RESUMO

In a granulometric analysis of coffee beans with different categories of defects, the data can be organized in contingency tables, and when considering the discrimination by harvest, they may have a structure that suggest a more complex model, by means of the counting of defective coffee beans compared to different crops interacting with the classification of defects and percentages of sieve grains, which characterizes a block design with multivariate responses. However, due to the techniques based on the analysis of variance, considering the uniform correlation structure for all plots, it becomes feasible to propose a model that allows contemplating different structures between the plots, associating the effects of the crops to the defects in the granulometric procedure applied to the coffee beans. Thus, the hypothesis of incorporating the effects of crops associated with defects arises using the biplot multivariate technique. This work aims to propose the use of corrected biplots by predictions obtained trhough the fit to the Generalized Linear Model in the coffee grain size classification, broken down by components of the effect of the harvests. In conclusion, the use of GEE models with the corrected biplot technique by the predictions is feasible for application to be applied to the granulometric analysis of defective coffee beans, presenting discrimination regarding the effects of harvests.

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Pereskia grandifolia Haworth (PGH) and Pereskia aculeata Miller (PAM) are recognized sources of proteins; dietary fiber; vitamins and minerals make this plant leaves, raw, cooked, and braised, an important ally against protein and micronutrient deficiencies. One of the main problems is the presence of antinutritional factors that may interfere in the digestibility and bioavailability of some nutrients. The objective was to evaluate the amino acid profile and the chemical score of the raw leaves and the effects of heating media and time on the total dietary fiber, minerals, trypsin inhibition, oxalic acid and tannins of leaves of PGH and PAM. The samples had similar amino acid profiles and total dietary fiber. With regard to antinutritional compounds, heating the leaves of PGH led to a decrease in trypsin inhibition, primarily after the first minutes of wet cooking. Oxalic acid and tannins predominated in both species. Considering the interaction with time, the variables related to iron and zinc minimized the tannin responses in PGH and PAM, respectively. Heating media and times interfered with the chemical components present in the leaves of Pereskia species and led to high antinutrient retention after heat treatment.

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Correspondence analysis is a multivariate dimensionality-reduction technique applied to data structured into contingency tables. The main outcome of this approach is the generation of perceptual maps aimed at the study of similarity between categorical levels. In most cases, interpretations of these similarities present subjectivity when different metrics are considered; e.g., Hellinger distance and Chi-square. Thus, in an attempt to minimize this subjectivity, the present study proposes an index that quantifies the shortest distance between those levels. A simulation study was undertaken in which the generated maps were discussed in relation to real data involving the similarity of blends formed by coffees of different species, with sensory evaluations considering the flavor and acidity attributes. In conclusion, the proposed indexnamed metric selection index (MSI)made it possible to include a statistic that justifies the most suitable metric for correspondence analysis, thus preventing subjectivity in interpretations of similarities between blend types and grade classes. In the simulation studies, with the metric proposed by Hellinger distance, MSI showed stabler results regarding total inertia distribution on the first two axes.(AU)

A análise de correspondência é uma técnica multivariada de redução de dimensionalidade aplicada a dados estruturados em tabelas de contingência. Como principal resultado, mapas perceptuais são gerados com o propósito de estudar a similaridade entre os níveis categóricos. Na maioria das vezes, as interpretações dessas similaridades apresentam certa subjetividade, ao considerar diferentes métricas, como por exemplo, a distância de Hellinger e Qui-quadrado. Assim, com o intuito de minimizar essa subjetividade, esse trabalho teve como objetivo propor um índice que quantifique a menor distância entre esses níveis. Foi realizado um estudo de simulação, discutindo-se os mapas gerados em relação a dados reais envolvendo a similaridade de blends formados por cafés de diferentes espécies com avaliações sensoriais considerando os atributos sabor e acidez. Concluiu-se que a proposta do índice, denominado índice de seleção de métrica (ISM), permitiu agregar uma estatística que justifique a métrica mais adequada na análise de correspondência, evitando a subjetividade nas interpretações das similaridades entre os tipos de blends e classe de notas. Em relação aos estudos de simulação a métrica proposta pela distância de Hellinger, o ISM apresentou resultados mais estáveis em relação à distribuição da inércia total nos dois primeiros eixos.(AU)

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Oligonychus ilicis (McGregor) (Acari: Tetranychidae), commonly known as the southern red mite, or as the coffee red spider mite in Brazil, is one of the main species of herbivorous mites that causes serious damage to coffee plants (Coffea spp.) and thus negatively affects coffee production. Among the biocontrol agents, predatory mites of the family Phytoseiidae play an important role in many biological control programs worldwide due to their potential as suppressor of mite populations mainly from the family Tetranychidae. One of the phytoseiid mites usually associated with O. ilicis is Euseius concordis (Chant), which often occurs abundantly in the coffee crops of Minas Gerais State, Brazil. This study was conducted to assess the predation potential of E. concordis feeding on the larvae and nymph stages of O. ilicis on coffee plants (Coffea arabica L.). Logistic regression analysis revealed a Holling type II functional response, showing that the number of O. ilicis killed by E. concordis increased gradually as the density of O. ilicis increased. Average daily oviposition also increased with prey densities above 6.3 mites/cm2, indicating that maximum oviposition rate is about 1 egg/day. Results of this study suggest that E. concordis has the potential to reduce O. ilicis populations, and this predatory mite can therefore be considered an important natural enemy of the pest O. ilicis in coffee plantations.

Assuntos

RESUMO

Correspondence analysis is a multivariate dimensionality-reduction technique applied to data structured into contingency tables. The main outcome of this approach is the generation of perceptual maps aimed at the study of similarity between categorical levels. In most cases, interpretations of these similarities present subjectivity when different metrics are considered; e.g., Hellinger distance and Chi-square. Thus, in an attempt to minimize this subjectivity, the present study proposes an index that quantifies the shortest distance between those levels. A simulation study was undertaken in which the generated maps were discussed in relation to real data involving the similarity of blends formed by coffees of different species, with sensory evaluations considering the flavor and acidity attributes. In conclusion, the proposed indexnamed metric selection index (MSI)made it possible to include a statistic that justifies the most suitable metric for correspondence analysis, thus preventing subjectivity in interpretations of similarities between blend types and grade classes. In the simulation studies, with the metric proposed by Hellinger distance, MSI showed stabler results regarding total inertia distribution on the first two axes.

A análise de correspondência é uma técnica multivariada de redução de dimensionalidade aplicada a dados estruturados em tabelas de contingência. Como principal resultado, mapas perceptuais são gerados com o propósito de estudar a similaridade entre os níveis categóricos. Na maioria das vezes, as interpretações dessas similaridades apresentam certa subjetividade, ao considerar diferentes métricas, como por exemplo, a distância de Hellinger e Qui-quadrado. Assim, com o intuito de minimizar essa subjetividade, esse trabalho teve como objetivo propor um índice que quantifique a menor distância entre esses níveis. Foi realizado um estudo de simulação, discutindo-se os mapas gerados em relação a dados reais envolvendo a similaridade de blends formados por cafés de diferentes espécies com avaliações sensoriais considerando os atributos sabor e acidez. Concluiu-se que a proposta do índice, denominado índice de seleção de métrica (ISM), permitiu agregar uma estatística que justifique a métrica mais adequada na análise de correspondência, evitando a subjetividade nas interpretações das similaridades entre os tipos de blends e classe de notas. Em relação aos estudos de simulação a métrica proposta pela distância de Hellinger, o ISM apresentou resultados mais estáveis em relação à distribuição da inércia total nos dois primeiros eixos.

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Tetrazolium tests use conventional sampling techniques in which a sample has a fixed size. These tests may be improved by sequential sampling, which does not work with fixed-size samples. When data obtained from an experiment are analyzed sequentially the analysis can be terminated when a particular decision has been made, and thus, there is no need to pre-establish the number of seeds to assess. Bayesian statistics can also help, if we have sufficient knowledge about coffee production in the area to construct a prior distribution. Therefore, we used the Bayesian sequential approach to estimate the percentage of viable coffee seeds submitted to tetrazolium testing, and we incorporated priors with information from other analyses of crops from previous years. We used the Beta prior distribution and, using data obtained from sample lots of Coffea arabica, determined its hyperparameters with a histogram and O’Hagan's methods. To estimate the lowest risk, we computed the Bayes risks, which provided us with a basis for deciding whether or not we should continue the sampling process. The results confirm that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average percentage of viability obtained with the conventional frequentist method was 88 %, whereas that obtained with the Bayesian method with both priors was 89 %. However, the Bayesian method required, on average, only 89 samples to reach this value while the traditional estimation method needed as many as 200 samples.

RESUMO

Tetrazolium tests use conventional sampling techniques in which a sample has a fixed size. These tests may be improved by sequential sampling, which does not work with fixed-size samples. When data obtained from an experiment are analyzed sequentially the analysis can be terminated when a particular decision has been made, and thus, there is no need to pre-establish the number of seeds to assess. Bayesian statistics can also help, if we have sufficient knowledge about coffee production in the area to construct a prior distribution. Therefore, we used the Bayesian sequential approach to estimate the percentage of viable coffee seeds submitted to tetrazolium testing, and we incorporated priors with information from other analyses of crops from previous years. We used the Beta prior distribution and, using data obtained from sample lots of Coffea arabica, determined its hyperparameters with a histogram and OHagan's methods. To estimate the lowest risk, we computed the Bayes risks, which provided us with a basis for deciding whether or not we should continue the sampling process. The results confirm that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average percentage of viability obtained with the conventional frequentist method was 88 %, whereas that obtained with the Bayesian method with both priors was 89 %. However, the Bayesian method required, on average, only 89 samples to reach this value while the traditional estimation method needed as many as 200 samples.(AU)

RESUMO

Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.(AU)

A cultura do café desempenha papel relevante na agricultura do Brasil, com expressiva participação social e econômica tanto pelos empregos gerados na cadeia produtiva, bem como pela renda obtida pelos produtores e pelas divisas geradas para o país na exportação do grão. No crescimento das plantas de café, as folhas desempenham papel decisivo para que tenha maior produção, portanto a contagem do número de folhas por planta fornece informações importantes aos produtores para o manejo adequado da cultura como, por exemplo, a aplicação de adubações foliares. Em geral, na descrição de dados obtidos por contagem, o modelo mais utilizado é o Poisson, sendo que quando os dados apresentam superdispersão, o modelo Binomial Negativo tem se mostrado mais adequado. O objetivo deste trabalho foi comparar o ajuste dos modelos de Poisson e Binomial Negativo em dados de contagens do número de folhas por planta em mudas do cafeeiro. Os dados foram obtidos de um experimento usando o delineamento em blocos casualizados com trinta tratamentos e três repetições com quatro plantas por parcela. Foram utilizados os dados de apenas um tratamento no qual foi feita a contagem do número de folhas ao longo do tempo. A primeira avaliação foi feita em 8 de abril de 2016 e as demais aos 18, 32, 47, 62, 76, 95, 116, 133 e 153 dias após a primeira avaliação, totalizando dez medidas. A adequação dos mesmos foi verificada com base nos valores da Deviance e no envelope simulado para os resíduos. Os resultados do ajuste indicaram que o modelo Poisson foi inadequado para descrição dos dados devido a superdispersão. O modelo Binomial Negativo se ajustou adequadamente e foi indicado para descrever o número de folhas das plantas do cafeeiro. Com base no modelo Binomial Negativo o aumento relativo esperado para o número de folhas foi de 0,9768% para cada dia.(AU)

RESUMO

ABSTRACT: Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.

RESUMO: A cultura do café desempenha papel relevante na agricultura do Brasil, com expressiva participação social e econômica tanto pelos empregos gerados na cadeia produtiva, bem como pela renda obtida pelos produtores e pelas divisas geradas para o país na exportação do grão. No crescimento das plantas de café, as folhas desempenham papel decisivo para que tenha maior produção, portanto a contagem do número de folhas por planta fornece informações importantes aos produtores para o manejo adequado da cultura como, por exemplo, a aplicação de adubações foliares. Em geral, na descrição de dados obtidos por contagem, o modelo mais utilizado é o Poisson, sendo que quando os dados apresentam superdispersão, o modelo Binomial Negativo tem se mostrado mais adequado. O objetivo deste trabalho foi comparar o ajuste dos modelos de Poisson e Binomial Negativo em dados de contagens do número de folhas por planta em mudas do cafeeiro. Os dados foram obtidos de um experimento usando o delineamento em blocos casualizados com trinta tratamentos e três repetições com quatro plantas por parcela. Foram utilizados os dados de apenas um tratamento no qual foi feita a contagem do número de folhas ao longo do tempo. A primeira avaliação foi feita em 8 de abril de 2016 e as demais aos 18, 32, 47, 62, 76, 95, 116, 133 e 153 dias após a primeira avaliação, totalizando dez medidas. A adequação dos mesmos foi verificada com base nos valores da Deviance e no envelope simulado para os resíduos. Os resultados do ajuste indicaram que o modelo Poisson foi inadequado para descrição dos dados devido a superdispersão. O modelo Binomial Negativo se ajustou adequadamente e foi indicado para descrever o número de folhas das plantas do cafeeiro. Com base no modelo Binomial Negativo o aumento relativo esperado para o número de folhas foi de 0,9768% para cada dia.

RESUMO

The modeling of generalized estimating equations used in the analysis of longitudinal data whether in continuous or discrete variables, necessarily requires the prior specification of a correlation matrix in its iterative process in order to obtain the estimates of the regression parameters. Such a matrix is called working correlation matrix and its incorrect specification produces less efficient estimates for the model parameters. Due to this fact, this study aims to propose a selection criterion of working correlation matrix based on the covariance matrix estimates of correlated responses resulting from the limiting values of the association parameter estimates. For validation of the criterion, we used simulation studies considering normal and binary correlated responses. Compared to some criteria in the literature, it was concluded that the proposed criterion resulted in a better performance when the correlation structure for exchangeable working correlation matrix was considered as true structure in the simulated samples and for large samples, the proposed criterion showed similar behavior to the other criteria, resulting in higher success rates.

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