State-space approach to evaluate the relation between soil physical and chemical properties

Timm, Luís Carlos; Reichardt, Klaus; Oliveira, J. C. M.; Cássaro, Fábio Augusto Meira; Tominaga, Tânia Toyomi; Bacchi, Osny Oliveira Santos; Neto, Durval Dourado; Nielsen, D. R.

doi:10.1590/s0100-06832004000100005

Cited by 19 publications

(6 citation statements)

References 13 publications

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“…In this model, at each addition/decrease of one unit in soil penetration resistance, soybean yield was reduced by -0.680. The better performance of state-space models compared to classical multiple regression models was also reported by Timm et al (2004). These authors observed a better estimation of the soil water content by state-space than by the equivalent linear regression equations.…”

Section: Resultssupporting

confidence: 55%

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Oliveira

Tavares

Uribe-Opazo

et al. 2011

Rev. Bras. Ciênc. Solo

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SUMMARYStatistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plantatmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.Index terms: autocorrelation, cross correlation, linear regression, state-space model, soil and plant properties.(1) Extracted from the dissertation of the first author.

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Section: Resultssupporting

confidence: 55%

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Oliveira

Tavares

Uribe-Opazo

et al. 2011

Rev. Bras. Ciênc. Solo

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“…Most of the variables had an obvious spatial distribution trend along the transect (Fig. 2), which caused a strong spatial dependence of each variable (Timm et al, 2004). Spatial relationships among variables had to be examined before estimating TNPP with the state-space method.…”

Section: Spatial Patterns Of Tnpp and Other Variablesmentioning

confidence: 99%

Estimating total net primary productivity of managed grasslands by a state-space modeling approach in a small catchment on the Loess Plateau, China

Jia¹,

Shao

Wei³

et al. 2011

Geoderma

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“…Several statistical tools have been used to evaluate the variabilities of soil attributes, which can contribute to improve management and the understanding of atmosphere-plant-soil interactions (Vieira et al, 1981;Cassel, 1983;Morkoc et al, 1985;Wendroth et al, 2001;Dourado-Neto et al, 1999;Timm et al, 2003;Tominaga et al, 2002;Martínez & Zinck, 2004). This work studies the importance of soil water content and bulk density changes, using some of these tools to characterize spatial and temporal patterns of changes during different climatic periods of the year, with the objective of examining possible differences that might occur when a soil is submitted to wetting or drying processes.…”

Section: Introductionmentioning

confidence: 99%

Field spatial and temporal patterns of soil water content and bulk density changes

Timm¹,

Pires²,

Roveratti³

et al. 2006

Sci. agric. (Piracicaba, Braz.)

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Soil water content (θ) and bulk density (ρ s ) greatly influence important soil and plant processes, such as water movement, soil compaction, soil aeration, and plant root system development. Spatial and temporal variability of θ and ρ s during different periods of the year and different phases of crops are of fundamental interest. This work involves the characterization of spatial and temporal patterns of θ and ρ s during different climatic periods of year, aiming to verify whether there are significant temporal changes in ρ s at the soil surface layer when submitted to wetting and drying cycles. The field experiment was carried out in a coffee plantation, Rhodic Kandiudalf soil, clayey texture. Using a neutron/gamma surface probe, θ and ρ s were measured meter by meter along a 200 m spatial transect, along an interrow contour line. During the wet period there was no difference of spatial patterns of θ while during the dry period differences were observed, and can be associated to precipitation events. It was also observed that there are ρ s temporal changes at the soil surface along the studied period as a consequence of the in situ wetting and drying cycles. Key words: neutron/gamma surface probe, wetting/drying cycles, spatial and temporal variability PADRÕES ESPACIAL E TEMPORAL DE MUDANÇAS DA UMIDADE E DENSIDADE DO SOLO NO CAMPORESUMO: Umidade (θ) e densidade do solo (ρ s ) influenciam importantes processos no solo e planta tais como: movimento de água, compactação do solo, aeração do solo e desenvolvimento radicular. Baseado neste fato, questões referentes à variabilidade espacial e temporal de θ e ρ s para diferentes períodos do ano e diferentes fases de desenvolvimento da cultura do café tornam-se de extremo interesse. Este trabalho teve como objetivo caracterizar padrões espaciais e temporais de θ e ρ s durante diferentes períodos do ano e verificar se existem mudanças temporais significativas de ρ s na superfície do solo quando submetida a ciclos de umedecimento/secagem. O experimento foi conduzido em campo cultivado com café em um solo classificado como Nitossolo Vermelho Eutrófico. O experimento iniciou-se em maio de 2001 com espaçamento de 1,75 m entre linhas e 0,75 m entre plantas. Medidas de θ e ρ s foram feitas metro a metro ao longo de 200 m usando uma sonda de superfície nêutron/gama. Durante o período úmido não houve padrões de diferença espacial para θ, enquanto que para o período seco o contrário foi observado. Estes padrões podem ser associados a eventos de precipitação alterando a estrutura de correlação espacial para observações adjacentes de θ. Para ρ s existem mudanças temporais na superfície do solo ao longo do período estudado como uma conseqüência dos ciclos de umedecimento/secagem no campo. Palavras-chave: sonda de superfície nêutron/gama, ciclos de umedecimento/secagem, variabilidade espacial e temporal

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State-space approach to evaluate the relation between soil physical and chemical properties

Cited by 19 publications

References 13 publications

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Estimating total net primary productivity of managed grasslands by a state-space modeling approach in a small catchment on the Loess Plateau, China

Field spatial and temporal patterns of soil water content and bulk density changes

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