Strategi Pengembangan Lumbung Pangan Nasional di Kabupaten Merauke Propinsi Papua Selatan

Abstract

Merauke Regency has significant potential as a food production hub in Eastern Indonesia. The food estate program in Merauke has been initiated since 2007 through various program including The Merauke Integrated Rice Estate (MIRE), Merauke Integrated Food and Energy Estate (MIFEE), and The National Food Estate (LPM). These programs aim to enhance food production and improve farmers' welfare. However, recently, these objectives have not been fully realized, as evidenced by the stagnation in rice production and the persistently low-income levels of local farmers. This situation indicates the need for a new approach in managing the ongoing FE programs, which are currently continued under the framework of the National Strategic Project (Proyek Strategis Nasional, PSN). The general objective of this study is to develop a sustainable planning model for the LPN (National Food Estate) program based on rice and maize commodities in Merauke Regency. The specific objectives are: (1) To assess the current status of rice and maize production and productivity in the central food-producing areas of Merauke Regency; (2) To estimate the factors influencing rice production using an input-output approach; (3) To optimize rice and maize production using both single-and multiple-objective approaches; (4) To analyze the competitiveness of rice commodities through private and social pricing approaches; and (5) To formulate policy recommendations for LPN implementation based on stakeholder perspectives. The novelty of this study lies in: (1) the development of a mathematical modeling based on the actual conditions of rice and maize farming system in Merauke; (2) the competitiveness analysis applied to various producer’s types-small-scale farmers (independent and contract farmer) and large-scale agricultural companies; and (3) the application of the multipol method in the context of the food estate program, which has not been previously explored. Data collection was conducted from July 2023 (during the second planting season/dry season) to April 2024 (the first planting season/wet season). The study took place in two districts—Kurik and Malind—which were LPN location. Data sources included interviews with farmers, agricultural companies, and contracted farmers. The sample size for farmers was determined using a quota sampling method based on respondent categories, with quotas set according to the level of respondent homogeneity. The sample consisted of 60 farmers in Kurik District, 40 farmers in Malind District, and 20 contrated farmers affiliated with the company, resulting in a total of 120 respondents. Focus Group Discussions (FGDs) were conducted with farmer groups leader (Gapoktan and Poktan) from each village, farmer groups of corporate, company representatives, local government officials from Food Crops and Horticulture, and the Regional Development Planning Agency (Bappeda) of Merauke Regency. FGDs were implemented to support policy analysis. Secondary data were obtained from previous research, official statistical publications such as Kabupaten and District-in-Figures issued by the Central Bureau of Statistics (BPS), the Department of Food Crops and Horticulture of Merauke Regency, and other relevant sources. Independent farmers defined as ownership and cultivate their land using self-funded inputs. Contract farmers is farmer who rent land and have agreements with agricultural companies. Agricultural company is business entities engaged in food crop cultivation on concession land. Independent farmers in Merauke generally adopt the tabela (direct seeding) system, achieving rice productivity 3,144 kg/ha in MT1, 2,908 kg/ha in MT2, and 2,688 kg/ha in MT3. On average, input use per hectare includes 67 kg of seed, 155–167 kg of urea and NPK fertilizer, 4.4 liters of liquid pesticides, and 1.8 kg of solid pesticides. Estimations of the production function indicate that variables significantly influencing productivity include farmer age and experience, irrigation water usage, and application of both liquid and solid pesticides, and labor. Production optimization analysis, using data from both independent and contract farmers, indicates that optimal rice production is 38,649 tons from 12,864.04 ha of land—comprising 7,783.94 ha in MT1, 4,303.23 ha in MT2, and 777.1 ha in MT3. This optimal area represents 53% of the existing farmland (27,594 ha) in the two districts and contributes 25% to total rice output in those areas. With the same input configuration, optimal maize production is estimated at 7,836 tons from 1,800 ha, equally divided across three planting seasons (600 ha each). Linear programming (LP) results show resource competition between rice and maize cultivation. Meanhile Goal programming (GP) suggests that the objective of optimizing production and improving farmer income can be achieved if maize is cultivated in all three planting seasons, with the largest area allocated in MT1 (13,157.1 ha), along with increased seed, fertilizer, and labor inputs. The participation of agricultural companies in the LPN program has not yet contributed significantly to increasing rice production in Merauke. The PAM analysis reveals that under existing policy conditions, large-scale companies are less competitive than smallholder farmers. However, under social price conditions without government policy intervention, companies demonstrate higher profitability. This suggests the need for differentiated input and output policies tailored to corporate producers. Market segmentation and product differentiation could serve as alternative output policy strategies. Results from the multipol analysis indicate that key strategies to support the food estate program include continuous technological support, timely availability of agricultural inputs, and output subsidies. These measures are essential under intensification and optimization policies. In contrast, for extensification policies, the initial steps should involve commodity specialization per producer (score: 13.8), timely and adequate input availability (score: 12.8), and output subsidies. Scenario-based policy evaluation suggests that under intensification and optimization, a realistic early-stage approach should prioritize independent farmers within designated priority zones.