Oral presentation at MODSIM 2017, Hobart, Tasmania, Australia 03 - 08 December 2017

1. Calibration & validation
of AquaCrop for
irrigated peanut
(Arachis hypogaea) in
lowland rice systems of
southern Laos
Khov, Vote, Hornbuckle, Inthavong, Oeurng,
Sengxua, Sihathep, Song, Eberbach

2. Increase in rural household income & nutrition
2
Vote, C., Newby, J., Phouyyavong, K., Inthavong, T., & Eberbach,
P. (2015). Trends and perceptions of rural household
groundwater use and the implications for smallholder
agriculture in rain-fed Southern Laos. International Journal of
Water Resources Development, 1-17.
doi: 10.1080/07900627.2015.1015071
Agricultural entrepreneurship ‘Pathway
out of poverty’ (World Bank, 2007)

3. Binding constraints
3

4. More binding constraints
4

5. And more…
5

6. But not only
that!
6
Photo: P. Eberbach
(2016)

7. An impossible situation?
7
No, but can the average smallholder afford it?

8. Why
AquaCrop??
8
Vote, C., Oeurng, C., Ty, S., Phongpacith, C., Inthavong, I.,
Seng, V., Eberbach, P., & Hornbuckle, J. (2015). A comparison
of three empirical models for assessing cropping options in a
data-sparse environment, with reference to Laos and
Cambodia, ACIAR Technical Reports, 30
Photo: L. Sinavong

9. AquaCrop data requirements
9
• Met data
• Irrigation
depth
• Soil water
Oklahoma State University,
Stillwater, OK, USA

10. Phone Ngam Rice Research Centre, Pakse
10

11. Effect of varying water treatments on DS peanut
production
11
Original
W1: -30 kPa
W2: -60kPa
W3: -90kPa
Modified
W1: Mon/Fri
W2: Mon
W3: ~ 10 days

12. Meteorological conditions
12
Unseasonal rainfall
Sharp drop in Ta

13. Cumulative irrigation & soil sensor response
13

14. Hostile
takeovers
14
09 March 2016 (t left),
23 March 2016 (t right),
31 March 2016 (b left),
21 April 2016 (b right)

15. Model calibration
15
Parameter Default Calibrated
Soil water depletion factor (canopy expansion) 0.60 0.55
Soil water depletion factor (stomatal closure) 0.55 0.50
Soil water depletion factor (canopy senescence) 0.80 0.85
Water Productivity normalized for ETo and CO2 (g/m2) 17 13
Normalized WP during yield formation (% WP) 100 87
Reference Harvest Index (%) 0.50 0.40

16. r = 0.90
RMSE= 14.1 % CC
NRMSE= 27.0 %
E= 0.61
d = 0.91
DAP
0 20 40 60 80 100 120
21 Apr 2016
CC(%)
0
20
40
60
80
100
19 Dec 2015
Simulated
Observed
r = 0.85
RMSE= 18.7 % CC
NRMSE= 44.2 %
E= 0.00
d = 0.85
DAP
0 20 40 60 80 100 120
21 Apr 2016
CC(%)
0
20
40
60
80
100
19 Dec 2015
Simulated
Observed
Canopy cover
16
r = 0.92
RMSE= 13.7 % CC
NRMSE= 24.6 %
E= 0.65
d = 0.93
DAP
0 20 40 60 80 100 120
21 Apr 2016
CC(%)
0
20
40
60
80
100
19 Dec 2015
Simulated
Observed W1
W2
W3
r values
W1 0.92
W2 0.85
W3 0.90

17. r = 0.95
RMSE= 0.6 ton/ha
NRMSE= 35.1 %
E= 0.56
d = 0.89
DAP
0 20 40 60 80 100 120
21 Apr 2016
AB(ton/ha)
0
1
2
3
4
19 Dec 2015
Simulated
Observed
r = 0.94
RMSE= 0.7 ton/ha
NRMSE= 30.6 %
E= 0.70
d = 0.95
DAP
0 20 40 60 80 100 120
21 Apr 2016
AB(ton/ha)
0
1
2
3
4
5
6
7
19 Dec 2015
Simulated
Observed
r = 1.00
RMSE= 0.6 ton/ha
NRMSE= 38.8 %
E= 0.49
d = 0.92
DAP
0 20 40 60 80 100 120
21 Apr 2016
AB(ton/ha)
0
1
2
3
4
5
6
19 Dec 2015
Simulated
Observed
Aboveground
biomass
17
W1
W2
W3
r values
W1 0.94
W2 1.00
W3 0.95

18. Grain yield
18
Yield (t ha-1)
Treatment Observed Simulated
W1 1.23 1.29
W2 0.61 0.85
W3 0.73 0.45

19. Soil water
19
W1
W2
W3
r values
W1 0.28
W2 0.26
W3 0.48

20. Conclusions & further work
20
• Satisfactory simulation of canopy cover, biomass & yield production under
well-watered conditions
• Degree of reliability under water-stressed conditions, but should be further
investigated due to ‘cold-snap’ (W2)
• Very poor simulation of soil water
• Further experimentation required to alleviate issues of poor lateral water
movement and/or water monitoring to conduct a thorough evaluation to
improve confidence in recommendations for the farmer.

21. Acknowledgements
21
‘Improving water and nutrient
management to enable double cropping
in the rice growing lowlands of Lao PDR
and Cambodia’ [SMCN/2012/071]
http://aciar.gov.au/project/smcn/2012/071

22. Camilla Vote
+856 20 9178 2204
cvote@csu.edu.au