A pedotransfer function for estimating bulk density of forest soil in Japan affected by volcanic ash

Nanko, Kazuki; Ugawa, Shin; Hirano, Shoji; Imaya, Akihiro; Kobayashi, Masahiro; Sakai, Hisao; Ishizuka, Shigehiro; Miura, Satoru; Tanaka, Nagaharu; Takahashi, Masamichi; Kaneko, Shinji

doi:10.1016/j.geoderma.2013.07.025

Cited by 58 publications

(26 citation statements)

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“…Soil properties, readily available from routine soil surveys, underpin most PTFs to estimate ρ b (Benites et al, 2007;Bernoux et al, 1998;Han et al, 2012;Nanko et al, 2014). With the growing use of Digital Soil Mapping techniques, ρ b is increasingly predicted using a combination of environmental data (covariates) and/or selected soil properties (Calhoun et al, 2001;Jalabert et al, 2010;Martin et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Pedotransfer functions to estimate bulk density from soil properties and environmental covariates: Rio Doce basin

Souza

Filho

Schaefer

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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Soil bulk density (ρ b ) data are needed for a wide range of environmental studies.However, ρ b is rarely reported in soil surveys. An alternative to obtain ρ b for data-scarce regions, such as the Rio Doce basin in southeastern Brazil, is indirect estimation from less costly covariates using pedotransfer functions (PTF). This study primarily aims to develop region-specific PTFs for ρ b using multiple linear regressions (MLR) and random forests (RF). Secondly, it assessed the accuracy of PTFs for data grouped into soil horizons and soil classes. For that purpose, we compared the performance of PTFs compiled from the literature with those developed here. Two groups of data were evaluated as covariates: 1) readily available soil properties and 2) maps derived from a digital elevation model and MODIS satellite imagery, jointly with lithological and pedological maps. The MLR model was applied step-wise to select significant predictors and its accuracy assessed by means of cross-validation. The PTFs developed using all data estimated ρ b from soil properties by MLR and RF, with R 2 of 0.41 and 0.51, respectively. Alternatively, using environmental covariates, RF predicted ρ b with R 2 of 0.41. Grouping criteria did not lead to a significant increase in the estimates of ρ b . The accuracy of the 'regional' PTFs developed for this study was greater than that found with the 'compiled' PTFs. The best PTF will be firstly used to assess soil carbon stocks and changes in the Rio Doce basin.

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Section: Introductionmentioning

confidence: 99%

Pedotransfer functions to estimate bulk density from soil properties and environmental covariates: Rio Doce basin

Souza

Filho

Schaefer

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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“…2016 DOI: 10.1590/S0100-204X2016000900041 área e é, portanto, obrigatória para comparar diferentes tipos de uso do solo, quanto ao potencial de sequestro de carbono (Brahim et al, 2012). Além disso, Ellert et al (2002) (Benites et al, 2007;Suuster et al, 2011;Brahim et al, 2012;Carvalho Junior et al, 2013;Nanko et al, 2014;Nasri et al, 2015;Rodríguez-Lado et al, 2015). Assim, os modelos estatísticos têm sido amplamente empregados para estimar o valor de algum atributo, ao invés de realizar a análise propriamente dita.…”

Section: Introductionunclassified

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Carvalho

Filho

Chagas

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi o desenvolvimento de modelos com diferentes conjuntos de dados, para estimar a densidade de solos de regiões tropicais montanhosas, a partir de atributos de solos comumente encontrados nas análises de perfis de solos descritos nos levantamentos regionais. O conjunto total de dados compõe-se de 163 amostras e foi dividido em seis grupamentos, dos quais três com 73 amostras, com o máximo de 32 covariáveis, e três com 163 amostras, com o máximo de 18 covariáveis. Testaram-se modelos de regressão linear múltipla (RLM) e randomForest (RF). A menor incerteza entre os modelos foi alcançada pelo RLM2, com R 2 de 0,56, 13 covariáveis e 73 amostras. Nos grupamentos com 163 amostras, os melhores modelos foram os RF, com R 2 médio de 0,48. A raiz quadrada da média do erro ao quadrado variou entre 0,09 e 0,14. As covariáveis mais importantes no modelo RF foram: carbono orgânico, hidrogênio, areia fina e grossa, saturação por bases e capacidade de troca catiônica. Pelo método "stepwise regression", as variáveis mais importantes foram: a relação silte/argila; areia grossa e fina; carbono orgânico; saturação por bases; e potássio.Termos para indexação: estoque de carbono, funções de pedotransferência, modelos dirigidos pelos dados, stepwise. Multiple linear regression and Random Forest model to estimate soil bulk density in mountainous regionsAbstract -The objective of this work was the development of models with different sets of data for estimating soil bulk density in tropical mountainous regions, from soil attributes commonly found in the analyses of soil profiles described in regional surveys. The complete dataset is composed of 163 samples and it was divided into six groups, of which three groups have 73 samples and the maximum of 32 covariables, and three have 163 samples and the maximum of 18 covariables. The linear regression (RLM) and randomForest (RF) models were tested. The lowest uncertainty between the models was achieved by RLM2, with R 2 of 0.56, 13 covariables, and 73 samples. Considering the groups with 163 samples, the best models were the RFs with mean R 2 of 0.48. The root mean squared error ranged between 0.09 and 0.14. The most important covariables in the RF model were: organic carbon, hydrogen, fine and coarse sand, base saturation, and cation exchange capacity. By the stepwise backward regression, the main covariables were: silt and clay relation; fine and coarse sand; organic carbon; base saturation; and potassium.

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“…It has been shown that most pedotransfer functions are suitable only for the agro-pedo-climatic conditions prevailing at the sites used to fit these functions (Martin et al, 2009). Under different conditions, they lead to substantial systematic errors (De Vos et al, 2005;Nanko et al, 2014;Vasiliniuc and Patriche, 2015). For a soil with a BD of 1.4 g cm −3 , a systematic measurement error of −0.01 to −0.51 g cm −3 (De Vos et al, 2005) would result in SOC stocks being underestimated by 1 to 36%.…”

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Determining Soil Bulk Density for Carbon Stock Calculations: A Systematic Method Comparison

Walter¹,

Don²,

Tiemeyer³

et al. 2016

Soil Science Soc of Amer J

128

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Accurate and effective determination of soil bulk density (BD) is needed to monitor soil organic C (SOC) stocks and SOC stock changes. However, BD measurements are often lacking in soil inventories and BD is estimated by pedotransfer functions with substantial uncertainty. In a systematic method comparison, we evaluated different methods for BD determination in the field by comparing the performance of MINI (5 cm 3 ) and BIG (250 cm 3 ) sample rings and of three driving hammer probes differing in diameter, material, and extraction method. Bulk density determined with 100-cm 3 sample rings was defined as the reference method (REF). All methods were tested at five depth increments in nine subplots at four sites with differing soil texture and SOC content. All methods determined BD in the depth increments with low systematic error (8% for probes and 2% for sample rings). The random error of the probe samples was, on average, 50% higher than that of the ring samples when the cores of the probes were adequately corrected for compaction or stretching. The BD was significantly overestimated (by 2%) when determined with MINI rings, and the variation in BD was not reduced with BIG sample rings rather than the smaller REF sample rings. The performance of the driving hammer technique varied widely among probe types and sites. The sheath probe had the smallest systematic error of all probes tested and is recommended for soil inventories. All methods for estimating BD had smaller errors than pedotransfer functions.Abbreviations: BD, bulk density; CV, coefficient of variation; MPE, mean prediction error; SDPE, standard deviation of the prediction error; SOC, soil organic carbon. C arbon storage in soils exceeds that in vegetation and the atmosphere (Ciais et al., 2013). Thus, small changes in soil organic C (SOC) stocks could have severe impacts on the global C cycle. Reliable measurements of C concentration are an important prerequisite for detecting such small changes in SOC stocks (Goidts et al., 2009). Information on soil bulk density (BD) is essential in converting weight-based concentration data to volume-or area-based stock data. However, BD is a parameter that is only partly or never sampled in many soil inventories (Gruneberg et al., 2014;Reynolds et al., 2013;Saby et al., 2008). Pedotransfer functions are often applied instead to predict soil BD on the basis of SOC or soil organic matter content and soil texture data (Arrouays et al., 2012). It has been shown that most pedotransfer functions are suitable only for the agro-pedo-climatic conditions prevailing at the sites used to fit these functions (Martin et al., 2009). Under different conditions, they lead to substantial systematic errors (De Vos et al., 2005;Nanko et al., 2014;Vasiliniuc and Patriche, 2015). For a soil with a BD of 1.4 g cm −3 , a systematic measurement error of −0.01 to −0.51 g cm −3 (De Vos et al., 2005) would result in SOC stocks being underestimated by 1 to 36%. Katja Core Ideas• Little is known about the methodological errors of bulk densit...

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A pedotransfer function for estimating bulk density of forest soil in Japan affected by volcanic ash

Cited by 58 publications

References 58 publications

Pedotransfer functions to estimate bulk density from soil properties and environmental covariates: Rio Doce basin

Pedotransfer functions to estimate bulk density from soil properties and environmental covariates: Rio Doce basin

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Determining Soil Bulk Density for Carbon Stock Calculations: A Systematic Method Comparison

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