Nonparametric Techniques for Predicting Soil Bulk Density of Tropical Rainforest Topsoils in Rwanda

Gharahi, Nasrin; Nemes, Attila; Verdoodt, Ann; Ranst, Éric Van; Cornelis, Wim; Boeckx, Pascal

doi:10.2136/sssaj2011.0330

Cited by 29 publications

(25 citation statements)

References 39 publications

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“…For instance, our RMSE for RFM PTFs is within the range reported by Ghehi et al . () for BD PTFs in Rwanda using k‐NN, but it is slightly better than the performance of PTFs derived using BRT. However, our MLR PTFs performed slightly less well than those reported for highly weathered soils in Central Africa (Botula et al ., ).…”

Section: Discussionmentioning

confidence: 78%

“…Several studies have been conducted to develop PTFs for predicting soil properties from basic soil data using various modelling techniques (Suuster et al, 2011;Ghehi et al, 2012;Haghverdi et al, 2012;Sequeira et al, 2014). Nonetheless, the reliability of many of these PTFs is largely dependent on the amount (data size) and structure (range) of the input parameters (Chirico et al, 2010;Ghehi et al, 2012;Haghverdi et al, 2012). For instance, in a relatively small area, with fairly homogeneous soil properties and topography, high reliability could be obtained from a reasonably few number of soil samples (Ghehi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Akpa

Ugbaje

Bishop

et al. 2016

Soil Use and Management

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Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R2; 0.29–0.69) outperformed both PTF‐1 (R2; 0.11–0.18) and PTF‐2 (R2; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R2; 0.61–0.86) performed comparably as PTF‐1 (R2; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R2; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).

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Section: Discussionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Akpa

Ugbaje

Bishop

et al. 2016

Soil Use and Management

View full text Add to dashboard Cite

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“…Nemes, Rawls, Pachepsky, and Van Genuchten (2006) analyzed the sensitivity of KNN variant to different data and algorithms. Subsequent applications of KNN to develop PTFs include, for example, Ghehi et al (2012), Botula et al (2013), and Nguyen et al (2015). A kriging-based Gaussian process approach similarly utilizes nearest neighbors, measuring the "distance" between neighbors based on a covariance function (Rasmussen, 2004).…”

Section: K-nearest Neighbor Methodsmentioning

confidence: 99%

“…The regression tree approach was first used to develop PTFs by McKenzie and Jacquier (1997). Subsequent applications of regression trees to develop PTFs include McKenzie and Ryan (1999), Rawls and Pachepsky (2002a), Pachepsky and Rawls (2003), Pachepsky et al (2006), Lilly et al (2008), Nemes et al (2011), Gharahi Ghehi et al (2012), Koestel and Jorda (2014), Jorda et al (2015), and Tóth et al (2015).…”

Section: Decision/regression Treesmentioning

confidence: 99%

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Looy

Bouma

Herbst

et al. 2017

Reviews of Geophysics

415

297

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Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.

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“…These methods are quite varied, ranging from simple regressions to more powerful methods such as neural networks, regression trees (Martin et al, 2009;Jalabert et al, 2010;Ghehi et al, 2012), and the nearestneighbor method (Nemes et al, 2010). In the case of the PTFs assessed in our study, the methods used were mostly simple or multiple regressions using the least squares method.…”

Section: General Evaluationmentioning

confidence: 99%

How accurate are pedotransfer functions for bulk density for Brazilian soils?

Boschi

Bocca

Lopes-Assad

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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ABSTRACT:The aim of this study was to evaluate the performance of pedotransfer functions (PTFs) available in the literature to estimate soil bulk density (ρb) in different regions of Brazil, using different metrics. The predictive capacity of 25 PTFs was evaluated using the mean absolute error (MAE), mean error (ME), root mean squared error (RMSE), coefficient of determination (R 2 ) and the regression error characteristic (REC) curve. The models performed differently when comparing observed and estimated ρb values. In general, the PTFs showed a performance close to the mean value of the bulk density data, considered as the simplest possible estimation of an attribute and used as a parameter to compare the performance of existing models (null model).The models developed by Benites et al. (2007)

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Nonparametric Techniques for Predicting Soil Bulk Density of Tropical Rainforest Topsoils in Rwanda

Cited by 29 publications

References 39 publications

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

How accurate are pedotransfer functions for bulk density for Brazilian soils?

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