Rothamsted Research where knowledge grows
Rothamsted Research where knowledge grows
How do we become champions
for transforming agri-food
systems
Achim Dobermann
achimdobermannrothamstedacuk
iCROPM 2016 Berlin 17 March 2016
Focus on solutions
Start fresh
Work differently
Mathematical models in agriculture a quantitative
approach to problems in agriculture and related
sciences
J France J H M Thornley
Butterworths 1984 335 pages
2006 edition CABI 928 p
This is a completely rewritten and expanded
version of the successful 1984 book of the
same name The objective remains the same
to teach students of agriculture and related
ecological problems how to express ideas
mathematically and solve the resulting
mathematical problem
httphybridmaizeunledu
2004
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015
0
50
100
150
200
250
300
350
400
Papers
1980-2000 903 papers 2001-2015 3207 papers (78)
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015 top 15 orginst
0 50 100 150 200 250 300 350
Rothamsted Research
University of Reading
IRRI
Indian Agricultural Research Institute
Michigan State University
University of Queensland
Universitat Bonn
University of Nebraska - Lincoln
China Agricultural University
Chinese Academy of Agricultural Sciences
CIRAD
University of Georgia
Chinese Academy of Sciences
Wageningen UR
CSIRO
University of Florida
USDA ARS
INRA
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Focus on solutions
Start fresh
Work differently
Mathematical models in agriculture a quantitative
approach to problems in agriculture and related
sciences
J France J H M Thornley
Butterworths 1984 335 pages
2006 edition CABI 928 p
This is a completely rewritten and expanded
version of the successful 1984 book of the
same name The objective remains the same
to teach students of agriculture and related
ecological problems how to express ideas
mathematically and solve the resulting
mathematical problem
httphybridmaizeunledu
2004
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015
0
50
100
150
200
250
300
350
400
Papers
1980-2000 903 papers 2001-2015 3207 papers (78)
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015 top 15 orginst
0 50 100 150 200 250 300 350
Rothamsted Research
University of Reading
IRRI
Indian Agricultural Research Institute
Michigan State University
University of Queensland
Universitat Bonn
University of Nebraska - Lincoln
China Agricultural University
Chinese Academy of Agricultural Sciences
CIRAD
University of Georgia
Chinese Academy of Sciences
Wageningen UR
CSIRO
University of Florida
USDA ARS
INRA
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Mathematical models in agriculture a quantitative
approach to problems in agriculture and related
sciences
J France J H M Thornley
Butterworths 1984 335 pages
2006 edition CABI 928 p
This is a completely rewritten and expanded
version of the successful 1984 book of the
same name The objective remains the same
to teach students of agriculture and related
ecological problems how to express ideas
mathematically and solve the resulting
mathematical problem
httphybridmaizeunledu
2004
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015
0
50
100
150
200
250
300
350
400
Papers
1980-2000 903 papers 2001-2015 3207 papers (78)
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015 top 15 orginst
0 50 100 150 200 250 300 350
Rothamsted Research
University of Reading
IRRI
Indian Agricultural Research Institute
Michigan State University
University of Queensland
Universitat Bonn
University of Nebraska - Lincoln
China Agricultural University
Chinese Academy of Agricultural Sciences
CIRAD
University of Georgia
Chinese Academy of Sciences
Wageningen UR
CSIRO
University of Florida
USDA ARS
INRA
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
httphybridmaizeunledu
2004
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015
0
50
100
150
200
250
300
350
400
Papers
1980-2000 903 papers 2001-2015 3207 papers (78)
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015 top 15 orginst
0 50 100 150 200 250 300 350
Rothamsted Research
University of Reading
IRRI
Indian Agricultural Research Institute
Michigan State University
University of Queensland
Universitat Bonn
University of Nebraska - Lincoln
China Agricultural University
Chinese Academy of Agricultural Sciences
CIRAD
University of Georgia
Chinese Academy of Sciences
Wageningen UR
CSIRO
University of Florida
USDA ARS
INRA
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015
0
50
100
150
200
250
300
350
400
Papers
1980-2000 903 papers 2001-2015 3207 papers (78)
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015 top 15 orginst
0 50 100 150 200 250 300 350
Rothamsted Research
University of Reading
IRRI
Indian Agricultural Research Institute
Michigan State University
University of Queensland
Universitat Bonn
University of Nebraska - Lincoln
China Agricultural University
Chinese Academy of Agricultural Sciences
CIRAD
University of Georgia
Chinese Academy of Sciences
Wageningen UR
CSIRO
University of Florida
USDA ARS
INRA
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Scopus 12 March 2016 total of 4110 papers from 1980 to 2015 top 15 orginst
0 50 100 150 200 250 300 350
Rothamsted Research
University of Reading
IRRI
Indian Agricultural Research Institute
Michigan State University
University of Queensland
Universitat Bonn
University of Nebraska - Lincoln
China Agricultural University
Chinese Academy of Agricultural Sciences
CIRAD
University of Georgia
Chinese Academy of Sciences
Wageningen UR
CSIRO
University of Florida
USDA ARS
INRA
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Climate models and measurements had now proven the existence of global climate change Marshall wrote and the questions for the organization would now be ldquoWhat do we do about itrdquo and ldquohow can we find solutions for the climate we will be living withrdquo
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Chinarsquos new policy lsquoNational Plan on Sustainable Agricultural Development 2015-2030rsquo
1 Increasing productivity
2 Protecting land resource
3 Increasing water use efficiency
4 Pollution mitigation
5 Increasing ecological functions
MOA NDRC MOST MOF MOLR MOEP MOWR SFB
May 2015
Five key goals
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Lin Ma et al Assessing pathways to sustainable food production and consumption (China) in subm
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
5
10
15
20
25
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N u
se e
ffic
ien
cy
()
0
5
10
15
20
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
P u
se e
ffic
ien
cy i
n(
)
0
500
1000
1500
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
GH
G e
mis
sio
ns
(Tg
CO
2e)
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
10
20
30
40
50
602
01
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Nr
losses
(Tg
N)
00
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P lo
sses
(Tg
P)
0
100
200
300
400
201
0
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+CWhellip
Wate
r u
se
(bil
lio
n m
3)
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
2010
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
200
400
600
800
1000
1200
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Pla
nt
foo
d
pro
du
cti
on
(T
g) Rice
Wheat
Maize
Soybean
Vegetables
Fruits 0
50
100
150
200
250
300
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
An
ima
l fo
od
p
rod
uc
tio
n (
Tg
) Milk
Egg
Mutton
Beef
Chicken
Pork0
200
400
600
800
1000
20
10
BA
U
PM
B
CW
L
PMB+hellip
IMF
PMB+hellip
Fe
ed
p
rod
uc
tio
n (
Tg
) Rice
Wheat
Maize
Soybean
Vegetables
Grass
2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F 2010
BA
U
PM
BC
WL
PM
B+
CW
L
IMF
PM
B+
CW
L
+IM
F
0
50
100
150
200
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Cro
pla
nd
(m
illi
on
ha
)
0100200300400500600700
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
Gra
ss
lan
d
(mil
lio
n h
a)
0
10
20
30
40
50
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
N f
ert
iliz
er
(Tg
N)
0
5
10
15
20
10
BA
U
PM
B
CW
L
PM
B+
CW
L
IMF
PMB+Chellip
P f
ert
iliz
er
(Tg
P)
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Plant and animal
genetics
Human animal and soil
microbiota
Digital technologies
New food technologies
Value chain system
transformation
httpwwwteagasciepublicationsview_publicationaspxPublicationID=3897
Breakthrough technologies which will transform the Irish agri-food and bioeconomy sector
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Broadbalk Mean wheat yields and major
technology changes 1843-2014
0
2
4
6
8
10
12
14
1840 1860 1880 1900 1920 1940 1960 1980 2000 2020
Gra
in t
ha
at
85
d
ry m
att
er
Fallowing Liming
Herbicides
Fungicide
Red Rostock Red Club Sq
Master Red Standard Sq Master Cappelle
Desprez
Flanders Apollo
Hereward Brimstone
Modern lsquoGreen Revolutionrsquo short-strawed cultivars
Continuous wheat unmanured
Cont wheat FYM
Cont wheat N3PK
1st wheat FYM+N2
(N3 since 2005)
1st wheat Best NPK
Crusoe
Crusoe 2014
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Lack of power for deeper explanation relying on data from conventional
agronomic research
Poor extrapolation and prediction due to empirical nature
Query of modelers March 2016
Not very useful for breeding and biotech canrsquot describe the process of reproduction
Lack of interactions with other components of the ecosystem Crop rotation and its effects are
rarely reflected
Poor usability Not usable until it is simple intuitive
and easy to use
Most models are outdated by decades in terms of technologies Younger
generations view them as antiques
Policy makers find them difficult to
understand and use
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
The biggest challenge in my view is to link plant dynamics to genetics
and signalling
There are few generic models built upon a robust and mechanistic basis that can be directly used by farmers for guidance on field management
Query of modelers March 2016
I donrsquot think many of our models work very well to be
honest even retrospectively
Models are often assembled over time with
increased complexity added in a stepwise fashion
Scales moving up scales and partitioning the
uncertaintyvariability are clearly important issues
Sharing large quantities of data is
not a straightforward problem
Modelers in our community rarely seem to work closely with software engineers
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Too much of the same
DSSAT STICS APSIM WOFOST Aquastat Ecosys SWIM MONICA SALUS Hybrid Maize MaizSim AgMaize hellip hellip
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
ldquoThe relative error averaged over models was 24ndash38 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC) There was little relation between error of a model for GY or GPC and error for in-season variables Thus most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamicsrdquo
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
My ideal crop model
bull Problem-oriented cool tool
bull Universal amp modular integrated systems approach
bull Process-based no more (genetic) ldquocoefficientsrdquo and ldquocalibrationrdquo
bull Intelligent self updating and adapting
bull Inter-operable with many data and knowledge sources
bull Flexible portable software design ndash interactive UI
bull Co-developed and -owned open innovation amp open access
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
170 papers since 1997
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Query of modelers March 2016
Few groups can develop such new
models independently
Knowledge and
experience across several
disciplines
Capability of system
analysis and model
formulation
Integration of observational science with
system analysis using
digital technology
Software design
database management
and programing
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Crop modeling
Weather amp climate
Math data science amp software
development
Social sciences
economics amp comms
Pests amp agronomy
Soils nutrients amp
water
Genomics physiology amp
breeding
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Hughes et al BIS 2013
Impact pathways of UK academics reporting having taken part in activity in past 3 years
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Start fast ndash stop fast
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Will CRISPRCAS9 become the breakthrough tool for modelers
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
4-1
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
Keynote speakers iCROPM 2016 J Jones 284 cited papers since 1974 G Hammer 166 cited papers since 1978 B Keating 54 cited papers since 1979 S Savary 83 cited papers since 1986 M Kropff 129 cited papers since 1987 A Dobermann 109 cited papers since 1994 F Ewert 118 cited papers since 1996 A Challinor 66 cited papers since 2003
2030
Female Other regions Under 40
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
We are recruiting
bull Genome Engineering Specialist
bull Molecular Crop Physiologist
bull Quantitative Statistical Genomicist
bull Computational Systems Biologist
bull Systems Agronomist
bull Grazing Livestock Systems Specialist
bull Nutrient Management Specialist
bull Agro-Eco Informatician
![Agri-A4a Information AGRI Oh! e O Oh! SERIES]](https://static.documents.pub/doc/80x56/5acab7477f8b9a42358e1546/agri-a4a-information-agri-oh-e-o-oh-series-agri-800mm-d-w-oomm-h-agri-800mm.jpg)
