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| Bortolini,Diego; Albuquerque,Jackson Adriano. |
| ABSTRACT: Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to use other analysis techniques such as artificial neural networks and regression trees. Thus, the objective of the study was to estimate the field capacity (FC), permanent wilting point (PWP), and available water (AW) in soils of Santa Catarina (SC), through multiple linear regressions (MLR), artificial neural networks (ANN), and regression trees (RT), more efficiently than the current pedotransfer... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Pedotransfer functions; Water retention curve; Artificial neural networks; Regression trees; Multiple linear regressions. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100424 |
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| Souza,Eliana de; Fernandes Filho,Elpídio Inácio; Schaefer,Carlos Ernesto Gonçalves Reynaud; Batjes,Niels H.; Santos,Gerson Rodrigues dos; Pontes,Lucas Machado. |
| ABSTRACT Soil bulk density (ρb) data are needed for a wide range of environmental studies. However, ρb is rarely reported in soil surveys. An alternative to obtain ρb 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 ρb 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... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Multiple linear regressions; Random forests; Soil predictors; Spatial prediction. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000600525 |
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