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International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 6, June 2017, pp. 423–429, Article ID: IJCIET_08_06_046
Available online at http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=6
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
SRI PADDY GROWTH SIMPLE PREDICTION
MODEL
N. A. I. Hasanah
Student, Department of Mechanical and Biosystem Engineering,
Bogor Agricultural University, Bogor - 16680, Indonesia
B. I. Setiawan
Professor, Department of Civil and Environmental Engineering,
Bogor Agricultural University, Bogor - 16680, Indonesia
C. Arif
Lecturer, Department of Civil and Environmental Engineering,
Bogor Agricultural University, Bogor - 16680, Indonesia
S. Widodo
Lecturer, Department of Mechanical and Biosystem Engineering,
Bogor Agricultural University, Bogor - 16680, Indonesia
ABSTRACT
The System of Rice Intensification (SRI) is a paddy cultivation system that is known
for its water management specialty. In practice, farmers regulate a water table in the
paddy field to serve sufficient soil moisture to support the paddy growth. The paddy
plant growth is represented by a tiller number produced during a cultivation season.
The tiller number (N) increases with time (t), and differs on the different water level (h)
applications. In this research, SRI paddy is cultivated in experimental pots under
various water table treatments. The water table is controlled by using mariotte tubes
and are set at -12, -7, -5, -3, 0, and +2 cm from the soil surface. The tiller number was
measured in each pot every 3 days. A 3D surface from 3 parameters (N, h, t) was made.
A simple multi-polynomial equation with R2 0.95 to predict N(h,t) was found after fitting
the 3D surface. The SRI paddy growth simple prediction model can be used as a
farmer’s guide when planning to apply different water tables in their land.
Key words: Fitting Surface, Multi Polynomial, Plant Growth and SRI
N. A. I. Hasanah, B. I. Setiawan, C. Arif and S. Widodo
http://www.iaeme.com/IJCIET/index.asp 424 [email protected]
Cite this Article: Hasanah, N. A. I., Setiawan, B. I., Arif. C, and Widodo, S. SRI Paddy
Growth Simple Prediction Model. International Journal of Civil Engineering and
Technology, 8(6), 2017, pp. 423–429.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=6
1. INTRODUCTION
The System of Rice Intensification (SRI) involves changes in certain management practices to
provide better growing conditions for rice plants than traditional practices, particularly in the
rhizosphere[1]. SRI is actually a strategy of irrigated rice production, adapted to local
conditions, that alters plant, soil, water and nutrient management practices (the means) with the
purpose (the end) of: (a) inducing larger, better-functioning root systems, and (b) more
abundant, diverse and active communities of soil biota that live in association with those root
systems[2]. In SRI, rice was recognized as having great unattained internal potential for tillering
and seeks to provide an optimum environment for plant to manifest such potential[3].
For SRI practice, the importance of keeping the soil unsaturated to get more air to plant
roots is evident. It because soil moisture still play a crucial role [4]. The optimal value of soil
moisture for SRI was found at 0.622 (wet), 0.593 (wet), 0.455 (medium), and 0.350 (dry)
cm3/cm3 for the initial, crop development, mid-season, and late season stages, respectively[5].
However, it was hard for farmers to understand the value of optimum soil moisture that should
be kept in their field. In practice, farmers tended to apply sufficient water tables to serve good
soil moisture. The different water table application could affect paddy growth[6], [7].
The reduction of water tables applied in a paddy field is one way of increasing water
productivity based on a civil water engineering study[8]. Agriculture is the largest consumer of
water and hence more effective use of water in agriculture is the highest priority[9]. The amount
of water saved through water table reduction could be used for expanding the scheme to areas
that were not receiving adequate irrigated water. This is especially important during the dry
season and in years where rainfall is below normal, when rice farming needs to be sustained
mainly through canal irrigation[10]. However, in fact farmers are generally hesitant to change
the water table that they normally apply. It because possibly lead to decrease in
productivity[11]. The aim of this research is to make a simple predictive model of rice plant
growth that can be used to estimate the potential result when farmers implement another water
table set-point.
2. METHODS
The paddy growth parameter used is tiller number. Tillering in paddy is an important agronomic
trait for grain production[12]. In this study, tillers that grow on the main stem of a paddy are
calculated directly every 3 days during one SRI cultivation season.
Paddy (Ciherang sp.) was cultivated in Bogor, Indonesia (S 6.55635°; E 106.72914°) with
an elevation of 191.0 m above sea level (Figure 1). The location belongs to A rainfall type (very
wet) with the general rainy season occurring from September to February[13]. This experiment
was conducted between July 2 and October 23 2014. For cultivation, 12 pots containing silt
loam soil (Table 1) were used.
Water treatment used for this experiment are water table commonly used by local farmers
(-12, -7, -3, -5, 0, +2 from soil surface)[14]. Mariotte tubes were utilized in cultivation pots and
used as a simple water level controller). The detail of the mariotte tube can be seen in Figure 2.
In this system, water flows due to the pressure head difference between water level point (A)
inside the tube and at the tube water output point (B). This tube is equipped with air holes and
the height of the inside air hole (C) will be parallel to the height of the pot water table[15].
SRI Paddy Growth Simple Prediction Model
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Figure 1 Paddy cultivation
Figure 2 Detail of water table control by mariotte tube
Table 1 Soil properties
Parameter Unit Value
Sand % 27
Silt % 62
Clay % 11
Bulk density g/cc 0.55
Particle density g/cc 2.05
Soil moisture at pF 1 %Volume 71.0
Soil moisture at pF 2 %Volume 27.5
Soil moisture at pF 2.54 %Volume 21.1
Soil moisture at pF 4.2 %Volume 13.3
N. A. I. Hasanah, B. I. Setiawan, C. Arif and S. Widodo
http://www.iaeme.com/IJCIET/index.asp 426 [email protected]
In this study, water table set-point in treatment (h) and paddy age (t) were used as inputs model
to predict the tiller number (N). The tiller number data used in the model building is the average
of the tiller numbers from two observation pots in every water table treatment.
The prediction model built using Table Curve 3D v4.0 software, a three dimensional curve
fitting program[16]. This program combines a powerful surface fitter with the ability to find the
ideal equation to describe three dimensional empirical data[17].
N, h, t data will be used as XYZ data in 3D surface. It will automatically fit using Table
Curve 3D. Fitted equations will be listed using R2 as the statistical criteria and equation number
as simplicity criteria. A simple SRI paddy growth prediction equation will be chosen from that
list.
3. RESULTS AND DISCUSSION
The number of tillers in various water table treatments were investigated at the interval of 3
days from transplanting to harvesting time. In this study, the first tiller appear in 18 days after
transplantation (DAT), because the tiller is usually counted after around 5 leaves develop within
the rate 3-4 days/leaves[18]. The number of tillers then increase along with the paddy age,
shown in Figure 3.
Figure 3 Tiller number during one season’s cultivation in various water table treatment
The maximum tiller number was found between 63 to 72 DAT before subsequently decreasing
due to tiller death. The results show that, water table treatments -5, -7, and -12 cm showed more
tillers than another treatments. Better aeration conditions in the soil are expected to occur in
those treatments, indicated by soil moisture values that are always below 0.71 m3/m3.
Meanwhile, saturation conditions are created in the water table treatments of 0 and +2 cm from
the soil surface.
The favorable conditions for growth in an unsaturated condition were allowed to complete
more phyllochrons of growth, producing more tillers and roots, before the flowering phase[19].
In saturated conditions, there might be an appearance of aerenchyma in the root, an adaptation
mechanism that allows plants to transport atmospheric oxygen to the underground organs to
maintain aerobic respiration and to oxidize various reducing compounds in the rhizosphere[20].
The aerenchyma could decrease root nutrient absorption capacity needed in tiller production. A
simple prediction function was found by using Table Curve 3D. A curve in 3 dimension was
built using the following variable data:
SRI Paddy Growth Simple Prediction Model
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a. Independent variables: water table (h) and paddy age (t)
b. Dependent variables: tiller number (N)
From analysis, there are 418 equations with 230 listed with R2 more than 90% with various
complexities. The simplest model found with the highest R2 0.95 is the multi polynomial model
(5 order):
���, �� � � � ��� �� ��� ��� �� ��� ��� ��� ���
Detail value of coefficient a, b, c, d, e, f, g, h, i, j, and k and the surface fitting can be seen in
Figure 4.
Figure 4Tiller numbers during one season of cultivation in various water table treatments
The following SRI paddy growth simple prediction model can be used as a farmer’s guide when
planning to apply different water tables in their land. However, it should be noted that the tiller
number (SRI plant growth) prediction function could be different in another location. It is
because other growth factors (x), such as soil nutrient and radiation, differ. Therefore, N is the
function of h, t, and x and can be list as:
���, �, �� � ����.���, �� � �.���, ��
N(h,t) found in this study can be used in another location if then value is known. Another
way is if there is a set tiller number data in certain water table applications, the tiller number
production potential in the same location could also be predicted using this approach:
����, �� � ����, ��� �� ���
� ��
� ���
� ���
� �� ��� ��� ��� ���
� �� ���� ��
� ���
� ���
� �� ��� ��� ��� ���
Farmers can predict their potential yield if they are willing to change the water table in their
field using this approach. Farmers’ scepticism of SRI cultivation (unflooding) method can also
be addressed.
N. A. I. Hasanah, B. I. Setiawan, C. Arif and S. Widodo
http://www.iaeme.com/IJCIET/index.asp 428 [email protected]
4. CONCLUSION
Tiller number is a paddy growth parameter that easy to measure. The number of tillers during
one cultivation season increases along with the paddy age and differs in various water table
treatment. A multi polynomial model (5 order) was found during analysis using Table Curve
3D. The equation works well in predicting SRI paddy growth in various water table set-points
(R2 = 0.95) and could be used as a model predicting approach in SRI paddy cultivation in
different locations.
ACKNOWLEDGEMENTS
The authors would like to thank the Ministry of Research, Technology and Higher Education,
Indonesia and especially to Directorate General of Resources for Science, Technology, and
Higher Education for their support, by a grant under PMDSU research project “Automation of
Irrigation and Drainage to Improve the Productivity of Land and Water and Reduce Greenhouse
Gas Emissions Factor”.
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