Optimum Plot Size Research Articles

Proposal of methods for determining the optimal size of experimental plots

Adequate planning of experiments is extremely important and for this to occur, the appropriate choice ofplot size is essential. Thus, determining the plot size seeks to increase experimental precision, since precision decreases when a plot size smaller than the ideal is chosen; on the other hand, when opting for plot sizes larger than the ideal, the researcher may use more resources than necessary, as well as increasing the time to set up the experiment. Therefore, this work aimed to propose two new methods for determining the optimal size of experimental plots, which were applied in experiments with yellow passion fruit in the field, and compare them to the modified maximum curvature method. The maximum curvature method of the modified Vx function uses the equation proposed by Smith and the maximum curvature method of the modified CVx function uses the equation proposed by Thomas. The modified maximum curvature method of the CV(x) function proved to be suitable for estimating the optimal size of plots in experiments with yellow passion fruit. It is recommended to use the optimal plot size of 4 plants per plot for variables related to the fruit, 5 plants per plot for variables related to the pulp and for the production variables of 9 plants per plot.

OPTIMAL LIMA BEAN PLOT SIZE

It is important to determine optimal experimental plot size in order to ensure accurate results and correct decision making. There are no studies of this nature for lima bean cultivation. As such, the aim of this study was to determine the optimal plot size to estimate lima bean yield in family farming and experimental areas, combining the number of treatments, repetitions, and accuracy levels. Experiments were conducted in two 12[Formula: see text]m × 16[Formula: see text]m plots, each subdivided into 192 basic units (BUs) measuring 1[Formula: see text]m2. Bean weight was assessed in each of the units (g). Smith’s index of soil heterogeneity and optimal plot size were estimated using the Modified Maximum Curvature Method and Hatheway’s Method. The index of soil heterogeneity was higher than 0.7 for all the variables analyzed. Plot size between 10[Formula: see text]m2 and 15[Formula: see text]m2 is considered efficient in estimating lima bean weight. These first estimated optimal plot sizes will allow researchers and farmers who work with the crop to obtain more robust yield estimates.

Determining Optimal Plot Size Using Saturation and Unsaturation Curves in the Western Taurus Area of the Mediterranean Region, Turkey

Determining Optimal Plot Size Using Saturation and Unsaturation Curves in the Western Taurus Area of the Mediterranean Region, Turkey

Tamanho ótimo de parcela experimental para melancia híbrido Liverpool

Watermelon is a Curbitaceae with relevance in the industry, in which experiments are implemented to evaluate several factors. The objective was to determine the optimal portion size (Xo) of Liverpool hybrid watermelon, to increase the experiment’s precision and maximize experimental results. The work was implemented at the Experimental Farm of the Centro Universitário Norte do Espírito Santo, of the Federal University of Espírito Santo. The equatorial and longitudinal perimeter and mass of the fruit were evaluated. The Meier and Lessman method was used with bootstrap simulation, using 2000 resamples to determine the Xo. Based on the variability of the data, the plot sizes for each characteristic evaluated were different, and the optimal plot size was four fruits per plot for fruit mass.

Tamaño óptimo de parcela experimental para pasto Mombaça

The objective of this study is to use statistical means in the optimal plot sizing for the culture of Mombasa grass. For this, data from a uniformity test will be analyzed in a total area of 100 m², with 400 basic experimental units (BEU), where each sampled plot had a usable area of 0.25 m². The experiment was conducted in the rural area of the municipality of Adelândia, Goiás. The climate of the region is classified as AW-tropical, with a dry season in the winter. In the implementation of the experiment, an application of chicken litter at 18 tons per hectare was made. For the selection of the experimental area, a place where the soil cover was more uniform was taken into account. However, due to the lack of management, the height of the grass within the experimental area was uneven. In view of this, it was necessary to perform a standardization cut throughout the plot. The cut samples collected were stored in paper bags and the production of green mass was measured. For the dry mass, 40 samples were randomly selected, where each one went through the drying process in a microwave. This drying process consists of weighing and marking the initial weight of the sample, putting two containers in the microwave for three minutes, one with water and the other with the sample. The methods used for this design were: maximum curvature model method of the coefficient of variation; linear segmented model method with plateau, and quadratic segmented model method with plateau. The analyzed models presented different plot sizes, with approximate values of 1 m2, 7.4 m2 and 14 m2, respectively. As a methodology for selecting the model that best fits the observed data, the Akaike information criterion (AIC) will be used. This is a relative measure of the quality of a statistical model’s fit. For a set of experimental data, the accepted model will be the one that presents the lowest value for AIC, that is, the lower the value for AIC, the better the model fits the data. We can conclude that the unsegmented method presented optimal plot size, with an area of 1 m2, plots of 4.3, a coefficient of variation of 27.2 and an AIC of 188. The linear segmented model with plateau and quadratic segmented model with plateau presented AIC values of 199 and 198, respectively.

Spatial scale of non-target effects of cotton insecticides.

Plot size is of practical importance in any integrated pest management (IPM) study that has a field component. Such studies need to be conducted at a scale relevant to species dynamics because their abundance and distribution in plots might vary according to plot size. An adequate plot size is especially important for researchers, technology providers and regulatory agencies in understanding effects of various insect control technologies on non-target arthropods. Plots that are too small might fail to detect potential harmful effects of these technologies due to arthropod movement and redistribution among plots, or from untreated areas and outside sources. The Arizona cotton system is heavily dependent on technologies for arthropod control, thus we conducted a 2-year replicated field experiment to estimate the optimal plot size for non-target arthropod studies in our system. Experimental treatments consisted of three square plot sizes and three insecticides in a full factorial. We established three plot sizes that measured 144 m2, 324 m2 and 576 m2. For insecticide treatments, we established an untreated check, a positive control insecticide with known negative effects on the arthropod community and a selective insecticide. We investigated how plot size impacts the estimation of treatment effects relative to community structure (27 taxa), community diversity, individual abundance, effect sizes, biological control function of arthropod taxa with a wide range of mobility, including Collops spp., Orius tristicolor, Geocoris spp., Misumenops celer, Drapetis nr. divergens and Chrysoperla carnea s.l.. Square 144 m2 plots supported similar results for all parameters compared with larger plots, and are thus sufficiently large to measure insecticidal effects on non-target arthropods in cotton. Our results are applicable to cotton systems with related pests, predators or other fauna with similar dispersal characteristics. Moreover, these results also might be generalizable to other crop systems with similar fauna.

Optimal plot size in wheat with comparison of three methods

Optimal plot size in wheat with comparison of three methods

Estimação do tamanho ótimo de parcela em experimento em campo com maracujá-roxo

Abstract The species P. edulis Sims f. edulis, native to Brazil, known as purple passion fruit, has purple fruits and lower acidity. With the growing demand for passion fruits, there is greater need for research on their cultivation to reduce production costs and improve fruit quality. The adequate determination of the size and number of plots has been a fundamental limitation in studies with several crops, as it is difficult to obtain constant data on plants per plot in most experiments, making it impossible to use usual methodologies for data analysis. As a result, testing can be performed with less labor and implementation costs, making plot size optimization a step of interest. Thus, this work aims to determine the ideal size of experimental plots with purple passion fruit in the field using three methods. The variables analyzed were fruit length, fruit diameter, peel thickness, juice yield, soluble solids content, citric acid, number of fruits, and average fruit weight. The use of optimal plot size of six basic units for fruit-related variables, five for pulp-related variables, and seven basic units for production variables, is recommended.

Optimal plot size in lettuce seed germination experiments

Lettuce in works under controlled conditions is widely used in experiments with a direct focus on the species itself, or as an indicator species used in allelopathy works. But despite its frequent use, there is no support in the literature for using a single plot size and, therefore, there are works from the use of 5 to 100 seeds per experimental plot. It is understood that the works have been developed using different experimental precisions. The low experimental precision caused by a smaller-than-optimal plot size can lead to a high residual value in the analysis of variance and consequent difficulties in detecting statistical difference between treatments. On the other hand, the use of a high number of seeds per plot can lead to unnecessary expenditure and does not contribute to improving experimental precision. The objective of this work was to determine the optimal plot size in seed germination experiments under controlled conditions. The modified maximum curvature method according to Meier and Lessman was used. After the subsequent analysis, the high variations between different sizes and the factors that can be interfered because of this were observed, and the optimal plot size was defined as six seeds for the biogerminative test of lettuce by petri dishes of 10 cm in diameter.

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.

Morpho-Agronomic Characterization, Sample Size, and Plot Size for the Evaluation of Capsicum chinense Genotypes

The Amazon is a center of diversity for Capsicum chinense Jacq., with wide genetic and morphological variability, but little exploration has been performed there to facilitate their improvement. This study aimed to characterize and evaluate C. chinense genotypes for the development of cultivars by determining the optimal size of the experimental plot and the minimum sample size to ensure a precise estimation of yield. A total of 23 genotypes were evaluated, and in multivariate analyses, the plants were characterized by 21 morphological descriptors and eight quantitative traits related to biometry and yield. The recommended sample size for fruit evaluation was defined based on simulations with subsample resampling and evaluation of the semi-amplitudes of the confidence interval of the mean estimate. The optimal plot size was estimated by the modified maximum curvature method. The similarity coefficients among the genotypes ranged from 0.54 to 0.93, indicating that the established clusters contained important information for future crosses. According to the sample size methodologies, 25 to 40 fruits should be sampled for valid evaluations of biometric traits. Experiments with five to eight plants per plot are recommended to test progenies of the species, ensuring good experimental precision combined with high selection accuracy for yield traits.

Optimizing the airborne laser scanning estimation of basal area larger than mean ([formula omitted]): An indicator of cohort balance in forests

Airborne laser scanning (ALS) assisted basal area larger than mean (BALM) estimation measures the cohort balance in forests and provides adequate opportunities to describe forest structure. However, a problem still exists that how the plot size, sample size (number of trees), and ALS point density affect the BALM estimation. We tackled this question by using both field and ALS data from a typical managed boreal forest area in Finland. Various concentric circular plots (1–15 m radii) were simulated within the actual field plots (squared) and the optimal plot size and sample size were selected by observing changes in the absolute correlation between BALM estimates and various ALS metrics. Instability in the correlation was found at the smaller concentric circular plots (1–5 m radii) and sample sizes (less than 6 trees) but as the plot size and sample size increased, the correlation followed a convex curve. The maximum correlation was found between a concentric circular plot size 11–14 m radii (380–615 m2 area) and sample size 50–80 trees which could be the optimal plot size and sample size for a reliable BALM estimation. With regards to the ALS point density, no major effects were observed on the relationship between BALM estimates and various ALS metrics unless the point density is less than at least 5 points m−2. The point density of the current nationwide ALS survey is matching the minimum point density requirement obtained in this study and thus it is suitable for a reliable forest structural assessment.

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.

High-Density UAV-LiDAR in an Integrated Crop-Livestock-Forest System: Sampling Forest Inventory or Forest Inventory Based on Individual Tree Detection (ITD)

Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.

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%.

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%.

Onion culture: experimental techniques for carrying out high precision experiments

The cultivation of onion Allium cepa L. is of extreme economic importance globally, and, because of this, research must be constantly updated in order to increase productivity. This study was designed to estimate the optimal plot size, sample size and number of repetitions for experiments evaluating variables in the onion crop. Uniformity tests were carried in the years 2016 and 2017 in the experimental area of the Crop Science Department at Universidade Federal de Santa Maria, Santa Maria (RS), Brazil. Each plant was considered a basic experimental unit. Bulb mass, bulb height, and bulb diameter were measured for each basic experimental unit. The optimal plot size, sample size, and number of repetitions were estimated by the method of maximum curvature of the variation coefficient. For onion culture, the optimal plot size to evaluate the bulb mass, bulb height and bulb diameter is eight, six and six, respectively. The optimal sample size for evaluating the bulb mass of the onion crop is six plants, while for the height and diameter variables the optimal sample size is four plants in the direction of the row considering a semi-amplitude of the confidence interval (D%) equal to 20% of the average. Bulb mass variables require 10 repetitions to assess up to 20 treatments in a randomized block design for the least significant difference of the Tukey’s test, expressed as a percentage of the average of 35%. Bulb height and bulb diameter require just six repetitions for the same assessment.

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.