Equation to Predict Paddy Biomass Macronutrient Availability in Saline Soil Using Soil-Vegetation Indices from Multispectral UAV
DOI:
10.25047/agriprima.v10i1.784Downloads
Abstract
The average paddy production in Indonesia has decreased by -6,83% in the last five years (2015-2019), and salinity is the main factor causing the problem with an effect of 42% compared to climate change (21%) and drought (9%). Salinity will inhibit the availability of macronutrients in rice biomass, which must be detected quickly to avoid crop failure. This study aimed to obtain a formula equation to quickly estimate the macronutrient content in paddy biomass due to salinity. The formula equation was formed based on an algorithm resulting from the transformation of the SR, NGRDI, NDVI, TNDVI, and GNDVI index from the multispectral UAV and Nitrogen (N), Phosphorus (P), and Potassium (K) content in paddy biomass in saline soil. When the salinity source's distance gets closer, the macronutrient content decreases, and the transformation index value increases. The SR index is the most sensitive index to macronutrient content, indicated by the highest correlation value compared to other indices. Formula to predict macronutrient content was N: -0.04149 (SR) + 1.38314, P: -0.07243 (SR) + 0.61766, and K: -0.7059 (SR) + 5.3279. There was no difference between the estimation results and the macronutrient content from the laboratory analysis.
Keywords:
Salinity Macronutrients Deficiency Paddy Biomass UAVReferences
Andianto, R., & Handayani, H. H. (2014). Studi Indeks Vegetasi Untuk Identifikasi Vegetasi Hutan Gambut Menggunakan Citra Airborne Hyperspectral Hymap Di Daerah Hutan Gambut Kalimantan Tengah. Geoid, 9(2), 186. Retieved from https://doi.org/10.12962/j24423998.v9i2.757
Banyo, Y. E., Nio, A. S., Siahaan, P., & Tangapo, A. M. (2013). Konsentrasi Klorofil Daun Padi Pada Saat Kekurangan Air Yang Diinduksi Dengan Polietilen Glikol. JURNAL ILMIAH SAINS, 13(1), 1. Retieved from https://doi.org/10.35799/jis.13.1.2013.1615
Bewick, V., Cheek, L., & Ball, J. (2003). Statistics review 7: Correlation and regression. Critical Care, 7(6), 451. Retieved from https://doi.org/10.1186/cc2401
BPS. (2020). Luas Panen, Produksi, dan Produktivitas Padi Menurut Provinsi 2018–2019.
BRAY, R. H., & KURTZ, L. T. (1945). Determination Of Total, Organic, And Available Forms Of Phosphorus In Soils. Soil Science, 59(1), 39–46. Retieved from https://doi.org/10.1097/00010694-194501000-00006
Bremner, J. M., & Mulvaney, C. S. (1982). Nitrogen—Total (pp. 595–624). Retieved from https://doi.org/10.2134/agronmonogr9.2.2ed.c31
Dam, T. H. T., Amjath-Babu, T. S., Bellingrath-Kimura, S., & Zander, P. (2019). The impact of salinity on paddy production and possible varietal portfolio transition: a Vietnamese case study. Paddy and Water Environment, 17(4), 771–782. Retieved from https://doi.org/10.1007/s10333-019-00756-9
Gkotzamani, A., Ipsilantis, I., Menexes, G., Katsiotis, A., Mattas, K., & Koukounaras, A. (2024). The Impact of Salinity in the Irrigation of a Wild Underutilized Leafy Vegetable, Sonchus oleraceus L. Plants, 13(11), 1552. Retieved from https://doi.org/10.3390/plants13111552
Gupta, G. S. (2019). Land Degradation and Challenges of Food Security. Review of European Studies, 11(1), 63. Retieved from https://doi.org/10.5539/res.v11n1p63
Hessini, K., Issaoui, K., Ferchichi, S., Saif, T., Abdelly, C., Siddique, K. H. M., & Cruz, C. (2019). Interactive effects of salinity and nitrogen forms on plant growth, photosynthesis and osmotic adjustment in maize. Plant Physiology and Biochemistry, 139, 171–178. Retieved from https://doi.org/10.1016/j.plaphy.2019.03.005
Himayah, S. (2019). Perubahan Temperatur Permukaan Lahan Di Kota Bandung Tahun 2009-2018. Jurnal Geografi Gea, 19(2), 105–112. Retieved from https://doi.org/10.17509/gea.v19i2.20697
Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., Fu, T., Wang, F., & Shi, Z. (2019). Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images. Remote Sensing, 11(7), 736. Retieved from https://doi.org/10.3390/rs11070736
Iswari, A. R., Hani’ah, H., & Nugraha, A. L. (2017). Analisis Fluktuasi Produksi Padi Akibat Pengaruh Kekeringan Di Kabupaten Demak. Jurnal Geodesi Undip, 5(4), 233–242. Retieved from https://doi.org/10.14710/jgundip.2016.13981
Kim, H., Willers, J. L., & Kim, S. (2016). Digital Elevation Modeling Via Curvature Interpolation For Lidar Data. In Electronic Journal of Differential Equations (Vol. 23). Retieved from https://ui.adsabs.harvard.edu/link_gateway/2018JSoSe..18..336K/doi:10.1007/s11368-016-1446-x
Kozak, M., & Pudełko, R. (2021). Impact Assessment of the Long-Term Fallowed Land on Agricultural Soils and the Possibility of Their Return to Agriculture. Agriculture, 11(2), 148. Retieved from https://doi.org/10.3390/agriculture11020148
Li, N., Zare, E., Muzzamal, M., Sefton, M., & Triantafilis, J. (2023). Improved prediction of soil exchangeable sodium percentage (ESP) using wavelet. Computers and Electronics in Agriculture, 209, 107810. Retieved from https://doi.org/10.1016/j.compag.2023.107810
Mardiansyah, Palupi, T., & Maulidi. (2018). Respon beberapa varietas padi lokal terhadap cekaman salinitas pada fase pembibitan. Jurnal Sains Mahasiswa Pertanian, 7(3), 1–9. Retieved from https://doi.org/10.26418/jspe.v7i3.25222
Maulana, E. (1997). Pemotretan udara dengan UAV untuk mendukung kegiatan konservasi kawasan Gumuk Pasir Parangtritis. Prosiding Simposium Nasional Sains Geoinformasi, 399–407. Retieved from https://doi.org/10.13140/RG.2.2.13837.13280
Montolalu, C., & Langi, Y. (2018). Pengaruh Pelatihan Dasar Komputer dan Teknologi Informasi bagi Guru-Guru dengan Uji-T Berpasangan (Paired Sample T-Test). D’CARTESIAN, 7(1), 44. Retieved from https://doi.org/10.35799/dc.7.1.2018.20113
Nowakowski, M., Dudek, E., & Rosiński, A. (2023). The Influence of Varying Atmospheric and Space Weather Conditions on the Accuracy of Position Determination. Sensors, 23(5), 2814. Retieved from https://doi.org/10.3390/s23052814
Perdinan, P., Boer, R., Kartikasari, K., Dasanto, B. D., Hidayanti, R., & Oktavariani, D. (2018). Economic And Adaptation Costs Of Climate Change: Case Study Of Indramayu, West Java Indonesia. Jurnal Kesejahteraan Sosial, 1(02). Retieved from https://doi.org/10.31326/jks.v1i02.143
Purwanto, T. H. (2017). Pemanfaatan Foto Udara Format Kecil untuk Ekstraksi Digital Elevation Model dengan Metode Stereoplotting. Majalah Geografi Indonesia, 31(1), 73. Retieved from https://doi.org/10.22146/mgi.24246
Rayes, M. L. (2007). Metode inventarisasi sumber daya lahan. Andi.
Rhoades, J. D., Manteghi, N. A., Shouse, P. J., & Alves, W. J. (1989). Soil Electrical Conductivity and Soil Salinity: New Formulations and Calibrations. Soil Science Society of America Journal, 53(2), 433–439. Retieved from https://doi.org/10.2136/sssaj1989.03615995005300020020x
Robbins, CharlesW. (1984). Sodium adsorption ratio-exchangeable sodium percentage relationships in a high potassium saline-sodic soil. Irrigation Science, 5(3). Retieved from https://doi.org/10.1007/BF00264606
Royston, P. (1992). Approximating the Shapiro-Wilk W-test for non-normality. Statistics and Computing, 2(3), 117–119. Retieved from https://doi.org/10.1007/BF01891203
Rusdiana, O., & Lubis, R. S. (2012). Pendugaan korelasi antara karakteristik tanah terhadap cadangan karbon (carbon stock) pada hutan sekunder. Jurnal Silvikultur Tropika, 3(1). Retieved from https://doi.org/10.29244/j-siltrop.3.1.%25p
Sarani, F., Ahangar, A. G., & Shabani, A. (2016). Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region, Sistan and Baluchestan Province, Iran). Archives of Agronomy and Soil Science, 62(1), 127–138. Retieved from https://doi.org/10.1080/03650340.2015.1040398
Silleos, N. G., Alexandridis, T. K., Gitas, I. Z., & Perakis, K. (2006). Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years. Geocarto International, 21(4), 21–28. Retieved from https://doi.org/10.1080/10106040608542399
Sipayung, R. (2003). Stres Garam dan Mekanisme Toleransi Tanaman. Universitas Sumatra Utara.
Sulistyo, B., Gunawan, T., Hartono, H., & Danoedoro, P. (2013). Modeling of Percentage of Canopy in Merawu Catchment Derived From Various Vegetation Indices of Remotely Sensed Data. Forum Geografi, 27(1), 23. Retieved from https://doi.org/10.23917/forgeo.v27i1.5075
USDA. (2020). America: Rice Outlook.
World Agricultural Production. (2020). Production Rice World 2020.
Zakiyah, Z. N., Rahmawati, C., & Fatimah, I. (2019). Analysis Of Phosphorus And Potassium Levels In Organic Fertilizer In The Integrated Laboratory Of Jombang District Agriculture Office. INDONESIAN JOURNAL OF CHEMICAL RESEARCH, 38–48. Retieved from https://doi.org/10.20885/ijcr.vol3.iss2.art1
Zhao, S., Liu, J.-J., Banerjee, S., Zhou, N., Zhao, Z.-Y., Zhang, K., & Tian, C.-Y. (2018). Soil pH is equally important as salinity in shaping bacterial communities in saline soils under halophytic vegetation. Scientific Reports, 8(1), 4550. Retieved from https://doi.org/10.1038/s41598-018-22788-7
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Copyright (c) 2026 Aditya Putra, Qoid Luqmanul Hakim, Alberth Fernando Sitorus, Martiana Adelyanti, Istika Nita, Sudarto, Michelle Talisia Sugiarto, Novandi Rizky Prasetya (Author)

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