干旱区生态环境知识资源中心(Arid): Estimating the soil respiration under different land uses using artificial neural network and linear regression models

Arid

DOI	10.1016/j.catena.2018.11.035
	Estimating the soil respiration under different land uses using artificial neural network and linear regression models
	Ebrahimi, Mitra 1,2; Sarikhani, Mohammad Reza 2; Sinegani, Ali Akbar Safari 1; Ahmadi, Abbas 2; Keesstra, Saskia 3,4
通讯作者	Sarikhani, Mohammad Reza
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2019
卷号	174 页码:371-382
英文摘要	Soil respiration is a biological process in microbes that convert organic carbon to atmospheric CO2. This process is considered to be one of the largest global carbon fluxes and is affected by different physicochemical and biological properties of soil, land use, vegetation types and climate patterns. Soil respiration recently received much attention, and it could be measured in two states basal respiration (BR) and substrate induced respiration (SIR) which together gives a good representation of the general soil microbial activity. The aim of this study was to estimate the BR and SIR of 150 data points obtained from soil samples collected from the surface to 20 cm of depth under different land use categories using the Artificial Neural Network (ANN) and Linear Regression Methodology (LRM). This study is bringing data from an arid area, and there is little information on this issue. Soil samples were chosen from three provinces of Iran, with humid subtropical and semi-arid climate patterns. In each soil sample a variety of characteristics were measured: soil texture, pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), organic carbon (OC), OC fractionation data e.g. light fraction OC (LOC), heavy fraction OC (HOC), cold water extractable OC (COC) and warm water extractable OC (WOC), population of fungi, bacteria, actinomycete, BR and SIR. Our goal was to use the most efficient ANN-model to predict soil respiration with simple soil data and annual precipitation (AP) and mean annual temperature (MAT) and compare it with LRM. Our results indicated that for an ANN model containing all the measured soil parameters (14 variables), the R-2 and RMSE values for BR prediction were 0.64 and 0.05 while these statistical indicators for SIR obtained 0.58 and 0.15, respectively; whereas the addition of AP and MAT data to this model (16 variables) caused a decrease in statistical indicators. When the R-2 and RMSE values of the BR-ANN and SIR-ANN predicted using an ANN model with only 7 variables (including OC, pH, EC, CCE and soil texture) they were estimated to be 0.66, 0.043 and 0.52, 0.16, respectively. Overall, LRM in comparison to ANN had a lower R-2. Therefore, the results show that ANN modeling is a reliable method for predicting soil respiration, even when based on easy to measure data. Our results revealed that highest and lowest BR and SIR were recorded in rice paddy soils and saline lands, respectively. In total, soil respiration (BR: 0.09 vs 0.06 and SIR: 0.46 vs 0.32 mg CO2 g(-1) day(-1)) was higher in agricultural land compared to natural covered land.
英文关键词	Artificial neural network Land use Linear regression Soil physicochemical properties Soil respiration Soil microorganisms
类型	Article
语种	英语
国家	Iran ; Netherlands ; Australia
收录类别	SCI-E
WOS记录号	WOS:000456754600035
WOS关键词	ORGANIC-MATTER ; FOREST ECOSYSTEMS ; MICROBIAL BIOMASS ; CARBON INPUTS ; CO2 EFFLUX ; PREDICTION ; SALINITY ; SODICITY ; TEMPERATURE ; IMPACT
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/2XILL650/214820
作者单位	1.Bu Ali Sina Univ, Fac Agr, Dept Soil Sci, Hamadan, Iran; 2.Univ Tabriz, Fac Agr, Dept Soil Sci, Tabriz, Iran; 3.Team Soil Water & Land Use, Droevendaalsesteeg 3, NL-6708 PB Wageningen, Netherlands; 4.Univ Newcastle, Civil Surveying & Environm Engn, Callaghan, NSW 2308, Australia
推荐引用方式 GB/T 7714	Ebrahimi, Mitra,Sarikhani, Mohammad Reza,Sinegani, Ali Akbar Safari,et al. Estimating the soil respiration under different land uses using artificial neural network and linear regression models[J],2019,174:371-382.
APA	Ebrahimi, Mitra,Sarikhani, Mohammad Reza,Sinegani, Ali Akbar Safari,Ahmadi, Abbas,&Keesstra, Saskia.(2019).Estimating the soil respiration under different land uses using artificial neural network and linear regression models.CATENA,174,371-382.
MLA	Ebrahimi, Mitra,et al."Estimating the soil respiration under different land uses using artificial neural network and linear regression models".CATENA 174(2019):371-382.