This paper intends to investigate the service life time and to formulate a model of performance index degradation of surface irrigation system. The study conducted in all of technical surface irrigation area in Indonesia which has the assessment of irrigation performance and the real value of operation and maintenance demand during six months. The secondary data and site visit to location (primary data) are used for analysis in this study. The methodology consists of analysis on the physical aspects index that are physical infrastructure index and supporting aspect index. The supporting aspect index consists of 5 parameters that are crop productivity, supporting means, management organization, and institutional condition. Result shows that performance index degradation of surface irrigation system is as the addition between physical aspect and supporting aspect. The dominant parameter in the supporting aspect is crop productivity

1. http://www.iaeme.com/IJCIET/index.asp 455 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 11, November 2018, pp. 455–465, Article ID: IJCIET_09_11_045
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=10
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
PERFORMANCE INDEX DEGRADATION
MODEL OF SURFACE IRRIGATION SYSTEM
Hendra Ahyadi
Doctoral Program on Department of Water Resources, Faculty of Engineering, University of
Brawijaya, Malang, Indonesia
Suhardjono, Lily Montarcih Limantara and Pitojo Tri Juwono
Department of Water Resources, Faculty of Engineering, University of Brawijaya, Malang,
Indonesia
ABSTRACT
This paper intends to investigate the service life time and to formulate a model of
performance index degradation of surface irrigation system. The study conducted in
all of technical surface irrigation area in Indonesia which has the assessment of
irrigation performance and the real value of operation and maintenance demand
during six months. The secondary data and site visit to location (primary data) are
used for analysis in this study. The methodology consists of analysis on the physical
aspects index that are physical infrastructure index and supporting aspect index. The
supporting aspect index consists of 5 parameters that are crop productivity,
supporting means, management organization, and institutional condition. Result
shows that performance index degradation of surface irrigation system is as the
addition between physical aspect and supporting aspect. The dominant parameter in
the supporting aspect is crop productivity.
Keywords: performance index, surface irrigation, physical aspect, supporting aspect
Cite this Article: Hendra Ahyadi, Suhardjono, Lily Montarcih Limantara and Pitojo Tri
Juwono, Performance Index Degradation Model of Surface Irrigation System, International
Journal of Civil Engineering and Technology, 9(11), 2018, pp. 455–465.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=10
1. INTRODUCTION
Seepage is very significant as the water losses in irrigation canal. During the flowing, water
through the canal side and bed will be as a loss to the demand (Arunkumar and Ambujam,
2010). By the time, when water reaches the area, it has been estimated that the water is
depending upon the specific condition, the water losses may be on the 45% of the water
supply on the head of main canal (Sharma and Chawla, 1975). Therefore, analysis of the
losses from canals is an important aspect for the sustainable management of water resources.

2. Performance Index Degradation Model of Surface Irrigation System
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A perfect check all the losses by the time will become ineffective in controlling the seep at
(Dhillon et.al., 1986).
The freshwater worldwide presently in about 70% is the major need for irrigated
agriculture (Colaizzi et.al, 2008 and FAO, 2006). However, water availability for irrigated
agriculture is continuously ignored due to the competitive demand from the other sectors that
are industrial, recreational, municipal, and recently environmental uses (Colaizzi et.al., 2008,
Lorite et.al., 2004, Mangkoedihardjo, 2010; Razif et al., 2006; Samudro et al., 2018; Santos
et.al., 2010; Utama et al., 2018) The evaluation of irrigation performance is critically urgent
for improving the irrigation water management and the sustainability of irrigated agriculture
(Abuzar et.al., 2017). Many indicators have been suggested and evaluated for measuring the
irrigation performance (Boss, 1993, Nurrochmad, 2007, and Levidow et.al., 2013, Lorite
et.al., 2004, and Santos, et.al., 2010). The most parameters which are frequently used of the
indicators include crop water use, crop water supply, and crop productivity.
The number of Indonesian population is predicted becoming to 275 million persons on
2025. Therefore, to fulfill the material production of staple food like paddy, is indispensable
the irrigation network. For supporting the implementation of good management, the Ministry
of General Work and Housing publishes the Ministerial Regulation No. 12/2015. The
Ministerial Regulation regulates the assessment of irrigation system performance that consists
of 6 parameters that are physical infrastructure, crop productivity, supporting means, personal
organization, documentation, and institutional condition. The performance assessment of
irrigation system is a value as the irrigation system performance. In the Ministerial
Regulation, there is also mentioned that the physical infrastructure parameter has quality
weight of 45% with the evaluation weight is 45 for the maximum and 25 for the minimum.
By knowing the performance index degradation of irrigation infrastructure after development
and rehabilitation, it is hoped to be able to determine the design life time, what actions that is
necessary to be carried out for maintaining the performance and by the end can estimate how
much cost is needed to maintain the performance that is usually mentioned as the real number
of operation and maintenance demand (AKNOP). Based on the argumentation as above, it is
needed a benchmark that is mentioned as the performance index of irrigation system which in
turn can be related with the funding estimation on the operation and maintenance of irrigation
network.
2. MATERIALS ANG METHODS
This research conducted in all surface irrigation area of Indonesia. Based on the Ministerial
Decree of General Work (KEPMEN PU) No. 293/2014, the number of irrigation area in
Indonesia is 9,136.028 ha as presented in the Figure 1. Based on the elevation, data of
irrigation area can be classified into three that are plain area, transition area, and mountains
area. The classification of irrigation area in Indonesia is presented as in the Table 1.
Table 1 Classification of irrigation area in Indonesia based on the elevation and area
Type of area
Area < 1,000 ha
1,000 ha < area < 3,000
ha
Area > 3,000 ha
Number Area (ha) Number Area (ha) Number Area (ha)
Plain area 28 9,399 29 51,808 75 605,654
(slope < 2%) Total 132 666,861
Transition area 47 14,235 32 51,209 22 157,769

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(2%<slope<5%) Total 101 223,213
Mountains area 26 9,301 22 29,291 10 50,761
(slope > 5%) Total 58 89,353
Figure 1 Recapitulation of irrigation area in Indonesia
Based on the ministerial regulation of Ministry of General Work and Housing No.
12/2015, the performance assessment produces some categories as follow: a) 80-100: very
good; b) 70-79: good; c) 55-69: less and needs attention; d) <55: bad and needs attention. The
maximum value is 100 and the optimal value is 77.5.
2.1. Criteria of assessment
Assessment of irrigation system performance is intended to know the performance condition
of irrigation system with the parameters as follow: 1) Physical infrastructure (maximum
45%); 2) Supporting infrastructure (55%) that consists of cropping productivity (maximum
15%), supporting means (maximum 10%), personal organization (maximum 15%),
documentation (maximum 5%), institutional condition (maximum 10%).
2.2. Performance index degradation model of irrigation system
Performance index degradation model of irrigation system is obtained by adding the physical
infrastructure performance index to the supporting aspect (non-physical) degradation index as
follow:
ḟ (IKSI) = ḟ(IKpf) + ḟ(IKnf) (1)

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Where ḟ (IKSI) = performance index degradation model of irrigation system, ḟ(IKpf)
=performance index degradation model of physical infrastructure, ḟ(IKnf = performance index
degradation model of supporting aspect (non-physical).
3. RESULTS AND DISCUSSION
3.1. Performance index degradation model of physical performance on irrigation
system
The performance index data of irrigation physical infrastructure without the intervention of
selected operation and maintenance cost regarding to the classification of time limitation for
finishing the physical development or rehabilitation, area limitation, and elevation, is
analyzed by regression. This analysis intends to obtain a trend formula of physical
infrastructure performance index degradation. The polynomial regression is used for this
analysis due to the most appropriate trend approach value.
Based on the Ministerial Regulation No 12/2015, it is known that the limitation of
infrastructure performance index is optimum of 45 and minimum of 25. Therefore, the
analysis result by the infrastructure life time < 10 years is beginning from the index value of
45 and then the data is adjustment regarding to the data trend due to the analysis result.
However, for the analysis result by the infrastructure life time > 10 years is beginning from
the last number of analysis result on the infrastructure life time < 10 years and then is
continued regarding to the data trend with the infrastructure life time > 10 years. The data of
performance index regarding to the plain, transition, and mountains area are presented as in
the Table 2, 3, and 4.
Table 2 Data of performance index regarding to the area in plain area
Yea
r
Performance index value of physical infrastructure
Initial data Becoming into
<10 years >10 years <10 years >10 years
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3000
>3,00
0
1 35.02
35.1
6
33.86 - - - 45.00
45.0
0
45.00 - - -
2 34.83
34,6
3
32.67 - - - 44.81
44.4
7
44.40 - - -
3 34.45
33.9
9
31.17 - - - 44.42
43.8
2
43.80 - - -
4 33.86
33.2
2
29.38 - - - 43.84
43.0
6
43.19 - - -
5 33.08
32.3
5
27.28 - - - 43.06
42.1
8
42.59 - - -
6 32.11
31.3
5
24.87 - - - 42.08
41.1
9
41.99 - - -
6 - - - 33.86
19.7
9
31.18 - - - 42.08
41.1
9
41.99

5. Hendra Ahyadi, Suhardjono, Lily Montarcih Limantara and Pitojo Tri Juwono
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7 - - - 32.67
19.2
6
30.83 - - - 40.89
40.6
6
41.64
8 - - - 31.17
18.5
7
29.79 - - - 39.40
39.9
6
40.60
9 - - - 29.38
17.7
2
28.07 - - - 37.60
39.1
2
38.88
10 - - - 27.28
16.7
1
25.67 - - - 35.50
38.1
1
36.48
11 - - - 24.87
15.5
5
22.58 - - - 33.10
36.9
5
33.39
Table 3 Data of performance index regarding to the mountains area
Yea
r
Performance index value of physical infrastructure
Initial data Becoming into
<10 years >10 years <10 years >10 years
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
1 35.67
35.0
4
- - - - 45.00
45.0
0
- - - -
2 35,12
34.4
8
- - - - 44.45
44.4
4
- - - -
3 34.62
33.5
3
- - - - 43.95
43.4
9
- - - -
4 34.16
33.2
0
- - - - 43.49
42.1
6
- - - -
5 33.73
30.4
8
- - - - 43.06
40.4
4
- - - -
6 33.35
28.3
8
- - - - 42.68
38.3
4
- - - -
6 - - - 30.99
23.0
2
- - - - 42.68
38.3
4
-
7 - - - 28.66
22.7
1
- - - - 40.35
38.0
3
-
8 - - - 26.32
22.1
9
- - - - 38.01
37.5
3
-
9 - - - 23.98
21.4
4
- - - - 35.67
36.7
6
-
10 - - - 21.63
20.4
7
- - - - 33.32
35.7
9
-
11 - - - 19.27
19.2
7
- - - - 30.96
34.5
9
-

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Table 4 Data of performance area regarding to the transition area
Yea
r
Performance index value of physical infrastructure
Initial data Becoming into
<10 years >10 years <10 years >10 years
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
<1,00
0
1,00
0-
3,00
0
>3,00
0
1 35.08
30.6
8
45.31 - - - 45.00
45.0
0
45.03 - - -
2 34.23
29.8
9
44.62 - - - 44.15
44.2
1
44.34 - - -
3 33.38
29.1
6
43.96 - - - 43.30
43.4
7
43.68 - - -
4 32.54
28,4
8
43.32 - - - 42.46
42.8
0
43.04 - - -
5 31.70
27.8
7
42.72 - - - 41.62
42.1
9
42.44 - - -
6 30.87
27,3
2
42.15 - - - 40.79
41.6
4
41.87 - - -
6 - - - 27.70
23.4
7
28.95 - - - 40.79
41.6
4
41.87
7 - - - 25.83
21.9
1
27.31 - - - 38.92
40.0
7
40.22
8 - - - 23.99
20.5
3
25.58 - - - 37.07
38.7
0
38.50
9 - - - 22.15
19.3
4
23.80 - - - 35.24
37.5
1
36.71
10 - - - 20.34
18.3
4
21.94 - - - 33.43
36.5
0
34.86
11 - - - 18,54
17,5
2
20.01 - - - 31.63
35.6
8
32.93
The formula of performance index degradation model of physical infrastructure in the
plain, transition, and mountains area are obtained by regression analysis of the performance
index data due to the optimization result by using simplex. The results are presented as in the
Figure 2.

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Figure 2 Performance index degradation model of physical infrastructure in the plain, transition, and
mountains area
3.2. Performance index degradation model of non-physical infrastructure on
irrigation system
Based on the previous research, it is concluded that the performance index degradation of
crop productivity is changing based on the regression coefficient. However, for the
performance index degradation of supporting means, personal organization, documentation,
and institutional assessment have the fixed value regarding to the maximum performance
index. Table 5 presents the correlation, p-value, and regression coefficient for each
supporting factor to the physical infrastructure for mountains, plain, and transition area.
Table 5 Correlation, p-value, and regression coefficient in mountains, plain, and transition area
Dependent
variable
Mountains area Plain area Transition area
Physical
infrastructure
Physical infrastructure Physical infrastructure
Independent
variable
Corre-
lation
p-
value
Coef-
ficient
Corre-
lation
p-
value
Coef-
ficient
Corre-
lation
p-
value
Coef-
ficient
Crop
productivity
0.481 0.010 0.291 0.396 0.010 0.111 0.459 0.002 0.082
Supporting
means
0.512 0.005 0.381 0.144 0.364 -0.043 0.153 0.334 0.016
Personal
organization
0.200 0.307 0.075 0.180 0.253 -0.097 0.783 0.000 0.190
Documentation 0.147 0.455 0.047 0.140 0.376 -0.031 0.358 0.020 0.044
Institutional
condition
0.565 0.002 0.483 0.714 0.000 -0.857 0.472 0.002 0.241
28 42 42
Rtable 0.374 0.304 0.304

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Explanation: green: significant influence and meet to the theoretical role, yellow: not
significant influence and does not meet to the theoretical role. Correlation limitation: > 0.20
(0.00-0.199: very low correlation), p-value limitation: 1% (meets to the condition for < 0.01),
regression coefficient due to the model rationalization: >0 (positive).
The influence means that the changes of performance index value on the physical
infrastructure due to the intervention also gives the impact to the other variable of
performance index value. However, the theoretical role means if the performance index of
physical infrastructure gets the intervention so it has the positive trend, the other variable
should be directly proportional to produce the positive influence too to the regression
coefficient.
The formula of non-physical aspect degradation by 2 approaches that are: 1) based on the
non-physical index data of irrigation area that has filtered on the analysis of physical
infrastructure performance index degradation. Data of non-physical performance index which
has been classified based on the criteria of implementation time, area, and elevation are also
analyzed with polynomial regression and then there is carried out the correlation test to the
physical infrastructure performance index data that has got the intervention of operation and
maintenance cost (AKNOP); 2) Based on the assessment criteria which has been set by the
regulation of Ministry of General Work and Housing No, 12/2015, the non-physical
performance index degradation model consists of crop productivity, supporting means,
personal organization, documentation, and institutional assessment that is formulated as
follow:
ḟ (IKnf) = ḟ(IKpt) + ḟ(IKsp) + ḟ(IKop) + ḟ(IKd) + ḟ(IKp3a) (2)
where ḟ(IKnf) = non-physical performance index degradation model, ḟ(IKpt) =
performance index degradation of crop productivity, ḟ(IKsp) = performance index degradation
of supporting means, ḟ(IKop) = performance index degradation of personal organization,
ḟ(IKd) = performance index degradation of documentation, ḟ(IKp3a) = performance index
degradation of institutional assessment. Figure 3, 4, and 5 present the non-physical
performance index each for the plain, mountains, and transition area.
Figure 3 Non-physical performance index degradation of crop productivity in plain area

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Figure 4 Non-physical performance index degradation of crop productivity in mountains area
Figure 5 Non-physical performance index degradation of crop productivity in the transition area
3.3. Performance index degradation model of irrigation system in the plain,
mountains, and transition area
Performance index degradation model of irrigation system is as the addition between physical
infrastructure index degradation and non-physical or supporting aspect degradation model as
follow:
ḟ (IKSI) = ḟ(IKpf) + ḟ(IKnf) (3)
Figure 6 presents the combined three performance index degradation model of irrigation
system in the plan, mountains, and transition area.

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Figure 6 Performance index degradation model of surface irrigation system in the plain, mountains,
and transition area
4. CONCLUSION
Performance index of irrigation system is as the addition between physical structure and non-
physical (crop productivity, supporting means, personal organization, documentation, and
institutional condition) performance index as follow: IKSI = IKpf + IKnF. Model of physical
infrastructure index degradation without the intervention of operation and maintenance cost
for the plain, transition, and mountains area are as follow:
 Plain : Y = -0.0808X^2+0.029X +45.00
 Transition : Y = -0.0795X^2-0.2007X+45.00
 Mountain : Y = -0.0805X^2-0.2424X+45.00
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