Next Article in Journal
Optimizing Overhead Irrigation Droplet Size for Six Mississippi Soils
Next Article in Special Issue
Exploring Innovation Adoption Behavior for Sustainable Development: The Case of Hungarian Food Sector
Previous Article in Journal
Genotypic Variation in Nitrogen Use-Efficiency Traits of 28 Tobacco Genotypes
Previous Article in Special Issue
Use of Aloe Vera Gel-Based Edible Coating with Natural Anti-Browning and Anti-Oxidant Additives to Improve Post-Harvest Quality of Fresh-Cut ‘Fuji’ Apple
Open AccessArticle

Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

1
Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, 72070 Tübingen, Germany
2
Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan 8951656767, Iran
3
Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj 6617715175, Iran
4
Department of Geography, Brigham Young University, Provo, UT 84602, USA
5
CRC 1070 ResourceCultures, University of Tübingen, 72070 Tübingen, Germany
6
DFG Cluster of Excellence “Machine Learning”, University of Tübingen, 72070 Tübingen, Germany
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(4), 573; https://doi.org/10.3390/agronomy10040573
Received: 27 March 2020 / Revised: 13 April 2020 / Accepted: 14 April 2020 / Published: 17 April 2020
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. View Full-Text
Keywords: random forests; support vector machine; parametric method; rain-fed wheat; barley random forests; support vector machine; parametric method; rain-fed wheat; barley
Show Figures

Figure 1

MDPI and ACS Style

Taghizadeh-Mehrjardi, R.; Nabiollahi, K.; Rasoli, L.; Kerry, R.; Scholten, T. Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy 2020, 10, 573.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop