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16. Adaptability and stability of grain yield and maize tassel traits.

Author

Lixinski Silveira, Daniela, Cargnelutti Filho, Alberto, Maronez de Souza, Jéssica, Severo Trivisiol, Vinícius, and Manfio Somavilla, Felipe

Subjects
*GRAIN yields, *GENOTYPE-environment interaction, *ANALYSIS of variance, *BLOCK designs, *GENOTYPES
Abstract

Due to the economic importance of maize, it is essential to evaluate the performance of genotypes under environmental variations (years, locations, and seasons). Consequently, researchers have scrutinized genotype-environment (G×E) interactions to ensure reliability in genotype recommendations. Furthermore, when a G×E interaction exists, adaptability and stability analysis can be performed. This analysis enables one to identify genotypes with predictable responses and adaptability to environmental variability. Hence, this study sought to determine if there is a G×E interaction and evaluate the response of 16 maize genotypes across seven environments through adaptability and stability analyses. Seven experiments were conducted with 16 maize genotypes using a randomized complete block design with three replications. Tassel length (TL), tassel branch number (TBN), tassel dry matter (TDM), and grain yield (GY) were evaluated. Individual and joint variance analyses were performed, along with the F-test at a 5% significance level. The complex parts of the interaction between genotypes and pairs of environments were estimated, followed by adaptability and stability analyses using the Eberhart and Russell method. A genotype-environment interaction was found for TL, TBN, TDM, and GY, indicating that genotypes respond differently in various environments. However, considering all four traits, there is no indication of the ideal genotype. [ABSTRACT FROM AUTHOR]

Published
2024
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28. A sustainable approach to control whitefly on soybean: Integrating entomopathogenic fungi with insecticides

Author

Bevilaqua, Júlia Guimarães, primary, Padilha, Guilherme, additional, Pozebon, Henrique, additional, Marques, Rafael Paz, additional, Cargnelutti Filho, Alberto, additional, Ramon, Paulo César, additional, Boeni, Lucas, additional, Castilhos, Lauren Brondani, additional, da Luz, Giulian Rafael, additional, Brum, André Luís Soares de Souza, additional, Biruel, Nicolle, additional, Wojahn, Bruna, additional, Leão, José Domingos Jacques, additional, and Arnemann, Jonas André, additional

Published
2023
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30. Tamanho de amostra para avaliar caracteres produtivos de teosinto.

Author

Augusto Andretta, João, Cargnelutti Filho, Alberto, Loro, Murilo V., Ortiz, Vithória M., Reis, Mikael B., and Schuller, Bruno R.

Subjects
CORN, SAMPLE size (Statistics)
Abstract

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023

31. Árvore de regressão para previsão da produtividade de matéria fresca da parte aérea de teosinto em função de variáveis meteorológicas.

Author

Reis, Mikael B., Cargnelutti Filho, Alberto, Loro, Murilo V., Augusto Andretta, João, Ortiz, Vithória M., and Schuller, Bruno R.

Subjects
GLOBAL radiation, SOLAR radiation, CORN, REGRESSION trees
Abstract

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023

32. Estimativa do filocrono para a cultura de teosinto.

Author

Denardin Junior, João Carlos, Cargnelutti Filho, Alberto, Loro, Murilo V., Augusto Andretta, João, Reis, Mikael B., Ortiz, Vithória M., and Schuller, Bruno R.

Subjects
CORN
Abstract

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023

33. Predição da massa fresca e massa seca da parte aérea da planta de teosinto em função de caracteres morfológicos.

Author

Konrad, Marcelo, Cargnelutti Filho, Alberto, Loro, Murilo V., Reis, Mikael B., Ortiz, Vithoria M., Augusto Andretta, João, and Schuller, Bruno R.

Subjects
REGRESSION trees, FORAGE plants
Abstract

Copyright of Sigmae is the property of Universidade Federal de Alfenas (UNIFAL-MG) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023

35. Sampling sufficiency for estimating the mean of wheat traits

Author

Cargnelutti Filho, Alberto, primary, Souza, Jéssica Maronez de, additional, Neu, Ismael Mario Márcio, additional, Silveira, Daniela Lixinski, additional, Bubans, Valéria Escaio, additional, Costa, Samanta Luiza da, additional, Osmari, Lucas Fillipin, additional, and Osmari, Bruno Fillipin, additional

Published
2023
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40. Optimal plot size in forage sorghum with comparison of the methods of modified maximum curvature, linear response and plateau model and quadratic response and plateau model

Author

Cargnelutti Filho, Alberto, primary, Silveira, Daniela Lixinski, additional, Trivisiol, Vinicius Severo, additional, Bubans, Valéria Escaio, additional, Somavilla, Felipe Manfio, additional, Ortiz, Vithória Morena, additional, Osmari, Bruno Fillipin, additional, and Osmari, Lucas Fillipin, additional

Published
2022
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41. Tamanho de parcela e número de repetições em trigo forrageiro

Author

Bubans, Valéria Escalo, Cargnelutti Filho, Alberto, Neu, Ismael Mario Márcio, Silveira, Daniela Lixinski, Somavilla, Felipe Manfio, and Osmari, Bruno Fillipin

Subjects
Triticum aestivum L. Soil cover crop. Uniformity trial. Experimental planning, Triticum aestivum L. Cultura de cobertura de solo. Ensaio de uniformidade. Planejamento experimental
Abstract

Forage wheat (Triticum aestivum L.) is an annual crop, alternative for use as a ground cover crop in the winter period, in production systems in southern Brazil. Given its magnitude, research with field experiments is carried out and plot size definition is of great importance in the experimental design. The objectives of this study were to estimate the plot size and number of repetitions to evaluate the fresh matter of forage wheat and to investigate the variability of plot size among agricultural years, sowing dates and cultivars. Thirty-six uniformity trials were conducted (two agricultural years × three sowing dates × two cultivars × three repetitions). Plot size was determined by the method of maximum curvature of the coefficient of variation model. The number of repetitions was determined for experiments in completely randomized and randomized block designs in scenarios formed by the combinations of i treatments (i=3, 4, …, 50) and d least significant differences among treatment means to be detected as significant by the Tukey test, at 5% of significance, expressed as a percentage of the overall mean of the experiment (d=5%, 10%, …, 30%). The plot size to evaluate the fresh matter of forage wheat is 4.05 m². Seven repetitions are sufficient to identify the fresh matter in completely randomized designs and randomized block designs with up to 50 treatments, making it possible to obtain least significant difference lower than or equal to 20% of the mean of the experiment. O trigo forrageiro (Triticum aestivum L.) é uma cultura anual, alternativa de utilização como cultura de cobertura de solo no período de inverno, em sistemas produtivos da região Sul do Brasil. Dada sua magnitude, pesquisas com experimentos a campo são desenvolvidas e a definição do tamanho de parcela tem grande importância no planejamento experimental. Os objetivos deste trabalho foram estimar o tamanho de parcela e o número de repetições para avaliar a matéria fresca de trigo forrageiro e investigar a variabilidade de tamanho de parcela entre anos agrícolas, épocas de semeadura e cultivares. Foram conduzidos 36 ensaios de uniformidade (dois anos agrícolas × três épocas de semeadura × duas cultivares × três repetições). O tamanho de parcela foi determinado por meio do método da curvatura máxima do modelo do coeficiente de variação. O número de repetições foi determinado para experimentos nos delineamentos inteiramente casualizado e blocos ao acaso em cenários formados pelas combinações de i tratamentos (i=3, 4, ..., 50) e d diferenças mínimas entre médias de tratamentos detectadas como significativas pelo teste de Tukey, a 5% de significância, expressas em percentagem da média do experimento (d=5%, 10%, ..., 30%). O tamanho de parcela para avaliar a matéria fresca de trigo forrageiro é 4,05 m2. Sete repetições são suficientes para avaliar a matéria fresca em delineamentos inteiramente casualizado e blocos ao acaso com até 50 tratamentos, possibilitando obter diferença mínima significativa menor ou igual a 20% da média do experimento.

Published
2022

45. SAMPLE SIZE AND LINEAR RELATIONS IN SLENDER LEAF RATTLEBOX

Author

Cargnelutti Filho, Alberto, primary, Silveira, Daniela Lixinski, additional, Bubans, Valéria Escaio, additional, Osmari, Bruno Fillipin, additional, Somavilla, Felipe Manfio, additional, Ortiz, Vithória Morena, additional, and Osmari, Lucas Fillipin, additional

Published
2022
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46. Soybean yield variability per plant in subtropical climate: sample size definition and prediction models for precision statistics

Author

Souza, Rafael Rodrigues de, primary, Toebe, Marcos, additional, Marchioro, Volmir Sergio, additional, Cargnelutti Filho, Alberto, additional, Lúcio, Alessandro Dal’Col, additional, Benin, Giovani, additional, Mello, Anderson Chuquel, additional, Tartaglia, Francieli de Lima, additional, and Manfio, Guilherme Liberalesso, additional

Published
2022
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47. Harvest interval for phytomass production of peppermint transplanted in summer and winter

Author

de Souza, Jéssica Maronez, Cargnelutti Filho, Alberto, Mario Márcio Neu, Ismael, Pereira Camargo, Darcila, Fillipin Osmari, Bruno, and Fillipin Osmari, Lucas

Abstract

The objective of this study was to determine the appropriate harvest interval for the phytomass production of peppermint (Mentha x piperita L.) transplanted in summer and winter. Two experiments were carried out in soilless cultivation, one with transplantation in summer and the other with transplantation in winter, in a completely randomized design, with five harvest intervals (30, 45, 60, 72, and 90 days) and four replicates. Fresh and dry matter weights of leaves, branches and shoots were determined. The fresh matter weight of peppermint leaves and the daily growth rate of this trait were similar between the harvest intervals for the transplantation performed in the summer. The dry matter weight of leaves was higher for the intervals of 72 days (90.80 g plant-1) and 90 days (90.24 g plant-1). For the transplantation performed in winter, the fresh matter and dry matter weights of leaves were higher for the intervals of 60 days (660.54 g plant-1, 107.14 g plant-1) and 90 days (630.40 g plant-1, 105.95 g plant-1). The same was observed for the daily growth rates of these traits. Peppermint harvests at 60-day intervals in winter transplantation are more appropriate for phytomass production. In summer transplantation, one can opt for the 45-day interval.

Published
2022

48. Variation in the hectolitre weight of wheat grain for equipment and sample size

Author

Martin, Thomas Newton, Cargnelutti Filho, Alberto, Deak, Evandro Ademir, Cechin, Joanei, Burg, Giovane Matias, and Grün, Eduarda

Subjects
bulk density, grain measurement, variância amostral, sample variance, densidade a granel, mensuração de grãos, Triticum aestivum L, classification system, sistema de classificação
Abstract

Hectolitre weight (HW) is the principal quantitative parameter used by receiving units as an indicator of the quality of wheat grain (Triticum aestivum L.). The moisture content and the equipment used can be considered important sources of variation to assess a batch, requiring correct sizing of the sample used to measure the HW. This research identified the variability between the equipment used to evaluate HW with or without correcting the grain moisture content, and to estimate the ideal number of samples for the commercial classification of wheat batches. The experiment was carried out in a randomised block design with four replications. Seven batches of wheat grain from different cultivars and/or production sites were used to measure the HW using three types of equipment (Equipment ‘A’, Equipment ‘B’ and Equipment ‘C’), with and without correcting the grain moisture content to 13%. Higher values for HW were determined with Equipment ‘A’ (77.15) compared to Equipment ‘B’ (75.08) and Equipment ‘C’ (74.69), classifying the wheat according to type. The moisture content affected the HW, but did not change the final classification of the wheat in terms of type. The ideal number of samples for HW ranged from 1 to 18 at the lowest level of precision (HW=mean±1 kg hl-1) and from 67 to 1820 samples for the highest precision (HW=mean±0.1 kg hl-1). Equipment ‘B’ requires a smaller number of samples (possibly as few as one) for the same level of precision (HW=mean±1 kg hl-1), whereas Equipment ‘A’ requires a greater number of samples for the majority of batches. Correctly classifying wheat by HW therefore depends on a larger number of samples. RESUMO: A massa de hectolitro (MH) é o principal parâmetro quantitativo usado nas unidades de recebimento como indicador de qualidade dos grãos de trigo (Triticum aestivum L.). O teor de umidade e o equipamento utilizado podem ser considerados fontes de variação importantes na classificação de um lote, necessitando correto dimensionamento amostral para mensuração da MH. O objetivo dessa pesquisa foi identificar a variabilidade entre equipamentos para avaliação da MH com correção ou não do teor de umidade da massa de grãos e estimar o número ideal de amostras para classificação comercial de lotes de trigo. O experimento foi realizado em delineamento experimental de blocos ao acaso com quatro repetições. Sete lotes de grãos de trigo, oriundos de diferentes cultivares e/ou locais de produção, foram usados para mensuração da MH em três equipamentos (Equipamento “A”, Equipamento “B” e Equipamento “C”), com e sem correção da umidade dos grãos para 13%. Maiores valores da MH foram determinados no Equipamento “A” (77,15) comparado ao Equipamento “B” (75,08) e Equipamento “C” (74,69), com classificação distinta do trigo quanto ao tipo. O teor de umidade afetou a MH, mas não alterou a classificação final do trigo quanto ao tipo. O número ideal de amostras para MH variou de uma a 18 amostras para o menor nível de precisão (MH = média ± 1 kg hl-1) e, de 67 a 1820 amostras para a maior precisão (MH = média ± 0,1 kg hl-1). O Equipamento “B” requer menor número de amostras (podendo chegar a uma) para um mesmo nível de precisão (MH = média ± 1 kg hl-1), enquanto o Equipamento “A” necessita maior número de amostras para a maioria dos lotes. Assim, a correta classificação do trigo pela MH depende de maior número de amostras.

Published
2022

49. Sample size to evaluate the degree of multicollinearity in rye morphological traits.

Author

Márcio Neu, Ismael Mario, Cargnelutti Filho, Alberto, Toebe, Marcos, Carini, Fernanda, Vieira Pezzini, Rafael, and Lixinski Silveira, Daniela

Subjects
MULTICOLLINEARITY, SAMPLE size (Statistics), RYE, MULTIVARIATE analysis
Abstract

Copyright of Revista Caatinga is the property of Revista Caatinga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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50. Number of trials to estimate the condition number in rye traits.

Author

Márcio Neu, Ismael Mario, Cargnelutti Filho, Alberto, Lixinski Silveira, Daniela, Vieira Pezzini, Rafael, and Tolfo Bandeira, Cirineu

Abstract

Multicollinearity must be diagnosed in multivariate analyses. Among the indicators, the condition number can be used to quantify the degree of multicollinearity. Hence, this study sought to determine the number of measurements (trials) necessary to estimate the number of condition in linear correlation matrices between rye traits. Five uniformity trials were carried out with 'BRS Progresso' rye, and eight morphological traits and eight productive traits were evaluated, forming two groups. In each group of traits, six cases (combinations of traits) were planned and the multicollinearity diagnosis was performed. Repeatability analyses were performed using the following methods: analysis of variance, principal component analysis, and structural analysis, and the number of measurements (trials) was determined for different levels of precision. A higher condition number of repeatability coefficients was obtained by the principal component methods (based on correlation and variance and covariance matrices) and structural analysis based on the variance and covariance matrix. A greater number of measurements (trials) is necessary to estimate the number of conditions in productive traits compared to morphological ones. One trial is enough to efficiently estimate the condition number with a minimum accuracy of 80% in morphological and productive traits of rye, whereas at least three trials are required for 95% accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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