This research aims to know (1) labor absorption of oil palm farmers; and (2) the productivity of oil palm farmers, (3) farmer perception, and (4) the palm plantation contribution to the regional income. The research was conducted in September 2013 until November 2013. Location of research in sub-districts of North Tambusai, Kunto Darussalam, Tandun, and Kabun in Rokan Hulu District. The method used interviews, questionnaires, and documentation. Sampling was done by purposive. Results of this research is the most of labor absorption in sub-district Kabun (4.22 HOK / ha), followed by North Tambusai (3.30 HOK / Ha), Kunto Darussalam (3.21 HOK / ha), and Tandun (2.99 HOK / Ha). The highest productivity of palm oil in sub-district Kabun (21.16 ton/ha/ year), followed by Kunto Darussalam (19.40 ton/ha/year), North Tambusai (15.76 ton/ha/ear), and Tandun (11,97 ton/ha/year). The most reason of farmers perception on palm plantation is marketing easier, followed by production facilities are supported, easy palm cultivation, the selling price and the income of farmers is high. The most farmers perception to use the income is education of children, followed by repair and extension the house, purchase of motor vehicles, and extend of palm plantations. The largest palm plantation contribution to the regional income is North Tambusai Sub-Distric, followed by Kunto Darussalam, Kabun, and Tandun.
The Malolo agropolitan area is a strategic food crop production center in Takalar. The implementation of klassen tipology is used to identify superior commodities with export value from food plants cultivated by farmers in the area. This study aims to determine the superior commodities of food crops using klassen typology and to map these superior commodities in the Malolo Agropolitan Area. The analytical methods used were klassen typology and Ar-GIS mapping. The results showed that the implementation of klassen typology on food commodities in the Agropolitan Malolo area resulted in maize as the only superior commodity out of 4 other food commodities. Maize is a leading commodity in 4 areas, that is Massamaturu, Timbuseng, Barugaya, and Towata. Mapping results show that 4 areas are superior commodity development, 11 areas for mainstay commodity development, 37 areas for prospective commodity development, and 38 areas for slow commodity development. The number of areas for slow commodity development shows that the production of food commodities in the Malolo agropolitan area is less able to compete with food commodity production in other areas in a larger area.
The problem of agricultural crops cultivation today is the productivity of land that has not been optimally, where the availability of land among the main crops should be utilized maximally by planting intercrops. This research aims to determine the cropping patterns and the best of inorganic fertilizers dosage, well as the interaction of cropping patterns with inorganic fertilizer to the growth and yield of sweet corn. The research was conduted from November 2017 until January 2018 in the village of Dundangan, district of Pangkalan Kuras, Pelalawan regency and Agronomic Laboratory of Faculty of Agriculture and Animal Science, Universitas Islam Negeri Sultan Syarif Kasim Riau. This research uses Randomized Block Design (RBD) with two factors and three replications. The first factor is cropping pattern with two levels that is cropping pattern of sweet corn monoculture and cropping pattern of sweet corn with pegagan plant. The second factor is the application of inorganic fertilizers with three levels is 0%, 50% and 100% recommended dosage. The results showed that the cropping pattern of sweet corn planted with intercropping pegagan gave the same results as good with the croping pattern of sweet corn monoculture, except on the parameter of leaf age of 6 weeks after plant which yield more leaf number on monoculture cropping pattern. Inorganic fertilizers application 50% recommended dosage (Urea 250 kg/ha + TSP 175 kg/ha + KCl 150 kg/ha) increased yield weight of corn cobs weighted per plot, corn cob weight without weight per cob and weight of corn cobs without weight per plot.There is not interaction between cropping patterns and inorganic fertilizers on the growth and yield of sweet corn crops.
Pengembangan kawasan agropolitan Malalo saat ini belum optimal karena perencanaan pengembangan wilayah berbasis potensi komoditas yang tidak terkait sehingga komoditas unggulan yang dikembangkan tidak tepat sasaran. Oleh karena itu, komoditas pertanian yang menjadi komoditas basis yang dijadikan komoditas unggulan perlu dikenali. Pemetaan komoditas dikawasan agropolitan Malolo dilakukan dengan menggunakan software ArcGIS dan dianalisis secara deskriptif kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa komoditas pertanian yang menjadi komoditas pokok di kawasan agropolitan Malolo adalah padi, jagung, kacang hijau, ubi kayu, dan ubi jalar. Dari komoditas tersebut, komoditas unggulan yang dapat dijadikan komoditas unggulan di kawasan agropolitan Malolo adalah komoditas jagung yang tersebar di desa-desa dan keluarga, yaitu Towata, Barugaya, Timbuseng, dan Massamaturu.
The policy for commodity development in Polongbakeng Utara District has not been able to optimize its natural resource potential. One of the efforts to optimize this potential is the identification of food crops basis commodities by potential mapping in each village in Polongbangkeng Utara District. This study aims to identify the food crops commodity which is a basis commodity and to make the basis commodities mapping in Polongbangkeng Utara District. The analytical method used is LQ analysis and Ar-GIS mapping. The results showed that the food crops commodities which were the basis commodities in Polongbangkeng Utara District were rice, corn, green beans, cassava and sweet potatoes. Palleko is a village that has the most basis commodities with 4 basis commodities, namely rice, green beans, cassava and sweet potato. Rice and sweet potato commodities are the most basis commodities because they are the basis for 12 villages out of 18 villages in Polongbangkeng Utara district. Base commodity mapping was carried out on 5 food crop commodities. Mapping results show that there are more non-basis commodity polygons (50 polygons) than basis commodity polygons (40 polygons).
Bontolempangan District is one of the districts included in the spatial planning area of the Gowa Regency. The development of tourism potential and agricultural land resources is a major part of the Gowa Regency spatial plan (RTRW 2012-2032). Therefore, this research investigates the potential of agricultural commodities and the carrying capacity of their land so that they can be used as a reference to become an agricultural-based tourism area. The agricultural potential was analyzed using the location quotient (LQ) and dynamic location quotient (DLQ) methods, while the carrying capacity of agricultural land was analyzed using land suitability analysis. From the LQ and DLQ analysis, robusta coffee, cocoa, cashew, candlenut, and arabica coffee are the leading agricultural commodities. Based on the land suitability analysis, the carrying capacity of the land for superior agricultural commodities is categorized into themoderate suitable level (S2) with an area of 2968.22 ha for Robusta coffee, 1202.30 ha of cocholate, 2227.22 ha of cashew nuts, 2253.47 ha of candlenut, 3235.91 ha of Arabica coffee ha, and sweet potatoes covering an area of 952.78 ha. Thus, leading agricultural commodities can be developed into agricultural-based tourism areas or agro-tourism.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.