scholarly journals COMPARISON OF METHODS FOR ESTIMATING THE OPTIMUM PLOT SIZE FOR PEARL MILLET, SLENDER LEAF RATTLEBOX, AND SHOWY RATTLEBOX

2021 ◽  
Vol 34 (2) ◽  
pp. 249-256
Author(s):  
ALBERTO CARGNELUTTI FILHO ◽  
MARCOS VINÍCIUS LOREGIAN ◽  
VALÉRIA ESCAIO BUBANS ◽  
FELIPE MANFIO SOMAVILLA ◽  
SAMANTA LUIZA DA COSTA
Keyword(s):  
Pearl Millet ◽  
Linear Response ◽  
Pennisetum Glaucum ◽  
Comparison Of Methods ◽  
Fresh Matter ◽  
Plot Size ◽  
Maximum Curvature ◽  
Quadratic Response ◽  
Uniformity Trials ◽  
Crotalaria Spectabilis

ABSTRACT This study aimed to compare three methods of estimating the optimum plot size to evaluate the fresh matter productivity of pearl millet (Pennisetum glaucum L.), slender leaf rattlebox (Crotalaria ochroleuca), and showy rattlebox (Crotalaria spectabilis). Twenty-seven uniformity trials were carried out with pearl millet, slender leaf rattlebox, and showy rattlebox cultivated alone and intercropped. Fresh matter productivity was evaluated in 972 basic experimental units (BEU) of 1 m × 1 m (36 BEU per trial). The optimum plot size was determined using the methods modified maximum curvature, linear response with plateau model, and quadratic response with plateau model. The optimum plot size differs between methods and decreases in the following order: quadratic response with plateau model (9.94 m2), linear response with plateau model (7.41 m2), and modified maximum curvature (3.49 m2). The optimum plot size to evaluate the fresh matter productivity of pearl millet, slender leaf rattlebox, and showy rattlebox cultivated alone or intercropped is 7.41 m2. This size could be used as a reference for future experiments.

scholarly journals Rice bran as a substitute for soy protein and erythorbate in chicken nuggets

2021 ◽  
Vol 42 (2) ◽  
pp. 501-516
Author(s):  
Ruth dos Santos da Veiga ◽  
◽  
Daneysa Lahis Kalschne ◽  
Rosana Aparecida da Silva-Buzanello ◽  
Éder Lisandro de Moraes Flores ◽  
...  
Keyword(s):  
Rice Bran ◽  
Soy Protein ◽  
Linear Response ◽  
Chicken Nuggets ◽  
Fresh Matter ◽  
Plot Size ◽  
Maximum Curvature ◽  
Quadratic Response ◽  
Fagopyrum Esculentum Moench ◽  
Uniformity Trials

The aim of this work was to compare three methods of estimating the optimal plot size for evaluating fresh matter in the IPR91-Baili and IPR92-Altar cultivars of buckwheat (Fagopyrum esculentum Moench). Sixteen uniformity trials (blank experiments) were conducted, eight with the IPR91-Baili cultivar and eight with the IPR92-Altar cultivar. The trials were carried out at eight different sowing times. The fresh matter was evaluated in 576 basic experimental units (BEU), each 1 m × 1 m in size (36 BEU per trial). The optimal plot size was determined using the method of modified maximum curvature, the linear response plateau model and the quadratic response plateau model. The optimal plot size differs between methods, and decreases in the following order: quadratic response plateau model, linear response plateau model and modified maximum curvature. The optimal plot size for evaluating fresh matter in the IPR91-Baili and IPR92-Altar cultivars of buckwheat is 7.60 m2. This size can be used as a reference for future experiments with buckwheat.

scholarly journals Basic experimental unit and plot sizes for fresh matter of sunn hemp

2018 ◽  
Vol 48 (5) ◽  
Author(s):  
Giovani Facco ◽  
Alberto Cargnelutti Filho ◽  
André Lavezo ◽  
Denison Esequiel Schabarum ◽  
Gabriela Görgen Chaves ◽  
...  
Keyword(s):  
Experimental Unit ◽  
Fresh Matter ◽  
Crotalaria Juncea ◽  
Evaluation Period ◽  
Plot Size ◽  
Maximum Curvature ◽  
Sunn Hemp ◽  
Sowing Season ◽  
Uniformity Trials

ABSTRACT: This study aimed to verify the influence of the basic experimental unit (BEU) size in the estimation of the optimum plot size to evaluate the fresh matter of sunn hemp (Crotalaria juncea L.) using the modified maximum curvature method. The fresh matter of sunn hemp was evaluated in uniformity trials in two sowing season in flowering. In each sowing season, 4,608 BEUs of 0.5×0.5m (0.25m2) were evaluated and 36 BEU plans were formed with sizes from 0.25 to 16m2. In each evaluation period for each BEU plan, using fresh matter data, optimum plot size was estimated through the modified maximum curvature method. Estimation of the optimum plot size depends on the BEU size. Assessing fresh matter in BEUs that are as small as possible is recommended in order to use it to estimate the optimum plot size.

scholarly journals Methods for Estimating Optimum Plot Size for ‘Gigante’ Cactus Pear

2019 ◽  
Vol 11 (14) ◽  
pp. 205 ◽  
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
High Precision ◽  
Linear Response ◽  
Basic Unit ◽  
Phenotypic Characteristics ◽  
Cactus Pear ◽  
Plot Size ◽  
Maximum Curvature ◽  
Use Of Resources ◽  
Quadratic Response ◽  
Practical Feasibility

The optimum plot size for ‘Gigante’ cactus pear can be estimated by several methods; thus, ultimately aiming for efficiency, simple use and high precision, the objective of this study was to compare methods for estimating plot sizes: modified maximum curvature method, Hatheway’s convenient plot size method, linear and quadratic response plateau models, and comparison of variances method for evaluating phenotypic characteristics in experiments with ‘Gigante’ cactus pear. Plot sizes were estimated by conducting a uniformity trial. Estimated optimum plot sizes varied with the method and vegetative characteristic. The quadratic response plateau regression estimated the largest plot sizes, whereas Hatheway’s method estimated the smallest plot sizes. Comparison of variances method estimated intermediate plot sizes in comparison with the other methods for most measured characteristics. Plots sizes estimated by modified maximum curvature method are more consistent with results reported by studies on ‘Gigante’ cactus pear. 10 basic unit plot sizes estimated by the linear response plateau model can be used with high precision and practical feasibility for growing cactus pear, thereby improving the use of resources.

scholarly journals Plot size and number of replications for ryegrass experiments

2020 ◽  
Vol 50 (1) ◽  
Author(s):  
Marcos Toebe ◽  
Alberto Cargnelutti Filho ◽  
Juliana Oliveira de Carvalho ◽  
Francieli de Lima Tartaglia ◽  
Alessandra Ferreira Cortes ◽  
...  
Keyword(s):  
Variation Coefficient ◽  
Fresh Matter ◽  
Tukey Test ◽  
Plot Size ◽  
Maximum Curvature ◽  
Uniformity Trials

ABSTRACT: The objective of this research was to determine the optimal plot size and the number of replications to evaluate the fresh matter of ryegrass sown to haul. Twenty uniformity trials were conducted, each trial with 16 basic experimental units (BEU) of 0.5 m2. At 117, 118 and 119 days after sowing, the fresh matter of ryegrass in the BEUs of 5, 10 and 5 uniformity trials, respectively, were determined. The optimal plot size was determined by the maximum curvature method of the variation coefficient model. Next, the replications number was determined in scenarios formed by combinations of i treatments (i = 3, 4, ... 50) and d minimum differences between means of treatments to be detected as significant at 5% of probability by the Tukey test, expressed in experimental mean percentage (d = 10, 11, ... 20%). The optimal plot size to determine the fresh matter of ryegrass seeded at the haul is 2.19 m2, with a variation coefficient of 9.79%. To identify as significant at 5% probability, by the Tukey test, differences between treatment means of 20%, are required five, six, seven and eight replications, respectively, in ryegrass experiments with up to 5, 10, 20 and 50 treatments.

scholarly journals Plot size and number of replications to assess the vegetable matter in oat

2018 ◽  
Vol 9 (2) ◽  
pp. 252-263
Author(s):  
André Lavezo ◽  
Alberto Cargnelutti Filho ◽  
Bruna Mendonça Alves ◽  
Denison Esequiel Schabarum ◽  
Daniela Lixinski Silveira ◽  
...  
Keyword(s):  
Dry Matter ◽  
Flowering Stage ◽  
Fresh Matter ◽  
Tukey Test ◽  
Plot Size ◽  
Randomized Design ◽  
Sowing Dates ◽  
Completely Randomized Design ◽  
Uniformity Trials

The determination of the optimum plot size in agricultural crops is important for obtaining accurate inferences in the treatments in question. This study aimed at determining the optimum plot size (Xo) and the number of replications to evaluate the fresh matter (FM) and the dry matter (DM) of oat and at verifying the variability of Xo among cultivars and sowing dates. Ninety-six uniformity trials of 3×3 m were performed and each assay was divided into 36 basic experimental units (BEU) of 0.5×0.5 m. The 96 uniformity trials were distributed in four cultivars and three sowing dates. At the flowering stage, FM and DM were determined in each BEU. Then, the Xo was determined in each uniformity assay, using the maximum curvature method of the coefficient of variation model. In oat, there is variability of Xo among cultivars and sowing dates to measure FM and DM. For the four cultivars on the three sowing dates, the Xo of 1.66 m2 and of 1.73 m2 are suitable to evaluate FM and DM, respectively. Four replications to evaluate the maximum of 50 treatments in completely randomized design and randomized blocks design are sufficient so that the differences among treatment means of 44.75% of the experiment mean may be significant, using the Tukey test at 5% probability to measure FM and DM in oat.

scholarly journals Plot Size and Number of Replications for Evaluation of the Yield of Grains in Cultivars and Dates of Sowing of Rye

2017 ◽  
Vol 10 (1) ◽  
pp. 122 ◽  
Author(s):  
Gabriela Görgen Chaves ◽  
Alberto Cargnelutti Filho ◽  
Cláudia Marques de Bem ◽  
Cirineu Tolfo Bandeira ◽  
Daniela Lixinski Silveira ◽  
...  
Keyword(s):  
Secale Cereale ◽  
Coefficient Of Variation ◽  
Tukey Test ◽  
Plot Size ◽  
Maximum Curvature ◽  
Sowing Dates ◽  
Secale Cereale L ◽  
The Mean ◽  
Uniformity Trials ◽  
Variation Model

The objectives of this study were to determine the optimum plot size (Xo) and the number of replications to evaluate the grains yield of rye (Secale cereale L.) and investigate the variability of Xo between two cultivars and three sowing dates. Eighteen uniformity trials were conducted with rye. The Xo was determined by the method of maximum curvature of the coefficient of variation model. The number of repetitions was determined in scenarios formed by combinations of i treatments (i = 3, 4, ... 50) and d minimum differences between means of treatments to be detected as significant at 0.05 of probability, by Tukey test, expressed in percentage of the average of the experiment (d = 10, 12, ... 30%). There is variability in optimum plot size to evaluate the grains yield among the cultivars BRS Progresso and Temprano and among sowing dates in the rye crop. The optimum plot size to evaluate the grains yield of rye is 6.08 m2. Seven replicates are sufficient to evaluate the grains yield of rye in experiments with up to 50 treatments, and identify, as significant at 5% probability by Tukey test, differences among averages of treatments of 29.65% of the mean of the experiment in designs completely randomized and randomized block.

scholarly journals Plot size and replications number for triticale experiments

2020 ◽  
Vol 50 (11) ◽  
Author(s):  
Marcos Toebe ◽  
Alberto Cargnelutti Filho ◽  
Anderson Chuquel Mello ◽  
Rafael Rodrigues de Souza ◽  
Franciéle dos Santos Soares ◽  
...  
Keyword(s):  
Experimental Design ◽  
Coefficient Of Variation ◽  
Fresh Weight ◽  
Human Food ◽  
Tukey Test ◽  
Plot Size ◽  
Maximum Curvature ◽  
Experimental Area ◽  
Uniformity Trials

ABSTRACT: The hybridization between wheat and rye crops resulted in the triticale crop, which presents rusticity, versatility in animal and human food and possibility of use as a cover plant. The objective of this research was to determine the optimal plot size and the replications number to evaluate the fresh weight of triticale in two evaluation moments. An experiment was carried out with the triticale cultivar IPR111. The experimental area was divided into 48 uniformity trials, each containing 36 basic experimental units of 0.51 m2. The fresh weight was evaluated in 24 uniformity trials at 99 days after sowing (DAS) and in 24 uniformity trials at 127 DAS. The optimal plot size was determined by the method of the maximum curvature of the coefficient of variation and the replications number was determined in scenarios of treatments number and differences between means to be detected as significant by Tukey test. To determine the fresh weight of triticale, the optimal plot size is 3.12 m2, with coefficient of variation of 13.69%. Six replications are sufficient to identify as significant, differences between treatment means of 25% for experiments with up to seven treatments and of 30% for experiments with up to 28 treatments, regardless of the experimental design.

scholarly journals Plot size and number of replications in Sudan grass

2020 ◽  
Vol 41 (3) ◽  
pp. 783
Author(s):  
Alberto Cargnelutti Filho ◽  
Cirineu Tolfo Bandeira ◽  
Gabriela Görgen Chaves ◽  
Jéssica Andiara Kleinpaul ◽  
Rafael Vieira Pezzini ◽  
...  
Keyword(s):  
Coefficient Of Variation ◽  
Iterative Process ◽  
Block Design ◽  
Mean Value ◽  
Fresh Weight ◽  
Sudan Grass ◽  
Plot Size ◽  
Maximum Curvature ◽  
The Mean ◽  
Uniformity Trials

The aim of this study was to determine the optimal plot size and the number of replications to evaluate fresh weight in Sudan grass [Sorghum sudanense (Piper) Stapf.]. Twenty-six uniformity trials were carried out in two cultivars (BRS Estribo and CG Farrapo), in four sowing seasons (20 Dec, 20 Jan, 7 Feb and 24 Feb) and two methods for evaluating fresh weight (cutting and at flowering). The fresh weight was evaluated in 936 basic experimental units (BEU) (26 trials × 36 BEU per trial). One BEU comprised three rows of plants, 1 m in length (1.2 m2). The optimal plot size was determined using the maximum curvature method of the model of the coefficient of variation. For experiments in a completely randomised or randomised block design, in combinations of number of treatments and levels of experimental precision, the number of replications was determined by an iterative process. The optimal plot size to evaluate fresh weight in Sudan grass is 7.95 m2. Eight replications, to evaluate up to 50 treatments in a completely randomised or randomised block design, are sufficient to identify as significant at 0.05% probability by Tukey’s test, differences between the mean value of each treatment of 30.2% of the mean value of the experiment.

scholarly journals Plot size and number of replicates in times of sowing and cuts of millet

2016 ◽  
Vol 20 (2) ◽  
pp. 119-127 ◽  
Author(s):  
Cláudia Burin ◽  
Alberto Cargnelutti Filho ◽  
Bruna M. Alves ◽  
Marcos Toebe ◽  
Jéssica A. Kleinpaul
Keyword(s):  
Coefficient Of Variation ◽  
Iterative Process ◽  
Block Design ◽  
Probability Level ◽  
Fresh Matter ◽  
Tukey Test ◽  
Plot Size ◽  
Randomized Block Design ◽  
The Mean ◽  
Uniformity Trials

ABSTRACT The objective of this study was to determine the optimum plot size (Xo) and number of replicates to evaluate millet shoot fresh matter in times of sowing and cuts. Uniformity trials of 6 × 4 m (24 m2) were carried out in three sowing times, in the agricultural year of 2013-2014. Each uniformity trial was divided into 24 basic experimental units (BEU) of 1 × 1 m (1 m2) and the shoot fresh matter of plants in each BEU was weighed. The Xo was determined by the method of maximum curvature of the coefficient of variation model. The number of replicates for experiments in completely randomized and randomized block design, in scenarios of combinations of i treatments (i = 3, 4, ..., 50) and d minimal differences between treatment means, to be detected as significant at 0.05 probability level by Tukey test, expressed in percentage of the experiment mean (d = 10, 12, ..., 30%), was determined by iterative process until convergence. The optimum plot size to evaluate millet shoot fresh matter is 4.97 m2, for the three times of sowing and cuts. For the evaluation of up to 50 treatments, in completely randomized and randomized block design, five replicates are sufficient to identify as significant, at 0.05 probability level by Tukey test, differences between treatment means of 28.66% of the mean of the experiment.

scholarly journals Size of uniformity trials for estimating the optimum plot size for vegetables

2015 ◽  
Vol 33 (3) ◽  
pp. 388-393 ◽  
Author(s):  
Diogo V Schwertner ◽  
Alessandro D Lúcio ◽  
Alberto Cargnelutti Filho
Keyword(s):  
Confidence Interval ◽  
Coefficient Of Variation ◽  
Winter Season ◽  
Plot Size ◽  
Maximum Curvature ◽  
Snap Beans ◽  
Autumn And Winter ◽  
Fruit Mass ◽  
Uniformity Trials ◽  
Variation Model

The aim of this work was to determine the uniformity trial size for estimating the optimum plot size in order to evaluate the fruit mass of tomato, snap-beans and zucchini. The mass of fruits was evaluated in uniformity trials with tomato grown in plastic tunnel in spring-summer and autumn-winter seasons, with snap-beans in plastic greenhouse in autumn-winter season and, with zucchini in plastic greenhouse in summer-autumn and winter-spring seasons. These data were used for planning different sizes of uniformity trials and resampling with replacement was used to estimate the optimum plot size by the method of maximum curvature of the coefficient of variation model. The size of uniformity trials influences the estimation of the optimum plot size for evaluating the mass of fruits of tomato, snap-beans and zucchini. Uniformity trials with tomato with 12 basic experimental units (12 plants) and with snap-beans with 21 basic experimental units (42 plants) are enough for estimating the optimum plot size for evaluating the mass of fruits in plastic tunnel with a confidence interval of 95% minor or equal to two basic experimental units. Uniformity trials with snap-beans with 18 basic experimental units (36 plants) and with zucchini with ten basic experimental units (ten plants) in plastic greenhouse are enough for estimating the optimum plot size for evaluating the mass of fruits with a confidence interval of 95% minor or equal to three basic experimental units.

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