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Assessment of Various Pedotransfer Functions for the Prediction of the Dry Bulk Density of Cultivated Soils in a Semiarid Environment
Communications in Soil Science and Plant Analysis ( IF 1.8 ) Pub Date : 2020-12-29 , DOI: 10.1080/00103624.2020.1869760
Pelin Alaboz 1 , Sinan Demir 1 , Orhan Dengiz 2
Affiliation  

ABSTRACT

Bulk density (BD) is a key soil indicator for the assessment of the sustainability of production from agricultural land. However, it is difficult to obtain data on BD from field studies because sampling is an extremely labor-intensive, time-consuming and expensive task at the large scale needed to generate meaningful amounts of data. Therefore, the purpose of this study was to evaluate different pedotransfer functions (PTFs) for their usefulness in the estimation of the BD values of an intensely cultivated area through the use of different geostatistical methods. PTFs are estimation functions of certain soil properties derived from raw data for other soil properties. Different approaches have been used for the development of PTFs, including regression methods and artificial neural networks. In this study, the multivariate analysis methods, general and stepwise regression, were used for BD estimation. In addition, different learning algorithms were evaluated with artificial neural networks (ANN) for their efficacy in estimating the BD. Seven basic soil properties, namely, sand, silt, clay, organic matter, pH, EC and CaCO3, were used in the development of models. The estimation power of the general regression model (normalized root mean square error (NRMSE): 7.10%) was higher than that of stepwise regression (NRMSE: 19.93%). Additionally, the lowest NRMSE (6.74%) and the highest R2 for BD estimation determined with different learning algorithms through artificial neural networks (ANN) were obtained with the Levenberg-Marguardt algorithm. Moreover, for BD estimation, ANN performed better than the multivariate regression equations. Spatial distribution maps of soil BD were generated with the commonly used ordinary kriging method by utilizing the real BD values in combination with BD values obtained from estimation models. Maps of BD values produced by stepwise regression estimation deviated significantly from maps generated with real values whereas ANN-II (Bayesian regularization algorithm) values were closest to the real values and that was reflected in the increased accuracy of mapping.



中文翻译:

评估半干旱环境下耕种土壤干容重的各种Pedotransfer函数评估

摘要

堆积密度(BD)是评估农业用地生产可持续性的关键土壤指标。但是,很难从实地研究中获得有关BD的数据,因为在生成有意义的数据量所需的大规模采样工作中,抽样是一项极为费力,耗时且昂贵的工作。因此,本研究的目的是通过使用不同的地统计学方法,评估不同的pedotransfer函数(PTF)在估算密集耕种区的BD值方面的有用性。PTF是从其他土壤特性的原始数据得出的某些土壤特性的估计函数。PTF的开发已使用了不同的方法,包括回归方法和人工神经网络。在这项研究中,多元分析方法 一般和逐步回归用于BD估算。另外,使用人工神经网络(ANN)对不同的学习算法进行了评估,以评估其BD的有效性。七种基本土壤特性,即沙子,淤泥,粘土,有机物,pH,EC和CaCO3,被用于模型的开发。一般回归模型的估计能力(归一化均方根误差(NRMSE):7.10%)高于逐步回归(NRMSE:19.93%)。此外,最低的NRMSE(6.74%)和最高的R 2使用Levenberg-Marguardt算法获得了通过不同学习算法通过人工神经网络(ANN)确定的BD估计值。此外,对于BD估计,ANN的性能优于多元回归方程。通过将实际BD值与从估计模型获得的BD值结合使用常用的普通克里格法生成土壤BD的空间分布图。通过逐步回归估计生成的BD值地图与使用实际值生成的地图显着不同,而ANN-II(贝叶斯正则化算法)值最接近于实际值,这反映在映射准确性的提高中。

更新日期:2020-12-29
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