Model Pemetaan Potensi Wilayah Perkebunan dan Daya Saing Petani Swadaya dalam Upaya Hilirisasi Kelapa Sawit

Abstract

Kelapa sawit sebagai salah satu komoditas utama yang memiliki peran dalam pembangunan nasional, mengalami peningkatan permintaan sebagai dampak positif dari upaya hilirisasi di dalam negeri. Hal ini menjadi prospek dalam pengembangan usaha kelapa sawit dan peningkatan kemampuan daya saing pelakunya. Pelaku dalam usaha perkebunan kelapa sawit terdiri atas perkebunan negara, perkebunan swasta, dan perkebunan rakyat. Namun, permasalahan utama dihadapi oleh perkebunan rakyat, khususnya milik petani swadaya, dalam upaya penyediaan bahan baku tandan buah segar (TBS) yang berkualitas. Permasalahan ini disebabkan tata kelola perkebunan yang kurang sesuai dan minimnya kesadaran petani dalam upaya peningkatan keberlanjutan usahanya. Padahal, pasokan TBS untuk kebutuhan hilirisasi diprediksi akan bergantung pada perkebunan swadaya. Kebutuhan ini diakibatkan kebijakan pemerintah dalam pelarangan ektensifikasi perkebunan kelapa sawit, sehingga optimalisasi perkebunan yang telah ada menjadi sangat penting. Oleh sebab itu, posisi penting petani swadaya perlu menjadi perhatian, berkaitan dengan potensi lahan perkebunan dan daya saingnya. Berdasarkan hal tersebut, tujuan penelitian ini antara lain untuk (1) mengidentifikasi konfigurasi bisnis petani swadaya dalam mendukung upaya hilirisasi kelapa sawit, (2) merancang model pemetaan penentuan tingkat potensi perkebunan kelapa sawit petani swadaya, dan (3) merancang model penilaian indeks daya saing petani swadaya dalam upaya hilirisasi kelapa sawit. Metode yang digunakan adalah kerangka Van der Vorst untuk identifikasi konfigurasi bisnis petani swadaya; Sistem Informasi Geografis-perbandingan berpasangan untuk melakukan pemodelan perkebunan kelapa sawit dan menilai potensi wilayahnya; dan modifikasi metode Diamond Porter-Indeks Daya Saing digunakan untuk pemodelan tingkat daya saing petani swadaya. Hasil identifikasi konfigurasi bisnis petani swadaya kelapa sawit menunjukkan bahwa mekanisme aliran bisnisnya terdiri atas petani swadaya, koperasi dan pedagang, serta pabrik kelapa sawit. Berdasarkan pada luas perkebunan kelapa sawit, diketahui Kabupaten Kampar di Provinsi Riau merupakan daerah dengan jumlah petani swadaya dan luas perkebunan yang paling tinggi. Pada aliran proses bisnisnya, diketahui bahwa petani swadaya memiliki jaringan rantai pasok yang cukup panjang hingga akhirnya masuk ke pabrik kelapa sawit. Pemetaan perkebunan petani swadaya dilakukan pada 128 946.8 hektar lahan di Kabupaten Kampar, Provinsi Riau. Model pemetaan tingkat potensi perkebunan dilakukan dengan memperhatikan beberapa aspek, yaitu jenis tanah, kemiringan lereng, jaringan jalan, jaringan sungai, lokasi pemukiman, lokasi pabrik, dan kawasan hutan. Model ini dilakukan melalui analisis spasial dan pemodelan matematis. Hasil pemetaan diklasifikasikan ke dalam tiga kategori tingkat potensi perkebunan. Hasil menunjukkan bahwa hanya 13.88% perkebunan petani swadaya yang bersifat potensial, 71.21% petani swadaya memiliki perkebunan yang berkembang; sedangkan 14.91% lahan perkebunan bersifat tidak potensial. Pengukuran tingkat daya saing petani swadaya dilakukan pada 177 responden dari lima kecamatan di Kabupaten Kampar. Model penilaian terhadap daya saing secara terintegrasi memperhatikan keterhubungan yang menggambarkan keunggulan dari satu individu petani swadaya dengan individu lainnya, terkait kegiatan dan proses bisnis yang dilakukan. Hasil penilaian indeks daya saing menunjukkan bahwa 7.91% petani swadaya berada pada daya saing mandiri (kemampuan maksimasi sumber daya untuk usahanya), 73.45% petani swadaya berada pada daya saing yang berkembang, dan 18.64% petani swadaya berada pada daya saing tertinggal. Beberapa saran untuk penelitian selanjutnya, diantaranya adalah 1) Perumusan model penguatan kelembagaan petani swadaya, 2) Pengembangan model perencanaan strategis usaha kelapa sawit petani swadaya dalam upaya mendukung hilirisasinya, dan 3) Penyatuan keseluruhan model dalam sistem penunjang keputusan spasial-cerdas juga perlu dilakukan untuk mempermudah proses pengambilan keputusan.

Palm oil as a leading commodity has a role in national development, experiencing the demands’ increase as positive impact of domestic downstreaming policy. This is becomes a prospect to develop oil palm business and improvement of its actors’ competitiveness. The actors of oil palm business known are state-owned plantation, private-owned plantation, and smallholder plantation. However, the main challenge faced by smallholder plantation, especially in the independent smallholder farmers' plantation, related to the planning of qualified fresh fruit bunch (FFB). The challenge is caused by the inappropriate plantation management and the lack of farmers’ awareness in attempt sustainability improvement of their business. In fact, the supply of FFB for downstream is predicted will depend on smallholder plantations. This need is due to government policy due to the banning of oil palm plantations extentification, so the optimization of existing plantations is crucial. Therefore, the important position of independent smallholder farmers need to be a major concern, related to the plantation area potencies and its competitiveness. Based on that, this research aimed to (1) identify the independent smallholder farmers’ business configuration in supporting palm oil downstreaming, (2) construct a potency mapping model of independent smallholder farmers’ oil palm plantations, and (3) construct a competitiveness index measurement model in relation to supporting palm oil downstream. The methods used were the Van der Vorst framework to identify independent smallholder farmers’ business configuration; Geographic Information System-pairwise comparison to modelling the oil palm plantations condition and to assess the plantation area potency; and the modified Diamond Porter-competitiveness index method applied to modelling the independent smallholder farmers’ competitiveness level. The identification of smallholder farmers’ business configuration showed that the business flow mechanism consisted of independent smallhoder farmers, cooperatives and traders, and palm oil mills. Based on the area of oil palm plantations, it is known that Kampar Regency in Riau Province is an area with the highest number both of the independent smallholder farmers and its plantation area. In the business processes flow, it is known that the independent smallholder farmers have a long supply chain network to reach the palm oil mill. The plantation area mapping model of independent smallholder farmers was done in 128 946.8 hectares area of Kampar Regency, Riau Province. The plantation potential area mapping model was done by including several aspects, namely soil type, slope, road system, river system, residence locations, mill locations, and forest areas. The model was done through spatial analysis and mathematical modelling. The results were classified into three categories of potency level. The results shows that only 13.88% of plantations owned by independent smallholder farmers that are potential, 71.21% of independent smallholder farmers were in developing category; while 14.91% plantations were non-potential. The measurement of independent smallholder farmers’ competitiveness model was conducted on 177 respondents from five districts in Kampar Regency. The integrated competitiveness’ assessment model was done by observing and illustrating the connectivity of one independent smallholder farmer’s advantage to others, related to the activities and business processes. The competitiveness index shows that there were only 7.91% at developed competitive position (ability to maximize business resources), 73.45% at developing competitive position, and 18.64% at least-developed position. Some suggestions for further research, there are 1) The formulation of the independent smallholder farmers’ institutional strengthening model, 2) The development of strategic planning model in achieving better independent smallholder farmers’ business to support its downstreaming, and 3) The integration of all the models into spatial intelligent decision support system also needs to be done to simplify the decision making process.