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This is a repository copy of Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/101358/ Version: Accepted Version Article: Ramirez-Villegas, J and Challinor, AJ orcid.org/0000-0002-8551-6617 (2016) Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations. Climatic Change, 138 (1). pp. 223-238. ISSN 0165-0009 https://doi.org/10.1007/s10584-016-1717-y (c) Springer Science+Business Media Dordrecht 2016. This is an author produced version of a paper published in Climatic Change. Uploaded in accordance with the publisher's self-archiving policy.The final publication is available at Springer via http://dx.doi.org/10.1007/s10584-016-1717-y [email protected] https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request.

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Page 1: Towards a genotypic adaptation strategy for Indian ...eprints.whiterose.ac.uk/101358/11/Ramirez-Villegas... · 41" SI Text 1: Parameterisation of CO 2 response 42" Groundnut is a

This is a repository copy of Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations.

White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/101358/

Version: Accepted Version

Article:

Ramirez-Villegas, J and Challinor, AJ orcid.org/0000-0002-8551-6617 (2016) Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations. Climatic Change, 138 (1). pp. 223-238. ISSN 0165-0009

https://doi.org/10.1007/s10584-016-1717-y

(c) Springer Science+Business Media Dordrecht 2016. This is an author produced version of a paper published in Climatic Change. Uploaded in accordance with the publisher's self-archiving policy.The final publication is available at Springer via http://dx.doi.org/10.1007/s10584-016-1717-y

[email protected]://eprints.whiterose.ac.uk/

Reuse

Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website.

Takedown

If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request.

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Supplementary Information 1"

2"

Towards a genotypic adaptation strategy for Indian groundnut cultivation using an 3"

ensemble of crop simulations 4"

5"

Julian Ramirez-Villegas and Andrew J. Challinor 6"

7"

Contents: 8"

•! SI Text 1: Parameterisation of CO2 response 9"

•! Table S1 Parameterisations of CO2 response used and changes to relevant GLAM model 10"

parameters 11"

•! SI Text 2: Crop model calibration and evaluation 12"

•! Figure S1 Taylor diagram showing the performance of the 50 parameter ensemble members 13"

•! Figure S2 Spatial distribution of two crop model skill metrics for two selected parameter 14"

ensemble members 15"

•! Figure S3 Percent of parameter ensemble members for which the normalised-by-observed 16"

predicted mean yields falls in each of three categories 17"

•! Figure S4 Probability density function of the Spearman rank correlation between the two yield 18"

gap parameter (CYG) values 19"

•! SI Text 3: Changes in groundnut productivity under no-adaptation scenarios 20"

•! Figure S5 Baseline and future projected changes in mean crop yield for no-adaptation scenarios 21"

•! Figure S6 Baseline and future projected changes in crop yield variability (CV) for no-22"

adaptation scenarios 23"

•! Figure S7 Projected mean yield changes by 2030s as a result of crop improvement related to 24"

drought scape and water use efficiency 25"

•! Figure S8 Projected yield variability (CV) changes by 2030s as a result of crop improvement 26"

related to drought scape and water use efficiency 27"

•! Figure S9 Projected mean yield changes by 2030s as a result of increased crop duration 28"

•! Figure S10 Projected yield variability (CV) changes by 2030s as a result of increased crop 29"

duration 30"

•! Figure S11 Projected crop yield mean changes by 2030s as a result of combined-trait 31"

improvement scenarios 32"

•! Figure S12 Projected yield variability (CV) changes by 2030s as a result of combined-trait 33"

improvement scenarios 34"

•! Figure S13 Robustness (R, fraction) of model projections of adaptation 35"

•! Figure S14 Relative contribution of different sources to total yield uncertainty in genotypic 36"

adaptation simulations 37"

•! Table S2 CMIP5 GCMs used in the study and main characteristics 38"

•! Table S3 Summary of studies of genotypic adaptation and ideotype design 39"

•! Supplementary References 40"

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SI Text 1: Parameterisation of CO2 response 41"

Groundnut is a grain legume featuring a C3 photosynthesis pathway (Seeni and Gnanam 42"

1982). Physiologically, therefore, the effects of increase in atmospheric CO2 concentrations 43"

have a direct impact on the production of assimilate (Schmidt et al. 2006; Leakey et al. 44"

2009). Under climate change scenarios of increased CO2 concentrations, C3 crops are 45"

expected to increase their rate of photosynthesis (Chen and Sung 1990; Long et al. 2006; 46"

Leakey et al. 2009). The additional production of assimilate is expected to increase water 47"

use efficiency, leaf area index, biomass, specific leaf area, radiation use efficiency (RUE) 48"

and the harvest index (Tubiello and Ewert 2002). As a result, crop yields in C3 crops are 49"

expected to increase with increased CO2 concentrations (Vara Prasad et al. 2003; Challinor 50"

and Wheeler 2008a). The parameterisation of CO2 response in GLAM is thus important for 51"

assessing crop growth CO2 stimulation and its combined effect with high temperature or 52"

drought stress on reproductive plant processes (i.e. flowering and grain filling) (Clifford et 53"

al. 2000; Vara Prasad et al. 2003). 54"

55"

The CO2 response of the crop was parameterised after Challinor and Wheeler (2008a) 56"

(CW2008 hereafter). The methodology developed by CW2008 mainly consisted of 57"

perturbing certain crop model parameters to enhance biomass production while increasing 58"

water use efficiency. They also introduced a factor (Tfac) that controls the response of the 59"

normalised transpiration efficiency to varying humidity levels (also see CW2008). 60"

Specifically, they introduced changes to the baseline values of the maximum rate of 61"

transpiration (TTmax), transpiration efficiency (ET) and specific leaf area (SLAmax) in order to 62"

account for the increased production of assimilate at higher-than-normal CO2 63"

concentrations using an 18-member model ensemble. In the study of CW2008, first, the 64"

baseline value of TTmax (physiologically limited transpiration rate) was reduced by 17 % 65"

owing to the expected reduction in transpiration (Stanciel et al. 2000). To reflect increased 66"

biomass production they increased the value of ET (increases of either 24 % or 40 % were 67"

used). They also used two values of Tfac (0 and 0.4) to quantify uncertainty in the 68"

differential response to high and low VPD (vapour pressure deficit) conditions. Finally, 69"

they reduced the baseline value of SLAmax by 10 %. Similar approaches to CO2 stimulation 70"

are used in other crop models, where either the radiation use efficiency (Jones et al. 2003) 71"

or the transpiration efficiency (Keating et al. 2003) are increased to reflect increases in net 72"

photosynthesis. 73"

In this study, the same four GLAM parameters were changed, but the factors differed 74"

(Table S1). This was because the factors employed by CW2008 were defined for doubled 75"

CO2 conditions (350 ppm x 2 = 700 ppm). Scaling was thus needed for 2030s climate as 76"

used here. For the concentrations projected by 2030s in RCP4.5 (450 ppm) these four 77"

consisted in moderate (+8.8 %) and large (+14.7 %) increases in transpiration efficiency in 78"

combination with a decrease of 6.2 % in physiologically limited transpiration (TTmax), a 79"

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decrease of 3.7 % in specific leaf area (SLAmax), and a moderate and low sensitivity of the 80"

crop to CO2 enhancement under low VPD conditions (Tfac). Genotypic adaptation 81"

perturbations were in all cases applied over the CO2-perturbed values. For additional details 82"

on the parameterisation of CO2 response in GLAM the reader is referred to Challinor and 83"

Wheeler (2008a). 84"

Table S1 Parameterisations of CO2 response used and changes to relevant GLAM model 85"

parameters 86"

ID Description Tfac ET TTmax SLAmax

C1 No stimulation at low VPD

Moderate increase in ET

0.0 +8.8 % –6.23 % –3.67 %

C2 No stimulation at low VPD

Large increase in ET

0.0 +14.7 % –6.23 % –3.67 %

C3 Moderate stimulation at low VPD

Moderate increase in ET

0.4 +8.8 % –6.23 % –3.67 %

C4 Moderate stimulation at low VPD

Large increase in ET

0.4 +14.7 % –6.23 % –3.67 %

87"

88"

89"

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SI Text 2: Crop model calibration and evaluation 90"

This section describes methods for model calibration and evaluation, and summarises 91"

results of model evaluation. 92"

i. Model calibration: In order to calibrate GLAM, the definition of 23 site-independent 93"

(i.e. global) model parameters and 1 ‘local’ parameter is required. The values of the 23 94"

global parameters are constant across large and relatively uniform areas, such as those 95"

where duration requirements are known not to vary significantly. The only local parameter 96"

that needs to be calibrated is the yield gap parameter (CYG), which is obtained separately for 97"

each grid cell. In this study, the domains across which ‘global’ parameters were defined 98"

were those of Fig. 1A. Model calibration was carried out separately for each of these zones 99"

by minimising the Root Mean Square Error (RMSE, Eq. S1) between observed and 100"

simulated yield. 101"

( )2

1

n

i i

i

O P

RMSEn

=

=∑

[Equation S1] 102"

where O and P refer to observed and predicted quantities of a series of n elements (here, n 103"

= 28 years). RMSE was used as it provides a complete measure of the model errors (Taylor 104"

2001). Calibration of model parameters was then conducted as follows: 105"

(1)!First, in order to minimise the interactions between CYG and all other parameters, the 106"

single grid cell with the highest yield per growing zone was selected for global 107"

parameter calibration. We assume that this single grid cell is close to the average 108"

potential on-farm yields for a large and relatively homogeneous region (i.e. the growing 109"

zone of Fig. 1A, main text), and thus is assigned a value of CYG = 1.0 throughout the 110"

rest of the calibration process. This assumption was made given the high average yield 111"

levels of some grid cells per growing zone. In addition, the use of a single grid cell also 112"

provided the opportunity to evaluate the skill of the model and its global parameters in 113"

grid cells not used for global calibration. 114"

(2)!We then developed a parameter ensemble for each growing zone by performing a total 115"

of 50 parallel calibration chains. We attempted 50 chains due to the computational 116"

needs for the genotypic adaptation simulations (see main text). Each calibration chain 117"

had a different order for parameter calibration, and started at a different point of the 118"

parameter space (i.e. a different value for each of the parameters). Both the starting 119"

values for each of the 23 parameters as well as the order of parameters during 120"

calibration were chosen at random for each chain. In each chain, parameters were 121"

calibrated by iteratively testing values within known parameter ranges (Challinor et al. 122"

2004; Ramirez-Villegas et al. 2015). As we ran 15 iterations for each chain, this 123"

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resulted in a total of 17,250 calibration runs being performed per growing zone (23 124"

parameters x 15 iterations x 50 chains). Similar to other parameter estimation methods 125"

[e.g. Beven and Freer (2001)], this method also allows a random sampling of the 126"

parameter space, and accounts for co-variation in parameter values. 127"

(3)!Next, from all calibration runs we selected all unique parameter sets that were skilful 128"

enough and hence could be considered as ‘behavioural’ (Beven and Freer 2001). More 129"

specifically, from the 50 initial parameter sets that resulted from the 50 calibration 130"

chains, the single one with lowest RMSE (‘reference’) was selected and compared with 131"

the remaining 49 through a Kolmogorov-Smirnov non-parametric test. This yielded 19 132"

parameter sets whose distribution was found statistically similar to the reference. 133"

(4)!Finally, for each chain, CYG was calibrated on a grid cell basis by iteratively testing 134"

values between 0.0 and 1.0 (at steps of 0.01) that minimised the RMSE. 135"

ii. Model evaluation: To provide a general idea of GLAM’s skill across the full set of 136"

potential parameter combinations the skill of the 50 parameter ensemble members was 137"

assessed in the following ways: 138"

(1)!A Taylor diagram (Taylor 2001) was constructed to summarise the skill of all 139"

parameter ensemble members. A Taylor diagram summarises how well a model 140"

simulation matches observations in terms of correlation, RMSE and ratio of 141"

variances. A diagram for each characteristic (i.e. crop yield mean and standard 142"

deviation) was finally produced. 143"

(2)!The correlation coefficient and the RMSE were calculated for each grid cell and 144"

parameter ensemble member to produce maps showing the variation of these two 145"

metrics across the geographic and parameter space. 146"

(3)!The 28-year calibration period was split into two halves so as to cross-calibrate CYG. 147"

The performance of the 50 parameter sets is shown in the form of two Taylor diagrams 148"

(Fig. S1) for both the spatial consistency of the mean yields and of the interannual 149"

variability of yields (i.e. the standard deviation). Each dot in the figure represents a single 150"

parameter ensemble member where all the three metrics have been calculated pair-wise 151"

using the time-mean (Fig. S1A) and the time-standard deviation (Fig. S1B) of all grid cells. 152"

Blue coloured dots show the 19 parameter ensemble members that were considered to 153"

represent crop yields reliably. GLAM represented mean yields with a higher degree of 154"

accuracy as compared to interannual variations. 155"

156"

The spatial correlation coefficient of mean yields was in all parameter ensemble members 157"

above 0.8 (maximum r=0.98, p≤0.001). The representation of standard deviations was 158"

much more limited in the model, with all parameter sets showing a spatial correlation 159"

coefficient below 0.5 (maximum r=0.45, p≤0.001), and the RMSE of the normalised 160"

standard deviations (grey arcs concentric to the unity in the x-axis) being relatively large. 161"

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The statistical characteristics of the crop yields were, however, well captured by the crop 162"

model, particularly in the selected parameter ensemble members (blue dots that are close to 163"

the black continuous standard deviation arc in Fig. S1). 164"

165"

A

B

Figure S1 Taylor diagram showing the performance of the 50 parameter ensemble members in 166"

relation to the spatial variation in mean (A) and standard deviation (B) of yields. Spatial standard 167"

deviations are normalised to observed (hence the “perfect” standard deviation is the continuous 168"

black arc at 1.0 –concentric to the origin). Grey arcs concentric to 1.0 in the x-axis represent the 169"

RMSE. Blue and red colours indicate selected and discarded parameter ensemble members, 170"

respectively. Large filled dots indicate parameter sets shown in detail in Figure S2. 171"

172"

The low performance parameter ensemble member marked in Fig. S1 showed one of the 173"

lowest correlations (r=0.25 and r=0.83 for standard deviation and mean yields, 174"

respectively), a significantly higher spatial standard deviation of the yield variability (about 175"

1.3 times higher), and the largest centred RMSE for both the mean and variability of yields 176"

(1.5 and 0.5, respectively) (Fig. S2). By contrast, the high performance parameter ensemble 177"

member showed a near-perfect representation of the standard deviations, a near-perfect 178"

correlation for mean yields (r=0.97, p≤0.0001) and a relatively strong correlation for yield 179"

variability (r=0.38, p≤0.0001). Most of the statistically significant correlations were found 180"

across western, northern and central-north India, where the strongest climate signals on 181"

crop yields are reported (Challinor et al. 2003). 182"

183"

The simulation of interannual variability was mostly in agreement with observations across 184"

western and central India (predicted σ is between 0.8-1.2 with respect to observations), but 185"

interannual variation was over-estimated in the southern zone and the east, and under-186"

estimated in the northern India (predicted σ up to 1.5-2 times larger than observed). Crop 187"

model errors were spatially consistent across parameter sets (Fig. S3). Most of the 188"

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parameter ensemble members showed mean yield prediction between +20 and –20% of 189"

observed yields in nearly 80% of the analysed areas, regardless of the parameter ensemble 190"

member. In the remainder of areas, a trend to under-estimate mean crop yields beyond -191"

20% (nearly up to -50%) was observed. 192"

193"

194"Figure S2 Spatial distribution of two crop model skill metrics for two selected parameter ensemble 195"

members (marked with large filled circles in Fig. S1). The captions “high” and “low” indicate that 196"

the parameter ensemble members are high- and low-skill, respectively (differentiated by blue and 197"

red colours in Fig. S1). Filled dots in the correlation coefficient maps indicate statistically 198"

significant correlations (p≤0.1). 199"

200"

The values of the yield gap parameter varied only slightly from one period to the other, 201"

particularly for the most skilful parameter sets. The areas where the most significant 202"

changes in CYG occurred are located towards the very north of India. In these areas, CYG 203"

increased by 30-40 % between the two periods. A PDF of the spearman rank correlation 204"

(rho) between the two time periods showed that the relationship is strong and statistically 205"

significant (Fig. S4). In particular, for the selected parameter sets (blue line in Fig. S4), the 206"

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values of rho were high (0.75-0.9). Differences in the values of the CYG through time can be 207"

attributed to changes in the main drivers of crop production through time (i.e. from water- 208"

to radiation-limited), noise in the yield time series, the assumption that the technology trend 209"

is linear [whereas it could in some cases be non-linear, see e.g. Baigorria et al. (2010)], the 210"

fact that this area is largely irrigated (Mehrotra 2011), or to structural errors in the crop 211"

model. 212"

213"

214"Figure S3 Percent of parameter ensemble members for which the normalised-by-observed 215"

predicted mean yields falls in each of three categories: underestimating (predicted yield [YP] by 216"

observed yield [YO] < 0.8; i.e. YP/YO < 0.8), normal (YP/YO between 0.8 and 1.2), and overestimating 217"

(YP/YO > 1.2). Parameter ensemble members are classified in three categories: all 50 members, 218"

selected 19 members and 31 discarded members (shown in different rows). 219"

220"

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221"

Figure S4 Probability density function of the Spearman rank correlation between the two yield gap 222"

parameter (CYG) values (CYG1: 1966-1979 and CYG2: 1980-1993) for all parameter ensemble 223"

members (n=50, black line), selected parameter ensemble members (n=19, blue line) and discarded 224"

parameter ensemble members (n=31, red line). Each PDF curve is calculated using the n parameter 225"

sets of each category. For each parameter set a single value of the Spearman correlation (rho) was 226"

computed using 195 pairs of [CYG1, CYG2] values, each corresponding to a grid cell of the analysis 227"

domain. Dashed vertical lines show the low (red) and high (blue) parameter sets indicated as large 228"

dots in Fig. S1). 229"

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SI Text 3: Changes in groundnut productivity under no-adaptation scenarios 230"

231"

Baseline values and projected changes in mean yields are shown in Figure S5. There were 232"

significant yield increases projected across the major growing areas in the west of India 233"

(Gujarat state). Yield losses below 20 % were found highly unlikely across the entire 234"

region. There was significant uncertainty as per the direction of the change in central India, 235"

although the probability of a negative impact was generally larger than that of a positive 236"

impact. Conversely, in eastern India, yield gains were found more often in the ensemble of 237"

model runs than those of negative impacts. These results broadly agree with those of refs. 238"

(Challinor and Wheeler 2008b; 2009), which projected yield losses in central India, and 239"

yield gains in north-west and western India. The choice of how GCM outputs are bias-240"

corrected did not affect the direction of change but did so for the extent of the change 241"

(Figure S5D, E, F). 242"

243"

(A) Baseline –DEL

(B) Baseline –SH

(C) Baseline –LOCI

(D) Change –DEL

(E) Change –SH

(F) Change –LOCI

Figure S5 Baseline (A, B, C) and future projected changes (D, E, F) in mean crop yield for (A, E) 244"

simulations using DEL-corrected GCM outputs (A, D), SH-corrected GCM outputs (B, E), and 245"

LOCI-corrected GCM outputs (C, F). Values shown are means across all simulations (i.e. GLAM 246"

parameter ensemble members, GCMs, CO2 response parameterisations) for each bias correction 247"

method. 248"

Projections of changes in yield variability (CV) showed less consistent patterns (Fig. S6). In 249"

general, the eastern part of the peninsular zone showed decreases in yield CV. Minor 250"

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decreases (0-5 %) were observed in western and central India. Relative changes in yield 251"

variability in northern India varied substantially between LOCI and the other two input 252"

types (SH and DEL). Since in LOCI temperature bias is not corrected, this suggested that 253"

temperature bias played an important role in the changes in interannual yield variability in 254"

this region. 255"

256"

(A) Baseline –DEL

(B) Baseline –SH

(C) Baseline –LOCI

(D) Change –DEL

(E) Change –SH

(F) Change –LOCI

Figure S6 Baseline (A, B, C) and future (2030s) projected changes (D, E, F) in crop yield 257"

variability (i.e. coefficient of variation) for (A, E) simulations using DEL-corrected GCM outputs 258"

(A, D), SH-corrected GCM outputs (B, E), and LOCI-corrected GCM outputs (C, F). Values shown 259"

are means across all simulations (i.e. GLAM ensemble members, GCMs, CO2 response 260"

parameterisations) for each bias correction method. 261"

262"

263"

264"

265"

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(A) Mean yield change (%) –LOCI

(B) Mean yield change (%) –SH

Figure S7 Projected mean yield changes by 2030s as a result of crop improvement related 266"

to drought scape and water use efficiency, expressed as percentage variation from no-267"

adaptation simulations. Shown are the ensemble mean results of A-LOCI (A) and A-SH (B) 268"

simulations for each of the genotypic properties. Model parameters are as follows: decrease 269"

in thermal time from sowing to flowering (tTT0), increase in transpiration efficiency (TE), 270"

increase in maximum transpiration efficiency (ETN,max), increase in rate of harvest index 271"

(∂HI/∂t), increase in maximum transpiration rate (TTmax), and increase in specific leaf area 272"

(SLAmax). 273"

274"

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275"

(A) Yield CV change (%) –DEL

(B) Yield CV change (%) –LOCI

(C) Yield CV change (%) –SH

Figure S8 Projected yield variability (CV) changes by 2030s as a result of crop 276"

improvement related to drought scape and water use efficiency for (A) A-DEL, (B) A-277"

LOCI and (C) A-SH simulations, expressed as percentage variation from no-adaptation 278"

simulations. Associated model parameters are as follows: decrease in vegetative duration 279"

(tTT0), increase in transpiration efficiency (TE), increase in maximum transpiration 280"

efficiency (ETNmax), increase in rate of harvest index (∂HI/∂t), increase in maximum 281"

transpiration rate (TTmax), and increase in specific leaf area (SLAmax). 282"

283"

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284"

285"

286"

(A) Mean yield change (%) –DEL

(B) Mean yield change (%) –LOCI

(C) Mean yield change (%) –SH

Figure S9 Projected mean yield changes by 2030s as a result of increased crop duration. 287"

Shown are the ensemble mean results of A-DEL (A), A-LOCI (B) and A-SH (C) 288"

simulations for each of the genotypic properties, expressed as percentage change from no-289"

adaptation simulations. Associated GLAM model parameters are as follows: increase in 290"

thermal time from sowing to flowering (tTT0), increase thermal requirement for flowering to 291"

start of pod-filling (tTT1), increase in thermal time from start of pod-filling to maximum leaf 292"

area index (tTT2), increase in thermal time from maximum LAI to physiological maturity 293"

(tTT3). 294"

295"

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296"

297"

(A) Yield CV change (%) –DEL

(B) Yield CV change (%) –LOCI

(C) Yield CV change (%) –SH

Figure S10 Projected yield variability (CV) changes by 2030s as a result of increased crop 298"

duration, expressed as percentage change from no-adaptation simulations. Shown are the 299"

ensemble mean results of A-DEL (A), A-LOCI (B) and A-SH (C) simulations for each of 300"

the genotypic properties. Associated GLAM model parameters are as follows: increase in 301"

thermal time from planting to flowering (tTT0), increase thermal requirement for flowering 302"

to start of pod-filling (tTT1), increase in thermal time from start of pod-filling to maximum 303"

leaf area index (tTT2), increase in thermal time from maximum LAI to physiological 304"

maturity (tTT3). 305"

306"

307"

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(A) Mean yield change (%) –DEL

(B) Mean yield change (%) –LOCI

(C) Mean yield change (%) –SH

Figure S11 Projected crop yield mean changes by 2030s as a result of combined-trait 308"

improvement scenarios, expressed as percentage change from no-adaptation simulations. 309"

Shown are the ensemble mean results of A-DEL (A), A-LOCI (B) and A-SH (C) 310"

simulations for each genotypic improvement scenario. Scenario “tTT0_d” refers to 311"

increases in TE, ETN, max, TTmax, SLAmax, ∂HI/∂t, tTT1, tTT2, and tTT3 combined with decreases in 312"

tTT0, whereas scenario “tTT0_i” refers to increases in the same genotypic properties 313"

combined with increases in tTT0. 314"

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(A) Yield CV change (%) –DEL

(B) Yield CV change (%) –LOCI

(C) Yield CV change (%) –SH

Figure S12 Projected crop yield variability (CV) changes by 2030s as a result of combined-315"

trait improvement scenarios for (A) A-DEL, (B) A-LOCI and (C) A-SH simulations, 316"

expressed as percentage change from no-adaptation simulations. Scenario “tTT0_d” refers 317"

to increases in TE, ETN, max, TTmax, SLAmax, ∂HI/∂t, tTT1, tTT2, and tTT3 combined with decreases 318"

in tTT0, whereas scenario “tTT0_i” refers to increases in the same genotypic properties 319"

combined with increases in tTT0. 320"

321"

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322"

323"

324"

325"

326"

Figure S13 Robustness (R, fraction) of model projections of adaptation. Maps show 327"

robustness calculated using simulations pooled by each of the modelling choices. BC refers 328"

to bias correction method (2 ensemble members), GCM refers to choice of global climate 329"

model (13 ensemble members), CO2 refers to parameterisations of CO2 response (4 330"

ensemble members) and GLAM refers to choice of parameter set. Grey areas all have R<0, 331"

indicating poor model robustness. 332"

333"

334"

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335"

336"

337"

338"

339"

340"

Figure S14 Relative contribution of different sources to total yield uncertainty in genotypic 341"

adaptation simulations. Maps show the geographic variation of importance in different 342"

sources, whereas the boxplots show the general trend across the country (spread being 343"

spatial variation). Thick horizontal red line is the median, blue boxes mark the 25 and 75 % 344"

of the data and black whiskers extend to 5 and 95 % of the data. BC uncertainty refers to 345"

the choice bias correction method. 346"

347"

348"

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349"

350"

351"

352"

353"

Table S2 CMIP5 GCMs used in the study and main characteristics 354"

Model name1 NC

2 HRx

3 NR

2 HRy

3 Calendar

4

BCC-CSM1.1 128 2.8125 64 2.8125 365

BNU-ESM 128 2.8125 44 4.0909 365

CCCMA-CanESM2 128 2.8125 64 2.8125 365

CNRM-CM5 256 1.4063 128 1.4063 366

CSIRO-Mk3.6.0 192 1.875 96 1.875 365

INM-CM4 180 2.000 120 1.500 365

IPSL-CM5a-LR 96 3.750 96 1.875 365

IPSL-CM5b-LR 96 3.750 96 1.875 365

MOHC-HadGEM2-CC 192 1.875 145 1.2414 360

MOHC-HadGEM2-ES 192 1.875 145 1.2414 360

MPI-ESM-LR 192 1.875 96 1.875 366

MPI-ESM-MR 192 1.875 96 1.875 366

MRI-CGCM3 320 1.125 160 1.125 366

1 In all cases only one ensemble member was used (r1i1p1) as described in ref. (2012). 355"

2 NC and NR Number of columns (NC) and rows (NR) in the climate grid. 356"

3 HRx and HRy refer to horizontal resolution in the x-axis (longitude, HRx) and the y-axis (latitude, HRy), in 357"

decimal degree. 358"4 Calendar type refers to that used in the climate model run: 365 is a calendar without leap years, 366 is the 359"

standard Gregorian calendar (with leap year), and 360 refers to the calendar in which all months have 30 days 360"

only used by the UK MetOffice climate models. 361"

362"

363"

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Table S3 Summary of studies of genotypic adaptation and ideotype design 364$

365$

366$

Study Region Genotypic property Crop response

Challinor et al. (2009) India Total thermal requirement Increases in thermal requirement are needed between 20-30 % to

counter yield loss by 2100

Challinor et al. (2007) India

Tolerance to high temperature Increased heat stress tolerance reduces yield loss by 50-80 % by 2100

Change in optimal temperature for development No beneficial effect observed with increases in Topt from 28 ºC to 36

ºC

Suriharn et al. (2011) Thailand

Thermal requirement during vegetative phase Yield gain when vegetative duration was decreased

Thermal requirement during pod-filling phase Yield gain when pod-filling duration was increased

Thermal requirement during flowering to max. LAI Increases are crucial for achieving high LAI

Maximum leaf size Minimal effect due to increased light competition

Specific leaf area Increases in yield, but countered increases in maximum photosynthetic

rate

Maximum rate of photosynthesis Increase of 7 % in PN, max increased yield by up to 150 %

Partitioning to seed Increases of up to 10 % boosted yields by 200 %

Singh et al. (2012, 2013) India

West Africa

Thermal requirement from emergence to flowering Increases produced little gain or yield loss

Thermal requirement during pod-filling phase Increase of 10 % produced yield gains of 2.5 - 8 %

Maximum leaf size Little or no yield gain

Specific leaf area Gains restricted to low temperature areas, where VPD is low

Maximum rate of photosynthesis Gains between 4-5 % at all locations

Seed filling duration Gains between 3-5 % at all locations

Nitrogen mobilisation rate Small yield gains between 1-2.5 % at all locations

Pod adding duration Moderate (2-5 %) yield gain restricted to warm sites

Fraction assimilate partitioned to seed Increased yield by up to 5 % at all locations

Fraction assimilate partitioned to roots Detrimental to yield

Root biomass across soil profile Little gain or yield loss

Velocity of extraction front Little to no yield gain

Temperature tolerance for pod-set, partitioning to pods

and individual seed growth Large yield gains (8-13 %) in warm areas

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