Abstract
The objective of this study was to estimate the coefficient of repeatability and the number of measurements required for production and quality variables in a strawberry crop. An experiment was conducted with two strawberry cultivars from two origins grown in four substrate mixtures, totaling 16 treatments, evaluated in a randomized block design with four replications. Mass (MF) and number (NF) of fruits per plant were evaluated as measures of production, and total soluble solids (SST), titratable acidity (AT) and firmness (FIR) of fruits during the crop cycle were evaluated as measures of quality. Subsequently, the repeatability coefficient was estimated by the following methods: analysis of variance (ANOVA), principal component analysis using a correlation matrix (PCcor), principal component analysis using a variance-covariance matrix (PCcov) and structural analysis (SA). The number of measurements was adjusted for each studied variable based on determination coefficients of 0.80, 0.85, 0.90, and 0.95. The repeatability coefficients ranged from low to medium. The ANOVA method gave the lowest r values, while the PCcov method presented the highest values of r. When using the PCcov method, 3.6, 2.9, 6.2, 3.2, and 3.8 measurements were needed to reach 80% confidence for the variables MF, NF, SST, AT, and FIR, respectively, and this increased to 7.3, 14.0, 29.6, 15.4, and 18.1 for 95% confidence in the results for MF, NF, SST, AT, and FIR, respectively.
Highlights
With high taste and organoleptic properties (Šamec et al, 2016), the strawberry culture Fragaria x ananassa Duch. is preferred among rural producers who seek to increase crop production in various parts of the world, generating employment and income for family farmers
Considering the high cost of implementing experiments with strawberry cultures grown on a substrate and the high labor and cost demands associated with experimental measurements and analysis of variables, the objective of this study was to estimate the coefficient of repeatability via the following methods: analysis of variance, principal component analysis using a correlation matrix, principal component analysis using a variance-covariance matrix and structural analysis
The determination of the coefficient of repeatability is used to measure the ability of the expression of a given genetic characteristic in time (Cruz et al, 2012); several studies have been carried out with diverse cultures, such as Panicum maximum (Fernandes et al, 2017; Martuscello et al, 2015), Prunus persica (Della Bruna et al, 2012), Musa spp. (Lessa, Ledo, Amorim, & Silva, 2014; Tenkouano, Ortiz, & Nokoe, 2012), Pennisetum purpureum (Shimoya, Pereira, Ferreira, Cruz, & Carneiro, 2002), Cicer arietinum (Kumar, Rheenen, Rao, & Weber, 1998), and Carthamus tinctorius (Mohammadi & Pourdad, 2009), among others
Summary
With high taste and organoleptic properties (Šamec et al, 2016), the strawberry culture Fragaria x ananassa Duch. is preferred among rural producers who seek to increase crop production in various parts of the world, generating employment and income for family farmers. When conducting an experiment, researchers use areas with dimensions defined by the budget of their institution without paying the attention that should be given to the minimum plot size required to prevent an increase the variability of the experiments (Lúcio, Haesbaert, Santos, & Benz, 2011). For the strawberry crop, Cocco, Boligon, Andriolo, Oliveira, and Lorentz (2009) showed that experimental variability is reduced with an increase in the number of plants per plot and that the number of plants per plot should be six plants for hydroponic cultivation and ten plants for cultivation in the soil. Several factors are evaluated to obtain satisfactory results for a crop; this is often one of the major obstacles in agricultural experimentation because labor is required for such evaluations as well as laboratory and/or field materials, resulting in high expenditures (Lúcio et al, 2011). Measurements of the variables are performed only once for each characteristic, which can reduce experimental accuracy
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