Land Suitability Analysis for Agricultural Land Use using Hyperscale DEM Data

Abstract

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.