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Maurits, Yuhanna
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Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Mathematics Physics

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ANALISIS VARIABILITAS DAN PERUBAHAN IKLIM TERHADAP KESESUAIAN AGROKLIMAT KEDELAI DI PROVINSI NUSA TENGGARA BARAT Maurits, Yuhanna
Megasains Vol 6 No 3 (2015): Megasains Vol. 6 No.3 Tahun 2015
Publisher : Stasiun Pemantau Atmosfer Global Bukit Kototabang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46824/megasains.v6i3.216

Abstract

Provinsi Nusa Tenggara Barat (NTB) ditetapkan oleh pemerintah sebagai salah satu sentra produksi kedelai nasional untuk mengejar target swasembada kedelai yang mampu memproduksi satu juta ton kedelai per tahun. Kebutuhan kedelai secara Nasional maupun di NTB dari tahun ke tahun semakin meningkat. Selain (El nino/ La nina), perubahan iklim akan memicu meningkatnya frekuensi cuaca/iklim ekstrim, kemarau panjang, hujan ekstrim. Perubahan iklim di Nusa Tenggara Barat tentu akan memberikan dampak terhadap luasan kesesuaian agroklimat kedelai. Dengan memanfaatkan data model iklim regional Conformal Cubic Atmospheric Model (CCAM) resolusi 14 km dari project Climate Change Adaptation Project CSIRO-UNRAM, data tersebut digunakan untuk membuat proyeksi iklim yang kemudian digunakan untuk menghitung klasifiklasi kesesuaian agroklimat kedelai sesuai standard FAO di NTB dimasa depan. Terjadi penurunan rata-rata curah hujan tahunan di periode masa depan dan kenaikan temperatur rata-rata dari 20- 30 ºC periode baseline menjadi sekitar 23-32 ºC pada periode future. Analisis kelas kesesuaian agroklimat untuk tanaman kedelai di NTB diproyeksikan mengalami perubahan signifikan dengan berkurangnya luas kelas kesesuaian S1 (Sangat Sesuai) turun sebesar 3.0 %, kelas S3 ( Sesuai Marginal) turun 12.5 % dan kelas N (Tidak Sesuai) turun sebesar 9.2 namun demikian terjadi pula peningkatan kelas S2 (Cukup Sesuai) sebesar 24.7% baik pada periode (2040-2069) serta pada periode (2070-2099) relatif terhadap periode baseline (1981-2010)
Study of Developing Models of Crop Failure Risk Information Agustiarini, Suci; Sampelan, David; Maurits, Yuhanna; Baihaqi, Anas; Patria Megantara, Restu; Ulfah, Afriyas; Permana, Angga; Kirana, Nindya; Sulistio Adi Wibowo, Dewo; Purwaningsih, Ni Made Adi; Pamungkas, Cakra Mahasurya Atmojo; Putrantijo, Nuga; Fajariana, Yuaning
Jurnal Pijar Mipa Vol. 19 No. 1 (2024): January 2024
Publisher : Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpm.v19i1.5981

Abstract

Climate is one factor that can influence plant growth. The risk of crop failure due to climate variability can be in the form of reduced water sources, which impact water needs in the land and the emergence of pests and diseases in plants. The risk of planting failure can impact product quality, which has the potential to decrease, higher plant handling costs, and various things that cause losses to farming businesses. The availability of climate forecast information, such as rainfall and other parameters, encourages writers to apply it to information that is easier for users to understand. One of the machine learning algorithms, Decision Tree, is used as a model in determining the risk of planting failure based on each attribute/parameter, including monthly rain, ENSO and IOD phenomena, drought, groundwater availability, and Oldeman climate type. This study aims to make a model prediction of crop failure risk potential, and the calculation is based on climate prediction data. The results of this study show differences in climatic conditions for each commodity when there is an increased potential risk of planting failure. Monthly rainfall is the most dominant factor influencing rice, maize, and soybean planting failure. Validation of the decision tree model shows that this model is quite good in determining the potential risk of crop failure in all commodities studied, with the proportion of correct proportion of more than 65%. However, the Heidke Skill Score (HSS) shows that this model is good for Paddy and Soybean; Maize shows an HSS of less than zero.
Co-Authors Agustiarini, Suci Angga Permana Baihaqi, Anas Fajariana, Yuaning Kirana, Nindya Pamungkas, Cakra Mahasurya Atmojo Patria Megantara, Restu Purwaningsih, Ni Made Adi Putrantijo, Nuga Sampelan, David Sulistio Adi Wibowo, Dewo Ulfah, Afriyas