Uniformity Trials Research Articles

Sampling sufficiency to estimate the mean productive traits of buckwheat

The objective of this work was to determine the sample size (number of plants) necessary to estimate the mean productive traits of buckwheat (Fagopyrum esculentum), cultivars IPR91-Baili and IPR92-Altar, in the vegetative and flowering stages of the crop. Twenty-one uniformity trials were conducted for each cultivar (IPR91-Baili and IPR92-Altar). Six productive traits (leaf fresh matter, stem fresh matter, shoot fresh matter, leaf dry matter, stem dry matter and shoot dry matter) were evaluated in 2280 plants of each cultivar (IPR91-Baili and IPR92-Altar). Kolmogorov-Smirnov normality test was performed. Measures of central tendency, variability and skewness and were calculated. The sample size was calculated to estimate the mean for six traits, with estimation errors (semi-amplitudes of the 95% confidence interval) equal to 5%, 10%, 15% and 20% of the mean. There is variability in sample size between productive traits, cultivars and phenological stages of buckwheat. In an experiment to estimate the mean of these six traits of buckwheat, cultivars IPR91-Baili and IPR92-Altar, in the vegetative and flowering stages, with a maximum error of 20% of the mean at a 95% confidence interval, 80 plants per treatment are needed.

Plano experimental para a cultura da cenoura

ABSTRACT: The carrotculture stands out on the world stage due to its nutritional characteristics and economic importance, an aspect that demands the constant development of research aiming greater productivity. Thus, this study proposed an experimental plan, determining the estimates of plot size, sample size, and number of repetitions, with the purpose of increasing the precision and reliability of the results of the experiments with the carrot crop. Six uniformity trials were conducted, using three cultivars in two growing seasons (Season: 2019 and 2021).Each plant was considered a basic experimental unit and in each BEU, the variables shoot height, root length, shoot fresh mass, root fresh mass, and root diameter were measured. The size of the plot, sample, and the number of repetitions was estimated by the method of maximum curvature of the coefficient of variation. The results recommend that for experiments with the carrot crop, plots with twelve plants should be used. For a sampling of carrot plants in the plot, samples of eleven plants must be used in the direction of the row, considering a semi-amplitude of the confidence interval (D%) equal to 20% of the mean, with a confidence level of 95%. For a minimum significant difference in the Tukey test expressed as a percentage of the 50% mean, plots of twelve plants per crop row, with eight replicates, are recommended.

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

ABSTRACT Investigation of multicollinearity allows parameters in multivariate analysis to be estimated with higher precision and with biological interpretation. In order to generate reliable estimates of the degree of multicollinearity, it is necessary to use appropriate sample size. Thus, the objectives of this study were to determine the sample size (number of plants) necessary to estimate the indicators of the degree of multicollinearity - condition number (CN), correlation matrix determinant (DET), and variance inflation factor (VIF) - in morphological traits of rye and to verify the variability of the sample size between the indicators. Five and three uniformity trials were conducted with the cultivars BRS Progresso and Temprano, respectively. Eight morphological traits were evaluated in 780 plants in eight trials. For each trial, 22 cases were selected among the 28 formed by the combination of eight traits, taken six by six, totaling 176 cases. In each case, 197 sample sizes were planned (20, 25, 30, ..., 1,000 plants) and in each size 2,000 resampling procedures with replacement were performed, CN, DET, and VIF were determined and the average among 2,000 estimates was calculated. For each case and indicator (CN, DET, and VIF), the sample size was determined through three models: modified maximum curvature method and linear and quadratic segmented models with plateau response. There is variability between sample sizes between indicators, with larger sample sizes required for DET, followed by CN and VIF, in that order, with at least 180, 116 and 85 plants, respectively.

Plot size and number of repetitions in forage wheat

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.

Suficiência amostral para estimar a média de caracteres de trigo

Abstract The objective of this work was to determine the sample size necessary for estimating the means of wheat (Triticum aestivum) traits, obtained through measurement, counting, and weighing. Seventeen uniformity trials were performed with 1,790 plants harvested randomly, whose following traits were evaluated: lengths of the main stem and main stem ear (measurement); number of leaves, stems, and ears (counting); and mass of fresh and dry matter of leaves, stems, and ears (weighing). The Bartlett and Kolmogorov-Smirnov tests and Welch’s analysis of variance were performed. Skewness, central tendency, and variability were determined, and sample size was calculated to estimate the means of the 13 evaluated traits, considering estimation errors (semi-amplitudes of the 95% confidence interval) equal to 5, 10, 15, and 20% of the mean. There is a decrease in the sample size to estimate the means of wheat traits obtained through weighing, counting, and measuring, in this order. In an experiment to estimate the mean of wheat traits obtained by weighing, counting, and measuring with a maximum error of 10% of the mean at a 95% confidence interval, 117, 76, and 9 plants per treatment are needed, respectively.

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

The objective of this study was to compare three methods for estimating the optimal plot size to evaluate the fresh matter productivity of forage sorghum (Sorghum bicolor (L.) Moench) sown in rows, evaluated in three cuts. Two uniformity trials (repetitions) were carried out. In each trial, three plant cuts were performed, totaling six uniformity trials (2 trials per cut × 3 cuts). The first cut was performed at 41 days after sowing (DAS), the second at 82 DAS and the third at 133 DAS. Fresh matter productivity was evaluated in 216 basic experimental units (BEU) of 1 m × 1 m (36 BEU per trial). The BEU was formed by two rows of 1.0 m in length, spaced 0.50 m apart, totaling 1.0 m2. The optimal plot size was determined using the methods of modified maximum curvature, linear response and plateau model and quadratic response and plateau model. The optimal plot size differs between the methods and decreases in the following order: quadratic response and plateau model (9.10 m2), linear response and plateau model (7.16 m2) and modified maximum curvature (4.13 m2). The optimal plot size to evaluate the fresh matter productivity of forage sorghum, sown in rows, evaluated in three cuts, is 7.16 m2 and the experimental precision stabilizes from this size on.

MÉTODOS DE ESTIMAÇÃO DO TAMANHO ÓTIMO DE PARCELA EM AVEIA PRETA, ERVILHACA E NABO FORRAGEIRO CULTIVADAS EM CONSÓRCIO

ABSTRACT The objective of this work was to compare three methods of estimating the optimal plot size to evaluate the fresh matter in black oat (Avena strigosa Schreb), common vetch (Vicia sativa L.) and forage turnip (Raphanus sativus L.) intercropping. Six uniformity trials with black oat, common vetch and forage turnip intercropping were carried out. Three trials were evaluated at 84 days after sowing and the other three trials at 119 days after sowing. The fresh matter was evaluated in 216 basic experimental units (36 per trial) of 1 m × 1 m. The optimal plot size was determined using the methods of modified maximum curvature, linear response and plateau model and quadratic response and plateau model. The optimal plot size differs between the methods and decreases in the following order: quadratic response and plateau model (15.13 m2), linear response and plateau model (8.24 m2) and modified maximum curvature (5.62 m2). The optimal plot size for assessing the fresh matter of black oat, common vetch and forage turnip, grown in intercropping, is 15.13 m2. This size can be used as a reference for future experiments.

Optimal plot size in single or intercropping sorghum and showy rattlebox

The objective of this study was to determine the optimal plot size to evaluate fresh matter productivity of sorghum (Sorghum bicolor (L.) Moench) and showy rattlebox (Crotalaria spectabilis Roth.). Fifteen uniformity trials (blank experiments) with sorghum and showy rattlebox, in single or intercropping, were carried out. The fresh matter productivity was evaluated in 540 basic experimental units (BEU) of 1 × 1 m (15 trials × 36 BEU per trial). The plot size was determined by the method of Hatheway (1961), in scenarios formed by combinations of treatment numbers, repetitions numbers, and levels of experimental precision. To evaluate the fresh matter productivity of sorghum and showy rattlebox, in single or intercropping, with 5 to 30 treatments and with five repetitions, plots of 10 m² of useful area are sufficient for differences between treatments of 24% of the overall average of the experiment to be considered significant at 0.05 probability.

Experimental design and precision in trials with black oat, common vetch, and forage turnip intercropping

The objective of this study was to determine the optimal plot size for evaluating the fresh matter of black oat (Avena strigosa Schreb), common vetch (Vicia sativa L.), and forage turnip (Raphanus sativus L.), grown in intercrop, in scenarios formed by combinations of treatments numbers, repetitions numbers, and experimental precision levels. Six uniformity trials were conducted with the consortium of black oat, common vetch, and forage turnip, with three trials evaluated at 84 days after sowing (DAS) and the other three trials at 119 DAS. Fresh matter was assessed in 216 basic experimental units (BEU) of 1 × 1 m (6 trials × 36 BEU per trial). The heterogeneity index of Smith (1938) was estimated. Plot size was determined by the method Hatheway (1961) in scenarios formed by combinations of i treatments (i = 5, 10, 15, 20, and 25), r repetitions (r = 3, 4, 5, 6, 7, 8, 9, and 10), and d precision levels (d = 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20%). To evaluate the fresh matter of black oat, common vetch, and forage turnip, in intercropping, with 5 to 25 treatments and with six repetitions, plots of 15 m² of usable area are sufficient for differences between treatments of 12% of the overall average of the experiment to show differences at the 0.05 level of significance.

Legacy Effects of Contrasting Long-Term Integrated Weed Management Systems

To reduce reliance on herbicides and maintain crop productivity, integrated weed management (IWM) seeks to optimize synergies between diverse sets of weed management practices combined at the cropping system scale. Nevertheless, data on weed community response to the long-term implementation of IWM practices remain scare. Here, we assessed the effects of four IWM systems with contrasting objectives and practices (S2: transition from superficial tillage to conservation agriculture; S3: no-mechanical weeding; S4: mixed mechanical and chemical weeding; S5: herbicide-free; all with 6 year rotations) compared to a conventional reference (S1: herbicide-based with systematic plowing and a 3 year rotation) on taxonomic and functional weed community composition and structure after 17 years of continuous implementation. We examined the legacy effects of these systems with a uniformity trial consisting of winter wheat managed uniformly across the systems as well as with a novel in situ weed seedbank approach involving tilled strips. We found that resulting weed communities in IWM systems were more species rich (species richness from 1.1 to 2.6 times greater) and more abundant (total density from 3.3 to 25 times greater) than those observed in the reference system, and differed in term of taxonomic and functional composition. In addition, we found that, when systems shared the same weed species, germination patterns of two thirds of the species differed between systems, highlighting the selection pressures some IWM practices exert on weeds. We showed that analyzing the superficial germinable seedbank in situ with tilled strips could provide a comprehensive view of resulting weed communities and be helpful in developing cropping systems that foster agroecological weed management.

Tamanho de parcela e número de repetições para experimentos de azevém semeados em filas

Abstract The objective of this work was to determine the optimal plot size and the number of replicates for the evaluation of the fresh weight of ryegrass sowed in rows. Seventy uniformity trials were performed with 'Barjumbo' ryegrass, in 16 basic experimental units (BEUs) of 0.51 m2 each. The fresh weight of ryegrass in the BEUs of 18, 18, 6, 6, and 22 uniformity trials was determined, respectively, at 130, 131, 133, 134, and 137 days after sowing. The optimal plot size was determined through the method of the maximum curvature of the coefficient of variation. The number of replicates was determined in scenarios formed by combinations of treatments and differences between means to be detected as significant by Tukey’s test, at 5% probabilit y. The optimal plot size ranged from 1.73 to 3.18 m2, and the variation coefficient in the optimal plot size from 7.58 to 13.96%. The number of replicates varied from 3.95 (~4) to 32.27 (~33), depending on the experimental design, the number of treatments, and the adopted minimum difference. The optimal plot size is 2.29 m2, and, in experiments with up to 50 treatments, eight replicates are required to identify as significant the differences between treatment means of 20.24%.

Nonlinear models in the height description of the Rhino sunflower cultivar

ABSTRACT: Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square’s method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.

Tamanho de parcela e número de repetições para experimentos com sorgo-forrageiro

Abstract The objective of this work was to define the optimal plot size and number of replicates for the evaluation of the fresh weight of sorghum (Sorghum bicolor) hybrids. Thirty-two uniformity trials were carried out with two hybrids, in two sowing dates and four evaluation periods. Each trial was divided into 48 basic experimental units (BEUs) of 0.5 m2, and fresh weight was determined for each BEU. The mean, variance, coefficient of variation, first-order spatial autocorrelation coefficient, optimal plot size, and coefficient of variation of the optimal plot size were calculated. The number of replicates was determined on the basis of the largest calculated plot size, through an iterative process, for the combinations of number of treatments and differences among means to be detected as significant by Tukey’s test, at 5% probability. The optimal plot size ranged from 1.79 to 2.58 m2, and the number of replicates from 2.6 (~3) to 49.2 (~50). The optimal plot size is 2.58 m2, and five replicates are sufficient to identify as significant the differences between treatment means of 35%.

Optimal plot size for experimentation of common beans (Phaseolus vulgaris L.) in the northern region of Minas Gerais, Brazil

The objective was to evaluate the minimum size of experimental plots for the common bean (Phaseolus vulgaris L.) using the modified maximum curvature method. The experiment consisted of a uniformity trial with the cultivar BRSFC-402 sown at a spacing of 0.5 m between plant rows and 10 plants per meter within the row. 20 central rows measuring 20 m in length were considered for measurements, totaling 4,000 plants on an area of 200 m2. Final bean stand (FS), mean number of pods per plant (NPP), mean number of grain per pod (NGP), mean 100-grain weight (M100), and grain yield (kg ha-1) were evaluated. At evaluations, each row with 10 plants was considered a basic unit (0.5 m2), amounting to 400 basic units whose dimensions were combined into 14 plot shapes. The methods of relative information and modified maximum curvature were used to obtain the best shape and the most appropriate plot size, respectively, for experimental evaluation with common bean. Using these methods, and considering that the optimum plot should enable an efficient evaluation of all evaluated characteristics, the appropriate plot size was five UB (25 plants) in the format with five rows x one UB per row. Highlights Support for experimental evaluation of common beans under edaphoclimatic conditions in the northern region of Minas Gerais Experimental plots with five basic units ensure maximum precision for joint evaluation of the main phenotypic descriptors of common beans. The characteristics mass of 100 grains and productivity were associated with the smallest and the largest plot sizes, respectively.

Experimental dimensions and precision in trials with millet and showy rattlebox

The objective of this study was to determine the optimal plot size to evaluate the mass of fresh matter in millet (Pennisetum glaucum L.) and showy rattlebox (Crotalaria spectabilis Roth.), in scenarios formed by combinations of treatment numbers, repetitions numbers, and levels of experimental precision. Fifteen uniformity trials with millet and showy rattlebox, in single or intercropping, were carried out. The mass of fresh matter was evaluated in 540 basic experimental units (BEU) of 1 × 1 m (15 trials × 36 BEU per trial). The heterogeneity index of Smith (1938) was estimated. The plot size was determined by the method of Hatheway (1961) in scenarios formed by combinations of i treatments (i = 5, 10, 15, 20, and 25), r repetitions (r = 3, 4, 5, 6, 7, 8, 9, and 10), and d precision levels (d = 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20%). To evaluate the mass of fresh matter of millet and showy rattlebox, in single or intercropping, with 5 to 25 treatments and with five repetitions, plots of 10 m² of useful area are sufficient for differences between treatments of 9% of the overall average of the experiment to be considered significant at 0.05 probability.

Automating Uniformity Trials to Optimize Precision of Agronomic Field Trials

Plot size has an important impact on variation among plots in agronomic field trials, but is rarely considered during the design process. Uniformity trials can inform a researcher about underlying variance, but are seldom used due to their laborious nature. The objective of this research was to describe variation in maize field trials among field plots of varying size and develop a tool to optimize field-trial design using uniformity-trial statistics. Six uniformity trials were conducted in 2015–2016 in conjunction with Iowa State University and WinField United. All six uniformity trials exhibited a negative asymptotic relationship between variance and plot size. Variance per unit area was reduced over 50% with plots 41.8 m2 in size and over 75% when using a plot size >111.5 m2 compared to a 13.9 m2 plot. Plot shape within a fixed plot size did not influence variance. The data illustrated fewer replicates were needed as plot size increased, since larger plots reduced variability. Use of a Shiny web application is demonstrated that allows a researcher to upload a yield map and consider uniformity-trial statistics to inform plot size and replicate decisions.

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

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.

Estimation of buckwheat leaf area by leaf dimensions

The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the plants, totaling 1,815 leaves. In these 1,815 leaves, L and W were measured and the LW of the leaf blade was calculated, which were used as independent variables in the model. The leaf area of each leaf was determined using the digital photo method (Y), which was used as a dependent variable of the model. For each sowing date, cultivar and thirds of the plant, 80% of the leaves (1,452 leaves) were randomly separated for the generation of the models and 20% of the leaves (363 leaves) for the validation of the models of leaf area estimation as a function of linear dimensions. For buckwheat, IPR91-Baili and IPR92-Altar cultivars, the quadratic model (Ŷ = 0.5217 + 0.6581LW + 0.0004LW2, R2 = 0.9590), power model (Ŷ = 0.6809LW1.0037, R2 = 0.9587), linear model (Ŷ = 0.0653 + 0.6892LW, R2 = 0.9587) and linear model without intercept (Ŷ = 0.6907LW, R2 = 0.9587) are indicated for the estimation of leaf area determined by digital photos (Y) based on the LW of the leaf blade (x), and, preferably, the linear model without intercept can be used, due to its greater simplicity.

Tamanho de amostra para avaliação do grau de multicolinearidade em caracteres produtivos de centeio

The objectives of this work were to determine the sample size (number of plants) necessary to estimate the indicators of the of multicollinearity degree - condition number (CN), determinant of the correlation matrix (DET), and variance inflation factor (VIF) - in productive traits of rye and to verify the variability of the sample size between the indicators. Five and three uniformity trials were conducted with the cultivars BRS Progresso and Temprano, respectively, and seven productive traits were evaluated in 780 plants. Twenty-one cases were obtained from seven traits, combined five to five. In each case, 197 sample sizes were planned (20, 25, 30, ..., 1,000 plants) and in each size 2,000 resampling were performed, with replacement. For each resample the CN, DET and FIV were determined and the average among 2,000 estimates of each indicator of the multicollinearity degree was calculated. Then, for each case and indicator, the sample size was determined through three models: models of maximum modified curvature, segmented linear with plateau response, and segmented quadratic with plateau response. There was superiority the quadratic model segmented with plateau in adjusting the degree of multicollinearity according to the sample size for all indicators. There is a need greater sample size to detect multicollinearity when diagnosed by DET and for sizes larger than 101, 258 and 102 plants when diagnosing for the number of conditions, determinant and inflation factor performed, respectively.

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

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.