Saran Saran Penelitian Lanjutan

organik yaitu umur petani, pendapatan di luar usahatani padi, luas lahan garapan, status lahan, dan pengalaman petani dalam usahatani padi.

7.2. Saran

Berdasarkan kesimpulan diatas, maka untuk keberhasilan penerapan usahatani padi organik perlu beberapa dukungan penting yang ditujukan kepada petani padi organik, antara lain : 1. Dilakukan penelitian mengenai kepastian kandungan mineral dan hara pada pupuk organik kompos dan pupuk kandang yang digunakan oleh petani organik, karena selama ini pupuk organik yang digunakan berasal pupuk kandang mempunyai kandungan hara yang tidak dapat dipastikan. 2. Perlu meningkatkang mutu benih yang digunakan petani padi organik. Bila benih yang digunakan kurang baik, akan menghasilkan produksi yang rendah walaupun dilakukan perawatan dan pemberantasan hama penyakit secara intensif. Benih yang bermutu akan menghasilkan tanaman padi yang sehat, pertumbuhannya seragam dan rumpun yang kokoh. 3. Petani perlu mendapatkan bantuan modal untuk membuka usaha sampingan sebagai sumber penghasilan lain misalnya bantuan modal untuk usaha ternak kecil, karena petani memerlukan back up pendapatan sebagai antisipasi apabila terjadi gagal panen. 4. Penataan status kepemilikan lahan, sehingga semua petani memiliki akses pada lahan pertanian. Dengan status lahan hak milik, kemungkinan petani menerapkan usahatani organik makin besar. Penataan kembali status kepemilikan lahan juga akan meningkatkan kesejahteraan petani kecil di pedesaan. 5. Perlu adanya asuransi pertanian untuk menunjang keberhasilan penerapan usahatani padi organik yang di dalamnya mengandung risiko lebih besar.

7.3. Saran Penelitian Lanjutan

Dalam penelitian ini, risiko produksi yang dianalisis adalah risiko produksi yang ditimbulkan karena penggunaan input usahatani. Untuk lebih memperluas cakupan penelitian, alangkah baiknya apabila : 1. Dilakukan penelitian lebih lanjut mengenai risiko produksi yang bukan hanya disebabkan karena penggunaan input usahatani, tetapi juga menganalisis risiko produksi yang ditimbulkan karena kondisi cuacaiklim. 2. Mengangkat permasalahan mengenai risiko harga hasil komoditi usahatani padi organik yang dihadapi oleh petani, karena petani padi organik juga menghadapi risiko harga beras organik. DAFTAR PUSTAKA Andoko, A. 2007. Budidaya Padi Secara Organik. Penebar Swadaya, Jakarta. Bachus, G.B.C., V.R. Eidman and A.A. Dijkhuizen. 1997. Farm Decision Making Under Risk and Uncertainty. Neitherlands Journal of Agricultural Science, 45 1997: 307-328. Beattie, B.R. and C.R. Taylor. 1985. The Economics Production. Montana State University. John Wiley Sons, Inc, Motana. Cacek, T. and L. Linda. 1986. The Economic Implication of Organic Farming. American Journal of Alternative Agriculture, 1 1: 25-29. Carter, M.R. 1984. Identification of the Inverse Relationship between Farm Size and Productivity : An Empirical Analysis of Peasant Agricultural Production. Oxford Economic Papers, 361: 131-145. Coelli, T., D.S.P. Rao and G.E. Battese. 1998. An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publisher, Boston. Debertin, D.L. 1986. Agricultural Production Economics. Macmillan Publishing Company, New York. Eggert, H. and R. Tveteras. 2004. Stochastic Production and Heterogeneous Risk Preferencec : Commercial Fishers Gear Choices. Amer. J. Agr. Econ, 86 1: 199-212 Ellis, F. 1988. Peasant Economics : Farm Households and Agrarian Development. Cambridge University Press, Cambridge. Fariyanti, A., Kuntjoro, S. Hartoyo dan A. Daryanto. 2007. Perilaku Rumah Tangga Petani Sayuran Pada Kondisi Risiko Produksi dan Harga di Kecamatan Pengalengan Kabupaten Bandung. Jurnal Agro Ekonomi, 25 2 : 178-206. Frisvold, G., T.M. Hurley dan P.D. Mitchell. 2009. Adoption of Best Management Practices to Control Weed Resistance by Cotton, Corn ang Soybean Growers. Selected Paper Prepared for Presentation at the Agricultural and Applied Economic Association 2009 AAEA ACCI Joint Annual Meeting, Milwaukee, Wisconsin, July 26-29, 2009. Fufa, B. and R.M. Hassan. 2002. Stochastic Technology and Crop Production Risk : The Case of Small-Scale Farmers in East Hararghe Zone of Oromiya Regional State in Ethiopia. Departement of Agricultural Economics Alemaya Unversity, Dire Dawa. Guan, Z. and F. Wu. 2009. Specification and Estimation of Heterogeneous Risk Preference. Contributed Paper Prepared for Presentation at the 27 th International Conference of Agricultural Economists IAAE 2009. Beijing. Graziano, A.M. and M.L. Raulin. 1989. Research Methods : A Process of Inquiry. Harper Collins Publishers, New York. Hartoyo, S., K. Mizuno and S.S.M. Mugniesyah. 2004. Comparatif Analysis of Farm Management Risk : Case Study in Two Upland Village, West Java. In : Hayashi, Y., S. Manuwoto and S. Hartono. Sustainable Agriculture in Rural Indonesia. Gadjah Mada University Press, Yogyakarta. Hong, C.W. 1994. Organic farming and The Sustainability Of Agriculture in Korea. Papers Delivered at 12 th Meeting of The Technical Advisory Committee of the Food and Fertilizer Technology Center for The Asian and Pacific Region, Taiwan. Kim, M.K. and A. Pang. 2009. Climate Change Impact on Rice Yield and Production Risk. Journal of Rural Development, 32 2: 17-19. Koundouri, P. and C. Nauges. 2005. On Production Function Estimation with Selectivity and Risk Considerations. Journal of Agriculture and Resource Economics, 30 3: 597-608. Koutsoyiannis, A. 1977. Theory of Econometrics : An Introductory of Econometric Methods. Second Edition. Harper Row Publishers, Inc., New York. Kumbhakar, S.C. 2002. Specification of Production Risk, Risk Preferences and Technical Efficiency. American Journal of Agricultural Economics. 84 1: 8-22. Lawal, J.O. and K.A. Oluyole. 2008. Factors Influencing Adoption of Research Result and Agricultural Technologies Among Cocoa farming Households in Oyo State, Nigeria. International Journal Sustainable Crop Production. 35: 10- 12. Lien, G., O. Flaten, A. Koraeth, K.D. Schumann, J.W. Richardson and R. Eltun. Are Organic Crop Farming More Risky than Integrated and Gonventional Crop Farming? Norwegian Agricultural Economics Research Institute NILF, Oslo. Madau, F.A. 2005. Technical Efficiency in Organic Farming : An Application on Italian Cereal Farms Using a Parametric Approach. Paper Prepared for Presentation at the XI th Congress of the Eroupean Association of Agricultural Economists. The Future of Rural Europe I the Global Agri- Food System, Copenhagen. McConnell, D.J. and J.L. Dillon. 1997. Farm Management for Asia : A System Approach. Food and Agriculture Organization of United Nations, Rome. Medina, F. and A. Inglesias. 2008. Economic Feasibility of Organic Farms an Risk Management Atrategies. 12 th Congress of the European Association of Agriculture Economists-EAAE, Madrid. Ogada, M., W. Nyangena and M. Yusuf. 2010. Production Risk and Farm Technology Adoption In The Rain-Fed Semi-Arid Lands of Kenya. AfJARE, 42010 : 159-174. Pa ek, K. and . Rozman. 2007. The Simulation Model For Cost-Benefit Analysis on Organic Farms. Agronomski Glasnik, 32007: 209-222. Robison, L.J. and P.J. Barry. 1987. The Competitive Firm s Response To Risk. Macmillan Publishing Company, New York. Rubinos, R., A. Theresa and P. Bayacag. 2007. Comparative Economic Study of Organic and Conventional Rice Farming in Magsaysay, Davao Del Sur. 10 th National Convention on Statistics NCS, EDSA Shangri-La Hotel, Manila. Rukka, H., Buhaerah dan Sunaryo. 2006. Hubungan Karakteristik Petani Dengan Respon Petani Terhadap Penggunaan Pupuk Organik Pada Padi Sawah ry ✁ ✂ ✄ ✂☎✆ v ✂ ✝ .. Jurnal Agrisistem, 22006: 23-31. Sauer, J dan D. Zilberman. 2009. Innovation Behaviour at Farm Level-Selection and Identification. Gewisola. University of California and Giannini Foundation, Berkely. Serra, T., D. Zilberman and J.M. Gil. 2008. Differential Uncertainties and Risk Attitudes Between Conventional and Organic Producers : The Case of Spanish Arable Corp Farmers. Centre de Recerca en Economia I Desenvolupament Agroalimentaris CREDA, Barkeley. Soekartawi, J.L. Dillon, J.B. Hardaker dan A. Soeharjo. 1985. Ilmu Usahatani dan Penelitian Untuk Perkembangan Petani Kecil, Universitas Indonesia Press, Jakarta. Sulaeman, A. 2009. Konsep dan Pemikiran untuk Menyongsong Revolusi Hijau Kedua. Di dalam: Sumardjo, Ari Purbayanto, Surjono Hadi Sutjahjo. Arief B Boediono. Toto T. Harini M. Tineke M. Alex H. Bonar MS. 2009. ✞✟✠ ✂ ✡ ☛ p t ✟ ☞ s ✌ ✂ ✍ ✂ ✎ ✞✟✡ ✏✟✍✑✍ ✂ ✂ ✡ ✞ ✂ ✡ ✏ ✂ ✡ ✒ Energi, SDM dan Lingkungan Yang Berkelanjutan. Buku II, IPB Press, Bogor. Sutanto, R. 2002. Pertanian Organik : Menuju Pertanian Alternatif dan Berkelanjutan. Kanisius, Yogyakarta. Villano, A.R., C.J. O Donnell and G.E. Battese. 2005.An Investigation of Production Risk Preferences and Technical Efficiency : Evidence from Rainfed Lowland Rice Farm in the Philippines. Working Paper Series in Agriculture and Resource Economics. University of New England Australia, 2005 1: 1-24. Lampiran 1 . Nilai AR per Input dari Petani Padi Organik Petani AR x 1 AR x 2 AR x 3 AR x 4 AR Std Rata-Rata Deviasi 1 0.04230 0.00007 0.00048 -0.01015 0.00817 0.02327 2 0.05032 -0.00081 0.00015 -1.05268 -0.25075 1.10814 3 0.05778 -0.02130 4.63070 -0.00224 1.16624 2.30989 4 0.05070 -0.00006 0.00027 -0.01666 0.00856 0.02918 5 0.05224 0.00017 0.00032 -0.01606 0.00917 0.02972 6 0.03360 0.00010 0.00016 -0.00631 0.00689 0.01806 7 0.04089 -0.00043 0.00024 0.19212 0.05821 0.09134 8 0.04270 0.00010 0.00050 -0.00747 0.00896 0.02279 9 0.02600 0.00062 0.00010 -0.00745 0.00482 0.01459 10 0.03116 0.00004 0.00026 -0.00202 0.00736 0.01590 11 0.03801 -0.03780 9.07059 -0.00060 2.26755 4.53546 12 0.03400 -0.00002 0.00011 -0.04124 -0.00179 0.03079 13 0.04571 -0.00162 4.64651 -0.00213 1.17212 2.31637 14 0.04110 -0.00021 0.00018 -0.01847 0.00565 0.02518 15 -1.01388 0.00006 0.00046 -0.00197 -0.25383 0.50670 16 0.05167 0.00044 0.00041 -0.01724 0.00882 0.02975 17 0.03448 0.00005 0.00011 -0.03277 0.00046 0.02746 18 0.02397 0.00005 0.00021 -0.00802 0.00405 0.01382 19 -4.42072 -0.16981 6.87292 -0.00125 0.57029 4.67289 20 0.03751 -0.00012 1.60711 -0.00117 0.41083 0.79772 21 0.14497 0.00001 1.12954 -0.00037 0.31854 0.54498 22 0.09349 0.00003 1.60071 -0.00023 0.42350 0.78604 23 0.01129 0.06893 3.70030 0.03080 0.95283 1.83180 24 0.05065 0.00000 0.00022 -0.00900 0.01047 0.02713 25 0.05353 -0.00103 0.00081 -0.13416 -0.02021 0.08007 26 0.06687 0.00031 0.00030 -0.02058 0.01173 0.03806 27 0.03443 -0.00016 0.00019 -0.11284 -0.01959 0.06424 28 0.03398 -0.00002 0.00015 -0.04214 -0.00201 0.03116 29 0.03011 0.00010 0.00013 -0.02848 0.00046 0.02392 30 0.04546 -0.00001 0.00031 -0.01381 0.00799 0.02583 Lampiran 2. Nilai AR per Input dari Petani Padi Non Organik Petani AR x1 AR x2 AR x3 AR x4 AR x5 AR x6 AR x7 AR Std. Rata-rata Deviasi 1 -0.00948 -1.06937 -0.00243 -0.00076 0.00006 2.70471 0.00002 0.23182 1.16067 2 -0.02701 -1.06668 -0.00614 19.41809 1.56040 40.17986 0.00002 8.57979 15.70439 3 -0.00366 -1.04501 -0.00647 -2.98173 0.00000 3.57533 0.00000 -0.06593 1.94659 4 -0.01031 -1.07387 -0.00526 0.00393 0.00002 3.15388 0.00000 0.29549 1.32219 5 -0.00306 -1.04126 -0.00248 -0.04594 0.00046 12.85629 0.00000 1.68057 4.94301 6 -0.04668 -1.06526 -0.04953 -0.00001 0.00021 3.30046 0.00000 0.30560 1.37712 7 -0.04267 -1.07133 -0.02300 0.01670 0.00001 1.46102 0.00001 0.04868 0.73806 8 -0.01503 -1.07651 -0.00566 -0.00037 0.00125 8.14803 0.00001 1.00739 3.17400 9 -0.02272 -1.05859 -0.01202 -0.00019 0.00035 6.98348 0.00001 0.84147 2.73660 10 -0.01393 -1.07543 -0.00089 -0.00029 0.01030 4.45085 0.00002 0.48152 1.79555 11 -0.02694 -1.08748 -3.83630 -0.00002 0.00034 2.86339 0.00001 -0.29814 1.97810 12 -0.00371 -1.02911 -0.00172 -0.35346 0.00100 3.08399 0.00001 0.24243 1.30917 13 -0.01562 -1.11088 -0.02758 0.00028 0.00935 3.33914 0.00001 0.31353 1.39624 14 -0.01179 -1.05876 -0.00081 11.50638 1.02629 1.75695 0.00002 1.88833 4.33393 15 -0.01797 -1.08387 -0.00802 -0.00002 0.00031 3.03966 0.00003 0.27573 1.28339 16 0.33229 -1.07240 -0.00288 -0.00002 0.00002 11.03242 0.00001 1.46992 4.23969 17 -0.00075 -1.02445 -0.32102 0.00000 0.00027 0.47087 0.00000 -0.12501 0.45914 18 -0.01239 -1.04729 -0.00122 -0.00002 0.00023 6.37854 0.00001 0.75970 2.50808 19 -0.01544 -1.07520 -0.00053 -0.00002 72.37625 3.20670 0.00002 10.64168 27.25510 20 -0.02080 -1.04258 -0.00888 0.00000 0.00034 3.13160 0.00001 0.29424 1.30948 21 -0.01591 -1.04316 -0.00050 -0.00002 1.91688 3.81426 0.00001 0.66737 1.64285 22 -0.01634 -1.05034 -0.00092 -0.00001 1.02421 1.70572 0.00002 0.23748 0.88197 23 -0.01133 -1.04294 -0.00075 0.00000 1.34602 5.03299 0.00001 0.76057 2.00754 24 -0.02480 -1.08496 -7.83804 0.00000 0.00002 11.28368 0.00001 0.33370 5.61478 25 -0.01267 -1.03996 -0.00096 -0.00003 0.00002 1.67124 0.00001 0.08824 0.79794 26 -0.00756 -1.06377 -0.00103 -0.07640 0.00078 3.27745 0.00001 0.30421 1.36817 27 -0.01072 -1.13168 -0.00615 0.00000 1.02579 4.47797 0.00000 0.62217 1.81088 28 -0.01044 -1.08865 -0.00604 -0.00026 0.00004 1.78841 0.00002 0.09758 0.84824 29 -0.15106 -1.04835 -0.00115 -0.00001 0.69611 1.64860 0.00001 0.16345 0.83039 30 -0.01049 -1.06210 -0.00187 0.00000 0.00007 3.14972 0.00001 0.29648 1.31869 Lampiran 3 . Hasil Estimasi Fungsi Produksi Usahatani Organik Fungsi Produksi Ustan Organik The REG Procedure Model: MODEL1 Dependent Variable: LY Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 5 12.96688 2.59338 18.31 .0001 Error 24 3.39931 0.14164 Corrected Total 29 16.36619 Root MSE 0.37635 R-Square 0.7923 Dependent Mean 3.00694 Adj R-Sq 0.7490 Coeff Var 12.51598 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 0.65839 0.96913 0.68 0.5034 0 LX1 1 0.39491 0.19904 1.98 0.0588 3.82423 LX2 1 0.12403 0.21794 0.57 0.5746 3.97806 LX3 1 0.13436 0.16662 0.81 0.4280 1.77488 LX4 1 0.09029 0.05029 1.80 0.0852 1.18522 LX5 1 0.49535 0.18586 2.67 0.0135 2.63973 Lampiran 4 . Hasil Estimasi Fungsi Produksi Usahatani Non Organik Fungsi Produksi Ustan Non Organik The REG Procedure Model: MODEL1 Dependent Variable: LY Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 8 17.07627 2.13453 56.93 .0001 Error 21 0.78742 0.03750 Corrected Total 29 17.86368 Root MSE 0.19364 R-Square 0.9559 Dependent Mean 3.76123 Adj R-Sq 0.9391 Coeff Var 5.14828 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 5.55666 1.43070 3.88 0.0009 0 LX1 1 1.13500 0.15066 7.53 .0001 9.25887 LX2 1 0.12222 0.13044 0.94 0.3594 5.29197 LX3 1 0.03534 0.14161 0.25 0.8054 8.97843 LX4 1 -0.05001 0.06064 -0.82 0.4188 3.54323 LX5 1 0.05350 0.05188 1.03 0.3142 2.40668 LX6 1 -0.03129 0.02455 -1.27 0.2163 1.11486 LX7 1 0.07137 0.06711 1.06 0.2997 1.97627 LX8 1 -0.38902 0.17545 -2.22 0.0378 7.04246 Lampiran 5 . Hasil Estimasi Fungsi Produktivitas Usahatani Organik Fungsi Produktivitas Ustan Organik The REG Procedure Model: MODEL1 Dependent Variable: LPY Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 4 1.67977 0.41994 2.79 0.0479 Error 25 3.75629 0.15025 Corrected Total 29 5.43606 Root MSE 0.38762 R-Square 0.3090 Dependent Mean 3.88578 Adj R-Sq 0.1984 Coeff Var 9.97542 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 1.61145 0.78360 2.06 0.0503 0 LPX2 1 0.07013 0.22173 0.32 0.7544 1.65222 LPX3 1 -0.00063796 0.14759 -0.00 0.9966 1.27169 LPX4 1 0.05776 0.04731 1.22 0.2335 1.02853 LPX5 1 0.41491 0.18418 2.25 0.0333 1.70341 Lampiran 6 . Hasil Estimasi Fungsi Produktivitas Usahatani Non Organik Fungsi Produktivitas Ustan Non Organik The REG Procedure Model: MODEL1 Dependent Variable: LPY1 Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 7 0.50026 0.07147 1.97 0.1068 Error 22 0.79984 0.03636 Corrected Total 29 1.30010 Root MSE 0.19067 R-Square 0.3848 Dependent Mean 4.21485 Adj R-Sq 0.1890 Coeff Var 4.52384 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 4.97874 1.00362 4.96 .0001 0 LPX2 1 0.13248 0.12723 1.04 0.3091 1.74147 LPX3 1 0.02545 0.13841 0.18 0.8558 1.71192 LPX4 1 -0.05080 0.05970 -0.85 0.4040 1.63822 LPX5 1 0.04450 0.04871 0.91 0.3709 1.52672 LPX6 1 -0.02632 0.02263 -1.16 0.2572 1.35757 LPX7 1 0.09074 0.05718 1.59 0.1268 1.43623 LPX8 1 -0.32858 0.13841 -2.37 0.0267 1.99453 Lampiran 7 . Hasil Estimasi Parameter Untuk Pembobotan Organik Estimasi Untuk Pembobotan Organik The REG Procedure Model: MODEL1 Dependent Variable: Y Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 4 0.12993 0.03248 0.30 0.8729 Error 25 2.67673 0.10707 Corrected Total 29 2.80666 Root MSE 0.32721 R-Square 0.0463 Dependent Mean 0.38200 Adj R-Sq -0.1063 Coeff Var 85.65730 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 0.06576 0.70752 0.09 0.9267 0 X1 1 -0.04637 0.18820 -0.25 0.8074 1.67034 X2 1 -0.03220 0.12456 -0.26 0.7982 1.27115 X3 1 0.01158 0.01402 0.83 0.4166 1.09139 X4 1 0.11752 0.15558 0.76 0.4571 1.70563 Lampiran 8 . Hasil Estimasi Parameter Untuk Pembobotan Non Organik Estimasi Untuk Pembobotan Non Organik The REG Procedure Model: MODEL1 Dependent Variable: Y Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 7 0.08678 0.01240 0.76 0.6237 Error 22 0.35752 0.01625 Corrected Total 29 0.44430 Root MSE 0.12748 R-Square 0.1953 Dependent Mean 0.14543 Adj R-Sq -0.0607 Coeff Var 87.65637 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 0.35935 0.68168 0.53 0.6034 0 X1 1 -0.06715 0.08737 -0.77 0.4503 1.83728 X2 1 -0.08308 0.08412 -0.99 0.3341 1.41447 X3 1 -0.02630 0.01841 -1.43 0.1673 1.33096 X4 1 -0.01259 0.01386 -0.91 0.3737 1.38777 X5 1 -0.00926 0.00745 -1.24 0.2272 1.28901 X6 1 0.03682 0.03993 0.92 0.3665 1.56702 X7 1 0.04601 0.08544 0.54 0.5957 1.70043 Lampiran 9 . Hasil Estimasi Parameter Setelah Dilakukan Pembobotan Organik MNLS Setelah Dibobot Organik The REG Procedure Model: MODEL1 Dependent Variable: Y Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 4 103.23562 25.80891 20.95 .0001 Error 25 30.79103 1.23164 Corrected Total 29 134.02665 Root MSE 1.10979 R-Square 0.7703 Dependent Mean 10.47501 Adj R-Sq 0.7335 Coeff Var 10.59467 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 20.30321 13.12366 1.55 0.1344 0 X1 1 1.02787 0.53236 1.93 0.0649 51.20309 X2 1 0.64285 0.40048 1.61 0.1210 35.51988 X3 1 -0.21039 0.15631 -1.35 0.1904 84.62010 X4 1 -1.93698 1.59447 -1.21 0.2358 381.28364 Lampiran 10 . Hasil Estimasi Parameter Setelah Dilakukan Pembobotan Non Organik MNLS Setelah Dibobot Organik The REG Procedure Model: MODEL1 Dependent Variable: Y Number of Observations Read 30 Number of Observations Used 30 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 4 103.23562 25.80891 20.95 .0001 Error 25 30.79103 1.23164 Corrected Total 29 134.02665 Root MSE 1.10979 R-Square 0.7703 Dependent Mean 10.47501 Adj R-Sq 0.7335 Coeff Var 10.59467 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 20.30321 13.12366 1.55 0.1344 0 X1 1 1.02787 0.53236 1.93 0.0649 51.20309 X2 1 0.64285 0.40048 1.61 0.1210 35.51988 X3 1 -0.21039 0.15631 -1.35 0.1904 84.62010 X4 1 -1.93698 1.59447 -1.21 0.2358 381.28364 Lampiran 11 . Hasil Estimasi Fungsi Risiko Organik Menggunakan SAS 9.1 dengan LIML Fungsi Risiko Organik Setelah Dibobot The SYSLIN Procedure Limited-Information Maximum Likelihood Estimation Model Y Dependent Variable Y Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 4 52.11051 13.02763 86.90 .0001 Error 25 3.747797 0.149912 Corrected Total 29 55.85831 Root MSE 0.38718 R-Square 0.93291 Dependent Mean 12.54974 Adj R-Sq 0.92217 Coeff Var 3.08520 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr |t| Intercept 1 18.94447 0.837184 22.63 .0001 X1 1 0.948097 0.222689 4.26 0.0003 X2 1 0.627791 0.147389 4.26 0.0003 X3 1 -0.23105 0.016593 -13.92 .0001 X4 1 -2.36875 0.184092 -12.87 .0001 Lampiran 12 . Hasil Estimasi Fungsi Risiko Non Organik Menggunakan SAS 9.1 dengan LIML Fungsi Risiko Non Organik Setelah Dibobot The SYSLIN Procedure Limited-Information Maximum Likelihood Estimation Model Y Dependent Variable Y Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 7 1.499604 0.214229 0.42 0.8787 Error 22 11.19276 0.508762 Corrected Total 29 12.69236 Root MSE 0.71328 R-Square 0.11815 Dependent Mean 0.93634 Adj R-Sq -0.16244 Coeff Var 76.17669 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr |t| Intercept 1 1.434506 1.507583 0.95 0.3517 X1 1 0.020367 0.141482 0.14 0.8868 X2 1 0.021832 0.130308 0.17 0.8685 X3 1 0.015297 0.033044 0.46 0.6480 X4 1 0.001915 0.022697 0.08 0.9335 X5 1 0.011383 0.014796 0.77 0.4498 X6 1 -0.01818 0.075228 -0.24 0.8113 X7 1 -0.02962 0.125170 -0.24 0.8152 Lampiran 13 . Hasil Estimasi Fungsi Risiko Organik Menggunakan Frontier 4.1 Ou t p u t f r o m t h e p r o g r a m FRONTI ER Ve r s i o n 4 . 1 c i n s t r u c t i o n f i l e = t e r mi n a l d a t a f i l e = Ri s MNLS. t x t t h e f i n a l ml e e s t i ma t e s a r e : c o e f f i c i e n t s t a n d a r d - e r r o r t - r a t i o b e t a 0 0 . 1 8 9 4 5 4 8 8 E+0 2 0 . 1 1 6 4 2 7 5 4 E+0 1 0 . 1 6 2 7 2 3 4 2 E+0 2 b e t a 1 0 . 9 4 8 0 9 4 4 4 E+0 0 0 . 2 0 0 3 4 2 6 4 E+0 0 0 . 4 7 3 2 3 6 4 8 E+0 1 b e t a 2 0 . 6 2 7 8 0 0 5 1 E+0 0 0 . 1 2 9 6 2 9 1 7 E+0 0 0 . 4 8 4 3 0 4 9 7 E+0 1 b e t a 3 - 0 . 2 3 1 0 5 1 3 2 E+0 0 0 . 1 4 6 8 5 3 1 6 E- 0 1 - 0 . 1 5 7 3 3 4 9 3 E+0 2 b e t a 4 - 0 . 2 3 6 8 7 4 7 7 E+0 1 0 . 1 6 5 9 8 9 1 4 E+0 0 - 0 . 1 4 2 7 0 4 9 8 E+0 2 s i g ma - s q u a r e d 0 . 1 2 4 9 2 6 2 8 E+0 0 0 . 3 0 0 7 8 8 5 5 E- 0 1 0 . 4 1 5 3 2 9 2 4 E+0 1 g a mma 0 . 1 4 3 1 4 8 3 1 E- 0 4 0 . 2 3 0 5 0 4 6 4 E- 0 1 0 . 6 2 1 0 2 1 3 8 E- 0 3 mu i s r e s t r i c t e d t o b e z e r o e t a i s r e s t r i c t e d t o b e z e r o l o g l i k e l i h o o d f u n c t i o n = - 0 . 1 1 3 6 7 6 9 9 E+0 2 t h e l i k e l i h o o d v a l u e i s l e s s t h a n t h a t o b t a i n e d u s i n g o l s - t r y a g a i n u s i n g d i f f e r e n t s t a r t i n g v a l u e s n u mb e r o f i t e r a t i o n s = 4 3 ma x i mu m n u mb e r o f i t e r a t i o n s s e t a t : 1 0 0 n u mb e r o f c r o s s - s e c t i o n s = 3 0 n u mb e r o f t i me p e r i o d s = 1 t o t a l n u mb e r o f o b s e r v a t i o n s = 3 0 t h u s t h e r e a r e : 0 o b s n s n o t i n t h e p a n e l Lampiran 14 . Hasil Estimasi Fungsi Risiko Non Organik Menggunakan Frontier 4.1 Ou t p u t f r o m t h e p r o g r a m FRONTI ER Ve r s i o n 4 . 1 c i n s t r u c t i o n f i l e = t e r mi n a l d a t a f i l e = Mn l s No n . t x t t h e f i n a l ml e e s t i ma t e s a r e : c o e f f i c i e n t s t a n d a r d - e r r o r t - r a t i o b e t a 0 0 . 1 4 3 6 4 3 6 2 E+0 1 0 . 1 6 4 6 8 3 1 7 E+0 1 0 . 8 7 2 2 4 2 2 5 E+0 0 b e t a 1 0 . 2 0 3 6 6 5 6 3 E- 0 1 0 . 1 1 1 9 4 3 7 8 E+0 0 0 . 1 8 1 9 3 5 6 3 E+0 0 b e t a 2 0 . 2 1 8 3 1 1 8 5 E- 0 1 0 . 1 0 4 2 5 3 9 5 E+0 0 0 . 2 0 9 4 0 3 9 0 E+0 0 b e t a 3 0 . 1 5 2 9 7 4 3 9 E- 0 1 0 . 2 5 9 5 8 0 3 2 E- 0 1 0 . 5 8 9 3 1 4 2 7 E+0 0 b e t a 4 0 . 1 9 1 4 8 8 0 7 E- 0 2 0 . 1 7 8 2 9 9 8 8 E- 0 1 0 . 1 0 7 3 9 6 6 3 E+0 0 b e t a 5 0 . 1 1 3 8 3 3 8 1 E- 0 1 0 . 1 1 5 1 1 8 5 8 E- 0 1 0 . 9 8 8 8 3 9 5 9 E+0 0 b e t a 6 - 0 . 1 8 1 8 0 2 9 2 E- 0 1 0 . 5 8 7 4 1 2 2 2 E- 0 1 - 0 . 3 0 9 4 9 8 0 2 E+0 0 b e t a 7 - 0 . 2 9 6 1 5 2 8 8 E- 0 1 0 . 9 8 3 1 8 8 6 7 E- 0 1 - 0 . 3 0 1 2 1 6 7 4 E+0 0 s i g ma - s q u a r e d 0 . 3 7 3 0 9 5 5 7 E+0 0 0 . 9 3 0 0 1 0 7 5 E- 0 1 0 . 4 0 1 1 7 3 4 0 E+0 1 g a mma 0 . 1 5 6 7 2 8 1 6 E- 0 4 0 . 1 9 4 5 7 3 1 0 E- 0 1 0 . 8 0 5 4 9 7 5 8 E- 0 3 mu i s r e s t r i c t e d t o b e z e r o e t a i s r e s t r i c t e d t o b e z e r o l o g l i k e l i h o o d f u n c t i o n = - 0 . 2 7 7 7 9 1 9 8 E+0 2 t h e l i k e l i h o o d v a l u e i s l e s s t h a n t h a t o b t a i n e d u s i n g o l s - t r y a g a i n u s i n g d i f f e r e n t s t a r t i n g v a l u e s n u mb e r o f i t e r a t i o n s = 6 5 ma x i mu m n u mb e r o f i t e r a t i o n s s e t a t : 1 0 0 n u mb e r o f c r o s s - s e c t i o n s = 3 0 n u mb e r o f t i me p e r i o d s = 1 t o t a l n u mb e r o f o b s e r v a t i o n s = 3 0 t h u s t h e r e a r e : 0 o b s n s n o t i n t h e p a n e l Lampiran 15 . Hasil Estimasi Fungsi Absolute Risk Aversion terhadap Pendapatan Petani Organik Fungsi AR thd Pendapatan Usahatani Organik The REG Procedure Model: MODEL1 Dependent Variable: AR Number of Observations Read 28 Number of Observations Used 28 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 1 3.49806 3.49806 2.25 0.1459 Error 26 40.48042 1.55694 Corrected Total 27 43.97849 Root MSE 1.24777 R-Square 0.0795 Dependent Mean 0.49369 Adj R-Sq 0.0441 Coeff Var 252.74602 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 1.25278 0.55864 2.24 0.0337 0 Pendapatan 1 -7.88709E-8 5.261856E-8 -1.50 0.1459 1.00000 Lampiran 16 . Hasil Estimasi Fungsi Absolute Risk Aversion terhadap Pendapatan Petani Non Organik Fungsi AR thd Pendapatan Usahatani Organik The REG Procedure Model: MODEL1 Dependent Variable: AR Number of Observations Read 28 Number of Observations Used 28 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 1 3.49806 3.49806 2.25 0.1459 Error 26 40.48042 1.55694 Corrected Total 27 43.97849 Root MSE 1.24777 R-Square 0.0795 Dependent Mean 0.49369 Adj R-Sq 0.0441 Coeff Var 252.74602 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 1.25278 0.55864 2.24 0.0337 0 Pendapatan 1 -7.88709E-8 5.261856E-8 -1.50 0.1459 1.00000 Lampiran 17 . Hasil Estimasi Fungsi Probit Fungsi Probit Ustan Organik dan NonOrganik The LOGISTIC Procedure Model Information Data Set WORK.USAHATANI Response Variable Y Number of Response Levels 2 Model binary probit Optimization Technique Fishers scoring Number of Observations Read 60 Number of Observations Used 60 Response Profile Ordered Total Value Y Frequency 1 0 30 2 1 30 Probability modeled is Y=0. Model Convergence Status Convergence criterion GCONV=1E-8 satisfied. Model Fit Statistics Intercept Intercept and Criterion Only Covariates AIC 85.178 80.935 SC 87.272 97.690 -2 Log L 83.178 64.935 Lampiran 17 . Lanjutan Fungsi Probit Ustan Organik dan NonOrganik The LOGISTIC Procedure Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr ChiSq Likelihood Ratio 18.2422 7 0.0109 Score 12.3801 7 0.0887 Wald 11.9083 7 0.1036 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr ChiSq Intercept 1 2.0818 1.7498 1.4154 0.2342 Umur 1 -0.0601 0.0409 2.1640 0.1413 Pddkan 1 -0.00281 0.00560 0.2525 0.6153 IncLain 1 0.4553 0.4139 1.2100 0.2713 LuasLhn 1 -0.4007 0.4822 0.6906 0.4060 StatusLhn 1 0.6026 0.5863 1.0566 0.3040 PnglmnUstan 1 0.0421 0.0308 1.8719 0.1713 Risk 1 -0.1502 0.0730 4.2294 0.0397 Association of Predicted Probabilities and Observed Responses Percent Concordant 80.2 Somers D 0.607 Percent Discordant 19.6 Gamma 0.608 Percent Tied 0.2 Tau-a 0.308 Pairs 900 c 0.803 Lampiran 18 . Uji-t Perbedaan Aset Petani Organik dan Non Organik Test Statistik Aset Petani The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper CL Difference N Mean Mean Mean Std Dev Std Dev Std Dev Std Err Org - Non 30 -2323 1153.2 4629.1 7413.4 9308.6 12514 1699.5 T-Tests Difference DF t Value Pr |t| AsetOrg - AsetNon 29 0.68 0.5028 Lampiran 19 . Uji-t Perbedaan Nilai Absolute Risk Aversion Petani Organik dan Non Organik Test Statistik Nilai AR Petani The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper CL Difference N Mean Mean Mean Std Dev Std Dev Std Dev Std Err AROrg - ARNon 30 -1.779 -0.851 0.0762 1.9785 2.4843 3.3397 0.4536 T-Tests Difference DF t Value Pr |t| AROrg - ARNon 29 -1.88 0.0706 Lampiran 20 . Uji-t Perbedaan Keuntungan Usahatani Organik dan Non Organik Test Statistik Keuntungan Usahatani The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper CL Difference N Mean Mean Mean Std Dev Std Dev Std Dev Std Err Org - Non 30 -378E4 -148E4 827583 4.92E6 6.17E6 8.3E6 1.13E6 Difference Minimum Maximum Org - Non -888E4 1.32E7 T-Tests Difference DF t Value Pr |t| Org - Non 29 -1.31 0.2002 Lampiran 21 . Uji-t Perbedaan Pengalaman Usahatani Petani Organik dan Non Organik Test Statistik Pengalaman Petani The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper CL Difference N Mean Mean Mean Std Dev StdDev Std Dev Std Err POrg - PNon 30 -8.069 -0.133 7.8022 16.925 21.252 28.569 3.88 Difference Minimum Maximum POrg - PNon -46 35 T-Tests Difference DF t Value Pr |t| POrg - PNon 29 -0.03 0.9728 Lampiran 22 . Hasil Estimasi Fungsi Absolute Risk Aversion terhadap Faktor Sosial Ekonomi Petani Fungsi AR thd Aset, off-farm income, Pengalaman, StatusLhn The REG Procedure Model: MODEL1 Dependent Variable: AR Number of Observations Read 55 Number of Observations Used 55 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr F Model 4 1.68296 0.42074 2.08 0.0972 Error 50 10.10935 0.20219 Corrected Total 54 11.79231 Root MSE 0.44965 R-Square 0.1427 Dependent Mean 0.33452 Adj R-Sq 0.0741 Coeff Var 134.41786 Parameter Estimates Parameter Standard Variance Variable DF Estimate Error t Value Pr |t| Inflation Intercept 1 0.79942 0.22019 3.63 0.0007 0 Aset 1 -0.00000587 0.00000908 -0.65 0.5204 1.03676 IncLain 1 -0.21407 0.13225 -1.62 0.1118 1.07584 Pengalaman 1 0.00034002 0.00425 0.08 0.9366 1.11676 StatLhn 1 -0.41162 0.16777 -2.45 0.0177 1.04791 Lampiran 23 . Uji-t Perbedaan Penggunaan Tenaga Kerja Usahatani Organik dan Non Organik Uji-t Perbandingan Tenaga Kerja Usahatani The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper CL Difference N Mean Mean Mean Std Dev Std Dev Std Dev OX4 - NX4 30 -103.5 -84.54 -65.56 40.485 50.835 68.338 Difference Std Err Minimum Maximum OX4 - NX4 9.2811 -265 -29.65 T-Tests Difference DF t Value Pr |t| OX4 - NX4 29 -9.11 .0001 Lampiran 24 . Uji-t Perbedaan Luas Lahan Usahatani Organik dan Non Organik Uji t Perbedaan Luas Lahan Petani The TTEST Procedure Statistics Lower CL Upper CL Lower CL Upper CL Difference N Mean Mean Mean Std Dev Std Dev Std Dev Std Err Org – Non 30 -5289 -2580 129.08 5778 7255 9753.1 1324.6 T-Tests Difference DF t Value Pr |t| LhnOrg - LhnNon 29 -1.95 0.0612 ✓ B ✔✕ ✖✓ C ✕ RETNO BUDI RAHAYU. Farmer Risk Preference on Organic Paddy Farming in Sragen NUNUNG KUSNADI as a Chairman and ANNA FARIYANTI as a Member of the Advisory Committee. Organic paddy farming has more production risk than non organic paddy farming. Greater production risk is shown in productivity variance in organic farming than non organic farming. The purposes of this study are : 1 to determine inputs effect on risk production, 2 determine farmer risk preference and analyze relationship between socio economic factors and farmer risk preference, and 3 analyze effect of risk preference on farmer decision in organic paddy farming implementation. In this study we use Just-Pope production function model. Just Pope model construct the production function as the sum of two components, that are mean production function and variance function as a risk function. Probit model is used to analyze the relation between socio-economic factors and probability farmer implement organic paddy farming, and Arrow-Pratt absolute risk averson AR is used to estimate farmer risk preference. The result shows that most farmers are risk averse. Organic paddy farmer tend to risk taker than non organic paddy farmer. Pesticides and labor are inputs that have a risk reducing effect in organic paddy farming. Seeds and manure inputs have a risk increasing effect. Farmer s off-farm income and land owner status are have significant effect to farmer s risk preference. Probability of farmer to adopt organic paddy farming have a positive relations with off-farm income, land owner status, experiences in paddy farming and have negative relation with age, and risk preference. Organic paddy farming have more production risk than non organic farming. Key words : Organic paddy farming, Just-Pope production function model, expected utility, risk preference I. PENDAHULUAN

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