Identifying climate patterns during the crop growing
cycle from 30 years of CIMMYT elite spring Wheat
international yield ...
Climate change for wheat development
Reduced yields. High temperatures and drought-related stress.
Increased irrigation.
P...
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...
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...
Where hot and where cold? VEG
Where hot and where cold? REP
Where hot and where cold? GF
-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...
But patterns are lost!
Self-Organized Maps SOM
SOM for Tmin
SOM for Tmax
SOM for stages length
Clusters
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Cluster6
Cluster7
Cluster8
Cluste...
red for VEG,
green for REP
and blue for GF
Relevance of all climatic variables for the SOM-
map grids
Clustering all locations now
Conclusions
• The wheat cycle is facing a significant rise in temperature, especially tmax
• Humidity and precipitations w...
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THEME – 2 Identifying climate patterns during the crop growing cycle from 30 years of CIMMYT elite spring Wheat international yield trials

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THEME – 2 Identifying climate patterns during the crop growing cycle from 30 years of CIMMYT elite spring Wheat international yield trials

  1. 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. 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. 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. 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. 5. Where hot and where cold? VEG
  6. 6. Where hot and where cold? REP
  7. 7. Where hot and where cold? GF
  8. 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. 9. But patterns are lost!
  10. 10. Self-Organized Maps SOM
  11. 11. SOM for Tmin
  12. 12. SOM for Tmax
  13. 13. SOM for stages length
  14. 14. Clusters
  15. 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. 16. red for VEG, green for REP and blue for GF Relevance of all climatic variables for the SOM- map grids
  17. 17. Clustering all locations now
  18. 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

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