Conclusion ANALYSIS ON THE PRODUCTIVITY OF ORGANIC RICE FARMING INTENSIFICATION

44 factors are well maintained. Otherwise, the yield may fall down below the maximum possible yield.

4.4. Conclusion

The models used in this study are quite good in analyzing the production and productivity of organic rice farming with SRI method and predicting the production yield through time. Based on the production factors S, F, L, and W and values of the constants, the development of production yield model resulted in a mathematical model of Y LD =2.664S -0.002 F 0.00019 L 0.002 W 0.94 . Calculation using this model gave values agreed nicely with the data values as depicted in the regression curve with an error of 0.002 and R 2 =0.9990. The prediction model for yield or productivity indicated that the maximum sustainable yield of 10.39 tonha was reached after approximately four years and starts to level off at the maximum value of 10.4 tonha in 2010, with an error of 2.1 and R 2 =0.9221. However, there were some limitations that should be taken into account to the results of this study due to the limiting study area in the District of Sukabumi, i.e., the model is valid only in the range of minimum and maximum values of S, F, L and W variables. Errors may also have been encountered during data collection. Therefore, this conclusion may not be applied to other areas without considering data collection from those areas.

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