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

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Table of contents

Opening Remarks

Dr. Fi-John Chang i

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

Sustainable Paddy 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

Session

IHGFEDCBAI I I -

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

54

Feasibility of SRI Application for Reduction of Irrigation and NPS Pollution in Korea 56

(4)

Evapotranspiration

over W~t· ~nd Dry System of Rice Intensification

(SRI) Paddy Field Environment:

Srlation

Using Surface Energy Balance Model

58

Jstimation

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

59

EDCBA

S e s s i o n I V - S u s t a i n a b l e W a t e r M a n a g e m e n t

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 i o n V - I m p a c t s o f C l i m a t e C h a n g e a n d N a t u r a l H a z a r d s o n P a d d y a n d W a t e r

E n v i r o n m e n t

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

hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA 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

Dl~DC model

6(

Impact assessment method of climate change on agricultural water use by using a distributed water

circulation model

6(

The importance of infrastructure

development

in farmland from the viewpoint of a crop science

student-through 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 i o n

VI---

R i v e r E n v i r o n m e n t a l D e s i g n a n d R e s t o r a t i o n

Population Model of Rana japonica Crossing Agricultural

Concrete Canals: Evaluating Population

Conservation by Improving the Migration Routes of Frogs

7

l

Fish migration from paddy fields to canals during mid-summer

drainage

7

Extension of Space-Time Conservation Method to Analysis of One-Dimensional

Unsteady Flow in

(5)

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

77

hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

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

79

EDCBA

P o s t e r S e s s i o 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

(6)

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

101

EDCBA

S u b m i t t e d A b s t r a c t s

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

(7)

P AEDCBAW 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 ezyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Capacity Building for Participatory Irrigation and Environmental Management

27 October 2011, National Taiwan UniversityIHGFEDCBA

2 0 1 1 0 5 4

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', Ryoichi Doi1PONMLKJIHGFEDCBA

1D e p a r t m e n tWVUTSRQPONMLKJIHGFEDCBAo f G l o b a l A g r i c u l t u r a l S c i e n c e , t h e U n i v e r s i t y o f T o k y o , J a p a n

Phone: (+81)-3-5841-1606

Email: chusnul_ar@yahoo.com

2D e p a r t m e n t o f C i v i l a n d E n v i r o n m e n t a l E n g i n e e r i n g , B o g o r A g r i c u l t u r a l U n i v e r s i t y ( I P B ) , B o g o r , I n d o n e s i a

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 e y w o r d s : s y s t e m o f r i c e i n t e n s i f i c a t i o n ( S R I ) , w a t e r m a n a g e m e n t , w a t e r b a l a n c e , s o i l

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PA WEES 2011 International

Conference

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

In tro d u c ti

n

Recently, the System of Rice Intensification (SRI) is well-known as a set crop management

practices for raising the productivity 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 al. 2011).

In the SRI paddy field, water is irrigated according to intermittent 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, water

balance analysis is usually performed to evaluate water productivity and water use efficiency

in field scale.

However, particular water balance variables such as crop evapotranspiration and percolation

are not easily measured in the field. The typical field measurement 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 inhgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBApaddy 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 water balance

(9)

P A 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 ezyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Capacity Building for Participatory Irrigation and Environmental Management

27 October 2011, National Taiwan University

The present stud proposes novel method to estimate non-measurable water balance variables

in SRI paddy field by considering 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

first rice seasonPONMLKJIHGFEDCBA2 0 1 0 / 2 0 1 1 (October 2010 to Februari 2011, wet season). The field is located 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

[image:9.571.15.548.11.840.2]

(10)

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

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 (DA T) to 40 days

after together with supplying local indigenous microorganism (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 Page), which is comprise of meteorological and soil

sensors. The meteorological data including precipitation 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 (AlIen et al. 1998).

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

(11)

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

t u . Deep percolation (DP)

I

1'_-I , I , ''I'' Hl ~!H2

l _ _

,

Hardpan [image:11.571.20.562.56.827.2]

G roundwater (G w)

Fig. 2 Water balance in paddy field

Table 1. Review measured and estimated water balance variables of the current study

Variables Measured Calculated Estimated Method

Soil moisture Precipitation

Crop evapotranspiration Irrigation water Percolation

Soil moisture sensor

Runoff! drainage

Rain gauge Excel Solver Excel Solver

Based on Darcy's law and

their parameter was estimated

by Excel Solver 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

(12)

P A 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 ezyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

Capacity Building for Participatory Irrigation and Environmental 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 l v e r e s t i m a t i o 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

[image:12.566.16.532.38.820.2]

at. 1998; Mohan and Arumugam 1994).

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

Growth stage Total day 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

(13)

P AWEES 2011 International

Conference

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

the FAO recommendation value (AlIen et al. 1998) and by considering current rice cultivation

and soil moisture at the field as well particularly 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):

Ks=1 ifSa(t)

=

Ss (6)

KshgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA=

InCI

+1 0 0 ( S a ( t ) / S s ) In101

if11SS ~ Sa(t) < Ss (7)

K

(

sa(t) - 11SS)

s

=

s exp

11SS

if Sa(t) < 11SS (8)

where Sa and Ss are the actual and saturated water contentWVUTSRQPONMLKJIHGFEDCBA( m3/ m \ respectively and 11and e 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) (9)

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PA WEES 2011 International Conference

where Ks is saturated hydraulic conductivity (mm/day) with a constant value,!1Z 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 which has positive correlation

(Chen et al. 2003). Accordingly, as initial condition, runoff was occurred when precipitation

occurred and its value was given by the following formula:

Qr(t)

=

hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA0 ifP(t) < ETomax (10)

Qr(t)=pet) - ETo_{m ax} ifP(t)

2:

ETomax (11)

where ETomax is maximum reference evapotranspiration (mm).

4. Irrigation water (I)

Irrigation water was given when the precipitation doesn't meet to the plant water requirement

through crop evapotranspiration, 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 ETcini is 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 (R2) to compare

between observed and estimated soil moisture. The value ofR2 ranged from 0.0 to 1.0 with

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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

non-measurable 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

Crop development 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

has higher

R2

value, andWVUTSRQPONMLKJIHGFEDCBAv i c e v e r s a . Accordingly, the number estimated data were not

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

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PA WEES 2011 International Conference

development and late season stages. The observed soil moisture values varied fromPONMLKJIHGFEDCBA0 . 5 1 1 to

0.608WVUTSRQPONMLKJIHGFEDCBAm3/ m3 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 in the current

wet season was commonly drained directly as runoff. Accordingly, the runoff trend's was

similar to that precipitation. 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

250

c::::::::J

hgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBAPred pitai 011

0.400

0.300

:D

25

I

X l

-.6

15

"&

₁₀ ~

-- Cl>ser.ed soil rnoisure 20J

- - - - Estim aed soil m oisture

w . < W . < W ~Estim ated Irrigaion water

20J ... -EstsnatedRJnoff

5

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 stage consumed most water

through crop evapotranspiration and loss through percolation as well. Therefore, the most

water inflow was should supplied through precipitation and irrigation water. The highest

[image:16.571.18.554.30.678.2]

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PA WEES 2011 International

Conference

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

Growth stage Water balance variables Percentage

Inflow (mm) Outflow (mm) error (%)

P ETc 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

Total 1331.8 107.7 296.5 1010.7 116.0

Total inflow-outflow 1439.5 1423.1 1.14%

The 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.

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 during

planting period and no standing water needed in the SRI water management. In addition, total

water consumption through crop evapotranspiration 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

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P AEDCBAW 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 ezyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

estim ted those ariables by using Excel solver estimation. 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 minimum irrigation water was needed to meet

plant water requirement since no standing water and high precipitation occurred in the wet

season. Also, the total crop evapotranspiration 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 water balance variables measurements. However,

those field measurements 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.

R e fe re n c e s

Alien RG, Pareira LS, Raes D, Smith M (1998) Crop Evapotranspiration Guidelines for computing crop water

requirements. FAO - Food and Agriculture Organization of the United Nations, Rome

Bouman BAM, Tuong TP (2001) Field water management to save water and increase its productivity in irrigated

lowland rice. Agr Water Manage 49 (1):11-30. doi:1O.1016/S0378-3774(00)00128-1

ChapagainT, Yamaji E (2010) The effects of irrigation method, age of seedling and spacing on crop

performance, productivity and water-wise rice production in Japan. Paddy Water Environ 8 (1):81-90.

doi: 10.1007/s 10333-009-0187-5

ChenRS, Pi LC, Huang YH (2003) Analysis of rainfall-runoff relation in paddy fields by diffusive tank model.

Hydrol Process 17 (13):2541-2553. doi:1O.1002lhyp.1266

DobermannA (2004) A critical assessment of the system of rice intensification (SRI). Agr Syst 79 (3):261-281.

doi: 10.10 16/S0308-521 x(03)00087-8

DoorenbosJ, Kassam AH (1979) Yield response to water. FAO Irrigation and Drainage Paper 33. FAO, Rome

FAO(2004) Rice Fact Sheet - Rice and Water. FAO. http://v.'Ww.fao.org/rice2004/enlf-sheetlfactsheetl.pdf.

(19)

P AEDCBAW 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 ezyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

Capacity Building for Participatory Irrigation and Environmental Management

GuerraLC, Bhuiyan SI, Tuong TP, Barker R (1998) Production more rice with less water from irrigated systems.

SWIM Paper 5. International Water Management Institute, Colombo, Srilanka

Hameed KA, Mosa AKJ, Jaber FA (2011) Irrigation water reduction using System of Rice Intensification

compared with conventional cultivation methods in Iraq. Paddy Water Environ 9 (1):121-127.

doi: 10.1007I s lWVUTSRQPONMLKJIHGFEDCBA0333-0 10-0243-1

Janssen M, Lennartz B, Wohling T (2010) Percolation losses in paddy fields with a dynamic soil structure:

model development and applications. Hydrol Process 24 (7):813-824. doi: 1O.1002lhyp.7525

Jury W, Horton R (2004) Soil Physic. 6th edn. John Wiley & Sons, Inc., New Jersey

Kim HK, Jang Tl, Im SJ, W PS (2009) Estimation of irrigation return flow from paddy fields considering the soil

moisture. Agr Water Manage 96:875-882. doi: 10.10PONMLKJIHGFEDCBA1 6 / j . a g w a t . 2 0 0 8 . I 1.009

Li YH, Cui YL (1996) Real-time forecasting of irrigation water requirements of paddy fields. Agr Water Manage

31 (3):185-193

Lin XQ, Zhu OF, Lin XJ (2011) Effects of water management and organic fertilization with SRI crop practices

on hybrid rice performance and rhizosphere dynamics. Paddy Water Environ 9 (1):33-39.

doi:10.1007I sI0333-0 10-0238-y

Manzano VJP, Mizoguchi M, Mitsuishi S, Ito T (2011) IT field monitoring in a Japanese system of rice

intensification (J-SRI). Paddy Water Environ 9 (2):249-255. doi:10.10 0 7 / s 1 0 3 3 3 - 0 1 0 - 0 2 2 6 - 2

Mohan S, Arumugam N (1994) Irrigation crop coefficient for lowland rice. Irrigation and Drainage Systems

8:159-176. doi: I0 . 1 0 0 7 / B F 0 0 8 8 1 0 1 6

Morrison FA (2005) Using the Solver add-in in Microsoft Excel®. Michigan Technological University Web.

http://www.chem.mtu.edu/-fmorriso/cm4650/Using Solver in Excel.doc.

ReshmideviTV, Jana R, Eldho TI (2008) Geospatial estimation of soil moisture in rain-fed paddy fields using

SCS-CN-based model. Agr Water Manage 95 (4):447-457. d o i : 1 O . 1 0 1 6 / j . a g w a t . 2 0 0 7 . 1 1 . 0 0 2

Sato S, Yamaji E, Kuroda T (2011) Strategies and engineering adaptions to disseminate SRI methods in

large-scale irrigation systems in Eastern Indonesia. Paddy Water Environ 9 (1):79-88. doi: 10.1007I sI 0333-0

10-0242-2

SheehyJE, Peng S, Dobermann A, Mitchell PL, Ferrer A, Yang JC, Zou VB, Zhong XH, Huang JL (2004)

Fantastic yields in the system of rice intensification: fact or fallacy? Field Crop Res 88 (1):1-8.

doi:l 0.10 1 6 / j . f c r . 2 0 0 3 . 1 2 . 0 0 6

Sinc1airTR, Cassman KG (2004) Agronomic UFOs. Field Crop Res 88 (1):9-10. d o i : 1 0 . 1 0 1 6 / j . f c r . 2 0 0 4 . 0 1 . 0 0 1

TyagiNK, Sharma OK, Luthra SK (2000) Determination of evapotranspiration and crop coefficients of rice and

sunflower with Iysimeter. Agr Water Manage 45 (1):41-54. d o i : 1 0 . 1 0 1 6 / S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 7 1 - 2

UphoffN, Kassam A, Harwood R (2011) SRI as a methodology for raising crop and water productivity:

productive adaptations in rice agronomy and irrigation water management. Paddy Water Environ 9:3-11.

doi:10.1007I s10333-0 I 0-0224-4

Vander Hoek W, Sakthivadivel R, Renshaw M, Silver JB, Birley MH, Konradsen F (2001) Alternate wet/dry

irrigation in rice cultivation: a pratical way to save water and control malaria and Japanese encephalitis?

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Capacity Building for Participatory Irrigation and Environmental Management

Vu SH, Watanabe H, Takagi K (2005) Application of FAO-56 for evaluating evapotranspiration in simulation of

pollutant runoff from paddy rice field in Japan. Agr Water Manage 76 (3):195-210.