Determination of Drainage Networks From Plot-Size and Basin-Size Areas

1992 ◽  
Vol 8 (2) ◽  
pp. 185-189 ◽  
Author(s):  
G. Couger ◽  
B. N. Wilson ◽  
C. T. Rice
Keyword(s):  
Plot Size ◽  
Drainage Networks

Determination of the Optimum Plot Size for Tomato Seedlings: An Advanced Research

2021 ◽  
pp. 41-50
Author(s):  
Jeniffer Ribeiro De Oliveira ◽  
Jalille Amim Altoé ◽  
Gleison Oliosi ◽  
Alex Silva Lima ◽  
Luã Víthor Chixaro Almeida Falcão Rosa ◽  
...  
Keyword(s):  
Tomato Seedlings ◽  
Plot Size

Miscellaneous note

Bothalia ◽  
1981 ◽  
Vol 13 (3/4) ◽  
pp. 575-576
Author(s):  
B. M. Campbell ◽  
E. J. Moll
Keyword(s):  
Plot Size

DETERMINATION OF PLOT SIZE

Determination of Adequate Plot Size by Use of Mean Distance between Salt Desert Shrubs

1968 ◽  
Vol 13 (1) ◽  
pp. 61
Author(s):  
Neil E. West ◽  
Mustafa M. Baasher
Keyword(s):  
Desert Shrubs ◽  
Plot Size ◽  
Salt Desert

scholarly journals OPTIMUM PLOT SIZE AND NUMBER OF REPLICATIONS IN PAPAYA FIELD EXPERIMENT

2016 ◽  
Vol 38 (2) ◽  
Author(s):  
EDILSON ROMAIS SCHMILDT ◽  
OMAR SCHMILDT ◽  
COSME DAMIÃO CRUZ ◽  
LAERCIO FRANCISCO CATTANEO ◽  
GERALDO ANTÔNIO FERREGUETTI
Keyword(s):  
Field Experiments ◽  
Optimal Number ◽  
Optimum Size ◽  
Plot Size ◽  
Maximum Curvature ◽  
The North ◽  
Significant Difference ◽  
Different Seasons ◽  
Planting Season

ABSTRACT The aim of this study was to estimate the optimum plot size and number of replications in papaya field experiments. Eleven variables were evaluated in four cultivars of papaya with planting in different seasons between 2011 and 2013 in the north of the Espírito Santo state. Analysis were made from blank test applied to 240 selected for planting season and cultivate plants in commercial fields. The determination of optimum plot size was performed by applying the methodologies of modified maximum curvature and maximum curvature of coefficient of variation. The determination of the number of repetitions was taken from the least significant difference in average 20% and 30%. The optimum plot size proved the same by the two methods studied for most evaluations. The optimum size required differs among cultivars, between variables and between planting seasons, with the largest number of plants was required for the variables number of fruits per plant and yield per plant. We conclude that the optimal number of papaya plants planted in the field is six plants per plot using three replications.

scholarly journals Experimental plot size of passion fruit

2020 ◽  
Vol 42 (1) ◽  
Author(s):  
Glaucia Amorim Faria ◽  
Beatriz Garcia Lopes ◽  
Ana Patrícia Bastos Peixoto ◽  
Antonio Flávio Arruda Ferreira ◽  
Kátia Luciene Maltoni ◽  
...  
Keyword(s):  
Passion Fruit ◽  
Basic Unit ◽  
Experimental Plot ◽  
Experimental Planning ◽  
Plot Size ◽  
Practical Matter ◽  
Uniformity Test ◽  
Number Of Leaves ◽  
Barnyard Manure

Abstract The determination of the plot size is a practical matter pertinent to the experimental planning, and its optimal characterization allows to obtain higher precision and better quality in the results. Therefore, in this study, the main goal was to determine the plot size in experiments of passion fruit in two uniformity tests with Passiflora setacea and Passiflora alata. The experiment was constituted of a substrate at planting with 3 thirds of soil and 1 of barnyard manure. The soil was fertilizer with 3 kg of simple superphosphate and 0.5 kg of KCl by 1m³. Each species of Passiflora was considered a uniformity test with 40 basic units (BU). The evaluations of the experiments were done on 60 days after the transplant, noticing the tree’s height, stem’s diameter, number of leaves, number of buds, number of meristems and chlorophyll. Several plot sizes were simulated, in which each plant was first considered as a basic unit up to 40 plants per unit basic. For the estimation of optimum plot size, the maximum modified curvature method was used. The plot sizes varied with the specie, founding values as three to seven BU for Passiflora setacea and four BU to five for Passiflora alata.

Efficiency of Optimum Plot Size using Information of Previous Experiments Conducted in Split Plot Design

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
RAMESH CHANDRA BHARATI ◽  
KAUSHAL KISHOR CHAUDHARY ◽  
ANIL KUMAR SINGH ◽  
ABHAY KUMAR ◽  
UJJWAL KUMAR ◽  
...  
Keyword(s):  
Real Data ◽  
Soil Heterogeneity ◽  
Accurate Estimate ◽  
Experimental Result ◽  
Alternative Procedure ◽  
The Past ◽  
Plot Size ◽  
Split Plot ◽  
Plot Design

The optimum plot size is required at the time of experiment lay out to obtain the accuracy and reliability of the experimental result. In absence of uniformity trail, an alternative procedure is described to get the idea of optimum plot size. The process involves for determining the accurate estimate of soil heterogeneity coefficient followed by optimum plot size through the past experimental data of split plot design and the expression for the determination of soil heterogeneity has been derived and illustrated through several artificial and real data. The result indicated the considerable gain in efficiency to the tune of 19 and 22 per cent in some cases. This procedure leads to the saving of plot size from 20 per cent to 75 per cent.

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

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

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

scholarly journals Determination of 3D Displacements of Drainage Networks Extracted from Digital Elevation Models (DEMs) Using Linear-Based Methods

2017 ◽  
Vol 6 (8) ◽  
pp. 234 ◽  
Author(s):  
Antonio Tomás Mozas-Calvache ◽  
Manuel Antonio Ureña-Cámara ◽  
Francisco Javier Ariza-López
Keyword(s):  
Digital Elevation Models ◽  
Drainage Networks ◽  
Digital Elevation

scholarly journals ESTIMATION OF THE OPTIMUM SIZE OF PLOTS FOR SOYBEAN RADIOMETER EXPERIMENTS

2021 ◽  
Vol 39 (2) ◽  
pp. 362-371
Author(s):  
Roger Nabeyama MICHELS ◽  
Marcelo Giovanetti CANTERI ◽  
Marcelo Augusto de AGUIAR E SILVA ◽  
Janksyn BERTOZZI ◽  
Tatiane Cristina DAL BOSCO
Keyword(s):  
Field Studies ◽  
Financial Resources ◽  
Optimal Size ◽  
Optimum Size ◽  
Agricultural Field ◽  
Phenological Stages ◽  
Distance Method ◽  
Experimental Planning ◽  
Plot Size

The lack of error of experimental planning in agricultural field studies can result in rework, causing the waste of financial resources. The determination of the optimal size of the experimental plot for carrying out the treatments can minimize these problems. The objective of this paper was to estimate the optimal plot size for measuring reflectance in soybeans, without treatment, using the modified maximum curvature method and the maximum distance method. Reflectance readings were taken in the soybean crop with the aid of the GreenSeeker® equipment, in basic experimental units of 0.45 m², in an area of 7 lines and 8 meters in length. The data were collected in three phenological stages of soy (R4, R5.5 and R6), obtaining 63 simulations of experimental area in each stage. Based on the results, it is recommended to use plots of 7.20 m², with grouping of 4 lines of 4 m in length.

scholarly journals Determination of robust optimum plot size and shape – a model-based approach

2015 ◽  
Vol 52 (1) ◽  
pp. 13-22
Author(s):  
Satyabrata Pal ◽  
Goutam Mandal ◽  
Kajal Dihidar
Keyword(s):  
Real Life ◽  
Radius Of Curvature ◽  
Data Sets ◽  
Life Data ◽  
Plot Size ◽  
Real Life Data ◽  
Empirical Variogram ◽  
General Method ◽  
Uniformity Trials

SummaryDetermination of optimum plot size has been regarded as an important and useful area of study for agriculturists and statisticians since the first remarkable contribution on this problem came to light in a paper by Smith (1938). As we explore the scientific literature relating to this problem, we may note a number of contributions, including those of Modjeska and Rawlings (1983), Webster and Burgess (1984), Sethi (1985), Zhang et al. (1990, 1994), Bhatti et al.(1991), Fagroud and Meirvenne (2002), etc. In Pal et al. (2007), a general method was presented by means of which the optimum plot size can be determined through a systematic analytical procedure. The importance of the procedure stems from the fact that even with Fisherian blocking, the correlation among the residuals is not eliminated (as such the residuals remain correlated). The method is based on an application of an empirical variogram constructed on real-life data sets (obtained from uniformity trials) wherein the data are serially correlated. This paper presents a deep and extensive investigation (involving theoretical exploration of the effect of different plot sizes and shapes in discovering the point – actually the minimum radius of curvature of the variogram at that point – beyond which the theoretical variogram assumes stationary values with further increase in lags) in the case of the most commonly employed model (incorporating a correlation structure) assumed to represent real-life data situations (uniformity trial or designed experiments, RBD/LSD).

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