Land Suitability Analysis for Agricultural Land Use using Hyperscale DEM Data

Sativandi Riza, Masahiko Sekine, Ariyo Kanno, Koichi Yamamoto, Tsuyoshi Imai, Takaya Higuchi


Cropland suitability analysis is a crucial process for achieving the optimum utilization of land use. It is frequently used today and continues to develop, especially in the methods. Digital soil mapping is a new technique that can generate spatial predictions of soil attributes obtained from digital soil covariates, reducing the cost, time, and land-use suitability evaluation accuracy. This study aims to determine the land suitability for agriculture commodities based on a hyperscale soil texture predictive model and compare it with the conventional land-use suitability evaluation based on determining soil texture. The target agricultural commodities in this study are leafy vegetables, carrots, apples, and coffee. This study finds that the Hyperscale Soil Texture estimation model can be used as a parameter for land suitability assessment for agricultural commodities. The prediction obtained by this model is not significantly different from that obtained by the traditional approach. This research discovers that the two procedures produced similar outcomes. The hyperscale approach can be an alternative method for land suitability estimation and reduce the time and cost compared to traditional techniques.


Digital Soil Mapping; Hyperscale Modeling; Land Suitability; Land-Use; Soil Texture

Full Text:



Aarthi, R., & Sivakumar, D. (2020). An enhanced agricultural data mining technique for dynamic soil texture prediction. Procedia Computer Science, 171, 2770–2778. DOI

Adi, S. H., Grunwald, S., Tafakresnanto, C., & Sosiawan, H. (2021). Modeling paddy field soil conditions in East Java, Indonesia. Soil Security, 5, 100025. DOI

Akpoti, K., Kabo-bah, A. T., & Zwart, S. J. (2019). Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis. Agricultural Systems, 173, 172–208. DOI

AL-Taani, A., Al-husban, Y., & Farhan, I. (2020). Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an Governorate, Jordan. The Egyptian Journal of Remote Sensing and Space Science, 24(1), 109-117. DOI

Amsili, J. P., van Es, H. M., & Schindelbeck, R. R. (2021). Cropping system and soil texture shape soil health outcomes and scoring functions. Soil Security, 4, 100012. DOI

Barman, U., & Choudhury, R. D. (2020). Soil texture classification using multi class support vector machine. Information Processing in Agriculture, 7(2), 318–332. DOI

Behrens, T., Schmidt, K., Ramirez-Lopez, L., Gallant, J., Zhu, A. X., & Scholten, T. (2014). Hyper-scale digital soil mapping and soil formation analysis. Geoderma, 213, 578–588. DOI

Bring, J. (1994). How to standardize regression coefficients. American Statistician, 48(3), 209–213. DOI

da Silva Chagas, C., de Carvalho Junior, W., Bhering, S. B., & Calderano Filho, B. (2016). Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. CATENA, 139, 232–240. DOI

Erb, I. (2020). Partial correlations in compositional data analysis. Applied Computing and Geosciences, 6, 100026. DOI

Fang, H. (2021). Effect of soil conservation measures and slope on runoff, soil, TN, and TP losses from cultivated lands in Northern China. Ecological Indicators, 126, 107677. DOI

FAO. (2007). Land evaluation: Towards a revised framework. In Land and Water Discussion Paper No. 6. Rome, IT: Food and Agriculture Organization of the United Nations. Retrieved from PDF

Florinsky, I. V. (2008). Global lineaments: Application of digital terrain modelling. In Z. Qiming, L. Brian, & T. Guo-an (Eds.), Advances in Digital Terrain Analysis. Lecture Notes in Geoinformation and Cartography (pp. 365–382). Springer. DOI

Florinsky, I. V. (2012). Digital terrain analysis in soil science and geology. Academic Press. DOI

Florinsky, I. V., Eilers, R. G., Manning, G. R., & Fuller, L. G. (2002). Prediction of soil properties by digital terrain modelling. Environmental Modelling & Software, 17(3), 295–311. DOI

Goto, H. (2021). Three-dimensionally consistent contour-based network rendered from digital terrain model data. Geomorphology, 395, 107969. DOI

Grunwald, S. (2009). Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152(3–4), 195–207. DOI

Lilja, H., & Nevalainen, R. (2006). Developing a digital soil map for Finland. In P. Lagacherie, A. B. McBratney, & M. Voltz (Eds.), Developments in Soil Science Vol. 31, Digital Soil Mapping: An Introductory Perspective (pp. 67–74). Elsevier. DOI

Ma, Y., Minasny, B., Malone, B. P., & Mcbratney, A. B. (2019). Pedology and digital soil mapping (DSM). European Journal of Soil Science, 70(2), 216–235. DOI

MacMillan, R. A., Pettapiece, W. W., Nolan, S. C., & Goddard, T. W. (2000). A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, 113(1), 81–109. DOI

Malone, B., Stockmann, U., Glover, M., McLachlan, G., Engelhardt, S., & Tuomi, S. (2022). Digital soil survey and mapping underpinning inherent and dynamic soil attribute condition assessments. Soil Security, 6, 100048. DOI

Mazahreh, S., Bsoul, M., & Hamoor, D. A. (2019). GIS approach for assessment of land suitability for different land use alternatives in semi arid environment in Jordan: Case study (Al Gadeer Alabyad-Mafraq). Information Processing in Agriculture, 6(1), 91–108. DOI

McBratney, A. B., Mendonça Santos, M. L., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1–2), 3–52. DOI

Ostovari, Y., Honarbakhsh, A., Sangoony, H., Zolfaghari, F., Maleki, K., & Ingram, B. (2019). GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions. Ecological Indicators, 103, 479–487. DOI

Pilevar, A. R., Matinfar, H. R., Sohrabi, A., & Sarmadian, F. (2020). Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming. Ecological Indicators, 110, 105887. DOI

Ritung, S., Nugroho, K., Mulyani, A., & Suryani, E. (2011). Petunjuk teknis evaluasi lahan untuk komoditas pertanian (Edisi Revisi). Bogor: Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian. Retrieved from website

Searle, R., McBratney, A., Grundy, M., Kidd, D., Malone, B., Arrouays, D., … Andrews, K. (2021). Digital soil mapping and assessment for Australia and beyond: A propitious future. Geoderma Regional, 24, e00359. DOI

Shary, P. A. (1995). Land surface in gravity points classification by a complete system of curvatures. Mathematical Geology, 27(3), 373–390. DOI

Sinshaw, B. G., Belete, A. M., Mekonen, B. M., Wubetu, T. G., Anley, T. L., Alamneh, W. D., … Mossie Birhanu, M. (2021). Watershed-based soil erosion and sediment yield modeling in the Rib watershed of the Upper Blue Nile Basin, Ethiopia. Energy Nexus, 3, 100023. DOI

Tashayo, B., Honarbakhsh, A., Akbari, M., & Eftekhari, M. (2020). Land suitability assessment for maize farming using a GIS-AHP method for a semi- arid region, Iran. Journal of the Saudi Society of Agricultural Sciences, 19(5), 332–338. DOI

Tercan, E., & Dereli, M. A. (2020). Development of a land suitability model for citrus cultivation using GIS and multi-criteria assessment techniques in Antalya province of Turkey. Ecological Indicators, 117, 106549. DOI

Yalew, S. G., van Griensven, A., Mul, M. L., & van der Zaag, P. (2016). Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Modeling Earth Systems and Environment, 2(2), 101. DOI

Zhang, X., Song, J., Wang, Y., Deng, W., & Liu, Y. (2021). Effects of land use on slope runoff and soil loss in the Loess Plateau of China: A meta-analysis. Science of The Total Environment, 755, 142418. DOI


Copyright (c) 2022 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.