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Based on the data of the production factors S, F, L, and W and values of the constants, the model was used to predict the production. The results are presented in
Table 4.1. The values of the model agreed nicely with the data values as depicted in the following regression curve with an error of 0.002 Fig. 4.1. Data is presented in
Appendix 4.1. For the purpose of financial farm analysis, which is not discussed in detail in this analysis, labor should be expressed in m-days with its unit price.
Labors are needed in land preparation, transplanting, fertilizing, weeding, harvesting, etc. The total labor needed per hectare varies from 200 to 300
m-daysha DISIMP NTT 2008; Fitriadi Nurmalina 2009; Rachmiyanti 2009.
Production Regression Curve
y = 1.0012x - 0.0095 R
2
= 0.9990 2.00
3.00 4.00
5.00 6.00
7.00 8.00
9.00 10.00
11.00
2.00 4.00
6.00 8.00
10.00 12.00
Field Data toha Mo
d e
l to
n h
a
Model vs. Field Data
Figure 4.1. Regression of observation data vs. model for rice production yield
Furthermore, optimization of parameters of the production factors used in this model resulted in the following values of variables as presented in Table 4.2. The
yield components of the production factors was also calculated and presented in Table 4.3.
4.3.2. Prediction of Yield
It has been claimed from many sources that the yields of organic and non-organic rice farming using SRI method in various countries vary from 8.75 to
17.5 tonha. The high yields of the rice production might be due to the technique
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used for estimating the yield. Many experiments were conducted on plots of paddy fields of less than one hectare, and mostly smaller than 0.5 ha. The production was
then converted to an area of 1 ha, resulting in a yield of rice production in tonha. Depending on the method used, the yield may vary from one method to another and
it may result in fantastic yield as much as 17.5 tonha as stated above. It is shown in Table 4.1 that the maximum productivity of organic rice in the study area is around
10.4 tonha which is used as the target yield in the prediction of rice productivity later on see Table 4.2.
Table 4.2. Variable values of the production factors
Variable Max
Min Initial
Optimal
Seed S kgha 16.7
3.3 8.3
3.3 Fertilizer Ftonha
33.3 0.4
8.6 33.3
Labor Lmenha 300.0
160.0 235.9
300.0 Water Wfraction
4.2 1.5
3.0 4.2
Yield Datatonha 10.4
4.0 Yield Targettonha
7.5 10.4
Many field experiments have been conducted in conjunction with the maximum yield of organic farming using SRI method. It is clearly understood that
rice production is dependent upon, among others, the production factors such as seed seedling, fertilizer, labor, and water, beside the climatic condition. In a
certain point, the yield will reach a maximum, and then levels off or it may be fall down through time. Through a modeling technique, the yield in a rice production
can be predicted. Using Verhulst’s growth model as expressed in Equation 3, the prediction
of a rice production yield with the application of SRI method in organic rice farming in the District of Sukabumi, West Java, was conducted. The maximum
sustainable yield or target yield used in the simulation was 10.386 tonha as presented in Table 4.2. Through optimization process the result and the parameters
obtained in the prediction of the production yield through time, i.e., 8 years starting
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from 2006, are presented in Table 4.3. The result of the prediction with an error of 2.1 and R = 0.9221 is depicted in Fig. 4.4. Result of calculation of trend in rice
productivity is presented in Appendix 4.2. Table 4.3. Prediction of Rice Productivity and Parameters
Year Data tonha
Verhulst tonha Parameters
Values
2006 6.2
6.2
γ
1.262 2006
6.7 6.2
Po
6.2 2007
7.3 8.7
P∞
10.4 2007
8.7 8.7
Error
2.1 2008
10.4 9.9
R
0.9221 2008
10.9 9.9
2009 11.2
10.2 2009
10.2 2010
10.4 2011
10.4 2012
10.4 2013
10.4
Productivity
2.0 4.0
6.0 8.0
10.0 12.0
14.0
2004 2006
2008 2010
2012 2014
Year Yi
e ld
to n
h a
Data Verhulst
Figure 4.2. Prediction of yield of SRI organic rice farming It is shown from Table 4.3 that the maximum optimum productivity begins
in 2010, i.e., approximately four years from 2006. The increase in the productivity is undoubtedly due to the increase in experience and skill of farmer in practicing the
new technique. The production yield starts to level off at 10.4 tonha in 2010 and remains at that level provided that the technique is well practiced and all production
44
factors are well maintained. Otherwise, the yield may fall down below the maximum possible yield.
4.4. Conclusion
