Production Yield Results and Discussion

39 software. Model for predicting the yield of organic rice farming production could be developed using Verhulst growth model Burghes Borrie 1981. The following Equation 3 was used as the model for the prediction of the yield of organic rice production through time. 3 where: Yt = yield with respect to time tonha Y = initial yield tonha Y ∞ = maximum sustainable yield tonha = coefficient t = time year Equation 3 implies that the yield will level at its maximum value through time. How long the leveling condition will be reached depends upon the limitations on soil fertility and land area. Some historical data is needed to run the model in order to predict the yield.

4.3. Results and Discussion

4.3.1. Production Yield

Rice production data was collected randomly from farmers practicing SRI organic rice farming in the study area of Sukabumi District. In general, farmers in this area are small land holders less than 0.5 ha. However, their lands are of technical irrigation paddy fields so that there is no problem for them to apply the SRI method. The production data including some of the production factors is presented in Table 4.1. Most farmers in the areas do not have good farm management practice. They do not have well recorded production data, particularly in water requirement. Consequently, the water requirement had to be calculated using the following Equation 4 Doorenbos Kassam in Tarjuelo de Juan 1999: 1 1 ETm ETa Ym Ya     4 1 1 1                      t o e Y Y Y t Y  40 where: Ya = actual yield kg ETa = actual seasonal evapotranspiration ET mm Ym = maximum yield kg ETm = seasonal ET for maximum yield mm = water yield sensitivity coefficient. Table 4.1. Rice production data of farmers practicing SRI organic rice farming in Sukabumi District No. Name of Farmer Area Water 1 Seed Fertilizer Labor Production Yield ha 1000 m 3 ha kgha tonha m-daysha 2 tons tonha 1. H. Baban 0.200 3.220 7.500 7.500 225.000 1.600 8.000 2. H. Uk ar 0.250 3.220 6.400 7.200 220.000 2.000 8.000 3. Jalal 0.200 3.310 7.500 8.000 225.000 1.640 8.200 4. Maman 0.170 3.070 7.100 7.600 265.000 1.300 7.647 5. Baen 0.150 3.220 6.700 6.700 240.000 1.200 8.000 6. Oben 0.120 3.190 7.100 7.000 300.000 0.950 7.917 7. Barnas 0.050 3.220 10.000 16.000 160.000 0.400 8.000 8. Suhendi 0.200 3.650 7.500 10.000 225.000 1.800 9.000 9. Udin 0.150 3.220 3.300 6.700 240.000 1.200 8.000 10. Suparman 0.300 2.970 5.000 3.300 183.000 2.220 7.400 11. F. Sarifudin 0.070 4.230 7.100 18.600 243.000 0.727 10.386 12. Endang 0.100 3.220 10.000 8.000 170.000 0.800 8.000 13. Pahrudin 0.060 2.660 16.700 33.300 283.000 0.400 6.667 14. Neneng Hamidah 0.070 2.250 14.300 0.400 243.000 0.400 5.714 15. Nanang 0.075 3.360 6.700 0.700 227.000 0.625 8.333 16. Usup Pakot 0.120 2.040 8.300 0.400 300.000 1.200 5.208 17. Iep 0.070 1.530 7.100 1.000 243.000 0.280 4.000 18. Romi 0.060 1.840 8.300 8.300 283.000 0.285 4.750 19. Hasan 0.040 2.800 12.500 12.500 200.000 0.280 7.000 20. Ujang Zaenal 0.070 3.550 7.100 8.600 243.000 0.615 8.786 1 Water requirement was calculated using formula of Equation 4 2 Labor required per season Cobb-Douglas production function was used to develop model for predicting the yield by conducting optimization for the constant values of Equation 2. The optimization resulted in the following model: 94 . 002 . 00019 . 002 . 664 . 2 W L F S Y LD   4 where the constant and parameters of A, , , , are 2.664, -0.002, 0.00019, 0.002, and 0.94, respectively. The model is valid only in the range of minimum and maximum values of S, F, L and W variables as presented in Table 4.2. 41 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