Optimum Plot Size Research Articles

Tamanho ótimo da parcela experimental de abacaxizeiro 'Vitória'

No planejamento experimental, a parcela experimental é um fator determinante para o sucesso da pesquisa. Portanto, é necessário que o pesquisador defina adequadamente a constituição da parcela ou unidade experimental, no intuito de aumentar a eficiência do experimento e diminuir o erro experimental. Em decorrência da grande variabilidade no tamanho da parcela encontrada nos experimentos com abacaxizeiro, realizou-se uma pesquisa objetivando estimar o tamanho ótimo para parcelas experimentais, considerando diferentes características. Os dados utilizados para este trabalho foram coletados em 100 plantas de abacaxizeiro 'Vitória'. Foram coletados 100 frutos e 100 folhas "D" de plantas cultivadas na mesma área experimental e submetidas às mesmas práticas de cultivo. Foram avaliados nos frutos: o comprimento, o diâmetro e a massa. Na folha "D", foram avaliados o comprimento e a massa, além dos teores de N, P e K. As estimativas do tamanho da parcela foram baseadas nos métodos do modelo linear de resposta a platô e da curvatura máxima. O tamanho ótimo da parcela para a avaliação do abacaxizeiro 'Vitória' é de 15 a 20 unidades experimentais por parcela, para as características dos frutos e das folhas, pelo método do modelo linear de resposta a platô. Para a avaliação dos teores de N, P e K, 10 folhas representam o tamanho ótimo da parcela pelo método do modelo linear de resposta a platô. Por outro lado, o tamanho ótimo da parcela para avaliação dos frutos e das folhas do abacaxi 'Vitória' é de 1,7 a 2,8 unidades experimentais pelo método da curvatura máxima. Enquanto, para os teores de N, P e K, 3 a 5 unidades experimentais representam o tamanho ótimo da parcela pelo método da curvatura máxima.

On optimum plot size and shape for field experimentation on brinjal (Solanum melongena L.) under middle Gujarat condition

Correspondence to : M.S. SHITAP Department of Agriculture Statistics, B.A. College of Agriculture, Anand Agricultural University, ANAND (GUJARAT) INDIA ABSTRACT : An uniformity trial on JBGR-1 of brinjal was conducted at the Main Vegetable Research Station, Anand Agricultural University, Anand to determine the optimum size and shape of the plot using maximum curvature method. The variability as judged by co-efficient of variation (CV) per unit area decreased with the increase in plot size. The optimum plot (net) size obtained and advocated was 6.48 sq. m. covering single row of 7.2 m length and spaced at 0.9 m (7.2 m × 0.9 m) for field experiments on brinjal under Anand condition.

UTLIZATION OF UNIFORMITY TRIALS TO ESTIMATE THE OPTIMUM PLOT SIZE AND SHAPE AND THE NUMBER OF REPLICATIONS IN WHEAT YIELD TRIALS

In the present work, two uniformity trials were carried out during thefirst and the second winter seasons of 2011/2012 and 2012/2013 in theExperimental farm Demo, Faculty of Agriculture, Fayoum University. Themain objectives were to estimate the optimum plot size, plot shape andnumber of replicates for wheat yield traits using the variety Sakha 93 as plantmaterial. The cultivated area of each field trial was divided into 12 strips;each of which consisted of 100 rows, 0.2 m width and 3.0 m long. Twostatistical methods including soil variability index and maximum curvaturewere used to estimate the optimum plot size and shape using the yield data of1200 basic units (each of 0.6 m2). The data were subjected to two proceduresof statistical analysis to estimate the optimum plot size, when the cost ofconducting the experiment is not taken into consideration and to evaluate theeffect of changing the plot shape on the variability. The first statistical methodwas that of maximum curvature which is based on the exponentialrelationship between plot size and the coefficient of variability. The secondmethod was that developed by smith’s method (1938). Bartlett's test forhomogeneity of variances, as outlined by Steel and Torrie (1980), was usedto study the effect of changing plot shape. The obtained results could besummarized as follows: Increasing the plot size decreased the variance perbasic unit and the coefficient of variability. However, the reduction was not inproportion with the increase in plot size. The index of soil variability rangedfrom 0.6433 to 0.6018 as an average for the 1st and the 2nd seasons,respectively. The relationship between the coefficient of variability (C.V.)and plot size (X) were mathematically expressed by the following equationC.V. = 19.21 X-0.2595 for the 1st season and C.V. = 19.60 X – 0.2725 for the 2ndone. Accordingly, using the soil variability index, the optimum plot size was2 basic units (1/3500 fed.) for the two seasons, while it was 4 basic units(1/1750 fed.) in both seasons when the maximum curvature method wasapplied. The required number of replications for the optimum plot size usingSmith method detecting a 15% difference among treatment means varied 13and 14 in the 1st and the 2nd seasons, respectively. But, for detecting a 20%difference among treatment means, 7 replications in the 1st season and 8replications in the 2nd one were found necessary. Optimum plot size estimatedusing the maximum curvature method detecting a 15% difference amongtreatment means varied 7 and 8 in the 1st and the 2nd seasons, respectively. But, for detecting a 20% difference among treatment means, 4 replications inthe 1st and 2nd season's one were found necessary. Generally, the plot shapedid not affect on the precision of wheat yield trial in most cases in the twogrowing seasons.

Effect of neighborhood and plot size on experiments with multiple-harvest oleraceous crops

The objective of this work was to determine the efficiency of the Papadakis method on the quality evaluation of experiments with multiple-harvest oleraceous crops, and on the estimate of the covariate and the ideal plot size. Data from nine uniformity trials (five with bean pod, two with zucchini, and two with sweet pepper) and from one experiment with treatments (with sweet pepper) were used. Through the uniformity trials, the best way to calculate the covariate was defined and the optimal plot size was calculated. In the experiment with treatments, analyses of variance and covariance were performed, in which the covariate was calculated by the Papadakis method, and experimental precision was evaluated based on four statistics. The use of analysis of covariance with the covariate obtained by the Papadakis method increases the quality of experiments with multiple-harvest oleraceous crops and allows the use of smaller plot sizes. The best covariate is the one that considers a neighboring plot of each side of the reference plot.

<b>Scaling the number of plants per plot and number of plots per genotype of yellow passion fruit plants</b> - doi: 10.4025/actasciagron.v36i1.17697

There is a demand for research on yellow passion fruit plants (Passiflora edulis Sims) used for juice production. Thus, the present study aimed to determine the number of plants per experimental plot and the number of plots per genotype for different properties of passion fruit. The data used were obtained from an experiment (Tangara da Serra, Mato Grosso State, Brazil) that assessed 13 fruit properties in eight genotypes of yellow passion fruit plants, with 10 replicates and 10 plants per plot. For each of the 80 experimental plots, the optimum plot size (Xo) was estimated by the method of maximum curvature of the coefficient of variation model. The number of replicates for each genotype and the means were estimated through the use of 10 plants per plot, combined with Xo plants per plot. The optimum plot size for the assessment of properties in yellow passion fruits independent of genotype is less than five plants, as the number of replicates is a function of the desired magnitude of accuracy. The number of plants per experiment is lower when using Xo plants per plot.

Selecting the optimum plot size for a California design-based stream and wetland mapping program

Accurate estimates of the extent and distribution of wetlands and streams are the foundation of wetland monitoring, management, restoration, and regulatory programs. Traditionally, these estimates have relied on comprehensive mapping. However, this approach is prohibitively resource-intensive over large areas, making it both impractical and statistically unreliable. Probabilistic (design-based) approaches to evaluating status and trends provide a more cost-effective alternative because, compared with comprehensive mapping, overall extent is inferred from mapping a statistically representative, randomly selected subset of the target area. In this type of design, the size of sample plots has a significant impact on program costs and on statistical precision and accuracy; however, no consensus exists on the appropriate plot size for remote monitoring of stream and wetland extent. This study utilized simulated sampling to assess the performance of four plot sizes (1, 4, 9, and 16 km(2)) for three geographic regions of California. Simulation results showed smaller plot sizes (1 and 4 km(2)) were most efficient for achieving desired levels of statistical accuracy and precision. However, larger plot sizes were more likely to contain rare and spatially limited wetland subtypes. Balancing these considerations led to selection of 4 km(2) for the California status and trends program.

Tamanho de parcela e efeito de bordadura no melhoramento de Urochloa ruziziensis

O objetivo deste trabalho foi avaliar a necessidade de bordadura e estimar o tamanho ótimo de parcela em experimentos para avaliação de Urochloa ruziziensis. Foram avaliadas a altura das plantas e a produção de massa verde de oito progênies de meio-irmãos de U. ruziziensis, em dois cortes. Utilizou-se o delineamento de blocos ao acaso, com quatro repetições e parcelas de 16 m². Cada parcela foi subdividida em 32 estratos de 0,5 m², sendo cada estrato considerado como uma unidade básica. Para determinar a necessidade de bordadura, foi realizada análise de variância, tendo-se considerado a posição da unidade básica na parcela. As estimativas do tamanho ótimo da parcela foram realizadas pelo método da máxima curvatura do coeficiente de variação e pelo método da reamostragem. O uso de bordaduras não altera o desempenho médio e a classificação das progênies de meio-irmãos avaliadas. O emprego de parcelas com 3 m² é suficiente para obter boa precisão experimental em experimento com progênies de meio-irmãos de U. ruziziensis.

Optimum Plot Size and Number of Replications for Determining Watermelon Yield, Fruit Size, Fruit Firmness, and Soluble Solids

Plot size and number of replications were evaluated for watermelon [Citrullus lanatus (Thunb.)] over a 3-year period. Four different methods were used, including plotting basic unit plots against changes in cvs, Hatheway’s method for detecting a true difference as a percent of the mean with a 20% threshold, Bartlett’s homogeneity of variance test, and computed least significant differences (lsds). An initial evaluation was done in a trial with several varieties. Plotting cv against number of basic units using plots with different watermelon varieties suggests a plot size of ≈7 basic units (1 basic unit = 3.34 m2). With a single variety and basic units of 6.69 m2 for inside rows, the plot size was ≈4 basic units and for the outside row 5 basic units. With plot sizes of 2.23 m2, the number of basic units per plot was estimated at ≈5 basic units. Bartlett’s test suggests larger basic unit plot sizes of 14 to 20 with a 3.34-m2 basic unit size with multiple varieties. With a single variety, 4 to 8 basic unit plot sizes are required with 6.69-m2 basic unit size. Results were unreliable with 2.23-m2 plot sizes using Bartlett’s test. Computed lsds, which were 5% of the mean or less, could be achieved with plots sizes of 10 basic units and five replications with 3.34-m2 basic unit plots and using multiple varieties. Other combinations meeting these criteria included 14 basic unit plots and two or three replications. With a 6.69-m2 basic unit size, plots of 6 basic units and four replications would meet the 5% criteria. Finally, with the 2.23-m2 basic unit size, a plot size of 8 basic units and three replications would result in an lsd of 5% of the mean. Results with Hatheway’s method were similar to plots of basic units against cv. Hatheway’s method also has an estimate of number of replications and with 3.34-m2 basic unit, the 20% threshold of detecting true differences occurred with 10 to 14 basic units and four replications. For fruit size, firmness, and soluble solids, the basic unit plot sizes ranged from 5 to 7. Plot size estimates were larger with 6.69 m2 compared with 2.23 m2 for fruit characteristics.

Efficiency of inventory plot patterns in quantitative analysis of vegetation: a case study of tropical woodland and dense forest in Benin

The main issue in forest inventory is the reliability of data collected, which depends on the shape and size of inventoried plots. There is also a need for harmonisation of inventoried plot patterns in West Africa. This study focused on the impact of plot patterns on the quantitative analysis of two vegetation types of West Africa based on case studies from Benin. Twenty and fifteen plots of 1 ha each were demarcated in dense forest and woodland, respectively. Each 1 ha plot was divided into 100 quadrats of 100 m2 each and diameter at breast height (dbh) of trees was recorded in each quadrat. The required time to measuring trees diameter in each 1 ha plot was also recorded to compute the mean inventory effort. From the 100 quadrats in each 1 ha plot, 14 subplots of different shapes and sizes were considered by grouping together adjacent quadrats. The basal area of each subplot was computed and the relationship between estimation bias of the basal area and the size of subplots was modeled using Smith's Law (Smith 1938). The mean absolute error of the shape parameter c of Weibull distribution was computed for each of the subplot shape, size and direction. The direction and shape of subplots did not influence significantly (P > 0.05) the precision of the quantitative analysis of vegetation. However, square subplots were suitable in practice. On the contrary, plot size was significantly (P < 0.05) and inversely correlated to estimation efficiency. The optimal plot size for quantitative analysis of vegetation was 1 800 and 2 000 m2 with an inventory effort of 0.51 and 0.85 man-days per subplot in woodland and dense forest, respectively. It is concluded that use of standard sample sizes will help to harmonise a forestry database and to carry out comparisons at regional level.

Optimum Plot Size for Field Trials of Taro (Colocasia esculenta)

Taro (Colocasia esculenta L. Schott) is a root crop widely grown in the Tropics. To determine the optimum plot size for taro field trials, fresh and dry weights of individual corms were collected from two field trials conducted under flooded culture and two conducted under upland culture. For a given maximum test plot with a single border row surrounding inner measured plants, all possible combinations of smaller plot sizes were investigated. A plot size was defined as a given number of adjacent plants. A strong linear relationship was found between the natural logarithm of variance of yield and the natural logarithm of plot size. Expressed on the non-log-transformed scale, the point of maximum curvature in this relationship indicates a sudden decrease in advantage to larger plot sizes and is taken as optimum. Calculating maximum curvature mathematically, optimum plot size was 21 inner plants (5.7 m2) for the second flooded trial and 18 inner plants (4.9 m2) for the second upland trial. Another method of estimating optimum plot size minimized the cost per unit of research data by using the index of degree of correlation between neighboring plots. In three of four trials, the optimum plot size ranged from 16 to 24 inner plants (4.3 to 6.5 m2). In this second method, we calculated a non-linear relationship between plot size and outer border plants to estimate the fixed and per-unit cost of a single border row surrounding the inner measured plants. Both methods of calculating optimal plot size sometimes resulted in estimates that exceeded the maximum test plot size for particular field trials, indicating limitations of each method and the importance of managing field trials to ensure uniformity across treatments. No evidence of spatial autocorrelation was found in the corm yield of taro, indicating that the two methods used were adequate in calculating optimum plot size. In addition, we conducted an analysis based on statistical power but found that plot size did not materially affect the power to detect differences between treatments. To our knowledge, this is the first report of optimum plot size for field trials of taro.

Tamanho ótimo de parcela para experimentos com rabanetes

O estabelecimento de tamanho ótimo de parcela, em qualquer cultura, é uma das maneiras de se aumentar a precisão experimental e maximizar as informações obtidas em um experimento. O objetivo deste trabalho foi determinar o tamanho ótimo de parcelas, em experimentos com rabanete, para as variáveis massa e diâmetro de raiz, e comparar diferentes métodos de estimação. Foi conduzido um ensaio de uniformidade, sendo o plantio do rabanete realizado nos espaçamentos de 15 cm entre linhas e de 5 cm entre plantas . A unidade básica foi definida como um segmento linear de três plantas consecutivas. Após 30 dias do plantio, foram avaliados a massa (g) e o diâmetro (mm) dos rabanetes. Foram simulados 35 tamanhos de parcelas, determinando a média e o coeficiente de variação para cada tamanho de parcela e, por meio de modelos não lineares, foi determinado o tamanho ótimo de parcela para ensaios com rabanete. Constatou-se que o método da máxima curvatura, modificado, e os modelos segmentados de resposta linear e quadrática estimam tamanhos de parcela diferentes; portanto, em experimentos com a cultura do rabanete recomendam-se parcelas com 21 a 63 plantas (0,1575 a 0,4745 m²) por parcela.

Tamanho ótimo de parcela para a cultura do feijão-vagem

With the aim of estimating the optimum plot size and verify the behavior of the experimental precision in different plot sizes and shapes to consider the production of snap beans, the following white experiments were conducted: 1) in a greenhouse during the autumn and winter; 2) in a poly-tunnel during the autumn and winter; 3) in an unprotected environment during the autumn and winter; 4) in a poly-tunnel during the spring and summer; and 5) in an unprotected environment during the spring and summer. The fresh pod mass was measured for pairs of plants. For each different plot size (X) the variation and its relation to X was estimated. The optimum plot size was estimated using the method of maximum modified curvature, and the significant minimum differences between averages were estimated using the Hathaway method. Analyze the total fresh biomass of pods reduces variability in experiments with green beans and optimum plot size recommended for this crop is 16 plants in a greenhouse for experiments and plans for 12 tunnel experiments and experiments in non-protected.

Tamanho ótimo de parcela em milho com comparação de dois métodos

O objetivo deste trabalho foi comparar dois métodos de estimação do tamanho ótimo de parcela (Xo) para avaliar 12 caracteres de híbridos simples, triplo e duplo de milho e verificar a variabilidade do Xo entre caracteres e entre híbridos. Foram mensurados caracteres morfológicos (altura de planta e de espiga) e produtivos (peso de espiga, número de fileiras por espiga, comprimento e diâmetro de espiga, peso e diâmetro de sabugo, massa de cem grãos, número de grãos por espiga e comprimento e produtividade de grãos), em unidades experimentais básicas de um metro linear, em 48 ensaios de uniformidade. Foi determinado Xo por meio dos métodos da curvatura máxima modificada e da curvatura máxima do modelo do coeficiente de variação para cada caractere. O tamanho ótimo de parcela obtido por meio do método da curvatura máxima modificada é concordante, mas maior que o obtido pelo método da curvatura máxima do modelo do coeficiente de variação. O tamanho ótimo de parcela dos caracteres produtivos é maior que dos morfológicos. O tamanho ótimo de parcela entre os híbridos de milho é de 5,04 unidades experimentais básicas para o híbrido simples (4,03m²), 5,24 para o híbrido triplo (4,19m²) e 5,53 para o híbrido duplo (4,42m²).

Modelos de regressão com platô na estimativa do tamanho de parcelas em experimento de conservação in vitro de maracujazeiro

A determinação do tamanho de parcela é uma prática pertinente ao planejamento experimental e sua caracterização otimizada, em conjunto com o controle do material experimental, permite a obtenção de resultados com maior precisão e qualidade. Neste trabalho, determinou-se o tamanho de parcelas para experimentos de conservação in vitro de maracujazeiro, em dez ensaios de uniformidade com a espécie Passiflora Giberti N. E. Brown, utilizando-se o modelo de regressão linear segmentado com platô e o modelo de regressão quadrática segmentado com platô, que utilizam a técnica de resposta com platô a modelos que possuam mínimo. Os ensaios de uniformidade foram oriundos de experimento conduzido no delineamento inteiramente casualizado, com 20 unidades básicas (ub), e os tratamentos dispostos em esquema fatorial com três concentrações de sacarose, três concentrações de sorbitol e uma testemunha. A coleta dos dados foi realizada aos 60 dias após a incubação, medindo-se o comprimento das brotações. Os tamanhos de parcelas variaram com o método utilizado, encontrando-se parcelas formadas por seis explantes pelo modelo da regressão linear segmentado com platô e de dez explantes pelo modelo de regressão quadrática segmentado com platô.

Tamanhos de parcela e de ensaio de uniformidade em nabo forrageiro

Os objetivos deste trabalho foram determinar o tamanho ótimo de parcela e o tamanho de ensaio de uniformidade para a estimação do tamanho ótimo de parcela, a fim de avaliar a massa verde de nabo forrageiro (Raphanus sativus L.). Foram usados os dados de massa verde de 3.456 unidades experimentais básicas de 0,5m x 0,5m (0,25m²) e planejados 46 tamanhos de ensaio de uniformidade. O tamanho ótimo de parcela foi determinado por meio do método da curvatura máxima do modelo do coeficiente de variação. O tamanho de ensaio de uniformidade foi determinado a partir de bootstrap, com reposição de 2.000 amostras de cada tamanho de ensaio planejado. O tamanho ótimo de parcela para avaliar a massa verde de nabo forrageiro é de 4,82 unidades experimentais básicas de 0,25m² (1,20m²). Ensaios de uniformidade com 225 unidades experimentais básicas de 0,25m² (56,25m²) são suficientes para a estimação do tamanho ótimo de parcela, para uma amplitude do intervalo de confiança de bootstrap de 95%, igual a uma unidade experimental básica.

Métodos de estimativa do tamanho ótimo de parcelas experimentais de híbridos de milho simples, triplo e duplo

O objetivo deste trabalho foi comparar os métodos de estimativa do tamanho ótimo de parcela e verificar a variabilidade do tamanho ótimo de parcela entre híbridos simples, triplo e duplo de milho. Para isso, foram usados os dados de produtividade de grãos de milho de 16 ensaios de uniformidade com o híbrido simples (P32R21), 16 com o híbrido triplo (DKB566) e 16 com o híbrido duplo (DKB747). Cada ensaio foi composto de quatro linhas de 6m de comprimento, dividido em unidades experimentais básicas de um metro linear. Em cada um dos 48 ensaios foi determinado o tamanho ótimo de parcela por meio dos métodos da curvatura máxima modificada, do modelo linear de resposta com platô e da curvatura máxima do modelo do coeficiente de variação. Foi utilizado o coeficiente de correlação de Spearman para verificar a associação entre os métodos e o teste t para comparar as médias. Há concordância na estimativa do tamanho ótimo de parcelas experimentais de milho simples, triplo e duplo por meio dos métodos da curvatura máxima modificada e da curvatura máxima do modelo do coeficiente de variação. Por esses métodos, o tamanho ótimo de parcela entre os híbridos de milho é de seis unidades experimentais básicas para o híbrido simples e cinco para os híbridos triplo e duplo.

Optimized Frequency Measures for Monitoring Trends in Tallgrass Prairie

Prairies exhibit a bimodal frequency distribution of patch occupancy with a few dominant species accounting for most of the primary production and plant biomass and numerous other species occurring infrequently over time and space. Consequently, management interest is often focused on the abundance of common species, and on the persistence of less common species. We used spatially nested arrays of various sized plots to simultaneously measure species’ persistence and abundance at Tallgrass Prairie National Preserve, Kansas. Larger plots captured more species, with 127 species identified at the 10-m scale. Smaller plots, however, provided useful frequency data for measuring the abundance of common species. We defined a plot frame size that delivers a frequency in a 20–50% target range as the optimal plot size, and the frequency of a species sampled at its optimal plot size is termed the optimized frequency. Optimized frequency was estimated for 27 common species for the years 2002– 2006. Of the nine perennial grasses in this group, five exhibited significant interannual variation within the 5-yr period. Bouteloua curtipendula exhibited a significant linear change over the 5-yr period, decreasing in frequency over time. From year to year, changes in optimized frequency among the 27 common species varied in both their trajectory (i.e., the average change), and amplitude (i.e., the average magnitude of change). Between 2005 and 2006, the average change in optimized frequency was 29.1%, the only year with an overall declining trajectory. The declining trajectory between 2005 and 2006 was significantly different from the increasing trajectories observed between 2002 and 2003 and between 2004 and 2005. No significant differences were observed in amplitude. We offer these techniques for aggregating information from multiple spatial scales to characterize the amplitude and trajectory of community-wide changes as tools to help scientists convey the results of multiscale research projects to land managers.

Optimum plot size and number of replications related to selective precision

Planning agricultural trials and evaluating their precision is important in determining the credibility of the results obtained from the research. There are various methods for estimating optimum plot size and various statistics used to measure precision. Selective precision has been cited recently as being an adequate measurement for evaluating experimental precision. The aim of this study is to propose a modification in the way the optimum plot size is estimated, related to the variability of the experimental area and prefixed selective precision. The method was applied to a set of six trials with potatoes. The optimum plot size, which was estimated on the basis of the relation between the variation coefficients and different plot sizes, can be estimated by considering simultaneously and in a prefixed way, the number of replications and the selective precision related to the treatments that are being evaluated. The optimum plot size for potato varies between four and thirteen hills, depending on the number of repetitions and the selective precision that will be used.

Partial collection of data on potato yield for experimental planning

The aim of this study was to estimate the number of blank experiments (BE) (i.e., a uniformity trial) required to estimate the optimum plot size for use in experiments involving potato crops. The study was based on data on the mass of potato tubers (Solanum tuberosum L.) from 3456 hills (i.e., 24 rows of 144 hills each) obtained from a BE. Using these data, BE of different sizes (i.e., 2 rows of 24, 36, 48 and 72 hills) were planned to estimate optimum plot size. For each BE, 11 plot sizes ( X) were planned based on the sum of adjacent hills, and the mean, variance and coefficient of variation (CV) between plots of the same size were calculated. Regression models for CV were adjusted in terms of X to estimate the optimum plot size. For each BE size, a bootstrap resampling method was used to estimate the sufficient number of BE to enable precise estimates of optimum plot size, mean and other statistics. It was found that a sampling potato hill yield of 39% of subdivisions within an experimental area where a potato experiment is to be performed is sufficient to estimate optimum plot size for the experiment. Plots composed of one row of six hills are sufficient to estimate potato yield.

Plot size and experimental precision for sunflower production

In order for sunflower cultivation to be economically sustainable, research should be based upon suitable experimental techniques. Since this kind of information is not readily available, the aim of this study was to estimate the seed production heterogeneity index and the optimal experimental plot size, and to verify experimental precision in sunflower experiments. Sunflower seed yield figures for plots of 1-meter rows (0.4 m spacing) were collected. The experiments were carried out in the 2004/05 and 2005/06 growing seasons in a 1-ha area, by marking out 12 randomized blocks (12 uniformity trials) of two rows 48 plots long on land used for commercial production of sunflowers in Bossoroca, state of Rio Grande do Sul, Brazil. Plots of different sizes were simulated and estimates made for the mean, variance and coefficient of variance for each plot size, and the production heterogeneity index, optimal plot size and experimental precision estimated. The sunflower seed production heterogeneity index was high, the plots should be large and the rows are the blocks. The optimal plot size is two 3-meter rows (2.4 m²).