Evaluation of the effect of grouping based on different characteristics on the performance of functions in estimating soil cation exchange capacity

Document Type : Research Paper

Authors

1 Department of Soil Science, Faculty of Agriculture, Bu Ali Sina University, Hamadan, IRAN

2 Department of Soil Science, Faculty of Agriculture, Bu Ali Sina University, Hamadan,, IRAN

3 Ph. D. Student, Soil Science Department, Faculty of Agricultural Sciences, University of Guilan, RASHT, IRAN

Abstract

This study addresses the challenge of measuring soil cation exchange capacity (CEC), a vital factor influencing soil fertility, by exploring the impact of grouping soil samples based on different characteristics on the performance of estimation models. Recognizing the difficulties associated with traditional CEC measurement methods, the study employs a cost-effective and rapid approach using various models and equations. The research, conducted at Bu Ali Sina University in Hamedan, utilizes a substantial dataset of 45,948 soil samples from the standardized database of world soils. Soil samples are initially categorized into different groups, and nine estimator variables are examined across 11 models for the entire dataset and specific classes within each group. These variables include soil texture components, organic carbon, calcium sulfate, calcium carbonate, bulk density, base saturation percentage, total exchangeable base cations, and soil reaction. The results demonstrate that grouping soil samples, especially based on texture classes, significantly improves the performance of artificial neural network models, with a remarkable 87% relative improvement coefficient in the test section. The study reveals that data grouping enhances the model's estimation capabilities, as evidenced by reduced root mean square error (RMSE) values in the test sections for different texture classes. In conclusion, the findings suggest that utilizing functions derived from grouped data offers an effective and cost-efficient method for estimating soil cation exchange capacity. This approach provides valuable insights for soil fertility management, offering a simplified yet accurate means of assessing this critical soil parameter.

Keywords

Main Subjects

Evaluation of the Effect of Grouping Based on Different Characteristics on the Performance of Functions in Estimating Soil Cation Exchange Capacity

EXTENDED ABSTRACT

 

Introduction

The cation exchange capacity of the soil is one of the most important characteristics, as it greatly influences soil fertility. This capacity is influenced by various physical and chemical characteristics of the soil, resulting in significant variability. However, measuring soil cation exchange capacity can be challenging and expensive. The objective of this study is to assess the impact of grouping based on different characteristics on the accuracy of estimating soil cation exchange capacity. Additionally, we aim to introduce a grouping method that yields the best estimation results and compare the effectiveness of linear regression and artificial neural networks in estimating cation exchange capacity.

Material and Methods

For this research, we utilized 45,948 soil samples from a standardized global soils database. Initially, these samples were grouped into different categories. Subsequently, we evaluated nine predictor variables including soil texture components, organic carbon content, calcium sulfate and carbonate levels, apparent specific gravity, base saturation percentage, total exchangeable base cations, and soil reaction in 11 models for both the entire dataset and each group separately. To determine which grouping method yields more accurate estimations for soil cation exchange capacity and which characteristic contributes to better results, transfer functions were developed for each group using all data and then again for each class within the groups. In this study, we employed multilayer perceptron neural networks with one hidden layer and a hyperbolic tangent activation function. The number of cells in the hidden layer ranged from 3 to 8. The network with the optimal number of hidden cells was selected as the final network based on its performance. Additionally, linear regression modeling was conducted using Datafit 9 software to compare its effectiveness with artificial neural networks in estimating cation exchange capacity.

Results and Discussion

The correlation between the cation exchange capacity of soil and clay, apparent specific gravity, the ratio of silt to positive sand, calcium carbonate, total exchangeable base cations, and soil reaction was found to be significant at the 1% level. When comparing the RMSE and RI statistics in two sets of training and test data for the entire dataset and for classes separated based on soil texture groups, it was observed that data separation improved the mean in estimating the cation exchange capacity of the soil. Two methods, linear regression and artificial neural networks, were used to estimate the cation exchange capacity. The results showed that for all coarse, medium, and fine texture groups, the artificial neural network test section had a relative improvement coefficient of 87%, while linear regression had a value of 17%. The grouping of data generally increased the estimation ability of the models. Among the groups studied in this research, the texture group showed higher accuracy in predicting cation exchange capacity compared to other groups. Overall, using functions obtained through grouping proved to be an easy and cost-effective method for estimating cation exchange capacity.

Conclusion

In general, artificial neural networks outperformed linear regression in estimating cation exchange capacity in most classes within each group. This may be due to not requiring a specific type of equation in designing artificial neural networks. By establishing a suitable relationship between output and input data, accurate results can be achieved.

References

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Iranian Journal of Soil and Water Research
Volume 55, Issue 3
June 2024
Pages 361-379

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