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Article info.
1.  Introduction
2.  Materials and methods
3.  Results
4.  Discussion
5. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE B 2014 Vol.15 No.11 P.986-996


Positional variation in grain mineral nutrients within a rice panicle and its relation to phytic acid concentration*

Author(s):  Da Su, Faisal Sultan, Ning-chun Zhao, Bing-ting Lei, Fu-biao Wang, Gang Pan, Fang-min Cheng

Affiliation(s):  . College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China

Corresponding email(s):   chengfm@zju.edu.cn

Key Words:  Grain position, Minerals, Phytic acid, Rice, Oryza sativa L.

Da Su, Faisal Sultan, Ning-chun Zhao, Bing-ting Lei, Fu-biao Wang, Gang Pan, Fang-min Cheng. Positional variation in grain mineral nutrients within a rice panicle and its relation to phytic acid concentration[J]. Journal of Zhejiang University Science B, 2014, 15(11): 986-996.

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author="Da Su, Faisal Sultan, Ning-chun Zhao, Bing-ting Lei, Fu-biao Wang, Gang Pan, Fang-min Cheng",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Positional variation in grain mineral nutrients within a rice panicle and its relation to phytic acid concentration
%A Da Su
%A Faisal Sultan
%A Ning-chun Zhao
%A Bing-ting Lei
%A Fu-biao Wang
%A Gang Pan
%A Fang-min Cheng
%J Journal of Zhejiang University SCIENCE B
%V 15
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%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1400157

T1 - Positional variation in grain mineral nutrients within a rice panicle and its relation to phytic acid concentration
A1 - Da Su
A1 - Faisal Sultan
A1 - Ning-chun Zhao
A1 - Bing-ting Lei
A1 - Fu-biao Wang
A1 - Gang Pan
A1 - Fang-min Cheng
J0 - Journal of Zhejiang University Science B
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SP - 986
EP - 996
%@ 1673-1581
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B1400157

Six japonica rice genotypes, differing in panicle type, grain density, and phytic acid (PA) content, were applied to investigate the effect of grain position on the concentrations of major mineral nutrients and its relation to PA content and grain weight within a panicle. grain position significantly affected the concentrations of the studied minerals in both the vertical and horizontal axes of a rice panicle. Heavy-weight grains, located on primary rachis and top rachis, generally had higher mineral concentrations, but were lower in PA concentration and molar ratios of PA/Zn, compared with the small-weight grains located on secondary rachis and bottom rachis, regardless of rice genotypes. However, on the basis of six rice genotypes, no significant correlations were found among mineral elements, PA, and grain weight. These results suggested that some desired minerals, like Zn and Fe, and their bioavailability, can be enhanced simultaneously by the modification of panicle patterns, and it will be helpful in the selection of rice genotypes with low PA and high mineral nutrients for further breeding strategy without sacrificing their high yields.


阐明水稻穗内不同粒位间的主要矿质营养元素和植酸含量差异、粒位分布特点及其与品种穗型间的联系。 将水稻品种的穗型变化与稻米营养品质结合起来,从水稻穗粒结构角度,对同一稻穗内不同籽粒间的主要矿质营养元素与植酸含量差异、粒位分布特点及其与水稻品种穗型间的相互关系进行了较系统的探讨分析。 以典型的直立穗型和弯穗型粳稻品种为材料,通过对两类水稻品种在相同栽培条件下籽粒矿质营养元素和植酸含量的测定分析,并依据水稻籽粒在稻穗上的着生部位,将同一稻穗内的不同籽粒划分为六个粒位,比较分析了两类品种同一稻穗内不同部位间矿质营养元素和植酸含量的差异变化及其粒位分布特点。 水稻穗型虽然与品种间的籽粒矿质营养元素和植酸含量高低没有直接关系,但对其穗内不同籽粒间的主要矿质营养元素和植酸含量存在着较大影响;与稻穗中下部的弱势粒相比,同一稻穗内着生在稻穗上中部的强势粒通常具有相对较高的锌、铁矿质元素含量,而籽粒植酸含量和植酸/锌(铁)摩尔比则有所降低,稻米营养品质也相对较好;不同矿质营养元素相比,粒位效应对铁矿质营养的影响作用要略大于对钙和锌营养元素含量。

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Article Content

1.  Introduction

 Micronutrient malnutrition, often called ‘Hidden Hunger’, now affects more than two billion people worldwide (McLaughlin et al., 1999). Populations in developing countries are particularly at risk as they live largely on plant-based diets without sufficient micronutrients gained from meat, fruits, or vegetables (Frank et al., 2009). The case is particularly severe for women, infants, and children (Umeta et al., 2005). It is therefore imperative to improve the nutrient composition of plant food so as to alleviate the widespread micronutrient malnutrition (Ferguson et al., 1988).

 Rice is one of the major cereal crops, consumed by nearly two billion people worldwide. As a primary dietary source of energy, especially for the low-income and rural population, rice plays an important role in providing plant proteins, carbohydrates, and other nutrients (Ferguson et al., 1988). However, rice grain is relatively low in some micronutrients such as iron (Fe), zinc (Zn), and calcium (Ca), compared with other staple crops such as wheat, maize, and legumes (Adeyeye et al., 2000).

 In the past few decades, great efforts have been made to elucidate the effects of genotype and environment on mineral uptake and accumulation in rice plants (Schachtman and Barker, 1999). It has been demonstrated that mineral concentrations in rice grain vary greatly according to genotype, geographical location, and other environmental factors (Anandan et al., 2011). Although the extent of difference is cultivar-dependent, the former studies showed that grain position within a panicle had a considerable impact on grain chemical composition, like protein or microelements (Calderini and Ortiz-Monasterio, 2003; Liu et al., 2005a), suggesting that the improvement of spikelet architecture and morphological pattern is possible for obtaining the cultivars with high yield and balanced nutrition (Calderini and Ortiz-Monasterio, 2003).

 Large variation among rice grains in a panicle is unfavorable for commercial value and quality (Jeng et al., 2006). Some research has been performed on the effect of grain position within a panicle on rice quality, so as to reduce the variation through improved agronomic practices, including application of plant growth regulators, and better water and fertilizer management (Jongkaewwattana and Geng, 1991). It was reported that there were large variations in grain quality (chalky occurrence, palatability, amylose, protein, and amylographic characteristics) between primary and secondary rachides (Matsue et al., 1995) and among the different positions within a rice panicle (Kobayasi et al., 2002). However, to date, little research has been done on the positional variation of micronutrient concentration in relation to rice panicle morphology.

 Phytic acid (PA) in crops, including cereals and legumes, is an anti-nutritional factor for animals and human, as it can strongly chelate mineral nutrients such as Zn, Fe, Ca, and magnesium (Mg) (Raboy, 2001), leading to a marked reduction in their bioavailability (Raboy et al., 1984). Moreover, animals possibly excrete more phytates if they are given high PA feed, which poses a threat of contaminating water. Therefore, it was important to reduce PA concentration in rice grain for the improvement of crops’ nutritional quality (Raboy, 2001).

 In this experiment, six japonica rice genotypes, differing in panicle type, grain density, and PA content, were used with the following objectives: (1) to investigate the effect of grain position on mineral elements (potassium (K), Mg, sodium (Na), Ca, Zn, Fe, manganese (Mn), and copper (Cu)), as well as PA concentration; (2) to identify the positional distribution of mineral elements in grains within a panicle as affected by the panicle morphology of rice genotypes; and, (3) to illustrate the relationships among mineral elements, PA content, and grain weight at different positions within a panicle.

2.  Materials and methods

2.1.  Plant materials and field experiments

 According to previous surveys on grain PA and panicle type of 56 rice cultivars and 32 breeding lines, collected from different locations of China (Liu et al., 2005a; Cheng et al., 2007), six japonica rice genotypes (98110, HIPj, Xiushui63, Xiushui994, Chunjiang15, and Xiushui11) were selected for the current study. All genotypes were seeded in late May and transplanted in late June at the experimental farm of the Huajiachi Campus of Zhejiang University, China. Each genotype was grown in two replicates of an eight-row plot, 2 m long and 20 cm wide between rows with nine hills in each row and three seedlings per hill. The trial was managed according to locally recommended agronomic practices.

2.2.  Grain sampling and analysis

 Grains from six positions within a panicle, i.e., top primary, top secondary, middle primary, middle secondary, bottom primary, and bottom secondary rachides, were sampled for analysis. The classification of these positions for each genotype is shown in Table 1. Panicle traits, including panicle length and bending degree of panicle (an angle from the tip of panicle to the extending top of stem), were determined at maturity with 20 individual measurements of main stems in each plot (Table 1).

Table 1

Classification of grain position within a panicle and panicle morphological traits for six rice cultivars
Cultivar Total number of primary branches Classification of rachis within a panicle
Panicle morphological character
Upper rachis Middle rachis Lower rachis PL (cm) GPP GD (grain/cm) BD (°) Yield (kg/hm2)
98110 14 4 5 5 16.2 151.2 9.3 9.0 8427.5
HIPj 14 4 5 5 16.0 148.7 9.3 9.2 8132.7
Xiushui63 12 3 5 4 15.8 126.5 8.0 9.7 7737.1
Xiushui994 14 4 5 5 15.4 132. 6 8.6 7.8 7945.3
Chunjiang15 12 4 4 4 21.5 103.7 4.8 76.2 6863.8
Xiushui11 12 4 4 4 19.2 110.0 5.7 86.1 7142.0

  • PL: panicle length; GPP: grains per panicle; GD: grain density; BD: bending degree of panicle

  •  At maturity, 30 panicles were harvested from each replication. After being dried naturally, rice grains were collected separately from six positions, then husked, milled, and passed through a 0.5-mm sieve. The flour was then stored in a desiccator until analysis.

     The concentrations of K, Na, Mg, Ca, Zn, Fe, Mn, and Cu were determined by an atomic absorption spectrophotometer (Perkin-Elmer Model AA6300) after wet digestion with nitric acid. PA was determined as described by Miller et al. (1980) with modification (Liu et al., 2005a). Triplicate measurements were performed for each sample.

    2.3.  Data analysis

     Statistical analysis was performed using statistics software version SPSS 11.0. The data were submitted for variance analysis and the means were tested by least significant difference. Correlation analysis was conducted to characterize the relationships among eight mineral elements, PA content, and grain weight for different grain positions and six genotypes, respectively.

    3.  Results

    3.1.  Positional variation in K, Mg, Na, Ca, Zn, Fe, Mn, and Cu concentrations within a panicle

     As shown in Table 2, grain position was an important source of variation for grain mineral concentrations. Of all eight minerals, there were significant differences among grains located on the different positions within a panicle. The grains on the top primary and middle primary rachides had substantially higher minerals concentrations than those on the bottom secondary and middle secondary rachides. The order of positional differences in mineral concentrations was: top primary rachides>middle primary rachides>bottom primary rachides/top secondary rachides>middle secondary rachides>bottom secondary rachides, although the extent of difference was genotype-dependent and varied largely with various mineral elements. This result clearly indicated that grain position affected the concentrations of all eight minerals in both vertical and horizontal axes of the rice panicle. In comparative terms, the effects of grain positions on Na, Fe, and Cu concentrations were more substantial than those on Ca and Zn concentrations, as reflected by their differences in coefficients of variation (CVs) and the variable range of different positions (Table 2).

    Table 2

    Positional variations in the concentrations of K, Mg, Na, Ca, Zn, Fe, Mn, and Cu in six different rice cultivars
    Cultivar Grain position Concentration (μg/g)
    K Mg Na Ca Zn Fe Mn Cu
    98110 TPR 1627a 1518a 260.6a 263.7a 25.28a 10.27a 24.82bc 2.780a
    TSR 1568b 1453ab 219.8ab 256.5b 24.58b 9.57c 24.20c 2.471b
    MPR 1468c 1398b 203.3bc 253.9bc 24.55b 9.89b 26.08a 2.425b
    MSR 1396d 1315c 191.7bcd 250.7bc 24.07c 9.31d 26.03a 2.273b
    BPR 1440cd 1378bc 170.4cd 247.8cd 23.48d 8.96e 25.19b 1.866c
    BSR 1314e 1324c 156.3d 242.3d 22.78e 8.59f 24.63c 1.671c
    Mean 1469 1398 200.4 252.5 24.12 9.43 25.16 2.248
    CV 7.76% 5.56% 18.57% 2.93% 3.69% 6.49% 3.04% 18.28%
    HIPj TPR 1862a 1704a 265.6a 282.1a 26.76a 11.93a 31.62a 2.815a
    TSR 1732b 1585b 200.5cde 269.4b 25.97b 10.90c 28.78b 2.567bc
    MPR 1583c 1440c 241.7ab 267.7b 25.83b 11.33b 27.52c 2.688ab
    MSR 1285e 1321d 215.1bc 263.8c 25.29c 10.92c 25.55c 2.459cd
    BPR 1536d 1389c 192.6de 257.6d 24.75d 10.42d 26.81c 2.302d
    BSR 1328e 1329d 184.1e 253.1e 24.38d 9.88e 25.07d 2.022e
    Mean 1554 1461 216.6 265.6 25.50 10.90 27.56 2.476
    CV 14.43% 10.47% 14.49% 3.83% 3.41% 6.53% 8.71% 11.49%
    Xiushui63 TPR 1836a 1757a 312.2a 290.1a 22.87a 9.68a 46.83a 2.505a
    TSR 1790a 1671ab 267.7b 283.2b 22.50a 9.42bc 43.27c 2.167b
    MPR 1802a 1655b 275.8b 281.8b 21.99b 9.33cd 45.03b 2.225b
    MSR 1570b 1415c 249.9bc 276.9c 21.28c 9.16d 38.87de 2.168b
    BPR 1737a 1715ab 209.7c 270.3d 20.88cd 8.70e 42.87c 2.010b
    BSR 1498b 1429c 191.9c 262.4e 20.44d 8.40f 38.73e 1.643c
    Mean 1706 1607 251.2 277.5 21.66 9.11 42.60 2.120
    CV 8.12% 9.19% 17.66% 3.57% 4.39% 5.25% 7.66% 13.40%
    Xiushui994 TPR 1655a 1359a 312.2a 304.2a 20.84a 10.81a 36.88a 3.270a
    TSR 1587ab 1297ab 239.1b 296.1b 20.46ab 10.18b 32.36c 2.908b
    MPR 1492b 1305ab 218.4b 292.3c 19.89ab 10.23b 33.81b 2.868b
    MSR 1571ab 1245b 202.2bc 287.4d 19.75ab 9.76c 32.52c 2.673bc
    BPR 1514b 1291ab 167.6cd 282.7e 19.21b 9.17d 32.98c 2.442cd
    BSR 1465b 1225b 150.6d 276.1f 18.79b 8.96e 30.29d 2.198d
    Mean 1547 1287 215.0 289.8 19.82 9.85 33.14 2.727
    CV 4.54% 3.68% 26.80% 3.44% 3.84% 7.09% 6.55% 13.84%
    Chunjiang15 TPR 1571a 1456a 228.9a 254.0a 24.81a 9.72a 34.02a 3.208a
    TSR 1308bc 1339b 179.9bc 247.8b 22.40b 9.26b 32.30b 2.975b
    MPR 1395b 1310bc 202.0abc 247.0b 21.55c 9.14b 31.93b 2.832b
    MSR 1336bc 1250c 218.7ab 240.2cd 22.40b 8.81c 31.22bc 2.870b
    BPR 1278c 1274bc 187.0abc 236.9d 21.26c 8.40d 30.84c 2.603c
    BSR 1285c 1375b 174.9c 231.6e 20.45d 8.18e 30.18c 2.525c
    Mean 1362 1334 198.7 242.9 22.15 8.92 31.75 2.836
    CV 8.13% 5.59% 10.99% 3.37% 6.77% 6.41% 4.24% 8.78%
    Xiushui11 TPR 1527a 1458a 273.8a 229.6a 24.48a 11.71a 30.58a 2.868a
    TSR 1347bc 1343a 228.2b 223.9b 23.74a 11.42b 26.95b 2.672b
    MPR 1421ab 1333a 228.9b 218.8cd 24.04a 11.20c 25.15bc 2.568c
    MSR 1345bc 1365a 216.1bc 214.6d 23.49a 10.86d 24.30c 2.458d
    BPR 1276c 1368a 206.0bc 209.5e 22.33b 10.34e 24.06c 2.283e
    BSR 1098d 864b 191.5c 203.2f 21.92b 10.08f 22.65d 2.132f
    Mean 1336 1289 224.1 216.6 23.33 10.94 25.62 2.497
    CV 10.81% 16.50% 12.57% 4.43% 4.29% 5.78% 10.99% 10.65%

  • TPR: top primary rachis; TSR: top secondary rachis; MPR: middle primary rachis; MSR: middle secondary rachis; BPR: bottom primary rachis; BSR: bottom secondary rachis; CV: coefficient of variation. The values followed by the same letters within a column are not significantly different in term of the same cultivar (P>0.05)

  •  The ratio of the eight mineral concentrations was relatively stable or showed only slight variations among different grain positions in a rice cultivar (Table 2). However, relatively great variability in the ratio of various minerals was found among cultivars, although the concentrations of Zn, Fe, Mn, and Cu were found to be remarkably lower than those of K, Mg, Na, and Ca in rice grains, with the lowest values for Cu and Fe, and the highest for K and Mg. For instance, 98110 had much higher Ca and Mg concentrations than Xiushui11, whereas the concentrations of Fe, Mn, and Cu for 98110 were lower than those for Xiushui11, in terms of an average of six different positions. This result suggested that grain positions had a relatively small impact on the ratio of mineral concentrations compared with the rice genotypes, despite the different sensitivities to the effect of grain positions among different mineral elements.

     Considering the yield potential of rice genotypes (Table 1) and grain mineral concentrations (Table 2), little relationship was found between the eight mineral concentrations and rice genotypes, differing in grain density in a panicle and grain yield, in terms of average different positions. In contrast, the extent of positional variation in some mineral elements (K, Na, Fe, and Cu), particularly the variable range between top primary rachides and bottom secondary rachides, even tended to be more pronounced than that of the six rice genotype variations (Table 2). This implied that the concentrations of mineral elements in rice grains were not inherently related to the level of grain yield for different rice genotypes.

    3.2.  Positional variation in PA content, grain weight, molar ratios of PA/Zn and PA/Fe within a panicle

     From Table 3, the marked differences among different positions were found in PA concentration, grain weight, and molar ratios of PA/Zn and PA/Fe. For grain weight, the grains located on the primary rachis (PR) and top rachis (TR) were significantly heavier than those on the secondary rachis (SR) and bottom rachis (BR), irrespective of rice genotype. On the other hand, the grains on PR and TR had somewhat lower PA concentrations relative to those on SR and BR. This result suggested that the grains with heavier weights were generally lower in PA concentrations in a comparison of different positions within a panicle, which was opposite to the effect of grain positions on mineral concentrations. However, the extent of positional variation in PA concentrations was considerably smaller compared with that in grain weight and most of minerals, in terms of their small CVs and narrow-range in PA among different grain positions for all six rice genotypes presented here (Tables 2 and 3).

    Table 3

    Positional variations in phytic acid (PA) concentration, grain weight, and the molar ratios of PA/Zn and PA/Fe in six different rice cultivars
    Cultivar Grain position PA concentration (mg/g)
    Grain weight (mg)
    PA/Zn molar ratio
    PA/Fe molar ratio
    98110 TR 4.68b 4.55b 25.47a 20.12c 18.35c 18.34c 45.17e 47.09d
    MR 4.89a 4.49b 24.35a 20.63c 19.74b 18.50c 49.00c 47.81cd
    BR 5.07a 5.25a 22.16b 17.06d 21.39a 22.85a 56.02b 60.58a
    Mean 1 4.88 4.76 23.99 19.27 19.83 19.89 50.06 51.83
    Mean 2 4.82 21.63 19.86 50.95
    CV 6.24% 14.10% 9.48% 11.80%
    HIPj TR 4.54a 4.43ab 24.12a 19.05d 16.82a 16.90a 37.73c 40.26b
    MR 4.18c 4.32ab 23.16b 19.01d 16.03b 16.91a 36.54cd 39.17b
    BR 3.68d 4.23c 20.50c 18.09e 14.73c 17.17a 35.00d 42.39a
    Mean 1 4.13 4.32 22.59 18.72 15.86 16.99 36.42 40.61
    Mean 2 4.23 20.65 16.43 38.52
    CV 7.10% 11.89% 5.57% 6.90%
    Xiushui63 TR 5.64bc 5.60c 26.54a 23.04c 24.66d 25.24d 58.26c 60.30c
    MR 6.13a 6.06a 25.68ab 22.40c 27.88c 28.48b 65.70b 66.16b
    BR 5.97ab 5.98ab 25.26b 22.45c 28.59b 29.26a 68.62b 71.19a
    Mean 1 5.91 5.88 25.83 22.63 27.04 27.66 64.19 65.88
    Mean 2 5.90 24.23 27.35 65.04
    CV 3.77% 7.48% 7.01% 7.56%
    Xiushui994 TR 4.58ab 4.46b 25.71a 23.18ab 21.98c 21.80c 42.37d 43.81c
    MR 4.28c 4.36c 24.14a 18.62c 21.52c 22.07c 41.84d 44.67c
    BR 4.43bc 4.64a 20.29bc 17.57c 23.06b 24.69a 48.31b 51.78a
    Mean 1 4.43 4.49 23.28 19.79 22.19 22.85 44.17 46.75
    Mean 2 4.46 21.58 22.52 45.46
    CV 3.01% 15.04% 5.27% 8.47%
    Chunjiang15 TR 4.87bc 4.90b 27.69a 21.41d 19.63c 21.88b 50.10c 52.92bc
    MR 4.98ab 5.09a 26.51ab 25.07bc 23.11a 22.72b 54.49b 57.78a
    BR 4.85bc 4.75c 24.68c 21.03d 22.81b 23.23a 57.74a 58.07a
    Mean 1 4.90 4.91 26.29 22.50 21.85 22.61 54.11 56.26
    Mean 2 4.91 24.40 22.23 55.18
    CV 2.38% 11.01% 6.12% 5.90%
    Xiushui11 TR 4.23b 4.31a 29.00a 28.61ab 17.28c 18.15b 36.12c 37.74c
    MR 4.49a 4.25b 28.48ab 24.26c 18.68b 18.09b 40.09b 39.13b
    BR 4.51a 4.38a 27.49b 19.73d 20.21a 19.98a 43.62a 43.45a
    Mean 1 4.41 4.31 28.32 24.20 18.72 18.74 39.94 40.11
    Mean 2 4.36 26.26 18.73 40.03
    CV 2.74% 13.84% 6.14% 7.57%

  • PR: primary rachis; SR: secondary rachis; TR: top rachis; MR: middle rachis; BR: bottom rachis. The crossing column between TR and PR means TPR (top primary rachis), and the corresponding expressions were for TSR (top secondary rachis, TR-SR), MPR (middle primary rachis, MR-PR), MSR (middle secondary rachis, MR-SR), BPR (bottom primary rachis, BR-PR), and BSR (bottom secondary rachis, BR-SR), respectively. Mean 1 is the average of three different positions for PR or SR; Mean 2 is the average of all six different positions; CV is the coefficient of variation. The values followed by the same letters within a column are not significantly different in term of the same cultivar (P>0.05)

  •  The molar ratio of PA/Zn was commonly considered as an important parameter for the evaluation of Zn bioavailability in cereal and legumes (Raboy et al., 1984). As shown in Table 3, the average molar ratio of PA/Zn ranged from 18.73 (Xiushui11) to 27.35 (Xiushui63) among the six genotypes, and the extent of positional variation in the molar ratio of PA/Zn was much larger than that in PA concentrations among different grains within a panicle, as reflected by their CVs. In general, PR and TR tended to have lower molar ratio of PA/Zn relative to SR and BR, which was almost consistent with the tendency of positional differences in PA concentration. This result implied that grain position was closely related to Zn bioavailability in rice grains, with relatively high Zn bioavailability noted for PR and TR within a panicle.

     The average molar ratio of PA/Fe ranged from 38.52 to 65.04 among all six genotypes, wider variation compared with that of average molar ratio of PA/Zn (Table 3). Moreover, the extent of the variation in the molar ratio of PA/Fe among different grain positions was also somewhat greater than that in the molar ratio of PA/Zn, despite the similar trend for difference between PR and SR for both molar ratios of PA/Fe and PA/Zn.

    3.3.  Correlations among K, Mg, Na, Ca, Zn, Fe, Mn, Cu, PA contents and grain weight

     The Pearson correlation coefficients among eight minerals, PA and grain weight were calculated for six different grain positions in a panicle (Table 4). There were highly positive correlations (P<0.01) between Zn and Fe, and statistically positive correlations (P<0.05 or P<0.01) among Ca, Zn, Fe, and Cu, regardless of genotypes. However, the statistical significance varied greatly with rice genotype for most of their correlations among other mineral concentrations, and also among minerals, PA, and grain weight. Generally, there were positive correlations among eight minerals, and between minerals and grain weight, but negative correlations between minerals and PA content.

    Table 4

    Correlation coefficients of K, Mg, Na, Ca, Zn, Fe, Mn, Cu, phytic acid (PA), and grain weight (GW) among different positions within a panicle
    Cultivar Feature Correlation coefficient
    K Mg Na Ca Zn Fe Mn Cu PA GW
    98110 K 1.000
    Mg 0.960** 1.000
    Na 0.932** 0.889* 1.000
    Ca 0.953** 0.891* 0.991** 1.000
    Zn 0.904* 0.810 0.959** 0.981** 1.000
    Fe 0.856* 0.796 0.936** 0.955** 0.981** 1.000
    Mn −0.302 −0.430 −0.151 −0.092 0.064 0.143 1.000
    Cu 0.874* 0.777 0.962** 0.971** 0.993** 0.970** 0.054 1.000
    PA −0.577 −0.353 −0.662 −0.677 −0.730 −0.612 −0.117 −0.768 1.000
    GW 0.702 0.663 0.711 0.770 0.794 0.854* 0.361 0.733 −0.315 1.000
    HIPj K 1.000
    Mg 0.963** 1.000
    Na 0.601 0.650 1.000
    Ca 0.816* 0.891* 0.880* 1.000
    Zn 0.826* 0.886* 0.859* 0.990** 1.000
    Fe 0.709 0.751 0.951** 0.957** 0.953** 1.000
    Mn 0.961** 0.982** 0.744 0.923** 0.907* 0.825* 1.000
    Cu 0.743 0.753 0.883* 0.934** 0.956** 0.978** 0.812* 1.000
    PA 0.351 0.567 0.512 0.665 0.669 0.537 0.492 0.492 1.000
    GW 0.684 0.624 0.902* 0.744 0.729 0.839* 0.741 0.795 0.161 1.000
    Xiushui63 K 1.000
    Mg 0.938** 1.000
    Na 0.746 0.541 1.000
    Ca 0.807 0.614 0.985** 1.000
    Zn 0.821* 0.651 0.953** 0.972** 1.000
    Fe 0.753 0.533 0.984** 0.994** 0.963** 1.000
    Mn 0.956** 0.925** 0.763 0.782 0.800 0.718 1.000
    Cu 0.780 0.618 0.947** 0.963** 0.881* 0.947** 0.767 1.000
    PA −0.473 −0.531 −0.483 −0.533 −0.661 −0.503 −0.477 −0.433 1.000
    GW 0.789 0.836* 0.552 0.547 0.503 0.465 0.896* 0.634 −0.201 1.000
    Xiushui994 K 1.000
    Mg 0.669 1.000
    Na 0.880* 0.853* 1.000
    Ca 0.848* 0.875* 0.978** 1.000
    Zn 0.879* 0.807 0.963** 0.989** 1.000
    Fe 0.772 0.819* 0.961** 0.979** 0.962** 1.000
    Mn 0.734 0.924** 0.873* 0.852* 0.780 0.830* 1.000
    Cu 0.827* 0.865* 0.974** 0.995** 0.977** 0.990** 0.872* 1.000
    PA 0.142 −0.053 0.056 −0.110 −0.098 −0.179 −0.081 −0.164 1.000
    GW 0.587 0.945** 0.862* 0.909* 0.854* 0.898* 0.834* 0.905* −0.162 1.000
    Chunjiang15 K 1.000
    Mg 0.690 1.000
    Na 0.805 0.191 1.000
    Ca 0.802 0.460 0.607 1.000
    Zn 0.869* 0.542 0.792 0.850* 1.000
    Fe 0.820* 0.488 0.641 0.993** 0.881* 1.000
    Mn 0.888* 0.618 0.655 0.967** 0.933** 0.971** 1.000
    Cu 0.818* 0.480 0.715 0.944** 0.949** 0.972** 0.956** 1.000
    PA 0.111 −0.567 0.575 0.309 0.251 0.329 0.161 0.367 1.000
    GW 0.764 0.124 0.863* 0.592 0.615 0.567 0.602 0.540 0.439 1.000
    Xiushui11 K 1.000
    Mg 0.871* 1.000
    Na 0.921** 0.690 1.000
    Ca 0.916* 0.751 0.938** 1.000
    Zn 0.944** 0.733 0.887* 0.940** 1.000
    Fe 0.909* 0.714 0.911* 0.990** 0.970** 1.000
    Mn 0.842* 0.645 0.973** 0.938** 0.813 0.892* 1.000
    Cu 0.928** 0.744 0.950** 0.998** 0.955** 0.992** 0.938** 1.000
    PA −0.337 −0.189 −0.507 −0.503 −0.458 −0.500 −0.548 −0.522 1.000
    GW 0.838* 0.882* 0.709 0.804 0.734 0.771 0.700 0.782 0.041 1.000

  • *Correlation is significant at the 0.05 level (2-tailed)

  • **Correlation is significant at the 0.01 level (2-tailed)

     Table 5 presents the correlations among the eight minerals, PA and grain weight for six genotypes. The correlation coefficients among the minerals were statistically significant only for K-Ca (r=0.833), but no significant correlations (P>0.05) were observed among PA and minerals, except for PA-Mn (r=0.818). Furthermore, grain weight was poorly correlated with minerals and PA. This result implied that grain PA concentration was relatively independent of grain weight and most metal minerals for different rice genotypes.

    Table 5

    Correlation coefficients of K, Mg, Na, Ca, Zn, Fe, Mn, Cu, phytic acid (PA) and grain weight (GW) among six rice cultivars
    Feature Correlation coefficient
    K Mg Na Ca Zn Fe Mn Cu PA GW
    K 1.000
    Mg 0.801 1.000
    Na 0.670 0.657 1.000
    Ca 0.833* 0.403 0.270 1.000
    Zn −0.195 0.200 −0.210 −0.444 1.000
    Fe −0.216 −0.269 0.076 −0.304 0.490 1.000
    Mn 0.724 0.621 0.699 0.597 −0.611 −0.562 1.000
    Cu −0.573 −0.759 −0.590 −0.112 −0.318 0.036 −0.236 1.000
    PA 0.562 0.726 0.568 0.285 −0.339 −0.744 0.818* −0.543 1.000
    GW −0.420 −0.133 0.316 −0.661 −0.209 −0.057 0.153 0.030 0.285 1.000

  • *Correlation is significant at the 0.05 level (2-tailed)

  • **Correlation is significant at the 0.01 level (2-tailed)

    4.  Discussion

     The yield potential of a rice cultivar is closely related to grain number per panicle and grain weight (Cheng et al., 2007). However, it was practically difficult to make a balance of grain number and grain weight for a rice panicle owing to the limiting assimilation supply from leaves (Mohapatra and Sahu, 1991). In the past two decades, the enhancement of yield level for newly released japonica cultivars was mostly attributable to the elevated grain number per panicle (Padmajarao, 1995), and there was a narrow variation of grain weight between modern japonica cultivars and landraces (He et al., 2011). In fact, the japonica cultivars now being widely planted in China were mainly characterized by compact and erect or semi-erect panicles with more grains per panicle (Cheng et al., 2007). On the other hand, the compact-panicle cultivars were often found to show a great variation in floret development and grain filling among different rachides within a panicle (Liu et al., 2005b; Wang et al., 2006); also there were dramatic differences in protein composition, amylose content, grain weight, and chalkiness among different positional grains (Cheng et al., 2007). In our current study, grain position significantly affected the concentrations of minerals in both vertical and horizontal axes of the rice panicle. The grains located on top primary and middle primary rachides were substantially higher than those on bottom secondary rachides and middle secondary rachides for all measured minerals (K, Mg, Na, Ca, Mn, Zn, Fe, and Cu). This result was generally in agreement with several previous findings, which demonstrated that the levels of Zn and Fe concentrations in wheat grain decreased at grain positions far distal from rachis (Calderini and Ortiz-Monasterio, 2003), and the heavy-weight grains had higher Zn and Fe concentrations relative to the small-weight grains within a wheat spike (Liu et al., 2006). However, little association was found between mineral concentrations and the rice yield for the rice genotypes differing in grain density and grain weight (Table 2), and between grain weight and the mineral concentrations, in term of an average of six different grain positions (Table 5). The results indicated the feasibility of enhancing the desirable mineral concentrations in rice grains without causing reduction in grain weight and yield level by appropriate breeding strategies and agronomic management for the compact rice cultivars. Moreover, in light of the fact that the high yield potentials of compact rice cultivars were mostly achieved by high grain density and floret numbers in a panicle (Wang et al., 2006; Cheng et al., 2007), it might be practically efficient to increase grain mineral concentrations of whole rice panicles, with the variation of mineral concentration among grains within a panicle being simultaneously decreased, through the modification of their rachis patterns in a panicle, i.e., reducing grain number on the secondary rachis, or increasing grain number on the secondary rachis at the top rachis instead of at the bottom rachis.

     Previous studies have showed that the enhancement of yield potentials could cause a significant reduction in nitrogen (N) and phosphoros (P) concentrations of wheat (Calderini et al., 1995), rice (Jongkaewwattana and Geng, 1991), maize (Simmonds, 1995), and sunflower grains (López Pereira et al., 2000). Additionally, initial findings indicated that grain weight was negatively related to grain PA, prolamin, glutelin, and total protein contents among different positions in a wheat spike (Herzog and Stamp, 1983; Ishimaru et al., 2005) or a rice panicle (Matsue et al., 1995; Liu et al., 2005b). Our present research concluded that the heavy-weight grains in a rice panicle generally had lower PA relative to the light ones (Table 3), which coincided well with previous reports (Liu et al., 2005b). Interestingly, our experimental results revealed that Fe and Zn concentrations of heavy-weight grains were generally higher than those of light-weight grains (Table 2), but the positional variations in Fe and Zn concentrations were poorly related to those in grain PA concentration for the different rachides within a rice panicle (Table 4). Thavarajah et al. (2010) investigated the impact of environmental temperature on PA, Zn and Fe concentrations in lentil seeds, concluding that high PA content was independent of the increase in total Zn and Fe concentrations in lentil seeds for high temperature regimes. Furthermore, Iwai et al. (2012) revealed that total Fe, Zn, and K concentrations in filling grains increased constantly with rice grain development, and the well-developed mature grains had higher concentrations of Fe, Zn, and K minerals than the poorly-developed filling grains. According to our findings, the heavy-weight grains in a rice panicle generally had higher Fe and Zn concentrations relative to the small ones (Table 2). This trend was basically similar to the results reported previously by Thavarajah et al. (2010) and Iwai et al. (2012), but it was contradictory to a previous hypothesis, which presumed that the low N and P concentrations in heavy-weight grains were partially caused by the dilution of additional starch accumulation to other chemical components (Calderini and Ortiz-Monasterio, 2003). Nevertheless, these discordant phenomena did not exclude the possibility that grain N and P concentrations were quite different from many metal minerals in their positional variations within a panicle/spike, which could be caused by their difference in the translocation and accumulation in the rice plant and grain location (Schachtman and Barker, 1999; Vasconcelos et al., 2003). Considering the extent of variation in grain mineral concentration among different positions within a rice panicle, the effect of grain position was more substantial for Na, Fe, and Cu than for Ca and Zn (Table 2). For rice breeding strategies, the different sensitivities to the effect of grain position on these metal minerals suggested that the improvement of panicle architecture and morphological pattern should be attended to more carefully if the deficiency of desired minerals was caused by low Fe concentration rather than low Zn and Ca concentrations in a compact rice cultivar. However, it should be stressed that the effect of grain position on the concentrations of PA and metal minerals (e.g., Fe, Zn, and Ca) was somewhat variable, depending on rice cultivars, and also the extent of positional variations in PA and metal minerals within a panicle was, in many cases, not so great as the cultivar-dependent difference in PA and metal minerals (Tables 2 and 3). For instance, Xiushui11 had relatively higher Fe, but inherently lower Ca concentration in rice grains than Chunjiang15, irrespective of grain position in the rice panicle. Thus, the selection of rice genotypes with low PA and high desirable minerals was an effective approach to directly reduce grain PA and to enhance the desirable mineral concentrations in rice grains for the improvement of its nutritional quality.

     The inhibitory effect of PA on mineral absorption can be estimated by molar ratios of PA/mineral concentrations. The molar ratio of PA/Zn was most widely applied to evaluate Zn bioavailability for human and animal absorption (Raboy et al., 1984). The molar ratios of PA/Zn for cereals and legumes, maize, wheat, sorghum, and rice ranged approximately from 20 to 40, and were relatively high compared with millet and soybean. In our study, the molar ratio of PA/Zn in brown rice grains was very close to those reviewed by Adeyeye et al. (2000), but it was slightly lower than the previous report of Wei et al. (2012), possibly owing to differences in PA and mineral determination methods. Considering the variations of the molar ratios of PA/Zn and PA/Fe among different grain positions, PR and TR generally were lower in both the molar ratios of PA/Zn and PA/Fe compared with BR and SR (Table 3), and also there was a significantly positive association between Zn and Fe concentrations for different grain positions within a panicle (Table 4) and positive correlation for various rice cultivars (Table 5). Our result was in agreement with the previous finding that transgenic rice with the ferritin gene enhanced Zn accumulation in rice grains (Vasconcelos et al., 2003). Therefore, it could be concluded that grain position had a large impact not only on grain Zn and Fe concentrations, but also on their bioavailability. Meanwhile, the lower PA and higher Zn or Fe concentrations for the heavier weight grains within a panicle implied that grain Zn and Fe concentrations in rice grains and their bioavailability could be concomitantly enhanced by a selection of good panicle architecture and morphological pattern for the rice genotypes, particularly for the compact rice genotypes with high grain density and yield levels.

    * Project supported by the National Natural Science Foundation of China (Nos. 31071366 and 31271655)Compliance with ethics guidelines Da SU, Faisal SULTAN, Ning-chun ZHAO, Bing-ting LEI, Fu-biao WANG, Gang PAN, and Fang-min CHENG declare that they have no conflict of interest.


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