干旱区生态环境知识资源中心(Arid): Predicting soil aggregate stability using readily available soil properties and I machine learning techniques

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DOI	10.1016/j.catena.2019.104408
	Predicting soil aggregate stability using readily available soil properties and I machine learning techniques
	Rivera, Javier, I 1; Bonilla, Carlos A.1,2
通讯作者	Bonilla, Carlos A.
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2020
卷号	187
英文摘要	Aggregate stability is a measurement of soil quality, as the presence of stable aggregates relates to a wide range of soil ecosystem services. However, aggregates stability is not reported in most soil surveys, so predictive models have been focus of increasing attention as an alternative method in the absence of direct measurements. Therefore, the objective of this study was to develop a new model for predicting aggregate stability, using two machine learning techniques: An Artificial Neural Network (ANN) model and Generalized Linear Model (GLM). These techniques were applied to a soil dataset described in terms of soil texture, organic matter content, pH, and water-stable aggregates. This dataset included 109 soil samples obtained at 0-17 cm soil depth from hyperarid, arid, semiarid, and humid regions in Chile, including agricultural soils, shrubland, and forestland. Most soil textures in this dataset were sandy loam, loam, and clay loam, and each soil property had a large range of values. Aggregate stability was measured and computed as the percentage of water-stable aggregates using a wet sieving apparatus, and the ANN and GLM models were constructed and evaluated by repeated cross-validation (80% and 20% of dataset for training and testing, respectively). The ANN and GLM models were compared by computing the modified r(2) (r(adj)(2)), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results demonstrated a positive gradient of aggregate stability from arid (40% in average) to humid (87% in average) regions, which is related to the increase in organic matter content and decrease in pH. Organic matter content and pH exhibited a significant correlation to the aggregate stability, with r = 0.56 and r = -0.73, respectively. Moreover, among the fractions used to compute the soil texture, the clay content exhibited the highest correlation with aggregate stability (r = 0.30). These variables were used for training and testing the ANN and GLM models. The ANN model achieved superior performance in terms of the RMSE, (r(adj)(2)) and MAE in the cross-validation procedure, and showed r(2) = 0.80 for training and r(2) = 0.82 for testing. The GLM yielded r(2) = 0.59 and r(2) = 0.63 for training and testing, respectively. Therefore, despite the limitations observed when implementing ANN, its use is recommended instead of GLM as a reference model. Considering the small number of easily measured variables, this study provides two models that can be coupled with other existing soil routines or can be used directly to complete soil surveys where the aggregate stability was not measured.
英文关键词	Aggregate stability Machine learning Neural networks Soil aggregates Water-stable aggregates
类型	Article
语种	英语
国家	Chile
收录类别	SCI-E
WOS记录号	WOS:000514020400010
WOS关键词	PEDOTRANSFER FUNCTIONS ; ORGANIC-MATTER ; CARBON ; ERODIBILITY ; MANAGEMENT ; NITROGEN ; MODELS ; SPECTROSCOPY ; TILLAGE ; EROSION
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314232
作者单位	1.Pontificia Univ Catolica Chile, Dept Ingn Hidraul & Ambiental, Av Vicuna Mackenna 4860, Santiago 7820436, Chile; 2.Ctr Desarrollo Urbano Sustentable CONICYT FONDAP, Comendador 1916, Santiago 7520245, Chile
推荐引用方式 GB/T 7714	Rivera, Javier, I,Bonilla, Carlos A.. Predicting soil aggregate stability using readily available soil properties and I machine learning techniques[J],2020,187.
APA	Rivera, Javier, I,&Bonilla, Carlos A..(2020).Predicting soil aggregate stability using readily available soil properties and I machine learning techniques.CATENA,187.
MLA	Rivera, Javier, I,et al."Predicting soil aggregate stability using readily available soil properties and I machine learning techniques".CATENA 187(2020).