SRI PADDY GROWTH SIMPLE PREDICTION MODEL€¦ · The following SRI paddy growth simple prediction...

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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

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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,


C. Arif

Lecturer, Department of Civil and Environmental Engineering,


S. Widodo

Lecturer, Department of Mechanical and Biosystem Engineering,


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


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


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



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:



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.



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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