ASIAN CROP SCIENCE ASSOCIATION

Organizers:
Assoc. Prof. Dr. Pham Van Cuong
Prof. Jun-Ichi Sakagami

PROCEEDINGS OF 8th ASIAN CROP SCIENCEASSOCIATION CONFERENCE
Ha Noi, 23-25 September, 2014

AGRICULTURAL UNIVERSITY PRESS - 2015

2

CONTENTS
Kernel Quality and Ripening Ability in Japonica and Indica Type Rice Cultivars
Grown under Different Temperature Conditions
Miyazaki, A., Y. Ishida, K. Masai, K. Tsuda, Y. Yamamoto, N. Tu, J. Ju, J. Cui ..................... 7
Relationships among Global Climate Indices, Rainfall and Rain-fed Crop Yields in
the Southern Highlands of Java: A Case Study in Gunungkidul, Yogyakarta,
Indonesia
Nugroho, B.D.A. ..................................................................................................................... 14
Growth Response of Three Rice Cultivars to Nitrogen Foliar Application under
Submerged Condition
Irmawati, H. Ehara, R. A. Suwignyo and J. Sakagami ........................................................... 25
Evaluation of Rice Productivity with Plant Canopy Analyzer in Farmers’ Fields in
Lao PDR
Hirooka, Y., K. Homma, M. Maki, K. Sekiguchi ..................................................................... 37
Greenhouse Gas Emmission From on-Field Straw Burning in The Mekong Deltaof Viet Nam
Nguyen Vo Chau Ngan, Tran Sy Nam .................................................................................... 43
Alleles for Good Grain Filling in Rice Extra-Heavy Panicle Types and Their
Distribution among Rice Cultivars
Kato, T., A. Horibata .............................................................................................................. 51
Residue Management Effects on Survival Rate, Growth and Yield of Rice Cultivar
IR64-Sub1 Subjected to Submergence at Young Seedling Stage in Pots
Pham Phuoc Nhan, Phan Thi Be Sau, Le Van Hoa, Ben Macdonald .................................... 62
Effects of Alternate Wetting and Drying Irrigation and Phosphorous Fertilizer Rateson the Growth and Yield of Rice in Double Rice Cropping System at An Giang
Province
Pham Phuoc Nhan, Le Van Hoa, N. T. Giang, T. P. Tuong, Ben Macdonald ....................... 71
Developing A Mathematical Model to Measure The Spreading of Soil Moistureunder Drip Irrigation
Ngo Thi Dung, Nguyen Van Dung, Hoang Thai Dai, Le Duc Vinh ....................................... 82
Growth and Sodium Absorption of Sweet Sorghum in Agricultural Land Damaged
from Tsunami of the Great East Japan Earthquake
Nakamura S., M. Saito, K. Kimura ......................................................................................... 933

Traits of Drought Resistance of NERICA with The Effects of Compost, Comparedwith Asian Rice and African Rice in The Field
M. Fujii ................................................................................................................................. 100
Activity for Simulating Rice Productivity in Musi River Basin, Indonesia
Homma, K., K. Tsujimoto, T. Ohta, T. Koike, A. Wijaya, S. Yamaoka, Y.
Matsumoto ............................................................................................................................ 110
The Number of Unemerged Leaves in Young Sucker of Sago Palm
Nabeya Keita, Satoshi Nakamura, Akihiro Fujii and Yusuke Goto...................................... 116
Traits Related to Seed Yield in Early Varieties of Adzuki Bean in Hokkaido, Japan
Kasajima Shinya, Satoshi Ogawa, Hirotake Itoh, Hideki Kurosaki, Yuki Horiuchi,
Hitoshi Sato .......................................................................................................................... 118
Dihaploid Rice Line Production through Anther Culture to Accelerate theDevelopment of New Rice Varieties
Dewi, I. S., N.H. Putri, B.S. Purwoko ................................................................................... 122
The Effects of Phosphorus Application on Salinity Tolerance under Different
Fertility Upland Conditions in The Interspecific Rice Cultivar NERICA1
Honda, T., M. Tsuda and Y. Hirai ........................................................................................ 127

4

Foreword

We are now living in a world which is affected by global challenges such as population growth,
climate change, food un-security and environmental degradation. The recent financial and foodcrises remind us that the world is changing quickly and dramatically. While world food production
has increased dramatically, 820 million people worldwide are still undernourished. It is important toincrease not only crop productivity to meet the world’s food demand but also the value in crop
production chains for farmers. Therefore, providing a growing population with food, water, shelter,and livelihoods, without further degradation of the environment and sustaining crop production
under global climate changes present great challenges worldwide.
From 23-25 September 2014, the 8th Asian Crop Science Association Conference (ACSAC8-2014)
on “Sustainable Crop Production in Response to Global Climate Change and Food Security” was holdat Viet Nam National University of Agriculture, Ha Noi, Viet Nam.
The aims of ACSAC8-2014 are to provide a platform for leading academic scientists, researchersand scholars to share and exchange the most current advances in environmental and crop science; aswell as to explore and discuss approaches, technologies and strategies to sustain and increase crop
productivity under the pressure of global climate changes.
In particular, the ACSAC8-2014 includes General Session with invited speakers and five specificsessions as follows:
Session 1: Climate change and its impacts on crop production and society
Session 2: Molecular approaches for improving crop adaptation to climate change
Session 3: Approaches of crop morphology, physiology and ecology to cope with climate change
Session 4: Innovative cultivation technology for enhancing crop production under climate change
Session 5: Plant physiology-based approaches to climate changes; impact of low to hightemperatures and other disaster on crop productivity (Special session in cooperation with the Crop
Science Society of Japan and the Korean Crop Science Society)We would like to express our sincere thanks to all participants. The acknowledgement is also
addressed to our sponsors for financial support of the conference.

On behalf of the Organizing committee

Assoc. Prof. Pham Van Cuong

5

6

Kernel Quality and Ripening Ability in Japonica and Indica Type Rice Cultivars
Grown under Different Temperature Conditions
Miyazaki, A.1*, Y. Ishida1, K. Masai1, K. Tsuda1, Y. Yamamoto1, N. Tu2, J. Ju3, J. Cui4
1

Faculty of Agriculture, Kochi University, 200 Monobe Otsu, Nankoku Kochi 783-8502, Japan
2
College of Agronomy, Hunan Agricultural University, Changsha Hunan 410128, China
3
College of Environmental Sciences, Yangzhou University, Yangzhou Jiangsu 225009, China
4
Tianjin Agricultural University, Tianjin 300384, China
Email*: miyazaki@kochi-u.ac.jp
ABSTRACTThe effect of high temperature during ripening on the occurrence of white immature kernels and ripening ability
was compared among rice cultivars, and the percentages of white immature kernels and grain filling were thenanalyzed in relation to yield characters. Five indica and nine japonica cultivars in 2011, and eight indica and eightjaponica cultivars in 2013 from China (collected from Hunan, Jiangsu and Tianjin), with six Japanese japonica
cultivars in each year, were cultivated in a paddy field and transplanted to pots (one plant/pot) at the flag leaf
emergence stage. From full heading stage to the mature stage, plants were grown under low (23C daytime/19C atnight), medium (28C/23C) and high (34C/26C) temperature conditions. The results were as follows: (1) thepercentage of white immature kernels in Chinese cultivars was significantly higher than that in Japanese cultivars,
because of the higher percentage of milky white kernels. In japonica, the Chinese cultivars had a wide and thickkernel compared with Japanese cultivars. (2) With increasing temperature during ripening, the percentage of white
immature kernels significantly increased because of the increase in milky white kernels. High temperature decreased
the grain filling percentage and kernel weight with a decrease in kernel width and thickness. (3) The percentage ofwhite immature kernels in middle temperature conditions was significantly related to kernel thickness in japonica,
while in indica it was significantly related to kernel width. These results indicated that cultivars with a wide or thickkernel had a high percentage of white immature kernels. However, these relationships were not significant under hightemperature conditions.
Keywords: Chinese cultivar, Grain filling percentage, High temperature, White immature kernel.

1. INTRODUCTION
A rapid temperature increase in the summer seasons (about 1.34C) has been observed in Kochi
Prefecture, Southeast Japan during the last three decades (Japan Meteorological Agency, 2011).
Severe high temperatures in the summer seasons (37-40C) have also been reported often in Hunan
Province, China, where a temperature increase of 1.02C in July and August has been observed
during these three decades (Matsui, 2009; Miyazaki et al., 2011). High temperatures in summer
have degraded the quality of rice kernels in various regions in Japan, and the percentage of firstgrade rice kernels declined to only 14 % in 2010 in Kochi Prefecture, Japan (Morita, 2008; Sakata etal., 2011). This is caused by a decrease in perfect kernels with an increase in white immature
kernels. The percentage of white immature kernels shows large variation among rice cultivars(Takata et al., 2010; Sakata et al., 2011), which is clear under high-temperature conditions (Sonoda
et al., 2012).
High temperature during the ripening stage decreases the kernel weight, beginning at an
average temperature of 25C and decreasing significantly at more than 30C (Matsushima andManaka, 1957). The decrease in kernel weight is due to a decrease in the kernel size, causing adecrease in grain filling below a certain threshold.

7

Recent Chinese cultivars have high-yield with high grain filling, a large number of spikelets per
panicle, and heavy kernels compared with Japanese cultivars, resulting from a high capacity tosupply starch (Yao et al., 2000a, 2000b; Gendua at al., 2009b). By comparing Chinese cultivars
including both japonica and indica cultivars with Japanese cultivars, the effect of high temperatureon white immature kernels and grain filling was elucidated in this experiment. In addition, therelationships between these characters and yield-related characters were examined under different
temperature conditions to identify the crop characters affecting cultivar differences.

2. MATERIALS AND METHODS
2.1. Plant materials and cultural conditions
Five indica and nine japonica cultivars in 2011, and eight indica and eight japonica cultivars in2013 collected from China (Hunan, Jiangsu, and Tianjin), with six Japanese japonica cultivars ineach year, were used.
Experimental plants were firstly cultivated in a paddy field of Kochi University, Japan. One
seedling/hill was transplanted at 22.2 hill/m2 on 24 May, 2011 and 27 May, 2013. Basal dressing wasapplied with slow-release fertilizer at a rate of 12 g/m2 each of N, P2O5, and K2O.
Plants with an average number of panicles were transplanted from the paddy field to pots (oneplant/pot) at the flag leaf emergence stage. From the full heading stage to the mature stage, plants
were grown under low- (23C daytime/19C at night), medium- (28C/23C) and high- (34C/26C)
temperature conditions using temperature-controlled rooms with natural sunlight.
2.2. Measurements
Three pots were sampled at the root base in the full heading and mature stages to determine thepanicle number and dry matter weight after drying at 95C for 2 h and 65C for 2 days. The panicles
were dried at 40C for 2 days to measure the dry weight and yield components in the mature stage.
After selection with salt solution, hulled kernels were used to measure kernel shapes and classify
white immature kernels with a grain quality analyzer (model RGQI10A, Satake Corporation,
Hiroshima, Japan) for japonica cultivars. The white immature kernels of indica cultivars wereclassified by visual inspection because the indica cultivar kernels did not fit into the analyzer.
The increase in dry matter weight after heading (∆W) was calculated by the difference inaboveground dry matter weight between the mature and heading stages. The amount ofcarbohydrate translocated from leaves and stems to panicles (∆T) was estimated by the decrease in
dry matter weight of leaves and stems after heading (Gendua et al., 2009a; Kokubo et al., 2013).

3. RESULTS
Similar results were obtained in both 2011 and 2013, and the common results are shown below.
3.1. Differences among production areas
The percentage of white immature kernels in Chinese japonica cultivars was significantly higher
than that in Japanese japonica cultivars, because of a higher percentage of milky white kernels(Table 1). The percentage of white immature kernels in indica cultivars classified by visualinspection was quite high; milky white kernels were predominant (Table 2). The kernel thickness in
Chinese japonica cultivars was greater than that in Japanese japonica cultivars (Table 3). Compared8

with japonica cultivars, indica cultivars had a large number of spikelets per panicle, a highpercentage of grain filling, and long and narrow kernels (Table 3, 4). A lower number of panicles,
larger number of spikelets per panicle, and heavier 1000-kernel weight were found in Tianjin
japonica, and Hunan and Jiangsu indica cultivars than Japanese japonica cultivars.

Table 1 Effects of production area and temperature treatment on the percentage of white immature
kernel in japonica cultivar (2013).
Production
area

SubspeciesWhite
immature (%)

Milky white(%)

White-based(%)

White belly
and back (%)

Japanjaponica

40.0

c

19.4

c

9.7

a

10.9

b

Tianjinjaponica

51.0

b

34.5

b

11.6

a

4.8

c

Jiangsujaponica

77.0

a

47.9

a

10.9

a

18.2

a

Low temp

37.4

c

15.3

b

10.7

b

11.4

b

Medium temp

50.1

b

16.4

b

17.7

a

16.0

a

High temp

73.7
***

a

64.0
***

a

3.4
ns

c

6.3
***

c

Area

***
***
***
***
Temp
**
***
***
***
Area × Temp
Classified by the inspection analyzer.
Means followed by the same letter in the same column do not differ significantly at the 5% level amongproduction areas and among temperature treatments.
** and ***, significant at the 1 and 0.1% levels, respectively; ns, not significant at the 5% level.

Table 2 Effects of production area and temperature treatment on the percentage of white immature
kernel in indica cultivar (2013).
Production
area

SubspeciesWhite
immature (%)

Milky white(%)

White-based(%)

White belly
and back (%)

Jiangsu

indica

95.7

a

85.6

b

0.0

a

10.0

a

Hunan

indica

96.7

a

93.8

a

0.0

a

3.0

b

Low temp

91.4

b

77.1

b

0.0

a

14.2

a

Medium temp

97.2

a

92.8

a

0.0

a

4.5

b

High temp

a

99.1
**

a

0.0
ns

a

0.8
**

b

Area

99.9
ns

Temp

*

***

ns

***

ns
**
ns
**
Area × Temp
Classified by the visual inspection.
Means followed by the same letter in the same column do not differ significantly at the 5% level amongproduction areas and among temperature treatments.
*, ** and ***, significant at the 5, 1 and 0.1% levels, respectively; ns, not significant at the 5% level.

3.2. Differences among temperature conditions
The percentage of white immature kernels in both japonica and indica cultivars increased withan increase in temperature, mainly because of an increase in milky white kernels (Table 1, 2). The
kernel width and thickness significantly decreased (Table 3). The grain filling percentage and 1000kernel weight in high-temperature conditions were significantly lower than in medium- and lowtemperature conditions, resulting in a lower yield (Table 4).

9Table 3 Effects of production area and temperature treatment on the kernel
shape in japonica and indica cultivars (2013).
Production
area

Subspecies

Length
(mm)

Width
(mm)

Thickness
(mm)

Japan

japonica

5.03

b

2.57

b

1.99

b

Tianjinjaponica

5.10

b

2.69

a

2.01

b

Jiangsujaponica

4.91

b2.64 ab2.08

aJiangsu

indica

6.26

a

2.26

c

1.99

b

Hunan

indica

6.54

a

2.21

c

1.90

c

Low temp

5.53

a

2.54

a

2.01

a

Medium temp

5.51

a

2.50

a

2.01

a

High temp
Area

5.53
***

a

2.40
***

b

1.97
***

b

ns
***
**
Temp
ns
ns
ns
Area × Temp
Means followed by the same letter in the same column do not differ
significantly at the 5% level among production areas and among temperature
treatments.
** and ***, significant at the 1 and 0.1% levels, respectively; ns, not significant
at the 5% level.

Table 4 Effects of production area and temperature treatment on the yield components in japonica and indica cultivars(2013).
Production
area

Subspecies

Panicle No.

Spikelet No.

Spikelet No.

Grain filling

1000 kernel

Brown rice(/hill)(/panicle)(/hill)(%)

weight (g)yield (g/hill)Japanjaponica

15.3

a

74.4

c

1124

b

52.2

b

20.2

c

12.7

c

Tianjinjaponica

12.8

b

110.2

b

1396

a

53.2

b

21.6

b

16.9

b

Jiangsujaponica

15.3

a

72.4

c

1104

b

52.3

b

20.8 bc

12.5

c

Jiangsu

indica

9.4

c

145.5

a

1367

a

60.1 ab

23.9

a

20.7

a

Hunan

indica

12.0

b

121.4

b

1422

a

63.3

a

21.8

b

20.1

a

Low temp

13.1

a

99.8

a

1246

a

72.1

a

23.3

a

20.8

a

Medium temp

13.1

a

103.7

a

1275

a

70.5

a

22.9

a

20.5

a

High temp

a

102.7
***

a

1283
***

a

25.0
**

b

21.6
***

b

7.4
***

b

Area

13.2
***

Temp

ns

ns

ns

***

***

***

Area × Temp

ns

ns

ns

***

ns

***

Means followed by the same letter in the same column do not differ significantly at the 5% level among production areas
and among temperature treatments.
** and ***, significant at the 1 and 0.1% levels, respectively; ns, not significant at the 5% level.

3.3. Relationship between
yield-related charactersthepercentage

of

white

immaturekernelsand

The percentage of white immature kernels in japonica cultivars was significantly related tokernel thickness in medium-temperature conditions (r=0.86, p<0.01), suggesting that the white
immature kernel percentage was low in cultivars with thin kernels (Table 5). However, thepercentage of white immature kernels in indica cultivars was significantly related to kernel width inmedium-temperature conditions (r=0.75, p<0.05); in addition, the kernel width was negatively
related to kernel length (r=−0.85, p<0.01). These results suggested that the percentage of white
immature kernels was low in indica cultivars with long slender kernels. However, these
relationships were not significant under high-temperature conditions, in which other factors may
have more influence on the occurrence of white immature kernels. Similar relationships between

10kernel shapes and white immature kernels were reported in the experiments using different
cultivars (Takata et al., 2008; Nagaoka et al., 2012).
Table 5 Correlation coefficients between the percentage of white immature kernel and the yield-related charactersin japonica and indica cultivars (2013).
Japonica

IndicaLow temp Medium temp High temp

Low temp Medium temp High temp

Panicle No.

-0.07

-0.08

-0.05

-0.83

Spikelet No. (/panicle)

-0.18

0.03

-0.03

Spikelet No. (/hill)

-0.36

-0.23

Grain filling (%)-0.081000 kernel weight

*

0.39

0.23

0.71

-0.16

-0.11

-0.03

-0.18

0.40

0.19

-0.37

0.08

0.04

0.64

0.15

0.49

0.39

0.07

-0.22

-0.79

Brown rice yield

-0.15

-0.35

0.16

-0.22

0.51

0.12

Kernel length

-0.02

-0.12

-0.14

-0.20

-0.56

-0.21

Kernel width

0.32

0.32

0.21

0.16

0.75

Kernel thickness

0.28

0.86 **

0.42

-0.03

0.08

0.48

∆W (/spikelet)

0.40

-0.01

0.00

-0.69

-0.23

0.13

∆T (/spikelet)

-0.37

0.02

-0.12

0.52

0.03

-0.11

∆W+∆T (/spikelet)

0.07

-0.02

-0.37

-0.26

-0.44

-0.02

*

*

-0.26

0.08

* and **, significant at the 5 and 1% levels in 2013, respectively.
Correlation coefficients with the underline, significant in 2011.
∆W, dry matter increase after heading; ∆T, dry matter decrease in leaves and stems after heading;∆W+∆T, dry matter increase in panicles after heading.

3.4. Relationship between the of grain filling and dry matter production
The percentage of grain filling of japonica cultivars was significantly related to ∆W+∆T under
low temperature and ∆T under high temperature, consistently in both years (Table 6). This result
suggested that ripening ability at high temperature might be related to the dry matter decrease inleaves and stems. The reduction in ripening ability depends on reduced starch accumulation
resulting from the deterioration of source capacity (Nagata et al., 2001; Gendua et al., 2009a). This ispartly explained by the shortage of carbohydrate translocated from leaves and stems to panicles(Hirai et al., 2003). Therefore, the kernel weight is enhanced by an increase in carbohydrate stored
in leaves and stems (Wada, 1969). However, in indica cultivars, consistent results between both
years were not obtained.
Table 6 Correlation coefficients between the percentage of grain filling and the ∆W, ∆T, ∆W+∆T in japonica
and indica cultivars (2013).
Japonica

IndicaLow temp Medium temp High temp
∆W (/spikelet)

0.07

0.34

-0.46

∆T (/spikelet)

0.23

0.16

0.56

∆W+∆T (/spikelet)

0.82 **

0.78 **

0.28

Low temp Medium temp High temp
-0.63

*

0.78
0.57

*

-0.47

-0.39

0.46

0.66

0.19

0.89 *** and **, significant at the 5 and 1% levels in 2013, respectively.
Correlation coefficients with the underline, significant in 2011.

11

4. CONCLUSIONSThe percentage of white immature kernels in medium-temperature conditions was significantly
related to kernel thickness in japonica, while in indica it was significantly related to kernel width.
These results indicated that cultivars with a wide or thick kernel had a high percentage of white
immature kernels. However, these relationships were not significant in high-temperature
conditions. On the other hand, the percentage of grain filling in japonica cultivars was significantly
related to the dry matter decrease in leaves and stems after heading under high-temperature
conditions. This result suggested that the cultivar difference in ripening ability under hightemperature conditions might be affected by the amount of assimilation supply.

ACKNOLEDGEMENT
We thank Mr. Yasuo Takemura and Mr. Nobuyuki Kira for their technical support on field
experiments. This study was supported by Grant-in Aid for Scientific Research (No. 25450023) from
the Ministry of Education, Culture, Sports, Science and Technology of Japan.

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regulation in sink and source formation of high-yielding rice cultivars, Soil Science and Plant Nutrition,
46(4): 825-834.

13

Relationships among Global Climate Indices, Rainfall
and Rain-fed Crop Yields in the Southern Highlands of Java:A Case Study in Gunungkidul, Yogyakarta, Indonesia
Nugroho, B.D.A.
Faculty of Agricultural Technology, Gadjah Mada University
Email: bayu.tep@ugm.ac.id; by_ugm@yahoo.com
ABSTRACT
Although there has been a high interest to investigate the relationships among global climate indices, rainfall andrain-fed crop yields in Indonesia, little evidence is available for crops in highland agriculture of the country. This study
evaluates the relationships among global climate indices (Southern Oscillation Index, SOI; sea surface temperature,SST), rainfall and rain-fed crop yields in Gunungkidul district, highland areas of southern Indonesia, an importantarea of rain-fed crop production. Rainfall data were collected during the rainy season (October-March) from 1981 to
2009, and crop yields data were collected from 1990-2009 from Gunungkidul district. Global climate indices (SOI and
SST) were collected from the Japan Meteorological Agency (JMA). Rainfall was highest in the southern and western
mountainous areas, especially near the coast, and SOI and SST Niño.West values were highly correlated with rainfall
in those areas. Soybean and dryland paddy yields were highly correlated with the amount of rainfall in January, andthe average SOI during June-September was highly correlated with maize yield during January-April.
Keywords: correlations, rain-fed agriculture, rainfall variability, SOI-SST, yields

1. INTRODUCTION
In Indonesia, variable rainfall during the growing season, mostly due to El Niño and La Niña
events, critically affects rain-fed agriculture. El Niño events affect delaying the beginning of therainy season (typically, October-March) and thereby negatively affecting agricultural production(Hamada et al., 2002). During the past 20 years, several El Niño events (McPhaden, 1999) delayed
the paddy harvest in Indonesia and threatened the stability of food security (Harger 1995; Amien etal., 1996). In 1997/1998 and 1982/1983, decrease of harvest area for wetland paddy was about 670
thousand hectares in 1982/1983 and 700 thousand hectares in 1997/1998. In percentage terms thedecrease was 4.5% and 3.8% in 1982 and 1983, respectively, and in 1997 and 1998 these values were4.0% and 3.0%, respectively. Harvest area decrease for all food crops was two million hectares forboth 1982/1983 and 1997/1998 (Irawan, 2002). The total loss to the Indonesian economy due to thedecreased agricultural production in 1997/1998 and 1982/1983 was about US$2.75 billion out of total
losses to the national economy that were close to US$9 billion (Kirono and Tapper, 1999;
BAPPENAS, 1999). Especially in Java, the rain-fed paddy yield in some subdistricts of Java
dramatically decreased, by 58% in wetland areas and by 52% in dryland areas, mostly in southern ofJava highland areas (Irawan, 2002) due to the delayed start of the rainy season. The delayed start of
the rainy season due to El Niño events has a greater effect on crop yields in highland areas, where
agriculture is rain-fed (Irawan, 2002; Kirono and Tapper, 1999). Therefore, detailed investigations of
the relationship between rainfall variability in the rainy season and rain-fed crop yields in highlandareas are needed.
Many studies have investigated the relationship between rainfall variability and topography inIndonesia. Hamada et al. (2008) showed that rainfall and wind speed are greater on the windwardside than on leeward side of the mountains of western Sumatera and suggested that surface

14

topography plays a role in determining the rainfall distribution not only in Sumatera but also in andaround the entire Indonesian archipelago.
Rainfall variability in Indonesia also correlates with global climate indices, such as the Southern
Oscillation Index (SOI) and sea surface temperature (SST) (Saji et al.,., 1999). SST fluctuations aremost pronounced around Indonesia and in the nearby tropical Pacific (Trenberth and Shea, 1987;
Trenberth and Hoar, 1996). Nicholls (1981, 1984) shshowedowed that surface pressure at Darwin, northern
Australia and SST around Indonesia could be used to predict Indonesian climate variations andIndonesian rainfall. Kirono and Tapper (1999) found that rainfall from June to November was
positively correlated with
ith the SOI from 1951 to 1997, and that the relationship was particularly
strong in southern Java, including Yogyakarta Special Province. Thus, rain
rain--fed crop production inhighland areas of Indonesia is simultaneously influenced by rainfall variability, glglobal climate asreflected by the SOI and SST indices, and the highland topography.
Therefore, the aim of this study was to clarify the relationships among global climate indices(SOI and SST), rainfall and rain
rain-fed crop yields during the rainy season from 1981 to 2009.

2.. MATERIALS AND METHODMETHODS2.1. Study Area
Our study area was Gunungkidul district (7(7°46’-7°09’S, 110°21’-110
110°50’E), a rain-fed
agricultural area in the highlands of southern Java bordering the Indian Ocean. This area of 1485.36
km2 is divided into 18 subdistricts and includes 144 villages (www.gunungkidulkab.go.id;(www.gunungkidulkab.go.id accessed 5
December 2011). The district can be divided into three areas according to elevation (Agricultural
Service for Food Crops and Horticulture, ASFCH): the northern part range
rangess between 200 and 700 m
above sea level (a.s.l.); the central lowland area is at 150-200
200 m a.s.l.; and the southern area is hilly
with elevations from 0 to 300 m a.s.l. ((Fig. 1).

Fig. 1. Topography of Gunungkidul district15

Of the total agricultural land area, rain-fed agriculture is practiced in 61,989 ha, with Octoberprecipitation being especially important (ASFCH, 2006), and the remaining 7804 ha are irrigated.
The mean annual rainfall in the district during 1989-1998 was 2041 mm. On average, the rainy anddry periods each last for 4 to 6 months (ASFCH, 2006). The main crops are dryland paddy andother dryland food crops. Two main cropping patterns are used: monoculture and multiple
cropping. Monoculture is the repetitive growing of the same crop on the same piece of land andusually planted in wetland or irrigated areas. Whereas multiple cropping is the intensificationof cropping in time and space dimensions, growing two or more crops on the same field in a calendar
crop (Huang, 2003; Beets, 1975), and usually planted in dryland or rain-fed agricultural areas(Sarjiman and Mulyadi, 2005).
In the rain-fed highlands of Gunungkidul district, multiple cropping is used as the main
cropping pattern. Crop production is dominated by cassava (62%), and other crops include dryland
paddy (11.4%), maize (9.6%), peanut (8%), soybean (5.5%), and wetland paddy rice (3.3%) (ASFCH,
2006). Cassava is the dominant crop in multiple cropping system of the district because it can begrown year-round and is resistant to drought (ASFCH, 2006).
2.2. Data and Methods
2.2.1. Crop yields and rainfall data
Crop yields from 1990 to 2009 and rainfall data from 1981 to 2009 were collected at 12 stations(see Fig. 1) (ASFCH, 2009). We used data from 12 of the 18 rainfall observation stations from whichadequate data were available. Rainfall data in Indonesia, especially for large areas, are often
incomplete or unreliable. Because the records are updated by hand, both the human error and thetime required for records to be updated are considerations. Data for all of Indonesia are particularly
hard to obtain. There are 6000 rainfall stations in 39 regions of Indonesia, and although some data
are collected and recorded at Jakarta, other data are kept only at each provincial office (Hamada etal., 2002). In this study, to minimize errors in the rainfall data, we limited the size of the study area
and obtained rainfall data directly from the rainfall observation station in each sub district.
2.2.2. Global climate indicesWe used SOI and SST data from the Japan Meteorological Agency (JMA)(http://www.data.jma.go.jp/gmd/cpd/db/elnino/index/dattab.html; accessed 20 November 2011)
averaged over the Niño.3 (5°N-5°S, 150°W-90°W), Niño.West (15°N-0°, 130°E-150°E), and the Indian
Ocean Basin-wide (IOBW; 20°N-20°S, 40°E-100°E) regions (Fig. 1). Munawar et al. (2000) reported
that anomalies in the Niño.3 area influence precipitation in Indonesia more strongly than those in
the Niño.4 area. We used JR-25 re-analysis data (Onogi et al., 2007) to analyze normal (i.e., the 26year mean from 1979 to 2004) specific humidity (kg/kg), wind direction, and wind speed in January
at 850 hPa at Java Island. Spatial data for Gunungkidul district (scale, 1:25,000) are from the
National Coordinating Agency for Survey and Mapping (Bakosurtanal) of Indonesia (2007).
Rainfall data are the averages of cumulative rainfall during the rainy season (October-March)from 1981 to 2009 at the twelve stations in Gunungkidul district (Fig. 2). We analyzed the data as
follows. First, we mapped the rainfall distribution during the entire rainy season (6 months) from1981 to 2009, and then we separately mapped the distribution during the first three months(October-December; OND) and the last three months (January-March; JFM) of the season. We chose
October as the first month because it is also the beginning of the growing season (Sarjiman and
Mulyadi, 2005). Second, we compared the distribution of rainfall in Gunungkidul district and theaverage SOI and SST values to identify global climatic influences on rainfall in the district by

16

conducting a regression analysis of the data for the entire rainy season and the data for OND and
JFM against the climatic indices. Third, we performed correlation analyses, comparing the average
rainfall during
ing the rainy season and crop yields and average values of the global climate indicesduring the rainy season and crop yields.

3. RESULTS
3.1. Correlations of rainfall in southern Java with global climate indices
During the El Niñoo years (low SOI years, Fig. 2) of 1982/1983, 1997/1998 and 2004/2005,
Gunungkidul district received less rainfall during the rainy season. The correlation between rainyseason rainfall and SOI variations during 1981
1981-20092009 was significant and positive (r( = 0.48, p<0.01;
Fig. 2a). Rainfall
ainfall variations also showed a significant, positive correlation with SST Ni
Niño.Westvariations (r = 0.39, p<0.05; Fig. 2b), and a significant negative correlation with SST Niño.3
Ni(r = 0.49, p<0.01; Fig. 2b) and SST IOBW variations ((r = -0.50, p<0.01; Fig. 2b). Therefore, SOI and SST
can be used to predict variations in precipitation in rain
rain-fed
fed agricultural areas of the southernJava highlands.

Fig. 2. Average precipitation (bars) and (a) average SOI (dashed line) and (b) average SST
Niño.West (solid line), Niñ
ño.3o.3 (dashed line), and IOBW (dotted line) during the rainyseason (October(October-March) from 1981 to 2009
3.1.1. Rainy season rainfall distribution from 1981 to 2009
We analyzed the rainfall distribution from 1981 to 2009 using data for the entire 6 months of therainy season and separately for OND and JFM (Fig. 3). Overall during the rainy season, rainfall
ranged from 1800 to 2200 mm in the southern coastal and western inland areas, and around 1500
15001600 mm of rain fell in the central lowland and nnorthern areas (Fig. 3a). During the early rainyseason (OND), rainfall was 750--900 mm in the southern coastal areas and 500--600 mm in the central
lowland area (Fig. 3b), and the highest amounts of rainfall occurred during the late rainy season(JFM, Fig. 3c).
c). During the rainy season, especially at its peak in January, westerly winds bringmoisture from the Indian Ocean to Java ((Fig. 4), and this moisture carried by the winds produces
dense clouds and orographic precipitation (Roe, 2005) along the mountain rranges
anges of southern andwestern Java. As a result, the windward side of the mountains in Gunungkidul district receives
much more precipitation than the leeward side. Orographic precipitation occurs particularly in themountainous northern Gunungkidul distric
districtt (Patuk, Nglipar, and Ngawen). Moreover, rainfall ishigher in Patuk sub district (in the northwest) than in Ngawen sub district (northeast), even thoughboth are mountainous, because the Patuk mountain block the southwesterly winds.

17

(a)

(b)

(c)

Fig. 3. Rainfall distribution in Gunungkidul district: (a) rainy season (October(October-March),(b) October-December
December (OND), and (c) January
January-March
March (JFM)

Fig. 4. Humidity (contours) and wind direction and wind speed (vectors)
at 850 hPa over Java Island in January from 1979 to 2004
3.1.2. Relationships between rainfall and climate indices during 1981
1981-20092009 by sub district
We compared the average rainfall with average values of the global climate indices during the rainyseasons from 1981 to 2009 in each subdistrict, and found significant correlations between the rainfall
variation in the subdistricts and SOI and SST values. SOI and SST Niño.Westo.West were both correlated with
rainfall in only three subdistricts (Figs. 5 and 6): Panggang (SOI, r = 0.58; SST Niño.West,Nir = 0.55),

18

Playen (SOI, r =0.44; SST Niño.West,o.West, r = 0.52), and Rongkop (SOI, r = 0.49; SST Niño.West,Nir = 0.38) in
p < 0.01 for all correlations. Rainfall variation was also correlated with SST Ni
Niñ
ño.3 in Panggang (r = 0.57), Playen (r = -0.38),0.38), and Rongkop ((r = -0.50) in p<0.01<0.01 for all correlations, and with SST IOBW in
Panggang (r = -0.44)0.44) and Rongkop ((r = -0.54) in p<0.01<0.01 for all correlations. Low significant correlation
between rainfall and SST IOBW and Ni
Niño.3o.3 is influenced by location of both SST. Also we focused on
SST Niño.Westo.West in our subsequent analysis because Ni
Niño.Westo.West is closest to our study area.
In Panggang subdistrict, rainfall ranged from 700 to 4000 mm, SOI from -25 to +14, and SST
Niño.Westo.West from 28.2 to 29.4. Most years, the average rainfall wa
wass between 1200 and 2500 mm, and it
was below 1000 mm in only one year. In Playen subdistrict, the average rainy season rainfall varied
greatly from year to year, from 1000 to 3700 mm, and SOI varied from -27
27 to +14 and SST Niño.West
Nifrom -27 to +14. The average
erage rainfall was usually between 1200 and 3500 mm and always above
1000 mm. The correlations between rainfall and SST Ni
Niño.Westo.West were similar in these two adjacentsubdistricts. In Rongkop subdistrict, rainfall variation was highly correlated with the indindices:average rainfall varied from 900 to 2400 mm, SOI from -27
27 to +14, and SST Niño.West
Nifrom -28.20 to29.40. In Rongkop, although the highest rainfall amount was lower than the highest amounts in
Panggang and Playen, the average rainfall was generally aabove 1500 mm.
Panggang, Tepus and Rongkop are the southernmost subdistricts in Gunungkidul district andborder directly on the Indian Ocean, and the SOI and SST Ni
Niño.Westo.West averages were Panggang andRongkop strongly correlated with the average rainfall in these subdistricts. In contrast, rainfall in
Playen subdistrict, in western--central
central areas of Gunungkidul district, is little influenced by winddirection from the west. Thus, rainfall in areas along the coast and west area shows stronger
correlations with SOI and SST Ni
Niño.West than central areas and northern areas, andpositive/negative SOI/SST Niño.Westo.West values influence the amount of rainfall in coastal areas.
The average rainfall in the other nine subdistricts of Gunungkidul district was not significantlsignificantly
correlated with either SOI or SST Ni
Niño.Westo.West averages, which we attribute to the effect of themountainous terrain on precipitation.

Fig. 5. Relationships between average rainfall and the average SOI
in the rainy season during 1981
1981-2009 for (a) Panggang,ng, (b) Playen, and (c) Rongkop

19

Fig. 6. Relationships between average rainfall and the average SST Ni
Niño.Westin the rainy season during 1981
1981-20092009 for (a) Panggang, (b) Playen, and (c) Rongkop
3.2. Relationships with crop yield
3.2.1. Relationships between
en rainfall and crop yield
We determined correlations between average rainfall and crop yields during 1990
1990-2009. During
January-April (JFMA), both soybean ((r = 0.62, P < 0.01) and dryland paddy (r( = -0.54, P < 0.05)
yields were significantly correlated wit
withh the amount of rainfall in January (Fig.
(7), but whereas
January rainfall was positively correlated with soybean yields, it was inversely correlated withdryland paddy yields. This can be explained as follows: traditional and economic factors determinethee planting system in Gunungkidul district. Sarjiman and Mulyadi (2005) divided the growing
season in dryland areas of the district into three stages, beginning in October, February, and May.
During the early growing season (October(October-December or January), farmers
rmers prefer to plant dryland
paddy for their own consumption (Sarjiman and Mulyadi, 2005). In contrast, in the late growing
season (January or February-March
March or April), farmers prefer to plant soybeans over dryland paddy
because they can get a higher pric
pricee for soybeans than for dryland paddy in the market. The average
price of late-season
season soybeans is IDR3.000 (per hectare per kg), compared with around IDR1.000 (perhectare per kg)1 for late-season
season dryland paddy (ASFCH, 2006). If precipitation is low in Jan
January,
however, farmers do not plant soybeans because the quality of the soybean crop would be poor.
Instead, they plant dryland paddy for their own consumption. Therefore, soybean yields correlate
positively, and dryland paddy yields correlate negatively, with January rainfall, and the amount of
rainfall in January can be used to predict soybean and dryland paddy yields during the late growing
season (JFMA).
1

IDR: Indonesian rupiah.

20

Fig. 7. Relationships between the amount of rainfall in January and (a) soybean yieldand (b) dryland
land paddy yield during January
January-April
April (JFMA)
3.2.2. Relationships between SOI and crop yield
The average SOI during June
June-September (JJAS) (x) was correlated with the maize yield during
JFMA (y) (r = 0.50, P < 0.05; Fig. 8), whereas we found no correlation between SST Niño.WestNiandany crop yield.

Fig. 8. Relationship between the average SOI
during June-September
September (JJAS) and maize yield during JFMA

4. DISCUSSION
Some sub-districts
districts in the coastal and western parts of the study area, the variations in rain
rainfallduring the rainy season were also significantly correlated with the SOI and with SST Niño.West,
Ni
Niño.3,o.3, and IOBW. Although we found SST Ni
Niño.West to be significantly correlated with rainfall in
Gunungkidul district, Surmaini and Susanti (2009) reportereported that SST Niño
o 3.4 (50N-50S, 1200-1700E)
is significantly correlated with rainfall in Indonesia generally. They contend that SST in the Ni
Niño 3.4
zone in May and June can be used to predict rainfall in Indonesia during AugustAugust-November. The
discrepancy between
een their results and ours reflects the difference in the scope of the study area andseasonal factors. Our microscale analysis focused on rainfall in highland areas during the rainyseason only (October-March),
March), whereas the mesoscale analysis of Surmaini aand Susanti (2009)
included the entire Indonesian archipelago during both the rainy and dry seasons.

21Average values of the indices SOI and SST Niño.West were correlated with rainfall in thesouthern and western mountainous areas of the district; coastal areas in particular received more
rainfall than inland areas. Similarly, annual rainfall is generally higher in the coastal regions ofWest Sumatera Province, Indonesia, than in the inland regions (Hamada et al., 2008). This patternof distribution can be attributed to the prevailing westerly winds being blocked by the mountains inthe southern and western areas (Hamada et al., 2008). Thus, in the future, the relationships
between rainfall variations and global climate indices in not only southern Java but also all ofIndonesia need to be examined.
By relating global climate indices to rainfall patterns, the suitability of crops can be
determined, which can help farmers make decisions about which crops to plant, especially inrain-fed agricultural areas in the highlands. Kirono and Khakhim (1998) proposed that if theweather can be predicted well in advance, and then so can crop production. We found that theamount of rainfall in January corresponded well to soybean and dryland paddy yields during
JFM: In Gunungkidul district, increased rainfall during January increases the soybean yieldand decreases the dryland paddy yield. Rice is the primary staple food for most Indonesian
peoples, but we showed that during the peak of the rainy season (JFMA), most farmers in
Gunungkidul district planted soybeans instead of dryland paddy rice. In the future, thecorrelation between soybean yield quality and rainfall variation in rain-fed agricultural areas
should be deeply investigated.
The average SOI in JJAS corresponded well to maize yields during JFMA. According to Naylor
et al. (2001), rice production in Java strongly depends on SST anomalies: Indonesia’s paddy riceproduction varies on average by 1.4 million t for every 1°C change in the August SST anomaly.
Furthermore, the SOI also corresponds to maize production (Naylor et al., 2002).
Overall, our results show that global climate indices and rainfall can be used as predictors ofcrop yields in rain-fed agricultural areas in the highlands of southern Java. Therefore,
policymakers and farmers can anticipate those areas that will have less rainfall during the next
growing season. The result of our study provides information to policymakers in the agricultural
sector that will be useful in the future. Further studies should investigate crop yields in rain-fed
croplands in both the highlands and the lowlands in relation to both rainfall and climate indices.

5. CONCLUSIONS
The rainfall distribution pattern shows that most rainfall during the rainy season from 1981 to
2009 fell in the southern and western mountainous areas, especially near the coast, and less rain
fell in the central lowland area. In the central lowlands, which have hilly topography, theprevailing wind direction and orographic effects cause the amount of rainfall to differ among subdistricts. The SOI and SST Niño.West were correlated with the average rainfall during the rainyseason in the coastal southern and western areas. Rainfall in January correlated well with soybean(r = 0.62) and dryland paddy rice yields (r = -0.54) in JFMA. The average SOI in JJAS correlated
well with the maize yield in JFMA (r = 0.50). Thus, the amount of precipitation in January can beused to predict soybean and dryland paddy yields, and the SOI during JJAS can be used to predictthe maize yield in JFMA.
Because precipitation in southern Java is significantly related to the global climate indices (SOIand SST), rainfall can predicted from variation in these indices, and precipitation and the SOI can beused to predict crop yields in rain-fed agricultural areas of the southern Java highlands.

22

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24

Growth Response of Three Rice Cultivars
to Nitrogen Foliar Application under Submerged Condition
Irmawati1, H. Ehara1*, R. A. Suwignyo2 and J. Sakagami3
1

Graduate School of Bioresources, Mie University, Japan
Faculty of Agriculture, Sriwijaya University, Indonesia
3
Faculty of Agriculture, Kagoshima University, Japan

2

Email*: ehara@bio.mie-u.ac.jp
ABSTRACT
Growth characteristics of three Indonesian rice cultivars (Inpara 5, IR 64, and Si Kuning) were evaluated under 7
and 14 days submergence condition. Nitrogen foliar treatment of 2300 ppm was applied to rice plants and split into
two application times: before (N1) and after (N2) submergence. Growth analysis then was calculated separately forboth under submerged condition and recovery period. Results showed that tiller number was identified as animportant character determining plant survival under submergence and growth after de-submergence. Greater tiller
number after de-submergence would lead to an improved leaf area ratio (LAR) and finally will affect relative growthrate (RGR) and the accumulation of total dry weight (TDW). Therefore, N2 treatment was assumed as an effective
effort to enhance tiller number and maintain plant growth after de-submergence. Nevertheless, under longer
submergence duration, the application of N1 appeared to give better performance.
Keywords: Growth analysis, nitrogen, recovery period, submergence, tiller number.

1. INTRODUCTION
Rice is a well-adapted species of plant to flooded condition. However, excessive flooding may
result in plant stress leading to decline of productivity. Flooding is a common problem occurred inlowland area across South and Southeast Asia (Ella and Ismail, 2006; Das et al., 2009), including
swamp areas in Indonesia. Swamp rice cultivation in Indonesia has suffered from an adverse impact
from high flooding which occurs in various stages of plant cycle. The risk of flooding to rice plant is
not only during early growth but also on the later stage or even during reproductive stage causing
the farmers suffer from massive harvest loss (Ito et al., 1999).
Poorly drained fresh-water swamp is the most prone area to flash-flooding that generally will lastless than a few weeks and is caused by heavy rain but the depth is not very deep (Catling 1992; Hattori
et al., 2009). Flash floods that result in stagnant flooding or complete submergence (Singh et al., 2011)of paddy fields can cause yield losses from 10% to 100% depending on water depth, duration of theflooding event, turbidity, temperature and the developmental stage of the plant (Das et al., 2009).
The increase in the frequency and intensity of flash floods due to changing global weather patterns(Singh et al., 2011) makes the development of more flood tolerant rice varieties critical (Sasidharan
and Voesenek, 2013). Nowadays, breeders are making efforts to develop and release new rice cultivars
that have submergence tolerance character (Septiningsih et al., 2015). However, this effort will takelonger time due to the complexity of floodwater characteristics among different environments (Das etal., 2009). Furthermore, existing submergence cultivars in Indonesia, such as INPARA cultivars, are
not preferable for both farmers and consumers due to the unfavorable taste. Farmers still cultivate
popular commercial cultivars which are less adaptive to flooding injury. Consequently, agronomists
should consider cultivation technique to cope with this submergence problem.

25

Proper nutrient adjustment then is considered as one of the subjects that can be modified toadapt to this situation. Nitrogen (N) is one of the essential macronutrients for plant growth and yield(De Datta, 1986; Ye et al., 2007). Many studies have reported the importance of N in plant body toshow better performance either in plant elongation, dry matter production or tillering (Ahmed et al.,
1998; Chaturvedi, 2005; Duan et al., 2006; Irmawati et al., 2015). Ehara et al (1993) mentioned thatapplication of 2300 ppm N would give the best effect on rice growth. The application of N fertilizer toimprove plant tolerance under submerged condition has also been investigated. As reported by Rao
et al. (1985), basal fertilization with N promoted early vigor allowing plants to tolerate submergenceat later stages of growth. Sharma (1995) also stated that top dressing of N after recession of flood
water improved recovery of plants and partially compensated for the mortality due to flooding.
Furthermore, the combination of N application through plant foliage along with soil application isbelieved more efficient compared to application only through soil (Mosluh et al., 1978; Khemira et al.,
1998; Saleem et al., 2013). However, very limited references of the effect of foliar N application to
growth of rice under submergence were found. Thus, the study was carried out to examine the effect offoliar application of high N concentration on growth parameters of rice under submerged condition.

2. MATERIALS AND METHODS
2.1. Plant materials, treatments and growth conditions
The experiment was conducted from September to December 2013 in Faculty of Agriculture,
Sriwijaya University, Indonesia. Three rice cultivars were evaluated in this research: IR 64 (common
commercial cultivar), Inpara 5 (IR 64 sub-1 cultivar), and Si Kuning (local fresh water swamp
cultivar in South Sumatra). Germinated seeds were first sown in a plastic tray, then 28-d-old
seedlings were transplanted into plastic pots with 1 seedling per pot. Submergence treatments werestarted at 25 days after transplanting (DAT) by shifting the pots into 4×1.5×0.8 m concrete watertank filled with tap water until whole plants were fully submerged. Two submergence treatments
were applied to the plants based on the duration: 7 days and 14 days. Water level and plantcondition was monitored to ensure submergence condition.
N treatment was applied to the plants by spraying 2300 ppm of N solution to the leaves. Tween
20 500 ppm was mixed in the N solution to confirm that foliar N will attach firmly on the leaflet.
Urea was used as N source considering its characteristic that can easily be absorbed by plants. The
application of N foliar treatment was divided into two treatments: before (N1) and after (N2)submergence and added with one non N treatment (N0) with three replicates. N treatment before
submergence was applied 7 days before submergence treatment (at same time for both 7 and 14 days
submergence), while after submergence treatment was applied one day after de-submergence foreach 7 days and 14 days submergence treatment.
2.2. Growth analysis
Plant samplings were conducted four times. First was one day before submergence treatment(t0: 25 DAT), second and third sampling were carried out at the end of submergence treatment [t1:end of 7 days submergence (32 DAT); t2: end of 14 days submergence (39 DAT)] and final sampling(t3: 49 DAT) was conducted 10 days after recovery period of 14 days submergence treatment. Plant
growth characters were measured at each sampling time, while plant length was measured on 25,
32, 35, 39, 42, and 49 DAT. During samplings, the treated plants were taken out and washedthoroughly in distilled water. The plants were separated into three parts: roots, stems and leaves.
Leaf area and dry weight were also measured. Growth analysis then was calculated separately forboth under submerged condition according to the following equations,26

Relative Growth Rate (RGR)=

(

)

(

)

Relative Tillering Rate (RTR)=

(

)
(

×
)

)
(

×

Net Assimilation Rate (NAR)=
Leaf Area Ratio (LAR)=

(

(1)
)

(

)

(

)

(
(

)
)

(2)
(3)

(4)

Where, W is total dry matter weight, t is time of treatment, A is leaf area and T is tiller number,respectively.
2.3. Statistical analysis
A statistical analysis was performed using two-way analysis of variance to determine thedifferences on growth parameters among cultivars and N treatments under both 7 days and 14 days
submergence. Differences among mean values of N treatments were evaluated by using 0.05 level of
LSD test. Pearson product moment correlation was also calculated for certain pairs of growth
analysis parameters.

3. RESULTS
3.1. Plant growth
N treatment had insignificant impact to plant length for all cultivars in both 7 days and 14 days
submergence either under submergence or after recovery period condition (P > 0.05). Fig. 1 showed thechange of plant length increment of three rice cultivars under both submergence durations and foliar N
treatments. From Fig. 1, it was found that rapid increase occurred during submergence treatment (2532 DAT for 7 days submergence treatment, and 25-39 DAT for 14 days submergence treatment) for all
cultivars. At the end of experiment, plants which had been submerged under 7 days duration reduced
their lengths due to N treatment, especially in Inpara 5 and IR 64 where the leaves suffered either
from rapid senescence or detached from the stems. While most plants (especially from N0 and N2 plot)
could not survive under 14 days submergence treatment so that the effect of N treatment could barely
be evaluated. Si Kuning could not survive under 14 days submergence treatment.
90

90

Plant length (cm)

90

IR 64

Inpara 5
80

80

80

70

70

70

60

60
7d

7d

recoveryrecovery
14d

50

14d

60

recoveryrecovery

31

37

43

49

7d
14d

recoveryrecovery

31

37

50

50
25

7d + N07d + N1
7d + N2
14d + N014d + N1
14d + N2

Si Kuning

25

31

37

43

49

25

43

49

Days after transplanting
Fig. 1. Plant length increment of three rice cultivars under 7 days and 14 days
submergence and after de-submergence condition
Tiller numbers of rice plants at the end of experiment (49 DAT) were measured as given in
Fig. 2. Increase in tiller number was found in all cultivars due to N treatment, except for N2 plot of27

IR64 under 14 days submerged duration. The increase caused by both N treatments was more
apparent in IR 64, while Si Kuning had a greater increase of tiller number due to N2 treatment in 7days submergence duration.
12

49 days after

Tiller number

10
N0
N1
N2

8
6
4
2
0
7d
14d
Inpara 5

7d

14d
IR 64

7d
14d
Si Kuning

Fig. 2. Tiller number of three rice cultivars measured
at the end of experiment (49 DAT). Error bars indicated standard error (n = 3)
3.2. Dry matter weight
Under submergence condition for both 7 days and 14 days, N1 treatment had significantly (P < 0.01)
increased total dry matter weight (TDW) of rice plants. The highest increase was obtained in Inpara 5
and IR 64 in 7 days submergence with 2.39 and 2.33 g respectively, while in 14 days submergence thehighest increase was Inpara 5 with 1.93 g as given in Fig. 3. However, TDW did not significantly change
after recovery period as it measured at the end of experiment (P > 0.05). N1 treatment showed opposite
response from the previous result (Fig. 3) by having decreased TDW in 7 days submergence in all
cultivars (Fig. 4). In contrast, N2 treatment showed various results among cultivars. TDW was decreased
in Inpara 5, increased greatly in IR 64 and only slightly increased in Si Kuning. In 14 days submergencetreatment, the application of N1 had improved TDW for Inpara 5 and IR 64.
6,0

Under submergence

Total dry weight (g)

5,0
4,0
3,0

C

D
c

2,0

N0

C
A

ab

a

1,0

b

B

b
b

N1

0,0
7d
14d
Inpara 5

7d

14d
IR 64

7d
14d
Si Kuning

Fig. 3. Total dry weight of three rice cultivars under submergence treatment,
measured on 32 DAT for 7 days submergence, and on 39 DAT for 14 days
submergence treatment. Error bars indicated standard error (n = 3)
Note: Values with the same letters are not significantly different at the 0.05 level by LSD test. Capital letter indicatescomparison among TDW of rice plants under 7 days submergence treatment. Lowercase letter indicates comparison among
TDW of rice plants under 14 days submergence treatment.

28

6,0

After recovery period
N0
N1
N2

Total dry weight (g)

5,0
4,0
3,0
2,0
1,0
0,0
7d
14d
Inpara 5

7d

14d
IR 64

7d
14d
Si Kuning

Fig. 4. Total dry weight of three rice cultivars, measured after recovery period
or at the end of experiment (49 DAT). Error bars indicated standard error (n = 3)
3.3. Growth analysis
Growth analysis was calculated to evaluate growth of rice plant both under submergencecondition and after de-submergence. The results then were divided as following.
(1) Under submergence
Results showed that N1 treatment had generally improved RGR of three cultivars under both 7days and 14 days submergence, except for Si Kuning under 14 days submergence (Fig. 5). Fig. 6
illustrated the effect of N treatments in NAR and it was found that NAR was escalated due to N1
treatment in Inpara 5 and IR 64 and conversely declined in Si Kuning for both submergence
durations. LAR change among cultivars was given in Fig. 7. The results showed that under 7 days
submergence, N1 treatment had caused a decrease in LAR value in Inpara 5 and IR 64, and only aslight change in Si Kuning. Different results were obtained under 14 days submergence where IR 64
and Si Kuning had a decreasing LAR though the result was more apparent in Si Kuning, while
Inpara 5 was opposite due to N1 treatment. N1 treatment also gave a positive impact to RTR as seenin Fig. 8 where all cultivars had an increasing RTR compared to N0 for both submergence durations.
Under 7 days
submergence

RGR (g g-1 d-1)

0,12Recovery
period

0,16
N0
N1
N2

0,08
0,04

Under 14 days
submergence

Recovery
period0,12RGR (g g-1 d-1)

0,16

N0
N1
N2

0,08
0,04
0,00

0,00

-0,04

-0,04
Inpara 5 IR64(a)

Si Inpara 5 IR64
Si
KuningKuning
Cultivar

Inpara 5 IR64

Si Inpara 5 IR64
Si
KuningKuning
Cultivar(b)

Fig. 5. Effect of N treatment to RGR of three rice cultivars under 7 days submergence (a)and 14 days submergence (b). Error bars indicated standard error (n = 3)29

Recovery
periodNAR (mg cm-2 d-1)

1,5
1,0
0,5
0,0
-0,5

Recovery
period

N0
N1
N2

1,5
1,0
0,5
0,0
-0,5

-1,0

-1,0

-1,5

-1,5
Inpara 5 IR64

Under 14 days
submergence

2,0

N0
N1
N2

NAR (mg cm-2 d-1)

Under 7 days
submergence

2,0Inpara 5 IR64

Si Inpara 5 IR64
Si
KuningKuning
Cultivar

Si Inpara 5 IR64
Si
KuningKuning
Cultivar(a)(b)

Fig. 6. Effect of N treatment to NAR of three rice cultivars under 7 days submergence (a)and 14 days submergence (b). Error bars indicated standard error (n = 3)
Under 7 days
submergence

Recovery
period

160
N0
N1
N2

120

LAR (cm2 g-1)

LAR (cm2 g-1)

160

80
40

Under 14 days
submergence

Recovery
period
N0
N1
N2

120
80
40
0

0
Inpara 5 IR64

Si Inpara 5 IR64
Kuning
Cultivar

Inpara 5 IR64

Si
Kuning(a)

Si Inpara 5 IR64
Kuning
CultivarSi
Kuning(b)

Fig. 7. Effect of N treatment to LAR of three rice cultivars under 7 days submergence (a)and 14 days submergence (b). Error bars indicated standard error (n = 3)

Under 7 days
submergence

Recovery
period

0,09

0,06

0,06

0,03

0,03

0,00

0,00
RTR

RTR

0,09

-0,03

Under 14 days
submergence

Recovery
period

-0,03
-0,06

-0,06

N0
N1
N2

-0,09
-0,12

N0
N1
N2

-0,09
-0,12
-0,15

-0,15
Inpara 5 IR64(a)

Si Inpara 5 IR64
Si
KuningKuning
Cultivar

Inpara 5 IR64

Si Inpara 5 IR64
Si
KuningKuning
Cultivar(b)

Fig. 8. Effect of N treatment to RTR of three rice cultivars under 7 days submergence (a)and 14 days submergence (b). Error bars indicated standard error (n = 3)30

(2) Recovery period
After de-submergence, N2 treatment then was applied to rice plants. During recovery period,
RGR of plants applied with N1 treatment after submerged for 7 days was significantly decreased.
There was only a slight increase of RGR by the application of N2 in IR 64 and Si Kuning compared to
N0 (Fig. 5). However, different response was shown by Inpara 5 by having a decreasing trend of RGR
due to N treatment. NAR showed a similar response to RGR due to N treatment in bothsubmergence durations (Fig. 6). LAR was only slightly changed in all rice cultivars during recovery
period among N treatments in 7 days submergence (Fig. 7). RTR of IR 64 and Si Kuning wereapparently improved by the application of N2 treatment in 7 days submergence, while N1 gave
better results in all the growth analysis parameters in 14 days submergence treatment (Fig. 8).

4. DISCUSSION
In this experiment, the evaluation of growth parameters could be mostly performed only for 7
days submergence duration since most of rice plants under 14 days submergence could not survive
until the end of experiment. Therefore, the evaluation of N2 treatment was also very limited. Somestudies reported that survival during submergence was closely related to plant elongation growththat led to plant avoidance mechanism under submerged condition (Setter et al., 1997; Visser et al.,
2003; Kotera and Nawata, 2007; Nishiuchi et al., 2012). As seen in Fig. 1, plant length increment
was not so apparent in 7 days submergence among N treatments, except for N1 in IR64 which later
resulted in drastic decrease of plant length during recovery period. Generally, plant length increase
was slower or relatively constant, and even declined due to detached leaf or enhanced leaf
senescence occurred after de-submergence. Nevertheless, enhanced stem elongation was actually an
unfavorable character for submergence rice. Kawano et al. (2009) reported that tolerance wasgreater in cultivars where acceleration of elongation caused by submergence was minimal.
Elongation growth competed with maintenance processes for energy and hence reduced survival
during submergence (Setter and Laureles, 1996). The report was also validated by result of thisstudy as shown by IR 64 which had the best survival under 14 days submergence since it had
minimal plant length under submergence, while Inpara 5 which was basically a sub-1 rice cultivar
and anticipated to have better survival performed an unexpected result. The application of N1
treatment improved survival of Inpara 5 by enhancing stem elongation under submergence.
As described above, N1 treatment had significantly affected TDW under submergenceconditions. However, the results were varied at the end of experiment. The relationship between
TDW and RGR then was investigated as given in Fig. 9 and showed significant positive correlations
for both under submergence and during recovery conditions. As RGR was a product of leaf area ratio(LAR) and net assimilation rate (NAR) (Yamauchi et al., 1988; Poorter and Werf, 1998), observing
the trend of both traits would be beneficial in differentiating the roles of morphological andphysiological response of rice plant under submergence condition. By comparing the tendency in
NAR results, it was understandable that RGR change was mostly attributed by the alteration in
NAR (Fig. 5, Fig. 6). Under both 7 and 14 days submergence, NAR was increased by the applicationof N1 as seen in Inpara 5 and IR64 and this trend was similar to RGR as aforementioned. Hence, theopposite result was shown by Si Kuning by having smaller NAR compared to N0 indicating thatRGR change of Si Kuning was not determined by the change in NAR and more subjected by thechange in LAR as given in Fig. 7, though the increase was quite small. Increase in LAR due to N1
treatment in Si Kuning defined plant investment in leaves and larger LAR was indicative of plantemphasizing on leaf production (Azarpour et al., 2014). Inpara 5 actually also had increased its LAR
under 14 days submergence. However, greater increase was initiated by NAR attributing to asuperior impact to RGR change.

31under submergencerecovery period
under submergencerecovery period

5,0

TDW (g)4,0
r = 0.726
P < 0.01

3,02,0r = 0.680
P < 0.05

1,00,0-0,03 0,00

0,03

0,06

0,09

0,12

0,15

RGR (g g-1 d-1)
Fig. 9. Correlation between TDW and RGR for bothunder submergence and recovery period
From the results, it was also noted that there was a negative relationship between NAR and
LAR caused by N1 treatment under submerged condition regarded as a compensatory response
between these two variables. Some studies also reported the occurrence of compensatory effect inrice. Yan and Wang (2009) reported the compensatory effect in rice seedling induced by water deficit.
Compensatory effect on yield was also found in rice planted at different seedling densities (Ehara etal., 1998). Contributions from both NAR and LAR to RGR then were further investigated (Fig. 10
and Fig. 11) and found that both variables affected RGR though the impact was not similar. From
the results, it was also considered that during recovery period, NAR gave a greater impact to RGR
rather than LAR.
under submergencerecovery period
under submergencerecovery period

0,15

RGR (g g-1 d-1)0,12r = 0.707

0,09
0,06

r = 0.848

0,03
0,00
-0,03
-1,0 -0,5

0,0

0,5

1,0

1,5

2,0

2,5

NAR (mg cm-2 d-1)
Fig. 10. Correlation between RGR and NAR for bothunder submergence and recovery period
Gautam et al. (2014) reported that a quick re-growth following submergence was a desirable
quality as it could ensure production of sufficient biomass for plant productivity indicating theimportance of plant growth during recovery period. Thus, growth characters in this period were32

beneficial. As NAR during recovery showed the great impact to RGR as mentioned, NAR related traits
then were identified. Some studies reported that NAR of rice was affected by leaf morphogenesis suchas thinning of the leaf blade when nutrients were supplied sufficiently (Ehara et al., 1990; Ehara,1993). Therefore, the relationship between NAR and SLA was also evaluated. However, the result
showed that the correlation coefficient between NAR and SLA was significant (r = 0.530, P = 0.076)
only under submergence and insignificant during recovery period (data not shown).
undersubmergencerecovery period

0,15
0,12
RGR (g g-1 d-1)

0,09

r = 0.654
P < 0.05

0,06
r = 0.500
P < 0.05

0,03
0,00
-0,03
0
30
60
LAR (cm2 g-1)

90

120

150

Fig. 11. Correlation between RGR and LAR for bothunder submergence and recovery period
under submergence

0,15

recovery period

RGR (g g-1 d-1)

0,12
0,09
0,06
0,03
0,00
-0,03
0

3
6
Tiller number

9

12

15

Fig. 12. Correlation between RGR and tiller number for bothunder submergence and recovery period
The present study also attempted to investigate the relationship between RTR and RGR as
they both showed a similar tendency as previously given in Fig. 5 and Fig. 8. The relation, however,was insignificant (data not shown). Similar insignificant result was shown between RGR andtiller number (r = 0.424, P = 0.080) as given in Fig. 12. Hence, when tiller number was associatedwith LAR, the correlation coefficient showed significant result (r = 0.815, P < 0.01) (Fig. 13).
Furthermore, the role of tiller number was more apparent to TDW for both under submergence andduring recovery period with r = 0.577, P < 0.05 and r = 0.828, P < 0.01 respectively (Fig. 14).

33

These findings indicated that tiller number was an important character determining plant
adaptation under submergence and survival after de-submerged condition. Corresponding to thisresult, Fageria et al. (1997) clarified that greater tiller number would increase the leaf area index(LAI) (or LAR in this case) and consequently the radiation interception capacity resulting in highbiomass production. Similar results were also reported by some studies indicating the contribution oftiller number to dry matter production (Wu et al., 1998; Ntanos and Koutroubas, 2002). Moreover,tiller number was also considered as a substantial trait affecting yield (Counce et al., 1996; Wang etal., 2007). The former study in submergence-prone swamp area in South Sumatra also discoveredthat fewer tillers led to less panicle number per hill resulting in a lower yield due to the absence of N
fertilizer (Irmawati et al., 2015).
under submergencerecovery period
recovery period

150,0

LAR (cm2 g-1)

120,0

r = 0.815
P < 0.01

90,0
60,0
30,0
0,0
0

3
6
Tiller number

9

12

15

Fig. 13. Correlation between LAR and tiller number for bothunder submergence and recovery period
5,0r = 0.828

undersubmergence

TDW (g)4,03,0r = 0.577

2,0
1,0
0,0
0 Tiller3 number
6

9

12

15

Fig. 14. Correlation between TDW and tiller number for bothunder submergence and recovery period

5. CONCLUSIONSIn conclusion, three cultivar tested showed different response to N treatment undersubmergence stress. IR 64 was the cultivar with highest dry weight increase in 7 days submergence,
Inpara 5 was more apparent in 14 days submergence treatment, and Si Kuning increased slightly

34during both submergence treatments. Tiller number was regarded as one of beneficial characters
determining plant adaptation under submerged condition. Greater tiller number after desubmergence would lead to an improved LAR and finally will affect RGR and the accumulation of
TDW. Considering these findings, the application of N after submergence (N2) was considered more
effective to enhance tiller number after de-submerged condition. However, this result was limited
only for relatively short duration submergence, while under longer duration, the application of N
before submergence (N1) appeared to give better performance. Further study would be beneficial toensure the effect of N application for either short or longer submergence durations.

ACKNOLEDGEMENT
This work was supported by JSPS KAKENHI Grant Number 24405021. We are also indebted
to Faculty of Agriculture, Sriwijaya University for granting of favorable treatment to conduct
the experiment.

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36Evaluation of Rice Productivity with Plant Canopy Analyzer
in Farmers’ Fields in Lao PDR
Hirooka, Y.1,2, K. Homma1, M. Maki3, K. Sekiguchi3
1

Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan
2
Research Fellow of Japan Society for the Promotion of Science3
Graduate School of Engineering, Kyoto University, Kyoto 615-8530, Japan
Email: yhirooka@kais.kyoto-u.ac.jp
ABSTRACT
Rice is the most important crop in Lao People’s Democratic Republic (Lao PDR). In crop monitoring for precision
farming and yield forecasting, the timely observation of leaf area index (LAI) is critical. The present study analyzed
the relationship between rice production and LAI growth rate measured by plant canopy analyzer, and discussed
suitability of these measurements to estimate grain yield in farmers’ fields in Lao PDR. 66 farmers’ paddy fields from33 places were selected in this area for surveying throughout the growth period. Because the LAI in the farmers’
fields increased almost linearly, a straight-line regression was used for the analysis. The LAI growth rate, the slope ofthe straight-line, was significantly correlated with the total dry weight (TDW) and the grain yield. However, comparedto TDW, correlation coefficient between the LAI growth rate and the grain yield was lower. The result might derivefrom larger variance of harvest index (HI). This large variance of HI was considered to be mainly caused by thedifference of cultivar and fertilizer and water management. Combining the measurements by plant canopy analyzer
with the simulation models to evaluate cultivar or crop management may help to estimate rice growth and then ricegrain yield in a regional scale.
Keywords: Lao PDR, Leaf area index (LAI), rice, plant canopy analyzer, yield.

1. INTRODUCTION
Rice is by far the most important crop in the Lao People’s Democratic Republic (Lao PDR), andhere, approximately 70% of the total calorie supply in diets comes from rice (Maclean et al., 2002).
The wet season lowland is the main rice-producing environment in Lao PDR (Schiller, 2006).
Rice productivity is strongly affected by water, temperature, and solar radiation (Inoue et al.,2014). Therefore, in crop monitoring for precision farming and yield forecasting, thetimely observation of plant biophysical and ecophysiological status is critical (Doraiswamy et al.,
2004; Inoue, 2003). The leaf area index (LAI) is an important biophysical variable that is
related to canopy photosynthetic rate and dry matter production during growth periods (Vaesen
et al., 2001). Indeed, Hirooka et al. (2015b) reported that rice productivity in Lao PDR is affectedby LAI growth.
This study used a plant canopy analyzer, LAI-2200, to measure the LAI. The analyzer also
reduces the laboriousness of the destructive measurements for LAI and makes frequent
measurements easier. Accordingly, the most advantageous aspect of this analyzer may be thequantification of the LAI dynamics based on frequent measurements (Hirooka et al., 2013).
The present study analyzed the relationship between rice production and LAI growth ratemeasured by plant canopy analyzer, and discussed suitability of these measurements to estimate
grain yield in farmers’ fields in Lao PDR.

37

2. MATERIALS AND METHODS
2.1. Sites
This study was conducted in 2013 in farmers’ fields in Vientiane province, Lao PDR (18°01’ 18°30’N, 102°24’ - 103°02’E, 168 - 178 m asl.). 66 farmers’ paddy fields from 33 places were selected inthis area for surveying throughout the growth period. The mean air temperature for the measuring
period (from 22 July to 16 September) was 28.0ºC. The longitude and latitude of the study sites wererecorded by the Global Positioning System (GPSMAP 62SJ, GARMIN). The rice plants of theinvestigated farmers’ fields varied in cultivation methods (direct seeding/transplanting, fertilizer,
planting density and cultivar). Preliminary interviews with farmers suggested that transplanting,non-fertilization, 25 hill m-2 of planting density and traditional cultivation were the majority.
2.2. Measurements
The leaf area index (LAI) was measured using a plant canopy analyzer (LAI-2200, LI-COR) witha single sensor mode in a sequence of two above and four below canopy at each field (Hirooka et al.,2015b). In order to reduce the influence of the adjacent fields and the operator, a 90º view-cap was
applied to the optical sensor. The measurement was conducted 4 times before the heading period(from 22 to 25 July, from 10 to 12 Aug, from 30 Aug to 1 Sep and from 16 to 18 Sep). The LAI for
each measurement was referred to as LAI1st, LAI2nd, LAI3rd and LAI4th. Because the peak of theheading in these study sites was on 22 Sep, LAI4th contained almost all of the maximum LAI values.
Nine rice plant samples were harvested, and the plant density was measured in 56 of the 66
farmers’ fields to determine the grain yield (g m-2) and total dry weight (TDW; g m-2) at the maturing
stage (from 22 to 25 Oct). The other 10 fields were excluded from the sampling because the rice
plants during these periods were too premature to harvest or had already been harvested. The grain
yield and TDW were determined after oven-drying at 70ºC for more than 2 days.
2.3. Data analysis
The air temperature (maximum, minimum and average temperature) was measured bythermometer/hygrometer (DCA, Decagon). The data was recorded using a field monitoring system(Mizoguchi, 2012) in Vientiane.
4

LAI

3

2

1

0
0

300

600

900

1200

Effective accumulated temperature (ºC d)Fig. 1. Changes with the effective accumulated temperature (°C d; base temperature of
10°C) of the LAI. The effective accumulated temperature was calculated from 22 July38

Because the measurements were conducted only 4 times in this study, linear growth was assumed(Hirooka et al., 2015b) (Fig. 1). LAI growth rate was calculated using the following linear function.
LAI = a T + b
where a and b are the regression coefficients, and T is the effective accumulated temperature (°Cd; base temperature of 10°C) from 22 July. The coefficient a represents LAI growth rate, and b
represents approximate LAI at the transplanting date.

3. RESULTS AND DISCUSSION
The grain yield ranged from 63.8 g m-2 to 411.8 g m-2 (235.8 g m-2 on average), and the total dry
weight at maturing stage (TDW) ranged from 248.4 g m-2 to 1089.0 g m-2 (660.0 g m-2 on average)(Fig. 2). The TDW was correlated with the grain yield (Fig. 2). The grain yield and the TDW of
Nipponbare (normal japonica rice cultivar) grown in Kyoto, Japan in 2013 was 563.4 and 1256.5 g m2
, respectively (Hirooka et al., 2014). These two values were much higher than those in this study
area (Fig. 2). This may be resulted in the poor soil fertility and cultivation method (less or no
fertilizer) in this area (Hirooka et al., 2015a).

600

Yield (g m-2)

500
400
300
200

Lao PDR
100

Japan

0
0

500
1000
Total dry weight (g m-2)

1500

Fig. 2. Relationship between the grain yield (g m-2) and the total dry weightat the maturing stage (g m-2). Japan; Nipponbare cultivated in Kyoto
in Japanese typical cultivation method
The LAI growth rate in the 66 fields varied widely from 0.56×10-3 to 3.63×10-3 m2 m-2 ºC-1
(1.94×10-3 m2 m-2 ºC-1 on average) (Fig. 3). The LAI growth rate was significantly correlated with the
TDW and the grain yield (Fig. 3). However, compared to TDW, correlation coefficient (r) between the
LAI growth rate and the grain yield was lower (TDW, r = 0.597; grain yield, r = 0.415). The result
might derive from larger variance of harvest index (HI) from 0.11 to 0.54 (0.36 on average) (Fig. 4).
This large variance of HI was considered to be mainly caused by the difference of cultivar andfertilizer and water management.
Because the LAI in this study area increased almost linearly against effective accumulated
temperature (Hirooka et al., 2015b), LAI growth rate theoretically can be determined by at least two
measurements. Hirooka et al. (2015b) also reported that LAI growth rate in the study area was39

closely associated with soil C and N levels, which is a major indicator of soil fertility and consequent
rice production. Consequently, LAI growth rate is thought to be useful indicator to evaluate riceproductivity such as in the study area, which would make it possible for remote sensing to evaluate
regional scale evaluations even when it is measured in wider area by remote sensing.

number ofof
field
TheThenumber
field

12

8

4

0
(0.11)-0.25 0.25-0.3

0.3-0.35

0.35-0.4

0.4-0.45

0.45-0.5

0.5-(0.54)

HI
HI
Fig. 3. The difference of harvest index (HI) in farmers’ fields in Lao PDR

1200

500

Total dry weight (g m-2)

Yield (g m-2)

400

300

200

900

600

300

100

r = 0.597***r = 0.415**
0

0
0

0.001
0.002
0.003
LAI growth rate (m2 m-2 Cº-1)

0.004

0

0.001
0.002
0.003
LAI growth rate (m2 m-2 Cº-1)

0.004

Fig. 4. Relationships of LAI growth rate (m2 m-2 Cº-1) with (a) total dry weightat the maturing stage (g m-2), and with (b) grain yield (g m-2)
Hirooka et al. (2015b) showed that LAI growth rate could be estimated by satellite based remotesensing of synthetic aperture radar (SAR) (Fig. 5). The remote sensing of SAR also provides
estimation of transplanting date (Miyaoka et al., 2015). Hashimoto et al. (2009) developed new index(TIPS: Time-series change Index of Plant Structure) to express seasonal change of plant canopy

40

structure by remote sensing for spectral reflectance. Rice cultivars would be distinguished by using
TIPS and NDVI (Kambayashi et al., 2014). Maki et al. (2015) and Tsujimoto et al. (2013) developed
the new simulation models to evaluate rice production combined with remote-sensing and hydrology,
respectively. These newly developed techniques may help to estimate rice growth and then rice grain
yield in a regional scale.

LAI growth rate (day-1)

0.06

0.04

0.02

r = 0.884
0
0.05

0.1

0.15

0.2

0.25

BSC increase rate (m2 m-2 day-1)

Fig. 5. Relationships between LAI growth rate (day-1)
and BSC increase rate (m2 m-2 day-1) (Hirooka et al., 2015b)

REFERENCES
Doraiswamy, P. C., Hatfield, J. L., Jackson, T. J., Akhmedov, B., Prueger, J., & Stern, A. (2004). Crop conditionand yield simulation using Landsat and MODIS, Remote Sensing of Environment, 92: 548-559.
Hashimoto, N., Maki, M., Tanaka, K., Tamura, M. (2009). Study of method for extracting LAI time-series patterns
for the estimation of crop phenology, Journal of the remote sensing society of Japan, 29(2): 381-391.
Hirooka, Y., Irie, T., Homma, K., Shiraiwa, T., and Toriumi, A. (2013). Analysis of genotypic variation of leaf
canopy dynamics in rice by using plant canopy analyzer, Journal of Crop Research, 58: 51-56. (in Japanese)
Hirooka, Y., Homma, K., Maki, M., Sekiguchi, K. (2015a) Applicability of synthetic aperture radar (SAR) to
evaluate leaf area index (LAI) growth rate of rice in farmers’ fields in Lao PDR (in press).
Hirooka, Y., Homma, K., Maki, M., Sekiguchi, K., Shiraiwa, T., Yoshida, K. (2015b). Evaluation of the dynamics
of the leaf area index (LAI) of rice in farmer’s fields in Vientiane Province, Lao PDR, Journal of Agriultural
Meteorology (in press).
Hirooka, Y, Homma, K, Shiraiwa, T, Zhengjin, X., and Liang, T. (2014). Yield and characteristics of response tofertilizer of erect panicle type rice in Kyoto, Japanese Journal of Crop Science, 84(Extra issue 1): 7. (inJapanese)Inoue, Y. (2003). Synergy of remote sensing andmodeling for estimating ecophysiological processes in plant
production. Plant Production Science, 6: 3-16.
Inoue, Y., Sakaiya, E., and Wang, C. Capability of C-band backscattering coefficients from high-resolution satellite
SAR sensors to assess biophysical variables in paddy rice,” Remote Sensing Environment, 140: 257-266.
Kambayashi, M., Homma, K., Maki, M., Hirooka, Y., Shiraiwa, T. (2012). Research on detection of rice ecotypes
by canopy spectral reflectance, The 33rd Asian Conference on Remote Sensing, PS1-24: 1-8.
Maclean, J.L., Dawe, D.C., Hardy, B., Hettel, G.L. (2002). Rice alamanac. Los Banos (Philippines): International
Rice Research Institute, Bouake (Cote d’Ivoire): West Africa Rice Development Association, Cali (Colombia):
International Center for Tropical Agriculture, Rome (Italy), Food and Agriculture Organization, 253.

41

Maki, M., Homma, K., Hirooka, Y., Oki, K. (2015) Estimation of rice yield by SIMRIW-RS, a model that integrates
remote sensing data into a crop growth model. J. Agric. Meteorol. (in press)
Miyaoka, K., Maki, M., Susaki, J., Homma, K., Noda, K., Oki, K. (2013) Rice-planted area mapping using small
sets of multi-remporal SAR data. IEEE Geoscience and Remote Sensing Letters, 10: 1507-1511.
Mizoguchi, M. (2012). Field monitoring system, Water, Land and Environ, 80: 50.
Schiller, J.M. (2006). Rice in Laos, International rice research institute, Metro Manila, 457.
Schiller, J.M., Linquist, B., Douangsila, K., Inthapanya, P., Douang Boupha, B., Inthavong, S. and Sengxua, P.
(2001). Constraints to rice production systems in Laos, In: Increased Lowland Rice Production in the Mekong
Region, Fukai S. and Basnayake J. (Eds.). ACIAR, Tokyo, p. 3-19.
Tsujimoto, K., Homma, K., Koike, T., Ohta, T. (2013) Development of a coupled model of a distributedhydrological model and a rice growth model for grasping necessary information for rain-fed agriculture. Annual
Journal of Hydraulic Engineering, JSCE, 57: 511-516. (in Japanese)
Vaesen, K., Gilliams, S., Nackaerts, K., and Coppin, P. (2001). Ground-measured spectral signatures as indicators ofground cover and leaf area index: the case of paddy rice, Field Crops Research, 69: 13-25.

42

Greenhouse Gas Emmission From on-Field Straw Burningin The Mekong Delta of Viet Nam
Nguyen Vo Chau Ngan*, Tran Sy Nam
College of Environment and Natural Resources, Can Tho University, Viet Nam
Email*: nvcngan@ctu.edu.vn
ABSTRACTGreenhouse gas emission from burning rice straw on file in the Mekong delta of Viet Nam was investigated by
prepared questionnaires and field surveys in An Giang, Dong Thap, Kien Giang and Can Tho provinces. The results
showed that the most common method to remove rice straw is field burning while other methods such as burying,cultivating mushroom, selling, raising cattle, giving away and leaving on fields accounted for the small proportion.
Removing rice straw methods vary depending on the rice crop seasons. The estimated quantity of rice straw in the
Mekong Delta region is approximate 28.98 million tons in 2014, in which 27.68 million tons is burned directly on thefields. The survey indicates most rice farmers tend to continue burning straw as the most popular method in the next
few years. This practice releases into the atmosphere with about 32.42 million tons of CO 2, 768.40 thousand tons of
CO and 68.65 thousand tons of NOx. The amount of CO2 accounting for more than 97% of the total greenhouse gas
released from field burning. Studying on the methods to utilize the straw after harvests is highly recommended inorder to limit the straw burning activities which waste a biomass resource and causes environmental pollution.
Keywords: Greenhouse gas, on-field burning, rice straw, the Mekong Delta.

1. INTRODUCTION
The Mekong Delta (MD) of Viet Nam is the most important rice producing area where riceproduction makes up approximately 51.5% of the national one and contributes to more than 90% of
the national rice export. The rice cultivating area in the MD keeps increasing over the years,
reaching 4.3 million ha in 2013 with the production nearly 25 million tons (GSO, 2014). Given that
big amount of cultivating area and rice production, there is a huge amount of rice straw which iseither discarded or burned every year in the MD (MONRE, 2010). At present, alternative of ricestraw uses is very limited and the most common rice straw practice is on-field burning which causes
air pollution, health impacts and nutrient loss. From Truc and Ni (2009), burning one tone rice straw
lost 91.3% carbon which equivalent to 291.2 kg C and may release 1,067.6 kg CO2 and 12.6 kg NO.
Concerning to health impacts, a former study of Kumar and Kumar (2010) showed that ricestraw burning leads to air pollution and several other problems. Irritation in eyes and congestion in
the chest were the two major problems faced by the majority of their survey household members.
Respiratory allergy, asthma and bronchial problems were the other smoke related diseases which
affected household members in the survey villages. Almost 50% of the survey households indicatedthat their health related problems get aggravated during or shortly after harvest when rice straw
burning is in full swing period. In the peak season, affected families had to consult doctor or use
some home medicine to get relief from irritation/itching in eyes, breathing problem and similar othersmoke related problems.
Therefore, studying on rice straw utilization is needed to understand gas emission from on-fieldrice straw burning in the MD which could help establish rice straw management strategy to reduce
the health effects at local communities and to mitigate greenhouse gas emission.

43

2. METHODOLOGIES
2.1. Household interview selection
To define the rice straw useage, data from various provinces in MD where rice is grown on large
hectarage, is needed. Based on the statistical rice production data, four districts were chosen forsurveying, including Chau Thanh district in An Giang province, Thap Muoi district in Dong Thap
province, Giong Rieng district in Kien Giang province and Thoi Lai district in Can Tho city. After
selecting the surveying districts, a list of all households including those who were agricultural
cultivators was worked out. At each district, 100 farmers were selected using random sampling
method. Thus, total 400 farmers were selected from four districts.
2.2. Questionnaire design
The questionnaire used for the household survey had four sections seeking detailed
information on various aspects of inputs - outputs used by the farmers for agricultural practices, anddisposal of crop residues. Section 1 of the questionnaire provides information on individual
household members’ profile in terms of their age, education, sex, occupation, land area, farm
activities such as number of rice crops per year, harvest forms, rice varieties used, and rice yield, etc.
Section 2 deals with the information on end use of straw in each crop of the year. Sub-sections also
ask information on rice straw utilization such as on-field burning, burying, cultivating mushroom,
selling, feefing cattle, giving away and leaving on fields. Section 3 recorded farmers’ opinion on ricestraw utilization, rice straw treatment, etc. In the last section, interviewers recorded any comments
on farmers’ situations.
To complete the questionnaire, 5 farmers were interviewed as testing phase. The questionnaire
was then revised, for officially interviews.
2.3. Survey for residue rice straw
At each selected district, five households who cultivate the popular rice variety at that area werechosen to collect rice straw. At these five selected rice field, we useed 1 square meter frame to fix fivesampling areas. The rice harvested following farmer’s procedure either by hand or by machine, wasseparated to grain and residue, then all the left rice straw was cut. The fresh weight of each part wasdetermined onsite. Then grain, rice residue and rice straw were put into plastic bags and broughtback to the laboratory for testing with dry weight.
The dry weight of grain was recorded when grain moisture reached 14% (good for storage andsell). The dry weight of rice residue and rice straw was determined using oven drying at 70oC until
constant weight.
According to Gadde et al. (2009), quantity of straw burnt on the fields is estimated based on thefollowing formula.
Qst = Qp × R × k
where
Qst: amount of straw burned on the fields (ton)Qp: rice production (ton)
R: proportion of rice straw to rice production
k: proportion of straw burned on the fields to amount of straw

44

The amount of greenhouse gas emitted from burning was estimated based on the formula(Gadde et al., 2009):
Ei = Qst × EFi × Fco
where
Ei: amount of gasi emitted into the environment due to burning straw on the fields (ton)EFi: coefficient of gasi emission from burning of straw on the fields (g/kg)
ECO2 = 1464ECO = 34.7ENOx = 3.1
Fco = 0.8: the conversion proportion for gas emission when burning straw

3. RESULTS AND DISCUSSION
3.1. Rice straw utilization in the MD
The results from the interviews involving 400 households revealed seven methods to deal withrice straw by local people, namely burning, burying, cultivating mushroom, selling, feeding cattle,
giving away and leaving on fields. These straw treatment methods were different between crops andbetween the farmer’s habits at each region (Table 1).
- In Winter-Spring crop, there were 4 ways to deal with straw, i.e. burning, using for mushroom
cultivation, selling and giving away. The surveys show that 98.23% of households burned their straw
after harvest, 0.99% used for mushroom cultivation, 0.73% sold the straw and 0.06% gave away.
- In Summer-Autumn crop the interview results show that there were two more methods thanthose used in Winter-Spring crop including burying the straw and using the straw to feed cattle.
Eventhough burning straw was the most popular treatment among farmer households in theSummer-Autumn crop, but the number of farmers burning rice straw only 87.9%, less than the ricestraw burning in Winter-Spring crop.
- In Autumn-Winter crop,burning straw still accounted for a large percentage yet it has muchdecreased (54.1%) compared with the Winter-Spring and Summer-Autumn crops. The percentage ofburying is higher than that in the Summer-Autumn crop ( 26.1%).
Table 1. The popular rice straw utilization practices in various crops
Utilization practice

Autumn-WinterWinter-Spring

Summer-Autumn

Selling (%)

2.92

0.73

1.27

Burning (%)

54.10

98.23

89.67

Cultivating mushroom (%)

8.14

0.99

1.26

Burying (%)

26.10

0.00

6.65

Giving away (%)

1.65

0.06

1.13

Raising cattle (%)

0.36

0.00

0.02

Leaving on fields (%)

6.74

0.00

0.00

Among three crops, the amount of straw burnt was highest in the Winter-Spring crop. The
reason is that there are favorable weather conditions in the harvest time of the Winter-Spring crop.
Thes hot weather helps burn rice straws more quickly. In addition, collecting large amount of straw
may be costly. In addition, burning can improve the field sanitation condition to prepare for the next
crop, and the ash from the burning can be utilized as fertilizer.

45

In the Summer-Autumn and Autumn-Winter crops, due to the less favorable weather conditions
than the Winter-Spring crop such as heavy rainfall, the straw burning decreases. In the recent
years, the harvest combines have been widely used in the MD; but by mechanized harvesting thestraw spreads all over the fields which makes costly for collection work. Therefore, local people often
let the straw on the fields, and then burn it if it is sunny or bury it in unfavorable conditions such as
rainy days or the fields are inundated.
In contrast, the Summer-Autumn and Autumn-Winter crops produce good quality straw formushroom cultivation, thus, so there were more straw buyers. This helps explain the highpercentage of dry straw in some regions from the surveys. However, for the fields harvested
manually, due to high rainfall and difficult in transportation (especially for farm households locating
far from their fields), many households can neither sell the straw nor transport the straw home, andthey therefore leave the straw on the fields for natural decomposition. The survey results also showsthat the majority of farmers (> 95%) tended to keep burning straw practices in the following years.
3.2. Trend of straw utilization by local people in the MD
The trend of rice straw utilization depends largely on the number of paddy rice crops per year,
the weather and farming conditions of individual households. In the selected areas, burning is themost popular method used by local people due to their long time habits (Fig. 1).
Besides burning straw directly on the fields, burying straw in the soil profile is a popularmethod in the MD due to specific condition. The Winter-Spring crop has the most favorable
conditions for rice production in the year, and its harvest time is in hot sunny days which are ideal
straw burning practices on the fields. In the Summer-Autumn and Autumn-Winter crops, straw
burning is not suitable due to high rainfall. Therefore, farming households usually bury the straw
directly under the soil layer on the fields. Other methods to deal with straw are less popular anddepend on the farming conditions.
100%
90%
80%

Raising cattle

70%
Burying60%
50%

Selling or
giving away
Mushroom
cultivation
Burning

40%
30%
20%
10%
0%
Autunm-WinterWinter-Summer

Summer-Autumn

Fig. 1. Trends of straw utilization in the following years
The trends in straw utilizations also reveal low perception in straw burning among local people.
Burning straw across large areas in the MD will have great impact on the environment of soil andair, human health conditions, and the increase of climate change (Gadde et al., 2009). Burning straw
also wastes a great biomass resource. Therefore, identifying the trends in dealing with straw in the46

following seasons among local farming households is essential in coming up with recommendations
and solutions to restrict burning straw practices, and at the same time reuse this resource in a mostlogical and efficient way.
3.3. Estimating the quantity of rice straw after harvest
3.3.1. The ratio of straw and grain
The ratio of straw to grain fluctuated between the lowest rate at Thoi Lai in the Winter-Spring
crop and the highest rate at Thap Muoi in the Autumn-Winter crop. This ratio is closely related to
the rice varieties and rice productivity in each crop (Table 2).
Table 2. The straw to grain ratio in differtent crops and locations
Autumn-WinterWinter-Spring

Summer-Autumn1

Giong Rieng

0.92 - 1.10

0.85 - 1.10

0.88 - 1.10

Thap Muoi

1.27 - 1.39

1.19 - 1.42

1.23 - 1.41

Thoi Lai

1.01 - 1.14

0.80 - 1.07

0.91 - 1.11

Chau Thanh

1.12 - 1.34

1.21 - 1.39

1.17 - 1.37

1.08 - 1.24

1.01 - 1.25

1.05 - 1.25

Locations

2

The MD

Note: 1: excluding surveyed result, presented as average value of Autumn-Winter and Water-Spring crops; 2: expressed as
average value of surveyed data

The research results show that the ratio is usually lower in the Winter-Spring crop than in the
Autumn-Winter crop. This can be explained by the fact that the rice productivity in the WinterSpring crop is the highest of the year whereas the rice productivity in the Autumn-Winter crop is
usually low due to rainy weather and low sunshine.
3.3.2. Estimating the amount of rice straw in the MD
The amount of rice straw in the MD is estimated based on the provincal rice outputs (whichreferenced from GSO, 2014) and the ratio of straw to grain (based on this survey results). The
average values of the ratio of straw to grain in selected areas are used to estimate the amount of ricestraw for whole MD. The estimated results show that the straw produced in Winter-Spring crop is
higher than it in the Autumn-Winter crop because the total cultivating areas and rice yields in theWinter-Spring crop are higher than those in the Autumn-Winter crop (Table 3).
Table 3. Estimation of annual quantity of rice straw (million tons)
Autumn-WinterWinter-Spring

Summer-Autumn1

Kien Giang

0.26 - 0.31

1.77 - 2.29

1.87 - 2.33

Dong Thap-

1.75 - 2.09

2.28 - 2.61

Can Tho-0.51 - 0.69

0.66 - 0.81

An Giang

0.03 - 0.04

2.12 - 2.43

2.61 - 3.01

The MD

2.03 - 2.33

10.97 - 13.58

12.86 - 15.31

Locations

According to the estimation, the total amount of straw in the whole MD in 2013 was 28.55
million tons in which An Giang accounted for the most and the lowest amount came from Can Tho.

47

The differences in the amount of straw among the selected areas came mmainlyainly from differences in theland areas for cultivation in 2013 among these regions. The total land area for rice cultivation in An
Giang was 641.3 thousand ha, while that in Can Tho was 236.6
36.6 thousand ha (GSO, 2014).
3.4. Estimating the greenhouse gas eemission from straw burning
The research results for the proportion of local farming households which adopt straw burningon the fields after harvest are presented in Table 1. The results show that the majority of households
choose straw burning method in tthe Winter-Spring and Summer-Autumn
Autumn crops. The AutumnAutumn
Winter crop showed a lower proportion of straw burning. In Giong Rieng district, the proportion for
straw burning is lower than those in other districts due to specific low terrain features. Besides
burning
ing straw, local people also bury straw under the soil.
The amount of straw burned on the field is estimated based on the straw burning proportion andthe amount of straw. The average proportion is considered for the estimation of straw burning in thewhole MD.. Most regions had low straw burning proportion in the Autumn
Autumn-WinterWinter crop and high one
in the Winter-Spring
Spring crop. The estimated results reveal that the total amount of straw burned in theWinter-Spring
Spring crop accounted for more than 50% while the Autumn
Autumn-WinterWinter crop had the lowest
amount. Among the regions in the research, the highest amount of burning was in An Giang and thelowest one was in Can Tho (Fig.. 2).
Million tons

Fig.. 2. Average quantity of straw burnedon the fields at different locations (million tons)
The amount of greenhouse gas emitted from burning straw practices is in linear proportional tothe amount of straw burned. Among the selected areas, An Giang had the highest amount ofgreenhouse gas emission, especially in the WinterWinter-Spring crop when there
e was big amount of straw
and high proportion of households adopting straw burning method (which releases 2,717 thousand tonsof CO2). At the same crop, Dong Thap and Kien Giang had nearly similar amounts of greenhouse gas
emission while Can Tho had the lolowest emission (703 thousand tons of CO2). These greenhouse gases
volumes could increase according to the development of rice cultivation area (Table 4).
4)48

In the composition of greenhouse gas emitted from straw burning, CO2 is of the biggest amount.
It is estimated that the amount of CO2 generated in the MD in 2014 was 32.419 thousand tons,
accounting for more than 97% of the total gas released. CO and NOx had also contributed the total
amount of gas released (Table 5).
Table 4. The amount of greenhouse gas (thousand tons)
emitted from burning rice straw in 2013
Locations

Autumn-WinterWinter-Spring

Summer-Autumn

CO2

CO

NOx

CO2

CO

NOx

CO2

CO

NOx

Kien Giang

23.42

0.56

0.05

1815.4

43.03

3.84

1569.4

37.20

3.32

Dong Thap---

2248.7

53.30

4.76

2834.3

67.18

6.00

Can Tho

-

-

-

702.7

16.66

1.49

808.1

19.15

1.71

An Giang

30.45

0.72

0.06

2717.2

64.40

5.75

3197.4

75.78

6.77

Table 5. The amount of greenhouse gas emitted from burning rice straw in the MD
Greenhouse gases (thousand tons)

Year

Grain
(mil. tons)

Residue straw(mil. tons)

Burning straw(mil. tons)

CO2

CO

NOx

2010

21.6

24.84

23.72

27783.44

658.53

58.83

2011

23.3

26.80

25.59

29970.10

710.36

63.46

2012

24.3

27.95

26.69

31256.37

740.84

66.19

2013

25.0

28.75

27.46

32156.76

762.19

68.09

2014

25.2

28.98

27.68

32418.82

768.4068.65

4. CONCLUSIONS AND RECOMMENDATIONSIn the MD, onsite burning is currently the most popular method to treat straw from riceproduction, from which the highest straw burning rate takes place in the Winter-Spring crop,
followed by the Summer-Autumn and Autumn-Winter crops. Other straw treatment methods
including mushroom cultivation, cattle raising, selling and giving shared a small proportion. Most of
the farmers tend to repeat the straw burning practices in the following years.
The annual amount of straw from rice production in the MD is very large, but most of thismaterial is burned off. This practice wastes an abundant biomass resource from agriculturalactivities and releases a large amount of CO2, CO, NOx into the atmosphere. There should beresearch studies on the methods to utilize the straw after harvests, like using straw to produce gases
as a renewable energy to limit the straw burning activities which waste a biomass resource andcauses environmental pollution.

ACKNOWLEDGEMENT
The authors acknowledge financial support of the project “Sustainable Biogas Production from
Waste Rice Straw” (DFC 11-016AU) by the Danish International Development Agency.

REFERENCES
Chandra R., Takeuch T. and Hasegawa T. (2012). Methane production from lignocellulosic agricultural crop wastes:
A review in context to second generation of biofuel production. Renewable and Sustainable Energy Reviews,
16: 1462-1476.

49

Gadde B., Bonnet S., Menke C., and Garivate S. (2009). Air pollutant emissions from rice straw open field burning
in India, Thailand and the Philippines. Journal of Environmental Pollution, 157: 1554-1558.
GSO - General Statistics Office (2014). Statistical Yearbook of Viet Nam 2013. Statistical Publisher. Ha Noi.
IPCC - Intergovernmental Panel on Climate Change (2006). 2006 IPCC Guidelines for National Greenhouse Gas
Inventories. National Greenhouse Gas Inventories Programme.
Kumar P. and Kumar S. (2010). Valuing the health effects of air pollution from agricultural residue burning. Invited
Paper to the ACIAR Workshop “Policy Options to Reduce Rice Stubble Burning”, Punjab State Marketing(Mandi) Board, Chandigarh, 13-15 October.
MONRE - Ministry of Natural Resources and Environment (2010). National Environment Report 2010 - Overview
of Viet Nam Environment. Ha Noi. pp. 112.
Nguyen Vo Chau Ngan, Nguyen Truong Thanh, Nguyen Huu Loc, Nguyen Tri Nguon, Le Ngoc Phuc and Nguyen
Truong Nhat Tan (2012). Potential use of water hyacinth and rice straw as additional loading materials forbiogas digester. Can Tho University Scientific Journal, 22a: 213-221.
Tipayarom D. and Nguyen Thi Kim Oanh (2007). Effects from Open Rice Straw Burning Emission on Air Quality
in the Bangkok Metropolitan Region. ScienceAsia, 33: 339-345.
Truc N. T. T. and Ni D. V. (2009). Mitigation of carbon dioxide emission: An environmental assessment of ricestraw burning practice in the mekong delta. Mekarn workshop 2009: livestock, climate change and theenvironment. Accessed at http://www.mekarn.org/workshops/environ/abstracts/truc_ctu.htm
Wassmann R., Lantin R. S., Neue H. U., Buendia L. V., Corton T. M., Lu Y. (2000). Characterization of methane
emissions from rice fields in Asia. III. Mitigation options and future research needs. Nutrient Cycling inAgroecosystems, 58: 23-36.

50

Alleles for Good Grain Filling in Rice Extra-Heavy Panicle Typesand Their Distribution among Rice Cultivars
Kato, T.*, A. Horibata
Faculty of Biology-Oriented Science and Technology, Kinki University,
930 Nishimitani, Kinokawa, Wakayama 649-6493, Japan
Email*: tkato@waka.kindai.ac.jp
ABSTRACT
Rice cultivars with numerous spikelets in a panicle (extra-heavy panicle type, EHPT) have been developed toattain higher yield, but could not realize this goal, mainly due to their low grain filling. EHPT with relatively better grain
filling, however, were also found and they showed a higher activity of ADPglucose pyrophosphorylase (AGPase) indeveloping endosperm. We then explored the single nucleotide polymorphisms (SNPs) among six cultivars including
EHPT for the loci of AGPase small subunit (APS2) and large subunit (APL2), as well as a locus of sucrosetransporter (SUT1). More than 30 SNPs in each locus were detected. Notably, in these six cultivars, one of the allelesin every locus, designated as APS2-2, APL2-2 and SUT1-2 at APS2, APL2 and SUT1, respectively, was shared incommon by the EHPT with good grain filling. Cultivars with the genotypes of APS2-2 APL2-2 SUT1-2 showed higher
AGPase activity and better grain filling in field experiments. These findings strongly suggests that these alleles could
contribute to good grain filling in EHPT. Cleaved amplified polymorphic sequences markers and others weredeveloped at several available SNPs to search for alleles among 179 rice cultivars. Results showed that
there were at least four, five and four major alleles at APS2, APL2 and SUT1, respectively. Of these, the plausible
alleles for good grain filling, APS2-2, APL2-2 and SUT1-2, existed most frequently in indica-type cultivars but not injaponica-types. These alleles could contribute to the improvement of yield sink strength, grain filling, and finally yield
in EHPT cultivars.
Keywords: ADPglucose pyrophosphorylase, grain filling, molecular marker, nucleotide polymorphism, rice,sucrose transporter.

1. INTRODUCTION
Increasing of grain yield is an ultimate goal for all research in crop science in order to supply
sufficient food for the growing population worldwide. In rice (Oryza sativa L.), many cultivars withnumerous spikelets in a panicle (heavy panicle type or extra-heavy panicle type [EHPT]) have beendeveloped in the course of breeding programs to attain higher yields, such as Hybrid Rice, New Plant
Type, Super Rice, etc. (Khush, 1996; Peng et al., 1999; Yang and Zhang, 2010). These EHPT
cultivars increased extensively the number of spikelets in a panicle, particularly on secondarybranches, and obtained higher yield sink potential to accumulate photoassimilates. The spikelets on
secondary branches, however, generally showed poor grain filling compared with those on primarybranches. Consequently, EHPT cultivars often failed to realize their potential and to produce higher
actual yield (Kato and Takeda, 1996; Yang et al., 2007; Yang and Zhang, 2010). This grain-fillingproblem has been pointed out in the first generation of New Plant Type, for example (Peng et al.,1999). Therefore, genetic improvement of poor grain filling is an important step to attain higher
yields consistently in EHPT cultivars.
On the other hand, there exists clear genetic variation in the degree of grain filling among
EHPT cultivars, which should contribute to the genetic improvement of their grain filling.
Venkateswarlu et al. (1986) showed that the proportion of high-density rice grains, which were
grains with a specific gravity of more than 1.20, varied including among EHPT cultivars. Peng et al.
(1999) also demonstrated the variation in grain filling among New Plant Type cultivars. Kato (2010)51

showed an apparent difference in the proportion of high-density grains, as well as the rate of grain
filling, among EHPT cultivars. Therefore, some internal physiological factors underlying the genetic
variation in grain filling among EHPT cultivars should be explored and utilized.
We have to consider the transportation of sucrose, the form of photoassimilates transported fromsource to sink, in this context. In rice, and many other cereal and pulse crops, most of thephotoassimilates to be accumulated in the sink and harvested were generated in source organs, not in
the sink. Patrick and Offler (2001) demonstrated that a steeper gradient of sucrose concentration
between source and sink termini along the network of sieve tubes (source > sink) results in distinct
differences in osmotic pressure, leading to the flow of water from higher pressure terminus (source) tothe lower pressure terminus (sink). This water flow consequently drives sucrose transport from source
to sink along sieve tubes, resulting in better grain filling. In other words, sucrose is not transported if
the sucrose concentrations are the same in both the source and sink termini, resulting in poorer grain
filling. This situation may the case in the spikelets on secondary branches in EHPT cultivars.
One of the factors to generate this gradient is to keep higher sucrose concentration in source
terminus, by supplying enough photoassimilates from higher photosynthetic activity in the source
organs. Another factor to be considered is to keep a low sucrose concentration in sink terminus. Thisshould be achieved by active unloading of sucrose from the sink terminus to the adjacent apoplast,and by metabolizing sucrose into starch, which is compartmented in amyloplasts. Kato et al. (2007)
examined the activities of two key enzymes of the metabolism of sucrose to starch in developing riceendosperm after anthesis, namely sucrose synthase (EC 2.4.1.13) and ADPglucose
pyrophosphorylase (EC 2.7.7.27, AGPase), using several EHPT cultivars with different degrees ofgrain filling. Results clearly showed that EHPT cultivars with higher AGPase activity exhibited
higher degrees of grain filling, as well as higher rates of grain growth. It was strongly suggested that
AGPase plays an important role in determining the activity of sink organs to decrease sucrose
concentration in the sink terminus. Liang et al. (2001) also demonstrated the importance of AGPaseactivity, as well as sucrose synthase activity, to determine grain filling in hybrid rice.
In higher plants, AGPase is a hetero-tetramer enzyme consisting of two large and two small
subunits (Tetlow et al., 2004; Lee et al., 2007). For each subunit, several isoforms, i.e., several
corresponding loci, have been identified. They function in an organ-specific manner: the loci of
OsAGPS2 (APS2, Os08g0345800) and OsAGPL2 (APL2, Os01g0633100) express the small and large
subunits, respectively, in the developing endosperm of rice (Ohdan et al., 2004). Thus, Kato et al.
(2010) explored nucleotide sequence polymorphisms in these two loci using six cultivars including
EHPT cultivars with different degrees of grain filling. The locus of sucrose transport (OsSUT1,
SUT1, Os03g0170900) (Aoki et al., 2003), which functions in unloading sucrose from the sinkterminus of sieve tubes into the apoplast, was also examined. Their results identified 28, 30 and 37
single nucleotide polymorphisms (SNPs) from translation start to translation stop codon comparedwith a reference cultivar, Nipponbare, for APS2, APL2 and SUT1, respectively (Kato et al., 2010;
Fig. 1). These SNPs did not alter any amino acid sequences within these three loci. Kato et al. (2010)
demonstrated that EHPT cultivars with better grain filling commonly shared the same haplotypes oralleles of APS2-2 and SUT1-2, and those with poorer grain filling shared APS2-1 and SUT1-1, at
APS2 and SUT1 loci, respectively. The former alleles, APS2-2 and SUT1-2, were strongly suggested
to be alleles for good grain filling in EHPT cultivars.
We recently re-examined the nucleotide sequences of the APL2 locus, resulting that one morenovel plausible allele for good grain filling, APL2-2, was identified at APL2 (Kato, unpublisheddata). The present study examined the effects of APL2-2, as well as APS2-2 and SUT1-2, on grain
filling and AGPase activity in developing endosperm. Kato and Horibata (2012) revealed a nonrandom distribution of alleles at APS2 and SUT among more than 300 cultivars, and detected atendency that indica-type cultivars have alleles for good grain filling in combination. This study
further explored the allelic variations in APS2, APL2 and SUT1 loci, for a wide range of rice52

cultivars, and using several cleaved amplified polymorphic sequences (CAPS) and insertion/deletion(Indel) markers available to detect SNPs. The distributions of the alleles including newly detected
ones at APS2, APL2 and SUT1 were evaluated again among 179 cultivars. The objectives of thisstudy were to characterize the function of these three alleles of good grain filling and theirdistribution among rice cultivars, in order to facilitate the utilization of these alleles in the
improvement of grain filling in EHPT cultivars.

2. MATERIALS AND METHODS
2.1. Materials
Seven rice EHPT cultivars; Akenohoshi (the genotype for APS2, APL2 and SUT1 is APS2-1
APL2-1 SUT1-1, hereafter 111.), Momiroman (111), Hokuriku 193 (121), IR65598-112-2 (121),
Takanari (121), Milyang 23 (222) and Nanjing 11 (222), were used in field experiment to evaluate thedegree of grain filling and AGPase activity in developing endosperm. For the molecular marker
study, 179 rice cultivars including 83 japonica-type and 96 indica-type were used to survey SNPs
and the distribution of alleles at APS2, APL2 and SUT1. These 179 cultivars were included in thoseused in Kato and Horibata (2012).
2.2. Methods
In the field experiment, the seven cultivars were sown in nursery boxes on 14 May, 2014, andgrown in a greenhouse. On 11 June, these seedlings were transplanted into a paddy field at theFaculty of Biology-Oriented Science and Technology, Kinki University, Kinokawa, Wakayama,
Japan, with a single plant per hill and at inter-hill and inter-row densities of 15 cm and 30 cm,
respectively. Each cultivar was arranged in the field in three rows and 12 hills per row. Fertilizers
were applied at the rate of 12:10:10 g m-2 for N:P2O5:K2O in total. Standard cultivation practices
were conducted throughout the growing season.
After recording heading dates of about 10 panicles individually, panicles were sampled at 15 days
after heading (DAH), frozen with liquid nitrogen, and their spikelets on secondary branches collected.
These spikelets were stored at -80˚C until use. Extraction of enzyme fractions from the endosperm andassays of AGPase activity were performed in accordance with Kato et al. (2007). The extractions andassays were replicated four times for each cultivar. The results were expressed as specific activity (mUmg protein-1), in which protein content was determined as described in Bradford (1976).
From 45 to 50 DAH, 5 panicles were sampled from every 6 hills at the center of each cultivar
plot, air dried and the spikelets on secondary branches collected. These spikelets were classified as
those showing specific gravities >1.00 and those >1.15. The former and latter spikelets were defined
as fine grains and well-filled grains, respectively. The proportions of fine grains and well-filled
grains to total grains were calculated and used as an index of grain filling in the earlier filling stage
and that in the later stage, respectively. Longest culm length, panicle length on the longest culm,
spikelet number per panicle on the longest culm, panicle number and spikelet number per panicle
were also recorded for every 6 hills.
For molecular marker studies, DNA was extracted from seedlings of all of the 179 cultivars by
CTAB method (Doyle and Doyle 1987). From the SNPs detected in six cultivars (Koshihikari,
Nakateshinsenbon, Akenohoshi, Takanari, Milyang 23 and Nanjing 11) (Kato et al., 2010; Kato,
unpublished data), 6, 5 and 5 SNPs for APS2, APL2 and SUT1 loci, respectively, from transcription
start to translation stop codon could be converted to CAPS or Indel markers (Fig. 1, Table 1). A totalof 179 cultivars were genotyped using these 16 markers. Table 1 also shows the primers for PCR and
restriction enzymes used for these markers. From the SNPs data, alleles for each of the three lociwere identified in every cultivar.

53

Table 1. Cleaved amplified polymorphic sequences (CAPS) and insertion/deletion (Indel)
markers available to detect single nucleotide polymorphisms ((SNPs)) from transcriptional
start to stop codons in APS2, APL2 and SUT1 of rice cultivars
cult
Forward primer (5’ - 3
3’)

Reverse primer (5’ - 3’)

Annealing
temp. (˚C)(

Restriction
enzyme-1

TGTAATTCTTTAATTTGTTGCAACCTGTAATTCTTTAATTTGTTGCAAAGATTGAAACATCATGCATGGAGAAGATTGAAACATCATGCATGGAGAA

61
57(Indel)2)549

AGGGGTTTTTGTCTGCATTG

TTCTCCATGCATGATGTTTCA

59

CviRI

2750

CAGATAATCCTAACTGGTTTCAGGTGTCAAGAAAATTCCCAATGAAATAA

58

CviRI

2837

CAGCATCTGCAGTACCCTGATGCAGAGTGTGCTTGGAATC

59

EcoRV

4461

GTCTGCTCCAATTTATACACAACCTTTGCAGCATGATAAATTACTTTGAA

58

TasI

5274

CGCAGAAAGCAGTTTTACGTT

TCCCCATTGTTCTTGGAATG

58

Hin1IIACCTGACAGGCCTAATTCCTGAGACGGGTCCATCACCATCAG

61

Eco47III

Position1)
APS2

APL2
-3680
-2549

TTGGCAGAGAAACAAATCCTGATAGGAGTTTCACAAGGCAATTCTG

60

Hin1II-455

ATTTGACTTGTTTTGGGTTTTGACGAATGGTTTGTAATTTTTGCTTGTTATAGAGCTGAACTACTGCTCCTGCTTCCTATGGCACTCAAGATAGCAC

59
58(Indel)1137

TCATTTGGTTATTTCGACTTGC

CATTTGTGTAGCAGCCAATACC

58

NdeI

1281

TCATTTGGTTATTTCGACTTGC

CATTTGTGTAGCAGCCAATACC

58

MunI

SUT1
1467

TTCCATTTTACCTCAGATGCTGT

AATCGCTCCACTTGCTCTGT

58

SspI

1908

AACTGCCAAACGATGTGTTTCCAGGGGTGATGAACTGCATA

58

HhaI

4743

CCTACTGGGATGCCTTCTGT

AGGATGAATGGGAACCAAGA

58

TasI

4751

CCTACTGGGATGCCTTCTGT

AGGATGAATGGGAACCAAGA

58

TaqI

5064

CCTACTGGGATGCCTTCTGT

CTGCAGTGATGGCTTTCTGA

58

CviRI

Note: 1 ) Number of nucleotides from the start of translation (minus
inus values mean upstream positions from the start
nucleotide); 2 ) Inn the case of Indel markers, two pairs of primers were individually used in separate PCR amplifications.

Fig. 1. Structure of rice APS2, APL2 and SUT1 loci, the sites of single nucleotide
polymorphisms (SNPs)) and the positions of molecular markers avail
available
able to detect SNPs
Note: Filled rectangles indicate exons. Straight and dotted vertical lines indicate the positions of translation start and st
stop
codons, respectively. Open downward arrows and filled upward arrows indicate the sites of SNPs and the positions
positofmolecular markers, respectively.

54

2.3. Data analysis
Analysis of variance by one-way classification was applied to the data of the proportions of finegrains and of well-filled grains after arcsine transformation, as well as to the data of AGPaseactivity, to obtain the respective least significant differences at a probability level of 0.05. Cluster
analysis was conducted to express the relationships among alleles at each of APS2, APL2 and SUT1
using an adin software of Excel (Mulcel) (Yanai 2005).

3. RESULTS
Table 2 shows the results of agronomic traits of the seven cultivars used. The number of
spikelets ranged from about 180 to 220 among these seven cultivars, indicating that all of them could
be regarded as EHPT cultivars. Three genotypes, 111, 121 and 222, were compared for the
proportion of fine grains and well-filled grains in spikelets on secondary branches (Fig. 2). Althoughsignificant variations were found within each genotype, the genotype of 222 apparently showedsignificantly higher proportions of fine grains and also of well-filled grains than other genotypes(P<0.05). Genotype 121 also showed significantly higher proportions than genotype 111 for wellfilled grains (P<0.05). Fig. 3 shows the results of AGPase activity in the developing endosperm of
these three genotypes. Similar to the results for the degree of grain filling, genotype 222 exhibited
significantly higher activity than genotypes 111 and 121 (P<0.05).
Table 2. Agronomic traits of seven rice cultivars examinedin the present field experiment. Mean ± S.D.
Genotype1)Days
to heading

Panicleshill-1

Culm
length (cm)

Paniclelength (cm)Spikelets
panicle-1

Akenohoshi

111

102.3 ± 1.2

9.2 ± 3.1

81.7 ± 5.5

20.5 ± 0.4

221.0 ± 27.7

Momiroman111

105.5 ± 0.5

8.7 ± 2.1

80.8 ± 6.0

20.6 ± 1.3

223.2 ± 35.6

Hokuriku 193

121

103.8 ± 2.3

9.4 ± 3.7

81.0 ± 4.5

20.7 ± 1.3

186.2 ± 29.6

IR65598-112-2

121

97.8 ± 1.3

9.3 ± 0.5

80.0 ± 2.2

20.8 ± 0.8

195.5 ± 37.4

Takanari

12198.8 ± 0.8

8.7 ± 2.1

77.3 ± 3.1

20.7 ± 2.1

194.7 ± 22.6

Milyang 23

222

98.7 ± 1.8

8.0 ± 2.5

76.0 ± 6.3

21.3 ± 0.8

210.3 ± 19.7

Nanjing 11222

92.0 ± 2.9

10.3 ± 1.6

75.0 ± 3.8

22.0 ± 2.5

180.8 ± 22.9

Cultivar

80
60

A
a
b

b

40
20
0

111

121

222

Proportion of well-filled grains(%, Specific gravities>1.15)

Proportion of fine grains(%, Specific gravities>1.00)

Note: 1 ) 111, 121 and 222 indicate APS2-1 APL2-1 SUT1-1, APS2-1 APL2-2 SUT1-1 and APS2-2 APL2-2 SUT1-2, respectively.

60
40

B

a
b
c

20
0

111

121

222

Fig. 2. Degrees of grain filling of the three genotypes of APS2, APL2 and SUT1 for the
spikelets on secondary branches. A - proportion of fine grains (specific gravity >1.00);
B - proportion of well-filled grains (specific gravity >1.15).
Note: Vertical bars on each mean indicate S.D. Means with different letters differed significantly at a probability level of 0.05.
111, 121 and 222 indicate APS2-1 APL2-1 SUT1-1, APS2-1 APL2-2 SUT1-1 and APS2-2 APL2-2 SUT1-2, respectively.

55

Fig. 3. Activity of ADPglucose
cose pyrophosphorylase (AGPase) in developing endosperm
at 15 days after heading of the three genotypes of APS2, APL2 and SUT1
for spikelets on secondary branches
Note: Vertical bars on each mean indicate S.D. Means with different letters differed significantly
icantly at a probability level of 0.05.
111, 121 and 222 indicate APS2-1
1 APL2APL2-1 SUT1-1, APS2-1 APL2-2 SUT1-1 and APS2-2 APL2-2
2 SUT1-2,
SUT1
respectively.

Table 3 shows SNPs detected for APS2, APL2 and SUT1 in 179 rice cultivars, resulting in 4, 5
and 4 alleles for APS2, APL2 and SUT1, respectively, except for rare alleles with the frequencies ofless than 5%. Fig. 4 shows dendrograms of these alleles for each locus, derived from a cluster
analysis based on dissimilarity index using the furthest neighborhoodborhood procedure.
proc
These
dendrograms demonstrate that APS2-1 and APS2-3, APL2-2 and APL2-4,, APL2-3 and APL2-5,
SUT1-1 and SUT1-3, and SUT1
SUT1-2 and SUT1-4,, showed similar nucleotide sequences to each other.

Fig. 4. Dendrograms representing dissimilarities of alleles at APS2,, APL2 and SUT1,
constructed using the furthest neighborhoodborhood procedure
Response to reviewer’s comments
P1, L17: loci => locus
P2, L11: higher rates of grain filling => higher rate
rates of grain growth
P2, L30: I deleted “which”.
P3, L16: I added “on the longest
ongest culm”.

56Distributions and associations of these alleles were evaluated separately in japonica-type andindica-type cultivar groups, as shown in Table 4. These tables clearly demonstrate that APS2-2,
APL2-2 and SUT1-2 were found most frequently in indica-type cultivars (68.2%, 58.1% and 51.8%,
respectively), whereas they appeared at very low frequencies in japonica-type cultivars (6.5%,
5.2% and 5.2%, respectively). Moreover, these three alleles tended to be shared in an indica-type
cultivar, although they are located on different chromosomes. Tests of independency from 2×2
contingency tables between the major allele and other alleles resulted in significant χ2 values inindica-type cultivars: χ2=29.733 (P<0.001) for APS2 vs. APL2, χ2=21.636 (P<0.001) for APS2 vs.
SUT1 and χ2=8.497 (P=0.004) for APL2 vs. SUT1. In japonica-type cultivars, APS2-3 (44.2%) andrelated APS2-1 (32.5%), and APL2-4 (40.3%) and APL2-1 (31.2%) were major alleles at APS2 andAPL2, respectively. SUT1-1 existed exclusively at the SUT1 locus at a frequency of 90.9%. The
same tests of independency in japonica-type cultivars demonstrated no significant association
among the three loci: χ2=0.104 (P=0.747) for APS2 vs. APL2, χ2=0.005 (P=0.942) for APS2 vs.
SUT1 andχ2=2.160 (P=0.142).
Table 3. Single nucleotide polymorphisms at the sites where molecular markers wereavailable and detected alleles at APS2, APL2 and SUT1 in 179 rice cultivars
Position1)

Allele2)
APS2-1

APS2-2APS2-3

APS2-4

-1

CC

del

CC

CC

549

T

G

G

G

2750

A

G

A

A

2837

G

A

G

A

4461

AT

del

AT

AT

5274

T

C

T

C

APL2-1

APL2-2APL2-3APL2-4APL2-5

-3680

G

C

C

C

C

-2549

C

C

T

C

T

-455

AC

GT

AC

AC

AC

1137

A

A

G

A

G

1281

C

C

T

C

C

SUT1-1

SUT1-2

SUT1-3

SUT1-4

1467

A

T

A

T

1908

G

C

C

C

4743

T

A

T

A

4751

A

C

A

A

5064

C

T

C

T

Note: 1 ) Number of nucleotides from the start of translation (minus values mean upstream positions from the start nucleotide);2)
Except for rare alleles.

57

Table 4. Frequencies of the alleles at APS2, APL2 and SUT1,
and their association among japonica-type and indica-type rice cultivars
Japonica-type cultivar
APL2-1

APL2-2APL2-4APL2-3APL2-5

Total

Freq. (%)

APS2-1

9

APS2-3

15

0

7

9

0

25

32.5

0

13

5

1

34

APS2-4

44.2

0

0

10

3

0

13

16.9

APS2-2
Total

0
24

4
4

1
31

0
17

0
1

5
77

6.5
100.0

Freq. (%)

31.2

5.2

40.3

22.1

1.3

100.0

APL2-1

APL2-2APL2-4APL2-3APL2-5

Total

Freq. (%)

SUT1-1

24

0

30

16

0

70

90.9

SUT1-3

0

0

1

1

1

3

3.9

SUT1-2

0

4

0

0

0

4

5.2

SUT1-4
Total

0
24

0
4

0
31

0
17

0
1

0
77

0.0
100.0

APS2-1

APS2-3

APS2-4APS2-2Total

SUT1-1

25

31

13

1

70

SUT1-3

0

3

0

0

3

SUT1-2

0

0

0

4

4

SUT1-4

0

0

0

0

0

Total

25

34

13

5

77

Indica-type cultivar

58

APL2-1

APL2-2APL2-4APL2-3APL2-5

Total

Freq. (%)

APS2-1

0

1

0

2

0

3

3.5

APS2-3

4

1

2

0

1

8

9.4

APS2-4

2

2

8

4

0

16

18.8

APS2-2

0

45

9

0

4

58

68.2

Total

6

49

19

6

5

85

100.0

Freq. (%)

7.0

58.1

22.1

7.0

5.8

100.0

APL2-1

APL2-2APL2-4APL2-3APL2-5

Total

Freq. (%)

SUT1-1

6

3

2

6

0

17

20.0

SUT1-3

0

2

6

0

1

9

10.6

SUT1-2

0

32

10

0

2

44

51.8

SUT1-4

0

12

1

0

2

15

17.6

Total

6

49

19

6

5

85

100.0

APS2-1

APS2-3

APS2-4APS2-2Total

SUT1-1

3

7

6

1

17

SUT1-3

0

1

5

3

9

SUT1-2

0

0

4

40

44

SUT1-4

0

0

1

14

15

Total

3

8

16

58

85

4. DISCUSSION
Kato et al. (2010) demonstrated that EHPT cultivars with the genotype APS2-2 SUT1-2
exhibited a higher degree of grain filling and also a higher AGPase activity than APS2-1 SUT1-1,
leading to the conclusion that APS2-2 and SUT1-2 could be alleles for good grain filling. The sametendencies could also be detected in the present results for alleles at the APS2 and SUT1 loci.
Additionally, a novel allele of APL2-2 also revealed an enhancing effect on grain filling, together
with APS2-2 and SUT1-2, compared with APL2-1. The genotype APS2-2 APL2-2 SUT1-2 showed asignificant increase in the proportion of well-filled grains compared with APS2-1 APL2-2 SUT1-1,
indicating that the alleles APS2-2, APL2-2 and SUT1-2 could function in an additive manner inenhancing grain filling. The present conclusion should be further elucidated using near-isogeniclines with all combinations of these three alleles.
The present results may propose a plausible mechanism for the enhancing effect of these threealleles on grain filling, i.e., these alleles strengthen the sink activity of developing endosperm of
EHPT cultivars through an increase in AGPase activity and sucrose transporter properties. This
strengthened sink activity maintained the concentration gradient of sucrose from the source
terminus to the sink along the phloem network, and allowed sustainable transportation ofphotoassimilates from the source organs. The fact that any SNPs detected in the present study do
not have altered amino acid sequences suggests that the three alleles should act quantitatively, notqualitatively. These three alleles might, therefore, function sufficiently to enhance grain filling not
in individual forms but in combined genotypes.
AGPase is well-known as a key enzyme in starch biosynthesis not only in sink organs but also in
source organs. Molecular manipulation of this enzyme in rice endosperm resulted in very highAGPase activity and higher grain weight (Smidansky et al., 2003; Sakulsingharoj et al., 2004). Mo etal. (2014) also reported several SNPs in APS2 and APL2 in Korean japonica rice cultivars, which
affected several grain quality traits, especially in different temperature conditions. These reportssuggested that the allelic variation in APS2 and APL2, and also in SUT1, could affect various traits
related to rice grain growth including grain filling.
The present survey for SNPs among 179 rice cultivars in APS2, APL2 and SUT1, detected
several additional novel alleles to those reported in Kato et al. (2010) and Kato and Horibata (2012).
The number of alleles in each locus will be the minimum estimates, because this study could not
examine polymorphic nucleotide positions that cannot be designed as CAPS or other markers.
Actually, reported SNPs in several databases corresponds to those in this study in many cases, but
do not match in several cases (for example in OryzaSNP@MSU, http://oryzasnp.plantbiology.
msu.edu/index.html).
The most pronounced results for the distribution of alleles at the three loci were that the alleles
were not distributed randomly among rice cultivars, particularly between japonica-type and indicatype cultivars. More than half of indica-type cultivars had APS2-2, APL2-2 and SUT1-2, all of which
are suggested to be for good grain filling as above, and tended to be associated to each other,resulting that the genotype APS2-2 APL2-2 SUT1-2 was much more frequent than expected fromindependent segregation in indica-type cultivars. On the other hand, in japonica-type cultivars, noneof the APS2-2, APL2-2 and SUT1-2 were frequently found. In addition, no association among thesethree alleles was detected. For the SUT1 locus, the SUT1-1 allele was mostly found in japonica-type
cultivars at a frequency of more than 90%. Kato and Horibata (2012) reported this tendency for theprevalence and association of APS2-2 and SUT1-2 in indica-type cultivars. The present study clearly
demonstrated that the tendency obtained from these two alleles was also revealed when one moreplausible allele for good grain filling, APL2-2, was newly involved. The association of these three59alleles might reflect natural and/or unconscious artificial selection toward obtaining good grain
filling genotypes. These three alleles should be utilized consciously to resolve grain filling problems
in EHPT cultivars in future breeding programs.

5. CONCLUSIONSThis study emphasized that the APL2-2 at the APL2 locus could be an allele for good grain
filling in EHPT cultivars, together with two known alleles of APS2-2 and SUT1-2 at other loci,compared with APS2-1, APL2-1 and SUT1-1. APS2 and APL2 encode AGPase small and large
subunits, respectively, and the alleles of APS2-2 and APL2-2 could enhance the activity of AGPasein developing endosperm, resulting in accelerating sucrose to starch metabolism, and finally in adecrease in sucrose concentration in the sink terminus of phloem network. The allele of SUT1-2
could also contribute to the decrease in sucrose concentration through enhanced unloading of sucrose
from the sink terminus to the endosperm apoplast. This decrease in sucrose concentration in thesink terminus may provide a steeper downward gradient of sucrose concentration from the source
terminus to the sink, leading to sustainable transportation of photoassimilates, and consequently
contribute to better grain filling. These plausible alleles for good grain filling, APS2-2, APL2-2 andSUT1-2, are frequently found in indica-type cultivars, and not so much in japonica-type cultivars. In
addition, these three alleles tended to result in the genotype of APS2-2 APL2-2 SUT1-2 in indicatype cultivars. These three alleles or this genotype should be introduced and utilized in breeding for
improving grain filling in EHTP cultivars.

ACKNOLEDGEMENT
The authors wish to express sincere thanks to M. Fukawa, M. Ishida, H. Kitano, Y. Endo and N.
Sakamoto, Faculty of Biology-Oriented Science and Technology, Kinki University, for technicalassistance. This study was in part supported by the grants from the JSPS KAKENHI Grant No.
22580020, and Wakayama Prefecture Collaboration of Regional Entities for the Advancement ofTechnological Excellence, JST.

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61

Residue Management Effects on Survival Rate, Growth and Yield of Rice Cultivar
IR64-Sub1 Subjected to Submergence at Young Seedling Stage in Pots
Pham Phuoc Nhan1*, Phan Thi Be Sau1, Le Van Hoa1, Ben Macdonald2
1

College of Agriculture and Applied Biology, Can Tho University, Viet Nam
2
CSIRO Agriculture Flagship, Canberra, Australia
Email*: ppnhan@ctu.edu.vn
ABSTRACT

Flooding is one of the factors that causes serious damage to agricultural production, in paddy rice areas. In
the Mekong river delta of Viet Nam, intensive rice production systems consist of 2 to 3 consecutive crops per yearwith short intervals between each crop. This causes straws and stubbles to remain un-decomposed under fieldwater, potentially change physical, chemical and biological characteristics of soil that affect rice growth and yield.
The study investigated the effects of straw and stubble incorporation equivalent to 5 tons per ha under artificial
flooding on growth and yield of IR64-Sub1 rice variety grown in pots. IR64-Sub1 seedlings’ survival decreased
consequently with prolonged submergence, around 10% after being flooded continuously for 20 days.
Organic residual incorporation under 60 cm depth of water continuously in 15 days at 3-day-seedlings altered pH,
EC, and dissolved oxygen of the water and affetced first internode elongation and delayed harvest time up to 20
days in comparison to that of the control. The incorporation of burnt straws and stubbles had a tendency toimprove rice yield regardless to submergence but flooded condition reduced seed discoloration and enhanced
spikelet filling significantly.
Keywords: Anaerobic germination, Mekong river delta, organic matter, rice seedlings, submergence, yield

1. INTRODUCTION
In many parts of the Mekong Delta (MD) of Viet Nam, farmers grow 3 crops of rice annually.
Typically, Winter - Spring crop (WS) is from December to March, Summer - Autumn (SA) cropfrom April - August, and Autumn - Winter (AW) crop from September to November. Among thethree, only WS is harvested in the dry season. With the average rice (Oryza sativa L.) yield of
about 5 tons/ha and with harvest index of the typical high yield rice cultivars grown in the MD,
the amount of rice residue (straw and stubble) after harvesting can be as high as 5 - 7 tons/ha.
Managing a large amount of residue is challenging and the solution is a trade-off between labour,decomposition rate, environmental regulation, and the cropping calendar. After the dry season
harvests, most farmers in MD burn the rice straw and stubble before establishing the next crop.
Residue burning may contribute to quick nutrient recycling (Gadde et al., 2009) and eliminate
pests (Dobermann and Fairhurst, 2002). But this practice contributes to the reduction in soilorganic matter leading to production issues and regional air pollution. In the rainy season crops,
particularly in AW crop, residue burning is not practical due to rains and increasing water level on
the field from annual flooding of the Mekong River. Farmers often leave the residues in theinundated fields and incorporated them into the soil during land preparation for the next crop.
Under these anaerobic conditions, organic matter from rice residual can undergo fermentation andmay lead to the toxic concentrations of in soil solution (e.g. Fe 2+, S2-, PO4; Ponnaperuma, 1984) andimmobilisation of soil nitrogen. This can cause mortality of rice seedlings and reduce yield of thesubsequent crop. These processes have not been fully investigated for highly intensive cropping

62

systems in the MD. Rice straw consisted about 5.4, 1.4, and 13.0 kg of N, P, and K per ton,
respectively. Analysis from IRRI also showed that it contained up to 0.03, 0.8, 6.5, 2.0, 0.3, 0.45,
0.003, and 0.01 kg of Zn, S, Si, Mg, Ca, Fe, Mn, Cu, and B, respectively per one ton of rice straw.
Incorporating residues may increase nutrient status of the soil. Ruensuk et al. (2010) showed thatincorporation of less than 5 tons/ha of rice straw in Thailand did not impact on plant density; buthigher levels of incorporation decreased number plants per m 2 after 15 days after crop
establishment and rice yield. Another long-term comparison reported that straw incorporation
improved microbial biomass, nitrogen and carbon in the soil than those of burnt straw (Bird et al.,2001). Long term research on straw management on the field at Cuu Long Delta Rice ResearchInstitute, Viet Nam reported that incorporation or burnt rice straw resulted in higher yield thanresidual removal, improved nitrogen and phosphorous level but caused no change in Ca, Mg, Na,
Zn and Cu in the soil (Tuyen and Tan, 2001). Most of previously researched residue incorporation
reported was not carried out under submerged conditions.
Rice in the MD is mostly established by direct sowing of pre-germinated seeds onto wet or
saturated soil. Sowing time of AW crop (September) coincides with period of high rainfall andannual flood of the Mekong river. Fields may become inundated after crop establishment,drowning the young seedlings. In these cases, farmers have to re-sow the seeds. The availability ofcultivars with high tolerance to submergence at young seedling stage is highly desirable.
Submergence tolerance gene in rice, Sub1, was discovered by Xu et al. (2006) and afterwards
transferred to other high yielding rice cultivars, e.g. IR64-Sub1 (Septiningsih et al., 2009) but
studies on this variety in practice are still limited in Mekong Delta. The tolerance to submergenceat vegetative stage of Sub1-cultivars has been well documented (Xu et al., 2006), but there is little
information on performance of IR64-Sub1 subjected to submergence during young seedling,
especially when the bio-chemistry of the flood water may be governed by anaerobic decompositionof the residue.
This research aimed to investigate the effects of rice residue anaerobic decomposition on
survival rate, growth and yield of IR64-Sub1 rice cultivar subjected to submergence at young
seedling stage in pots.

2. MATERIALS AND METHODS
The study was carried out at green house of College of Agriculture and Applied Biology, Can Tho
University from May to September 2013. It comprised of two experiments, the first one formed thebasis for designing the second. Both experiments used breeder’s seed of IR64-Sub1, obtained from
Cuu Long Rice Research Institute.
2.1. Experimental lay out and treatments
Experiment 1
Experiment 1 aimed at investigating the survival rate of IR64-Sub1 seedlings subjected todifferent durations of submergence, starting from 3 days after emergence (DAE). There were 5
submergence duration treatments: control (without submergence), 5, 10, 15, and 20 days. Rice wasgrown in pots of 23 cm high and 30-cm inner diameter. Experiment pots were arranged in
randomized complete blocks, with 4 replications. In each pot, 100 germinated seeds were sown
onto a layer of saturated soil. After 3 days, rice seedlings anchored their roots firmly into the soil
layer. Pots were lowered into 1-m height by 0.75-m diameter plastic container and were flooded to63

60 cm depth with domestic tap water, except for the control treatment, which was cover by a layerof water 1 cm above the soil surface. All the containers were covered by black plastic sheet. Rice
seedlings thus grew in darkness, to mimic conditions of submergence under high turbidity flood,
typical of the MD. At the end of the submergence treatment, water was gradually drained from thecontainers. Pots were exposed to natural light for 2 days and the survival rate was calculated fromnumber of turned-green seedlings.
Experiment 2
Experiment 2 investigated effects of residue managements under submerged and nonsubmerged conditions on field water chemistry and growth and yield of IR64-Sub1. The experimentconsisted of two factors:(i) Submergence was main factor (S): Seedlings were subjected to submergence for duration of
15 days (S1); or without submergence (S0, control). The exposure to submergence treatment was
similar to that of Experiment 1. The submergence duration in S1 was limited to 15 days - based on
results of Experiment 1 - to ensure adequate survived seedlings to carry out the experiment.
(ii) Residue management was sub-factor (R): With three levels, namely R0: without residue
incorporation; R1: Incorporation of burnt residue; and R2: Incorporation of fresh residue. The
residue was rice straw and stubble collected from surrounding rice fields. The amount of residue ineach pot of treatments R1 and R2 were equivalent to 5 tons/ha. The experimental pots werearranged in split plot design with 4 replications.
2.2. Monitored parameters
In Experiment 2, electrical conductivity (EC), pH (using Toledo meter), and dissolved oxygen(Oxi330 meter) of water during the submergence period were monitored daily around 9 am.
At the end of 15-day submergence period, 3 seedlings were left in each pot to grow with water
depth of 2 cm, until harvest. Inorganic fertilizer supplement was applied equivalent to 100 - 60 - 30
kg of nitrogen, P2O5, and K2O per ha, respectively.
The length of first, second, third and forth 4 internodes from randomly selected tiller of eachplant in each pot were measured at the harvest. Yield components were also examined at harvest,
yield recorded. Seed discoloration was evaluated following IRRI method (IRRI, 1998).
2.3. Data analysis
Microsoft Excel and SPSS 16.0 were employed for data comparison between treatments
for each experiment. For Experiment 2, analysis of variance, effects of submergence andresidue management treatments, and their interaction were carried out by the mentioned
statistic software.

3. RESULTS AND DISCUSSION
3.1. Experiment 1 - Survival rate of IR64-Sub1 subjected to submergence at young
seedling stage
Fig. 1 shows that the survival rate decreased sharply as the duration of submergenceincreased. Indeed, the survival rate was significantly negatively correlated with the submergence
duration.Despite carrying Sub1 gene, less than 10% of the 3-days old IR64-Sub1 seedlings
survived after 20 days of continuous submergence.

64Fig. 1. Survival rate of IR64
IR64-Sub1
Sub1 seedlings submerged in pots for different duration.
In “control”“control”, seedlings were not submerged
Note: Columns with the same letters (a, b,c) inside are not significantlt different by Duncan’s Mul
Multipletiple Range Test at the 5%
level. Vertical and capped bars are standard deviations of the means of 4 replications. The dashed line shows correlation
between survival rate and submergence duration by relation equation showed on the top right corner.

3.2. Experiment 2 - Residue management and IR64
IR64-Sub1 performance
3.2.1. EC and pH
A few days at the beginning of submerged duration, EC values of non
non-submergedsubmerged treatments
were clearly higher than the submerged ones. Low EC of the submerged treatments could be
attributed
ributed to the dilution effects of added water to increase the flood depth in the containers. Amongthe S0, EC of treatments with burnt residue had a tendency to be higher than those with fresh
residue, this in-turn
turn higher than those without residues. This implies that materials from burnt
residues could dissolve more quickly than those of fresh ones. Due to large variations of the EC
values, however, the difference among the residue treatments were not significant. At the end of thesubmergence period, the water EC of all treatments were similar ((Fig. 2).

Fig. 2. Effects of submergence (S0 = control, no submergence, S1 = 15
15--day submergence)and residue treatments (R0 = without residue incorporation;
R1 = incorporation of burnt residue; R2= incorporation of fresh residue) on water EC
Note: Vertical and capped bars are standard deviations of the means of 4 replications

65

Fig. 3. Effects of submergence (S0 = control, no submergence,
S1 = 15-day
day submergence) and residue treatments (R0 = without residue incincorporation;
R1 = incorporation of burnt residue; R2= incorporation of fresh residue) on water pH
Note: Vertical and capped bars are standard deviations of the means of 4 replications.

3.2.2. Dissolved Oxygen
For the first 11 days, the nonnon-submerged treatments
ents had significantly higher level of dissolved
oxygen in solution than those in submerged ones (Fig. 4). The last 5 days of submerged duration,
there were no difference in soluble oxygen in water recognized.

Fig. 4. Effects of submergence (S0 = control
control, no submergence, S1 = 15--day submergence)and residue treatments (R0 = without residue incorporation; R1 = incorporationof burnt residue; R2= incorporation of fresh residu
residue)e) on dissolved oxygen in water
Note: Vertical and capped bars are standard devia
deviations of the means of 4 replications.

3.2.3. Effects of submergence and residual treatment on survival, growth and yield of
IR64-Sub1 rice
The survival rate of submerged treatments from Experiment 2 was less (<20%) than Experiment
1 (>20%, compare Fig. 1 andnd FigFig. 5). Similar to Experiment 1 submergence of the seedlings66significantly reduced the seedling survival rate compared with the non-submerged treatment. Thismay be related to the reduced dissolved oxygen in submerged treatments during the first 11 days oftreatments (Fig. 4). Straw incorporation regardless fresh or burnt enhanced the survival rate of
IR64-Sub1 seedlings under submergence, though the difference was small.

Fig. 5. Effects of submergence (S0 = control, no submergence, S1 = 15-day submergence)and residue treatments (R0 = without residue incorporation; R1 = incorporation of burnt
residue; R2= incorporation of fresh residue) on survival of IR64-Sub1 rice seedlings
Note: Vertical and capped bars are standard deviations of the means of 4 replications. Columns with the same letter are notsignificant difference by Duncan Multiple Range Test at the 5% level.

Submerged treatments contributed to improve the number of filled spikelet per panicle but nodifference in yield was found. Submerged plants produced more spikelets per panicle and resulted inlighter weight of 1000-grains. The incorporation of burnt residual in both water treatments
increased rice yield significantly in comparison to other residue treatments. Among them, fresh
residue incorporation caused negatively effect on yield in contrast with free-residual treatment. In
overall, submergence showed effectively impact on growth and yield components of IR64-Sub1 rather
than residual incorporation. The interaction between submergence and residual incorporation was
rarely found except for the final rice yield (Table 1).
Table 1. Effects of submergence and residue treatments on yieldand yield components of IR64-Sub1 rice
FactorUnfilled spikelets/panicle

Filled spikelets/panicle

1000-grain weight

Yield (g/pot)

24.6 ± 4.6
23.4 ± 5.2
25.6 ± 6.2

63.4 ± 17.3 b
73.9 ± 22.4 a
67.4 ± 15.8 ab

23.7 ± 1.3
24.3 ± 2.1
24.4 ± 1.4

52.1 ± 17.7 b
72.5 ± 12.5 a
40.0 ± 6.4 c

22.0 ± 4.2
27.1 ± 5.1

51.8 ± 5.7
84.6 ± 9.6

25.3 ± 1.1
23.0 ± 1.0

59.1 ± 19.9
50.7 ± 16.8

ns
*
ns

*
**
ns

ns
**
ns

**
ns
**

Residue management
No residue
Burnt residue
Fresh residue
SubmergenceNon-submergence
Submergence
F (R)
F (S)
F (R x S)

Note: In the same column, numbers followed by the same letter (a, b,c) are not significant different by Duncan Multiple Range
Test; ns = non-significance; * = significant difference at 5% level; ** = significant difference at 1% level.

67

The submerged plants were higher than non-flooded plants and tended to lodge. Submergenceenhanced elongation of the first internode from top of plant significantly but the length of followinginternodes among treatments were found no apparent difference (Fig. 6). In overall, the plant heightfrom submerged treatments was significantly higher than those of non-submerged ones.

Fig. 6. Effects of submergence (S0 = control, no submergence, S1 = 15-day submergence)and residue treatments (R0 = without residue incorporation; R1 = incorporationof burnt residue; R2= incorporation of fresh residue) on the length of 4 internodes
from top of IR64-Sub1 rice at harvest
Note: Vertical and capped bars are standard deviations of the means of 4 replications.

During the second experiment was that submergence treated plants appeared greener and lessdisease contaminated than those of non-submergence ones. There was also a significant in seeddiscoloration from the submerged plants than that in the non-submerged treatments (Fig. 7).

Fig. 7. Effects of submergence (S0 = control, no submergence,
S1 = 15-day submergence) and residue treatments (R0 = without residue incorporation;
R1 = incorporation of burnt residue; R2= incorporation of fresh residue)
on occurrence of seed discoloration of IR64-Sub1 rice at harvest
Note: Vertical and capped bars are standard deviations of the means of 4 replications. Columns with the same letter(s) inside
are not significant difference by Duncan Multiple Range Test at 5% level.

684. GENERAL DISCUSSION AND CONCLUSIONSSurvival under completely submerged conditions of 3-day seedlings IR64-Sub1 rice might be notlonger than 3 weeks. In the dark conditions, imposed to mimic the natural condition of water
turbidity, there was a low probability of survival after 15 days. This is similar to the findings of (Daset al., 2009, Singh et al., 2009, Bailey-Serres et al., 2010) for older plants >21 days after emergence.
In the field there may be better survival because plants would receive scattered natural sunlight.
Submergence delayed harvest by twenty days and affect yield relative to the control similar to otherstudies (Das et al., 2009, Singh et al., 2009, Bailey-Serres et al., 2010). The results indicate that a 15
day flood on starting 3 DAE on IR64-Sub 1would still significantly reduce yield due to seedlingmortalities of 70-80% (Fig. 1 and 5). Further testing of the survival of high yielding rice cultivars
under field condition is required.
Submerged rice plants elongated more in contrast to those of non-submergence, this is
surprising because IR64-Sub1, like other high yielding rice cultivars, uses the quiescence strategy
for survival under completely flooded conditions (Voesenek and Bailey-Serres, 2009; Nishiuchi et al.,2012). The presence of Sub1 should limit the elongation response (Bailey-Serres et al., 2010). It isalso possible that early flooded rice seedlings might result in more frequency of lodging at harvest.
The incorporation of the rice straw stubble and rice straw ash improved the level of riceseedlings survival (Fig. 5). This was unexpected because fresh rice straw incorporation typicallycauses seedling mortality. It is not clear why rice straw stubble or ash improved survival rates. The
residue treatments did not affect the EC or pH of the water, rather the depth of water was more
important probably through dilution. The ash had a significant effect relative to the othertreatments on yield. Rao et al. (1976) found that the incorporation of fresh straw cause theimmobilization of mineral N and subsequently effected yield. This could be overcome if the planting
was delayed by 15 days to all the straw to begin decomposing and subsequent mineralization of theorganic nitrogen pool.
In conclusion, IR64-Sub1 can survive a 3 week-duration of complete submergence in darkness
beginning 3 days after emergence, but there is significant mortality. This indicates that early season
flooding could still have a significant effect on yield if growers are unable to replant. The
incorporation of rice straw or ash increased the survival rate relative to the control. At harvest theash had the greatest effect on yield when the rice under both flooding treats. The management ofrice straw is an important factor for intensive rice production systems whilst incorporation of fresh
residue boosted survival it also had a negative impact on yield.

ACKNOWLEDGEMENT
This research was undertaken under project “Climate change affecting land use in the Mekong
delta: adaptation of rice-based cropping system” (CLUES), funded by Australian Federal
Government Australian Center for International Agricultural Research.

REFERENCES
Bailey-Serres, J., Fukao, T., Ronald, P., Ismail, A., Heuer, S. and Mackill, D. (2010). Submergence tolerant rice:
Sub1's journey from landrace to modern cultivar, Rice, 2: 138-147.
Bird, J.A., Horwath, W.R., Eagle, A.J. and Kessel, C. (2001). Immobilization of fertilizer nitrogen in rice: effects ofstraw management practices. Soil Science Society of America Journal, 65: 1143-1152.

69

Das, K.K., Panda, D., Sarkar, R.K., Reddy, J.N. and Ismail, A.M. (2009). Submergence tolerance in relation tovariable floodwater conditions in rice, Environmental and Experimental Botany, 66: 425-434.
Dobermann, A. and Fairhurst, T.H. (2002). Rice straw management, Better Crops International, 16: 7-11.
Gadde, B., Menke, C. and Wassman, R. (2009). Rice straw as a renewable energy source in India, Thailand and thePhilippines: Overall potential and limitations for energy contribution and greenhouse gas migration, Biomass
and Bioenergy, 33: 1532-1546.
IRRI. (1998). Standard evaluation system for rice. IRRI, Los Banos, Laguna, Philippines.
Nishiuchi, S., Yamauchi, T., Takahashi, H., Kotula, L. and Nakazono, M. (2012). Mechanisms for coping withsubmergence and waterlogging in rice, Rice, 5:2.
Ponnaperuma, F.N. (1984). Straw as a source of nutrients for wetland rice. In Organic matter and rice. pp: 117-137.
IRRI. Philippines.
Ruensuk, N., Mongkonbunjong, P. and Inthaleang, W. (2010). Effect of rate of straw incorporation into the soils on
rice growth and yield. World Congress of Soil Science, Soil Solution for a Changing World. Brisbane,
Australia, pp. 140-142.
Rao, D.N. and Mikkelsen, D.S. (1976). Effect of Rice Straw Incorporation on Rice Plant Growth and Nutrition.
Agronomy Journal, 68: 752-756.
Septiningsih, E.M., Pamplona, A.M., Sangchez, D.L., Neeraja C.N., Vergara G.V., Heuer, S., Ismail, A.M. andMackill, D.J. (2009). Development of submergence-tolerance rice cultivars: the Sub1 locus and beyond, Annalsof Botany, 203: 151-160.
Singh, S., Mackill, D.J. and Ismail, A.M. (2009). Responses of SUB1 rice introgression lines to submergence in thefield: Yield and grain quality, Field Crops Research, 113: 12-23.
Tuyen, T.Q. and Tan, P.S. (2001). Effects of straw management, tillage practices on soil fertility and grain yield ofrice, Omonrice, 9: 74-78.
Voesenek, L.A.C.J. and Bailey-Serres, J. (2009). Genetics of high-rise rice, Nature, 460: 559-560.
Xu K., Xu, X., Fukao, T., Canlas, P., Maghirang-Rodriguez, R., Heuer, S., Ismail, A.M., Bailey-Serres, J., Ronald,
P.C. and Mackill, D.J. (2006). Sub1A is an ethylene-response-factor-like that confers submergence tolerance torice, Nature, 442: 705-708.

70Effects of Alternate Wetting and Drying Irrigation and Phosphorous Fertilizer Rates on
the Growth and Yield of Rice in Double Rice Cropping System at An Giang Province
Pham Phuoc Nhan1*, Le Van Hoa1, N. T. Giang1, T. P. Tuong2, Ben Macdonald3
1

College of Agriculture and Applied Biology, Can Tho University, Viet Nam
2
International Rice Research Institute, Philippines
3
CSIRO Agriculture Flagship, Canberra, Australia
Email*: ppnhan@ctu.edu.vn
ABSTRACT

Alternate wetting and drying (AWD) irrigation is a water saving irrigation technique in rice production and can be
an important adaptation strategy under climate change where water scarcity may become more prevailing. The
suitability of AWD in acid sulphate soils (ASS) has not been tested. There is a possibility that the drying cycles of
AWD may enhance oxidation and acidification. The drying cycles may also reduce the phosphorous availability to theplants. Rice irrigated with AWD may require more P fertilizer than under continuous flooded condition. In highly
intensive cropping areas of the Mekong river delta of Viet Nam, such as An Giang province, farmers have appliedhigh doses of P fertilizer to compensate for the P-binding of toxicities associated with ASS such as aluminum andiron. Excess of P, on the other hand can lead to phosphorous accumulation over time. This may reduce therequirement for P fertilizer input, even under AWD irrigation. This study investigated the interactive effects of AWD
irrigation and phosphorous rates on on water use, soil properties, rice growth and yield in double rice system grownon ASS in Ta Danh, An Giang, for 3 consecutive crops from March 2012 to June 2013. AWD application reduced
about 50% of irrigated water quantity and increased EC value of field water in contrast with continuous flooded offarmers’ practice. Difference in water management strategies and phosphorous levels showed no impact on pH
values of field water, rice growth, biomass accumulation, and rice yield in 3 continuous cropping seasons. Without or
reduced phosphorous fertilization varied the availability of phosphorous in the soil for at least 2 crops that could
waste farmers’ resource and create environmental issues. Obtained data promote the application of AWD irrigationand less phosphorous use for rice production in acid sulphate soil to improve resource management and local
farmers’ adaptation under impact of climate change.
Keywords: Acid sulphate soil, climate change adaptation, Mekong river delta, phosphorous, rice yield,
water saving.

1. INTRODUCTION
Rice is one of among majority crops to be grown to feed billion of people around the world(Grierson et al., 2011). About half of the world population depends on rice as staple food (Timmer,
2010) and rice is the most important crop in Viet Nam, particularly in the Mekong river delta (MRD)
where it accounts for more than 80 percents of rice export of the country (De, 2008).
Phosphorous (P) is one of three macronutrients and among 17 essential elements required forplant growth (Raghothama, 1999). P involves in many metabolic processes of plants such as energy
generation, nucleic acid synthesis, photo-synthesis, glycolysis, respiration, membrane synthesis andstability, enzyme activation/inactivation, redox reactions, signaling, carbohydrate metabolism, andnitrogen ﬁxation. On dry weight basis, P level in plant materials ranges from 0.05 to 0.50% (Vance etal., 2003) which exceeds the P concentration in the soil solution about 2000 folds (Schachtman et al.,1998). In arable land around the world, about 30 - 40% area is limited by P availability (von Uexküll
and Mutert, 1995). Application of P-containing fertilizers is usually the recommended treatment forenhancing soil P availability and stimulating crop yields (Vance et al., 2003). P limiting condition71

can lead to alterations in root architecture, carbon metabolism, exudation of organic acids, gene
expression, etc. (Vance et al., 2003).
In many soil types, P is abundant but presents in unavailable forms for plant and P is usuallyconsidered as limiting element for plant growth and development, particularly in acid soils with highpresense of aluminum and iron (Vance et al., 2003). Unfortunately, more than 50% of the MRD isoverlaid with acid sulphate soil (ASS) with low pH, high concentration of soluble aluminum andferrous ions (Foy et al., 1978), which are highly toxic to plants including rice (Becker and Asch, 2005;
Panda et al., 2009). P availability is enhanced by phosphate solubilizing bacteria via lowering soil
pH, producing organic acids, and mineralizing organic P (Awasthi et al., 2011). In addition,
mycorrhiza also plays an important role in plant P nutrition (Smith et al., 2011) and using microorganisms to improve plant acquisition of phosphorous appears to be a potential prospect(Richardson, 2001). Recently, high yield rice under P deficiency with Pup1 gene has been developed(Chin et al., 2011).
Fertilizer recommendation for the MRD was developed about 3 decades ago, mostly for single
crop of rice. Since then, farmers in many parts of the MRD have increased the cropping intensity to 2
or 3 crops/year. In ASS, farmers tend to use high rates of P, from 70 to 90 kg P2O5 ha-1, to reduce thenegative effects of ASS-toxicities. With most of residues either burned in the fields or ploughed into
the soil, apart from the amount of P in the grains, which are removed from the fields, the major partof phosphorous fertilizer is returned to the soil. Through the years, there might be substantial
accumulation of P in the soil. In other words, excessive phosphorus fertilization has occurred. We
hypothesized that farmers in ASS areas in the MRD can reduce P fertilizer rates withoutcompromising rice growth and yield.
The normal practice of rice production in MRD is keeping the field continuously flooded. This
practice requires high volume of water and actually supplies more water than the requirement ofrice plants and the field need (Guera et al., 1998). In many parts of the MRD, farmers experiencewater scarcity for irrigation the dry season rice crops. This water scarcity may become more
prominent with the eminent climate change. Alternate wetting and drying irrigation (AWD) has
been shown a viable irrigation technique to help farmers cope with water scarcity and has been
widely practiced in “normal” soil and water conditions In ASS, there are some concerns that thedrying cycles of AWD may exposed the soil to oxidation, which may lead to increased acidification.
There has not been systematic investigation on the use of AWD in ASS.
AWD has been proven a robust technology. It did not require any adjustment in N fertilizer
management compared with continuous flooded conditions (Cabangon et al., 2004; Cabangon et al.,2011). The effects of AWD on P fertilizer management has, however, not investigated. AWD exposes
the soil to temporary aerobic conditions, which may limit the bioavailability of phosphorus,
especially if soil is affected by salinity or acidity. Plants under AWD may need more P than thoseunder continuous flooding. Optimum P fertilization under AWD and continuously flooded conditions
on ASS need to be identified.
The aim of this study was to examine the effects of AWD irrigation in combination withphosphorous fertilizer rates on water use, chemical properties of soil and field water and yields ofrice grown on ASS in a double rice cropped areas of the MRD.

2. MATERIALS AND METHODS
2.1. Study site
Experiment was done at Ta Danh village, Tri Ton district, An Giang province, from March 2012 toJune 2013. The experimental site is a typical annually flooded area of An Giang province. Local

72

farmers usually culture double rice crops annually during (Winter-Spring and Summer-Autumn; see
Fig. 1) because their fields are located outside the flood protection area where triple rice cropping
systems are grown. During the wet season (AW) fields outside the flood protection area are left fallow
and inundated until the on-set of the dry season (WS). The weather data were collected at the An
Giang Provincial Station located 20 km away from the study site. Farmers always burn rice residue
after harvest and the average phosphorous application at study site is around 40 kg P2O5 per ha.

AW

SA

WS

SA
32

30

200

28

O

Rainfall and Evaporation (mm)Rainfall
Evaporation
Temperature

150
26
100
24
50

22

0
b ar
Fe M

Temperature ( C)

250

20
r
y
n
Ap Ma Ju

J

ul

p
g
ct ov ec
Au Se O N
D

J

an

b ar
Fe M

r
y
n
Ap Ma JulJu

Month (2012-2013)

Fig. 1. Cropping calendar, temperature, rainfall and evaporation
The soil is classified as Typic Sulfaquept (USDA) or Epi-Orthi-Thionic Fluvisol (IUSS WRB
2006) and is commonly known as an acid sulphate soil. The top soil is mixed with decomposedorganic matter and has a field pH <5.5; soil is less developed, ripe to nearly ripe down to 500 mm
depth. The sulphuric horizon with jarosite mottles occurs from 250-800 mm depth with a pH <4.5.
2.2. Treatments and lay out
The experiment was arranged in split-plot design with 3 replications. Main plot factor waswater management (W) and the sub-plot factor was P rates.
The main plot factor W had 3 levels:
WCF = Farmer Practice- continuously flooded irrigation water management. The field is always
flooded, irrigate when water level in the field is about 1 cm
WAWD5 = AWD irrigation water management. Irrigate when the water level in the field is 5 cm
below the soil surface.
WAWD15 = AWD irrigation water management. Irrigate when the water level in the field is 15 cm
below the soil surface.
In the AWD plots, a water tube (Tuong et al., 2009) water depth measuring device in form of a
PVC tube approximately 15 cm diameter x 25 cm depth is installed to a depth of 15 cm in the field73

at 20 days after direct sowing. The buried parts of these tubes were holed by 5 cm diameter witha distance at 5 cm from each other allowing water to percolate whenever field water increases
or decreases.
The sub-plot factor P had 3 levels
P0 = No P fertilizer
P20 = 20 kg P2O5 per ha
P40 = 40 kg P2O5 per ha
There were 27 subplots (5 x 8m). Small bunds (20 cm height and 30 cm width) were built
between subplots. For the two main plot of AWD the bunds (30 cm height and 40 cm width) werebuilt with the insertion of plastic film in the middle to a depth of 30 cm from soil surface to minimize
lateral water movement.
2.3. Management
The residue from previous crop was burnt and ash and remaining residue was incorporatedusing a rotary hoe. After tillage was completed, the field was flooded until the soil was saturated.
The field surface was then leveled off. Rice cultivars were OM4218 and OM2517 (certified seed) for
SA and WS, respectively and drum seeding was used to apply the seed at the density of 100 kg per
ha.Pre-germination herbicide, Sofit was used to control weeds.
Nitrogen and potassium were the fixed amounts as farmer practice (120 kg N and 60 kg K2O perha, respectively). One third of total nitrogen was applied at 10, 20 and 35 days after crop
establishment, and 30 kg K2O ha-1was applied at 20 and 35 days..
Water in the fields was controlled according to water treatments. Whenever ground water level
reached the critical depth of soil surface at 5 and 15 cm, water from nearby canal was pumped
mechanically into the respective treatments up to about 5 cm above the soil surface. The volume ofirrigation water that was applied to each treatment was measured by a 90 mm flow meter.
2.4. Parameters monitored
2.4.1. Water
Water level in each plot was measured every three days inside AWD tools using a tape measure
and the height was referenced to the ground surface.The water inside the AWD water tube was
sampled every 6 days for pH and EC measurement, using a hand held meter (FG2 and FG3 Fieldkit,
Mettler Toledo, Switzerland). At each measurement, water inside the water tube was purged byhand bailing prior to sampling. During the 2013 WS, the concentration of aluminum and ferrous ionsin the ground water was determined in the field using test strips (Al 1.10015.0001 and Fe
1.10004.0001 MERCK, Germany).
2.4.2. Soil
Available phosphorous was analysed after harvesting. Soil samples were collected by a using asoil core of 50 mm diameter and 200 cm depth at the centre of each plot. The soils were stored inplastic bags and transported bag to the laboratory for analysis. On receipt the soils were oven-driedat 60 degrees Celsius for 24 hours. Each soil sample was crushed, ground and sieved and the fraction> 2 mm was discarded. The soils at the site are acid sulfate soils and characteristically acidic andcontain iron and aluminum mineral. In soils such as these the Bray-Krutz method (Bray and Krutz,
1945) is the most suitable for the determination phosphorus. The input of nutrients from flood
sediments was determined after the 2012 annual wet season. Three plastic sheets (4 x 5 m) sheets74were placed in the field before the start of the flooding season. Immedaitely after the flood water
receded, the sediment was collected, weighed and samples for dried weight at 60oC in an incubator.
Total and available phosphorous was determined.
2.4.3. Plant
Plant biomass at flowering and physiological maturity was done by collection of 0.5 m2 at eachstage, made sub-sample and dried until constant weight at 60oC. For yield component: harvested 0.5
m2 at physiological maturity (about 5 days before harvesting) for counting the number of panicles/m2,
filled and unfilled spikelets, 1000 grains weight, and grain moisture content. Grain yield wasmeasured from 5 m2 at harvest, converted to tons per ha at 14% of moisture content.
2.5. Data analysis
Microsoft Excel and SPSS 16.0 were employed for data comparison between treatments, factors
and their interaction for each crop season.

3. RESULTS
3.1. Water quantity
During the crop systems when the ground water reach trigger values of -5 and -15cm thetreatments where irrigated (Fig. 2). Figure 1 shows that the use of water was not uniform across thefield, treatment WAWD15 lost water through seepage and evapo-transpiration at a greater rate than
WCF and WAWD5 (Fig. 2). Three consecutive crops were cultured but only two of them were recorded forvolume of pumped water quantity. Difference in water quantity between water management
treatments is presented in Table 1.

Fig. 2. Dynamics of field water in different water management treatment
in 2013 WS crop, from November 2012 to February 2013
at Ta Danh, Tri Ton, An Giang
Note: Vertical and capped bars indicate means of 3 measurements

75

Table 1. Effects of water management on the volume of pumped water. Total water
volume equates to the rainfall and the irrigated water inputs
Irrigation water volume (m3/ha)

Total water volume (m3/ha)

2013 WS

2013 SA2013 WS

2013 SAWCF

2,519a2,061 a

4135

5052

WAWD15

1,183b

1,433 b

2799

4424

WAWD301,167b

1,292 b

2783

4283

F

41.9**

65.4**

Water
management

Note: In a column, means followed by the same letter (a, b) are not significantly different at the 1% level by Duncan’s multiple
range test.

AWD treatments saved more than 50% of irrigated water for both cropping seasons compared with
continuous flooding treatment. The difference between the two AWD regimes was not significant. This
result also reveals that field water management as farmers' practice waste fresh water and might
contribute to increase the risk of seawater intrusion for the downstream areas in MD. During the 2013
WS crop rain contributed additional 1600 m3 water and in the 2013 SA crop 2991 m3.
3.2. pH and EC value of field water
Experimented factors caused very little change in pH of field water in 3 continuous crops but
data from 2013 WS chosen for presentation (Fig. 3). At no time did the pH drop below 5.5 and theconcentration of Fe2+ and Al3+ was below the detection limit of each test (<3 and <10 mg L-1
respectively). These outcomes also demonstrated that if there were not much change in waterchemistry characteristics of the field water, AWD technique is a promising application even on acidsulphate soil areas as in MD to save natural fresh water resource in rice production.

Fig. 3. Effect of water management on pH of field water in 2013 WSfrom November 2012 to February 2013 at Ta Danh, Tri Ton, An Giang
The EC of the field water during the WS season increased during the cropping seasons and theEC of WAWD5 and WAWD15 was greater that the WCF (Fig. 4). Variation in phosphorous application
showed no effect on pH and EC values of field water which were not presented here.

76Fig. 4. Effect of water management on EC of field water in 2013 WSfrom November 2012 to February 2013 at Ta Danh, Tri Ton, An Giang
These outcomes also demonstrated that if there were not much change in water chemistrycharacteristics of the field water (see below), AWD technique is a promising application even on acidsulphate soil areas as in MD to save natural fresh water resource in rice production.
3.3. Above ground biomass and rice yieldIn 3 consecutive crops, 2 experimented factors caused no significant difference in biomass
partitioning among leaves, culm, and panicles between treatments.. The water management orphosphorous levels did not contributed the observe differences in the above ground biomass (Table2). It was also recognized that biomass accumulation was higher in WS than in SA.
Table 2. Effects of water management and phosphorous levels
on above ground biomass at physiological maturity
Total biomass (tons/ha)
Factor2012 SA2013 WS

2013 SA

WCF

13.5

15.3

12.6

WAWD15

13.8

15.6

12.9

WAWD30

13.5

15.4

12.0

P0

14.0

16.0

12.6

P20

14.1

15.1

12.1

P40

12.7

15.1

12.8

F (W)

0.17ns

0.09ns

0.79nsF (P)2.77ns

0.97ns

1.61nsF (WxP)

0.79ns

1.38ns1.14nsWater management (W)Phosphorous level (P)Note: ns: non-significanceLocal farmers always chose suitable rice cultivars for their cropping seasons which explain ununi-formed cultivars among crops at the same experimental location leading to difficulty in
elucidation of rice yield difference between crops. However, water management or phosphorous77

levels showed no impact on rice yield in 3 consequent crops (Table 3). The combination of less waterand phosphorous use for rice production in double rice areas can be approaching strategy for
improvement of local farmers’ income.
Table 3. Effects of water management and phosphorous levels on rice yield
FactorYield (tons/ha)
2012 SA2013 WS

2013 SA

Water management (W)
WCF
WAWD5
WAWD15

6.3
6.6
6.2

7.4
7.7
7.3

6.7
6.5
6.5

Phosphorous level (P)
P0
P20
P40

6.4
6.3
6.5

7.6
7.5
7.4

6.0
6.9
6.7

0.50ns
1.77ns1.26ns0.53ns0.26ns
0.58ns0.29 ns
3.02 ns
0.75 ns

F(W)
F(P)
F(WxP)
Note: ns: non-significance3.4. Availability of phosphorous in the soil
Normally, less phosphorous application to the soil would lead to its phosphorous reduction but
the analysis of available phosphorous in soil in 2 crops at the same experimental site shown nodifference for both factors, water management or phosphorous applied levels, even for the null
phosphorous fertilized treatment (Table 4).
Before the first crop establishment, the total phosphorous and its availability in the soil were0.13 ± 0.01% and 6.80 ± 1.79 mg P/kg of dried soil, respectively. After harvesting, the available
phosphorous in the soil was comparable to its value at starting point (2012 SA). At the highest dose,
40kg of P2O5 per ha did not contribute to build up the availability of phosphorous in the soil after
consequent cropping seasons (Table 4).
Table 4. Effects of water management and phosphorous levels
on available phosphorous in the soil after harvesting each crop
Factor2012 SA2013 WS

Water management (W)
WCF
WAWD5
WAWD15

8.61
8.58
9.73

11.32
12.96
11.89

Phosphorous level (P)
P0
P20
P40

9.46
8.69
8.78

12.59
9.83
13.76

0.46ns0.19ns0.43ns0.19ns1.13ns0.33nsF (W)F (P)F (WxP)
Note: ns: non-significance78

Available P in soil (mg P/kg dried soil)

During the wet season flood a significant amount of nutrients are supplied to the soils (Table 5)
It is not clear how much of the supplied P will be mineralized during the cropping season andconverted to plant available P.
Table 5. Nutrients supplied to the non-flood protected areas
during the wet season floods
Replicate
Nutrients in dried sediment

Average1

2

3

Dry sediment (kg/ha)

9,511

12,694

10,648

10,951

Total nitrogen (kg N/ha)

30.4

40.6

36.2

35.7

Total phosphorous (kg P2O5/ha)

11.4

15.2

11.7

12.8

Total potassium (kg K2O/ha)

82.7

93.9

56.4

77.7

NH4+ (kg N/ha)

0.885

1.273

0.769

0.976

NO3- (kg N/ha)

0.027

0.096

0.063

0.062

Available P (kg/P2O5)

0.129

0.188

0.179

0.165

Available K (kg K/ha)

0.167

0.233

0.174

0.191

4. DISCUSSION
In the double rice area the application of AWD to 15 cm could reduce the application of irrigationwater by ~50% in the WS and SA seasons. This represents a significant saving in both labour andcapital for the farmer because the pumping time would be reduced by 50%. The application of AWD
in the acid sulfate soil areas of An Giang did not affect the paddy water pH, and Al3+ or Fe2+
concentrations. This indicates that the AWD did not promote further profile oxidation and acidity
generation. But under drier conditions, predicted to occur under future climate change scenarios,
when the ground water level is allowed drain to ~30 cm then profile oxidation and acidification may
become a significant issue. If AWD is applied broadly in acid sulphate soil double rice production
areas then it is recommended that farmers and extension offices monitor and evaluate the potential
profile oxidation. Further, the application of lime may be warranted to increase the buffering
capacity of the surface soil.
The phosphorous trial results where no fertilisation in 3 consecutive crops had no impact on rice
yield in free-dyke area of Ta Danh. This indicates that there has been soil phosphorous
accumulation from the application of phosphorous fertilizer and the yearly inputs of P with the flood
sediments. In double rice cropping areas farmers apply 40 kg P2O5 ha-1 which is barely enoughfertilizer to replace the P that is removed in the grain. This suggests that the flood sedimentsupplied P augments the applied fertilizer. Changes to the supply of flood sediments caused byclimate change adaptations such as dyke and dam constructions would cause farmers to increase
synthetic P applications.
The increased in available P after the end of the WS season is due to the increased EC of the
paddy water. During the dry season the irrigation source canal solute concentration increases
through evap-concentration and inputs of ground water. This water would contain sulphate which issubsequently reduced in the paddy soil releasing P. A similar process has been found to regeneratesalt marsh PO42- through mineralization due to SO42- metabolism (Portnoy and Giblin., 1997a;
Portnoy and Giblin, 1997b; Williams et al., 2014).

79

5. CONCLUSIONSThe combination of AWD irrigation and maintenance of phosphorous fertilization thru flooding
is perspective adaptation for farmers in double rice area in An Giang. Farmers could makesignificant water savings, which would improve farm profitably and hence climate change resilience.
The P balance at the site is dependent on the delivery of P within the flood water sediments. If
climate change adaptations or future developments change the supply of P then farmers will need toincrease the application of synthetic P to the crop.
Overall, there is significant potential for farmers in the double rice cropping areas in An Giangand possibly other provinces to apply AWD and apply 40 kg P2O5 ha-1 P without yield penalty.
Farmers will need to monitor and adjust AWD and P management if acidification and P deficiencies
become an issue.

ACKNOLEDGEMENT
The work was done under scope of “Climate change affecting land use in the Mekong delta:
adaptation of of rice-based cropping system” (CLUES), funded by ACIAR.

REFERENCES
Awasthi, R., Tewari, R. and Nayyar, H. (2011). Synergy between plants and P-solubilizing microbes in soils: effects
on growth and physiology of crops, International Research Journal of Microbiology, 2: 484-503.
Becker, M. and Asch, F. (2005). Iron toxicity in rice-conditions and management concepts, Journal of Plant
Nutrition and Soil Science, 168: 558-573.
Bray, R. H. and Kurtz, L. T. (1945). Determination of total, organic, and available forms of phosphorus in soils, Soil
Science, 59: 39-45.
Cabangon, R. J., Tuong, T. P., Castillo, E. G., Bao, L. X., Lu, G., Wang, G. H., Cui, Y., Bouman, B. A. M., Li, Y.,
Chen, C. D. and Wang, J. Z. (2004). Effect of irrigation method and N-fertilizer management on rice yield,
water productivity and nutrient-use efficiencies in typical lowland rice conditions in China, Paddy and Water
Environment, 2: 195-206.
Cabangon, R. J., Castillo, E. G. and Tuong, T. P. (2011). Chlorophyll meter-based nitrogen management of rice
grown under alternate wetting and drying irrigation, Field Crop Research, 121: 136-146.
Chin, J. H., Gamuyao, R., Dalid, C., Bustamam, M., Prasetiyono, J., Moeljopawiro, S., Wissuwa, M. and Heuer, S.
(2011). Developing rice with high yield under phosphorus deﬁciency: Pup1 sequence to application, Plant
Physiology, 156: 1201-1216.
Foy, C.D., Chaney, R.L. and White, M.C. (1978). The physiology of metal toxicity in plants, Annual Review ofPlant Physiology, 29: 511-566.
Grierson, C. S., Barnes, S. R., Chase, M. W., Clarke, M., Grierson, D., Edwards, K. J., Jellis, G. J., Jones, D. J.,
Knapp, S., Oldroyd, G., Poppy, G., Temple, P., Williams, R. and Bastow, R. (2011). One hundred importantquestions facing plant science research, New Phytologist, 192: 6-12.
Guera, L. C., Bhuiyan, S. I., Tuong, T. P. and Barker, R. (1998). Producing more rice with less water from irrigatedsystem, Discussion Paper Series, IRRI, 29.
De, N. N. (2008). Rice Culture. Mekong Delta Development Research Institute. Can Tho University Publisher.
Panda, S. K., Baluska, F. and Matsumoto, H. (2009). Aluminum stress signaling in plants, Plant Signaling and
Behavior, 4: 592-597.
Portnoy, J. W. and Giblin, A. E. (1997a). Effects of historic tidal restrictions on salt marsh sediment chemistry,
Biogeochemistry, 36: 275-303.
Portnoy, J. W. and Giblin, A. E. (1997b). Biogeochemical effects of seawater restoration to diked salt marshes,
Ecological Applications, 7: 1054-1063.
Raghothama, K. G. (1999). Phosphate acquisition, Annual Review of Plant Physiology and Plant Molecular
Biology, 50: 665-693.

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Richardson, A. E. (2001). Prospects for using soil microorganisms to improve the acquisition of phosphorus byplants, Australian Journal of Plant Physiology, 28: 897-906.
Schachtman, D. P., Reid, R. J. and Ayling, S. M. (1998). Phosphorus uptake by plants: from soil to cell, Plant
Physiology, 116: 447-453.
Timmer, C. P. (2010). Behavioral dimensions of food security, Proceedings of the National Academy of Sciences of
the United States of America, Early Edition 1-6.
Tuong, T. P., Bouman, B. A. M. and Lampayan, R. (2009). A Simple Tool to Effectively Implement Water Saving
Alternate Wetting and Drying Irrigation for Rice, ICID newsletter 2009/4:5.
Vance, C. P., Uhde-Stone, C. and Allan, D. L. (2003). Phosphorus acquisition and use: critical adaptations by plants
for securing a non-renewable resource, New Phytologist, 157: 423-447.
von Uexküll, H. R. and Mutert, E. (1995). Global extent, development and economic impact of acid soils, Plant andSoil, 171: 1-15.
Williams, A. A., Lauer, N. T. and Hackney, C. T. (2014). Soil phosphorus dynamics and saltwater intrusion in a
Florida Estuary, Wetlands, 34: 535-544.

81

Developing A Mathematical Model to Measure The Spreadingof Soil Moisture under Drip Irrigation
Ngo Thi Dung1*, Nguyen Van Dung1*, Hoang Thai Dai2, Le Duc Vinh3
1

Faculty of Land Management, Viet Nam National University of Agriculture, Ha Noi, Viet Nam
2
Faculty of Environment, Viet Nam National University of Agriculture, Ha Noi, Viet Nam
3
Faculty of Information Technology, Viet Nam National University of Agriculture, Ha Noi, Viet Nam
Email*: nvdung1@vnua.edu.vn
ABSTRACT
Water plays a crucial role to crops. Therefore, determining the suitable time for irrigation and the quantity ofwater needed is very important in the whole irrigating system and is a key factor to the crop growth and production. In
alluvial soils of the Red River delta, the field capacity (βđr) is 32.41%; if the average moisture of soils before irrigation3-1(β0) 28.42%, the standard irrigation was calculated as M = 221.84 m ha . Based on experiments, the function of
0.057362
2
moisture before irrigation was found to be g(y) = 25.28203(y-1.5), with the coefficient of determination R =-51.240306
0.933298; while the function of moisture at the end of drip irrigation was F(x;y) = 8.9918.10 (25 - x)
(25 2.311185
0.057362
2
y)+ 25.28203(y-1.5), with the coefficient of determination R = 0.946491. One day after finishing the drip
1.269
1.83194
0.057362
irrigation, the function of moisture became F(x;y) = 0.0003325(25 - x)
(25 - y)+ 25.28203(y-1.5)
with
2
R = 0.9256. The function can be used to simulate soil moisture as model validation resulting in small errors (5-10%).
Keywords: Soil moisture, the function of moisture content.

1. INTRODUCTION
Water plays a very important role for agricultural production. Crop needs a lot of water;
however, each type of crop (at different growth stages) requires a different amount of water for
growth, and irrigation is needed in order to compensate the lack of water. Therefore, determining theexact moment of irrigation and the amount of water needed is significant and important for crop
growth and yield (Nguyen, 2008).
In addition, the horizontal permeability and vertical permeability processes during irrigation
depend on the irrigation discharge (Shrivastava et al., 2011). For drip irrigation, these processes areaffected by the distance between sprinklers (Abdallah et al., 2011).
There have been several studies based on the Richard’s equation which represents onedimensional vertical flow to predict the movement of water in the unsaturated soil. Nguyen (1994)
has developed a general model of moisture transfer in the unsaturated soil area for all dryland crops
in the condition of changing upper and lower bounds. However, this model is one-dimensional
moisture equation in which the irrigation method, the root characteristics and crop density were nottaken into consideration. For one-dimensional moisture model studies of the unsaturated soil due tothe drip irrigation, several models were developed. For analytic methods, the simplified model in thehemispherical form for the stable unsaturated permeable form due to the drip irrigation in the hoseline/point style was used based on the assumptions of permeable in the hemispherical style andlinear solution. This model was developed by Bresler et al. (1971), Philip (1971), and Warrick (1974).
Nevertheless, some limitations exist in this model such as its assumption that the flow is stable, andthat the water pressure and permeability have an exponential relationship.
Healy and Warrick (1988) have successfully developed a model of unsaturated permeability inthe soil due to drip irrigation by applying the finite difference method for Richard dimensionless82

variant equations, but still, the method is complex because its output is humidity without units. The
group in Aristotle University of Thessaloniki (2001) has applied the finite difference method in theheterogeneous soil but the method is limited to one-dimensional mathematical problem.
In this paper, we introduce a mathematical model to build a function that determines the
moisture in the soil under drip irrigation. The aim is to find the coefficients of soil moisturespreading vertically and horizontally based on soil types, discharge, and density of crop torecommend an irrigation regime to obtain the high efficiency.

2. MATERIALS AND METHODS
2.1. Drip irrigation
Drip irrigation was carried out in the experimental room using 3 soil (Red river alluvial soil)
blocks each of of 40 x 40 x 40 cm in size. The soil blocks are covered around by the plastic to avoid
evaporation and seepage. One drip nozzle was positioned at the center of each soil block (Fig. 1). For
the drip system, PE main irrigation pipe with 25 mm in diameter and smaller branch pipe of 15 mm
were used. The nozzle discharge was 0.43 L hr-1.

Fig. 1. Drip irrigation in the soil block in the experiment room
2.2. Determination of physical and chemical characteristics of the soil
The physical and chemical characteristics of the soil were determined: pHKCl by pH meter,
OC% (Walkley and Black), P2O5 (Oniani), K2O by the photometer fire, N (Tiurin and Kononova). The
soil texture was measured by the Robinson suction method and the soil density measured by thecylinder method.
Soil moisture and field capacity were deterimned according to the percentage of the dry weight.
2.3. Determination of standard irrigation
The rrigation standards for each soil block were determined according to the following formula:M = 104.h.d. (βđr - β0). (m3 ha-1)
Where:
h: depth of the soil to be moistened (m)d: soil density (g cm-3)βđr: field capacity (% dry weight)
β0: moisture before irrigation (% dry weight)83

2.4. Monitoring criteria
To predict the distribution process of soil moisture before drip irrigation, immidiately after theirrigation and one day, two days and three days after the irrigation, five positions with a horizontal
spacing of 5 cm and at the depths of 0-5 cm, 10-15 cm, 15-20 cm, and 20-25 cm were fixed. Soilsampling was taken by hand drilling. Soil moisture was determined by the dried soil using theweight method.
2.5. Construction of mathematical model
The soil moisture function F(x,y) at position M(x,y) was formulated. The coefficients k, α, β, aand b of soil moisture function were determined by monitored data. Mathematical model ofthe spread of the moisture in the soil using was constructed using regression function in theMicrosoft Excel.

3. RESULTS ANG DISCUSSION
3.1. Physical and chemical characteristics of the soil and the standard irrigation
The soil used for experiment is clay soil, pHKCl: 7; OC: 1.92%; P2O5: 332 mg kg-1 soil; K2O: 55.3
mg kg-1 soil; N: 80.5 mg kg-1 soil
- The components of the grain: clay 5.4%; silt 40.3%; sand 54%- The soil density d = 1.39 g cm-3
- The field capacity βđr = 32.41%- The average moisture before irrigation β0 = 28.42%- Irrigation standard M = 221.84 m3 ha-1
3.2. Construction of a function to determine soil moisture
Soil moisture depends on many elements such as soil profile, the amount of irrigation water,
temperature, radiation, and specific time, etc. Based on the results of our experiment, we introduce
the following function to determine soil moisture:F(x,y) = f(x,y) + g(y)Where: y is the depth of soil layer,
x is the distance from the position where moisture is to be calculated to the irrigation
position on the vertical axis,
g(y): the function that determines the moisture before irrigation at M(x,y),f(x,y): the function that determines the additional moisture at M(x,y) after the irrigation
time (at the measured time),
F(x,y): the function that determines the moisture at M(x,y) after the irrigation time (atthe measured time),
Experimental data shows that the function g(y) has a linear form as follows:g (y) = a(y-1.5)b
Where 1.5 is the factor identifying natural moisture at the midpoint of the 0-5 cm layer (at the
midpoint of the 0-5 cm layer, y=2.5 then g(y) = a)
The function f(x,y) decreases when x increases, y increases and f(x,y) = 0 when x  x0 or y  y0
Where: x0, y0 are the maximum speading of moisture in horizontal and vertical directions
respectively.

84

Therefore, the function of f(x,y) is as follows:f (x, y) = k (x0-x) α (y0-y) β when 0 ≤ x ≤ x0; 0 ≤ y ≤ y0
f (x, y) = 0 when x> x0; y> y0
Where: k, α, β are positive constants depending on the physical and chemical properties ofthe soil.
The function f(x,y), as previously determined, is reasonable because when x increases, yincreases leading to the decrease of the function and when one of two parameter increases to thecritical levels, it will be cancelled, at that time the soil moisture at the position M(x,y) equals thenatural soil moisture before irrigation.
The function of F(x,y) in the range 0 ≤ x ≤ x0; 0 ≤ y ≤ y0 has the following form:αβF(x,y) = k(x0-x) (y0-y) + a(y-1.5)b
To define the function of F(x;y) we have to determine the coefficients k, α, β, a, b based on theexperimental data.
3.3. Defining the moisture function before irrigation g(y)The function of g(y) has the form: g(y) = a(y-1.5)ba; b are defined based on the observations of the moisture before irrigation
Table 1. The moisture before irrigation
Distance

Irrigation pointx = 5 cmx = 10 cm

x = 15 cm

x = 20 cm

0 - 5 cm

24.98

24.98

24.90

25.00

24.96

5 - 10 cm

28.87

28.89

28.85

28.88

28.91

10 - 15 cm

29.05

29.13

29.08

29.05

29.12

15 - 20 cm

29.67

29.66

29.59

29.57

29.56

20 - 25 cm

29.68

29.63

29.61

29.59

29.56

Depth

From Table 1, using regression function in the Microsoft Excel the moisture equation before
irrigation obtained depend on the weather condition and soil characteristics and this equation can beused to predict the moisture before irrigation:g(y) = 25.28203(y-1.5)0.057362
With the correlation coefficient R = 0.966073, the coefficient of determination R2 = 0.933298.
3.4. Defining the function of moisture increment f(x,y) and the moisture function F(x,y)
When irrigated, the soil moisture will increase into the depth and over the width of soil layer.
Based on this equation, crop density and soil characteristics the time of efficient irrigation withspecific sprinklers discharge can be pedicted:
The function of f(x;y) has the following formαβf(x,y) = k(x0-x) (y0-y) when 0≤x≤x0; 0≤y≤y0
f(x,y) = 0 when x>x0; y>y085

To determine the coefficients k, α, β in the case experimental results were obtained immediately
after irrigation, 1 day, 2 days, and 3 days after irrigation finished, we need to perform the following
transformations:
From the function f(x,y) = k(x0-x)α(y0-y)β take logarithm on both sides we get:ln[f(x,y)] = lnk+ αln(x0-x)+ βln(y0-y)Assuming that z = ln[f(x,y)] ; u = ln(x0-x) ; v = ln(y0-y); lnk = c
z = c + αu + βv this is a linear function of z with two variables u and v
To find c, α, β we perform the following steps:+ Determining the constants x0, y0: From the monitoring soil moisture experiment, we found x0 =y0 = 25cm+ Finding out the moisture increment f(x y) at position M(x,y)+ Finding the corresponding values z = ln[f(x,y)]; u = ln(x0-x); v = ln(y0-y)+ Using Regression function to find c, α, β in the case of soil moisture monitoring
3.4.1. Soil moisture immediately after the drip irrigation
Table 2. The moisture after drip irrigation finished
Distance

Irrigation pointx = 5 cmx = 10 cm

x = 15 cm

x = 20 cm

0 - 5 cm

32.84

32.41

29.77

28.57

27.23

Depth

5 - 10 cm

32.31

32.26

29.64

29.49

29.08

10 - 15 cm

30.78

30.72

29.53

29.38

29.33

15 - 20 cm

30.23

30.15

29.65

29.59

29.48

20 - 25 cm

29.73

29.69

29.63

29.60

29.57

After irrigation finished, soil moisture in the surface layer was higher than in the deeper layers(Table 2). At the point of irrigation, soil moisture in the first layer and in the layer of 20-25 cm was
32.82% and 29.73%, respectively. Soil moisture was found to decrease when the measured position
was farther from the irrigation point. The lowest moisture was 27.23%. Based on the variation of
humidity, by performing transformations and using Regression Software, the moisture content
function F(x,y) at position M(x,y) was identified with the coefficient of determination R2 = 0.946941.
The linear regression function for predicting the spread of soil moisture after irrigation is as follows:
F(x,y) = 8.9918.10-5(25 - x)1.240306(25 - y)2.311185 + 25.28203(y-1.5)0.057362
3.4.2. Soil moisture at one day after drip irrigation
Table 3. The moisture at one day after the drip irrigation
Distance

Irrigation pointx = 5 cmx = 10 cm

x = 15 cm

x = 20 cm

0 - 5 cm

30.48

30.42

28.04

27.12

25.82

Depth

86

5 - 10 cm

32.14

32.01

29.12

29.09

29.00

10 - 15 cm

31.98

31.85

30.02

29.58

29.41

15 - 20 cm

30.85

30.26

30.21

30.05

29.66

20 - 25 cm

29.74

29.72

29.64

29.60

29.57

One day after the irrigation, the soil moisture tended to increase at the second layer (32.14%)
and the third layer (31.98%), the last layer reached only 30.48% due to the impact of weather
conditions (Table 3). Meanwhile, at the third layer and the fourth layer the soil moisture did notshow any significant change as compared to itself before irrigation. After performing
transformations and using the Regression software the moisture content F(x,y) at position M(x,y)
was determined as follows:
F(x,y) = 0.0003325(25 - x)1.269(25 - y)1.83194 + 25.28203(y-1.5)0.057362
The coefficient of determination R2 = 0.9256, the linear regression function is well accepted.
3.4.3. Soil moisture at two days after drip irrigation
Table 4. Soil moisture at two days after the drip irrigation
Distance

Irrigation pointx = 5 cmx = 10 cm

x = 15 cm

x = 20 cm

0 - 5 cm

27.11

27.09

26.73

26.33

25.12

5 - 10 cm

29.02

29.01

28.95

28.93

28.90

10 - 15 cm

29.18

29.14

29.10

29.07

29.04

15 - 20 cm

30.22

30.08

29.98

29.76

29.65

20 - 25 cm

29.77

29.73

29.67

29.61

29.58

Depth

Two days after irrigation, soil moisture at layers 1, 2 and even layer 3 are all affected by theevaporation process of the surface layer as well as by the subsoil layer. When performing
transformations and using the Regression software, the coefficient of determination R2 = 0.559009
can be determined according to the following equation:
F(x,y) = 0.00211(25 - x)1.315452 (25 - y)0.940766 + 25.28203(y-1.5)0.057362
Therefore, using the assumptions to build the model is not satisfactory, and we need to further
study to take into account climate factors into the equation.
3.4.4. The moment after the drip irrigation finished for three days
Table 5. Soil moisture at three days after the drip irrigation
Distance

Irrigation pointx = 5 cmx = 10 cm

x = 15 cm

x = 20 cm

0 - 5 cm

25.01

24.98

24.95

24.95

24.95

5 - 10 cm

29.02

29.01

28.95

28.93

28.90

10 - 15 cm

29.18

29.14

29.10

29.07

29.04

15 - 20 cm

30.22

30.08

29.98

29.76

29.65

20 - 25 cm

29.77

29.73

29.67

29.61

29.58

Depth

Three days after the irrigation, the soil moisture in the topsoil layer decreased very fast. At theirrigation point the soil moisture was only 25.01%, much lower compared to the moisture content
after the irrigation finished (32.84% - Table 2). Performing transformations and using the software
to determine the moisture content F(x,y) at position M(x,y) in this case with the coefficient of87

determination R2 = 0.217065, the linear regression function will not give good prediction. The
function F(x,y) is as follows:
F(x,y) = 0.019181(25 - x)0.755575 (25 - y)-0.13168 + 25.28203(y-1.5)0.057362
From the monitoring data of the soil moisture at three days after irrigation shown in Table 5 we
can see that the measured results were similar to the moisture before irrigation, meaning that after
three days the soil moisture returns to its original state, and we need to make the next irrigation.
3.5. Validation
When using the data to setup the model in the section 3.4 we used only half of the experimental
data measured on the right (north - south) of the experimental plot. The experimental data obtained
from the left side was used to validate the objectivity of each model:- The moisture before irrigation g(y)Using the function g(y) = 25.28203(y-1.5)0.057362 to evaluate the errors between the corresponding
values of the function g (y) and the value of the reference sample at the moisture measuring point(soil moisture values at the left plot), we found that none of the relative error (ratio f) of all thesamples exceeded 5%. This shows that using the function g(y) = 25.28203(y-1.5)0.057362 to determine
the characteristics of soil moisture before irrigating is very good (Appendix table 1).
- The moisture determination functions immediately after irrigation and one day after
irrigation F(x;y)
Validation of the level of errors between the corresponding values of moisture content
determination function F(x,y) at position M(x,y) with the values of control samples is the assessmentof the total error at this position of the moisture content increases f(x,y) compared with measured
values of moisture increase and errors between the experimental and the corresponding error of the
moisture function before irrigation g(y) compared with the value of humidity in M(x,y) of the control
sample. The results show that the errors between the modeled values and the values of the control
data (ratio f) were mostly less than 10%. Only 2 samples had error exceeding 10% but not greaterthan 15%. Therefore, using the function F(x,y) to determine the soil moisture immediately after theirrigation and one day after the irrigation is acceptable (Appendix table 2 and 3).

4. CONCLUSIONSIn the Red River alluvial soil condition, the maximum field capacity βdr = 32.41%; the averagesoil moisture content before irrigation was β0 = 28.42% dry weight, then the standard irrigation was
M = 221.84 m3 ha-1.
From the experiments on the Red River alluvial soil with the drip irrigation technique, amathematical model on the spread of the soil moisture was preliminary constructed. A function for
determining the soil moisture at the position M(x,y) in the vertical and horizontal directions wasestablished:+ The function of moisture content before irrigationg(y) = 25.28203(y-1.5)0.057362; With the correlation coefficient R = 0.966073, the coefficient ofregression determination R2 = 0.933298+ The function of moisture content immediately after the drip irrigation:
F(x,y) = 8.9918.10-5(25 - x)1.240306(25 - y)2.311185 + 25.28203(y-1.5)0.057362; the coefficient ofdetermination R2 = 0.946491.

88

+ The function of moisture content at one day after the drip irrigation:
F(x,y) = 0.0003325(25 - x)1.269(25 - y)1.83194 + 25.28203(y-1.5)0.057362 the coefficient of determination
R = 0.9256.
2+ At two days and three days after irrigation the coefficients of determination were R2 = 0.559009
and R2 = 0.217065, respectively. The reason of having low R2 values is, after the irrigation, soil
moisture increased with depth leading to reverse permeation. Hence, using a linear regression function
for this calculation is inappropriate. This result cannot allow prediction of the soil moisture evolutions.
The model validation by the functions resulted in acceptable errors, therefore the functions
above can be used to simulate the soil moisture.
Further experiments should be continued in the field to construct a model that determines the
moisture content more accurately based on physical characteristics of the soil and climate conditions.

ACKNOWLEDGEMENT
The authors are grateful to the members of Department of Water Resource, Faculty of Land
Management, Viet Nam National University of Agriculture, Viet Nam for supplying the soil blocks
for this study.

REFERENCES
Abdallah EL. Badr, Mohamed E. Abuarab (2011). Soil moisture distribution patterns under surface and subsurface
drip irrigation systems in sandy soil using neutron scattering technique. Irrigation Science, 31(3): 317-332.
Bresler E, Heller J, Diner N, Brandt A, Goldberg D (1971). Infiltration from a trickle source, II. Experimental data
and theoretical predictions. Soil Sci. Soc. Am. Pro., 35: 683-689
Healy R. W and Warrick A. W (1988). A generalized solution to infiltration from a surface point source, Soil Sci.
Soc. Am. J., 52: 1245-1251
Nguyen Van Duc (2008). Irrigation and drainage Methods, Hue University of Agriculture and Forestry. (in Viet
Namese)Nguyen Thu Hien (1994). Soil Moisture Transmission Model in Unsaturated Zone, Ha Noi Water Resources
University. (in Viet Namese)
Philip J R (1971). General theorem on steady infiltration from a surface sources with application to point and line
source. Soil Sci. Soc. Am. Pro., 35: 867-871.
Prashant Shrivastava, Govind Singh Rajput and Siddarth Nayak (2011). Soil moisture distribution as influenced bydrip irrigation supply and planting pattern in heavy soils of Madhya Pradesh. Journal of Agricultural
Technology 7(4): 1177-1186
Warrick A. W (1974). Time dependent linearized infiltration, I. Point sources. Soil. Sci. Soc. Am. Proc., 38.

89

APPENDIX

1. The moisture function before irrigation g(y) = 25.28203(y-1.5)0.057362
Table 1. Evaluation of the error of the moisture function before irrigation g(y)
Zt

x

y

error 1error

Ratio f

24.98

0

2.5

0.0120185

1.01209101

0.04051605

28.87

0

7.5

-0.0299301

0.97051338

0.03361667

29.05

0

12.5

-0.0013764

0.99862453

0.03437606

29.67

0

17.5

-0.0010012

0.9989993

0.03367035

29.68

0

22.5

0.01426048

1.01436264

0.03417664

24.98

5

2.5

0.0120185

1.01209101

0.04051605

28.89

5

7.5

-0.0306226

0.96984152

0.03357015

29.13

5

12.5

-0.0041265

0.995882

0.0341875

29.63

5

17.5

0.00034787

1.00034793

0.03376132

29.66

5

22.5

0.01493456

1.01504664

0.03422275

24.90

10

2.5

0.0152262

1.01534271

0.04077682

28.85

10

7.5

-0.0292371

0.97118618

0.0336633

29.08

10

12.5

-0.0024086

0.99759431

0.03430517

29.49

10

17.5

0.00508401

1.00509696

0.03408264

29.61

10

22.5

0.01662175

1.01676066

0.03433842

25.00

15

2.5

0.01121818

1.01128134

0.04045125

28.88

15

7.5

-0.0302764

0.97017733

0.0335934

29.05

15

12.5

-0.0013764

0.99862453

0.03437606

29.47

15

17.5

0.00576244

1.00577907

0.03412891

29.59

15

22.5

0.01729743

1.0174479

0.03438486

24.96

20

2.5

0.01281946

1.01290198

0.04058101

28.91

20

7.5

-0.0313146

0.96917058

0.03352371

29.12

20

12.5

-0.0037832

0.99622399

0.03421099

29.36

20

17.5

0.00950203

1.00954732

0.03438513

29.56

20

22.5

0.0183118

1.01848049

0.03445469• Zt is the moisture before irrigation measured at the soil sample corresponds with the left plot
• Error 1 is the error between lng(y) and ln(Zt).
• Error is the absolute error between g(y) and ZT.
• The ratio f is the relative error between g(y) and Zt.

90

2. The moisture function after the irrigation finished F(x;y) = 8.9918.10-5(25 - x)1.240306(25 y)2.311185 + 25.28203(y-1.5)0.057362
Table 2. Evaluation of the error of moisture function afterthe irrigation finished
Z0t

x

y

error 0error 1

Total error

Ratio f

32.84

0

2.5

1.01209101

0.82703755

1.83912855

0.056002697

32.31

0

7.5

0.97051338

1.05714928

2.02766267

0.062756505

30.78

0

12.5

0.99862453

0.9658725

1.96449703

0.063823815

30.23

0

17.5

0.9989993

0.91631433

1.91531363

0.063358043

29.73

0

22.5

1.01436264

0.81011604

1.82447869

0.061368271

32.41

5

2.5

1.01209101

0.66336872

1.67545972

0.051695764

32.26

5

7.5

0.96984152

0.81820266

1.78804418

0.055426044

30.72

5

12.5

0.995882

0.79682841

1.79271041

0.058356459

30.15

5

17.5

1.00034793

0.7482129

1.74856083

0.057995384

29.69

5

22.5

1.01504664

1.02374551

2.03879215

0.068669321

29.77

10

2.5

1.01534271

0.70834522

1.72368792

0.057900165

29.64

10

7.5

0.97118618

1.49599867

2.46718485

0.083238355

29.53

10

12.5

0.99759431

1.9705141

2.96810841

0.100511629

29.65

10

17.5

1.00509696

1.70191818

2.70701514

0.091298993

29.63

10

22.5

1.01676066

1.07476471

2.09152537

0.070588099

28.57

15

2.5

1.01128134

0.58437004

1.59565138

0.055850591

29.49

15

7.5

0.97017733

1.91325701

2.88343435

0.097776682

29.38

15

12.5

0.99862453

1.62502614

2.62365067

0.089300568

29.59

15

17.5

1.00577907

1.37233344

2.37811251

0.08036879

29.60

15

22.5

1.0174479

1.29994693

2.31739483

0.078290366

27.23

20

2.5

1.01290198

0.38899378

1.40189576

0.051483502

29.08

20

7.5

0.96917058

2.9058058

3.87497637

0.133252282

29.33

20

12.5

0.99622399

1.0808548

2.07707879

0.070817552• Z0t is the moisture after the irrigation finished measured at the left plot
• Error 0 is the error between the lnf (x, y) and ln (Z0t-Zt).
• Error 1 is the absolute error caused by g(y).
• Error total is the total absolute error caused by F (x, y).
• The ratio f is the relative error between the model and the control experimental value91

3. The moisture function one day after the irrigation finished F(x;y) = 0.00033248(25 x)1.269(25 - y)1.8319 + 25.28203(y-1.5)0.057362
Table 3. Evaluation of the error of the moisture function
one day after the irrigation finished
Z1t

x

y

error 0error 1

Total error

Ratio f

30.48

0

2.5

1.01209101

1.07764472

2.08973573

0.068560883

32.14

0

7.5

0.97051338

1.14380289

2.11431627

0.065784576

31.98

0

12.5

0.99862453

0.68919575

1.68782028

0.05277737

30.85

0

17.5

0.9989993

0.6713181

1.6703174

0.05414319

29.74

0

22.5

1.01436264

1.76453869

2.77890133

0.093439856

30.42

5

2.5

1.01209101

0.82083697

1.83292797

0.060254042

32.01

5

7.5

0.96984152

0.9031541

1.87299562

0.058512828

31.85

5

12.5

0.995882

0.55931803

1.55520003

0.048828886

30.26

5

17.5

1.00034793

0.94729954

1.94764747

0.064363763

29.72

5

22.5

1.01504664

1.32937864

2.34442527

0.078883758

28.04

10

2.5

1.01534271

0.98712955

2.00247226

0.071414845

29.82

10

7.5

0.97118618

2.01647441

2.98766059

0.100189825

30.02

10

12.5

0.99759431

1.12343477

2.12102909

0.070653867

30.21

10

17.5

1.00509696

0.66816571

1.67326266

0.055387708

29.64

10

22.5

1.01676066

1.8455537

2.86231437

0.096569311

27.12

15

2.5

1.01128134

0.87397989

1.88526123

0.069515532

29.29

15

7.5

0.97017733

2.85176735

3.82194469

0.130486333

29.58

15

12.5

0.99862453

1.1910592

2.18968373

0.074025819

29.20

15

17.5

1.00577907

0.5159031

1.52168217

0.052112403

29.41

15

22.5

1.0174479

0.47280646

1.49025436

0.050671688

29.66

20

2.5

1.01290198

0.89396465

1.90686663

0.064290851

29.57

20

7.5

0.96917058

1.67294417

2.64211475

0.089351192• Z1t is the moisture after the irrigation finished ,measured at the left plot
• Error 0 is the error between the lnf (x, y) and ln (Z1t-Zt).
• Error 1 is the absolute error caused by g(y).
• Error total is the total absolute error caused by F (x, y)• The ratio f is the relative error between the model and the control experimental value

92

Growth and Sodium Absorption of Sweet Sorghum in Agricultural Land Damaged
from Tsunami of the Great East Japan Earthquake
Nakamura S.*, M. Saito, K. Kimura
School of Food, Agricultural and Environmental Sciences,
Miyagi University, Sendai, Miyagi 982-0215, Japan
Email*: nakamurs@myu.ac.jp
ABSTRACTThe Great East Japan Earthquake on March 11, 2011, created a colossal tsunami that damaged agriculturalland along the east coast of Japan, washing away topsoil, flooding areas with salt-water and causing poor drainage
due to the destruction of aggregate structures. Cultivation experiments of sweet sorghum was carried out in an
agricultural land at a distance of 2 km from the coastline in Miyagi, Japan in 2012 so as to obtain knowledge of theeffect of sodium removal from soil by using sweet sorghum, which has a high dry matter production and salinity
tolerance. Sweet sorghum was sown on May 26 and grew vigorously. The plant length was about 3.5 m at the end of
September. After harvest, the amount of sodium absorbed by the plant and in soil was estimated using atomic
absorption analysis. Lower internodes in the stems accumulated more sodium than higher ones. The addition offertilizer on July 6 increased the internode length of the lower internodes, resulting in the increase of internodevolume and dry matter and also the increase of amount of sodium in the stems. However, despite sweet sorghum’s
ability to remove sodium from the soil, it was not enough as most of the sodium carried inland by the tsunami still
remained. This result suggested that plowing-in of organic matter produced by sweet sorghum would be useful for
improving the aggregate structure of soil because outflow of sodium would be expected by large amounts ofprecipitation in Japan.
Keywords: Growth, Sodium absorption, Sorghum bicolor Moench, sweet sorghum, the Great East Japan
Earthquake, Tsunami.

1. INTRODUCTIONOn March 11, 2011, the Great East Japan Earthquake created a colossal tsunami that damaged
agricultural land along the east coast of Japan, e.g. washed away soil, caused flooding and poor
drainage due to the destruction of aggregate structures. It was necessary to reduce the salt content
of soil in agricultural lands as soon as possible. Repeated use of puddling and washing or
underdrainage can be conducted using irrigation water to help remove the salt from paddy fields(Hoshi and Yusa, 2012). However, it is difficult to reduce the salt content in upland fields using thesame methods. Lately, Sweet sorghum (Sorghum bicolor Moench) has garnered attention as abiomass energy crop since it accumulates a lot of sugar in the stem (Smith et al., 1987). Sweetsorghum also has a high dry matter production and is a medium salt tolerance plant (Almodares andSharif, 2007). However, there is little information about the growth of sweet sorghum in the fields
damaged by tsunami and the amount of sodium absorption by sweet sorghum. We evaluated thegrowth of sweet sorghum in damaged fields from seawater and the effect of sodium removal from thesoil by growing sweet sorghum.

2. MATERIALS AND METHODS
2.1. Field condition
Field experiments were carried out in a field in Higashimatushima at a distance of 2 km fromthe coast in Miyagi Prefecture, Japan in 2012. On March 11 in 2011, after the tsunami, the field was93flooded with about 50 cm of seawater and about 3 cm of mud. In May of 2011, tillage was done
without removing the mud and no crop was grown in the field that year. In 2012, there were cracks
appeared in the soil’s surface before tilling (Fig. 1a). After tillage for seeding, poor infiltration of rainwater was observed (Fig. 1b). These occurrences are considered to have been caused by thedestruction of aggregate structure by sodium in the seawater.

a)b)Fig. 1. Soil surface before tillage (a) and after tillage (b) for seeding in 2012
Note: Sodium in seawater destroyed aggregate structure soil, resulting in soil cracking and crust (a) and poor infiltration of
rainwater (b).

2.2. Planting and measurement
Sweet sorghum (cv. KCS105, Super sugar sorgo) was sown on the 26th of May in 2012. The
seedlings were thinned to one plant per hill with 0.75 and 0.15 m spacing between and within therows. Fertilizer with 10g m-2 N, 9 g m-2 K2O, and 10g m-2 P2O5 were applied before seeding (control
plot, Cont) and 5g m-2 N (ammonium sulfate) was applied at 41 days after seeding (DAS41, the 10th
leaf stage) (additional fertilization plot, AF). Each plot has two replications. The plant height and the
expanded leaf number were recorded about every 2 weeks from DAS22. At harvest (DAS116), length,
diameter of internodes and head length, dry weight of head, leaf and stem were measured. Internodecross-section area was calculated by the major and minor of diameter in the middle of the internodeas an ellipse (Nakamura et al. 1995). Internode volume was calculated by the product of the crosssection area and the length. The internode enclosed with the n th leaf sheath was defined as the n thinternode (IN n) (Goto et al., 1994).
2.3. Na content in stem
Na concentration in plants, especially in the stems, were measured by atomic absorption analysis.
The lower internode (at 10 cm above ground), the middle internode (at the middle of stem) and theupper internode (just under the head) of 3 main stems with an average length was analyzed for Naconcentration (mg/g DW). The main stem was divided into 3 equal parts in length and dry weight ofeach part was calculated and then Na content in each part was obtained by Na concentration of 3internode positions. Na content in a stem was estimated by the sum of 3 parts in stem.
2.4. Na content in soil
Na content in the soil at different depths were also measured by atomic absorption analysis. Soilsamples at different depths (0-15 cm, 15-30 cm, 30-45 cm and 45-60 cm from the ground surface) in
the field was obtained at DAS-30, DAS 16, DAS 40, DAS70 and DAS116 using a soil auger. Soil94

samples from three different sections were mixed and air
air-dried in one plott so as to compare its Na
content with soil samples obtained using the same method mentioned above from a Miyagi
University experimental field not affected by the tsunami at DAS40 and DAS116. Soil samples(Fallow) were obtained from the sections of no cultcultivation
ivation in the field in Higashimatushima in order
to elucidate the effect of sweet sorghum cultivation to the Na content in the soil.

3.. RESULTS AND DISCUSSION
3.1. Plant growth
Sweet sorghum grew vigorously in the field in 2012 ((Fig. 2). However, an abundance
abundof plants
with some prop roots elongating on the ground were observed ((Fig. 3). This is due to the Na in theseawater having destroyed the aggregate structure of surface soil thus rendering it too hard for theprop roots to penetrate.

Fig. 2. Sweet sorghum plant growing vigorousl
vigorously
y in the field at DAS97 in 2012

Fig. 3. Prop roots elongating on the ground surface becau
becausese of hard surface soil (crust)95

In both Cont and AF, the leaf number increased straightly from DAS27 to DAS84 ((Fig. 4a).
During this period, the leaf number per day was 0.25 and 0.26 in Cont and AF respectively. At
harvest, there was no significant differences in the leaf number between 20.2 in Cont and 19.7 in
AF (Table 1). The plant height increased 7.3 cm
cm/day in Cont and 7.7 cm/day
day ini AF from DAS41
through DAS 70 (Fig. 4b). Although the leaf number of AF was smaller than Cont, the plant
length of AF was higher than Cont at harvest (DAS116). There were no significant differences inleaf number, plant length, stem length, head length and dry weight of main stem between Contand AF (Table 1).
Table 1. Leaf number, plant length, stem length, head lengthand dry weight of head, leaf aandnd stem of main stem at harvest
Dry weight (g)plotleaf number

plant length stem length
(cm)
(cm)

head length
(cm)

head

leaf

stem

total

Control

20.2 ± 0.3

365 ± 5338 ± 529.4 ± 0.6

65.4 ± 6.1

39.1 ± 1.8

80.5 ± 6.0

185 ± 13AF

19.7 ± 0.2

377 ± 4349 ± 431.4 ± 0.4

56.3 ± 4.2

40.2 ± 1.4

79.8 ± 4.1

176 ± 9Fig. 4. Changes in leaf number (a) and plant length (b)Note: An arrow indicates the time of additional fertili
fertilization (DAS41); * and ** represent significant differences
difference at 5 %3.2. Internodes characteristics
In both Cont and AF, the length of IN13 was the longest internode from IN 7 to IN18 ((Fig. 5a).
In AF, internode lengths were significantly longer than in Cont from the lower to the middle ofthe stem, but the internode diameter didn’t increase (Fig. 5b). The volume of internodes in AF
were larger than those in Cont in the lower to the middle of the stem ((Fig. 5c) and the dry weightof internodes in AF were larger
arger tha
than those in Cont in the lower of the stem (Fig.
(5d). Thus,
the additional fertilizer significantly increased the internode length and the dry weight of the lowerof the stem.

96

Fig. 5. Internode length (a), cross
cross-section area (b), volume (c) and dry weight (d) based on
the internode position (IN) from IN7 to IN18. The internode position including neckinternode just below head was exclude for analys
analysis
is of internode characteristics
Note: ** and * represent significant differendifferences at 5 % and 1 % level respectively.

3.3. Na content in stem
Concentration of Na in the stem increased as plants grew ((Fig. 6a, 6b). At harvest, concentrationof Na increased with the lower internodes ((Fig. 6b). In the middle and lower internode, Naconcentration was significantly
ntly higher in AF than in Cont. Estimated content of Na in the stem waslarger in AF than in Cont mainly due to larger dry weight ((Fig. 5d) and higher concentration of Na
in the lower internode in AF (Fig.
Fig. 6c). The result suggested that additional fertilizer
fertiliz was effective in
the increase of Na absorption.

Fig. 6. Concentration of Na in stem and leaf at DAS70 (the 15 leaf stage) (a), in upper,
middle and lower internode in stem at DAS116 (harvest) (b), and the estimated content
of Na in upper, middle and lower
ower part of stem at DAS116 (c)Note: ** and * represent significant differences at 5 % and 1 % level respectively.

97

3.4. Na content in soil
Fig. 7 shows changes of Na content in soil at different depths. Soil infiltrated with seawatercontained large quantities
tities of Na compared with the field not affected by seawater (DAS40 and
DAS116). Although variation of Na content was large during the growing season, Na content in thesoil tended to decrease in all depths over harvest time. At DAS116, soil in AF containcontained lower Na.
This result suggested that expansion of the roots by using additional fertilizer might absorbed more
Na. However, Chiba et al. (2012) reported that the infiltration of rainwater and its discharge
through underdrains eliminated considerable salsaltt from the paddy fields. Our experimental field
located in Higashimatsushima received 286 mm of precipitation from seeding to harvest in 2012. The
decrease of Na content in all depths as shown in Fig. 7 might be attributed to the infiltration of
rainwater though no drainage was installed in the field. In order to elucidate the effect of expansion
roots through additional fertilizer on Na content in the soil, further experiments are needed.

Fig. 7. Changes of Na content in soil (100g in air drying) at diffdifferent depths
from -30
30 days aafter seeding (DAS-30) to DAS116

4. CONCLUSIONSIn sweet sorghum, lower internodes in the stems accumulated more sodium than in the higher
ones. The additional fertilizer increased the middle and lower internodes in length, resu
resulting in theincrease of internode volume and dry matter in lower internodes and also the increase of Na content
in the stems in AF. However, the amount of sodium absorbed by only sweet sorghum was not enoughto remove all the sodium caused by the tsunami from the soil in a short period of time. This result98

suggested that, instead of salt removal, plowing-in of a large amount of organic matter produced bysweet sorghum would be useful for improving the aggregate structure of soil because outflow of
sodium can be expected by the large amount of precipitation in Japan. When sweet sorghum is usedas organic material for improving the aggregate structure in Japan, combining it with lime materials
for removal of exchangeable sodium and winter crops with salinity tolerance such as barley would be
more effective.

REFERENCES
Almodares, A. and Sharif, M.E. (2007). Effects of irrigation water qualities on biomass and sugar contents of sugar
beet and sweet sorghum cultivars, J. Environ. Biol., 28: 213-218.
Chiba, K., Kanmuri, H. and Kato, T. (2012). Desalinization technique for tsunami-hit farmland by infiltration of rain
and discharge from underdrain, Journal of the Japanese Society of Soil Physics, 121: 29-34. (in Japanese)
Goto, Y., Nakamura, S., Sakai, K. and Hoshikawa, K. (1994). Analysis of elongation and thickening of internodes insweet sorghum (Sorghum bicolor Moench), Japanese Journal of Crop Science, 63: 473-479. (in Japanese)
Hoshi, N and Yusa, T. (2012). Desalinization of a salt affected paddy field by Tsunami in Miyagi Pref. Bulletin of
the Society of Sea Water Science, Japan, 66: 74-78. (in Japanese)
Nakamura, S., Suzuki, Y. and Goto, Y. (1995). Effect of plant density on characteristics of internode of sweetsorghum (Sorghum bicolor Moench). 1. Internode of main stem, Japanese Journal of Crop Science, 64(Extra 2):
195-196. (in Japanese)
Smith, G.A., Bagby, M.O., Lewellan, R.T., Doney, D.L., Moore, P.H., Hills, F.J., Campbell, L.G., Hogaboam, G.J.,
Coe, G.E. and Freeman, K. (1987). Evaluation of sweet sorghum for fermentable sugar production potential,
Crop Science, 27: 788-793 (in Japanese).

99

Traits of Drought Resistance of NERICA with The Effects of Compost,
Compared with Asian Rice and African Rice in The Field
M. Fujii
Faculty of Education, Shizuoka University, Shizuoka, 422-8529, Japan
Email: eimfuji@ipc.shizuoka.ac.jp
ABSTRACT
Recently NERICA (New Rice for Africa) was developed by a crossing of African rice (Oryza glaberrima Steud.)and Asian rice (Oryza sativa L.) in West Africa, and is considered to be drought resistant, but drought resistance of
NERICA is not clarified enough. In this research, NERICA, Asian rice and African rice were cultivated in the field in
Shizuoka University under drought to compare dry matter production, stomatal conductance, characteristics of-2
absorbing water and leaf to clarify traits of drought resistance. Fertilization level was 4gN m and in compost plots-2compost levels were 2 and 4kg m . Stomatal conductance, soil water content, SPAD value, leaf thickness and top
dry weight at harvest were measured. Dry weight was higher in one of NERICA lines and NERICA 7. Ear weight was
higher in Dular and one NERICA line. Effects of compost on ear weight were different between lines. In one NERICA
line, compost increased ear weight but in another it decreased. In NERICA 7, Dular and sativa parent showed highstomatal conductance. Compost increased stomatal conductance. In soil water content in deep layer, Dular and IRAT
13 showed lower water content than NERICA. SPAD values of sativa parent and NERICA were higher than Dular andglaberrima parent. Leaf thickness of NERICA, sativa parent and Dular were higher than glaberrima parent. Cultivar
differences in top dry weight were significantly correlated with those in stomatal conductance (r=0.616*), cultivar
differences in ear weight were significantly correlated with those in leaf thickness (r=0.646**), SPAD value (r=0.537*)
and stomatal conductance (r=0.534*).
Keywords: Compost, drought resistance, leaf thickness, NERICA, stomatal conductance, water use efficiency.

1. INTRODUCTION
Global water shortages are getting worse and drought is the major constraint to crop production
and breeding for drought resistance in rice is important (Blum, 2009; Boyer, 2010; Kamoshita, 2011;
Kumar et al. 2014; Serraj et al., 2011). Recently NERICA (New Rice for Africa) was developed by acrossing of African rice (Oryza glaberrima Steud.) and Asian rice (Oryza sativa L.) in West Africa(WARDA, 1999), and is considered to be drought resistant, but drought resistance of NERICA andthe effects of compost under drought condition are not clarified enough.
Fujii et al. (2004, 2008 and 2013) have been investing the characteristics of NERICA underdrought condition but key characteristics contribute to yield and the effects of compost underdrought are still not clarified yet.
In this research, NERICA, Asian rice and African rice were cultivated in the field under drought
condition to compare dry matter production, stomatal conductance, water absorbing characteristics
and leaf characteristics to clarify traits of NERICA under drought condition. In two NERICA lines
effects of compost under drought condition was tested.

2. MATERIALS AND METHODS
2.1. Materials
In this study four NERICA cultivars and three NERICA lines which showed superior drought
resistant in our previous reports (Fujii et al., 2005, 2013) and their two parent cultivars as follows
were tested. Three Asian rice (O. sativa L.) cultivars were also tested.

100

Four NERICA cultivars tested were NERICA 1, 2, 3 and 7. Three NERICA lines tested were
WAB450-24-3-P3-1-HB, WAB450-I-B-P-82-2-1 and WAB450-I-B-P-157-2-1. Two parent cultivarsof NERICA tested were sativa parent: WAB56-104 and glaberrima parent: CG14. Three Asian rice
(O. sativa L.) cultivars, Koshihikari (japonica, lowland, Japan), Dular (indica, lowland-upland,
India), IRAT13 (japonica, upland, Cote d’Ivoire), were also tested.
2.2. Methods
2.2.1. Cultivation
Rice cultivars and lines were seeded on June 13, 2011 in paper pots and planted at upland field in
the vinyl house of Shizuoka University, Japan on July 4. Sides of vinyl house were kept open.
Irrigation was applied on July 4, 5, 6, August 4, 5, 10 and 18, and after that no irrigation was applied.
Fertilization level was 4gN m-2 by compound fertilizer (N:P2O5:K2O = 8:8:8%) )as basal
treatment. In compost plots in WAB450-24-3-P3-1-HB and WAB450-I-B-P-82-2-1, compost madefrom rice straw was applied at the rate of 2 kg m-2. In WAB450-I-B-P-82-2-1 compost application at
the rate of 4 kg m-2 was also created.
2.2.2. Measurements of dry weight
Plants were sampled at harvest on November 30. After dividing into leaf, dead leaf, stem andear, dry weight was measured after desiccating in the drying oven at 80℃ for 48 hours.
Measurements were replicated 10 times in each plot.
2.2.3. Measurements of stomatal conductance
Stomatal conductance was measured at the center of abaxial side of topmost fully expanded
leaves by dynamic diffusion porometer (AP4, Delta-T Devices Ltd., UK) on sunny days during thedaytime. Measurements were made on September 13, 16 and October 9. Measurements werereplicated six times in each plot.
2.2.4. Measurements of soil water content
Soil water content at individual depths were measured by TRIME-T3 tube access probe system(IMKO micromodultechnik, Germany) by TDR (Time domain reflectometry) method. Measurements
were made at intervals of 10cm depth on September 14, October 5 and 25. Data presented areaverage of 20cm depth increments from surface to 60cm depth. Measurements were replicated fourtimes in each plot.
2.2.5. Measurements of SPAD value
SPAD value was measured by SPAD meter (SPAD502, Konica Minolta, Tokyo, Japan) on
September 15, 20 and October 6. Measurements were replicated 10 times in each plot.
2.2.6. Measurements of leaf thickness
Leaf thickness was measured by micrometer (No.193-111, Mitutoyo, Kawasaki, Japan) at the
middle length of topmost fully expanded leaves avoiding midrib on September 15, 20 and October 9.
Measurements were replicated 10 times in each plot.
2.3. Data analysis
Tukey’s test was applied to determine any significant differences at the level of 5% between themeans of the data. Significance of regression coefficient was analyzed at the level of 5% and 1%.

101

3. RESULTS
3.1. Top dry weightTop dry weight att harvest on November 30 was maintained high in WAB450
WAB450-I-B-P-82-2-1 (oneof NERICA lines) and NERICA 7 (Fig. 1).. Top dry weight in IRAT 13 and Dular (drought resistantcultivars in Asian rice) and CG14 ((glaberrima parent of NERICA) also tended to be high.
Ear weight was highest in Dular (drought resistant cultivar in Asian rice) and followed by one of
NERICA line (WAB450-I-B-P-82
82-2-1)
1) which maintained highest top dry weight and tended to show
higher ear weight than sativa parent of NERICA (WAB56(WAB56-104).
Top dry weight of glaberrima parent of NERICA (CG 14) was almost as high as Dular but
proportion of dead leaf was large and got no ear weight.
Effects of compost on ear weight were different between NERICA lines. In one NERICA line
(WAB450-24-3-P3-1-HB),
HB), compcompostost tended to increase ear weight but in another line (WAB450-I-B(WAB450
P-82-2-1)
1) it tended to decrease ear weight by the application rate of compost at the rate of 2kg m-2
and at the rate of 4kg m-2in this line it tended to decrease ear weight more.

Fig. 1. Top dry weight of each oorganrgan at harvest on November 30
Note: Bars show standard error. Different letters indicate siginificant difference at 5% level (Tukey’s(Tukey test).

3.2. Stomatal conductance
NERICA 7, WAB450-I-B-P
P-82-2-1
1 (one of NERICA line that maintained
maintainhighest top dry
weight), sativa parent of NERICA (WAB56(WAB56-104)
104) and Dular tended to show high stomatal
conductance under drought condition on September 16 (Fig. 2). Application of compost tended toincrease stomatal conductance.
3.3. Soil water content
In soil water content at surface soil layer (0(0-20cm)20cm) on October 5, differences among cultivars andlines were small (Fig. 3).

102

At deep soil layer (40-60cm)
60cm) on October 5, Dular, IRAT 13 and sativa parent of NERICA(WAB56-104)
104) which are drought resistant Asian rice cultivars tended to show lower water content
than NERICA cultivars and lines (Fig. 4).
Application of compost at the rate of 2 kg m-2 tended to decrease soil water content at deep layer.

Fig. 2. Stomat
Stomatal conductance measured on September 16
Note: Bars show standard error. Different letters indicate siginificant difference at 5% level (Tukey’s(Tukey test).

Fig. 3. Soil water content at surface layer (0(0-20cm)0cm) on October 5
Note: Bars show standard error.
Same letters indicate differences were not signi
significant at 5% level (Tukey’s test).

103Fig. 4. Soil water content at dee
deep layer (40-60cm)
60cm) on October 5
3.4. SPAD value
Average SPAD values of sativa parent (WAB56-104),104), NERICA 3, IRAT 13, NERICA 1 andNERICA 2 tended to be higher than Koshihikari, glaberrima parent of NERICA (CG 14) andDular (Fig. 5).

Fig. 5. Average SPAD value
Note: Bars show standard error.
Different letters indicate siginificant difference at 5% level (Tukey(Tukey’s test).

1043.5. Leaf thickness
Leaf thickness of NERICA 3, sativa parent of NERICA (WAB56-104) and Dular were higher
than glaberrima parent of NERICA (CG 14) and Koshihikari (Fig. 6). In one NERICA line (WAB450(WAB450
24-3-P3-1-HB)
HB) compost tended to increase leaf thickness.

Fig. 6. Average leaf thickness
Note: Bars show standard error.
Different letters indicate siginificant difference at 5% level (Tukey(Tukey’s test).

3.6. Relationship between stomatal conductance and top dry weight
Cultivar and fertilization differences in top dry weight were significantly correlated with thosein stomatal conductance at 5% level (r=0.616*) (Fig. 7).

Fig. 7. Correlation between stomatal conductance and top dry weight
Note: * show significant at 5% level.

1053.7. Relationship between leaf thickness and ear weight
Cultivar and fertilization differences in ear weight were significantly correlated with those inleaf thickness at 1% level (r=0.646**) (Fig. 8).

Fig. 8. Correlation between leaf thickness and ear dry weight
Note: ** show significant at 1% level.

3.8. Relationship between leaf thickness and SPA
SPAD value
Cultivar and fertilization differences in leaf thickness were significantly correlated with thosein SPAD value at 5% level (r=0.537*) (Fig. 9). In standard fertilizer plots correlation coefficient
was 0.695*.

Fig. 9. Correlation between leaf tthickness
kness and SPAD value
Note: * show significant at 5% level.

1063.9. Relationship between leaf thickness and stomatal conductance
Cultivar and fertilization differences in leaf thickness were significantly correlated with those instomatal conductance at 5% level (r=0.534*) (Fig. 10). In sativa cultivars correlation coefficient washigh (r=0.962*) and was significant at 5% level.

Fig. 10.. Correlation between leaf thic
thicknesskness and stomatal conductance
Note: * show significant at 5% level.

4. DISCUSSION
Ear weight
eight was highest in Dular, drought resistant cultivar in Asian rice and followed by one of
NERICA line which showed higher top dry weight ((Fig. 1). In drought resistant Asian rice cultivars(Dular, IRAT13, sativa parent of NERICA (WAB56(WAB56-104)) soil water content
ontent at deep soil layer tended
to be lower than other cultivars ((Fig. 4). On the contrary in NERICA cultivars and lines soil watercontent tended to be maintained higher at deep soil layer. The tendency to maintain higher soil
water content in NERICA compared
pared with drought resistant Asian rice under drought condition arecoincide with author’s previous reports (Fujii et al. 2004, 2008, 2013).
Cultivars and lines that maintained high top dry weight showed high stomatal conductance(Fig. 7). Cultivar and line
ne differences in top dry weight were significantly correlated with those instomatal conductance (r=0.616*) ((Fig. 7). Cultivar and line differences in ear weight weresignificantly correlated with those in leaf thickness (r=0.646**) ((Fig. 8). Cultivar and line differencesin leaf thickness were significantly correlated with those in SPAD value (r=0.537*) ((Fig. 9) andstomatal conductance (r=0.534*) ((Fig. 10).
In the author’s previous reports (Fujii et al., 2004, 2008, 2013) correlation between stomatal
conductanceductance and top dry weight under drought condition and correlation between leaf thickness anddry weight at harvest were shown, but correlation between leaf thickness and yield (ear weight) havenot shown.
Vadez et al. (2014) pointed out the importance of water availability at reproductive stage. It
seems that in drought condition with little precipitation, consuming soil water conservative at
vegetative stage and remaining much water at reproductive stage may be essential. Luo (2010)
pointed out the importancetance of water
water-savingsaving characteristics by high water use efficiency in rice.

107

High leaf thickness and SPAD values of NERICA seem to be effective to maintain dry matterproduction and yield under limited water condition with high water use efficiency by increasing thepotential of photosynthesis. Condon et al. (2004) showed cultivar differences in water use efficiency
in wheat. In rice NERICA seems to be appropriate for the cultivation under drought condition withthe effective use of limited water for the sustainable crop production.
Effects of compost on ear weight were different between NERICA lines (Fig. 1). Compost
increased stomatal conductance and as the results it seems to consume more water at vegetative
stage and tended to decrease soil water content at deep layer at reproductive stage and in one
NERICA line tended to decrease ear weight. Though there are possibilities to increase growth underdrought condition, in this experiment the effects were different between NERICA lines and in somecases it tended to decrease ear weight. More research about appropriate cultivars to compost andadequate amount seem to be needed to clarify the effects of compost under drought.

5. CONCLUSIONS
Ear weight was highest in Dular, drought resistant cultivar in Asian rice and followed by one of
NERICA line which showed higher top dry weight. In drought resistant Asian rice cultivars soil
water content at deep soil layer tended to be lower than other cultivars. On the contrary in NERICA
cultivars and lines soil water content tended to be maintained higher at deep soil layer.
Cultivars and lines that maintained high top dry weight showed high stomatal conductance.
Cultivar and line differences in ear weight were significantly correlated with those in leaf thickness.
Cultivar and line differences in leaf thickness were significantly correlated with those in SPAD value
and stomatal conductance.
High leaf thickness and SPAD values of NERICA seem to be effective to maintain dry matterproduction under limited water condition with high water use efficiency. NERICA seems to beappropriate for the cultivation under drought condition with the effective use of limited water for thesustainable crop production.
Effects of compost were different between NERICA lines and more research about appropriate
cultivars and lines to compost and adequate amount seem to be needed to clarify the effects ofcompost under drought.

ACKNOLEDGEMENT
I thank Emeritus Professor Horie and Professor Shiraiwa, Kyoto University, for kindly giving an
opportunity to use seeds of NERICA lines in this study.

REFERENCES
Blum A. (2009). Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield
improvement under drought stress. Field Crops Res., 112:119-123.
Boyer J. S. (2010). Drought decision-making. J. Exp. Bot., 61(13): 3493-3497.
Condon A. G., Richards R. A., Rebetzke G. J. and Farquhar G. D. (2004). Breeding for high water-use efficiency. J.
Exp. Bot., 55: 2447-2460.
Fujii M., Andoh C. and Ishihara S. (2004). Drought resistance of NERICA (New Rice for Africa) compared with
Oryza sativa L. and millet evaluated by stomatal conductance and soil water content. In: New directions for adiverse planet: Proceedings for the 4th International Crop Science Congress, Brisbane, Australia, 26 September- 1 October 2004. T. Fischer et al. (Eds.), [online] www.cropscience.org.au/icsc2004, 5 pages.

108

Fujii M., Ishihara S. and Miyamoto Y. (2005). Comparison between Asian rice (Oryza sativa L.) and NERICA(New Rice for Africa) from the characteristics of stomata reaction and water absorption under drought
condition. Jpn. J.Crop Sci. 74(extra 2): 148-149. (in Japanese)
Fujii M., Ishihara S. and Shinohara R. (2013). Drought resistance of NERICA compared with Asian rice, African
rice and millets in the field with different fertilization levels. In: Proceedings of the 7th Asian Crop Science
Conference: improving food, energy and environment with better crops, Bogor, Indonesia, 27-30 September
2011. (Eds.) Suharsono et.al. [online], http://www.cropscience.jp/acsa/conference/Proceeding-ACSACISBN.pdf, pp. 113-118.
Fujii M., Miyamoto Y. and Ishihara S. (2008). Studies on drought resistance of Asian rice (Oryza sativa L.) andNERICA -Water use in the field-. In: Abstracts of the 5th International Crop Science Congress & Exhibition,
April 13-18, 2008, Jeju, Korea: p. 209.
Kamoshita, A. Current status of research on improvement of drought resistance of rice (Oryza sativa L.). (2011)
Japan. J. Crop Sci., 80(1): 1-12. (in Japanese with English abstract).
Kumar A., Dixit S., Ram T., Yadaw R. B., Mishra K. K. and Mandel N. P. (2014). Breeding high- yielding
drought-tolerant rice: genetic variations and conventional and molecular approaches. J. Experimental Botany,
65(21): 6265-6278.
Luo, L. J. (2010). Breeding for water-saving and drought-resistance rice (WDR) in China. J. Experimental Botany,
61(13): 3509-3517.
Serraj R., McNally K. L., Slamet-Loedin I., Kohli A., Haefele S. M., Atlin G. and Kumar A. (2011). Drought
resistance improvement and resource management strategy. Plant Prod. Sci., 14(1): 1-14.
Vadez V., Kholova J., Medina S., Kakkera A., and Andeberg H. (2014). Transpiration efficiency: new insights into
an old story. J. Experimental Botany, 65(21): 6141-6153.
WARDA (1999). New Rice for Africa--- with a little help from our friends. WARDA Annual Report, pp. 9-15.

109

Activity for Simulating Rice Productivity in Musi River Basin, Indonesia
Homma, K.1*, K. Tsujimoto2, T. Ohta3, T. Koike2, A. Wijaya4, S. Yamaoka5, Y. Matsumoto5
1Graduate School of Agriculture, Kyoto University, Sakyo, Kyoto 606-8502, Japan2
Department of Civil Engineering, The University of Tokyo, Japan
3
Earth Observation Data Integration and Fusion Research Initiative, The University of Tokyo, Japan
4
The Suboptimal Land Research Center, Sriwijaya University, Indonesia
5
Nippon Koei Co. Ltd., Japan
Email*: homma@kais.kyoto-u.ac.jp
ABSTRACT
Rice production in Musi River Basin is conducted in 4 ecotypes: irrigated, rainfed, tidal swamp and freshwater
swamp. The variation makes it difficult to estimate climate change impact on the rice production, because cultivation
methods and water requirements were much different among ecotypes. This study firstly conducted investigation at
150 farmers’ fields to characterize rice production in the ecotypes. Based on the research, parameters for rice growthmodel SIMRIW-rainfed were determined. To simulate weather and water availability, hydrological model WEB-DHM
were combined with SIMRIW-rainfed. The simulating results were validated with the investigated data. The impact ofclimatic change on rice production will be estimated in future studies.
Keywords: Farmers’ fields, fresh water swamp field, irrigated field, rainfed field, simulation model, Tidal
swamp field.

1. INTRODUCTION
One of the impacts caused by climate change in Indonesia is considered to be a change of thewater cycle. While rainfall tends to increase in Java, Bali, Nusa Tenggara and Papua in the rainyseason especially, it tends to decrease in other regions. In the dry season, rainfall is projected todecrease in most of Java and South Sumatra. In addition, there are growing concerns about anincrease of extreme events such as droughts and floods, owing to the increasing frequency of El Niño.
To evaluate climate change on rice production in Indonesia, we focused on Musi River Basin in
Sumatera Island, which occupies 60,000 km2 as basin area in Sumatra Island. Here we reported datacollection from farmer’s fields, development of a simulation model and preliminary results of thesimulation model.

2. MATERIALS AND METHODS
2.1. Stady area and data collection from farmer’s fields
Rice is planted in paddy fields which were agro-ecologically classified into 4 categories inMusi River Basin: tidal swamp, fresh water swamp, rainfed and irrigated. We seleclcted 6 locations
Telang, Upang (Tidal swamp), Rambutan (fresh water swamp), Leumping (rainfed), Belitang
and Musi Rawas (irrigated) (Fig. 1). 25 farmer’s fields were selected for each location, and total 150
fields were investigated, Because of the area and importance, we selected 2 tidal swamp and 2
irrigated locations. Biomass and nitrogen content in rice plant at 2 and 6 weeks after transplanting,heading and maturity were measured. Rice yield was measured at maturity. Soil chemical andphysical properties were measured in laboratory. Cropping management was obtained by interviews
to farmers.

110

Fig. 1. Location map for investigated fields
2.2. Model development
We used a coupled model of hydrological model (water and energy budget-based distributedhydrological model; WEB-DHM; Wang et al., 2009), and crop model (simulation model for riceweather relations for rainfed condition; SIMRIW-Rainfed; Homma and Horie, 2009) (Tsujimoto et al.
2013). WEB-DHM can describe spatiotemporal distributions of soil moisture and river discharge
with high accuracy and resolution at basin scale. The model of rice growth and production, SIMRIWrainfed, can simulate the growth of rice as represented by LAI and rice production by considering
yield decrease caused by stress from deficits of water and/or nitrogen. By dynamically coupling these
two models, soil moisture distribution can be described by considering soil characteristics andtopography of a river basin, and then the growth and production of rice responding to soil moisture
can be calculated over the entire basin.
Parameters in the crop model are set using data from the target area obtained in 2.1. Based on
the agricultural calendar and tables for characteristics of rice varieties, parameters such as forphenological development processes are established for each variety. Parameters such as for
production properties are set for each variety based on yield data.
For coupling SIMRIW-rainfed and WEB-DHM, meteorological data such as air temperature,solar radiation and others, plus hydrologic data such as soil moisture and surface storage water
depth output by WEB-DHM are input to SIMRIW-rainfed. Then the data of rice growth as
represented by leaf area of rice is output from SIMRIW-rainfed to WEB-DHM. Interfaces between
the two models are thereby developed.
The coupled model includes a module for irrigation management to investigate the effectivenessof water resource development. The irrigation-drainage system is regionally different and111

complicated. Consequently, an irrigation system model suitable to the target river basin wasdeveloped by modifying the previously developed irrigation model4 based on the information
obtained from field surveys of the target area, and then applied to the area.

3. RESULTS AND DISCUSSION
Crop management, e.x. water management and fertilizer application, varied among locations.
Table 1 shows the amount of nitrogen fertilizer for example. Musi Rawas applied 120 N kg ha-1 ofnitrogen fertilizer, which was more than double of amount in Upang. Although the management wasquite different, growth of biomass was similar among locations (Fig. 2).
TL
TL
UP
UP
RB
RB
LP
LP
BL
BL
MR
MR

1400

Biomass (g m-2)

1200
1000
800
600
400
200
0
0

20

40

60
DAT

80

100

120

Fig. 2. Biomass change of rice grown in representative fields
in the investigated locations (Symbols: measured; lines: simulated)
Table 1. Rice yield, harvest index (HI) and amountof nitrogen (N) fertilizer in each locations
Ecotypes

LocationYield (t ha-1)

HI (t t-1)

Fertilizer (N kg ha-1)

Rainfed

Leumping

4.2

0.4

60.8

Irrigated

Belitang

4.2

0.4

62.8

Musi Rawas

4.2

0.4

120.0

Average

4.2

0.4

91.4

Telang

5.0

0.4

117.7

Upang

2.1

0.2

50.6

Average

3.6

0.3

84.2

Rambutan

2.9

0.3

56.0

Tidal swamp

Fresh water swamp

Telang recorded the highest yield on average among the research locations. Upang andRambutan did not reach 3.0 t ha-1 of yield on average. Lower yield in Upang was mainly caused byinadequate water supply, while that in Rambutan was by deep water. The drought and deep water
decreased harvest index.

112

Using the investigated data of biomass and nitrogen uptake, the parameters in simulation
model were optimized. One of the parameters corresponds to soil fertility (Homma and Horie, 2009),
and roughly explained the variability of rice yield among farmers’ fields (Fig. 2). The relation
between parameter for soil fertility and soil properties will be analyzed in further study.
After developed the coupled model of WEB-DHM and SIMRIW-Rainfed, we have made sometest runs for sub-basin “Padang”. Since the input dataset and parameters for hydrology was notcompleted, the following results are merely examples and are not expected to be realistic. The landuse type in “Padang” is shown in Fig. 3. To examine the impact of irrigation during dry seasons, wehave made runs by changing planting dates over Julian days 90-180 at 15-day intervals in 1986, forwith and without irrigation. The results showed that if planting is after June, there is a difference ofrice growth between rainfed and irrigated conditions (Fig. 4). However, if planting is before May,
there is no difference because of adequate rainfall.
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■T da swamp paddy■ Fresh wa er swamp paddy■ Irr ga ed paddy■ Ra nfed paddy■ Wa er■ O her and-use■ Ou s de of he bas n
Fig. 3. Meshed map of Padang sub-basin in Musi River Basin

113

Fig. 4. One of the simulated result for Padang sub-basin. Simulation was conducted
for planting date 29 June 1986; non-irrigated (left) and irrigated for irrigated paddy field
and tidal swamp paddy field (right)

4. CONCLUSIONSThis study conducted research in Musi River Basin in Indonesia to simulate rice productivity
under climatic change. The preliminary results by a test simulation suggested that the coupledmodel represented rice production in farmers’ fields in each ecological categories. We are now
revising climate data and calibrating parameters for hydrology. Climate change impact on riceproduction in Musi river basin will be evaluated in next study.

ACKNOLEDGEMENT
The concept of this work was originally supported by the green network of excellence,
Ministry of Education, Culture, Sport, Science and Technology, Japan. The study was substantiallyconducted for the project for assessing and integrating climate change impacts into the water
resources management plans for Brantas and Musi river bains, funded by Japan International
Cooperation Agency.

114

REFERENCES
Homma, K. and Horie, T. (2009) The present situation and the future improvement of fertilizer applications by
farmers in rainfed rice culture. In: LR Elsworth and WO Paley (Eds.) Fertilizers: Properties, Applications andEffects, Nova Science Publishers, New York, pp. 147-180.
Tsujimoto, K., Homma, K., Koike, T. and Ohta, T. (2013) Development of a coupled model of a distributedhydrological model and a rice growth model for grasping necessary information for rain-fed agriculture, Annual
Journal of Hydraulic Engineering, JSCE 57: 511-516
Wang, L., Koike, T., Yang, K., Jackson, T., Bindlish, R., and Yang D. (2009) Development of a distributed
biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and
SGP99), J. Geophys. Res., 114, D08107.

115

The Number of Unemerged Leaves in Young Sucker of Sago Palm
Nabeya Keita1*, Satoshi Nakamura2, Akihiro Fujii1 and Yusuke Goto1
1

Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
School of Food, Agricultural and Environmental Sciences, Miyagi University, Sendai, Miyagi, Japan

2

Email*: keita.nabeya.s1@dc.tohoku.ac.jp
ABSTRACTA branch of sago palm (Metroxylon sagu Rottb.) is called ‘a sucker’. In sago palm cultivation, trunks
accumulating a large amount of starch can be harvested persistently by having young suckers grow with motherpalm. Young suckers are also used for propagation. They are cut off from the mother palm and nursed for about halfyear. After that, the suckers are transplanted to the field. Thus, suckers play an important role in sago palm
cultivation, but there is little scientific knowledge about the growth and morphology of sucker. In this study, to obtainbasic knowledge about the sucker, we investigated the number of unemerged leaf in suckers with proper size fortransplantation. Suckers were collected in two sago garden in Mukah, Sarawak, Malaysia. The youngest leafthemerged from leaf sheath was defined as the spear leaf. The diameter at the base of the 5 leaf from the spear leaf
was an average of 6 cm in the suckers used in this study. At least 6, up to 10 unemerged leaves were observed
initiated inside the spear leaf. The number of unemerged leaf was significantly positively correlated with the diameterof the base of spear leaf at a 1 % standard.
Keywords: Sago palm, Sucker, unemerged leaf

1. INTRODUCTION
A branch of sago palm (Metroxylon sagu Rottb.) is called ‘sucker’. In sago palm cultivation,
trunks accumulating a large amount of starch can be harvested persistently by having young
suckers grow with mother palm. Young suckers are also used for propagation (Sato et al., 1979).
They are cut off from the mother palm and nursed for about half a year. After that, the suckers aretransplanted in a field. Thus, suckers play an important role in sago palm cultivation, but there islittle scientific knowledge about the growth and morphology of sucker. In this study, to obtain basic
knowledge about the sucker leaf, we investigated the number of unemerged leaves in suckers inproper size for transplantation.

2. MATERIALS AND METHODS
Suckers in proper size for transplantation were collected in two sago gardens in Mukah,
Sarawak, Malaysia. Expanding leaves, a spear leaf (Jones, 1995) and unemerged leaves of thesuckers were removed carefully up to the growth point. Regarding unemerged leaves, in theyoungest hood-like leaf primordium covering the growth point, the leaf primordium which the tipof hood-like leaf was beyond the tip of growth point was assumed the first leaf primordium. The
number of unemerged leaves inside a spear leaf was counted to the first leaf primordium using astereomicroscope. In the sago palm, since a leaf sheath, which is a lower part of petiole, wraps
around the stem, a part of leaf sheath attached appears to be a node like ring after removed of theleaf sheath (Fig. 1). Therefore, considering the part of leaf attached to be a circle, we measured theperimeter of the part of a spear leaf attached to the stem and calculated the diameter of the stem.

116

3. RESULTS AND DISCUSSION
On the suckers we investigated, unemerged leaves initiated inside of a spear leaf were at least 5,
up to 10, an average of 6.7. The diameter of the part of a spear leaf attached ranged from 6.7 to 20.7mm, an average of 11.1 mm. Fig. 2 shows relationship between the diameter of the part of a spear
leaf attached and the number of unemerged leaves. The number of unemerged leaves was
significantly positively correlated with the diameter of the part of a spear leaf attached at over 0.1 %level. This result suggests that the number of unemerged leaves inside of a spear leaf increases withincrease of the diameter of the part of a spear leaf attached.

Unemerged leaf

Number of nuemerged leaves

Fig. 1. The part of a spear leaf attached to the stem and the unemerged leaf inside.
The solid line ( ) shows the part of a spear leaf attachedy = 0.27 x + 3.70
r = 0.85 ***

12
9
6
3
0
0

5
10
15
20
Diameter of the attached part of spear leaf (mm)Fig. 2. Relationship between the diameter of part of a spear leaf attachedand the number of unemerged leaves
Note: *** represents significant difference at 0.1 % level (n = 21)

REFERENCES
Jones, D. L. (1995). Palms. Smithonian Institution Press, Washington, D. C. pp. 1-410.
Sato, T., Yamaguchi, T. and Takamura, T. (1979). Cultivation, harvesting and processing of sago palm. Jpn. J. Trop.
Agr., 23: 130-136. (in Japanese).

117

Traits Related to Seed Yield in Early Varieties of Adzuki Bean in Hokkaido, Japan
Kasajima Shinya1*, Satoshi Ogawa1, Hirotake Itoh1, Hideki Kurosaki2,
Yuki Horiuchi3, Hitoshi Sato3
1

Faculty of Bioindustry, Tokyo University of Agriculture, Hokkaido, Japan
Hokkaido Research Organization Central Agricultural Experiment Station, Hokkaido, Japan
3
Hokkaido Research Organization Tokachi Agricultural Experiment Station, Hokkaido, Japan
2

Email*: s3kasaji@bioindustry.nodai.ac.jp
ABSTRACT
Seed yield-related traits of early varieties of adzuki bean [Vigna angularis (Willd.) Ohwi et Ohashi] were
examined for introduction in Hokkaido regions with short frost-free seasons. An elite line with early maturity,
Toiku No. 160; early varieties Sahoro-shozu and Kita-roman; and a medium variety, Erimo-shozu, were
evaluated. Flowering and podding ceased earlier in Toiku No. 160 than in other varieties; the podding rate of
Toiku No. 160 was approximately 10 % higher than that of the other varieties. Seed yield of Toiku No. 160 was
similar to that of Erimo-shozu, a leading variety in Hokkaido. Sahoro-shozu showed lower biomass production
and seed yield. The high seed yield of Toiku No. 160 may be useful for introducing early adzuki bean varieties
in extremely cold regions of Hokkaido.
Keywords: Adzuki bean, early variety, flowering, podding, seed yield

1. INTRODUCTION
Adzuki bean [Vigna angularis (Willd.) Ohwi et Ohashi] is widely cultivated in East Asia and is
the second most important leguminous crop in Japan after soybean [Glycine max (L.) Merr.](Lumpkin and McClary, 1994). Adzuki bean is mainly used for producing sweet bean paste (Ann),
which is a major ingredient in traditional Japanese sweets (Murata, 1999). Adzuki bean ismainly cultivated in Hokkaido, the northern region of Japan, and almost all adzuki bean varieties
have been bred by Tokachi Agricultural Experiment Station in Hokkaido (Shimada, 2006). It is
desirable to cultivate early adzuki bean varieties in extremely cold regions of Hokkaido owing toshort frost-free seasons.
The previously released early Sahoro-shozu variety has a lower seed yield than the standard
variety grown in Hokkaido (Aoyama, 2009). Therefore, it is important to identify the traits of earlyadzuki bean varieties with high yield potential. However, limited information is available regarding
the varietal differences in traits associated with seed yield in adzuki beans grown in extremely cold
regions. Knowledge of the flowering and podding habit is important in adzuki bean, as in many otherleguminous crops, for determining a variety’s earliness and seed yield (Kasajima et al., 2013). We
investigated the growth and seed yield of early varieties, focusing on flowering and podding. This
study describes the potential for cultivating early adzuki bean varieties as stable high-yielding
varieties in the extremely cold regions of Hokkaido, Japan.

2. MATERIALS AND METHODS
2.1. Varieties
Four adzuki bean varieties (Toiku No. 160, Sahoro-shozu, Kita-roman, and Erimo-shozu) wereused in this experiment. Toiku No. 160 is an elite line with early maturity, bred by Tokachi

118

Agricultural Experiment Station. Sahoro-shozu and Kita-roman are early varieties; the former areprone to soil-borne diseases, whereas the latter are tolerant against soil-borne diseases. Erimoshozu, a medium variety, is a leading adzuki bean variety in Hokkaido.
2.2. Culture
A field experiment was conducted at the Tokyo University of Agriculture farm at Abashiri,
Hokkaido, Japan (43°53′44″N, 144°21′45″E). The experimental site is in one of the coldest
regions of Hokkaido. The soil was Andosol, and the preceding crop was sugar beet (Beta vulgaris
L. var. rapa Dumort.). Four seeds per hill were seeded on June 1, 2011 with 16.6-cm hill spacing
in approximately 3.5-m-long rows with 72-cm row spacing. Seedlings were thinned to 2
seedlings per hill 3 wks after seeding. Compound fertilizer (N:P2O5:K2O = 5:25:14) was applied at−
80 g m 2 as the basal application. Eight experimental plots were prepared according to arandomized block design with 2 replications. Because variations in air temperature in 2011 weresimilar to the average over the preceding 5 yr, this experiment was performed under normal
climatic conditions.
2.3. Investigation
The change in flower and pod numbers was investigated for 4 plant samples in each variety.
The flowering date was recorded on a small label (9  22 mm) for each plant, which was wrappedaround the flower pedicel. The labels were attached to all flowers that had formed pods bymaturity. The podding rate was calculated from the flower and pod numbers during the entire
flowering period based on the label data. Twenty standard plants of each variety, except for thelabeled plants, were sampled at maturity from 10 hills. After air drying, the top dry weight, seedyield, and yield components, such as pod number, seed number per pod, and 100-seed weight
were recorded.

3. RESULTS AND DISCUSSION
The change in flower and pod numbers per plant differed among varieties (Fig. 1). Floweringand podding of the early varieties (Toiku No. 160, Sahoro-shozu, and Kita-roman) began earlier thanthat of the medium variety Erimo-shozu. Flowering and podding ceased more quickly in Toiku No.
160 than in the other 3 varieties. Our results revealed that cumulative temperature had minimal
effects on flowering and podding at the late flowering stage in Toiku No. 160. This trait of Toiku No.
160 is considered important for early maturity. In fact, the maturity date of Toiku No. 160 was
September 18, whereas that of Sahoro-shozu, Kita-roman, and Erimo-shozu was September 16, 20,
and 26, respectively. The highest flower and pod number per plant were observed in Sahoro-shozu,
although the podding rate of Toiku No. 160 was approximately 10% higher than that of the othervarieties, suggesting that Toiku No. 160 has highly efficient podding.
Table 1 shows the top dry weight, seed yield, and yield components for each variety. The top dry
weight was highest for Toiku No. 160, followed by Kita-roman. The lowest seed yield was found in
Sahoro-shozu, whereas few differences were observed between the other 3 varieties. The highest pod
number per hill was observed in Sahoro-shozu, followed by Erimo-shozu and Toiku No. 160. The
highest seed number per pod was observed in Toiku No. 160, followed by Kita-roman and Erimoshozu. The 100-seed weights of Toiku No. 160 and Erimo-shozu were lower than those of Sahoroshozu and Kita-roman.

119

開花数
Flower number

着莢数
Pod number

10
Flower no. per plant: 56.8
Pod no. per plant: 27.5
Podding rate (%): 48.4

8

Toiku No. 160

6
4
2
0

Jul-11

Jul-11

Aug-11 Aug-11 Aug-11

Sep-11

10

Number of flowers and pods per plant

8

Flower no. per plant: 94.0
Pod no. per plant: 35.8
Podding rate (%): 38.1

Sahoro-shozu

6
4
2
0

19-Jul

28-Jul

6-Aug

15-Aug 24-Aug

2-Sep

10
8

Flower no. per plant: 79.4
Pod no. per plant: 29.5
Podding rate (%): 37.2

Kita-roman

6
4
2
0

19-Jul

28-Jul

6-Aug

15-Aug 24-Aug

2-Sep

10
8

Flower no. per plant: 64.5
Pod no. per plant: 24.6
Podding rate (%): 38.1

Erimo-shozu

6
4
2
0

802

969

1153

1369

1541

1736

Fig. 1. Changes in flower and pod number per plant for each variety
Note: Values with the date on the abscissa indicate cumulative temperature (°C) from the seeding date. The plots show 5-dmoving averages for 8 plants in 2 replicates. The flower number, pod number, and podding rate in each figure are the final
values for the entire study period.

120

Table 1. Yield and yield components for each variety
Variety

Top dry weight(gm-2)

Pod no. per hill

Seed no. perpod

100-see weight(g)

Seed yield(g m-2)

Toiku No. 160

771.5 ± 26.8

62.8 ± 0.4

6.2 ± 0.1

13.5 ± 0.2

434.0 ± 12.5

Sahoro-shozu

656.5 ± 2.3

69.6 ± 1.6

5.2 ± 0.0

14.1 ± 0.4

418.8 ± 15.4

Kita-roman733.3 ± 53.3

55.0 ± 1.6

6.0 ± 0.2

15.7 ± 0.6

437.5 ± 17.3

Erimo-shozu

719.6 ± 50.2

65.3 ± 2.3

5.7 ± 0.0

13.6 ± 0.4

437.8 ± 20.3

ns

9,27

0.72

ns

ns

1)

LSD

Note: Data are mean ± standard error of 2 replicates.
1)
LSD shows significance level at p < 0.01; ns, not significant at p = 0.05.

In previous studies (Aoyama et al., 2009; Yamazaki et al., 2010), seed yield of early adzuki beanvarieties was lower than that of medium varieties. From the results of our experiment, the top dry
weight and seed yield of Sahoro-shozu was lowest among the varieties. Furthermore, the lowest pod
number per hill was observed in Kita-roman. However, the seed yield of Toiku No. 160 was almost
similar to that of the standard variety Erimo-shozu, although flowering and podding in the former
ceased earlier than those in the other varieties. This result may be attributed to the higher podding
rate of Toiku No. 160. These traits observed in Toiku No. 160 may be useful for introducing earlyadzuki bean varieties in the extremely cold regions of Hokkaido. By allowing expansion of thecultivation area, the development of early varieties such as Toiku No. 160 may promote stableadzuki bean production. In conclusion, we suggest extensive adzuki bean cultivation in theextremely cold regions of Hokkaido using early varieties.

REFERENCES
Aoyama S, H Shimada, N Hasegawa, K Murata, S Fujita and I Matsukawa. 2009. A new early maturing adzuki beanvariety “Kita-roman” with cool-weather tolerance and soil-borne disease resistance. Bulletin of Hokkaido
Prefectural Agricultural Experiment Station, 94: 1-16. (in Japanese with English abstract)
Kasajima S, H Itoh, Y Nakamaru, H Yoshida and H Sato. 2013. Effect of source or sink restriction on flowering,
podding, and yield performance in field-grown adzuki bean. The Proceedings of the 7th Asian Crop Science
Association Conference, pp. 230-233.
Lumpkin, T.A. and D.C. McClary. 1994. Azuki bean: botany, production and uses. CABI. 1-279.
Murata K. 1999. Breeding of adzuki bean cultivar “Erimo-shozu” and cultivars with large seeds or resistance to soilborne diseases in Hokkaido. Breed. Res., 1: 173-179. (in Japanese)
Shimada H. 2006. Circumstances of adzuki bean production in Hokkaido and future direction of breeding adzuki
bean. J. Agr. Sci., 61: 11-15. (in Japanese)
Yamazaki H, M Sato, H Sato, S Kasajima and H Kurosaki. 2010. Characteristics of flowering and pod setting
pattern in early adzuki bean varieties. Report of the Hokkaido Branch, the Japanese Society of Breeding andHokkaido Branch, the Crop Science Society of Japan, 51: 93-94. (in Japanese)121Dihaploid Rice Line Production through Anther Culture to Accelerate
the Development of New Rice Varieties
Dewi, I. S.1*, N.H. Putri2, B.S. Purwoko2
1Indonesian Center for Agricultural Biotechnology and Genetic Resources Research and Development,
Jl. Tentara Pelajar 3A, Bogor 16111,West Java-Indonesia
2
Department of Agronomy and Horticulture, Bogor Agricultural University, West Java-Indonesia
Email*: iswari.dewi01@gmail. com
ABSTRACT

Crop improvement to environmental stresses is the key to agriculture adaptation due to the changing climatesand less favourable soil conditions. Anther culture is one of rice breeding methods which can be used to achieve
homozygous pure lines in 2-3 generations, while conventional rice breeding usually needs more than 5 generations.
Thus anther culture can reduce the time, costs, and labours in developing new rice varieties. The objective of thisresearch was to obtain spontaneous doubled-haploid or dihaploid plants (DH) produced from indica rice antherculture. The explants used for the experiment consisted of several kinds of F1s from crosses between lowland rice(high yielding, resistant to bacterial leaf blight) and upland rice (high yielding, tolerant to shade and drought,
moderately tolerant to Al toxicity, resistant to blast), i.e. IR83821-99-2-2-2/I5-10-1-1, IR85640-114-2-1-3/I5-10-1-1,
IR83821-99-2-2-2/O18-b-1, IR85640-114-2-1-3/O18b-1, Bio-R81/I5-10-1-1, Bio-R82-2/I5-10-1-1, Bio-R81/O18b-1,
and Bio-R82-2/O18b-1. Callus induction and regeneration media were N6 and MS medium. Observations wereconducted on number of anther inoculated, calli produced and producing plantlets, green plantlets, and albino
plantlets. The result from this research were 608 green plantlets (15.9% from the total plantlets) consisted of 66, 60,
26, 12, 91, 15, 140, and 198 green plantlets from IR83821-99-2-2-2/I5-10-1-1, IR85640-114-2-1-3/I5-10-1-1,
IR83821-99-2-2-2/O18-b-1, IR85640-114-2-1-3/O18b-1, Bio-R81/I5-10-1-1, Bio-R82-2/I5-10-1-1, Bio-R81/O18b-1,
and Bio-R82-2/O18b-1, respectively. The first generation of spontaneous doubled-haploid (185 DH) plants weregrown to be further selected to speed up the rice breeding program.
Keywords: Doubled-haploid, indica, lowland rice, upland rice.

1. INTRODUCTION
Rice yield has increased dramatically with the development of new high yielding varieties
resistant to lodging, pests and diseases. Although rice farmers commonly attain high yield, yieldfluctuations sometimes occur because of unfavorable weather conditions during the rice cropping
season as well as less favourable soil conditions in particular areas. Therefore, crop improvement toenvironmental stresses is the key to agriculture adaptation (Kochian et al., 2004; Vinocur andAltman, 2005; Naylor et al., 2007).
Anther culture is one of the in-vitro techniques that can be used to accelerate the obtainmentof pure lines in rice breeding programs through production of doubled-haploid (DH) plants. Antherculture will shorten the breeding cycle significantly by rapid development of completely homozygouslines (in 2-3 generations), instead of the conventional inbred line development process which takes
at least 6-8 generations to derive lines with ~99% homozygosity (Forster and Thomas, 2005).
Thus anther culture can reduce the time, costs, and labours in developing new rice varieties.
However, the technique should allow production of large number of genetically stable spontaneous
DH plants from a wide range of genotypes for effective utilization in rice breeding programs (Dewiand Purwoko, 2008).

122

Rice subspecies indica is generally used for rice variety development in Indonesia. Early anthernecrosis, poor callus proliferation and high albino-plant regeneration are recognized as the major
problems in indica rice varieties (Dewi and Purwoko, 2008). The objective of this research was toobtain spontaneous DH or dihaploid plants produced from rice anther culture of several F1s fromindica x indica crosses.

2. MATERIALS AND METHODS
2.1. Materials
F1 plants from crosses between lowland rice which is high yielding, resistant to bacterial leaf
blight (Purwoko et al., 2010 ) and upland rice which is high yielding, tolerant to shade and drought,
moderately tolerant to Al toxicity, resistant to blast (Herawati et al., 2010; Safitri et al., 2011) i.e.
IR83821-99-2-2-2/I5-10-1-1, IR85640-114-2-1-3/I5-10-1-1, IR83821-99-2-2-2/O18-b-1, IR85640114-2-1-3/O18b-1, Bio-R81/I5-10-1-1, Bio-R82-2/I5-10-1-1, Bio-R81/O18b-1, and Bio-R82-2/O18b1 were used in this study. These anther donor rice plants were grown in green house.
2.2. Methods
2.2.1. Preparation of Planting Materials
Panicles at booting stage were collected when the anthers occupy 1/3 to 1/2 of the spikelet lengthor when the auricle distance between the flag leaf and subtending leaf of the primary and secondary
tillers reach 5-10 cm depending on genotypes. The panicles were placed inside the refrigerator withan average temperature of 7-100C for 8 days. After cold treatment, panicles were surface sterilized in
20% commercial bleach containing sodium hypochlorite for 20 minutes under aseptic condition.
Individual spikelets were cut to expose the anthers.
2.2.2. Callus Induction and Plant Regeneration
The media used for callus induction and plant regeneration in anther culture was according toDewi and Purwoko (2008). Anthers containing pollen at the mid-uninucleate to early binucleatestage of development were plated onto the N6 medium supplemented with 2.0 mg/L napthalene
acetic acid (NAA) + 0.5 mg/L Kinetin + 10-3 M putrescine and 6% sucrose. Induced calli were
subculture onto the plant regeneration medium consisting of MS medium supplemented with 0.5
mg/L NAA + 2.0 mg/L Kinetin + 10-3 M putrescine and 3% sucrose. Cultures were kept under dark
conditions for callus induction and under 16 hrs daily illumination with 80-watt fluorescent bulb forplant regeneration.
Observation was done on number of anther plated, number of calli produced, number of calli
producing plantlet, number of green and albino plantlets.
The rate of callus induction and regeneration was calculated as follows:% formed calli (CF)= (No. calli/No. plated anthers) x 100

% calli forming plantlets (CFP)= (No. calli forming plantlet/No. formed calli) x 100

% calli forming green plantlets (CGP)= (No. calli forming green plantlets/No. formed calli) x 100

% calli forming albinoplantlets (CAP) = (No. calli forming albino plantlets/No. formed calli) x 100

% of green plantlets (GP) = (No. of green plantlets/No. of plantlets) x 100

% of albino plantlets (AP) = (No. of albino plantlets/No. of plantlets) x 100

% of anther culture ability (ACA) = (No.of green plantlets/No.of plated anthers) x 100

% Doubled haploid (DH) plants = (No. of DH plants/No. Grow out plants) x 100

123

3. RESULTS AND DISCUSSION
3.1. Callus Induction and Regeneration
There were strong genotypic effects on callus induction frequency among 8 crosses. Callus
induction started when anther colour changed from yellow to brown three to four weeks after
culture. Embryogenic calli were compact, globular in shape, rapidly growing and with clearly formed
somatic embryos. Callus formation varied between 11.8 to 26.2% depending upon the genotype(Table 1). The data indicated that anthers from Bio-R81/I5-10-1-1 produced the highest percentage
of calli, while the lowest induced from anthers of IR83821-99-2-2-2/I5-10-1-1.
The ability of calli to regenerate plantlet was varied between 1.0-12.7 % (Table 1). Percentage of calli
producing green plantlet ranged between 0.2-2.7%, while for calli producing albino plantlet the range
were between 3.5-14.6%. Therefore, high callus formation did not result in high levels of green plantregeneration. The result was similar to other indica rice anther culture (Niroula and Bimb, 2009).
Table 1. Callus formation and ability to regenerate plantlets in rice anther culture of F1s
Genotypes (F1s)

CF (%)CP (%)CGP (%)CAP (%)

1. IR83821-99-2-2-2/I5-10-1-1

11.8

5.2

1.7

3.5

2. IR85640-114-2-1-3/I5-10-1-1

20.8

12.7

1.5

11.2

3. IR83821-99-2-2-2/O18-b-1

13.8

1.1

2.2

5.4

4. IR85640-114-2-1-3/O18b-1

19.7

1.0

0.2

4.9

5. Bio-R81/I5-10-1-1

26.2

3.1

2.7

9.0

6. Bio-R82-2/I5-10-1-1

12.8

2.1

1.5

14.6

7. Bio-R81/O18b-1

13.1

2.0

4.1

11.2

8. Bio-R82-2/O18b-1

25.5

2.7

3.2

7.4

Note: AP= Anther plated; CI= calli formed; CP= calli producing plantlets; CGP= calli producing green plantlets; CAP= calli
producing albino plantlets.

The plant regeneration started after 5 days to 2 weeks after transferring calli onto regeneration
medium. Some calli differentiated only into green plantlets or albino plantlets. Some calli differentiatedinto both green and albino plantlets. The result showed that 2-4 mm callus with soft shape, light yellow
colour was given high green plant regeneration, while more than 4 mm callus with hard shape, yellow
colour was gradually browning and died after transfer to plant regeneration medium.
Table 2. Plant regeneration in rice anther culture of F1s
Genotypes (F1s)PF

GP (%)AP (%)ACA (%)

1. IR83821-99-2-2-2/I5-10-1-1

188

35.1

64.9

1.5

2. IR85640-114-2-1-3/I5-10-1-1

948

6.3

93.7

1.0

3. IR83821-99-2-2-2/O18-b-1

213

12.2

87.8

1.1

4. IR85640-114-2-1-3/O18b-1

423

2.8

97.2

0.2

5. Bio-R81/I5-10-1-1

444

20.5

79.5

2.0

6. Bio-R82-2/I5-10-1-1

236

6.4

93.6

1.0

7. Bio-R81/O18b-1

673

20.8

79.2

2.0

8. Bio-R82-2/O18b-1

702

28.2

71.8

3.5

Note: PF= Plantlet formed; GP= Green plantlet; AP= Albino plantlet; ACA= Anther culture ability

124

Number of plantlet regenerate from cultured callus derived rice anther culture was varied
depending upon the genotypes. Green plant regeneration showed differences between the genotypes.
Green plant regeneration rates varied from 2.8 to 35.1% (Table 2). Experiment by Gioi (2004) showed
the results of anther culture of F1 plants from crosses between IR64 and new plant types cultivars
reached the highest green plant regeneration of 5.72%. In this experiment, the genotypes that give
more than 20% rates of green plant regeneration were IR83821-99-2-2-2/I5-10-1-1 (35.1%), BioR82-2/O18b-1 (28.2%), Bio-R81/O18b-1 (20.8%), and Bio-R81/I5-10-1-1 (20.5%). Anther culture
ability (ACA) for these genotypes relatively high ranged between 1.5-3.5%. Thus, the results of thisexperiment indicated the ability to form green plant regeneration in all genotypes, except forIR85640-114-2-1-3/O18b-1, was superior.
However, high rate of albino plant regeneration was also occurred in this experiment (Table 2).
Percentage of albino plant ranged between 64.9-97.2%. According to Dewi and Purwoko (2008)
compared to japonica, the low androgenic response of rice subspecies indica in anther culture due toearly necrosis or senescence of anthers may results from a higher rate of ethylene productions byanthers of indica. In this experiment a senescence inhibitor, putrescine, was used in both callus
induction and regeneration media but also could not nullified albino plant regeneration.
3.2. Dihaploid Plants Production
All green plantlets (608 plantlets) were acclimatized and survival plants (589 plants) wereplanted in the green house. Spontaneous doubled-haploid or dihaploid (DH) plants obtained from theexperiment were 185 plants or approximately 31.4%. Bio-R82-2/O18b-1 had the highest number ofDH plants, followed by Bio-R81/I5-10-1-1, IR85640-114-2-1-3/I5-10-1-1, and Bio-R81/O18b-1(Table 3). Those DH plants can be used further to ease selection of phenotypes for quantitative
characters (Datta 2005; Herawati et al., 2010).
Table 3. Dihaploid plants obtained from rice anther culture of F1s
No. of Plants
Genotypes

DH (%)GP

GOP

DH

1. IR83821-99-2-2-2/I5-10-1-1

66

55

14

25.5

2. IR85640-114-2-1-3/I5-10-1-1

60

60

33

55.0

3. IR83821-99-2-2-2/O18-b-1

26

18

3

16.7

4. IR85640-114-2-1-3/O18b-1

12

12

4

33.3

5. Bio-R81/I5-10-1-1

91

91

34

37.4

6. Bio-R82-2/I5-10-1-1

15

15

5

33.3

7. Bio-R81/O18b-1

140

140

31

22.1

8. Bio-R82-2/O18b-1

198

198

61

30.8

608

589

185

31.4

Total

Note: GP= Green plant, GOP= Grow out plant; DH= Doubled-haploid or dihaploid

4. CONCLUSIONS
There were strong genotypic differences for anther culture ability both for callus induction andplant regeneration. Callus induction and plant regeneration from anther culture were found to be
independent of each other. 185 DH plants were obtained from IR83821-99-2-2-2/I5-10-1-1,
IR85640-114-2-1-3/I5-10-1-1, IR83821-99-2-2-2/O18-b-1, IR85640-114-2-1-3/O18b-1, Bio-R81/I510-1-1, Bio-R82-2/I5-10-1-1, Bio-R81/O18b-1, and Bio-R82-2/O18b-1.

125

REFERENCES
Datta, S.K. (2005). Androgenic haploids: factors controlling development and its application in crop improvement,
Curr. Sci., 89(11): 1870-1878.
Dewi, I.S. and Purwoko, B.S. (2008). Role of polyamines in inhibition of ethylene biosynthesis and their effects onrice anther culture development, Indon. J. Agric.Sci., 9(2): 60-67.
Forster, B.P. and Thomas, W.T.B. (2005). Doubled haploids in genetics and plant breeding, Plant Breed Rev., 25: 57-88.
Gioi, T.D. and Tuan, V.D. (2004). Anther culture from crosses between IR64 and new plant type cultivars,
OMonRice, 12: 27-32.
Herawati, R., Purwoko, B.S. and Dewi, I.S. (2010). Characterization of doubled haploid derived from anther culture
for new plant type upland rice, J. Agron. Indonesia, 38(3): 177-184.
Kochian, L.V, Hoekenga, O.A. and Piněros, M.A. (2004). How do crop plants tolerate acid soils? Mechanisms ofaluminum tolerance and phosphorus efficiency, Ann. Rev. Plant Biol., 55: 459-493.
Naylor, R.L., Battisti, D.S., Vimont, D.J., Falcon, W.P., and Burke, M.B. (2007). Assessing risks of climate
variability and climate change for Indonesian rice agriculture, PNAS, 104: 7752-7757.
Niroula, R.K. and Bimb, H.P. (2009). Effect of genotype and callus induction medium on green plant regenerationfrom anther culture of Nepalese rice cultivars, Asian J. Plant Sci., 1:1-7.
Purwoko, B.S., Dewi, I.S. and Khumaida, N. (2010). Rice anther culture to obtain doubled-haploids with multiple
tolerances, AsPac J. Mol. Biol. Biotechnol., 18(1) : 55-57.
Safitri, H., Purwoko, B.S., Dewi, I.S., Wirnas, D., Abdullah, B. (2011). Evaluasi karakter agronomi dan komponen
hasil 35 genotipe padi haploid ganda hasil kultur antera (The agronomic character evaluation and yieldcomponents of 35 doubled haploid genotypes produced from rice anther culture), pp. 87-97. In: B. Suprihatno,
A. Daradjat, Satoto, Baehaki, Sudir (Eds.). Proc. Seminar Ilmiah Hasil Penelitian Padi Nasional, 24-25
November 2010. Buku 1. BB Padi, IAARD-Indonesia.
Vinocur, B. and Altman, A. (2005). Recent advances in engineering plant tolerance to abiotic stress: achievements
and limitations, Curr. Opin. Biotechnol., 16: 123-132.

126

The Effects of Phosphorus Application on Salinity Tolerance under Different Fertility
Upland Conditions in The Interspecific Rice Cultivar NERICA1
Honda, T., M. Tsuda* and Y. Hirai
Graduate School of Environmental and Life Science, Okayama University
1-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan
Email*: tsuda@okayama-u.ac.jp
ABSTRACTNERICA is interspecific hybrid rice developed to increase rice production in Africa. Cropping area has increased,
but grain yield has remained low due to infertile soil and progressive salinization. Phosphorus (P) application
improves rice growth. However, the effects of P application on rice have not been explored in saline soils. This study
examined whether P application improves rice growth in saline soils with different fertilities under upland conditions.
NERICA1 seeds were sown in 14-L pots filled with fertile paddy soil or infertile decomposed granite soil (masa soil). P-1
was applied to half the pots containing each soil. NaCl was applied at 0, 13, 26, or 39 g pot , fourteen days aftersowing. Electrical conductivity of soil solution (ECs) was recorded. Plants were harvested 114 days after sowing.
Shoot dry weight was determined, and Na and P content in the shoots was analyzed. Plant dry weight was larger inthe paddy soil than in the masa soil, and decreased as the NaCl levels rose, and ECs, increased. The decrease waslarger in masa soil. Shoot P concentration increased with P application, while shoot dry weight changed with salinity
levels. P application increased shoot dry weight in the two soils at low salinities, but decreased it at high salinities.
Therefore, P fertilization efficiency was negative at high salinities. Therefore, P application may improve rice growth inlow saline soils, but not in high saline soils. Rice salinity tolerance may be reduced by P application, whereas
increased fertility may enhance tolerance.
Keywords: Electrical conductivity, Fertilization, Mineral soil, Sodium accumulation.

1. INTRODUCTION
In the past, rice was only grown over a limited area in Africa, but it is now becoming animportant food crop and is grown over a much wider area (Balasubramanian et al., 2007). Demand
for rice has risen due to population growth and a shift in consumer preference for rice, especially inurban areas, but domestic production is far below demand. NERICA was developed by the Africa
Rice Center (formerly the West Africa Rice Development Association) to improve rice production(Jones et al., 1997). It was derived from a cross between two rice species: Oryza sativa L. and O.
glaberrima Steud., and the end cultivar line combined the useful traits from both species. NERICA
has a high-yield potential; therefore, it has been disseminated extensively. However, its actual yieldremains low.
Soil infertility and salinity are the major soil constraints in Africa (Korb et al., 2000; Ash andWoperies, 2001: Balasubramanian et al., 2007). NERICA1, one of the NERICA cultivars, wasidentified as not being salt tolerant (Sone et al., 2010), so NERICA1 yield should increase withimproved cultivation management of infertile saline soils. Phosphorus (P) deficiency is one of the
major limiting factors for crop production in the acid soils throughout Africa because available P is
reduced by the reaction of soluble P with iron. A previous study showed that the growth and yield of
upland rice was improved by P fertilizer in these unfavorable soils (Sahrawat et al., 1995). However,
the responses of NERICA cultivars varied with soil fertility (Sone et al., 2011). This study examined127the responses of NERICA1 to P application under various soil salinity stress conditions in soils withdifferent levels of fertility.

2. MATERIALS AND METHODS
2.1. Materials
The upland cultivar: NERICA1 was used. Its salinity tolerance is reported to be comparable tojaponica upland rice cultivars (Sone et al., 2010).
2.2. Methods
Fertile organic soil from the experimental field at Okayama University and infertile decomposed
granite soil were used as rooting media, and are referred to as paddy and masa soils, respectively.
The soils were packed in 14-L pots with pumice stone at the bottom. Nitrogen and potassium weremixed with the soils as a basal fertilizer at a rate of 1.6 g pot-1. Phosphate was applied to a half thepots containing each soil to create pot treatment (P) and not applied to the other half to create pot
treatment (N). The amount of phosphate was 1.6 g pot-1 resulting in 0.44 g pot-1 for P. NERICA1
seeds were sown on May 31, 2013. A drain hole at the bottom of the side wall was connected to a
storage tank via tubing. Surface irrigation was conducted and water draining into the tank was used
for irrigation. The plants were grown under upland conditions. Leaf age, number of stems, and plant
length were assessed every week.
Salinization treatment began 21 days after sowing. Sodium chloride (NaCl) was dissolved in theirrigation water and applied to pots at a rate of 13 g pot-1 for the saline pots. The same amount of
NaCl was added every two days. The different salinization treatments were 13, 26, and 39 g NaCl
pot-1. NaCl was not applied to the control pots. The electrical conductivity of soil solution (ECs) wasrecorded. Plant shoots were harvested 114 days after sowing and dried at 80°C for three days. Then,
their dry weights (DW) were determined.
Dry material (0.2 g) was extracted with 50 mL distilled water and 1 mL nitric acid at 80°C over
24 h. The extract solution was filtered and then analyzed with a flame spectrophotometer (Flame
Photometer, BWB Technologies Ltd. UK). The same amount of dry material was subjected to dry
ashing at 400°C for 24 h and then dissolved in nitrate. The solution was analyzed for P by vanadomolybdate absorption spectrometry using a spectroscope photometer (DU530, Beckman Coulter Inc.
US) at λ = 420 nm.
Phosphorus fertilization efficiency was calculated as follows: DW/amount of P applied, where
DW is the increase in shoot dry weight after a given level of P application at the same salinity.
2.3. Data analysis
The experiment included three factors, i.e., soil type, P application, and salinity, with five
replications, where one pot was one considered to be a replicate. The pots were arranged as arandomized complete block design. The data were subjected to analysis of variance and differences ofmeans were examined by Tukey’s test, with a significance level at 5%, using the Ekuseru-Toukei
2012 software (Social Survey Research Information Co., Ltd. Japan).

3. RESULTS
Electrical conductivity of the soil solution (ECs) increased as the amount of NaCl applied
increased (Fig. 1). At the same NaCl application level, ECs was higher in masa soil than in paddy
soil. P application had no significant effect on ECs.

128

ECs (dS m-1)

20

20

(a)

(b)

N

15

15

10

b B

5

c C

5

c C

d D

d D
0

B

b

a A
10

a A

P

0
0

13
26
Applied NaCl (g pot-1)

39

0

13
26
Applied NaCl (g pot-1)

39

Fig. 1. Effects of NaCl and P application on the electrical conductivityof the soil solution (ECs) in paddy (a) and masa (b) soils
Note: Values are the mean and standard error of five replications. The same lowercase and uppercase letters indicate no
significant differences for N (no P application) and P (P application), respectively (Tukey’s test, P < 0.05).

Plant growth i.e. leaf age, plant length and number of stems per plant, was suppressed by highsalinity, but the effects of P application were complex. Figure 2 shows the changes in the number ofstems as an example. P application increased stem numbers at low salinity levels, whereas it
decreased stem numbers at the high salinity levels. The number of stems was about three-foldgreater in the paddy soil than in the masa soil. Plants died about 90 days after sowing in NaCl-39 gtreated masa soil.

No. of stems (no plant-1)

50

50

(a)

40

40

30

30

20

20

10

10

(b)

0N

0P

13N

13P

26N

26P

39N

39P

0

0
0

40

80

120

0

40

80

120

Days after sowing (day)Fig. 2. Effect of NaCl and P application on number of stems
in paddy (a) and masa (b) soils
Note: Values are the mean and standard error of five replications. Arrows indicate application of NaCl. Numbers attached tothe symbols indicate the amount of NaCl applied to a pot (g pot-1). N is no P application and P is P application.

Na concentration in the shoots was hardly affected by NaCl and phosphate application, except
for the NaCl-39 g treated paddy soil and the NaCl-26 g treated masa soil where it was about 10 mg

129

g-1 (Fig. 3). P concentration in the shoots was averaged 2.5 mg g-1 without significant effects of NaClor P application in paddy soil (Fig. 4). In the masa soil, P concentration in the shoots was not affectedby salinity, but there was a P application effect. Without P application, it was as low as 1.2 mg g-1,
but increased by 3.7 mg g-1 when P was applied.

Plant Na (mg g-1)

20

20

(a)

15

(b)

N

15

a

A
10

P

A
10

a
5
B

a B

a

B

a

0
0

13
26
Applied NaCl (g pot-1)

39

5

b

B

b B

0
0

13
26
Applied NaCl (g pot-1)

39

Fig. 3. Effect of NaCl and P application on Na concentrationin the shoot (Plant Na) in paddy (a) and masa (b) soils
Note: Values are the mean and standard error of five replications. The same lowercase and uppercase letters indicate no
significant differences between applied NaCl for N (No P application) and P (P application), respectively (Tukey’s test, P < 0.05)

Plant P (mg g-1)

8

8

(a)

6

(b)

N

P

6
A

4
b A

a A

a A

a A

4

a

2

2

0
0

13
26
Applied NaCl (g pot-1)

A

A

39

a

a

0
0

13
26
Applied NaCl (g pot-1)

39

Fig. 4. Effect of NaCl and P application on P concentrationin the shoot (Plant P) in paddy (a) and masa (b) soils
Note: Values are the mean and standard error of five replications. The same lowercase and uppercase letters indicate no
significant differences between applied NaCl for N (No P application) and P (P application), respectively (Tukey’s test, P < 0.05)

Generally, shoot dry weight declined as the amount of NaCl applied increased, but the effect of P
application on dry weight was complex. There were interactions between soil type, salinity, and P
application (Fig. 5). Dry weight was greater for plants grown in the paddy soil compared to thosegrown in the masa soils. It was increased by P application at low salinities but P application
decreased dry weight at high salinities. As a result, the P fertilization efficiency, i.e. the increase in
dry weight per 1 g P application, was large in the low salinity treatments, but negative in the highsalinity treatments (Table 1).

130Dry weight (g plant-1)

200

200

(a)

150

a A

A

ab

a

(b)
N

P

150

A
b

100

B

100

A
a

50

50

0

0

B
b
bd C

0

13
26
Applied NaCl (g pot-1)

39

0

d D

13
26
Applied NaCl (g pot-1)

39

Fig. 5. Effect of NaCl and P application on shoot dry weight in paddy (a) and masa (b) soils
Note: Values are the mean and standard error of five replications. The same lowercase and uppercase letters indicate no
significant differences between applied NaCl for N (No P application) and P (P application), respectively (Tukey’s test, P , 0.05)

Table 1. Effect of NaCl application on phosphorus fertilization efficiency (PFE)in paddy and masa soils
P FE*

Applied NaCl(g pot-1)

Paddy

Masa

0

6.6

9.1

13

3.7

7.5

26

-6.1

-3.2

39

-14.5

-0.2

Note: *Fertilization efficiency of phosphorus. Increase in shoot dry weight due to fertilization is divided by
the amount of phosphorus applied.

To examine the factors responsible for the decrease in shoot dry weight at higher salinities, theP relative value against NaCl-0 g (no application of NaCl) was calculated. Relative dry weightdecreased as salinity increased i.e. amount of NaCl applied, ECs and sodium concentration in theshoot (Fig. 6). The size of the reduction was always larger in masa soil than in paddy soil, whichindicated that the plants seemed more salt tolerant in paddy soil.

Dry weight (Relative value)

1.5

(b)

(a)

(c)

Paddy-N
Paddy-P

1.2

Masa-N
0.9

Masa-P

0.6
0.3
0
0

20

40

Applied NaCl (g pot-1)

010ECs (dS m-1 )

20

0

5

10

15

Plant Na (mg g-1)

Fig. 6. Relationships between applied NaCl, electrical conductivity of the soil solution(ECs), Na concentration in the shoots (Plant Na) and shoot dry weights in salinized paddy(a) and masa (b) soils with and without phosphorus fertilization
Note: Values are the mean of five replications.

131

4. DISCUSSION
Soil salinity has hazardous effects on crop production (Ghassemi et al., 1995). In a similarmanner to previous studies (Makihara et al., 1999; Wang et al., 2007; Sone et al., 2010), shoot dry
weight decreased as soil salinity increased (Fig. 5). However, the degree of decrease with respect tothe amount of NaCl applied was different for the two soils, in that the decrease was smaller in the
paddy soil. Soil salinity should be expressed using soil electrical conductivity (ECs) instead of theamount of NaCl applied. The ECs’ for the two soils were not the same when a comparable amount of
NaCl was applied (Fig. 1). This suggests that relative dry weight was related to ECs (Fig. 6).
Although relative dry weight decreased as ECs increased, the degree of decrease was still smaller inthe paddy soil. Furthermore, relative dry weight was related to Na concentration in the shoot (Fig. 6)
because Na accumulation in plants is often responsible for reductions in dry matter production. In
this study, relative weight decreased as sodium concentration increased, and the degree of decrease
was smaller in the paddy soil. Shoot dry weight was larger in the paddy soil (Fig. 5), which indicatesthat the productivity of paddy soil was higher than that of masa soil. These results strongly suggestthat plants grown in fertile soil may be salt tolerant in terms of soil salinity and accumulation of Na
in plants. This confirmed the results reported by Sone et al. (2011).
Sone et al. (2011) indicated that there was a large variation in response to P application at agiven salinity between NERICA strains. Additionally, this study found that the effect of P
application on dry matter production changed, depending on the salinity level: it raised dry matterin the low soil salinity treatments, whereas it reduced dry matter production at high salinity levelsin the two soils (Fig. 5). Stem numbers also followed this trend (Fig. 2). As P concentration in theshoot increased by generally the same amount under both low and high soil salinities, it could not be
responsible for the complex changes in dry weight. P concentration did not seem to affect salt
tolerance (Fig. 6). Therefore, the role of phosphorous in dry matter production under salineconditions should be explored in the future.
Shoot dry weight increased with P application (Fig. 5) in non-saline soils, which confirms theresults reported by Sahrawat et al. (1995). However, fertilization efficiency may decline as salinity
levels rise (Table 1). Recently, fertilizers are being increasingly used by small farmers; therefore,
farmers are being urged to use fertilizers more appropriately (Kamara et al., 2010; Saito et al., 2010;
Vanlauwe et al., 2014). However, the price of fertilizer is still not within the means of many small
farmers. Therefore, fields that could benefit from P fertilization should be evaluated carefully,
especially for saline soils. This would reduce the cost and inappropriate use of P fertilizers.

5. CONCLUSIONS
Salinity tolerance in rice may be greater in fertile soil than in infertile soil. P application may
increase plant P concentration, but changes in dry matter production vary, depending on the salinity ofthe soils. This suggests that P application should be carefully evaluated before it is added to the soil.

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