Compute the Synthetic Variation Coefficient (\(CV\%\)) (Dong 1998; Dong et al. 2001) to compare quantitative traits of the entire collection (EC) and core set (CS).

scv.evaluate.core(data, names, quantitative, selected)



The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.


Name of column with the individual names as a character string


Name of columns with the quantitative traits as a character vector.


Character vector with the names of individuals selected in core collection and present in the names column.


The Synthetic Variation Coefficient values for EC and CS


Synthetic Variation Coefficient (\(CV\%\)) (Dong 1998; Dong et al. 2001) is computed as follows for the core set (CS).

\[CV(\%) = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{SE_{j}}{\mu_{i}} \right ) \times 100\]

Where, \(SE_{i}\) is the standard error of the \(i\)th trait, \(\mu_{i}\) is the mean of the \(i\)th trait and \(n\) is the total number of traits.


Dong YS (1998). “Exploration on genetic diversity center for cultivated soybean in China.” Chinese Crops Journal, 1, 18--19.

Dong YS, Zhuang BC, Zhao LM, Sun H, He MY (2001). “The genetic diversity of annual wild soybeans grown in China.” Theoretical and Applied Genetics, 103(1), 98--103.



ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL

core <- rownames(cassava_CC)

quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
           "ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
          "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",

ec[, qual] <- lapply(ec[, qual],
                      function(x) factor(as.factor(x)))

scv.evaluate.core(data = ec, names = "genotypes",
                  quantitative = quant, selected = core)
#>   EC_SCV   CS_SCV 
#> 10.75148 41.67945