1. Identifying climate patterns during the crop growing
cycle from 30 years of CIMMYT elite spring Wheat
international yield trials
Zakaria KEHEL, Jose CROSSA and Matthew REYNOLDS
Rabat-Morocco. 24-27 June 2014

2. Climate change for wheat development
Reduced yields. High temperatures and drought-related stress.
Increased irrigation.
Planting and harvesting changes. Shifting seasonal rainfall patterns  delay planting and harvesting.
More pests. Lower-latitude pests may move to higher latitudes.

3. Climatic variable Intercept Slope P(Intercept) P(Slope) R2
tmin_veg 13.61 -0.003 0.52 0.78 0.32
tmin_rep -13.40 0.012 0.57 0.31 4.23
tmin_gf -33.92 0.024 0.12 0.03 18.04
tmin_seas -35.12 0.023 0.05 0.01 23.76
tmax_veg -39.25 0.030 0.09 0.01 22.55
tmax_rep -41.28 0.032 0.19 0.04 15.96
tmax_gf -64.12 0.046 0.04 0.00 29.31
tmax_seas -80.13 0.052 0.00 0.00 56.44
dtr_veg -52.87 0.033 0.02 0.00 29.12
dtr_rep -27.88 0.020 0.25 0.09 11.18
dtr_gf -30.19 0.022 0.04 0.00 28.88
dtr_seas -45.01 0.029 0.00 0.00 43.37
tavg_veg -12.82 0.013 0.50 0.17 7.73
tavg_rep -27.34 0.022 0.27 0.08 12.18
tavg_gf -49.02 0.035 0.06 0.01 25.58
tavg_seas -57.62 0.037 0.00 0.00 46.55
srad_veg 0.76 0.007 0.98 0.59 1.22
srad_rep 68.20 -0.025 0.15 0.28 4.80
srad_gf -13.45 0.018 0.62 0.20 6.61
srad_seas -26.33 0.022 0.22 0.05 15.63
vpd_veg -7254.53 4.018 0.03 0.02 20.31
vpd_rep -6303.50 3.687 0.26 0.20 6.89
vpd_gf -15138.76 8.380 0.04 0.02 19.47
vpd_seas -11316.95 6.195 0.01 0.00 29.15
What does it mean? Constraints

4. Year tmin_gf tmax_gf tmin_rep tmax_rep tmin_veg tmax_veg
p-value cluster p-value cluster p-value cluster p-value cluster p-value cluster p-value cluster
1995 0.0000 High 0.0001 High 0.3658 Random 0.1679 Random NaN NaN 0.3392 Random
1996 0.0003 High 0.0000 High 0.4336 Random 0.0815 High 0.5036 Random 0.0501 High
1997 0.0320 High 0.0097 High 0.3213 Random 0.8897 Random NaN NaN 0.1084 Random
1998 0.0003 High 0.0000 High NaN NaN 0.8988 Random NaN NaN 0.4388 Random
1999 0.0000 High 0.0000 High 0.0968 High 0.0103 High NaN NaN 0.0101 High
2000 0.0021 High 0.0000 High 0.9595 Random 0.0859 High 0.1365 Random 0.0028 High
2001 0.0000 High 0.0000 High 0.0033 High 0.0021 High NaN NaN 0.1795 Random
2002 0.0010 High 0.0000 High 0.8590 Random 0.9247 Random 0.6714 Random 0.4215 Random
2003 0.1010 Random 0.1348 Random 0.1327 Random 0.0818 High NaN NaN 0.2048 Random
2004 0.0000 High 0.0000 High 0.5892 Random 0.9132 Random NaN NaN 0.1121 Random
2005 0.0514 High 0.0032 High 0.0253 High 0.0008 High 0.4310 Random 0.0037 High
2006 0.0001 High 0.0000 High 0.6581 Random 0.4251 Random NaN NaN 0.4108 Random
2007 0.0004 High 0.0000 High 0.2256 Random 0.1020 Random NaN NaN 0.5464 Random
2008 0.0001 High 0.0000 High 0.4245 Random 0.0955 High NaN NaN 0.1207 Random
2009 0.0000 High 0.0000 High 0.0148 High 0.0011 High 0.9666 Random 0.2163 Random
Any pattern?

5. Where hot and where cold? VEG

6. Where hot and where cold? REP

7. Where hot and where cold? GF

8. -0.8
-0.6
-0.4
-0.2
0
0.2
0.4
rep_days
rhum_gf
gdd_rep
rhum_seas
dl_seas
rhum_rep
rhum_veg
dl_veg
FDrep
dl_rep
dl_gf
TRveg
tmin_veg
srad_veg
tavg_veg
tmax_veg
dtr_veg
IDveg
IDCL
dtr_rep
FDgf
srad_seas
TRrep
SUveg
dth_mean
srad_rep
gdd_veg
gf_days
tmin_seas
vpd_veg
CL
veg_days
SUrep
tavg_seas
dtr_seas
edd_veg
srad_gf
tmax_seas
edd_rep
FDCL
SUgf
vpd_seas
FDveg
vpd_rep
SUCL
gdd_gf
tmax_rep
tmin_gf
tmin_rep
gdd_seas
tavg_rep
dtr_gf
R10mmgf
tavg_gf
vpd_gf
TRgf
TRCL
tmax_gf
R5mmgf
edd_gf
edd_seas
Rx5daygf
Rx5dayrep
R5mmrep
R10mmrep
Rx5dayveg
R10mmveg
R5mmveg
R10mmCL
R5mmCL
Rx5dayCL
PC1
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
vpd_seas
tavg_seas
tmax_seas
tmin_seas
vpd_veg
vpd_rep
SUCL
SUveg
tmax_rep
tavg_gf
tmax_veg
dtr_veg
SUrep
tmin_gf
tmax_gf
tavg_rep
vpd_gf
tavg_veg
dtr_rep
TRCL
edd_seas
TRgf
edd_gf
dtr_seas
edd_rep
SUgf
tmin_rep
edd_veg
tmin_veg
gdd_veg
gdd_seas
srad_seas
dtr_gf
TRveg
TRrep
srad_veg
gdd_gf
srad_rep
srad_gf
FDrep
IDveg
IDCL
dl_seas
dl_veg
gf_days
dl_gf
veg_days
dl_rep
dth_mean
FDgf
CL
FDveg
gdd_rep
FDCL
rhum_gf
rep_days
rhum_veg
rhum_rep
rhum_seas
R10mmgf
Rx5daygf
R5mmgf
Rx5dayrep
R10mmrep
Rx5dayveg
Rx5dayCL
R10mmveg
R5mmveg
R5mmrep
R5mmCL
R10mmCL
PC2
PCA analysis to understand

9. But patterns are lost!

10. Self-Organized Maps SOM

11. SOM for Tmin

12. SOM for Tmax

13. SOM for stages length

14. Clusters

15. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Cluster6
Cluster7
Cluster8
Cluster9
Cluster10
Y2009
Y2008
Y2007
Y2006
Y2005
Y2004
Y2003
Y2002
Y2001
Y2000
Y1999
Y1998
Y1997
Y1996
Y1995
Y1993
Y1992
Y1991
Y1990
Y1989
Y1988
Y1987
Y1986
Clusters and years

16. red for VEG,
green for REP
and blue for GF
Relevance of all climatic variables for the SOM-
map grids

17. Clustering all locations now

18. Conclusions
• The wheat cycle is facing a significant rise in temperature, especially tmax
• Humidity and precipitations were reduced over years for most ESWYT
locations
• Neighboring locations had similar climatic profile except for Middle East and
Central Europe
• PC analysis was not able to identify any of the spatial or temporal patterns
present in the data and hence cannot be used to investigate any climate
change
• The SOM approach was able to identify regional and temporal change  Non
linearity
• Patterns of change in climatic profiles + genotypic sensitivities  Breeding
strategies