EFISIENSI USAHATANI MELON DAN PERILAKU TERHADAP RISIKO PETANI MELON LAHAN PASIR DAN LAHAN SAWAH DI KABUPATEN KULON PROGO

ANANTI YEKTI, Prof. DR.Ir. Dwidjono Hadi Darwanto, MS; DR. Jamhari, SP. MP; DR.Ir. Slamet Hartono, SU. MSc

2017 | Disertasi | S3 Ilmu Pertanian

Penelitian ini memiliki tujuan: 1) mengukur efisiensi teknis, efisiensi alokatif dan efisiensi ekonomi usahatani melon serta faktor-faktor yang mempengaruhi inefisiensi, 2) mengukur risiko produksi, risiko pendapatan, faktor-faktor yang mempengaruhi: produksi, pendapatan, risiko produksi dan risiko pendapatan, 3) menganalisis perilaku petani melon terhadap risiko serta hubungan antara perilaku terhadap risiko dengan efisiensi teknis dan 4) mengidentifikasi strategi pengelolaan risiko dan usahatani melon dan selanjutnya digunakan untuk rencana perbaikan strategi pengelolaan risiko. Pengambilan data dilakukan pada April-Juni 2014. Data diperoleh dengan melakukan wawancara 50 petani di Kecamatan Galur (mewakili agroekosistem lahan pasir) dan 50 petani di Kecamatan Lendah (mewakili agroekositem lahan sawah). Data yang dikumpulkan berupa data antar petani (cross section) dan data antar musim tanam (time series). Efisiensi dianalisis menggunakan Cobb-Douglas stokhastik frontier menggunakan Maximum Likelihood Method, risiko usahatani diukur menggunakan fungsi produksi model Just and Pope (1979), perilaku petani melon terhadap risiko dianalisis menggunakan model Moschardi and de Janvry (1977), sedangkan pengelolaan risiko usahatani melon dianalisis secara deskriptif merujuk model yang digunakan Adiyoga dan Soetiarso (1999) serta Saptana (2010). Hasil penelitian menunjukkan bahwa petani melon di Kulon Progo belum efisien secara teknis, alokatif dan ekonomi. Luas lahan, benih, pupuk organik, agroekosistem dan musim tanam memberikan pengaruh nyata meningkatkan produksi melon. Faktor yang berpengaruh nyata terhadap biaya usahatani melon adalah jumlah produksi, luas lahan dan upah tenaga kerja. Umur petani berpengaruh meningkatkan inefisiensi teknis. Umur petani dan pendidikan formal berpengaruh menurunkan inefisiensi biaya secara nyata. Produksi dan pendapatan usahatani melon pada agroekosistem lahan sawah lebih tinggi dibanding lahan pasir. Tidak ada perbedaan risiko produksi maupun risiko pendapatan antara agroekosistem lahan pasir dengan lahan sawah. Pupuk ZA bersifat risk decreasing terhadap risiko produksi. Berkenaan dengan risiko pendapatan, harga pupuk ZA bersifat risk decreasing, sedangkan harga pupuk SP-36 bersifat risk increasing. Secara rata-rata petani melon di Kulon Progo berperilaku enggan terhadap risiko. Terdapat perbedaan strategi pengelolaan risiko pada kedua agroekosistem, khususnya pada strategi ex-ante dan interactive. Perbedaan strategi ex-ante terlihat pada keragaman pola tanam, pemilihan teknologi budidaya, dan lokasi usahatani, sedangkan pada strategi interactive terlihat bahwa petani pada agroekosistem lahan sawah menerapkan pemupukan anorganik lebih intensif dibanding petani lahan pasir, namun mengaplikasikan pupuk organik lebih rendah daripada petani lahan pasir. Penerapan strategi ex-post oleh petani pada agroekosistem lahan pasir maupun lahan sawah cenderung sama.

Objective of this study was : 1) measured technical efficiency, allocative efficiency, economic efficiency of melon farming and identified the factors that affected inefficiency, 2) measured production risk, risk of farm income, identified the factors that influenced: production, income, production risk and income risk, 3) analyzed attitude toward risk among melon farmers, and determined relationship between attitudes toward risk and technical efficiency and 4) identified melon farm risk management strategies, then be used for improvement plans for next risk management strategies. Primary data was collected during April-June 2014. Data was obtained by interviewing 50 farmers in Galur district (representing coastal agroecosystem) and 50 farmers in Lendah district (representing paddy fields agroecosystem). Data collected in the form of cross section data (among farmers) and time series data (among growing seasons). Production efficiency was analyzed using Cobb-Douglas stochastic frontier using Maximum Likelihood Method. Farm risk was measured using model of Just and Pope (1979). Attitude toward risk of melon farmer was determined using Moschardi and de Janvry (1977) model, while melon farm risk management analyzed descriptively referenced a model had used by Adiyoga and Soetiarso (1999) and Saptana (2010). The study revealed that the melon farmers in Kulon Progo did not efficient technically, allocatively and economically. Land area, seed, organic fertilizer, agroecosystem and planting season has significantly affected to melon production. The factors that had affected significantly on melon cost production were amount of melon production, land use and SP-36 fertilizer price. Age increased technical inefficiency. Age and formal education significantly decreased cost inefficiency. Melon production and farm income in paddy fields agro-ecosystem were higher than coastal agro-ecosystem, while production risk and income risk were insignificant between both agro-ecosystems. ZA fertilizer was risk decreasing due melon production. Price of ZA fertilizer was decreasing risk of farm income, while price of SP-36 fertilizer was increasing risk of farm income. On average melon farmers in Kulon Progo behaved aversion to the risk. There were differences in the risk management strategies for both agro-ecosystem, especially in the ex-ante and interactive strategy. Differences of ex-ante strategy was seen in the diversity of growing pattern, selection of cultivation technology, the use of varieties, and the location of the farm. The differences in interactive strategy was shown by farmers in paddy fields agro-ecosystem applied chemical fertilizers more intensively than coastal farmers, but they applied less organic fertilizers than coastal farmers. No differences were found in ex-post risk management strategy by the farmers in both agro-ecosystem.

Kata Kunci : melon, efficiency, risk, attitude toward risk, agro-ecosystem, farm risk management