Optimizing Non-flooded Irrigation Regime under System of Rice Intensification Crop Management using Genetic Algorithms

(1)

PAWEES 2012

International Confere ce on

"C al enges of Water

zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

&

Environmental Management

in Monsoon Asia"

27-29 November 2012

Royal Irrigation Department (Pakkred),

Thailand

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C O N T A C T IN F O R M A T IO N A N D D E T A IL S :

F o r U S -re la te d q u e s tio n s , p le a s e c o n ta c t:

tsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

Contact Person:

D r. S u c h a rit K o o n ta n a k u lv o n g

Water Resources System Research Unit

Room 203, Bldg. 2 Faculty of Engineering,

Chulalongkorn University, Bangkok

Tel: +66-2-218-6426, +66-81-646-9750

Fax: +66-2-218-6425

Email: sucharit.k@chula.ac.th

F o r m o re d e ta ile d in fo rm a tio n y o u a re m o re th a n w e lc o m e to v is it o u r w e b s ite :

http://project-wre.eng.chula.ac.th/watercu

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e n g l

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C o

c

O rg a n iz in g In s titu te s :

tsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

International Society of Paddy

and Water Environment

Engineering (PAWEES)

Faculty of Engineering,

Chulalongkorn

University (CU),

Thailand

Faculty of Engineering at

Kamphaengsaen,

Kasetsart University (KU),

Thailand

Royal Irrigation Department (RID),

Thailand

Irrigation Engineering Alumni

Association

under H.M.

The King's

Patronage (lEA),

Thailand

(4)

A d v is o ry C o m m itte e

tsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

1.

Mr. Lertviroj

Kowattana

2. Or Pirom

Kamol-ratanakul,

MD

3. Mr. Vudtechai Kapilakanchana

4. Dr. Tai-cheol

Kim

5

Or. Yohei

Sato

6. Or. Hiroyuki

Konuma

7. Or. Sho

Shiozawa

8. Or. Sun-joo

Kim

9. Mr. Wei-Fuu

Yang

S c ie n tific C o m m itte e

10. Or. Bancha

Kwanyuen

11. Dr. Ekasit

Kositsakulchai

12. Dr. Masaru

Mizoguchi

13. Or. Yoshiyuki

Shinogi

14. Or. Nobumasa Hatcho

15. Dr. Yutaka

Matsuno

16. Dr. Seong Joon

Kim

17. Dr. Sang Min

KIM

18. Or. Fi-John

Chang

19. Or. Ke-Sheng Cheng

20. Dr. Sucharit

Koontanakulvong

21. Dr. Chaiyuth

Sukhsri

22. Dr. Aksara

Putthividhya

23. Dr. Somchai

Donjadee

24. Or. Chirakarn Sirivitmitrie

25. Or. Pongsathorn Sopaphun

26. Or. Wisuwat

Taesombat

27. Mr. Santi

Thongphamnak

28. Dr. Varawoot

Vudhivanich

29. Dr. Watchara

Suidee

O rg a n iz in g C o m m itte e

30. Or. Bonsoom

31. Dr. Sucharit

32. Mr. Va-Son

33. Or. Thanet

34. Or. Bancha

35. Or. Ekasit

36. Or. Yutaka

37. Miss Mada

Lerdhirunwong

Koontanakulvong

Boonkird

Aksorn

Kwanyuen

Kositsakulchai

Matsuno

laumsupanimit

Royal Irrigation Department

Chulalongkorn University

Kasetsart University

PAWEES

FAO

JSIDRE, Japan

KSAE, Korea

TAES, Taiwan

Kasetsart Uuniversity

University of Tokyo, Japan

Kyusyu University, Japan

Kinki University, Japan

PAWEES, Kinki University.Japan

Konkuk University, Korea

Gyeongsang NationalUniversity,Korea

National Taiwan University.Taiwan

Chulalongkorn University

Kasetsart University

Kasetsart Uuniversity

PAWEES Kinki University.Japan

Chulalongkorn University

(5)

C O

T E

T S

WELCOME ADDRESS

111

CONFERENCE ORGANIZING INSTITUTES

IV

CONTENTS

VI

AIMS AND SCOPE

1

PROGRAMME

2

FEATURED SPEECH

Participatory management

for irrigation

projects

Va-son Boonkird

6

Radioactive contamination

of paddy soil and its transfer to rice in fukushima

Sho Shiozawa

7

Farmers' responses to climate change adaptation

in irrigation

project (Thailand

case study)

Assoc. Prof. Chaiyuth Suksri.

8

ABSTRACT

SESSION A Climate Change and Uncertainty

PAPER ID: 000

A review of land-use change scenarios by new climate change scenarios in Korea

10

PAPER ID: 008

Simulation of GHG emission from paddy by DNDC model for climate change impact in Korea .... 11

PAPER ID: 025

Coping with uncertainties in climate change by stochastic storm rainfall simulation

12

PAPER ID: 032

The study on parameter sensitivity analysis of the Denitrification-Decomposition

(DNDC) model

13

PAPER ID: 046

Climate change impact assessment in Sukhothai Province: intercomparisqn between three Global

Climate Models

:

14

PAPER ID: 049

Probability based assessment of Climate Change Impact on Irrigation Systems in Upper Chao

Phraya Basin

15

PAPER ID: 051

Assessment of water supply capacity of agricultural irrigation facilities using MODSIM-DSS

Coupled with SWAT

16

(6)

PAPER ID: 052

Assessment

of Climate

Change

Impact on Multi-purpose

Dam based on RCP emission

scenarios

Using SWAT model

17

PAPER ID: 057

Mitigation method of irrigation

systems

against

climate

change

in the Chao Phraya

Basin,

Thailand

18

PAPER ID: 075

Analysis Framework

for Water

Resource

Policy Decision-Making

under

Effects of Climate Change

19

SESSION

8

Participatory

Management

for Irrigation

Projects

PAPER ID: 005

Relationship between

irrigation

water management

by farmers'

group and the levy system

21

PAPER ID: 033

Duration of irrigation vulnerabilities

according

to agricultural

water supply

and demand

22

PAPER ID: 044

Assessment

of farmer participation

in irrigation

management

for

paddy irrigation development

in Myanmar

23

PAPER ID: 045

Impact of participatory

approaches

on irrigation

development

and management

for Communal

Irrigation System (CIS): Case studies

in three upland provinces

of north, Philippines

24

PAPER ID: 071

Investigation of paddy field irrigation

activities

by farmers

aiming for

demand-oriented

irrigation

service

25

SESSION C Emerging

Technologies

in Water

Management

PAPER ID: 003

Dam management

at Drought

in dry season

paddy irrigation,

Case study of Mae Suai Dam ...

27

PAPER ID: 012

Assessing flood damages

of rice in the Chao Phraya

Delta, using

MODIS satellite imageries

28

PAPER ID: 015

Optimizing non-flooded

irrigation

regime

under system

of rice intensification

crop management

using Genetic Algorithms

29

PAPER ID: 018

Investigating the interactive

recharge

mechanisms

between

surface

water and groundwater

over

the Jhuoshuei River Basin in Central Taiwan

30

PAPER ID: 020

jlrAls: Agent-Based

Modeling

for Simulating

Irrigation Water

Use in Paddy

Land

31

(7)

PAPER ID:

023

A bayesian uncertainty

analysis

of the modelled

surface-

and ground-water

flows in an agricultural

watershed. ..

.. . .. .

32

PAPER ID:

024

A study on drainage

efficiency

of shortcut

canal project in the Lower The chin River.

33

PAPER ID:

031

Drought response

by farmers

after decreasing

water supply

34

PAPER ID:

036

Development

of automated

irrigation

system for food production

land

35

PAPER ID:

047

Satellite Data Application

for flood simulation

36

PAPER ID:

048

Statistical forecasting

of rainfall with ENSO index in the Chao Phraya

River Basin in Thailand ..37

PAPER ID: 055

Est mation of Streamflow

by SWAT Model in Sedone

River Basin, LAO PDR

'38

PAPER ID: 059

Yoshino and Nan River basins development

and management

comparative

study

39

PAPER ID:

061

System of Environmental-Economic

Accounting

for water in case of Thailand

.40

PAPER ID:

062

Ap lication of Input-Output

Table for future water resources

management

under policy and climate

ch nqe in Thailand:

Rayong Province

Case study

41

PA

ER ID:

063

Wc

er Footprint of Bioethanol

Production

in Thailand

.42

PA

ER ID:

073

Ot..

si Real-time monitoring

system for Informing

the Optimum

Planting

and Harvesting

Dates of

Ca sava in Rain-fed

Upper Paddy Field in Northeast

Thailand

.43

pp

ER ID:

077

Re ional difference

in the citizen's

consciousness

of water resources

:

.44

SE

SION

0

Environmental

Sustainability

in Paddy

Irrigation

and Drainage

PAPER ID:

001

Car asian experience

be transferred

to Africa?

-Lessons

learned from drafting

a rice production

me ual in Africa

.45

PA

ER ID:

002

Wa 'r quality constituents

export from paddy field in Southern

Korea

.46

PAl ER ID:

004

Ml

.

regression

analysis

of water quality characteristics

in lowland

paddy fields

.47

(8)

PAPER ID:

006

Evalcation of field measurements

and estimated

rice crop water requirements

.48

PAPER ID:

007

Effect of rice straw mat mulch and soil amendments

on runoff under

laboratory rainfall simulations

.49

PAPER ID:

009

Runoff and NPS pollution

discharge

characteristics

from sloping

upland fields in Korea

16

50

PAPER ID:

011

Aqrlcuitura:

infrastructure

database

establishment

for vulnerability

assessment

according

to climate

chan e in Korea

51

PAPE RID:

013

Genr ic diversities

and population

structures

of small freshwater

fishes in

Mekong River basin

52

PAP[R

ID:

014

Char cteristics of drainage

water quality and loading from paddy field under cyclic irrigation

and its

mancqement

options

53

PAP

RID:

017

Estin ating regional total phosphate

concentration

in a river basin through

the NARX network ...

54

PAP

RID:

021

Deve

opment of the world atlas of irrigated

agriculture

for sustainability

science

55

PAP

RID:

022

Influ

ntial

factors in determining

the timing of transplanting

lowland

rice:

case study in Lao PDR

56

PAP

RID:

027

Hab

t potential maps of three frog species

for paddy field areas of

the

I

'ddle

Sakura River basin, Japan

57

PAP

RID:

040

Cha --nges in the decontamination

of radioactive

cesium

of Fukushima:

a rural planning

pers ective

58

PAF

RID: 058

Soil

acro Nutrient (N, P, K) during Growth

Stages

under Conventional

and SRI

(Sy_m

of Rice Intensification)

Practices

in Tropical

Soil.

59

PA

RID:

069

A

si

lyon the reason why the reported

yields of the System

of

Rice ntensification

(SRI) are so widespread

60

PA

RID:

072

Mal

xing and Analyzing

of Soil Water

Pollutant

Loads in Greenhouse

61

(9)

IN O F A U T H O S 6 2

R Y

L IR R IG A T IO D E P A R T M E N T (P A K K R E D ) M A P 6 3

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PA\\ EES 2012 International Conference

he lenges . Water

&Envrronmental Management In Monsoon ASIa '7-2'-1 member 20 2. Thailand

Optimizing

on-flooded Irrigation Regime under System of Rice

Intensification Crop Management

using Genetic Algorithms

Chusnul Arif. Budi Indra Setiawan, Masaru Mizoguchi

and Ryoichi Doi

Abstract: In this study, an optimal non-flooded

Irrigation regime that maximizes both the yield and

water productivity of System of Rice Intensification

(SRI) crop management was simulated by genetic

algorithms (GAs) model. Here, the field was classified

into wet

ONMLKJIHGFEDCBA

( \ \ I ) . medium ( M ) and dry (D) conditions in

each growth stage, namely initial, crop development,

mid-season and late season stages according to the soil

moisture level. The simulation was performed based on

the identification process according to the empirical data

during three cropping seasons. As the results, the

optimal combination was 0.622 (W), 0.563 (W), 0.522

(M), and 0.350 crnvcrrr' (D) for initial, crop

development, mid-season and late season growth stages,

respectively. The wet conditions in the initial and crop

development growth stages should be achieved to

provide enough water for the plant to develop root, stem

and tiller in the vegetative stage, and then the field can

be drained into medium condition with the irrigation

threshold of field capacity to avoid spikelet fertility in

mid-season stage and finally, let the field dry to save

more water in the late season stage. By this scenario, it

was simulated that the yield can be increased up to

6.33% and water productivity up to 25.09% with saving

water up to 12.71%compared to the empirical data.

Keywords: system of rice intensification (SRI),

non-flooded irrigation, crop productivity, water productivity,

genetic algorithms.

Chusnul Arif «(8])

Department of Civil and Environmental Engineering,

Bogor Agricultural University (IPB), Bogor, Indonesia

Department of Global Agricultural Sciences, the

University of Tokyo, Japan

Phone: (62)-251-8627225, (81 )-3-5841-1606

Fax: (62)-251-8627225, (81 )-3-5841-1606

Email: chusnul_ar@yahoo.com.chusnutarif@ipb.ac.id

Budi Indra Setiawan

Department of Civil and Environmental Engineering,

Bogor Agricultural University (IPB). Bogor, Indonesia

Email: budindra@ipb.ac.id

Masaru Mizoguchi and Ryoichi Ooi

Department of Global Agricultural Sciences, the

University of Tokyo, Japan Email: amizo@mail.ecc.u-tokyo.ac.jp;

aroird@mail.ecc.u-tokyo.ac.jp

Introduction

Recently, the scarcities of water resources and

competition for their use have made water saving the

main challenge in maintaining the sustainability of rice

farming. Therefore, water saving technology becomes

one of the priorities in rice research (Barker et al. 2000).

From the previous findings, rice is highly possible

produced under water saving technology with System of

Rice Intensification (SRI) crop management in which

continuous flooded irrigation is not essential anymore to

gain high yield and biomass production (Zhao • al.

201I; Sato et al.201I;Lin et al. 201I).

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

(Sinclair and Cassman 2004; Sheehy et al. 2004;

Oobermann 2004), however, its benefits have been

validated in 42 countries of Asia, Africa and Latin

America (Uphoff et al. 2011). In the SRI paddy field,

non-flooded irrigation is applied in which the field is

allowed dry during particular time instead of keeping

them continuously flooded, a practice called alternate

wetting and drying irrigation (AWOl) (Van der Hoek et

al. 2001).

Many experiments have been conducted by comparing

continuous flooded and non-flooded irrigations under

SRI crop management (Choi et al. 2012; Zhao et al.

2011; Sato et al. 2011; Hameed et al. 2011; Barison and

Uphoff 2011; Chapagain and Yamaji 2010). Water

productivity can be raised by saving water significantly,

as reported in studies that provide data for different

countries, e.g., 28% in Japan (Chapagain and Yamaji

2010),40% in Eastern Indonesia (Sato et al. 2011), and

38.5% in Iraq (Hameed et al. 2011). Also by SRI crop

management, the land productivity raised more than

double in Madagascar (Barison and Uphoff 2011), 78%

in Eastern Indonesia (Sato et al. 2011), 65% in

Afghanistan (Thomas and Ramzi 2011), 42% in Iraq

(Hameed et al. 2011), and 11.3% in China (Lin et al.

2011). However, the optimal wet and dry levels

(represented by soil moisture) in each growth stage is

still unclear because there is lack information study on

optimizing water irrigation regime under SRI crop

management. Thus, the current study was undertaken to

find optimal soil moisture level in each growth stage to

maximize both land and water productivity during

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P \ \\ Ef S 2012 International Conference

( tal enges of \\ ater

tsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

&Environmental Management In Monsoon ASia

'7-11) ver-ber 2012 Thailand

In the irrigation planning model, it has always been a difficult problem to find optimal or near optimal solutions with traditionally dynamic optimizations method because of multi-factors, uncertainty and non-lmearity in the model (Zhang et al. 2008). Also, traditional optimizations have limitations in finding global optimization results for a complex irrigation planning problem because they search from point to point (Kuo et al. 2000). Thus, genetic algorithms (GAs) model proposes global optimization search with many remarkable characteristic by searching the entire population instead of moving from one point to the next as the traditional methods (Kuo et al. 2000).

GAs model has ability to rapidly search a global optimal value of a complex objective function using a multi-point search procedure involving crossover and mutation processes (Goldberg 1989). GAs model differs from traditional optimization and other search procedures in the following ways: (l) GAs works with a coding of the parameter set, not the parameters themselves, (2) GAs searches from population of points, not single point, (3) GAs uses objective function information, not derivatives or other auxiliary knowledge, and (4) GAs uses probabilistic transition rules, not deterministic rules (Goldberg 1989). GAs model has been applied to several irrigation planning applications (Zhang et al. 2008; Ward law and Bhaktikul 2004; Raju and Kumar 2004; Kuo et al. 2000). However, optimizing non-flooded irrigation regime by finding optimal soil moisture in each growth stage has not yet been achieved particularly under SRI crop management.

Therefore, the objective of current study was to find optimal soil moisture level in each growth stage of paddy rice under SRI crop management using GAs model to maximize both land and water productivity.

Materials and Methods

Field Experiments

[image:11.563.278.503.109.638.2]

The optimization process was carried based the field experiment in the experimental paddy field in the Nagrak Organics SRI Center (NOSC), Sukabumi, West Java, Indonesia located at 06°50'43" S and 106°48'20" E, at an altitude of 536 m above mean sea level (Fig. 1) during three cropping seasons (Table 1).

Table I. Cultivation period of each cropping season Period Planting date Harvesting dale Season First

Second Third

14 October 20I0 20 August 2011 22 March 2012

8 February 20I I

15 December 2011 5July2012

Wet Dry- Wet Wet- Dry

There were four plots and each plot was planted with the variety of rice

tOryza

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s a l i v a L), Sintanur using the following SRI crop management: single planting of young seedlings spaced at 30 cm x 30 cm, applying an organic fertilizer at 1 kg/m ' in the land preparation, but no chemical fertilizer. The weeding was performed every 10 days in the period between 10 and 40 days

after transplantation supplying local indigenous microorganism to enhance biological activity in the soils (Uphoff et al. 2011)

Source: earth.google.com (2012)

Fig. I Experimental field location 111 West Java. Indonesia.

Each plot was irrigated under different water irrigation regimes according to the growth stages, namely, initial, crop development, mid-season and late season stages (Vu et al. 2005; Tyagi et al. 2000; Alien et al. 1998; Mohan and Arumugam 1994). Non-flooded condition was applied in all regimes and in each growth stage the soil was classified into three conditions i.e. wet (W), medium (M) or dry (D) to realize the soil moisture described by changes in soil suction head (i.e. pF value) (Arif et al. 2011 a) presented in Fig. 2. The wet condition was achieved when pF value was between 0 and 1.6 which was the air entry value for this soil. The medium condition was achieved when pF value was between 1.6 and 2.54 which was the field capacity value. When the soil was drier than the medium condition, the condition was regarded as the dry condition.

5.0

4.0

3.0

u,

Cl, 20

LO

Volumetric water content(cm'jcm')

L_

_

[image:11.563.279.504.623.734.2]

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P'\ WEES 2012 International Conference

l 'iallcngcs of Water

tsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

&Env ronmental Management in Monsoon ASIa 27-29 lovernber 2012 fhailand

oil moisture and meteorological data consisting of air

temperat re, wind speed, relative humidity, solar

radiation and precipitation were collected by a quasi real

tin-e monitoring system (Arif et al. 20 I1 b). Here,

meteorological data were used to calculate reference

evapotranspiration (ETo) based on the FAO

Penman-Monteith model (Alien et al 1998). Then, using

monitoring data. total irrigation, crop evapotranspiration

were estimated b) Excel Solver (Arif et al. 2012a).

Modeling Approach

Identification procedure

To determine optimal soil moisture level in each growth

stage to maximize yield and water productivity, the

relationship between their values and the yield as well as

water productivity should be identified firstly based on

empirical data. Since there was no mathematical model

between them, identification process was carried out

gradually and its procedure as follow:

I. Yield as function of plant height and tiller

numbers/hill

The yield has positive correlation to plant growth

represented by plant height and tiller numbers/hill.

Therefore, we used multiple linear regressions to show

the correlation between the yield and plant height as

well as tiller numbers.hill, The basic formula was given

as follow:

Y=aPH+bTH+e

(I)

Where Y is yield (ton/ha), PH is plant height (cm), TH

is tiller numbers/hill, a, b, c are coefficients of plant

height, tiller numbers/hill and the intercept, respectively.

Plant height and tiller numbers/hill were measured

manually every 5 days and we used their average values

in the end of mid-season stage when maturity time was

started to show their correlation to the yield (Alien et al.

1998). Here, all coefficients in equation I were

determined empirically according field experiments by

multiple linear regressions.

2. Plant height and tiller numbers/hill as function of

soil moisture

Plant height and tiller numbers/hill are affected by soil

condition represented by the soil moisture level in each

growth stage. In fact, it is difficult to identify the

relationship between soil moisture and plant growth by

mathematical model because it is characterized by

non-linearity. Thus, we implemented neural networks model

to show its correlation since neural networks model

deals with complex system such as in agricultural

system (Hashimoto 1997). The model consisted three

layers, i.e. input, hidden and output layers. Soil moisture

in the initial (SM 1), crop development (SM2),

mid-season (SM3) and late mid-season (SM4) were used as in the

inputs, while plant height (PH) and tiller numbers/hill

(TH) as the outputs (Fig.3).

3. Water productivity with respect total water input

Since we focused to find the minimum water input as

much as possible, thus we defined water productivity

with respect total water input (Bouman et al. 2005) with

the following equation:

Wp=

y

ONMLKJIHGFEDCBA

L ( !

+P)

Where I is total irrigation (mm) that affected by the soil

moisture level in each growth stage, P is precipitation

(mm) and WP is water productivity (g grain/kg water).

Input layer Hidden layer Output layer

(2)

SMl

TH

5M2

PH

5M3

5M4

Fig. 3 Structure of neural networks model to identify

plant height and tiller numberslhill as affected b) soil

moisture.

Optimization procedure

The objective function can be described as:

F(SM 1,SM2,SM2,SM4)=

dY +eWP

(3)

Maximize F(SMI ,SM2,SM3,SM4)

Subject to:

0.586~SMI~0.622(ern3fern3)

(4)

0.563 ~ SM2 ~ 0.593 (cm

3

/cm ')

(5)

0.455 ~ SM3 ~ 0.522 (cm 3/cm ')

(6)

0.350 ~ SM4 ~ 0.505 (cm i/cm ")

(7)

Where d and e are weights for the yield (Y) and water

productivity (WP) and their values were 0.6 and 0.4,

respectively. Since both Y and WP have different unit,

thus their values were normalized using their maximum

and minimum values based on empirical data. Here,

GAs model searched optimal combination of SM I,

SM2, SM3, and SM4 with their interval (minimum and

maximum soil moisture in each growth stage) according

experiment data to maximize the objective function by

multi-point searching procedure.

In order to employ GAs model, some parameters such as

individual, population, fitness function and operators of

GAs should be defined firstly as follows:

I. Definition of individual

An individual represented a candidate for the

optimal solution that consisting of particular values

SM I, SM2, SM3 and SM4. Meanwhile a set

individual was called population. An individual was

coded as six-bit binary strings as illustrated as

follow:

Individual = SMI, SM2, SM3, SM4

=

000101, 001000, 001100, 100001 [image:12.561.279.496.202.322.2]

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PA\\ EES 2012 International Conference

Cha enge of Water

tsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA

&Em ironmental Management In Monsoon Asia :7-29 ove+ber 2012 Thailand

0.598, 0.567, 0.468, 0.431 (decimal values in cm'zcrrr')

2 Fitness function

Fitness function is an indicator to show the quality of an individual. All individuals in a population were evaluated based on their performances in which the higher fitness function, the better ability to survive In this problem, fitness function was given same as an objective function (equation 3). 3. Operators of GAs model

The main operators were crossover and mutation. Crossover combined features from two individuals based on crossover rate (Pc). It is operated by swapping corresponding component in the binary strings represented an individual. Mutation inverted one or more bit binary string (also called gene) in

each indiv idual from 0 to 1 or 1 to 0 based on

mutation rate (Pm). Here, Pc and Pm were 60% and 5%, respectively.

K=O

Initial population No New population ote:

GN =generation numbers; 200

Fig. 4 Searching procedure of GAs model in the current study.

We adopted the searching procedure as developed previously (Suhardiyanto et al. 2009) and could be further explained as follow:

I. An initial population consisting of ten individuals were generated randomly as the first generation 2. Fitness function was calculated to show the

performance of each individual

3. The performance of each individual was evaluated by sorting maximum to minimum fitness value. Here, 60% of all individuals with the highest performance were selected to next step, another fitness values were eliminated

4. Crossover and mutation operations were applied to the selected individual based on Pc and Pm rates

5. Then, new population was created. Here, 60% of selected individuals previous generation were compared to the new population. We implemented the elitist strategy to find global optimum by sorting the fitness value and then selected ten individuals with highest performance as the next generation. 6. Steps 2 to 5 were repeated until the required

generation numbers achieved. The optimal values were given as an individual with highest fitness when the result was convergent

We developed both neural networks and GAs models in Microsoft Excel 2007 with Visual Basic Application with our own codes.

Results and Discussions

Correlation between plant growth, yield and soil

moisture

Based on empirical data, it was cleared observed that both plant height and tiller numbers/hill have positive correlation to the yield with R2 of 0.86 and 0.89, respectively (Fig.5). These results were consistent with previous findings that higher plant height with more tiller numbers/hill promoted more yield and vice versa (Kumar et al. 2012; Zeng et al. 2003). Therefore, the correlation between the yield and both plant height and tiller numbers/hill can be presented well by multiple linear regressions with R2 of 0.94 (Fig.6). Commonly

the yield of rice is highly dependent upon the number of fertile tiller numbers/hill, thus more tiller numbers/hill have high probability to produce more fertile tiller (Zeng et al. 2003).

a 12 v 02329)1-18531 10

""'WI

'"

s

'>

~

6 "0 -.; ;;: 4 2 0

50 70 90 110 130 ISO

Plant height [crnl

b

12

y 0.536,· 6.7421

10

"'/

'"

8

""

c

.g

6 -o -.; >: 4 1 0

10 IS 20 25 30 3S 40

[image:13.563.279.525.445.721.2]

Tiller numb ers/hdl

Fig. 5 Correlation between plant height, tiller

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&Environmental Management In Monsoon ASIa 27-2'1. ovember 2012 Thailand

12

--; y _a.107PH t0 323TH - 13.03

10 R- 0.9355 0

c

g

8 '0 -a:; s, '0 6

'"

to

e:

4 :;; w 2 0

0 4 6 8 10 12

Observed yield(ton/ha)

Fig. 6 Multiple linear regression plant height and tiller numbers/hill on the yield.

The yield also has positive correlation to the crop evapotranspiration (ET) with R~ of 0.94 as presented in Fig. 7. It was indicated that higher crop evapotranspiration promoted more evaporation process from the soil and transpiration process from the plant, thus more yield was achieved as well as biomass production (Shih 1987). The linear correlation between crop evapotranspiration and the yield is not only found for paddy rice (Shih et al. 1983), but also other crops such as corn (Ko and Piccinni 2009), cotton and wheat (Jalota et al. 2006). Therefore, crop evapotranspiration is usually used to estimate the yield (Shih 1987).

12.0 10.0

~

8.0

?

,g, 6.0 '0 Qj > 40 20 00 310

Y=0.l2ET -33.9

R'=0.94

•

• Ydata

-Ymodel

350 360 370

320 330 340

Crop evaporranspuatioo (mm)

Fig. 7 Correlation between yield and crop evapotranspiration.

Crop evapotranspiration is vital for irrigation scheduling and water resource allocation, management and planning (Jensen et al. 1990). Also, it is a main component of water consumption in paddy fields, thus its rate is depend on water availability in the field represented by soil moisture. In other words, plant growth and the yield were clearly affected by soil moisture level in each growth stage (Arif et al. 2012b; Anbumozhi et al. 1998). However, there is no mathematical model was found showed the relation between soil moisture level in each growth stage and plant growth as well as the yield.

Here, we used neural networks model to estimate plant growth represented by plant height and tiller

numbers/hill as affected by soil moisture level in each growth stage. Fig. 8 shows the comparisons between observed and estimated values of both plant height and tiller numbers/hill by neural networks model. It can be seen that the estimated values were closely related to the observed values with R2 of 0.82 and 0.92 for estimation of plant height and tiller numbers/hill, respectively. This means that more than 80% of observed values can be explained linearly by neural networks model. Therefore, the results suggest that a reliable simulation model by neural networks could be obtained to estimate plant height and tiller numbers/hill.

a 130

E

~

120

~

₀₀ ijj 110 s: C

'"

Ci 100 '0 B

'"

~

90 w 80 80

y=0.8008x+21.109 R' 0.8181 o

o

90 100 110 120

Observed plant height (cm)

130

35 35 b

i: Y 0.9231>+1.8232 '?;;- R'=0.9243

Q:; ₃₀ .D E :J C

~

25 '0 B_ro 20 E

~

w 15

15 20 25

o

30

Observed tiller numbers/hill

Fig. 8 Observed and estimated plant growth as affected soil moisture level by neural networks model: a) Observed vs estimated plant height, b) Observed vs estimated tiller numbers/hill.

Optimal soil moisture levels

by

Genetic

Algorithms

Fig. 9 shows the evolution curves of fitness values (equation 3) between their maximum, average and minimum values in each generation. All of values increased sharply from the first to the tenth generation, and then increased gradually until the fortieth generation. After the fortieth generation, the all fitness values were convergent until the end of generation and their values were 0.68. This means that global maximum value was obtained because all of maximum, average and minimum values were same.

[image:14.563.280.517.237.549.2]

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presented in Fig. 9. SM I and S'vI2 were convergent faster than others growth stages in which their values

we-e reached before the tenth generation. Meanwhile, SM3 was convergent latest in the fortieth generation, thus fitness value was also convergent started at this moment (Fig. 9). This means that the optimal soil moisture level in each growth stage that maximizes the yield and water productivity was obtained from the model simulation based on the GAs procedure shown in Fig. 4 after the fortieth generation.

70

r---,

./

',5

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, " ,I

/ "

,

.,

,

.60

,

,I f ' i' -r 5'. i, "

,

0.50

0 '0 20

- - - Mlllfitne:S5

'0 40 50 so 70 80 ?O 100

General 0" numbers

Fig. 9 Evolution curves in searching for a maximal value of itness function.

0700

r---,

...•.

.

---

,---!'

il~

c.seo 0.300 '---' -

..

,

• M •~. ~•••••••••••• _••••••• ~ ••••__ •• ••••••••••••• _

N W ~ ~ m m m % ~

ene at on"U'TI~'S

Fig. I0 Evolution curves in searching the optimal values of soil moisture in each growth stage.

Table 2 shows the optimal soil moisture level in each growth stage obtained from the GAs simulation, while Fig. II shows the optimal output simulated by GAs model. Four irrigation regimes with some combinations of soil moisture level from the field measurements are also represented in the table with the same precipitation during cropping season as the comparison. The optimal combination of soil moisture level in each growth stage obtained in this study was 0.622 (wet), 0.563 (wet), 0.522 (medium), and 0.350 crrr'zcrn:' (dry) for initial, crop development, mid-season and late season growth stages, respectively. By this scenario, it was simulated that the yield can be increased up to 6.33% and water productivity up to 25.09% with saving water up to 12.71% compared to the first regime (as base line). From this simulation, it can be concluded that during the first to the second stages keeping the field in the wet condition is important to fulfill plant water requirement.

In the SRI, avoid continuous flooding is one of the their elements because rice plants cannot grow best under hypoxic soil conditions, thus plants should be given just enough water at saturated condition to meet their requirement for root, stem and tiller development (Uphoff et al. 2011). Then, the field can be drained into medium condition in the third stage when plants focusing on reproductive stage (flowering and panicle development). The medium condition with the threshold of irrigation was reduced to the field capacity (pF 2.54) is important in developing aerobic condition to avoid spikelet sterility particularly around flowering time (Bouman et al. 2005). Finally, the field should be drained into dry condition in the last stage to save the water input as reported previous studies (Uphoff et al. 2011; Doorenbos and Kassam 1979; Zawawi et al. 2010). a 12.0 10.0

'"

-E. 8.0 c:

~

_6.0 '0 Oi ;;: 4.0 2.0 0.0 10.63 10.00

--r- 9.38 9.38

r- 8.75

-

r-Regime 1 r-Regime 2 Regime 3 Regime 4 Optimal

Regime b 14 1.2

~

10 JP _0.8 ? ;;;

O D ₀₆ : E c, 0.4

~

0.2 0.0 125

112 1.11 1.14

r--

--

102

----

[]VJitttr

producttvitv 1& grain!kRwater]

Regime 1 Regimt> 2 Regime 3 R(>gIl'fH:04 Optmal RegIme

Fig. II Optimal the yield and water productivity simulated by GAs model and their comparison to the empirical data.

Table Z Optimal soil moisture level in eacbg;ro\'\th stagf' andits ccmpansce to the empmcal data

Field :Ex.pmrnenrs GAs optmuzer

Regime Regsne Regime Regime (Jptm,.,

Components 1 1 3 4 Regone Ccndmon

Soil moisture {cm3!cm3)

lrutial (SM 1) 0.622 C 602 0.611 0586 O.6:!2 W<t

Crop development (SM.:!) 059" 058; 0.593 0'63 0.563 Wet

Mid- se ason(SM3) 0.522 oJgS 04-1 0455 o5~~ Mednan

Late st:a..son{S_M4) o50~ CAOI 0.~j6 0350 0350 Dry

Yield (ton ha) 1000 938 S -5 938 1063

Tctalcrigaricn (mm) 343 295 305

--,

:99

Tot alpreciprtation (mm) 551 551 551 ~51 551

Water prcducrrviry(g gram

kg+waterj 112 1.11 102 114 11~

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EES 2012 International Conference Challenges of Water&Environmental Management in Monsoon Asia 27-29 November 2012 Thailand

Conclusions

In this study, optimal combination of soil moisture level

in each growth stage that maximizes both the yield and

water productivity of SRI crop management was

simulated by GAs model. The simulation was performed

based on the identification process according to the

empirical data during three cropping seasons. As the

results the optimal combination was 0.622 (wet), 0.563

• 3 1

(wet), 0.522 (medium), and 0.350 cm /cm (dry) for

initial. crop development. mid-season and late season

growth stages, respectively. The wet conditions in the

initial and crop development growth stages should be

achieved to provide enough water for vegetative

development, and then the field can be drained with the

irrigation threshold of field capacity to avoid spikelet

fertility in mid-season stage and finally, let the field d~y

to save more water in the late season stage. By this

scenario it was simulated that the yield can be increased

up to 6.33% and water productivity up to 25.09% ~ith

saving water up to 12.71% compared to the first regime

(as base line).

Acknowledgments

We are grateful to the Directorate of Higher Educatio?,

Ministry of National Education, Republic of Indonesia

for generous financial support .through gr~nt. of

International Research Collaboration and Scientific

Publication and the Japan Society for the Promotion of

Science. Also. the study was partially supported by

GRE E (Green Network of Excellence) of MEXT in

Japan.

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