Trop. Agr. Develop. 60 （2）：132 － 136，2016 Short Report Statistic Reappraising in Taxonomical Identification of Wild Species of Job’s Tears (Coix spp.) Based on Involucre Samples from Southeast and East Asia Junko MIYAMOTO1, Naomi NAKAKURA2, and Yukino OCHIAI 3, * 1 Graduate School of Science and Engineering, Kagoshima University, Korimoto, Kagoshima 890-0065, Japan 2 Faculty of Science, Kagoshima University, Korimoto, Kagoshima 890-0065, Japan 3 Faculty of Agriculture, Ryukoku University, Seta Oe-cho, Otsu, Shiga 520-2194, Japan Key words: Coix lacryma-jobi, Morphology, Non-destructive identification Introduction in the first priority and male flowers in the second priority. This key could be applied to the growing plants Species of the genus Coix L., Poaceae, have been in the field observation. Furthermore, as involucres known as one of the useful plants in northeastern India are covered by hard shell, they are able to be stored and Southeast and East Asia according to the ethnobo- semi-permanently in herbariums and museums. Thus, tanical reports (Jain and Banerjee, 1974; Arora, 1977; this key could be applied effectively to identify taxon, Ochiai, 2007). A domesticated subspecies, C. lacryma- firstly in case of researches on herbarium specimen, jobi subsp. ma-yuen T. Koyama, has been cultivated as a as destructive methods for identification are frequently cereal crop and is evaluated as a function food in recent restricted, and secondly in case of researches on artifacts years (Ohta et al., 2007; Arora, 1977; Ochiai, 2002). The collection, as involucres were separated from the genus Coix includes six wild taxa, such as C. lacryma- inflorescences and stems. In other words, it is suggested jobi var. lacryma-jobi L., C. lacryma-jobi var. monilifer that non-destructive method based on the morphological Watt, C. lacryma-jobi var. stenocarpa Stapf, C. puellarum characteristics of involucre is necessary to identify Coix Balansa, C. aquatica Roxb., and C.gigantea Koenig ex taxa, although chemical identification techniques, such Roxb. Regarding the distribution of wild Coix species, as the DNA profiling, have been developed (Ma et al., C. lacryma-jobi var. lacryma-jobi L. distributes widely in 2006; Qin et al., 2005; Liang et al., 2008; Su et al., 2008; tropics and subtropics of Africa, South Asia, Southeast Lakkham et al., 2009). The morphological identification Asia, East Asia, Oceania and America, while others are is, however, sometimes confusable, due to the presence of mainly found in Southeast Asia and its surroundings. involucres with intermediate characters. And also, some The whole plants of wild Coix species have been used for inter-specific hybrids between Coix taxa were reported medicinal purpose, and the hard and shiny involucres (Christopher et al., 1997; Rao and Nirmala, 1994). Based have been used as beads for ornamentation (van den on such background, the taxonomical identification by Bergh and Lamsupasit, 1996; Bor, 1960; Hara et al., 2007; using morphological traits of involucres was reappraised Jain and Banerjee, 1974). Based on these interactions by testing five Coix taxa from Southeast and East Asia in between Coix species and people, herbarium specimen this study. The aims of this study are 1) to reappraise and artifacts made with its involucres have been col- the key characters suggested by Bor (1960), and 2) to lected and stored at herbariums and museums. find minimum numbers of the quantitative characters of From the standpoint of classical taxonomist, Bor (1960) described a standard key character of Coix, based on morphologies of involucres of female inflorescences involucres that are necessary for practical identification. Materials and Methods Dry plant samples of Coix species were collected Communicated by N. Kurauchi Received Feb. 23, 2015 Accepted Nov. 27, 2015 * Corresponding author aobana@agr.ryukoku.ac.jp from 46 sites of 9 countries and were temporarily stored as herbarium collection at Faculty of Agriculture, Ryukoku University. Scientific names to these samples were preliminary identified at the collection sites, by using Miyamoto et al.: Reappraising in Coix identification based on involucres Table 1. Sample numbers, taxonomic names identified using descriptions by Bor (1960), and collection locality. Sample Site name, country or area C. lacryma-jobi var. lacryma-jobi L. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Shan, Myanmar Shan, Myanmar Chiang Mai, Thailand Loei, Thailand Xiang Khoang, Laos Vientiane, Laos Hai Duong, Vietnam Lao Cai, Vietnam Central Surawesi, Indonesia West Java, Indonesia Cebu, Philippines South Cotabato, Philippines Taitung, Taiwan Miaoli, Taiwan Jeju, Korea Jeollanam-do, Korea Shizuoka, Japan Kagosima, Japan C. lacryma-jobi var. monilifer Watt 19 20 21 22 23 24 25 26 27 Shan, Myanmar Shan, Myanmar Chiang Rai, Thailand Chiang Rai, Thailand Luang Namtha, Laos Luang Namtha, Laos South Cotabato, Philippines South Cotabato, Philippines Jeollanam-do, Korea C. lacryma-jobi var. stenocarpa Stapf 28 29 30 31 32 33 34 35 36 37 Shan, Myanmar Shan, Myanmar Chiang Rai, Thailand Chiang Rai, Thailand Luang Namtha, Laos Luang Namtha, Laos Son La, Vietnam Hoa Binh, Vietnam South Cotabato, Philippines South Cotabato, Philippines 133 Tokyo). A page of a color sample book (GEK associe SE COLORs, GE Kikaku Center Inc. Tokyo) that was showing color charts similar to the sample color was put beside the sample. Eight parts; the radius from the center of the bottom face to the left side (a), the radius from the center of the bottom face to the right side (b), the radius from the center of the bottom face to the outer side (c), the radius from the center of the bottom face to the inner side (d), the largest diameter (e), the height (f), the height from the center of ‘e’ to the top (g), and the chord at the half length of ‘g’ (h) were measured (Fig. 1). Three parts, (e), (f) and (g), were measured using a calipers, and five parts were calculated using a free image analyzing software, ‘Image J (http://rsbweb://rsbweb.nih.gov/ij/)’ semi-automatically. The basic statistic analysis, the logistic regression analysis and the clustering were revealed using software (Mac Toukeikaiseki ver.1.5, Mac Tahenryokaiseki ver. 2.0, Esumi Co. Ltd., Tokyo) on a personal computer. Results and Discussion Digital images of typical involucres of C. lacrymajobi var. lacryma-jobi, C. lacryma-jobi var. monilifer, C. lacryma-jobi var. stenocarpa, C. puellarum and C.gigantea were shown in Fig. 2. Color numbers and values of redgreen-blue (RGB) according to the color sample book C. puellarum Balansa 38 39 40 41 42 43 Shan, Myanmar Shan, Myanmar Chiang Rai, Thailand Chiang Rai, Thailand Pong Sally, Laos Luang Namtha, Laos C. gigantea Koenig ex Roxb 44 Shan, Myanmar 45 Shan, Myanmar 46 Tak, Thailand Fig. 1. Positions of eight measured parts, (a) to (h), for statistical analysis on the side face (A) and the bottom face (B) of an involucre. following whole descriptions by Bor (1960). Table 1 indicates examined samples, their collecting sites, taxonomic names and sample sizes. Ten matured involucres were dried and selected at random from each sample. While in a case of No. 35 from Vietnam, five involucres were applied. Then, the shape and color of involucres were examined by the following methods. Digital images of a side face and a bottom surface of the involucres were taken on a section paper as a scale using a digital camera (TG-320, Olympus Corporation, Fig. 2. Digital images of the side face (bottom) and the bottom face (upper) of a typical involucre of C. lacryma-jobi var. lacryma-jobi (A), C. lacryma-jobi var. monilifer (B), C. lacryma-jobi var. stenocarpa (C), C. puellarum (D) and C. gigantea (E). A bar = 5 mm. S17 209 153 168 S18 214 189 194 S53 219 163 153 S54 235 204 204 S70 209 168 128 S71 S82 S83 219 51 82 184 26 51 153 5 20 S84 112 82 51 S88 209 163 112 S89 219 184 153 S90 S100 S101 S118 S170 S433 S434 S435 S436 S437 S438 S440 S441 S442 S443 S444 235 71 82 240 26 245 240 235 230 224 214 199 189 179 163 148 219 56 71 224 128 245 240 235 230 224 214 199 189 179 163 148 204 5 46 204 5 245 240 235 230 224 214 199 189 179 163 148 E40 133 102 102 E62 204 204 204 E63 153 153 153 o o E64 102 102 102 E65 E66 E72 E78 E107 E108 E118 E130 E132 E164 E172 E178 51 0 255 179 235 158 230 255 255 217 245 242 51 0 219 138 209 158 230 247 237 235 237 242 51 0 219 133 153 133 240 247 237 217 232 247 o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o Trop. Agr. Develop. 60 （2）2016 Color No. S8 S10 R-value 138 51 G-varue 5 5 Sample No. B-varue 26 10 1 2 3 4 5 o 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 o 43 44 45 46 134 Table 2. Color numbers and values of red-green-blue (RGB) according to the color sample book. Character ‘o’ indicates existence of the color on the involucre surface. The color name of each color number is as follows: branc-rouge (S8), sepia (S10), rose-gray (S17), ash-gray (S18), birchbark (S53), beige (S54), marron glace (S70), Japanese yellow-white (S71), susutake-iro or brown (S82), havane or brown (S83), rokoucha or Japanese dark yellow-brown (S84), yellow (S88), sand-gray (S89), ivory (S90), earth green (S100), dark green (S101), Japanese dark blue-green (S118), Japanese dark green (S170), snow white (S433), enpaku or white lead (S434), white lead (S435), Chalk (S436), grayish white (S437), grayish white (S438), Japanese light silver gray, S440), usukumonezu or Japanese light gray (S441), kmachinezu or Japanese light gray (S442), flint gray (S443), metal gray (S444), Japanese yellow-red-brown (E40), gray (E62), nezumi or Japanese dark gray (E63), nibi-iro or Japanese dark gray (E64), Japanese blue-black (E65), black (E66), Japanese light beige (E72), Japanese light brown (E78), mitsudasou or Japanese light yellow-brown (E107), rikyushiracha or Japanese light yellow-brown (E108), pearl white (E118), white (E130), pale beige (E132), nickel gray (E164), milky white (E172), off white (E178). o o o o o o 135 Miyamoto et al.: Reappraising in Coix identification based on involucres Table 3. Averages (AVE) and standard deviations (STD) of eight measured parts, (a) to (h), and sample size of No. 1-46 (See Fig. 1 for the sample No.). (a) Sample No. AVE 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 2.89 3.59 2.89 3.49 3.20 3.67 3.06 3.11 2.55 3.76 3.98 2.75 3.63 2.93 3.27 3.83 3.92 3.15 19 20 21 22 23 24 25 26 27 28 (b) STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.39 0.37 0.23 0.37 0.35 0.28 0.95 0.25 0.48 0.37 0.58 0.23 0.57 0.64 0.44 0.47 0.38 0.44 3.09 3.55 2.87 3.65 3.27 3.45 3.20 2.79 2.62 3.92 3.97 2.78 3.84 2.82 3.51 3.69 3.67 3.26 3.05 5.39 3.84 3.40 3.41 2.83 3.07 2.86 5.16 1.95 ± ± ± ± ± ± ± ± ± ± 0.28 0.53 0.36 0.41 0.34 0.37 0.48 0.30 0.45 0.46 29 30 31 32 33 34 35 36 37 1.54 1.61 1.23 1.74 1.33 1.63 2.05 1.79 1.38 ± ± ± ± ± ± ± ± ± 38 39 40 41 42 43 2.43 2.36 2.19 2.38 2.81 2.28 ± ± ± ± ± ± 44 45 46 4.27 ± 0.58 3.60 ± 0.69 3.39 ± 0.51 (c ) STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.51 0.36 0.33 0.52 0.47 0.35 0.77 0.32 0.68 0.46 0.41 0.24 0.34 0.51 0.42 0.33 0.41 0.52 3.03 3.08 2.91 3.27 3.04 3.18 2.98 2.75 2.61 3.79 3.92 2.74 3.65 2.72 3.10 3.78 3.88 2.94 3.07 5.45 4.17 3.06 3.21 3.05 2.89 3.01 4.90 1.91 ± ± ± ± ± ± ± ± ± ± 0.31 0.30 0.45 0.24 0.20 0.44 0.42 0.35 0.53 0.51 0.61 0.51 0.31 0.47 0.24 0.46 0.34 0.25 0.34 2.38 1.77 2.37 2.30 1.76 2.28 1.80 1.61 1.96 ± ± ± ± ± ± ± ± ± 0.31 0.28 0.32 0.25 0.31 0.29 2.44 2.62 2.27 2.77 3.25 2.61 ± ± ± ± ± ± (d) STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.62 0.36 0.33 0.47 0.39 0.32 0.43 0.27 0.43 0.54 0.61 0.38 0.56 0.47 0.32 0.65 0.62 0.31 2.26 2.71 2.03 3.11 2.25 2.65 2.48 2.12 2.33 2.88 2.69 1.98 2.93 2.38 2.73 2.58 2.66 2.47 2.98 5.21 3.91 2.92 3.07 2.64 3.01 2.97 5.05 2.00 ± ± ± ± ± ± ± ± ± ± 0.29 0.61 0.34 0.24 0.29 0.38 0.45 0.37 0.41 0.21 0.49 0.42 0.34 0.42 0.37 0.45 0.36 0.42 0.39 1.80 1.64 2.44 2.06 1.47 1.91 1.63 1.76 1.68 ± ± ± ± ± ± ± ± ± 0.24 0.33 0.23 0.22 0.24 0.30 2.40 2.42 2.18 2.40 2.72 2.43 ± ± ± ± ± ± 3.72 ± 0.38 4.04 ± 0.58 3.73 ± 0.38 (e) STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.33 0.67 0.35 0.47 0.43 0.30 0.49 0.30 0.36 0.46 0.50 0.33 0.45 0.55 0.32 0.49 0.52 0.34 6.37 7.46 5.58 7.39 6.62 7.14 6.68 6.14 5.63 7.86 8.23 5.89 7.55 6.11 7.15 7.83 8.07 6.53 2.24 4.60 2.93 2.57 2.59 2.43 2.31 2.20 3.70 1.58 ± ± ± ± ± ± ± ± ± ± 0.25 0.60 0.31 0.35 0.21 0.25 0.50 0.35 0.45 0.23 0.38 0.30 2.60 0.39 0.25 0.27 0.13 0.36 0.14 1.64 1.33 1.47 1.68 1.33 1.46 1.64 1.36 1.34 ± ± ± ± ± ± ± ± ± 0.32 0.33 0.44 0.24 0.38 0.17 1.84 1.99 1.61 2.03 2.34 1.79 ± ± ± ± ± ± 3.90 ± 0.29 3.65 ± 0.50 3.01 ± 0.29 (f) STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.73 0.49 0.17 0.28 0.26 0.45 0.38 0.30 0.27 0.63 0.42 0.28 0.53 0.64 0.28 0.42 0.53 0.29 7.65 7.34 6.04 9.43 8.35 7.32 9.23 7.88 7.48 9.50 10.99 6.70 9.57 8.60 10.30 9.26 9.97 8.71 6.18 10.46 8.02 6.59 6.74 5.65 5.78 5.54 9.99 4.34 ± ± ± ± ± ± ± ± ± ± 0.38 0.60 0.46 0.45 0.31 0.38 0.54 0.27 0.49 0.30 0.33 0.25 0.31 0.28 0.19 0.32 0.22 0.14 0.21 4.01 3.90 3.72 4.31 3.16 4.18 4.16 3.56 3.59 ± ± ± ± ± ± ± ± ± 0.34 0.32 0.30 0.20 0.12 0.27 4.79 4.77 4.36 4.94 6.01 4.84 ± ± ± ± ± ± 3.50 ± 0.27 3.30 ± 0.60 2.60 ± 0.47 (g) STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.62 0.31 0.19 0.64 0.32 0.40 0.35 0.37 0.55 0.82 0.50 0.24 0.53 0.46 0.40 0.66 0.75 0.58 5.13 4.44 3.78 6.04 5.28 4.44 5.93 4.92 4.37 6.07 6.89 4.01 5.56 5.58 6.52 5.66 6.42 5.61 4.99 7.94 6.94 4.88 4.79 5.68 5.17 4.96 10.26 11.89 ± ± ± ± ± ± ± ± ± ± 0.26 0.70 0.33 0.26 0.29 0.49 0.49 0.45 0.97 0.71 0.34 0.40 0.26 0.28 0.26 0.15 0.36 0.45 0.40 12.13 9.50 11.11 9.83 10.76 11.65 11.68 7.89 8.57 ± ± ± ± ± ± ± ± ± 0.39 0.41 0.37 0.21 0.44 0.25 3.82 3.38 3.52 3.34 6.11 4.45 ± ± ± ± ± ± 8.33 ± 0.35 7.67 ± 0.98 7.36 ± 0.35 (h) STD Sample size ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.52 0.47 0.27 0.33 0.19 0.43 0.53 0.29 0.31 0.41 0.64 0.30 0.57 0.49 0.29 0.33 0.71 0.36 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 5.21 9.08 6.76 5.79 5.78 4.62 5.05 4.74 8.67 3.09 ± ± ± ± ± ± ± ± ± ± 0.38 0.67 0.49 0.44 0.31 0.31 0.59 0.30 0.53 0.26 10 10 10 10 10 10 10 10 10 10 1.15 0.37 0.64 0.52 0.55 0.43 0.69 0.60 0.69 2.84 2.76 2.64 3.38 2.49 3.29 3.29 2.75 2.75 ± ± ± ± ± ± ± ± ± 0.32 0.27 0.22 0.21 0.21 0.22 0.23 0.28 0.23 10 10 10 10 10 10 5 10 10 0.29 0.31 0.20 0.21 0.25 0.30 4.16 4.10 3.69 4.23 5.27 4.07 ± ± ± ± ± ± 0.27 0.35 0.38 0.31 0.23 0.13 10 10 10 10 10 10 6.88 ± 0.53 6.06 ± 0.77 5.32 ± 0.29 10 10 10 STD AVE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 0.56 0.37 0.26 0.44 0.45 0.30 0.58 0.25 0.37 0.64 0.46 0.34 0.46 0.48 0.46 0.64 0.65 0.55 5.05 5.84 4.43 5.79 5.28 5.48 5.22 4.64 4.19 5.91 5.78 4.77 6.13 4.57 5.30 6.03 6.13 4.58 3.05 5.00 4.16 2.81 2.74 3.41 2.90 2.96 6.32 7.52 ± ± ± ± ± ± ± ± ± ± 0.43 0.55 0.37 0.32 0.22 0.32 0.32 0.28 0.82 0.31 0.92 0.42 0.53 0.53 0.68 0.27 0.79 0.83 0.74 8.27 5.08 6.84 6.17 7.40 7.80 7.58 4.83 5.59 ± ± ± ± ± ± ± ± ± 0.36 0.31 0.30 0.25 0.34 0.42 2.05 1.80 2.00 1.80 3.58 2.58 ± ± ± ± ± ± 11.22 ± 0.95 12.26 ± 2.07 9.34 ± 0.46 7.77 ± 0.82 8.17 ± 1.64 5.90 ± 0.46 are shown in Table 2. Character ‘o’ indicates existence parts and/or ratios of two parts that showed over 90% of of the color on the involucre surface. The surface of homing rate by logistic regression analysis were shown involucres of these five taxa showed 24, 12, 15, 14 and 6 in Table 4. The most valuable combination to identify colors, respectively. There were some samples with very taxa was a set of (c)/(d), (e), (f), and (g)/(h), because small stripes or spots of two or three kinds of colors. Ac- it showed the highest homing rate (97.8%). Thus, the cordingly, taxonomical identification was impossible by noteworthy morphological characters might be the using color variations because the surface of involucres ratio of inner and outer radiuses at the bottom face, the showed various colors within and among each taxon. diameter, the height, and the side shape of upper part of Eight parts ((a)—(h)) of 455 involucres of 46 involucres. A clustering dendrogram of 46 groups using samples (No. 1–46) were measured, and averages (c)/(d), (e), (f), and (g)/(h) as factors was shown in Fig. (AVE) and standard deviations (STD) of each sample 3. The cluster dendrogram constructed using Euclidean were calculated. Size of each sample was ten, except No. distances and the Wald methods. Six large clusters were 35 (Table 3). The identification effects were verified by recognized, as follows. Clusters I, II, III, and IV included logistic regression analysis. Combinations of measured C. puellarum, C. lacryma-jobi var. monilifer, C. lacryma- 136 Trop. Agr. Develop. 60 （2）2016 Table 4. Combinations of measured parts and/or ratios of two parts that showed over 90% of homing rate by logistic regression analysis. Combination of factors Homing rate (c)/(d), (e), (f), (g)/(h) 97.80% (e), (f), (g)/(h) (e), (f), (g), (h), 95.70% (a)/(b), (c)/(d), (e)/(f), (g)/(h) 93.50% 95.70% Table 5. The six clusters（I-VI）and their ranges (minimummaximum) of factors: (c)/(d), (e), (f), and (g)/(h). Ranges: minimum - maximum Cluster name (c)/(d) (e) mm (f) mm (g)/(h) I II III IV V VI 1.18-1.35 1.09-1.35 1.00-1.66 1.10-1.16 1.05-1.47 1.12-1.43 4.36-4.94 5.54-5.78 3.16-4.34 7.36-8.33 7.15-8.23 5.58-7.46 3.34-4.45 4.79-5.78 7.89-12.13 9.34-12.26 9.26-10.99 6.04-9.23 0.43-0.63 0.47-0.74 1.76-2.97 1.11-1.35 0.91-1.23 0.76-1.22 Fig. 3. A clustering dendrogram of 46 groups using (c)/(d), (e), (f), and (g)/(h). Numbers, 1-46 on the foot of the dendrogram are the sample numbers in Table 1. Six large clusters (I-VI) were recognized. Numbers at the bottom indicate sample numbers in Table 1. jobi var. stenocarpa, and C. gigantea, respectively. Clus- Acknowledgements ters V and VI consisted of mainly C. lacryma-jobi var. lacryma-jobi. Two or one C. lacryma-jobi var. monilifer We thank Queen Sirikit Botanic Garden, Thailand; were, however, included in the clusters V or VI, respec- Hasanuddin University, Indonesia; Central Agriculture tively. The widely distributed species, C. lacryma-jobi Research Institute, Myanmar; and Kangwong Univer- var. lacryma-jobi, might vary in its involucre morphology sity, Korea, for their kind assistance in the field survey. because it was divided into two large clusters V and VI. This research was financially supported by the JSPS KA- In case of Coix lacryma-jobi var. monilifer, it was hard to KENHI Grant Numbers 10041071, 13371007, 13575024 be identified using only involucre in a few cases because and 15710184. it might show the similar characters of var. lacryma-jobi. The ranges of (c)/(d), (e), (f) and (g)/(h) of major samples of each clusters were shown in Table 5. In conclusion, we suggested four indicators that are used effectively to identify Coix lacryma-jobi var. lacryma-jobi, var. stenocarpa, C. puellarum and C. gigantea. They are 1) the ratio of the radius from the center of the bottom face to the outer side (c) and the radius from the center of the bottom face to the inner side (d), 2) the largest diameter (e), 3) the height (f), and 4) the ratio of the height from the center of the largest diameter (g) to the top and the chord at the half length of ‘g’ (h). The ranges of these indicators shown in Table 5 may useful to rewrite a key to identification of these taxa. For example, quantitative characters can be added to the orthodox Bor’s key (1960). On the other hand, in case of Coix lacryma-jobi var. monilifer, it was slightly difficult to be identified by using these quantitative characters of involucres. Taxonomical identification methods using quantitative data of gross morphologies would be reappraised using the final idea of systematics of the genus. References Arora, R. K. 1977. Economic Botany 31: 358-366. Bor, N. L. 1960. Coix Linn. In: The Grasses of Burma, Ceylon, India and Pakistan (Excluding Bambuseae) Pergamon Press (Oxford) pp. 263-265. Christopher, J. L., S. Mini, and N. Omanakumari 1997. Caryologia 50(2): 175-184. Jain, S. K. and D. K. Banerjee 1974. Economic Botany 28: 38-42. van den Bergh, M. H. and N. Lamsupasit 1996. Coix lacryma-jobi L. In: Plant Resources of South-East Asia. No. 10. Cereals. (Grubben, G. L. H. and S. Rartoharadjono eds.) Backhuys Publishers (Leiden) pp. 84-87. Hara, T., T. Tetsuka, and K. Matui 2007. Jpn. J. Crop Sci. 76(3): 459-463. Lakkham, K., P. Wangsommuk, and C. Aromdee 2009. J. of Sci. and Tech. 31(4): 425-431. Liang, Y. T., C. B. Chen, Z. J. Xu, J. Huang, H. Z. Zeng, Q. Z. Lai, S. C. Liang, Y. X. Luo, and R. J. Huang 2008. Guangxi Agricultural Sciences 39: 413-418. Ma, K. H., K. H. Kim, A. Dixit, J. W. Yu, J. W. Chung, J. H. Lee, E. G. Cho, T. S. Kim and Y. J. Park 2006. Molecular Ecology Notes 6: 689-691. Ohta, Y., N. Suzuki, T. Ohta, K. Beppu, S. Ohno, K. Koike, and M. Inoue 2007. Clinic All-round 54(12): 3199-3201. (in Japanese) Ochiai, Y. 2002. Asian and African Area Studies 2: 24-43. (in Japanese with English summary) Ochiai, Y. 2007. Southeast Asian Studies 45(3): 382-403. (in Japanese with English summery) Qin, F., J. Li, X. Li, and H. Corke 2005. Genetic Resources and Crop Evolution 52: 209-214. Rao, P. N. and A. Nirmala 1994. Cytologia 59: 59-63. Su, T. C., M. S. Yeh, and S. H. Tseng 2008. Crop, Environment & Bioinformatics 5: 187-195.