Spatial variability of soil CO2 emission in different topographic positions

Brito, Liziane de Figueiredo; Júnior, José Marques; Pereira, Gener Tadeu; Scala, Newton La

doi:10.1590/s0006-87052010000500004

Cited by 33 publications

(43 citation statements)

References 22 publications

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“…For FCO 2 , Gaussian models were the best fit, although in most other studies, spherical models (Brito et al, 2010;Herbst et al, 2010;Teixeira et al, 2011a) and exponential models (Panosso et al, 2009) fit better. Each model describes variability in different ways, and, along with the parameters, is responsible for the spatial patterns of each variable.…”

Section: Resultsmentioning

confidence: 76%

“…However, due to the complexity and uncertainty involved in quantifying soil carbon (C) balance, in most studies, estimates of GHG emissions from agricultural soils are included in those related to forestry and land use changes (IPCC, 2007). This complexity is due to the high spatial variability (La Scala et al, 2000;Panosso et al, 2009;Brito et al, 2010;Teixeira et al, 2011a,b) and temporal variability (Herbst et al, 2010;Teixeira et al, 2011a,b) in soil CO 2 emissions (FCO 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…However, in OK, the variance calculations consider only the location of the data points and do not provide any measure of the spatial uncertainty of these estimates, which are obtained by SGS. Although OK is the most widely used method for the interpolation of FCO 2 values (La Scala et al, 2000;Ohashi & Gyokusen, 2007;Panosso et al, 2009;Brito et al, 2010), simulation has been preferred in R. Bras. Ci.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

Teixeira

Bicalho

Panosso

et al. 2012

Rev. Bras. Ciênc. Solo

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SUMMARYThe soil CO 2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO 2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO 2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO 2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO 2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO 2 ha -1 , according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories (r = 0,25, p < 0,05) e a porosidade livre de água (r = 0,27, p <0,01) e negativa com a densidade do solo (r = -0,41, p < 0,01)

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Section: Resultsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

Teixeira

Bicalho

Panosso

et al. 2012

Rev. Bras. Ciênc. Solo

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“…Many studies have used the quota as an auxiliary variable in the estimative of different main variables, with good results (Brito et al, 2010;Viola et al, 2010;Di Piazza et al, 2011). However, the quantification of its contribution in this aspect, regarding the interpolators still needs to be better determined.…”

Section: Introductionmentioning

confidence: 99%

Daily maximum annual rainfall statistical regionalization in Andalusia

Fernandes¹,

José²,

Wolff³

et al. 2016

Clima, Sociedad, Riesgos Y Ordenación Del Territorio

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The annual maximum daily rainfall (Amdr) is an important variable to model the runoff, justifying its study. This work aimed at regionalize the Amdr for the region of Andalusia through the spatialization of parameters of the probability distribution. Daily rainfall data between the years of 2001 and 2015 from 56 meteorological stations in Andalusia were evaluated and adjusted to ten probability distributions, as also the possibility of tendencies in the data was explored. The Gumbel II distribution was the one with better results for 20 of the 56 meteorological stations. Decreasing tendencies in three meteorological stations were verified. The Amdr was estimated for the payback periods of 10, 20, 50, 100 and 1000 years, obtaining average values of 76.76, 90.75, 110.79, 127.69 and 201.23 mm, respectively. Maps with the distribution of the parameters α and β of the Gumbel II distribution for Andalusia were obtained, apart from maps with the Amdr distribution for each payback period.

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“…As informações obtidas por meio do mapeamento da variabilidade dos atributos dos solos são utilizadas no planejamento das práticas de manejo para conservação do solo e da água (Mello et al, 2006;Silva et al, 2008;Sanchez et al, 2009;Vaezi et al, 2010), produção de culturas agrícolas (Sanchez et al, 2005;Siqueira et al, 2010b;Mann et al, 2010), estudos relacionados à gênese (Camargo et al, 2008a), otimização amostral (Montanari et al, 2005), emissão de CO 2 (Panosso et al, 2009;La Scala Júnior et al, 2010;Brito et al, 2010;Mendonça et al, 2011;Merbold et al, 2011). Pode-se identificar, por meio deste mapeamento, áreas com maior homogeneidade dos atributos dos solos.…”

Section: Introductionunclassified

Classificação numérica e modelo digital de elevação na caracterização espacial de atributos dos solos

Silva

Siqueira

Marques

et al. 2012

Rev. bras. eng. agríc. ambient.

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R ESU M OUma das necessidades da agricultura moderna é a predição da variabilidade espacial dos atributos dos solos em escalas mais detalhadas, visando o gerenciamento sustentável e a otimização das práticas de manejo dos solos. Associada ao conhecimento da variabilidade dos atributos do solo e ao mapeamento das formas do relevo, a modelagem matemática tem auxiliado no planejamento agrícola. Objetivou-se então, no presente trabalho, caracterizar a variabilidade espacial de atributos físicos e químicos de Latossolo e Argissolo utilizando-se a classificação numérica e o modelo digital de elevação em duas áreas, identificadas por duas pedoformas distintas: pela convexa, para o Latossolo (158 ha) e por meio da linear, para o Argissolo (172 ha). Foram retiradas 53 amostras no Latossolo e 57 amostras no Argissolo e realizada análise multivariada de agrupamentos dos atributos estudados a partir de suas distâncias euclidianas. Esta análise caracterizou, por meio de dendogramas e juntamente com os modelos digitais de elevação para os diferentes solos, grupos mais homogêneos em Argissolo e menos homogêneos para o Latossolo em pedoforma convexa. Esses métodos quantitativos mostraram que as pedoformas condicionaram o padrão espacial dos atributos dos solos.Palavras-chave: análise multivariada, agricultura precisão, mapeamento híbrido, relação solo-paisagem Numerical classification and digital elevation model to spatial characterization of soil attributes A B ST R A C T One of needs of modern agriculture is the prediction of spatial variability of soil properties at more detailed scales for sustainable management and optimization of management practices. The mathematical model associated with knowledge of variability of soil attributes and mapping of relief forms has helped in agricultural planning. In this regard the aim of this study was to characterize the spatial variability of physical and chemical properties of Oxisols and Ultisols using numerical classification and the digital elevation model. Two distinct landforms: convex for the Oxisol (158 ha) and linear for the Ultisol (172 ha). 53 samples from the Oxisol and 57 samples from the Ultisol were taken. Multivariate analysis of clusters of attributes studied from their euclidean distances was performed. This analysis by dendograms along with digital elevation models for different soils characterized was more homogeneous in Ultisol groups, and less homogeneous for the Oxisol in convex landform. These quantitative methods showed that the landforms conditioned the spatial pattern of soil attributes.

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Spatial variability of soil CO2 emission in different topographic positions

Cited by 33 publications

References 22 publications

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

Daily maximum annual rainfall statistical regionalization in Andalusia

Classificação numérica e modelo digital de elevação na caracterização espacial de atributos dos solos

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