干旱区生态环境知识资源中心(Arid): Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus

Arid

DOI	10.3390/agriculture12071062
	Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus
	Kaya, Fuat; Keshavarzi, Ali; Francaviglia, Rosa; Kaplan, Gordana; Basayigit, Levent; Dedeoglu, Mert
通讯作者	Keshavarzi, A
来源期刊	AGRICULTURE-BASEL
EISSN	2077-0472
出版年	2022
卷号	12 期号:7
英文摘要	Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models' results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties with a well-known relationship to SOC, was the most important predictor of SOC, followed by open-access multispectral satellite images-based vegetation and soil indices. Ava-P had a similar set of effective variables. Hybrid approaches did not improve model accuracy significantly, but they did reduce map uncertainty. In the validation set, Ava-P was calculated using the RF algorithm with a normalized root mean square (NRMSE) of 96.8, while SOC was calculated using the Cubist algorithm with an NRMSE of 94.2. These values did not change when using the hybrid technique for Ava-P; however, they changed just by 1% for SOC. The management of SOC content and the supply of Ava-P in agricultural activities can be guided by SOC and Ava-P digital distribution maps. Produced digital maps in which the soil scientist plays an active role can be used to identify areas where concentrations are high and need to be protected, where uncertainty is high and sampling is required for further monitoring.
英文关键词	digital soil mapping landsat 8 OLI sentinel 2A MSI soil organic carbon phosphorus environmental covariates machine learning hybrid techniques land use arid regions
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000831696500001
WOS关键词	RANDOM FOREST ; INTERPOLATION METHODS ; SPATIAL VARIABILITY ; DAN RIVER ; LAND ; MATTER ; NITROGEN ; DENSITY ; AREA ; GEOSTATISTICS
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391691
推荐引用方式 GB/T 7714	Kaya, Fuat,Keshavarzi, Ali,Francaviglia, Rosa,et al. Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus[J],2022,12(7).
APA	Kaya, Fuat,Keshavarzi, Ali,Francaviglia, Rosa,Kaplan, Gordana,Basayigit, Levent,&Dedeoglu, Mert.(2022).Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus.AGRICULTURE-BASEL,12(7).
MLA	Kaya, Fuat,et al."Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus".AGRICULTURE-BASEL 12.7(2022).