The document describes research using the ORYZA rice model to evaluate irrigation management strategies that optimize rice production in salt-affected areas of Bangladesh. The model was calibrated and validated using field experiments with different irrigation water quality treatments. Model results showed that alternating between fresh and saline water irrigation every 2 weeks maintained soil salinity similar to fresh water and led to higher yields than saline water alone. The model accurately simulated the effects of salinity on rice biomass and yield. Overall, the research identified irrigation strategies to improve rice productivity in salt-affected soils using a validated rice model.

1. Iden%fying
op%mized
use
of
fresh
and
saline
water
for
irriga%on
on
salt
affected
rice
systems
in
Bangladesh
using
ORYZA
ver.3
Radanielson
A.M.
,
O.
Angeles,
T.Li,
A.K.
Rahman,
D.
Gaydon.
Revitalizing
the
Ganges
coastal
zones
conference
Dhaka
Oct
21-­‐23,
2014

2. Outline
1. Ra>onale
and
objec>ves
2. Methodology
3. Results
4. Summary
and
perspec>ves

3. Rice
produc%on
challenges
in
Bangladesh
• Rice
demand
brought
by
increasing
popula>on:
–
By
2030,
40.0
M
tons
of
rice
for
about
190
M
popula>on
• Limited
resources
:
land,
water,
labour
• Environmental
constraints
aggravated
by
climate
change
– Soil
salinity
– Sea
level
rise
– Extreme
weather
events
Farmers livelihood

4. Opportuni%es
to
improve
rice
produc%on
in
salt-­‐affected
areas
• Over
30%
of
cropped
land
is
saline
• Saline-­‐tolerant
rice
varie>es
are
available
• Need
a
suitable
management
for
produc>ve
and
sustainable
cropping
systems

5. Objec%ves
1. Evaluate
irriga>on
water
management
op>ons
to
reduce
salinity-­‐stress
on
rice
produc>on
– Calibra>on
and
valida>on
of
a
modified
version
of
the
rice
model
ORYZA
ver.3
– Scenario
analyses
to
evaluate
performance
of
management
strategies
using
different
sowing
dates,
adapted
virtual
varie>es
and
the
mixing
fresh-­‐saline
water
as
an
irriga>on
approach
targeted
to
op>mize
yield
and
water
produc>vity
2. Iden>fy
poten>al
adapta>ve
strategies
for
salt-­‐
affected
rice
systems

6. Input:
Weather
Soil
Crop
management
Cul>var
parameters
Output:
Crop
phenology
LA
index
Crop
N
status
Biomass
produc>on
Crop
Yield
Phenology
Assimila%on
Biomass
produc%on
Biomass
par%%oning
Water
balance
N
balance
Dynamics
of
salinity
in
soil
The
model
ORYZA
and
its
improvement

7. Iden>fica>on
of
suitable
irriga>on
strategy
to
manage
saline
and
fresh
water
availability
for
increasing
salt
affected
areas
produc>vity
Site:
Satkhira
BARI
experiment
sta>on
Variety:
BR47
Boro
Rice:
2013
and
2014
Irriga>on
water:
1.
Freshwater
2.
Mixture
1:1
ra>o
of
fresh
and
saline
water
(AFS1:1)
3.
Mixture
of
2:1
of
fresh
and
saline
water
(AFS2:1)
4.
Saline
water
Experiments
for
model
calibra%on
and
valida%on

8. Satkhira
2013
Satkhira
2014
Variability
of
soil
salinity
with
irriga%on
water
• Salinity
range:
1-­‐
16
dS
m-­‐1
• Con>nuous
increase
of
soil
salinity
over
the
crop
growth
0
4
8
12
16
0 50 100 150
Soil
salinity
at
15
cm
depth
(dS
m-­‐1)
Days
after
sowing
2:1
ratio
1:1
ratio
Saline
water
Fresh
water
0
4
8
12
16
0 50 100 150
Soil
salinity
at
15
cm
depth
(dS
m-­‐1)
Days
after
sowing

9. Iden>fica>on
of
suitable
irriga>on
strategy
to
manage
saline
and
fresh
water
availability
for
increasing
salt
affected
areas
produc>vity
Site:
Infanta
Laguna
(Farmer’s
field)
Variety:
BR47
Dry
season:
2013
and
2014
Irriga>on
water:
1.
Freshwater
2.
Alternate
fresh
and
saline
water
(AFS1)
3.
Alternate
fresh
and
saline
water
(AFS2)
4.
Saline
water
Experiments
for
model
calibra%on
and
valida%on

10. Variability
of
soil
salinity
with
irriga%on
water
Infanta
2013
Infanta
2014
• Salinity
range:
1-­‐
15
dS
m-­‐1
• The
soil
salinity
of
alternate
fresh-­‐saline
water
irriga>on
with
2-­‐week
interval
was
not
significantly
different
from
fresh
water
irrigated
treatment.
0
5
10
15
50 80 110 140
Soil
salinity
at
15
cm
depth
(dS
m-­‐1)
Days
after
sowing
0
5
10
15
30 60 90 120
Soil
salinity
at
15
cm
depth
(dS
m-­‐1)
Days
after
sowing
Fresh
Water
Saline
Water
AFS2
AFS1

11. Oryza
ver.3’s
ability
to
simulate
BRRI
Dhan
47
performance
Yield
7.5t/ha
RMSEn:
20.19
Yield
5-­‐6t/ha
RMSEn
:
13.10
0
3000
6000
9000
12000
15000
0 30 60 90 120 150
Dry
biomass
(kg/ha)
Day
after
sowing
0
3000
6000
9000
12000
15000
0 50 100 150
Dry
biomass
(kg/ha)
Day
after
sowing
0
3000
6000
9000
12000
15000
0 50 100 150
Dry
biomass
(kg/ha)
Day
after
sowing
0
3000
6000
9000
12000
15000
0 50 100 150
Dry
biomass
(kg/ha)
Day
after
sowing
Simulated
above
ground
biomass Observed
above
gorund
biomass
Simulated
yield Observed
grain
yield

12. Model
ability
in
salinity
effects
simula%on
on
BR47
yield
under
Satkhira
condi%ons
0
2
4
6
8
10
0 2 4 6 8 10
Simulated(t/ha)
Measured (t/ha)
Rice Yields
Y
=
0.77
x
+
220.4
r2
0.61
P(t)
0.18
EF
0.97
RMSE
344
RMSE
n
9.8%
n
=
63
A
model
reproduces
experimental
data
best
when
α
is
1,
β
is
0,
R2
is
1,
P(t)
is
larger
than
0.05
(indica>ng
observed
and
simulated
data
are
the
same
at
the
95%
confidence
level),
and
the
RMSE
is
similar
to
standard
devia>on
of
experimental
measurements.

13. Scenario
simula%ons
Factors:
• Satkhira
weather
data
over
15
years:
2000
-­‐2014
• Virtual
varie>es:
BR47
with
long,
medium,
and
short
crop
dura>on
• Sowing
dates:
weekly
from
Dec
1
to
Feb
10
• Irriga>on
water
management
– Fresh
water
– Saline
water
– 1:
1
ra>o
fresh
to
saline
water
– 2:
1
ra>o
fresh
to
saline
water

14. Variability
of
yields
and
water
produc%vity
among
varie%es
0
0.05
0.1
0.15
0.2
0.25
Transpired
water
productivity
(mm/kg) Variety
X
water
irrigation
management
0
1000
2000
3000
4000
5000
Grain
yield
(t/ha)
Varieties
X
Irrigation
water
management
• Higher
yield
was
observed
for
long
dura>on
variety
• Higher
water
produc>vity
was
observed
for
medium
dura>on
variety
• Op>mized
produc>vity
for
medium
variety
under
mixture
2
:1
ra>o
SW
FW
2W
1W
SHORT
SW
FW
2W
1W
LONG
SW
FW
2W
1W
MEDIUM
SW
FW
2W
1W
SHORT
SW
FW
2W
1W
LONG
SW
FW
2W
1W
MEDIUM

15. Trends
of
yield
and
water
produc%vity
over
sowing
dates
0.1
0.12
0.14
0.16
0.18
0.2
5 12 19 26 33 40 336 343 350 357 364
Transpired
water
productivity
(mm/kg)
Date
of
sowing
(Julian
day)
2000
2100
2200
2300
2400
2500
5 12 19 26 33 40 336 343 350 357 364
Grain
yield
(kg/ha)
Date
of
sowing
(Julian
day)
• Windows
of
cropping
calendar
tested
was
op>mized
for
a
yield
mean
• Efficient
water
use
and
higher
yield
were
observed
for
third
week
of
December
and
second
week
of
January

16. Summary
and
perspec%ves
• ORYZA
ver.3
has
good
ability
to
simulate
rice
produc>on
under
saline
condi>ons
• The
model
is
now
calibrated
with
BRRI
Dhan
47
• Alterna>ng
saline
water
with
freshwater
in
2-­‐week
interval
or
mixing
2
parts
of
freshwater
with
1
part
of
saline
water
are
poten>al
irriga>on
approaches
in
rice
cul>va>on
along
saline
areas
where
freshwater
is
limited
• Op>mized
water
produc>vity
and
higher
yield
were
enhanced
using
medium
and
long
dura>on
varie>es
established
at
around
3rd
week
of
December
(357)
and
1st
week
of
January
(12)
under
Satkhira,
Bangladesh
condi>ons
• Matching
the
assessment
with
current
farmers’
prac>ces
will
generate
useful
informa>on
on
prac>cal
strategies
for
op>miza>on
• Mapping
of
sites
with
available
and
limited
freshwater
source
will
be
useful
in
es>ma>ng
yield
poten>al
and
targe>ng
appropriate
technologies

17. THANK
YOU

18. Variability
of
simulated
yields
Factors Df SS MS F value Pr(>F)
Year 13 1874169209 144166862 115.6612 <2e-16 ***
Date of sowing 10 8257068 825707 0.6624 0.7603
Treatment 3 2697853515 899284505 721.472 <2e-16 ***
Variety 2 947924737 473962368 380.2474 <2e-16 ***
Error 5297 6602487508 1246458

19. 0
5
10
15
20
25
01-­‐Jan-­‐13 11-­‐Apr-­‐13 20-­‐Jul-­‐13 28-­‐Oct-­‐13 05-­‐Feb-­‐14 16-­‐May-­‐14
Underground
water
River
water
Ponded
water
Soil
at
15
cm
depth
Salinity
build-­‐up
in
Satkhira
site