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Aditya Nugraha Putra
Faculty of Agriculture, Brawijaya University


S Sudarto
Faculty of Agriculture, Brawijaya University


Ananda Ginanthian Alpheratz Ridwan
Faculty of Agriculture, Brawijaya University


Aftomi Firman Aditama
Faculty of Agriculture, Brawijaya University


Sifa’ul Janahtin
Faculty of Agriculture, Brawijaya University



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Comparison of soil physical properties and soil-vegetation indices to predict rice productivity in Malang Regency of East Java

Aditya Nugraha Putra, S Sudarto, Ananda Ginanthian Alpheratz Ridwan, Aftomi Firman Aditama, Sifa’ul Janahtin
  J. Degrade. Min. Land Manage. , pp. 2891-2901  
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Rice has become the leading food commodity in Indonesia, with total production reached +54.60 million tons in 2019. However, the production tended to decrease by around 8% from 2018 to 2019, while the rice consumption increased by +1.53 tons. This study aims to develop a rice production estimation model using the soil-vegetation index transformation (MSAVI and SAVI) and soil physical properties, which has the advantage of being faster, cheaper, and more accurate than conventional methods. The soil physical properties were taken based on soil mapping units and analyzed with soil physical parameters. The results showed strong relationships between rice productivity - soil physical characteristics and rice productivity – MSAVI and EVI with r values of 0.97, 0.83, and 0.74, respectively. The soil physical properties have a better coefficient of determination and accuracy than soil-vegetation index. The prediction model of rice production by soil physical properties is formulated inward γ = -8.96+0.01 (Top Soil Sand) + 0.01 (Top Soil Silt) + 6.28 (Bulk Density) - 14.07 (Penetration) - 0.13 (Sub Soil Permeability). There is no difference in the productivity value between model and laboratory analysis result. These results indicate that the rice yield prediction model can be used for estimation purposes.


remote sensing;. rice productivity; soil index; soil physical properties; vegetation index

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Agus, F.A. and Hardjowigeno, S. 2004. Rice Fields and Its Management Technology. Puslitbang Tanah, Bogor, Indonesia (in Indonesian).

Andayani, N.N., Aqil, M. and Syuryawati. 2016. Application of step wise regression model in determining white corn yield. Informasi Pertanian 25: 21-28, doi: 10.21082/ip.v25n1.2016 (in Indonesian).

Armbruster, K., Hertwig, A. and Kutzbach, H., 1990. An improved design of cone penetrometer. Journal of Agricultural Engineering Research 46: 219-222, doi: 10.1016/S0021-8634(05).

Barus, B. and Wiradisastra, U.S. 2000. Geographic Information Systems. Remote Sensing and Cartography Laboratory. Soil Department, Faculty of Agriculture. IPB University (in Indonesian).

BBPadi. 2017. Sampling Technique: Estimation of Rice Productivity According to Planting Systems [WWW Document]. URL (accessed 5.20.20) (in Indonesian).

Bernardi, C., Rabello, L.M., Inamasu, R.Y., Grego, C.R. and Andrade, R.G. 2014. Spatial variability of soil physicochemical and surface biophysical parameters in sorghum cultivation. Revista Brasileira de Engenharia Agrícola e Ambiental 18(6): 623-630, doi: 10.1590/S1415-43662014000600009.

Bernoux, M., Cerri, C., Arrouays, D., Jolivet, C. and Volkoff, B. 1998. Bulk densities of Brazilian Amazon soils related to other soil properties. Soil Science Society of America Journal 62: 743-749.

Blake, G.R. and Hartge, K. 1986. Bulk density. Methods of Soil Analysis Part 1 Physical and Mineralogical Methods 5: 363-375.

Burke, M. and David, B.L. 2017. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Science 114: 89-94, doi : 10.1073/pnas.1616919114.

Camara, G., Palomo, D., de Souza, R.C.M. and de Oliveira, R.F. 2005. Towards a generalized map algebra: principles and data types. Natl. National Institute for Space Research (INPE) Av dos Astronautas, 1758 – 12227-001 – São José dos Campos – SP – Brazil.

Central Bureau of Statistics. 2020. Central Bureau of Statistics [WWW Document]. Area Rice Harvest Prod. 2019 Decreased Comp. 2018 615 776 Percent. URL

Central Statistics Agency of Malang Regency, 2019. Area of Harvest, Productivity and Production of Rice Fields by District In Malang, 2013-2018 [WWW Document]. URL

Chehbouni, A., Kerr, Y.H, Qi, J., Huete, A.R. and Sorroshian, S.S. 1994. Toward the development of a multidirectional vegetation index. Water Resources Research 30: 1281-1286, doi: 10.1029/93WR03063.

Daoud, J.I. 2017. Multicollinearity and regression analysis. Journal of Physics: Conference Series. IOP Publ. 012009.

Dinesh Kumar, M.L. and Phogat, V.K. 2009. Compactability in relation to texture and organic matter content of alluvial soils. Indian Journal of Agricultural Research 43: 180-186.

Erizilina, E., Pamoengkas, P. and Darwo, D. 2018. Relationship of the physical and chemical properties of soil with the growth of red meranti in KHDTK Haurbentes. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan 8: 216-222, doi: 10.29244/jpsl.8.2.216-222 (in Indonesian).

Gee, G.W. and Bauder, J.W. 1986. Particle-size Analysis. Methods of Soil Analysis Part 1 Physical and Mineralogical Methods 5: 383-411.

Hanafiah, K.A. 2007. Basic of Soil Science. Raja Grafindo Persada, Jakarta (in Indonesian).

Hanif, A. 2018. Using stepwise linear regression to determine factors that affect labour productivity. Jurnal Informasi 5: 73-80 (in Indonesian).

Haryati, U., 2014. Soil physical characteristics of highland vegetable farming area: it’s relationship with land management strategy. Jurnal Sumberdaya Lahan 8(2): 125-138 (in Indonesian).

Holilullah, Afandi, and Novpriansyah, H., 2015. Soil physical characteristics on low and high production lands at PT Great Giant Pinapple. Jurnal Agrotek Tropika 3(2): 278-282 (in Indonesian).

Indrajati, R.P. 2008. Evaluation of Changes in Technical Irrigation Soil Quality in Industrial Area at Sub Watershed Bengawan Solo Region, Karanganyar Regency. Sebelas Maret University, Surakarta, Indonesia (in Indonesian).

Ishaq, M., Rumiati, A.T. and Permatasari, E.O. 2017. Analysis of factors affecting rice production in East Java Province using spline semiparametric regression. Jurnal Sains dan Seni ITS 6: 94-100, doi: 10.12962/j23373520.v6i1.22451 (in Indonesian).

Keaton, J.R. and Degraff, J.V. 1996. Surface Observation and Geologic Mapping. Special Report - National Research Council, Transportation Research Board 247:178-230. In: Landslides: Investigation and Mitigation (pp.178-230) Chapter: 9, Transportation Research Board, National Academy Press.

Kravchenko, A.N. 2003. Influence of spatial structure on accuracy of interpolation methods. Soil Sci Society of America Journal 67: 1564-1571, doi: 10.2136/sssaj2003.1564.

Kuria, D., Ngari, D. and Waithaka, E. 2011. Using geographic information systems (GIS) to determine land suitability for rice crop growing in the Tana delta. Journal of Geography and Regional Planning 4(9): 525-532.

LPT (Soil Research Institute). 1979. Guide to Soil Physics Analysis. Indonesian Agricultural R&D Agency (in Indonesian).

Malang District Government. 2016. Geographical Condition of Malang Regency (in Indonesian).

Marwoto, and Danang, S.C., 2007. Creation of Geographic Information System for The Suitability of WEB-Based Sugarcane Planting Land in Merauke Regency. Jurnal Penginderaan Jauh 4: 60-71 (in Indonesian).

Ministry of Internal Affairs. 2020. Population Census in East Java [WWW Document]. URL (in Indonesian).

Mustaffa, A.A., Mukhtar, A.N., Rasib, A.W., Suhandri, H.F. and Bukari, S.M. 2020. Mapping of peat soil physical properties by using drone-based multispectral vegetation imagery. IOP Conference Series: Earth and Environmental Science 498, 012021, doi: 10.1088/1755-1315/498/1/012021.

Mutmainna, N.D., Achmad, M. and Suhardi, S., 2017. Estimation of Inceptisol soil moisture in horticultural plants using Landsat 8 images. Jurnal Agritechno 10(2): 135-151 (in Indonesian).

Parsa, M., Dirgahayu, D.D., Manalu, J., Carolita, I. and Harsanugraha, W. 2017. The testing of phase growth rice model based on multitemporal modis in Lombok Island. Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital 14(1), doi: 10.30536/j.pjpdcd.2017.v14.a2621 (in Indonesian).

Putra, A.N. and Nita, I. 2020. Reliability of using high-resolution aerial photography (red, green and blue bands) for detecting available soil water in agricultural land. Journal of Degraded and Mining Lands Management 7(3): 2221-2232, doi:10.15243/jdmlm.2020.073.2221.

Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H. and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 48: 119-126.

Rayes, M.L. 2007. Land Resource Inventory Methods. Andi Press Yogyakarta, Indonesia (in Indonesian).

Rudiana, E., Rustiadi, E., Firdaus, M. and Dirgahayu, D. 2017. Development of remote sensing use for rice production estimation (Bekasi District Case Study). Jurnal Ilmu Tanah dan Lingkungan 19: 6-12, doi: 10.29244/jitl.19.1.6-12 (in Indonesian).

Said, H.I., Subiyanto, S. and Yuwono, B.D. 2015. Analysis of rice production with remote sensing and geographic information system in Pekalongan City. Jurnal Geodesi Undip 4(1): 1-8 (in Indonesian).

Saputri, R.D., Darundiati, Y.H. and Dewanti, N.A.Y. 2016. Relationship of use and handling of pesticides on onion farmers to residues of pesticides in soil in agriculture of Wanasari Village, Wanasari District, Brebes Regency. Jurnal Kesehatan Masyarakat 4(3): 879-887 (in Indonesian).

Sari, V.D. and Sukojo, B.M. 2015. Analysis of rice production estimation based on growth phase and Autoregressive Integrated Moving Average (ARIMA) forecasting model using Landsat 8 Satellite Image (Case Study: Bojonegoro District). Geoid 10(2): 194-203, doi: 10.12962/j24423998.v10i2.828 (in Indonesian).

Sripada, R.P., Heiniger, R.W., White, J.G. and Meijer, A.D. 2006. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal 98: 968-977.

Sudarsono, N.W., Sudarsono, B. and Wijaya, A.P. 2016. Analysis of rice growth phases using the NDVI, EVI, SAVI and LSWI algorithms on Landsat 8 imagery. Jurnal Geodesi Undip 5(1): 125-134 (in Indonesian).

Susetyo, I. and Setiono, S. 2013. Remote sensing application to support plantation land management system in rubber plantation. Warta Perkaretan 32:105-113, doi: 10.22302/ppk.wp.v32i2.42 (in Indonesian).

Sutanta, H. and Tiera, A. 2019. Calculation the number of peaks in the sewu mountain area using DEMNAS, focal maximum function and slope position classification method. IOP Conference Series: Earth and Environmental Science 389, 012052, doi: 10.1088/1755-1315/389/1/012052.

Suwardjo, H. 1981. The Role of Plant Remains in Soil and Water Conservation in Plant Farming Patterns A Season. (Dissertation). Bogor Agricultural University, Bogor (in Indonesian).

Tan, K.H. 2005. Soil Sampling, Preparation, and Analysis. CRC Press Taylor and Francis Group. Boca Raton, FL.

Widhaningtyas, T.U., Putra, A.C.P. and Fariz, T.R. 2020. Comparison of topographic correction methods on Landsat 8 satellite imagery of Mount Telomoyo Region, Central Java. Jurnal Geografi 17(2): 32-38, doi: 10.15294/jg.v17i2.22863 (in Indonesian).


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