Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/36170
Title: Bulk density prediction for histosols and soil horizons with high organic matter content
Keywords: Pedotransfer functions
Multiple linear regression
Box-Cox transformation
Soil database
Issue Date: 2017
Publisher: Sociedade Brasileira de Ciência do Solo
Citation: BEUTLER, S. J. et al. Bulk density prediction for histosols and soil horizons with high organic matter content. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 41, 2017.
Abstract: Bulk density (Bd) can easily be predicted from other data using pedotransfer functions (PTF). The present study developed two PTFs (PTF1 and PTF2) for Bd prediction in Brazilian organic soils and horizons and compared their performance with nine previously published equations. Samples of 280 organic soil horizons used to develop PTFs and containing at least 80 g kg-1 total carbon content (TOC) were obtained from different regions of Brazil. The multiple linear stepwise regression technique was applied to validate all the equations using an independent data set. Data were transformed using Box-Cox to meet the assumptions of the regression models. For validation of PTF1 and PTF2, the coefficient of determination (R2) was 0.47 and 0.37, mean error -0.04 and 0.10, and root mean square error 0.22 and 0.26, respectively. The best performance was obtained for the PTF1, PTF2, Hollis, and Honeysett equations. The PTF1 equation is recommended when clay content data are available, but considering that they are scarce for organic soils, the PTF2, Hollis, and Honeysett equations are the most suitable because they use TOC as a predictor variable. Considering the particular characteristics of organic soils and the environmental context in which they are formed, the equations developed showed good accuracy in predicting Bd compared with already existing equations.
URI: http://repositorio.ufla.br/jspui/handle/1/36170
Appears in Collections:DCS - Artigos publicados em periódicos



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