Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

• Published in: Agronomy (Basel) Vol. 10; no. 4; p. 573
• Main Authors: Taghizadeh-Mehrjardi, Ruhollah, Nabiollahi, Kamal, Rasoli, Leila, Kerry, Ruth, Scholten, Thomas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.04.2020
• Subjects: Agricultural land; Agricultural management; Agricultural production; Arid regions; Arid zones; artificial intelligence; Barley; Chemical properties; Climatic data; Crops; Farming systems; Flowering; Food and Agriculture Organization; Gene mapping; Gravel; Information systems; Iran; Land improvement; land suitability; Land use; Learning algorithms; Machine learning; meteorological data; Methods; parametric method; planning; Precipitation; Rain; rain-fed wheat; Rainfall; random forests; Semi arid areas; Semiarid zones; Soil analysis; Soil depth; Soil layers; Soil profiles; Soil properties; soil sampling; Soils; support vector machine; Support vector machines; Sustainability; Sustainable agriculture; Topography; Wheat; Iran; Kurdistan

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...