scholarly journals Land Suitability and Direction of Strategic Agricultural Commodities in East Kalimantan to Support the Development of the New Nation’s Capital of Republic of Indonesia

2021 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
NFN Sukarman ◽  
Erna Suryani ◽  
Husnain Husnain
Keyword(s):  
Paddy Field ◽  
Agricultural Land ◽  
Upland Rice ◽  
Buffer Zone ◽  
Land Suitability ◽  
Agricultural Commodities ◽  
Dry Land ◽  
Suitability Assessment ◽  
East Kalimantan ◽  
The Republic

<p class="JSDLKatakunci"><strong>Abstra</strong><strong>ct</strong><strong>.</strong> The development of the new nation's capital in East Kalimantan must be supported with sufficient food supply. An Agricultural buffer zone must be provided as production area of food crops, horticulture, plantation, and livestock to suffice the food needs. The planning of landuse arrangement in the area required land suitability assessment for various agricultural commodities. The purpose of this paper is to provide information of land suitability in East Kalimantan Province that support the development plan of the new capital of the Republic of Indonesia. Literature studies of the previous research in East Kalimantan Province are carried out by the Indonesian Center for Agricultural Land Resources Research and Development (ICALRRD), as well as other research institutions. Based on the researches by ICALRRD conducted between year 2016-2019, the land suitable for agriculture is quite extensive (7.7 million ha), mostly for dry land farming. It is classified as suitable (S) mainly for plantation, forage, dry land food, horticulture, and upland rice, especially rainfed paddy. Only a small part is suitable for swamp lowland paddy field or tidal paddy field. The efforts to develop the regions include: (1) the expansion of new areas called as extensification (E), and a little through intensification (I). Extensification is conducted by cultivating superior commodities on new opening land that were previously in the form of shrubs or swampy shrubs, and open area or pasture. The available area for extensification program in East Kalimantan is 2.728 million ha. (2) The intensification program is carried out through the development of commodities in the existing land by strengthening the application of land technology, water management, crops varieties selection and cultivation techniques covering 73.2 thousand ha.</p><p><strong>Abstrak. </strong>Rencana pemindahan ibu kota negara ke Kalimantan Timur, perlu didukung oleh kawasan penyangga pertanian (tanaman pangan, hortikultura, perkebunan, dan peternakan) untuk memenuhi kebutuhan pangan masyarakat. Perencanaan penyusunan kawasan tersebut memerlukan data kesesuaian lahan berbagai komoditas pertanian. Tujuan dari tulisan ini adalah untuk memberikan informasi data tentang kesesuaian lahan di Provinsi Kalimantan Timur dalam mendukung rencana pembangunan ibukota baru Republik Indonesia. Metode yang digunakan dalam penulisan makalah ini adalah studi literatur dari hasil penelitian di Provinsi Kalimantan Timur, baik yang dilaksanakan oleh Balai Besar Litbang Sumberdaya Lahan Pertanian (BBSDLP), maupun lembaga penelitian lain. Berdasarkan data hasil penelitian BBSDLP antara tahun 2016-2019, lahan yang sesuai untuk pertanian cukup luas (7,7 juta ha), terutama untuk pertanian lahan kering. Lahan yang tergolong kelas sesuai (S) sebagian besar untuk tanaman perkebunan, pakan ternak, pertanian tanaman pangan lahan kering, hortikultura, dan padi sawah tadah hujan. Hanya sedikit yang sesuai untuk pertanian padi rawa lebak atau padi pasang surut. Upaya yang dapat ditempuh untuk membangun kawasan ini adalah: (1) melalui perluasan areal baru atau ekstensifikasi (E) tanaman perkebunan, pakan ternak, pertanian tanaman pangan lahan kering, hortikultura, dan padi sawah tadah hujan, pada lahan bukaan baru yang sebelumnya berupa semak belukar atau semak belukar rawa, lahan terbuka atau padang rumput seluas 2,728 juta ha. (2) melalui program intensifikasi (I) dilakukan melalui pengembangan komoditas di lahan sawah eksisting melalui penguatan aplikasi teknologi pengelolaan lahan, pengelolaan air, penggunaan varietas unggul, dan teknik budidaya, seluas 73,2 ribu ha.</p>

scholarly journals Analisis Perubahan Penggunaan Lahan dan Arahan Pemanfaatan Ruang untuk Pertanian di Kabupaten Penajam Paser Utara, Provinsi Kalimantan Timur

2018 ◽  
Vol 1 (3) ◽  
pp. 243 ◽  
Author(s):  
Benadikta Widjayatnika ◽  
Dwi Putro Tejo Baskoro ◽  
Andrea Emma Pravitasari
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Regional Development ◽  
Agricultural Land ◽  
Land Suitability ◽  
Forest Land ◽  
Dry Land ◽  
Retention Factors ◽  
Agriculture Sector ◽  
East Kalimantan

Penajam Paser Utara was one of the youngest regency in East Kalimantan which focused to develop agriculture sector, especially food crops. Contribution agriculture sector to Gross Regional Domestic Product (PDRB) in 2015 was in second position accounted for 20.97%. This research was aimed to compile direction for agricultural land use based on actual land use, regional development index and land suitability. Land use change was obtained by overlay method within two land use map (2010 and 2016) from BPN, regional development was analyzed by skalogram method using PODES data (2011 and 2014) from BPS and land suitability was evaluated referred to FAO framework using matching method between land unit mapping based on soil map 1:50,000 from BBSDLP and criteria for specific commodities. Actual land use in Penajam Paser Utara (2016) consist of forest (32.92%), plantation (25.51%), industrial forest (17.09%), bush (8.76%) and other land use (15.72%). Land use change pattern from 2010 to 2016 showed increasing of plantation area (3.55%) due to forest land decreasing (1.42%). Regional development indicated by increasing of average IPD from 21.72 (2011) to 32.04 (2014). Land suitability for agriculture was classified in S3 (marginally suitable)-N2 (permanently not suitable). Retention factors were erosion hazard (e), rooting media (r), nutrion retention (n) and nutrient availability (n). Available land for agriculture using largely 162,493 Ha consist of (a) plantation area largely 113,796 Ha (b) wetland crop largely 24,258 (c) dry land crop largely 15,101 Ha and (d) not suitable for agriculture largely 6,027 Ha.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

scholarly journals PERUBAHAN PENGGUNAAN LAHAN DAN POTENSI PERLUASAN LAHAN UNTUK SAWAH DI KABUPATEN CIANJUR

2019 ◽  
Vol 19 (1) ◽  
pp. 33-40
Author(s):  
Chaida Chairunnisa ◽  
Khursatul Munibah ◽  
Widiatmaka Widiatmaka
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Paddy Field ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Land Suitability ◽  
Limiting Factor ◽  
Land Use Land Cover ◽  
Land Use Plan ◽  
Field Area

Population growth, increasing income, and the rapid economic development create complexity of land issues. Land has a central role in  food production, however demand for land increased significantly to meet the needs of the population. Cianjur Regency is one of regencies in the southern part of West Java Province with the largest paddy field area. However, paddy field conversion into non agricultural land or another agricultural land resulted in the decrease of paddy field area. Therefore, in the context of maintaining the availability of rice in Cianjur Regency, this study aimed to: (1) analyze the patterns of land use/land cover, (2) evaluate land suitability for paddy field, and (3) analyze the potency of land for paddy field expansion. Land use change was identified using Landsat imagery of 2000 and 2015 by using fusion techniques. Land suitability for paddy field was analyzed using limiting factor method. Potential for paddy field expansion was analyzed according to land suitability and agricultural land allocation in official regional land use plan map (“RTRW”). The results showed that in the period of 2000 to 2015, most of paddy field were converted into settlements. Land suitability classes for paddy field in Cianjur Regency were not suitable (N) (61.19%), suitable (S2) (9.53%), and marginally suitable (S3)(29.28%). Cianjur Regency still has the potency of land to be used for paddy field expansion of 148,980 ha. Keywords: Land use change, potential area for paddy field priority, land suitability for paddy field

The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment

1994 ◽  
Vol 26 (2) ◽  
pp. 265-284 ◽  
Author(s):  
F Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Agricultural Land ◽  
Assessment Methods ◽  
Land Suitability ◽  
Geographical Information ◽  
Suitability Assessment ◽  
Artificial Neural ◽  
Land Suitability Assessment

Agricultural land-suitability assessment involves the analysis of a large variety and amount of physiographic data. Geographical information systems (GISs) may facilitate suitability assessment in data collection. To generate accurate results from the data, appropriate suitability-assessment methods are required. However, the assessment methods which can currently be used with GISs, such as that developed by the United Nations Food and Agriculture Organization and the statistical pattern—classification method, have limitations which may lead to inaccurate assessment. An artificial neural network is an effective tool for pattern analysis. A neural network allows decision rules of greater complexity to be applied in pattern classification. By formulating the land-suitability-assessment problem into a pattern—classification problem, neural networks can be used to achieve results of greater accuracy. In this paper, a neural-network-based method for land-suitability assessment is discussed, and a set of neural networks is described. The integration between the neural networks and a GIS is addressed, and some experimental results are presented and analyzed.

scholarly journals Strategi Pemanfaatan Sumberdaya Lahan untuk Pencapaian Swasembada Beras Berkelanjutan

2020 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Anny Mulyani ◽  
Dedi Nursyamsi ◽  
Muhammad Syakir
Keyword(s):  
Paddy Field ◽  
Local Governments ◽  
Agricultural Land ◽  
Upland Rice ◽  
Global Climate ◽  
Essential Element ◽  
Rice Fields ◽  
Food Crops ◽  
Land Conversion ◽  
Self Sufficiency

<p><strong>Abstrak.</strong> Lahan pertanian eksisting penghasil bahan pangan terutama sawah dan lahan kering menjadi tumpuan harapan untuk memenuhi kebutuhan pangan 258,7 juta jiwa penduduk pada tahun 2017. Usaha pencapaian swasembada berkelanjutan dihadapkan pada (i) peningkatan jumlah penduduk sekitar 3,4 juta jiwa setiap tahun, (ii) konversi lahan sawah ke non pertanian dengan laju sekitar 96.500 ha tahun-1, sementara laju perluasan lahan sawah hanya sekitar 20.000-30.000 ha tahun-1, dan (iii) perubahan iklim global yang menyebabkan peningkatan intensitas dan frekuensi kejadian iklim ekstrim berupa kekeringan, kebanjiran, longsor, yang selanjutnya meningkatkan intensitas serangan hama/penyakit tanaman. Upaya dan strategi untuk mengatasi permasalahan tersebut diantaranya melalui, pertama, intensifikasi dengan inovasi teknologi pada 4 juta ha sawah irigasi teknis, 4,1 juta ha lahan sawah sub-optimal (tadah hujan, irigasi sederhana, sawah rawa) melalui perbaikan saluran irigasi dan sistem drainase, pemupukan berimbang, pengembangan varietas unggul, dan peningkatan Indeks Panen dari 1 menjadi 1,5. Kedua, pengendalian konversi lahan melalui kesepakatan berbagai pemangku kepentingan, kerjasama lintas kementerian/ lembaga serta antara pemerintah dengan swasta dan masyarakat untuk meningkatkan kesadaran akan bahaya konversi lahan terhadap ketahanan pangan, kestabilan sosial, ekonomi dan politik. Ketiga,perluasan areal tanam di lahan perkebunan kelapa sawit muda (5,1 juta ha) dan karet (0,54 juta ha), serta pada perkebunan kelapa (2,15 juta ha). Tersedia varietas toleran naungan untuk padi gogo, jagung dan kedelai untuk mendukung usaha ini. Keempat, perluasan areal pertanian baru untuk tanaman pangan pada lahan potensial di lahan rawa (pasang surut, lebak, dan gambut) dan pada lahan basah non rawa untuk sawah irigasi dan tadah hujan, serta di lahan kering dengan lereng &lt; 15% untuk tegalan. Keempat pendekatan ini diharapkan dapat mewujudkan swasembada pangan secara berkelanjutan.</p><p><em><strong>Abstract.</strong> Existing agricultural land for food crops, especially paddy fields and upland, is a very essential element for fulfilling the needs of food for258.7 million people in 2017. The efforts to achieve permanent self-sufficiency are challenged by (i) an increase in the population of approximately 3.4 million people each year, (ii) conversion of paddy field to non-agricultural land at a rate of about 96,500 ha year-1, while the rate of paddy field expansion is only about 20,000-30,000 ha year-1, and (iii) global climate change which causes the increase in intensity and frequency of extreme climatic events in the forms of droughts, floods, landslides, that in turns increase the incident of pest/disease attacks. Efforts and strategies are required to overcome them through first, intensification by applying technological innovations on 4 million ha of existing fully irrigated rice fields, 4.1 million ha of sub-optimal rice fields (rainfed, simple irrigation, swampland) with improved irrigation and drainage systems, balanced fertilization, improved varieties, and increased Harvesting Index from 1 to 1.5. Second, control of land conversion by establishing multi-stakeholder agreements, cooperation among related ministerials/institutionals, local governments, private sectors and communities to raise awareness of the risk of land conversion to food security, social, economic and political stability.Third, expansion of rice farming areas into young oil palm (5.1 million ha), rubber (0.54 million ha), as well as on coconut (2.15 million ha) plantations. Shade tolerant varieties are now available for upland rice, maize and soybeans to support this effort. Fourth, expansion of new agricultural areas for food crops on potential lands in swamp land (tidal swamp lands, fresh water swamp land and peat land), as well as on non swamp wetlands for irrigated and rain-fed rice fields, and on upland with slopes of &lt;15% for annual upland crops. These four approaches are believed to enable achievement of sustainable food self-sufficiency.</em></p><p> </p>

scholarly journals Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 573 ◽  
Author(s):  
Ruhollah Taghizadeh-Mehrjardi ◽  
Kamal Nabiollahi ◽  
Leila Rasoli ◽  
Ruth Kerry ◽  
Thomas Scholten
Keyword(s):  
Machine Learning ◽  
Agricultural Land ◽  
Soil Depth ◽  
Traditional Approach ◽  
Soil Samples ◽  
Land Suitability ◽  
Agricultural System ◽  
Climate Data ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.

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