Biochar, farmyard manure and poultry litter on chemical attributes of a Distrophic Cambissol and soybean crop

Passos, A. M. A. dos; Rezende, Pedro Milanez de; Carvalho, Everson Reis; Ávila, Fabrício William

doi:10.5039/agraria.v10i3a4546

Cited by 4 publications

(3 citation statements)

References 16 publications

(24 reference statements)

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“…According to the fitted Gaussian linear spatial model (GSLM), the value associated with the potassium content (K) ( Table 3) was the highest and presented a positive sign, indicating that it was the covariate that contributed the most to an increase in the average of the soybean yield (t ha -1 ). This result corroborates with the study of Dos Passos et al (2015), considering that this nutrient is the second most absorbed by the soybean plant, being essential for the growth and development of the plants (Li et al, 2015). The estimates of the parameters associated to the covariates of phosphorus (P) and manganese (Mn) contents indicated that these covariates contributed little to the estimated soybean yield ( Table 3).…”

Section: Resultssupporting

confidence: 88%

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

et al. 2018

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This study aims to quantify the uncertainties associated to the parameters of a Gaussian spatial linear model (GSLM) and the assumption of normality residuals in the modeling of the spatial dependence of the soybean yield as a function of soil chemical attributes. The spatial bootstrap methods were used to determine the point and interval estimators associated with the model parameters. Hypothesis tests were carried out on the significance of the model parameters and the quantile-quantile probability plot was elaborated to verify the data normality. The uncertainties associated to the parameters of the spatial dependence structure were quantified and the potassium content, phosphorus content and soil pH covariates were significant to explain the soybean yield mean. These covariates were used in the elaboration of a new model, which provided the elaboration of a contour map of soybean yield. Analysis of the quantile-quantile plot indicated that soybean yield data follow a normal probability distribution.

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Section: Resultssupporting

confidence: 88%

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

et al. 2018

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“…Neste contexto, a utilização de resíduos orgânicos, advindos da própria propriedade rural, como fonte alternativa ao fertilizante inorgânico é uma importante estratégia para a agricultura brasileira, principalmente para a agricultura familiar (PASSOS et al, 2015). Além disso, a utilização do solo em cultivo intensivo pode provocar o esgotamento dos nutrientes e, para superar a rápida diminuição da fertilidade do solo, os insumos orgânicos são aplicados para melhorar e manter sua fertilidade (NDUBUISI-NNAJI et al, 2011).…”

Section: Introductionunclassified

CRESCIMENTO E QUALIDADE DE MUDAS DE Moringa oleifera Lam EM DIFERENTES PROPORÇÕES DE COMPOSTO ORGÂNICO

Medeiros

Cavalcante

Cavalcante³

et al. 2017

Ifes Ciência

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A produção de mudas de moringa é uma tecnologia pouco conhecida, mas materiais orgânicos vêm sendo utilizados na formulação de substratos. O objetivo foi avaliar o crescimento inicial e qualidade de mudas de Moringa em diferentes proporções de composto orgânico adicionado ao solo. Foi utilizado o DBC com quatro repetições, aplicando teste de média, com quatro proporções de composto bovino + solo (0:1; 1:1; 2:1 e 3:1v/v). A unidade experimental foi constituída por 10 plantas, e, aos 60 dias após a emergência, foram medidos a altura de planta, diâmetro caulinar, número de folhas, comprimento radicular, massa seca aérea e radicular e o índice de qualidade de Dickson. As diferentes concentrações de composto utilizadas influenciaram positivamente nas variáveis de crescimento e qualidade das mudas.

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“…Another issue that may be related to pH is the low value of the β 1 coefficient, since soil acidity decreases release of available P in organic matter of the soil (Santos et al, 2013;Passos et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

Fagundes

Uribe-Opazo

Guedes

et al. 2018

Rev. Bras. Ciênc. Solo

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In geostatistical modeling of soil chemical properties, one or more influential observations in a dataset may impair the construction of interpolation maps and their accuracy. An alternative to avoid the problem would be to use most robust models, based on distributions that have heavier tails. Therefore, this study proposes a spatial linear model based on the slash distribution (SSLM) in order to characterize the spatial variability of soybean yields as a function of soil chemical properties. The likelihood ratio statistic (LR) was applied to verify the significance of parameters associated with the model. We evaluated the sensitivity of the maximum likelihood estimators by means of local influence analysis for both the soybean response and the linear predictor. In the proposed model, we analyzed data gathered from a commercial grain production area (127.18 ha) located in the western part of the state of Paraná (Brazil). The results showed that the slash distribution allowed us to adjust the high kurtosis of the data set distribution and the LR test confirmed that the soil chemical properties of phosphorus, potassium, pH, and organic matter were significant for the SSLM. Diagnostic analysis indicated that the atypical value of the sample set was not influential in the parameter estimation process. Construction of the interpolation map based on the proposed model is not affected when considering the atypical and/or influential observations. Thus, SSLM becomes a robust alternative in the study of soybean yield variability as a function of soil chemical properties, making it possible to investigate the productive potential of the areas.

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Biochar, farmyard manure and poultry litter on chemical attributes of a Distrophic Cambissol and soybean crop

Cited by 4 publications

References 16 publications

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

Gaussian Spatial Linear Model of Soybean Yield Using Bootstrap Methods

CRESCIMENTO E QUALIDADE DE MUDAS DE Moringa oleifera Lam EM DIFERENTES PROPORÇÕES DE COMPOSTO ORGÂNICO

Slash Spatial Linear Modeling: Soybean Yield Variability as a Function of Soil Chemical Properties

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