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Selection of irrigation duration for high performance furrow irrigation on cracking clay soils Rod Smith, Jasim Uddin , Malcolm Gillies. Research question. Is there a simple objective way of estimating time to cut-off for furrows in real-time & - PowerPoint PPT Presentation
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Selection of irrigation duration for high performance furrow irrigation on
cracking clay soilsRod Smith, Jasim Uddin, Malcolm Gillies
Research question
Is there a simple objective way of estimating time to cut-off for furrows in real-time&that does not require substantial data or complex computation
Typical infiltration curves for a cracking clay soil
0 50 100 150 200 250 300 350 400 4500.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
F1F2F3F4
Time (min)
Cum
ulat
ive
infil
trat
ion
(mm
)
Irrigation performance – various flow rates – 5% runoff
3 4 5 6 7 8 90
200
400
600
800
1000
1200
1400
1600
1800
0
20
40
60
80
100
120
Tco Ea Er DU
Inflow (L/s)
Tim
e to
Cut
-off
(min
)
Ea, E
r, &
DU
(%)
3 4 5 6 7 8 90
200
400
600
800
1000
1200
1400
1600
1800
0
20
40
60
80
100
120
Tco Adv time to 580 m Xco (m) Ea
Inflow (L/s)
Tim
e (m
in) &
Cut
-off
dist
ance
(m)
Ea (%
)
Tco vs advance time to mid-way down furrow
100 200 300 400 500 600 7000
200
400
600
800
1000
1200
1400
1600
varying infiltration
varying inflow rate
Advance time (min)
Tco
(min
)
0 100 200 300 400 500 600 7000
200
400
600
800
1000
1200
1400
1600
1160 m 565 m
Advance time (min)
Tco
(min
)
Data for 4 furrows x 4 irrigations
100 150 200 250 300 350 4000
100
200
300
400
500
600
700
800
900
5 L/s7 L/s6 L/s
Advance time to half distance (min)
Tco
(min
)
Example application efficiencies (%) – one field – average of four furrows
Irrigation FarmerIndividual Optimum
(5% runoff)
Set distance
Guide-lines
‘Autofurrow’(5% runoff)
2 49.5 58.6 64.2 65.2 61.5
4 54.5 70.9 73.2 74.1 77.6
3 70.6 95.0 95.1 86.4 89.7
5 90.3 95.0 95.0 96.9 98.8
7 81.8 95.0 86.8 83.8 92.5
Mean 69.4 82.9 82.9 81.3 84.0
Application efficiencies (%) – single furrows
Furrow Farmer Optimum Set distance Guidelines
#12 52.1 92.2 79.3 71.7
#41 27.9 94.8 95.3 96.9
#61 63.6 75.4 82.8* 74.0
#74 87.8 87.8 93.7* 79.6
#87 25.7 83.4 72.7 71.6
#91 75.9 93.2 69.9 86.0
Ba 85.8* 79.9 87.1* 77.7
By 56.7 93.4 94.0 99.5
F 86.5 89.8 87.5 85.9
K 66.3 92.8 94.6 99.8*
* advance did not reach end of field
Summary Three methods compared:
‘Autofurrow’Set distance cut-offGuidelines based on advance rate
Common featuresData collected during an irrigation is used to
control that irrigationSpeed of advance is a function of flow rate, soil
properties, moisture deficitHence adapt to changes in those variables
Summary
‘Autofurrow’ is a reliable predictor of Tco but is data and computationally intensive.
The two simpler alternative methods give deliver performance generally equivalent to ‘Autofurrow’ and each other – but some variability
All methods deliver better performance than the ‘average’ grower
All three methods benefit from fine tuning, either manually or as self learning in automated systems
