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      Optimasi Sistem Irigasi Bawah Permukaan untuk Peningkatan Produktivitas Tanaman dan Air dengan Algoritma Genetika

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      Date
      2023-07
      Author
      Caroline, Maharani Bilqist
      Arif, Chusnul
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      Abstract
      Permintaan tanaman holtikultura pada tahun 2018 – 2019 meningkat menjadi 4.588 ton dari sebelumnya hanya sebesar 1.628 ton. Namun, kurangnya pengetahuan petani akan penanganan dan budidaya tanaman hortikultura menjadi salah satu penyebab tidak terpenuhinya permintaan. Irigasi bawah permukaan digunakan untuk memaksimalkan kebutuhan air tanaman langsung ke daerah perakaran tanaman. Skenario pengairan dibagi menjadi 3 dengan mengatur tinggi muka air (TMA) pada bak kontrol setinggi 13 cm (TMA1), 10 cm (TMA2) dan 7 cm (TMA3). Produktivitas air (WP) tertinggi terjadi pada TMA2 sebesar 1,60 kg/m3. WP pada TMA2 lebih besar 34% dibandingkan TMA1 dan lebih besar 9% dibandingkan TMA3. Luas lahan 0,795 m2 menghasilkan bobot panen pada TMA2 seberat 0,25 kg, TMA3 seberat 0,22 kg dan TMA1 seberat 0,18 kg. Identifikasi JST dilakukan menggunakan data rerata tinggi muka air (WL) sebagai input dan data bobot panen sebagai output, maka didapatkan nilai R2 > 0,9. Nilai bobot panen optimal berdasarkan identifikasi AG didapatkan sebesar 0,3 kg dengan WL optimum pada 3 fase pertumbuhan (initial stage, mid-season, late-season) secara berturutan adalah 12,8 cm, 7 cm, 7,2 cm dan WP sebesar 1,96 kg/m3 lebih besar 22,5 % dibandingkan WP pada skenario TMA2.
       
      The demand for horticultural crops in 2018-2019 increased to 4,588 tons from previously only 1,628 tons. However, the lack of knowledge of farmers about the handling and cultivation of horticultural crops is one of the reasons for the non-fulfillment of demand. Subsurface irrigation is used to maximize crop water demand directly to plant root areas. Irrigation scenarios are divided into 3 by adjusting the water level (TMA) in the control tank as high as 13 cm (TMA1), 10 cm (TMA2) and 7 cm (TMA3). The highest water productivity (WP) occurs at TMA2 of 1.60 kg/m3. WP at TMA2 greater than 34% compared to TMA1 and greater than 9% compared to TMA3. The land area of 0.795 m2 produces a harvest weight of TMA2 weighing 0.25 kg, TMA3 weighing 0.22 kg and TMA1 weighing 0.18 kg. ANN identification was carried out using average water level (WL) data as input and harvest weight data as output, so the value of R2 > 0.9 was obtained. The optimal harvest weight value based on AG identification was obtained at 0.3 kg with optimum WL at 3 growth phases (initial stage, mid-season, late-season) respectively 12.8 cm, 7 cm, 7.2 cm and WP of 1.96 kg/m3 greater than 22.5% compared to WP in the TMA2 scenario.
       
      URI
      http://repository.ipb.ac.id/handle/123456789/122243
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      • UT - Civil and Environmental Engineering [1042]

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