 64
Wheat 27 33
Rice 25 26
Conclusions
We provide clear, independent evidence, that climate change is likely to decrease production from staple food crops across the tropics and much mid-latitude cropland already by the middle of the 21st century. Attempts to intensify crop production in these regions are likely to provide much more limited benefits when climate change is considered. Our results suggest that large shifts in land-use patterns, taking advantage of increased yield potential in regions which are currently lightly-cropped, will likely be necessary to sustain production growth rates and keep pace with demand.
Acknowledgements
The authors thank members of the AgGRID global gridded crop model intercomparison project are thanked for making available their crop modelling data, and in particular Stefan Olin, Delphine Deryng and Christian Folberth for useful comments on an early version of the analysis. This work was funded by the European Commission's 7th Framework Programme, under Grant Agreement number 603542 (LUC4C).
References
Alexandratos, N., Bruinsma, J. (2012) World Agriculture towards 2030/2050: The 2012 Revision. ESA Working Paper No. 12-13.
Hempel, S. et al., (2013) Earth System Dynamics, 4: 219–236.
Koven, C.D. (2013) Nature Geoscience, 6: 452–456.
Mueller, N.D. et al., (2012) Nature, 490: 254–257.
Rosenzweig, C. et al., (2014) Proceedings of the National Academy of Sciences of the United States of America, 111: 3268-3273.
Williams, J.W., Jackson, S.T., Kutzbach, J.E. (2007) Proceedings of the National Academy of Sciences of the United States of America, 104: 5738–5742.
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Modelling adaptive traits to screen for salinity tolerance in rice
A. M. Radanielson1 – D. S. Gaydon2,3 – O. Angeles1 – T. Li1 – A. M. Ismail1
1 International Rice Research Institute, Philippines, a.radanielson@irri.org, o.angeles@irri.org, t.li@irri.org, a.ismail@irri.org
2 CSIRO Agriculture Flagship, Brisbane, Australia, 3 School of Agriculture and Food Sciences, University of Queensland, St Lucia, Brisbane, Australia, don.gaydon@csiro.au
Introduction
Climate change is expected to increase the severity and occurrence of salinity in irrigated rice systems, leading to reduction of cultivated areas and productivity. The use of salt-tolerant varieties is among potential adaptive strategies to maintain productivity in this condition (Ismail et al., 2007; Deryng et al., 2011). The conventional selection strategy to develop salt tolerant varieties is limited by time and site specific management, particularly with the temporal and spatial variability of salinity dynamics in the field. Simulation modelling has been useful to explore possible combinations of crop traits in a time-effective manner, and to assess variety performance in real environments (Cooper et al., 2005; Chenu et al., 2011). By integrating advances in crop physiology, crop modelling can be used to assist breeding programs by accelerating selection and delivery. In this work we define traits characterizing salinity tolerance in rice and provide orientation for breeding a new generation of salt tolerant cultivars.
Materials and Methods
The rice model ORYZA v3 was modified to account for salinity on rice growth and yield (Radanielson et al., in press). Salt stress factors applied to water extraction, plant transpiration and assimilation rate were determined by parameters related to plant tolerance (Tol_coeff) and resilience (Res_coeff) to salinity and by soil electrical conductivity. The model was calibrated and validated with field experimental data using 3 contrasting rice varieties: BRRIDhan 47 (salt-tolerant), IR64 (moderately-tolerant) and IR29 (sensitive). Long-term scenario simulations were performed for Satkhira, Bangladesh using historical weather data (1980-2014) and virtual varieties characterized by different combinations of values, for Tol_coeff and Res_coeff, within the range of variation observed for the 3 varieties. Variance analyses of the model outputs were performed and general linear regression was used to estimate the contribution of Tol_coeff and Res_coeff to variation in yield.
Results and Discussion
ORYZA v3 model simulated rice yield variability under saline conditions with acceptable accuracy. This suggested that Tol_coeff and Res_coeff were able to represent the difference in tolerance among the studied genotypes and they were genotype specific.
Yield responses to variations in Tol_coeff presented an increasing linear phase
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followed by a plateau phase (Figure 1). The breaking point between these two phases corresponded to an optimum value of tolerance suitable to the prevailing salinity conditions. From this framework, an increase of 1 % in the salinity tolerance of IR64 would result in 0.3-0.4 % yield gain (R2=0.85-0.88, p<0.001). A similar trend was observed with the resilience parameter (Res_coeff). The gain was about 0.07 % with an improvement of Res_coeff to a decrease of 1 % (R2= 0.85, p<0.001, Figure 1). BRRI Dhan47 presented a tolerance level suitable for saline conditions below 12 dS m-1, as reported for its release (Islam et al., 2008). Improvement in BRRI-Dhan47 tolerance and resilience is likely an opportunity to develop newly cultivar adapted to conditions with higher salinity.
Figure 1. Change in yield relatively to the genotype of reference IR64 with variation of model parameter related to salinity trait (Tol_coeff & Res_coeff).
Conclusions
A trait-based modelling approach was used to represent the effect of salinity on rice crop performance. The salinity tolerance traits represented by the model parameters had genotypic variability and contributed significantly to the yield variability. A novel linear framework developed to quantify the effect of their variability on yield suggested new opportunities and directions to increase rice productivity in saline environments. Further studies in genetic variability of these model parameters would be of interest for breeding purposes and application.
Acknowledgements
The authors thank their collaborators from Bangladesh Agricultural Research Institute (BARI) helping in the data collection and the financial support from Global Rice Science Partnership (GRiSP), Stress Tolerant Rice Varieties for Africa and South Asia (STRASA), and the Australian Centre for International Agricultural Research (ACIAR).
References
Chenu, K., Cooper, M., Hammer G, et al., (2011). Journal of Experimental Botany 62, 1743-1755.
Cooper, M., Podlich, D. W., and Smith, O. S. (2005). Australian Journal of Agricultural Research 56, 895-918.
Deryng, D., W. J. Sacks, C.C. Barford et al., (2011). Global Biogeochemistry Cycles doi:10.1029/2009GB003765
Islam, M.R., M.A. Salam, M.A.R. Bhuiyan et al., (2008). Journal of Biological Research 5, 1: 1-6 Ismail, A.M., Heuer S., Thomson M.J. et al., (2007). Plant Molecular Biology. 65:547–70
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