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.

PLOT SIZE, NUMBER OF TREATMENTS AND REPLICATES AND EXPERIMENTAL PRECISION IN BUCKWHEAT

ABSTRACT The objective of this work was to determine the optimal plot size to evaluate the fresh weight in buckwheat (Fagopyrum esculentum Moench) of the IPR91-Baili and IPR92-Altar cultivars, in scenarios formed by combinations of numbers of treatments, numbers of replicates, and levels of experimental precision. Sixteen uniformity trials (blank experiments) were carried out, eight with cultivar IPR91-Baili and eight with cultivar IPR92-Altar. The trials were performed in eight sowing dates. The fresh weight was evaluated in 576 basic experimental units (BEU) of 1 m x 1 m (36 BEU per trial). The soil 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, and 20), r replicates (r = 3, 4, 5, 6, 7, and 8), and d precision levels (d = 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, and 20%). To evaluate the fresh weight in buckwheat of the IPR91-Baili and IPR92-Altar cultivars, in experiments under completely randomized and randomized block designs, with 5 to 20 treatments and eight replicates, plots of 8 m3of useful area are sufficient to identify significant differences between treatments, at 5% probability level, of 15% of the overall mean of the experiment.

Plot size and number of replications in Sudan grass

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.

Optimal plot size for experiments with black oats and the common vetch

ABSTRACT: The aim of this study was to determine the optimal plot size for evaluating the fresh weight of black oats (Avena strigosa Schreb) and the common vetch (Vicia sativa L.) in scenarios comprising combinations of the number of treatments, number of replications and levels of precision. Fifteen uniformity trials were conducted with single-crop and intercropped black oats and vetch. Fresh weight was evaluated in 540 basic experimental units (BEU), each of 1 m × 1 m (36 BEU per trial). The Smith index of soil heterogeneity (1938) was estimated. Plot size was determined using the HATHEWAY method (1961), in scenarios comprising combinations of i treatments (i = 5, 10, 15 and 20), r replications (r = 3, 4, 5, 6, 7 and 8) and d levels of precision (d = 2%, 4%, 6%, 8%, 10%, 12%, 14%, 16%, 18% and 20%). To evaluate the fresh weight of monocropped or intercropped black oats and vetch in a completely randomized or randomized complete block design, with from 5 to 20 treatments and five replications, plots of 10 m2 are sufficient to identify, at a probability of 0.05, significant differences between treatments of 10% of the overall mean value of the experiment.

Plot size and replications number for triticale experiments

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.

Plot size and number of replications for ryegrass experiments

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.