Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Ruhollah Taghizadeh-Mehrjardi,Leila Rasoli,Thomas Scholten,Ruth Kerry,Kamal Nabiollahi

doi:10.3390/agronomy10040573

Ruhollah Taghizadeh-Mehrjardi, Leila Rasoli + Show 3 more

Open Access

https://doi.org/10.3390/agronomy10040573

Copy DOI

Abstract

Land suitability assessment is essential for increasing production and planning a sustainable agricultural system, but such information is commonly scarce in the semi-arid regions of Iran. Therefore, our aim is to assess land suitability for two main crops (i.e., rain-fed wheat and barley) based on the Food and Agriculture Organization (FAO) “land suitability assessment framework” for 65 km2 of agricultural land in Kurdistan province, Iran. Soil samples were collected from genetic layers of 100 soil profiles and the physical-chemical properties of the soil samples were analyzed. Topography and climate data were also recorded. After calculating the land suitability classes for the two crops, they were mapped using machine learning (ML) and traditional approaches. The maps predicted by the two approaches revealed notable differences. For example, in the case of rain-fed wheat, results showed the higher accuracy of ML-based land suitability maps compared to the maps obtained by traditional approach. Furthermore, the findings indicated that the areas with classes of N2 (≈18%↑) and S3 (≈28%↑) were higher and area with the class N1 (≈24%↓) was less predicted in the traditional approach compared to the ML-based approach. The major limitations of the study area were rainfall at the flowering stage, severe slopes, shallow soil depth, high pH, and large gravel content. Therefore, to increase production and create a sustainable agricultural system, land improvement operations are suggested.

Highlights

Rapid population growth in developing countries means that more food will be required to meet the demands of growing populations
In this study, we focus on using machine learning models to predict the spatial distribution of land suitability classes in the most economically feasible way and explore if it works better than the traditional approach—the most common approach to produce land suitability class maps in Iran
Soil m above sea level and the area is surrounded by mountains and hills from the southwest to the moisture and temperature regimes are Xeric and Mesic, respectively

Summary

Introduction

Rapid population growth in developing countries means that more food will be required to meet the demands of growing populations. Rain-fed wheat and barley, as major grain crops worldwide, are planted under a wide range of environments and are a major staple source of food for humans and livestock [1,2,3,4]. The production of such staple crops influences local food security [5]. Rain-fed wheat and barley are cultivated on approximately 6 and 0.64 million ha in Iran, respectively [6].

Objectives

Methods

Results

Conclusion