干旱区生态环境知识资源中心(Arid): Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models

Arid

DOI	10.3390/agronomy10040573
	Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models
	Taghizadeh-Mehrjardi, Ruhollah 1,2; Nabiollahi, Kamal 3; Rasoli, Leila 3; Kerry, Ruth 4; Scholten, Thomas 1,5,6
通讯作者	Nabiollahi, Kamal
来源期刊	AGRONOMY-BASEL
EISSN	2073-4395
出版年	2020
卷号	10 期号:4
英文摘要	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 km(2) 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 (approximate to 18%up arrow) and S3 (approximate to 28%up arrow) were higher and area with the class N1 (approximate to 24%down arrow) 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.
英文关键词	random forests support vector machine parametric method rain-fed wheat barley
类型	Article
语种	英语
国家	Germany ; Iran ; USA
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000534620300071
WOS关键词	RANDOM FOREST ; SEMIARID REGION ; SOIL TYPES ; CLASSIFICATION ; BARLEY ; WHEAT ; INFORMATION ; REGRESSION ; PROVINCE ; STOCKS
WOS类目	Agronomy ; Plant Sciences
WOS研究方向	Agriculture ; Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/318926
作者单位	1.Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, D-72070 Tubingen, Germany; 2.Ardakan Univ, Fac Agr & Nat Resources, Ardakan 8951656767, Iran; 3.Univ Kurdistan, Fac Agr, Dept Soil Sci & Engn, Sanandaj 6617715175, Iran; 4.Brigham Young Univ, Dept Geog, Provo, UT 84602 USA; 5.Univ Tubingen, CRC 1070 ResourceCultures, D-72070 Tubingen, Germany; 6.Univ Tubingen, DFG Cluster Excellence Machine Learning, D-72070 Tubingen, Germany
推荐引用方式 GB/T 7714	Taghizadeh-Mehrjardi, Ruhollah,Nabiollahi, Kamal,Rasoli, Leila,et al. Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models[J],2020,10(4).
APA	Taghizadeh-Mehrjardi, Ruhollah,Nabiollahi, Kamal,Rasoli, Leila,Kerry, Ruth,&Scholten, Thomas.(2020).Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models.AGRONOMY-BASEL,10(4).
MLA	Taghizadeh-Mehrjardi, Ruhollah,et al."Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models".AGRONOMY-BASEL 10.4(2020).