Unsaturated Hydraulic Conductivity Prediction Using Artificial Intelligence and Multiple Linear Regression Models in Biochar Amended Sandy Clay Loam Soil

Unsaturated Hydraulic Conductivity Prediction Using Artificial Intelligence and Multiple Linear Regression Models in Biochar Amended Sandy Clay Loam Soil

Improved productivity of crops grown in biochar amended soils largely depend on the unsaturated hydraulic conductivity (K (q)) and moisture content of the soil. However, their relationships in biochar amended soil have not been well elucidated in both field and laboratory studies. Moreso, it is impo...

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Bibliographic Details
Published in:Journal of soil science and plant nutrition Vol. 22; no. 2; pp. 1589 - 1603
Main Authors: Faloye, Oluwaseun Temitope, Ajayi, Ayodele Ebenezer, Ajiboye, Yinka, Alatise, Michael Olanrewaju, Ewulo, Babatunde Sunday, Adeosun, Sunday Samuel, Babalola, Toju, Horn, Rainer
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01-06-2022
Springer Nature B.V
Subjects:
Adaptive systems
Agriculture
Artificial intelligence
Artificial neural networks
Biomedical and Life Sciences
Carbon
Charcoal
Clay loam
Clay soils
Conductivity
Corn
Ecology
Environment
Field tests
Fuzzy logic
Growing season
Hydraulic conductivity
Hydraulics
Hydrology
Life Sciences
Loam soils
Moisture content
Neural networks
Original Paper
Plant Sciences
Regression analysis
Regression models
Root-mean-square errors
Sandy soils
Soil amendment
Soil improvement
Soil moisture
Soil Science & Conservation
Soil sciences
Water content
Water flow
Multiple regression model
Water movement
Maize crop
Artificial neural network
ANFIS
Soil amendments
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Summary:Improved productivity of crops grown in biochar amended soils largely depend on the unsaturated hydraulic conductivity (K (q)) and moisture content of the soil. However, their relationships in biochar amended soil have not been well elucidated in both field and laboratory studies. Moreso, it is important to propose a model, which can accurately predict the K (q), since its determination in field is laborious. The goals of this study were to determine the relationship between moisture content and K (q) in biochar amended soil; (ii) predict K(q) using multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), as innovative tools used in soil science; and (iii) evaluate their performances. Field experiments with five levels of biochar applications (0, 3, 6, 10 and 20 t/ha) were used during maize growing seasons. Soil moisture contents in relation to days after planting (DAP) of maize and biochar (B) application rates were recorded and used as model inputs. Results showed that measured K (q) decreased as moisture content increased in biochar amended soil. Also, ANN outperformed ANFIS and MLR in predicting K (q). The coefficients of determination, R 2 were 0.98, 0.92 and 0.95 for the ANN, MLR and ANFIS during validation, respectively. Also, the root mean square error (RMSE) values were 1.80, 8.83 and 6.84 mm h −1 for the ANN, MLR and ANFIS during validation, respectively. Artificial neural network is most suitable for modelling water flow in biochar amended soil, and moisture content is important for its determination.
ISSN:0718-9508
0718-9516
DOI:10.1007/s42729-021-00756-x