Artificial neural network (ANN) modelling for the estimation of soil microbial biomass in vineyard soils
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Artificial neural network (ANN) modelling for the estimation of soil microbial biomass in vineyard soils. / Pellegrini, Elisa; Rovere, Nicola; Zaninotti, Stefano; Franco, Irene; De Nobili, Maria; Contin, Marco.
I: Biology and Fertility of Soils, Bind 57, Nr. 1, 2021, s. 145-151.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Artificial neural network (ANN) modelling for the estimation of soil microbial biomass in vineyard soils
AU - Pellegrini, Elisa
AU - Rovere, Nicola
AU - Zaninotti, Stefano
AU - Franco, Irene
AU - De Nobili, Maria
AU - Contin, Marco
PY - 2021
Y1 - 2021
N2 - Soil microbial biomass (SMB-C) is one of the most frequently used parameters for the assessment of soil quality, but no threshold values have ever been proposed. We challenged the problem of a reliable numerical estimation of the SMB-C based on the knowledge of physicochemical soil properties. The aim was to evaluate artificial neural network (ANN) modelling for the prediction of SMB-C from a range of physical and chemical properties. The dataset used is composed of 231 vineyard soils of widely different characteristics and exposed to different temperature and moisture regimes. Each soil was described by ten physicochemical parameters: sand, clay, soil organic matter, total N, C/N ratio, pH, EC, exchangeable Na, active lime and total Cu. The ANN followed the topology: one input layer (1 to 11 nodes), one hidden layer (2 center dot n nodes) and one output node (SMB-C). Each soil sample was validated against the other 230 samples. The ANN model showed a much better fit than the linear model. The divergence between measured and predicted SMB-C was greatly restrained using the nonlinear approach, testifying the ability of the ANN to adapt to the highly variable dataset. The ANN analysis confirmed the primary importance of SOM for SMB-C prediction, being present in all of the best five models with the lowest root mean square relative error and in four out of five models with the lowest root mean square error. The prediction capability of SMB-C by ANN was limited at high SMB-C values, but the method can potentially be improved by expanding the dataset and introducing more parameters regarding soil physical properties and management.
AB - Soil microbial biomass (SMB-C) is one of the most frequently used parameters for the assessment of soil quality, but no threshold values have ever been proposed. We challenged the problem of a reliable numerical estimation of the SMB-C based on the knowledge of physicochemical soil properties. The aim was to evaluate artificial neural network (ANN) modelling for the prediction of SMB-C from a range of physical and chemical properties. The dataset used is composed of 231 vineyard soils of widely different characteristics and exposed to different temperature and moisture regimes. Each soil was described by ten physicochemical parameters: sand, clay, soil organic matter, total N, C/N ratio, pH, EC, exchangeable Na, active lime and total Cu. The ANN followed the topology: one input layer (1 to 11 nodes), one hidden layer (2 center dot n nodes) and one output node (SMB-C). Each soil sample was validated against the other 230 samples. The ANN model showed a much better fit than the linear model. The divergence between measured and predicted SMB-C was greatly restrained using the nonlinear approach, testifying the ability of the ANN to adapt to the highly variable dataset. The ANN analysis confirmed the primary importance of SOM for SMB-C prediction, being present in all of the best five models with the lowest root mean square relative error and in four out of five models with the lowest root mean square error. The prediction capability of SMB-C by ANN was limited at high SMB-C values, but the method can potentially be improved by expanding the dataset and introducing more parameters regarding soil physical properties and management.
KW - Soil microbial biomass
KW - Artificial neural networks
KW - Vineyard soils
KW - Critical values
KW - ENZYME-ACTIVITIES
KW - DIVERSITY
KW - PARAMETERS
KW - MANAGEMENT
KW - COMMUNITY
KW - AMENDMENT
KW - QUALITY
KW - SYSTEMS
KW - COPPER
U2 - 10.1007/s00374-020-01498-1
DO - 10.1007/s00374-020-01498-1
M3 - Journal article
VL - 57
SP - 145
EP - 151
JO - Biology and Fertility of Soils
JF - Biology and Fertility of Soils
SN - 0178-2762
IS - 1
ER -
ID: 247539456