V. SENSITIVITY ANALYSIS AND OPTIMIZATION OF THE PRODUCTION MODEL OF ORGANIC RICE FARMING IN
DISTRICT OF SUKABUMI
5.1. Background
The land productivity or yield of rice farming could be formulated by referring to the characteristics of agricultural systems involving among others:
power input, irrigation system, seed input, fertilizer and labor Kostrowicki 1976 as cited in van Dongen van Lier 1999. Using Cobb-Douglas production
function, a model for the calculation of the yield of organic rice farming production based on that characteristics
was developed for the District of Sukabumi area. The model was validated using field data from the area and the
result was a regression curve well agreed with the field data. Furthermore with the use of Verhulst’s growth model Burghes and Borrie, 1981, the development of
the productivity from year to year until reaching its maximum value was predicted based on the available field data Gardjito et al. 2010.
A sensitivity analysis on the model was necessary to conduct which would help to build confidence in the model by studying the uncertainties often
associated with parameters in the model. Using a comprehensive cropping system simulation model, Stockle et al. 1992 made a sensitivity analysis of crop growth
response to the combined effects of CO
2
concentration increase and CO
2
-induced climate change. Salam et al. 1994 developed a model to simulate the growth
duration, biomass, grain yield and quality, and to assess the economic implications for a rice crop in Bangladesh. Sensitivity to some of the biological parameters was
showed by the model, i.e., the extinction coefficient for visible light, initial light use efficiency for individual leaves, maximum rate of gross photosynthesis of
single leaves, and development rate constant at the reproductive stage. Green and Whittemore 2005 conducted sensitivity analysis of a model of growing pig for
weight gain and composition. The general equation used for the sensitivity was in terms of a differential equation of a model output to a change in a model
parameter. For comparison of sensitivity values, relative sensitivity rather than absolute sensitivity was more appropriate to use. Furthermore, Letcher et al.
2006 developed an integrated toolbox for water resources assessment and
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management in highland catchments. A scenario modeling approach was used in the toolbox which translated policy and uncontrollable drivers into scenario inputs
to the biophysical toolbox, a component of the integrated modeling toolbox. A sensitivity analysis of the model to changes in various input assumptions was
conducted which showed plausible levels and patterns of sensitivity. The sensitivity analysis used in this study was that of parameter sensitivity
which determine how sensitive the model developed to changes in the value of the parameters Jourdan et al. 1991; Pannell 1997; Breierova Choudhari 2001.
This was done as a series of tests in which different parameters values were set to see how a change in the parameter causes a change in the behavior of the yield.
Aside from that, the model can be used to determine the maximum profit that can be obtained by using optimization technique. The objective of this study was to
make sensitivity analysis of the production model based on Cobb-Douglas production function and used the model for the calculation of the optimum profit
that could be obtained through optimization process.
5.2. Method
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