Pedotransfer functions for water retention in different soil classes from the center-southern Rio Grande do Sul State

Santos, Walbert Júnior Reis dos; Curi, Nilton; Silva, Sérgio Henrique Godinho; Araújo, Elias Frank de; Marques, João José

doi:10.1590/s1413-70542013000100006

Cited by 11 publications

(9 citation statements)

References 21 publications

(28 reference statements)

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“…For different soils of Rio Grande do Sul State, Santos et al (2013) verified that data of water retention at -33 and -1500 kPa matric potentials can be estimated from particle size distribution and organic matter data and the stratification by soil classes increases the PTF accuracy due to the great pedological and mineralogical variability.…”

Section: Introductionmentioning

confidence: 62%

“…In Table 3, the junction of soil classes presented high predictive power of equations, allowing a higher data entry for generation of PTFs. The results found by Santos et al (2013) for Rio Grande do Sul State soils demonstrated that for regions where the pedological and mineralogical variability is expressive (Streck et al 2008), the stratification by soil classes for generating PTF results into higher coefficients than PTFs in which a small number of data of all soils is compiled in a sole model. Due to the smaller pedological and mineralogical variability in the Coastal Plains environment (Resende et al 2011;Carvalho Filho;Fonseca, 2013), the junction of different soil classes (Yellow Argisol and Yellow Latosol) and soil horizons (A and B) did not cause a deleterious effect on coefficient of PTFs.…”

Section: Resultsmentioning

confidence: 99%

“…However, this methodology is justified considering two main reasons: the development of these PTFs seeks maximum efficiency with simple and low cost input data, and most of the potential users of these PTFs are in countries with low amount of data, case of Brazil and other tropical countries. In addition, papers such as Oliveira et al (2002) and Santos et al (2013) obtained adequate PTF fitting utilizing disturbed soil samples.…”

Section: Methodsmentioning

confidence: 99%

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Pedotransfer Functions for Water Retention in the Main Soils From the Brazilian Coastal Plains

et al. 2015

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Pedotransfer functions (PTFs) are equations used to estimate soil characteristics difficult to determine from other easily obtained ones. Water retention in soil is used in several agronomic and environmental applications, but its direct determination is time consuming and onerous, therefore PTFs are alternatives to obtaining this information more quickly and economically. The aims of this study were to generate a database and develop PTFs for water retention at potentials of -33 kPa (field capacity) and -1500 kPa (permanent wilting point) for Yellow Argisol and Yellow Latosol from the Brazilian Coastal Plains region. The Coastal Plains soils are mostly developed from Barreiras formation (pre-weathered sediments) and their main uses are sugarcane, livestock, forestry and fruticulture. The database to generate the PTFs was composed from the selection of information derived from scientific works and soil survey reports of the region. Specific PTFs were generated for each soil class, in their respective A and B horizons and for solum, through multiple regression by stepwise package of R language programming. Due to the small pedological variability (small number of soil classes containing great geographical expression) and mineralogical uniformity, usually observed in this environment, non-stratification of soil classes to create general PTFs presented similar or superior results compared to equations for each soil class. The adjustment of data demonstrated that water retention values at -33 kPa and -1500 kPa potentials can be estimated with adequate accuracy for the main soils of the Brazilian Coastal Plains through PTFs mainly from particle size distribution and secondarily from organic matter data.Index terms: Database; pedofunction; permanent wilting point; field capacity. RESUMOFunções de pedotransferência (FPTs) são equações usadas para estimar atributos de solo de difícil determinação, a partir de outras mais facilmente obtidas. A retenção de água no solo é utilizada em diversas aplicações agronômicas e ambientais, porém sua determinação direta é demorada e onerosa. Com isso, as FPTs são alternativas para obtenção dessas informações de forma mais rápida e econômica. Conduziu-se este trabalho, com os objetivos de gerar um banco de dados e desenvolver FPTs para a retenção de água nos potenciais de -33 kPa (capacidade de campo) e -1500 kPa (ponto de murcha permanente) para Argissolo Amarelo e Latossolo Amarelo, da região dos Tabuleiros Costeiros brasileiros. Os solos dos Tabuleiros Costeiros são, principalmente, provenientes de sedimentos pré-intemperizados da Formação Barreiras e seus principais usos são cana-de-açúcar, pecuária, silvicultura e fruticultura. O banco de dados para geração das FPTs foi composto, a partir da seleção de informações obtidas de trabalhos científicos e de relatórios de levantamentos de solos da região. Foram geradas FPTs específicas para cada classe de solo em seus respectivos horizontes A e B e para o solum, e FPTs gerais, englobando as classes de solos e seus horizontes, po...

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Section: Introductionmentioning

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Pedotransfer Functions for Water Retention in the Main Soils From the Brazilian Coastal Plains

et al. 2015

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“…Recently, Obalum & Obi (2012) proposed point-based PTFs for kaolinitic and coarse-textured tropical soils from southeastern Nigeria. Santos et al (2013) generated and validated PTFs to predict gravimetric water content at -33 and -1500 kPa for different soil classes from the central-south portion of the State of Rio Grande do Sul in Brazil.…”

Section: Point-based Ptfsmentioning

confidence: 99%

Pedotransfer functions to predict water retention for soils of the humid tropics: a review

Botula

Ranst

Cornelis

2014

Rev. Bras. Ciênc. Solo

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SUMMARYOver the past three decades, pedotransfer functions (PTFs) have been widely used by soil scientists to estimate soils properties in temperate regions in response to the lack of soil data for these regions. Several authors indicated that little effort has been dedicated to the prediction of soil properties in the humid tropics, where the need for soil property information is of even greater priority. The aim of this paper is to provide an up-to-date repository of past and recently published articles as well as papers from proceedings of events dealing with water-retention PTFs for soils of the humid tropics. Of the 35 publications found in the literature on PTFs for prediction of water retention of soils of the humid tropics, 91 % of the PTFs are based on an empirical approach, and only 9 % are based on a semiphysical approach. Of the empirical PTFs, 97 % are continuous, and 3 % (one) is a class PTF; of the empirical PTFs, 97 % are based on multiple linear and polynomial regression of n th order techniques, and 3 % (one) is based on the k-Nearest Neighbor approach; 84 % of the continuous PTFs are point-based, and 16 % are parameter-based; 97 % of the continuous PTFs are equation-based PTFs, and 3 % (one) is based on pattern recognition. Additionally, it was found that 26 % of the tropical water-retention PTFs were developed for soils in Brazil, 26 % for soils in India, 11 % for soils in other countries in America, and 11 % for soils in other countries in Africa.Index terms: physical soil properties, soil hydraulic properties, tropical soils, data-mining techniques.( Durante as últimas três décadas, as funções de pedotransferência (FPTs) foram amplamente utilizadas pelos cientistas para estimar propriedades de solos em regiões temperadas para responder à falta de banco de dados deles. Diferentes autores indicaram que poucos esforços foram dedicados à estimação de propriedades de solo nos trópicos úmidos, onde a necessidade de informações sobre as propriedades dos solos é ainda mais prioritária. Este artigo buscou proporcionar um inventário atualizado dos artigos, assim como trabalhos publicados recentemente em anais de congressos sobre FPTs com retenção de água para solos nos trópicos úmidos. Entre 35 publicações encontradas na literatura sobre as FPTs para a estimação da retenção de água dos solos nos trópicos úmidos, 91 % dos FPTs estão com base num enfoque empírico e somente 9 %, num enfoque semifísico. Das FPTs empíricas, 97 % são contínuas e 3 % (uma) é uma classe. Das FPTs empíricas, 97 % são com base em regressão multilinear e polinomial de n técnicas de ordenamento e 3 % (uma), no método k-NN; 84 % das FPTs contínuas são com base em pontos e 16 %, em parâmetros; 97 % das FPTs contínuas são baseadas em equação e 3 % (uma), no reconhecimento padrão. Adicionalmente, foi encontrado que 26 % das FPTs da retenção de água "tropical" foram desenvolvidas para solos no Brasil; 26 % , na Índia; 11 %, em outros países na América; e 11 %, em outros países na África.Termos de indexação: meteorização de solos, sol...

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“…There is a wide variation in data i.e., % clay varied from 1.66 to 74.80, BD varied from 1.25 to 1.84 g cm -3 , which is favourable and essential for the generation of PTFs and they reflect the different parent materials and soil formation process of the databases. Santos and Curi [15], Das and Verma [16] have also argued the importance of heterogeneity of datasets in generating and validating PTFs.…”

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confidence: 99%

Modelling Soil Water Contents at Field Capacity and Permanent Wilting Point Using Artificial Neural Network for Indian Soils

Mohanty

Sinha

Painuli

et al. 2015

Natl. Acad. Sci. Lett.

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Soil field capacity (FC) and permanent wilting point (PWP) are important input parameters in many biophysical models. Although these parameters can be measured directly, their measurement is quite difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. A study has been conducted to evaluate PTFs of FC and PWP created using artificial neural networks (ANNs). A total of 721 different sampling locations spread all over India are selected to develop PTFs using ANN. Results indicate that six neurons in hidden layers are best suited for prediction of FC and PWP. The statistical criteria (value of R 2 , RMSE, MBE, ME, and d) is used to evaluate ANN, indicated an unbiased and higher predictability of developed models.Knowledge of the soil hydraulic properties is indispensable to solve many soils and water management problems related to agriculture, ecology, and environmental issues. These properties are useful in describing and predicting water and solute transport, as well as to model heat and mass transport near the soil surface [1]. Soil hydraulic properties such as soil water content at field capacity (FC) and permanent wilting point (PWP) are two critical input parameters required in various biophysical models [2] and also used to calculate the water depth that should be applied by irrigation [3], and to determine water availability, which is a crucial factor in assessing the suitability of a crop production [4].Direct measurements of FC and PWP are time-consuming and expensive. Many attempts have been made to establish the relationship between routinely measured soil properties such as soil texture, bulk density (BD), organic matter content, and FC and PWP [2]. Those relationships are referred to as pedotransfer functions (PTF) [5]. There are four types of PTFs that are generally used for predicting FC and PWP: (i) class PTF (ii) multiple linear regression (iii) extended nonlinear regression and (iv) artificial neural network (ANN). In past, PTFs were usually expressed as linear or nonlinear regression equations but, recently, ANN is in practice [6]. The recent research findings revealed that ANN method could predict water content at FC and PWP with more accuracy than other methods [7][8][9]. An advantage of using ANN technique compared to regression PTFs is that no a priori model structure needs to relate input and output data [10]. In addition, Minasny and Mc Brantney [11] have also stated that one of the advantages of ANNs is that they do not require assumed relationships, which relates input and output data. ANN offers a fundamentally different approach for modeling soil behavior. Najafi and Givi [12] used the ANNs and PTFs methods for prediction

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Pedotransfer functions for water retention in different soil classes from the center-southern Rio Grande do Sul State

Cited by 11 publications

References 21 publications

Pedotransfer Functions for Water Retention in the Main Soils From the Brazilian Coastal Plains

Pedotransfer Functions for Water Retention in the Main Soils From the Brazilian Coastal Plains

Pedotransfer functions to predict water retention for soils of the humid tropics: a review

Modelling Soil Water Contents at Field Capacity and Permanent Wilting Point Using Artificial Neural Network for Indian Soils

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