Linear relations between apple traits

Toebe, Marcos; Both, Vanderlei; Thewes, Fábio Rodrigo; Filho, Alberto Cargnelutti; Brackmann, Auri

doi:10.1590/1678-4499.650

Cited by 4 publications

(6 citation statements)

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“…The effects of total chlorophyll and total soluble protein on SCY were changed from non-significant (p-value = 0.7695 and p-value = 0.0166) to highly significant (pvalue = 0.0033 and p-value = 0.0093) by using Liu linear regression (Tables 2 and 3) since Liu estimator showed lowest standard errors of the estimates of total chlorophyll and total soluble protein. These results confirm that OLSE is inconsistent, and not reliable in the presence of a high degree of collinearity and these results correspond with the findings of Carvalho et al, (2001) and Toebe et al, (2013). A regression model gives better estimation if it has minimum MSE, AIC and SEs of the estimated parameters.…”

Section: Discussionsupporting

confidence: 83%

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Effect of Different‎ Biochemical Traits on Seed Cotton Yield: An Application of Liu Linear Regression

2020

THE JAPS

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This article aimed to study the associations among the biochemical traits and their effects on seed cotton yield using the regression analysis and to assess the alternative approach for reducing the impact of multicollinearity problem in estimating the regression coefficients. The field experiment was conducted where five explanatory variables (chlorophyll 'a', chlorophyll 'b', total chlorophyll, total soluble protein and total soluble sugar) and one dependent variable seed cotton yield were measured. The correlation matrix of showed that biochemical traits were significantly correlated. The multicollinearity problem among the biochemical traits was determined by condition index and correlation matrix. Using the least square regression analysis, the effects of biochemical traits on seed cotton yield were not satisfactory since least square regression model has high value of MSE (3352475), AIC (366.7) and inconsistent estimates of traits. The Liu regression analysis was efficient (MSE = 57212 and AIC = 363.8) and reliable in reducing the adverse effects of multicollinearity. The Liu regression results indicated that total chlorophyll and total soluble protein were contributed a significant (P ≤ 0.05) role in seed cotton yield. In contrast, ordinary least square regression analysis was showed insignificant (P > 0.05) effect of total chlorophyll on seed cotton yield.

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Section: Discussionsupporting

confidence: 83%

“…Therefore, we used an alternative biased method (Liu estimator). Similar biased estimation techniques were also used by Carvalho et al, (1999), Bizeti et al, (2004), Wang et al, (2011) and Toebe et al, (2013) for different crops. These authors were focused on path analysis for estimating direct and indirect effects.…”

Section: Discussionmentioning

confidence: 99%

Effect of Different‎ Biochemical Traits on Seed Cotton Yield: An Application of Liu Linear Regression

2020

THE JAPS

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“…Ou seja, para um experimento com cinco repetições, por exemplo, deve-se amostrar 58 folhas para estimar a severidade e cinco folhas para estimar a AACPD. Porém, caberá a cada pesquisador determinar o tamanho de amostra a ser utilizado em seu estudo, de acordo com a quantidade de recurso disponível (Michereff et al, 2008;Toebe et al, 2011;Stürmer et al, 2013) e precisão desejada lembrando, sempre, que com a redução do tamanho de amostra maior será o erro amostral e, consequentemente, menor será a qualidade do experimento.…”

Section: Resultsunclassified

“…A amostragem gera uma nova variância dentro da parcela, que deve ser minimizada através do dimensionamento amostral adequado (Cargnelutti Filho et al, 2009). A estimativa do tamanho de amostra sofre interferência da variabilidade dos dados, sendo ela afetada por fatores genéticos e ambientais (Cargnelutti Filho et al, 2008), pela aplicação de tratamentos (Toebe et al, 2011) e, no caso de doenças, pela sua distribuição espacial no campo (Michereff et al, 2011).…”

Section: Introductionunclassified

Amostragem para avaliação de mancha amarela em trigo

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et al. 2020

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O dimensionamento amostral adequado é um importante fator a ser levado em consideração para melhorar a precisão experimental. Assim o objetivo deste trabalho foi estimar o tamanho de amostra na parcela necessário para avaliar a severidade da mancha amarela do trigo e a área abaixo da curva de progresso da doença (AACPD). Utilizou-se dados de severidade da mancha amarela e da AACPD de dois experimentos de controle realizados com trigo na safra 2010. A severidade foi obtida em dez folhas por parcela, ou seja, 40 folhas por tratamento, após a segunda aplicação do fungicida e a partir delas calculou-se a AACPD. Posteriormente, calcularam-se as estatísticas descritivas em cada tratamento: mínimo, máximo, média, desvio padrão, variância e coeficiente de variação da média. A avaliação do dimensionamento dos experimentos quanto ao número de repetições e de amostras foi realizado por meio da análise de variância dos experimentos. A dispersão da doença foi verificada pela razão variância/média e do índice de Morisita e teve como objetivo verificar se a metodologia utilizada para o cálculo do tamanho de amostra foi adequado. A dispersão da doença não foi a mesma ao longo dos experimentos, variando entre tratamentos e avaliações. Diante disso a metodologia utilizada neste estudo para o cálculo do tamanho da amostra foi adequada. Para um erro de 10%, o tamanho da amostra para estimar a média da severidade da mancha amarela e da área abaixo da curva de progresso da doença são 286 e 22 folhas bandeiras, respectivamente.

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“…In the same 2016/2017 crop season, even with the increase of K dose a decrease of ethylene production and respiratory rate in the fruits was detected after 90 days in a refrigerated storage and 7 days under ambient conditions at 20 • C (Figure 4). This reduction in ethylene production may be related to the rise of K export to the fruits, which may have delayed its maturation and produced smaller size fruits with lower average weight (Jacomino et al, 2005;Toebe, Both, Thewes, Filho, & Brackmann, 2016). Still, the decrease in the fruit respiration rate can be associated with same reduction in the fruit diameter, which decreases the fruit surface area (Hunsche, Brackmann, & Ernani, 2003).…”

Section: Fruit Qualitymentioning

confidence: 99%

Potassium fertilization effects on quality, economics, and yield in a pear orchard

et al. 2020

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Potassium (K) nutrient existent in the soil does not always supply pear tree (Pyrus communis L.) demand, which makes the use of potassium‐based fertilizer necessary. The objective of this study was to evaluate the impact of potassium fertilization on yield and pears quality in order to establish critical K levels in soil and leaves. The treatments consisted of K application rates of control, 40, 80, 120 and 160 kg K2O ha−1 yr−1 during four crops (2013 to 2017). The fruit quantity, mass and yield were evaluated, and leaves were collected for nutrient analysis. Stratified soil samples were collected, prepared and subjected to exchangeable K extraction by Mehlich‐1. In the last two crops, peel color, ethylene production, and respiratory rate were also evaluated after 90 days inside a controlled atmosphere storage chamber. After storage, pears were submitted to a shelf life of 7 days to evaluate the epidermis color, ethylene production, respiratory rate, total titratable acidity (TTA), soluble solids (SS), and pulp firmness. Potassium fertilization increased the exchangeable K contents in the soil, but it was not always correlated with an increase of K concentration in the leaves and fruit. The most economical dose was 45.40 kg K2O ha−1 in the 2016/2017 crop season. It was not possible to estimate K critical levels in the soil and leaves. The fruits submitted to higher doses of K showed the lowest values of ethylene production and respiration rate, which resulted in an increase in storage life in cold rooms and on the shelves.

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Linear relations between apple traits

Cited by 4 publications

References 10 publications

Effect of Different‎ Biochemical Traits on Seed Cotton Yield: An Application of Liu Linear Regression

Effect of Different‎ Biochemical Traits on Seed Cotton Yield: An Application of Liu Linear Regression

Amostragem para avaliação de mancha amarela em trigo

Potassium fertilization effects on quality, economics, and yield in a pear orchard

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