Hierarchical Pedotransfer Functions to Predict Bulk Density of Highly Weathered Soils in Central Africa

Botula, Yves-Dady; Nemes, Attila; Ranst, Éric Van; Mafuka, Paul; Pue, Jan De; Cornelis, Wim

doi:10.2136/sssaj2014.06.0238

Cited by 35 publications

(17 citation statements)

References 39 publications

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“…For instance, our RMSE for RFM PTFs is within the range Estimation of bulk density and ECEC 653 reported by Ghehi et al (2012) 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, 2015). The difference between our result and those of Botula et al (2015) could be attributed to the difference in the data sets used, the varying extent of both studies as well as the weaker correlation between BD and predictors used in this study (Table 5) compared to theirs.…”

Section: Performance Of Ptf Modelscontrasting

confidence: 93%

“…However, our MLR PTFs performed slightly less well than those reported for highly weathered soils in Central Africa (Botula et al, 2015). The difference between our result and those of Botula et al (2015) could be attributed to the difference in the data sets used, the varying extent of both studies as well as the weaker correlation between BD and predictors used in this study (Table 5) compared to theirs. There is more variation in our data set as we have used legacy data which is a combination of data from disparate sources.…”

Section: Performance Of Ptf Modelscontrasting

confidence: 93%

“…() 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 ., ). The difference between our result and those of Botula et al .…”

Section: Discussionmentioning

confidence: 97%

See 2 more Smart Citations

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: Performance Of Ptf Modelscontrasting

confidence: 93%

Section: Performance Of Ptf Modelscontrasting

confidence: 93%

See 1 more Smart Citation

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

show abstract

“…PTF estimates of bulk density are always based on SOC content according to the model of Adams () where bulk density of a soil can be modeled as mixture of organic matter and mineral component. The mineral components expressed as clay and/or silt content are identified as statistically significant explanatory variables (Bernoux et al, ; Benites et al, ; Botula et al, ; De Souza et al, ; Manrique & Jones, ). Also, the coarse fraction (>2 mm) and soil depth has predictive potential (Nussbaum et al, ) as well as the sum of basic cations (Benites et al, ) and other environmental covariates (De Souza et al, ).…”

Section: Ptfs In Earth System Sciencesmentioning

confidence: 99%

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

Looy

Bouma

Herbst

et al. 2017

Reviews of Geophysics

Self Cite

364

284

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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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“…Then k-nearest neighbor algorithms (e.g. NEMES et al, 2006;BOTULA et al, 2015), artificial neural networks (e.g. MINASNY et al, 1999), classification and regression trees (e.g.…”

Section: Te C Hniq Ue S To Develop a Nd Eval Uate Pt Fsmentioning

confidence: 99%

Pedotransfer in soil physics: trends and outlook — A review —

Pachepsky

Rajkai

Tóth

2015

Agrokem

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Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships.This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from "big data" as a source of soil parameter variability are also described.Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation.Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services.

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Hierarchical Pedotransfer Functions to Predict Bulk Density of Highly Weathered Soils in Central Africa

Cited by 35 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

Pedotransfer in soil physics: trends and outlook — A review —

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