Estimation of Water Balance Variables in the SRI Paddy Field by Considering Soil Moisture

I

PA W EES

PONMLKJIHGFEDCBA
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Table of contents
Opening Remarks
i

Dr. Fi-John Chang
Councilof Agriculture,

Executive

Yuan, Taiwan,

RO C

iii

Director-G eneral W ei-Fuu Yang

vi

Dr. Tai-Cheol Kim

viii

O r. Yohei Sato

x

Dr. Sun-Joo Kim

xii

Session I: Keynote Speech
A Decision Support System for Sustainable Water

1

SustainablePaddy Production and Water Management in the Context of 21 s, Century Climate Change

20

Challenges in remediation of agricultural soil contaminated by radioactive substances

22

ImproveSoil Moisture Estimation in Arid/Semi-arid Region using In-Situ and us g Stu a d Remote Sensing
Information

32

Session 11- General session
Geographyof Damage: Tsunami Inundation Area of the Great East Japan Earthquake

.47

On-farmIrrigation Development and Management in Lower Myanmar for Sustainable Rice Production

..48

Effectsof suspended solids and water temperature on phosphorus in the drainage canal in a paddy-field
district

49

Applicability study of watershed model for Korea TMDL

50

ClimateChange Impact on Irrigation System and Farmers' response (The Plychomphol Irrigation Project,

PhitsanulokProvince, Thailand: case study)

51

Thedecision making of fallow strategies for irrigation water management in northern Taiwan

52

SessionIHGFEDCBA
III - Paddy Environmental

Sustainability and Irrigation Techniques

EvaluationIrrigation Water Requirements and Crop Yields with Different Irrigation Schedules for Paddy
Fieldsin ChiaNan Irrigated Area, Taiwan

53

Adaptingthe Water Management Practice to the Double Exposure: The System of Rice Intensification in
Taiwan

Feasibility of SRI Application

54
for Reduction

of Irrigation

and NPS Pollution

Effectof percolation rate on methane emission from paddy soil columns

in Korea

56

57

Evapotranspiration
Srlation
Jstimation

over W~t·~nd Dry System of Rice Intensification (SRI) Paddy Field Environment:

Using Surface Energy Balance Model

58

of Water Balance Variables in the SRI Paddy Field by Considering Soil Moisture

59E

S e s s io n

I V - S u s ta in a b le

W a te r

M anagem ent

Types of Damage to Drainage Facilities of Paddy Irrigation Schemes along Sendai Bay in Miyagi Prefecture
by the Tsunami Caused by the East Japan Earthquake

60

The Geographical Characteristics of Heavy-metal Contamination on Paddy-fields in Taiwan

61

Application of watershed scale model using NPS monitoring data

62

Application of a Distributed Kinematic Wave Storm Runoff Model (KIMSTORM)

for Flood Simulation

Considering Dam Release in the South Han River Basin of Korea

6]

Applying Genetic Algorithm to optimizing Artificial Neural Networks for estimating arsenic pollution in
groundwater systems

6

Estimating of the spatial rainfall data using inverse distance weighing (IDW) in the Middle Taiwan

6~

e s s io n V - I m p a c ts

o f C lim a te

C hange

a n d N a tu r a l

H azards

on Paddy

a n d W a te r

E n v ir o n m e n t

Factor Analysis of Urban Non-point Source Pollutant Discharge by LandusehgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

6f

Uncertainty of paddy irrigation requirement estimated from climate projections in the Geurnho river basin,
outh Korea

6'

Simulation of greenhouse gas emissions from a Korean rice paddy field by flooding water depth using the
6(

Dl~DC model
Impact assessment method of climate change on agricultural water use by using a distributed water

6(

circulation model
The importance of infrastructure development in farmland from the viewpoint of a crop science studentthrough my volunteer experience on Tohoku earthquake disaster

7

Modeling the rice-climate indices in Taiwan

7

The Impact of Climate Change on Paddy Field Irrigation in Southern Taiwan

7

S e s s io n

VI---

R iv e r

E n v ir o n m e n ta l

D e s ig n

a n d R e s to r a tio n

Population Model of Rana japonica Crossing Agricultural Concrete Canals: Evaluating Population
Conservation by Improving the Migration Routes of Frogs

7l

Fish migration from paddy fields to canals during mid-summer drainage

7

Extension of Space-Time Conservation Method to Analysis of One-Dimensional
Channels with Irregular Geometries

Unsteady Flow in
7

Comparative DNA and morphological analyses with respect to the distribution ranges of an introduced and .,.
native Rhynchocypris species in the Tama River Basin, Japan

76

Nonpoint source pollution control programs for reservoir water quality improvement in Korea

77hgfedc

An

Investigation of the Effectiveness of Ecological Construction and Fish Assemblages in Southern Branch

ofChia-Nan Irrigation Channel

78

Study on Agricultural Benefits by Increasing Capacity of Water Ponds-A Case Study at Taoyuan Paddy
Fields

79EDCBA

P o ste r

S e s s io n

Estimating Effective rainfall trends for Irrigation using dynamic factor analysis

80

Modeling Irrigation Return Flow for the Return Flow Reuse System in Paddy Fields

81

Assessment of Cooling Effect Range of Paddy Fields by LANDSA T Thermal Imagery

82

The Impact Assessment of Climate Change on Ecological Base Flow in Da-an River

83

Assessing the Accuracy of Flow Management using Propeller-type Flowmeter, Acoustic Doppler Profile
(ADP) and Radar Surface Velocimeter in Rivers

84

Geography of Damage: Tsunami Inundation Area ofthe Great East Japan Earthquake

85

Population Model of Rana japonica Crossing Agricultural Concrete Canals: Evaluating Population
Conservation by Improving the Migration Routes of Frogs

86

Comparative DNA and morphological analyses ,with respect to the distribution ranges of an introduced and
native Rhynchocypris species in the Tama River Basin, Japan

87

Long-term C02, CH4 and N20 flux measurement in rice paddy fields with relaxed eddy accumulation method
in Japan and Thailand

88

The yield and growth of paddy rice plants as functions of soil dryness during growing stages

89

Decomposition characteristics of organic nitrogen in digested slurry from methane fermentation after
application to paddy soil

90

Evaluation of soil temperature rise effect and soil moisture environment during a period of solar heat
disinfection in greenhouse

91

Agricultural Land Use and Prospect in Brantas River Basin in Indonesia

92

DSS for Agricultural Drought Management Using Risk Assessment

94

Study on the necessity of management for agricultural nonpoint source pollution in Korea

95

Estimation of croJ2water requirement for paddy rice with climate change in Korea

96

Surface Temperature and Energy Balance of Paddy Field during Wet and Dry Period in Tropical Watershed
Soil

97

Tillage and crop residue effects on carbon sequestration and crop yields in the lowland environment of
Chitwanvalley of Nepal

98

Evaluation Irrigation Water Requirements

and Crop Yields with Different Irrigation Schedules 'tor-Paddy

Fields in ChiaNan Irrigated Area, Taiwan

100

Adapting the Water Management Practice to the Double Exposure: The System of Rice Intensification in
Taiwan

101E

S u b m itte d

A b str a c ts

Mode1ing of Nitrate leaching from a potato field using Hydrus-2D

103

Investigation of Water Level to Maintain Advantage of Isoetes taiwanensis in Menghuan-Pond Wetland at
Yangmingshan National Park

104

Nitrogen and phosphorus losses from paddy field under water-saving irrigation in Taihu Lake Region of
China

10)

Wind-Induced Flow in a Closed Water Body with Floating Culture System

lOC

Using Tracer to Evaluated Efficiency of Domestic Water Purification in Oyster Shell Contacted Beds

10'

Study on Development of Farmlands outside region of irrigation association in Taiwan

101

Physicochemical and biological properties of constructed small- scale ponds for ecological improvement in
paddy fields

10(

P A EDCBA
W EES

2 0 1 1 I n te r n a tio n a l
C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK
Capacity Building for Participatory Irrigation and Environmental Management
27 October 2011, National Taiwan UniversityIHGFEDCBA

2011054
E s tim a tio n

o f W a te r

B a la n c e

V a ria b le s

in th e S R I P a d d y

F ie ld b y C o n S id e rin g

S o il M o is tu re

Chusnul Arif,2, Budi I Setiawarr', Masaru Mizoguchi',

WVUTSRQPONMLKJIHGFEDCBA
e p a r tm e n t
o f G lo b a l A g r ic u ltu r a l

1D

S c ie n c e ,

th e U n iv e r s ity

Ryoichi Doi1 PONMLKJIHGFEDCBA
of Tokyo, Japan

Phone: (+81)-3-5841-1606
Email: chusnul_ar@yahoo.com
2D

e p a r tm e n t

o f C iv il a n d E n v ir o n m e n ta l

E n g in e e r in g ,

B ogor,

Bogor

A g r ic u ltu r a l

U n iv e r s ity

(IP B ),

In d o n e s ia

Phone: (62)-251-8627225

A b s tra c t

Water Balance analysis in the System of Rice Intensification (SRI) paddy field is essential to evaluate water
management regarding its water productivity and water use efficiency. In the observed field, however, particular
water balance variables are not easily measured since the methods are often costly, complicated and time
consuming. The current study proposed the novel method to estimate non-measurable water balance variables by
considering monitored soil moisture in the field by using Excel solver estimation. The field experiment was
conducted by adopting SRI rice cultivation in the SRI experimental field in Nagrak Organics SRI Center,
Sukabumi, West Java, Indonesia during the first rice season 2010/2011 (October 2010 to Februari 2011, wet
season).Results of the developed model showed satisfactory result between observed and estimated soil moisture
with the values ofR2 higher than 0.70 in the all growth stages. Accordingly, each estimated water balance
variable has reasonable trend and value. We found that total inflows through precipitation and irrigation water
were 1331.8 and 107 mm, respectively. Meanwhile, the water has leaved the field through crop
evapotranspiration, runoff and percolation with their total values were 296.5, 1010.7 and 116 mm, respectively.
Minimum irrigation water was needed to meet plant water requirement since no standing water and high
precipitation occurred in the current wet season. Also, the total crop evapotranspiration and percolation were low
according to the FAO values as the results oflow reference evapotranspiration and reduction of hydrostatic
pressure, respectively. On the other hand, high runoff was estimated because almost precipitation was drained
directly and only retained the water in the field to meet crop evapotranspiration.
K eyw ords:

s y s te m

o f r ic e in te n s ific a tio n

m o is tu r e , e x c e l s o lv e r

e s tim a tio n

(S R I), w a te r

m a n a g e m e n t,

w a te r

b a la n c e ,

s o il

PA WEES 2011 International

Conference

Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan UniversityIHGFEDCBA

In tro d u c ti

Management

n

Recently, the System of Rice Intensification
practices for raising the productivity

(SRI) is well-known as a set crop management

of irrigated rice by changing the management

of plants,

soil, water and nutrients. Although some critics were dismissed to the SRI (Sinc\air and
Cassman 2004; Sheehy et al. 2004; Dobermann 2004), however, its benefits have been
validated in 42 countries of Asia, Africa and Latin America (Uphoffet
In the SRI paddy field, water is irrigated according to intermittent

al. 2011).

irrigation with an alternate

wetting and drying irrigation system instead of continuous flooding for conventional

rice

cultivation. By this method, SRI can save up to 28% of water in Japan (Chapagain and Yamaji
2010),40% in Eastern Indonesia (Sato et al. 2011) and 38.5% in Iraq (Hameed et al. 2011),
and can increase water use efficiency by up to 43.9% in China (Lin et al. 2011). Here, the
field is kept saturated and then letting the field dry for particular period. Accordingly,
balance analysis is usually performed to evaluate water productivity

water

and water use efficiency

in field scale.
However, particular water balance variables such as crop evapotranspiration
are not easily measured in the field. The typical field measurement

and percolation

methods are often costly,

complicated and time consuming especially for equipment preparation. Crop
evapotranspiration, the main route of water loss from both plant and soil surfaces, is
commonly measured empirically by using a lysimeter (Vu et al. 2005; Tyagi et al. 2000;
Mohan and Arumugam

1994). Meanwhile, percolation, as downward movement of excess

water through the soil, is measured by using range infiltrometer and Iysimeter technique and
then calculated based on Darcy's law (Janssen et al. 2010).
Soil moisture represents water availability in the field and it commonly affect pattern of water
balance variables such as evapotranspiration,

percolation and runoff inhgfedcbaZYXWVUTSRQPONMLKJIHGFEDCB
paddy field (Kim et al.

2009; Reshmidevi et al. 2008; Li and Cui 1996). In addition, it is easier measured than those
variables based on the dielectric constant of the soil by well-developed

sensor. Accordingly,

real time soil moisture monitoring has been developed for monitoring SRI paddy field in
Japan with intermittent irrigation (Manzano et al. 2011). Therefore, the method to estimate
non-measurable water balance variables based on the monitored soil moisture is vital for
water productivity and water use efficiency evaluation without complicated
variables measurements.

water balance

2 0 1 1 I n te r n a tio n a l
C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK
Capacity Building for Participatory Irrigation and Environmental Management
27 October 2011, National Taiwan University

PA W EES

The present stud

proposes novel method to estimate non-measurable

in SRI paddy field by considering

water balance variables

the monitored soil moisture on daily basis. Then, the model

is validated by comparing observed and estimated soil moisture during planting period.IHGFEDCBA

M e th o d s

F ie ld E x p e rim e n t

Water balance analysis was performed according to the field data on experimental

SRI paddy

field in the Nagrak Organics SRI Center (NOSC), Sukabumi West Java, Indonesia during the
2 0 1 0 /2 0 1 1
(October 2010 to Februari 2011, wet season). The field is located
first rice seasonPONMLKJIHGFEDCBA

at 06°50' 43" Sand 106°48'20" E, at an altitude 536 m above mean sea level (Fig. 1).

Fig. 1 Experimental field location in west java, Indonesia

The field was planted with the local variety of rice (Oryza sativa L), Sintanur, a rice variety
suitable for SRI in Indonesia. Here, we used the following SRI rice cultivation:

single planting

2 0 1 1 I n te r n a tio n a l
C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK
Capacity Building for Participatory Irrigation and Environmental Management
27 October 2011, National Taiwan University

PA W EES

x 30 cm, using an organic fertilizer
of young seedlings (5 days after sowing) spaced at 30 cmhgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

at I kg/m2 on the land preparation and no chemical fertilizer. Moreover, weeding process was
conducted regularly every 10 days, starting from 10 day after transplanting
after together with supplying local indigenous microorganism

(DA T) to 40 days

(IMO) to enhance biological

activity in the soils.
The field was kept continuous saturated and no standing water was applied during planting
period as recommendation

from local SRI center. Continuous soil saturation is recommended

as the less water consumptive

irrigation regime and its application

is feasible only when the

irrigation water supply is reliable (Janssen et al. 2010). Accordingly,

irrigation threshold was

performed to maintain water level at -5 cm to 0 cm soil depth.IHGFEDCBA
F ie ld M e a s u re m e n ts

The field was monitored by the new developed information system, Field Network System
(http://info.ga.a.u-tokyo.ac.jp/fns/Main
sensors. The meteorological

Page), which is comprise of meteorological

data including precipitation

and soil

were recorded by using Davis

Vantage Pro2 Weather Station every 30 minutes. Daily average values of air temperature,
wind speed and relative humidity as well as total solar radiation were used to calculate
reference evapotranspiration

(ETo) based on the FAO Penman-Mointeith

In the soil layer, soil moisture sensor (5TE: soil moisture, temperature,

(AlIen et al. 1998).
electrical

conductivity), developed by Decagon Devices, Inc., USA, was installed at the 10-cm depths
from the top of soil to measure soil parameters every 30 minutes. All of data were sent daily
to the web server on the particular time, thus we called this information system as a quasi real
time monitoring system.
M o d e lin g A p p ro a c h

Water balance analysis was performed according to the schema in Fig. 2. The inflow to the
field consists of precipitation

and irrigation water, while water leave the field through crop

evapotranspiration, runoff and percolation. Here, we define runoff as lateral water movement
either artificially through drainage process or naturally when water depth exceeds the height
ofthe drainage outlet and as seepage through bunds in paddy field. Accordingly,
balance equation can be expressed as:

water

2 0 1 1 I n te r n a tio n a l
C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK
Capacity Building for Participatory Irrigation and Environmental Management
27 October 2011, National Taiwan University

PA W EES

t;S(t)

= P(t)hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
+ I(t) - Qr(t) - DP(t) - ETc(t)
(1)

where t;S is the change of soil moisture equivalent to water depth (mm), P is precipitation
(mm), I is irrigation water (mm),Qr is runoff (mm), DP is percolation (mm) and ETc is crop
evapotranspiration (mm).

Evapotranspiration

t

(Elc)

Rain(P)

1

Irrigation (I)
Runoff (Q r)

~ WVUTSRQPONMLKJIHGFEDCBA

I

1'_I

,

I

,

''I''
Hl ~!H2

,
,
l

tu .

Hardpan

Deep percolation (DP)

__

G roundwater (G w)

Fig. 2 Water balance in paddy field
Table 1. Review measured
Variables

and estimated
Measured

water balance variables
Calculated

of the current study

Estimated

Method

Soil moisture

Soil moisture

sensor

Precipitation

Rain gauge

Crop evapotranspiration

Excel Solver

Irrigation water

Excel Solver

Percolation

Based on Darcy's
their parameter

law and

was estimated

by Excel Solver
Runoff! drainage

Excel solver

As mentioned above, however, only soil moisture, precipitation

were measured by the

developed monitoring system. Therefore, others variables were estimated and calculated
based on their parameters as presented in Table 1. Hence, we used Excel solver to estimate
non-measurable variables since it has ability to find optimal decision variables up to 200 data

PA W EES

2011

I n te r n a tio n a l

C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK

Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan University

Management

within one process by minimizing or maximizing objective function. The guidelines for using
Excel Solver could be referred to Morrison (2005).PONMLKJIHGFEDCBA
E x c e l s o lv e r e s t im a t io n

Excel solver was performed to estimate non-measurable

variables according to Eq. 1. Since

the number of decision variables was limited, the estimation process was carried out four
times according to each growth stage i.e. initial (I), crop development

(11), mid-

season/reproductive (Ill) and late season stage (IV) (Vu et at. 2005; Tyagi et at. 2000; Alien et
at. 1998; Mohan and Arumugam

1994).

Table 2. Minimum and maximum crop coefficient in each growth stage
Total day

Growth stage

Crop coefficient
Minimum

Maximum

Initial

27

0.80

1.05

Crop development

45

0.90

1.20

Mid-season

24

LlO

1.40

Late season

20

0.90

1.20

The decision variables consist ofETc, I, Qr and H2 by minimizing the following equation
objective function:hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
n

Error

= ~]So

(t) - Srn(t)1

(2)

1=1

subject to the constraints:
ETcrnin ~ ETc(t) ~ ETcrnax

(3)

H2(t) ~ Hl(t); Qr(t) ~ 0; let) ~ 0 (4)
where So(t) is observed soil moisture equivalent to water depth (mm), Sm(t) is estimated soil
moisture equivalent to water depth (mm), t is time (day, starting from 1 to 116 DAT), n is
total days (n

=

116), ETcmin is minimum crop evapotranspiration,

ETcmax is maximum crop

evapotranspiration, HI and H2 are water table at different point as parameters to determine
deep percolation (mm). ETcmin and ETcmax were given by multiplying ETo to minimum and
maximum crop coefficient, respectively

(Table 2). Those values were determined according to

PAWEES 2011 International

Conference

Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan University

the FAO recommendation

value (AlIen et al. 1998) and by considering

and soil moisture at the field as well particularly

Management

current rice cultivation

in the initial and crop development

stages.PONMLKJIHGFEDCBA

I n it ia l C o n d it io n s

The initial condition for each decision variable should be determined properly to gain reliable
estimated variables. Hence, we consider the actual field situation for each decision variable as
explained below:

I. Crop evapotranspiration

(ET c)

Initial condition was given according to actual crop evapotranspiration

under water stress

condition (AlIen et al. 1998) and can be calculated using following relationship.
ETc(t)

= Ks(t)xKc(t)xETo(t)

(5)

where Kc is a crop coefficient that varies with the crop growth stage, Ks is water stress
coefficient as the effect of soil moisture in the root zone and calculated using the relationship
between actual and saturated water content (Kim et al. 2009; Li and Cui 1996):
ifSa(t) = Ss

Ks=1

(6)

KshgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
= InCI + 1 0 0 ( S a ( t ) / S s )
if11SS ~ Sa(t) < Ss (7)
In101

Ks = s exp

sa(t) - 11SS)
(

if Sa(t) < 11SS

(8)

11SS
3

( m / m \ respectively and 11and e
where Sa and Ss are the actual and saturated water contentWVUTSRQPONMLKJIHGFEDCBA

are empirical coefficient for rice set equal to 0.80 and 0.95, respectively

(Li and Cui 1996).

2. Percolation (DP)

Percolation (DP) was calculated according to Darcy's law for vertical flow (Jury and Horton
2004). According to Fig. 2, DP was calculated based on two different water tables by
following equation:

DP(t)

= Ks Hl(t)

- H2(t)

/::,.Z

(9)

PA WEES 2011 International
Conference
Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan University

where Ks is saturated hydraulic conductivity

(mm/day) with a constant value,!1Z

Management

is

secondary soil layer (mm), HI is water table according to effective depth (mm) and H2 is
lower water table at different points in the field (mm) which was estimated by Excel solver.
Percolation loss is primary function of soil texture, and its rate is relatively low for clay soil
range to 1-5 mm/d (Guerra et al. 1998) as the current location. Thus, percolation was given at
the rate of 1 mm/d on determined H2 as initial condition.
3. Runoff (Qr)
In paddy field, runoff is primary function of precipitation
(Chen et al. 2003). Accordingly,

which has positive correlation

as initial condition, runoff was occurred when precipitation

occurred and its value was given by the following formula:
ifP(t) < ETomax (10)
0
Qr(t) =hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Qr(t) = pet) - ETom ax

ifP(t) 2: ETomax

(11)

where ETomax is maximum reference evapotranspiration

(mm).

4. Irrigation water (I)
Irrigation water was given when the precipitation
through crop evapotranspiration,

doesn't meet to the plant water requirement

and also no runoff on the current day. Accordingly,

initial

condition was given as the following formula:
I(t) = 0

ifQr(t) > 0

(12)

I(t) = ETcini (t) - pet)

ifQr(t) = 0

(13)

where ETciniis initial crop evapotranspiration

(mm).PONMLKJIHGFEDCBA

M o d e l V a lid a t io n

The model was validated by the indicator of the coefficient of determination
between observed and estimated soil moisture. The value ofR
higher values indicating better agreement.

2

(R2) to compare

ranged from 0.0 to 1.0 with

P A W E E S 2 0 1 1 I n te r n a tio n a l
C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK
Capacity Building for Participatory Irrigation and Environmental Management
27 October 2011, National Taiwan UniversityIHGFEDCBA

R e s u lts a n d D is c u s s io n s

E v a lu a tio n o f th e m o d e l fe a s ib ility

The model feasibility was evaluated by comparing observed and estimated soil moisture
during planting period. Estimated soil moisture was resulted based on estimated nonmeasurable water balance variables (Table 1). Estimated soil moisture showed good
agreement with observed data pointed by high R2 at the values of 0.71, 0.78, 0.77 and 0.83 for
initial, crop development,

mid-season and late season stages, respectively

(Table 3). R2 value

greater than 0.6 is acceptable or satisfactory for model prediction (Kim et al. 2009).
Therefore, the current method was feasible to estimate non-measurable

water balance

variables.
Table 3. Model validation for estimated soil moisture in the experimental field
Growthstage

Total number estimated data

R2

Initial

108 (each variables was 27 data)

0.71

Cropdevelopment

180 (each variables was 45 data)

0.78

Mid-season

96 (each variables was 24 data)

0.77

Late season

80 (each variables was 20 data)

0.83

The highest R2 was achieved when total estimated data were lowest number on the late
season. However, when the total number estimated data more than 100, higher estimated data
v i c e v e r s a . Accordingly, the number estimated data were not
has higher R2 value, andWVUTSRQPONMLKJIHGFEDCBA

corresponding to R2 value, despite higher number data need more time for the estimation
process. The major factor to gain high R2 with reliable estimated data is the initial conditions.
This should consider the actual field condition such as weather, soil texture and crop as
considering in this study.
E s tim a tio n o f w a te r

b a la n c e

v a ria b le s

Excel solver has estimated non-measurable

water balance variables properly with reliable

values and trend during planting period as shown in Fig. 3. Estimated soil moisture trend deal
with those observed data in the entire planting period. However, over and underestimation
occurred when observed soil moisture increased and decreased suddenly in the crop

PA WEES 2011 International
Conference
Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan University

Management

development and late season stages. The observed soil moisture values varied fromPONMLKJIHGFEDCBA
0 . 5 1 1 to
3

3

0.608WVUTSRQPONMLKJIHGFEDCBA
m /m
as the result of continuous saturated irrigation threshold at water level ranged
from -5 cm to 0 cm from soil surface. Therefore, high intensity of precipitation
wet season was commonly drained directly as runoff. Accordingly,
similar to that precipitation.

in the current

the runoff trend's was

High intensity of irrigation water was supplied when precipitation

was limited particularly in the end of mid-season and early late season stage (Fig. 3).

0.500

c::::::::JhgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Pred pitai 011
0.400

--

Cl>ser.ed soil

- - - - Estim aed

rnoisure

20J

soil m oisture

250

0.300

:D
25

I
.6
"&

Xl

15
10

w .< W .< W ~

Estim ated

Irrigaion

water

........ -Estsnated RJnoff

~

5
20J

Fig. 3 Observed and estimated water balance variables during planting period.

The non-measurable water balance variables were estimated on daily basis and the total result
in each growth stage is presented on Table 4. Crop development
through crop evapotranspiration

stage consumed most water

and loss through percolation as well. Therefore, the most

water inflow was should supplied through precipitation and irrigation water. The highest
water consumed and water loss were caused by the longest total day in the crop development

PA WEES 2011 International

Conference

Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan University

Management

stage since SRI rice cultivation used younger seedling, therefore plant has more opportunity to
remain its growth potential for roots and shoots development

(Uphoff et al. 2011).

Table 4. Total water balance variables in each growth stage
Water balance variables

Growth stage

Inflow (mm)

error (%)

Outflow (mm)
ETc

P

Percentage

Qr

DP

Initial

374.2

26.4

68.3

311.8

27.1

Crop development

670.6

19.0

114.7

516.5

45.1

Mid-season

106.6

38.9

65.3

59.9

23.9

Late season

180.4

23.4

48.2

122.5

19.9

107.7

296.5

1010.7

116.0

Total
Total inflow-outflow

1331.8

1439.5

The total inflows through precipitation

1423.1

1.14%

and irrigation water were 1331.8 and 107 mm,

respectively. Meanwhile, the water has leaved the field through crop evapotranspiration,
runoff and percolation with their total values were 296.5, 1010.7 and 116 mm, respectively.
There was a low difference between total inflow and outflow at the value of 16.4 mm or
1.14% as the total percentage error estimation. This error was low and can be tolerated, thus
each water balance variable was reliable and can be accepted.
Minimum total water irrigation was supplied to the field since high precipitation
planting period and no standing water needed in the SRI water management.
water consumption through crop evapotranspiration

during

In addition, total

was very low according to the FAO value

(Doorenbos and Kassam 1979), because the effect of low total reference crop
evapotranspiration. We have calculated that total reference evapotranspiration

value of279

mm based on weather data. Therefore, total evaporated water through soil evaporation

and

crop transpiration was low in the wet season. Moreover, total percolation was low according
to the FAO noted (FAO 2004) due to the soil texture and application of continuous saturated
soil irrigation which can reduce water loss through that percolation by the reduction of
hydrostatic pressure (Van der Hoek et al. 2001; Bouman and Tuong 2001).IHGFEDCBA

C o n c lu s io n s
The novel and simple method was applied to estimate non-measurable

water balance variables

in SRI paddy field by considering the monitored soil moisture. The proposed method has

P A EDCBA
W E E S 2 0 1 1 I n t e r n a t i o n a l C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONML

Capacity Building for Participatory Irrigation and Environmental
27 October 2011, National Taiwan University

estim ted those

Management

ariables by using Excel solver estimation. Results of the developed model

showed satisfactory result between observed and estimated soil moisture with the values ofR
higher than 0.70 in the all growth stages. Accordingly,

2

each estimated water balance variable

has reasonable trend and value. We found that minimum irrigation water was needed to meet
plant water requirement

since no standing water and high precipitation

season. Also, the total crop evapotranspiration

occurred in the wet

and percolation were low according to the FAO

values as the results of low reference evapotranspiration

and reduction of hydrostatic pressure,

respectively.
This method can provide a valuable approach for evaluating the water productivity

and water

use efficiency not only for SRI rice cultivation but also others rice cultivation system with less
effort and lower cost without complicated
those field measurements

water balance variables measurements.

However,

are still needed to the future research to make the method more

acceptable and satisfactory.IHGFEDCBA

A c k n o w le d g m e n t

This research was supported by a grant of International Research Collaboration and Scientific Publication from
Directorate Higher Education, Ministry of National Education, Republic Indonesia in the year of201O/2012.

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27 October 2011, National Taiwan University

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2011

I n te r n a tio n a l

C o n f e r e n c e zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLK

Capacity Building for Participatory Irrigation and Environmental Management
27 October 2011, National Taiwan University

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