Department of Employment, Economic Development and Innovation

Environmental characterisation for Environmental characterisation for genotype by environment genotype by environment

matchingmatching  

Solomon Fekybelu, Yash Chauhan, Daniel Rodriguez, John Dimes

SIMLESA workshop, Brisbane, Sep. SIMLESA workshop, Brisbane, Sep. 20112011

2© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

E-characterization using simulation models

helps identify stress types & frequency

Thermal time pre- and post-flowering (ºCd)

-1500 -1000 -500 0 500

Str

ess in

de

x:

Wa

ter

su

pp

ly/d

em

an

d r

atio

0.0

0.2

0.4

0.6

0.8

1.0

Env. type 1Env. type 2Env. type 3Env. type 4

Temp/Radiation.Rain Soil

Comprehensivecheaper

3© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Simulations (APSIM)• 32 soil-site combinations

(environments)• 1890-2010 climate records used• ‘best bet management’

Simulations outputs:• Simulated yield• Water supply demand ratios for

Pioneer hybrid 3153

Statistics : Cluster analysis

Approaches of E-characterization

4© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Clustering of probability distribution of simulated yield

5© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Yield cluster distribution follows a geographical pattern

6© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Environment stress patterns

0.0

0.2

0.4

0.6

0.8

1.0

-400 -200 0 200 400

Degree days from anthesis

Supp

ly d

eman

d ra

tio E1

E2

E3

E4

E5

7© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Clusters 4 & 5 have higher frequencies of terminal drought (sky blue and purple sections)

No stress

MildModerate

Severe

Flowering

8© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Over 80% the variability in yield across environments was accounted by drought frequencies in those environments

y = -1.0312x - 6E-05

R2 = 0.897

-6

-5

-4

-3

-2

-1

0

1

2

3

-4 -2 0 2 4 6

PCA1 of drought frequencies

PC

A1

of

yie

ldc

9© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Summary

• The traditional maize breeding trial sites don't represent the potential TPE for maize in NE Australia

• Drought seems to contribute to over 80% of production risk to maize in different environments in north-eastern Australia

• Characterization drought frequencies will enhance our ability to breed, select and target drought tolerant/resistant germplasm in different environments

• Combined analyses of G-M-E may help identify the most profitable scenarios, E.g. matching phenology with seasonal available soil moisture to minimize yield variability between seasons

10© The State of Queensland, Department of Employment, Economic Development and Innovation, 2010

Thank you