Factors affecting head rice yield and chalkiness in indica rice

Field Crops Research 172 (2015) 1–10

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Factors affecting head rice yield and chalkiness in indica rice Lijie Zhou a,b , Shanshan Liang b , Kimberley Ponce b , Severino Marundon b , Guoyou Ye b,∗ , Xiangqian Zhao b,∗ a b

Longping Branch, Graduate School of Central South University, Changsha 410125, Hunan, China Plant Breeding, Genetics and Biotechnology Division, International Rice Research Institute (IRRI), Los Ba˜ nos, Laguna, Philippines

a r t i c l e

i n f o

Article history: Received 24 September 2014 Received in revised form 4 December 2014 Accepted 4 December 2014 Keywords: Rice Head rice yield Chalkiness Grain quality Nitrogen

a b s t r a c t Effects of nitrogen application, genotype, seasons, grain shapes and amylose content (AC) on head rice yield (HRY) and chalkiness were determined in 2012 dry (DS) and wet seasons (WS) in IRRI using 351 indica advanced breeding lines/cultivars from several breeding programs and three N rates (0, 90, and 180 kg ha−1 and 0, 45, and 90 kg ha−1 for DS and WS, respectively). HRY was improved with increasing N rates in WS while no significant differences were observed in DS. The average HRY was 10% higher in DS than in WS. Degree of endosperm chalkiness (DEC) and percentage of grains with chalkiness (PGWC) were decreased with increasing N rates in both seasons. AC was only slightly affected by season and N treatment. DEC, PGWC, grain length (GL) and the ratio of grain length to width (LWR) were negatively correlated with HRY in both seasons. 78 and 23 lines had high HRY and low DEC under 180 (DS) and 90 N (WS) rates, respectively. 74 and 21 of these high quality lines had PGWC less than 30%, indicating that visual selection on PGWC could be a quick and simple method for screening for HRY. The low AC subset had lowest DEC but highest HRY in both seasons. Furthermore, the proportions of lines with high HRY and low DEC in different shape classes were significantly different, suggesting that it is possible to select for low chalkiness and high HRY by selecting for PGWC and grain shape. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Rice is the most important cereal crop for human caloric needs, feeding more than half of the world’s population. It is estimated that 40% increase in rice production will be required by 2030 to satisfy the rapid growth of population in rice consuming countries (Khush, 2005). However, in the last two decades the annual growth rate for rice yield is only about 0.88% (Zhu et al., 2010). Furthermore, agricultural land is being lost in rice producing countries due to economic growth and rapid urbanization. Thus, increasing paddy yield and edible rice yield through breeding and improved agronomy practices is needed. On the other hand, with continuous economic development and rising standard of living, preference for high grain quality gradually increases. Grain quality is a combination of physical and chemical characteristics required for a specific use by a specific customer class. Although preferences for some of the quality characteristics vary across countries and regions (Calingacion et al., 2014), the preferences for some of the characteristics

∗ Corresponding authors. Tel.: +63 2 5805600 2499/+63 2 5805600 2731; fax: +63 2 580 5699. E-mail addresses: [email protected] (G. Ye), [email protected] (X. Zhao). http://dx.doi.org/10.1016/j.fcr.2014.12.004 0378-4290/© 2014 Elsevier B.V. All rights reserved.

are widely shared. Consumers prefer rice with uniform shape and translucent endosperm (Unnevehr et al., 1992; Fitzgerald et al., 2009; Zhao and Fitzgerald, 2013). HRY, defined as the ratio of head rice to rough rice, is an important parameter used in milling industry. Market value of head rice is higher than broken rice grains. Low chalkiness which is associated with more translucent rice along with HRY determines the price of rice in almost all markets. The average HRY of 39 varieties developed by the International Rice Research Institute (IRRI) was about 51.27% and 43.79% in DS and WS, respectively. However, HRY has remained at about 60% over the past 10 years in US. Thus, HRY is one of the top priorities for rice breeding (Nelson et al., 2010; Zhao and Fitzgerald, 2013). It is well known that milling quality aspects are associated with chalkiness, immature kernels, kernel dimensions, fissuring, amylose content and amylopectin chain length (Wassmann et al., 2009). Endosperm chalkiness, comprising percentage of grains with chalkiness (PGWC), influences consumer acceptability, cooking and milling quality (Cheng et al., 2005; Zhao and Fitzgerald, 2013). Eliminating chalkiness is a viable strategy to produce more head rice in the same land area, since ∼1% increase in HRY follows for every ∼1% decrease in chalkiness (Zhao and Fitzgerald, 2013). Also, it has been revealed that grain size and shape are closely linked

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L. Zhou et al. / Field Crops Research 172 (2015) 1–10

to head rice recovery (Jennings et al., 1979). Amylose is a major component of rice starch. Amylose content (AC), controlled mainly by GBSSI, influences the rice texture. AC is considered to be the most important characteristic for predicting rice processing, cooking and eating behavior (Bao et al., 2002; Juliano and Hicks, 1996). Although highly influenced by the environment, AC, grain length (GL), grain width (GW) and ratio of length to width (LWR) are mainly under genetic control (Fitzgerald et al., 2009). The amount of rough rice required for HRY analysis is 125 g per sample (Cruz and Khush, 2000). It is not possible to screen for HRY at early generation in breeding, since the total seed weight of a single plant is usually less than 40 g. However, some of the quality traits such as GL, GW, PGWC can be measured quickly using a small sample and thus applicable in early generation selection. With this, comprehensive understanding on how the chalkiness, AC and grain shape affect HRY might be helpful to define indirect criteria for improving HRY. Furthermore, nitrogen (N) fertilizer management is an important strategy for rice yield improvement as adequate N fertilization is essential for high yield, especially with modern cultivars (Mulvaney et al., 2009). Many studies reported that N fertilizer application affects rice grain quality such as milling and nutritional quality (Leesawatwong et al., 2005; Ning et al., 2009; Perez et al., 1996), and has adverse effect on occurrence of imperfect grains (Qiao et al., 2011). The average N fertilizer application per ha varies greatly across countries, ranging from 3 to 4 kg in Lao PDR to about 180 kg in China (http://www.fao.org/docrep/006/y4751e/y4751e0k.htm). Previous studies on the effects of N fertilizer on grain quality were conducted with limited number of genotypes (varieties) (Qiao et al., 2011; Perez et al., 1996; Wang et al., 2012; Wopereis-Pura et al., 2002) and the results were not informative for breeding. With this, a large breeding population with wide variations for quality traits was used in this study to (1) elucidate the overall trend about how N fertilizer affects rice grain quality in different seasons, (2) examine the effect of seasonal variation on rice grain quality, (3) investigate the associations between key grain quality traits with the objective of defining effective selection criterion for improving HRY and chalkiness.

A total of 36 plants from middle rows were harvested in each plot. Freshly harvested paddy was dried to moisture content of 12–14% and equilibrated in paper bags at room temperature for more than 3 months for grain quality analysis. Milled rice yield (MRY) was determined from the weight of dehulled and polished grains recovered in 125 g rough rice (Satake Engineering Co., Japan and Grainman Machinery MFG. Corp., Miami FL, USA). From the milled rice, head rice and broken grains were separated manually. Grains with length greater than or equal to 3/4 of its total length were considered as head rice. The weight of recovered head rice was used to calculate HRY relative to the original 125 g rough rice. PGWC was determined manually using more than 100 grains of polished head rice. DEC, GL and GW of polished grains were measured using a Cervitec Grain Inspector 1625 (Foss, Denmark). LWR was calculated based on the recorded grain length and width data. AC was measured by the standard iodine colorimetry method described in ISO 6647-2-2011. GL, GW and LWR, the physical dimensions of grains, were used to describe grain shape. The lines were grouped into subsets based on different quality traits to allow more detailed analysis and comparison. Usually three classes are defined based on GL, long (6.61–7.50 mm), medium (5.51–6.60 mm) and short (≤5.50) grains. Based on LWR, grains are divided into slender (>3.01), medium (2.1–3.0) and bold (1.1–2.0) (Jennings et al., 1979). Traditionally, AC was classified as glutinous (∼0%), low (LAC) (2–19%), intermediate (IAC) (20–25%) and high (HAC) (>25%) (Juliano, 1985). In this study, there were no glutinous lines. Grain shape within each of the two classes was further categorized into few subclasses. Long and medium class GL was grouped into 2 and 3 subsets, GL6.6–6.8 and GL>6.8 and GL<6.0 , GL6.0–6.4 and GL6.4–6.6 , respectively. GW was classified into 5 subsets, GW<1.9 , GW1.9–2.0 , GW2.0–2.1 , GW2.1–2.2 , and GW>2.2 . Slender (LWR > 3.01) was divided into LWR3.0–3.3 , LWR3.3–3.6 and LWR>3.6 . PGWC varied from low (<1%) to high (100%) chalkiness. Thus, it was grouped into 10 subsets, i.e., PGWC<10 , PGWC10–20 , PGWC20–30 and so on. The smallest subset contains 14 lines, that is, about 4% of all samples used in this study.

2. Materials and methods

4. Statistical analysis

2.1. Plant materials and experimental design

Basic descriptive statistics including mean, standard deviation and range were computed for all the 8 traits measured in the six nitrogen-season combinations. The Pearson’s phenotypic correlation coefficients were calculated on a mean basis for DS and WS separately. Analysis of variance combined with multiple comparisons using LSD was used to study the effects of nitrogen-season combination, genotype and Genotype-by-environment interaction. Principal component analysis (PCA) was carried out using the mean values of 8 traits in each environment. Two components of PCA were extracted based on a correlation matrix and presented as biplot ordination of environments and entries.

Three hundred and fifty one (351) indica advanced breeding lines/cultivars were used in this study. They were from the following programs at IRRI, International Network for Genetic Evaluation of Rice (INGER), the irrigated breeding program, the green super rice (GSR) project and the multiple environment test (MET). Field experiments were performed at the experimental farm of ˜ Laguna, Philippines (14◦ 11 N, 121◦ 15 E) during the IRRI, Los Banos, 2012 dry (DS) and wet (WS) seasons. In DS the three nitrogen treatments were 0, 90 and 180 kg N ha−1 while in WS they were 0, 45, 90 kg N ha−1 . Due to the serious lodging, N rates for WS was lower than that in DS. The N and season combinations were designated as DN0 , DN90 , DN180 , WN0 , WN45 and WN90 . Seeds were sown in seedling nursery and 21-day-old seedlings were transplanted with single seedling per hill. Experiments were laid out in row-column design with 2 replications. Each plot consisted of 8 × 8 hills with a spacing distance of 0.2 × 0.2 m N in the form of urea was applied 3 times in split; basal, 14 and 42 days after transplanting with 1:1:1 ratio during whole growing season. 40 kg P ha−1 and 40 kg K ha−1 were also applied basally. Field management including pest control, weeding and irrigation followed the IRRI’s experimental farm practices to avoid adverse effects on grain quality.

3. Grain quality analysis

5. Results 5.1. Overall performance of grain quality traits A wide range of variations for HRY, MRY, DEC, PGWCGL, GW and LWR were observed among genotypes, nitrogen rates and seasons (Table 1). The main sources of variations were genotype and treatments. HRY ranged from 4.19% to 70.68%. Few lines were almost chalk free while some lines were very high in chalkiness, PGWC varied from 1.17% to 100%. AC was found to be highly variable among genotypes but average AC was relatively stable across 6

L. Zhou et al. / Field Crops Research 172 (2015) 1–10

nitrogen-season combinations. The first two major principal components explained 48.0% to 62.8% of the total variations for different traits (Fig. 1). For GL, GW, LWR, MRY and AC, the 3 N treatments in DS were grouped together, indicating that the influence of season was stronger than that of N treatment. 6. Correlations among grain quality traits In general, the estimates of correlation coefficients in each N treatment were similar in DS and WS (Suppl. Table 1). Therefore, only the correlation coefficients computed using the DS and WS average data were presented respectively (Table 2). The correlation between PGWC and DEC was highly significant and positive in both seasons (Table 2). DEC and PGWC were strongly correlated with HRY in both seasons. The correlation between DEC and HRY was similar to the correlation between PGWC and HRY in both seasons. GL and LWR were negatively correlated with HRY and MRY while GW was positively correlated with HRY and MRY. Among the three grain shape traits, GL had the strongest correlation with to HRY. The

3

correlation between AC and HRY was negative but only significant in DS. The correlation between GW and DEC was negative and highly significant in DS (−0.438) and WS (–0.376) (Table 2). The correlation between GL and DEC was also negative but significant only in DS. GW was more strongly related to LWR than GL. AC was positively related to DEC (P < 0.01). No significant correlations were observed between AC and the three grain shape traits. 7. Factors effecting HRY, chalkiness and grain shapes Average HRY and MRY under N180 were higher than the other two N rates in DS, although the differences were not statistically significant (Table 1). Among the three N rates, N0 showed the highest MRY but lowest HRY (P < 0.05) in WS. Under the same N rate, average HRY in DS was about 10% higher than WS. There was no significant difference in HRY among N rates in each of the subsets in DS. For the high PGWC (>60%), extreme long (>6.8 mm) and slender (GW < 2.0 or LWR > 3.6) subsets had higher HRY under high N

Fig. 1. Bioplot of two major principal components (PC1 and PC2) for the principal components analysis of grain quality traits. Letter (A–H) represent DEC, PGWC, GL, GW, LWR, AC, HRY and MRY, respectively. Trait abbreviations are as in Table 1.

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L. Zhou et al. / Field Crops Research 172 (2015) 1–10

Fig. 1. (Continued ).

(N180) (Fig. 3A, E, G and I). N application increased HRY in most of the subsets in WS (Fig. 3B, F, H and J). HRY had a wide range of variation among subsets (Fig. 3, Suppl. Table 1). Among GL or LWR classifications, HRY decreased quickly with GL or LWR increasing. HRY of GL<6.0 and GL>6.8 was higher than 60% and lower than 53% under 3 N treatments in DS, respectively, especially which was lower than 50% in DN0 . HRY of GW>2.2 was highest among the 5 GW subsets. HRY of the LAC group was relatively higher in both seasons as compared with those of IAC and HAC. Significant differences were observed among N rates for DEC and PGWC in both of DS and WS (Table 1). The variation between seasons was smaller than that among N rates. DEC and PGWC decreased significantly with increasing N rate. DEC and PGWC were 11.64% and 28.62% lower under DN180 than these under DN0 , respectively. The differences in DEC and PGWC between WN0 and WN90 were 6.75% and 10.29%, respectively. Under the same N rate, DEC and PGWC were slightly lower in DS. For most of the PGWC subsets DEC was lowest under the highest N rate. It is interesting that average DEC of PGWC<10 and PGWC10–20 subsets was lower than

2% under 3 N treatments in DS (Fig. 2A and B). DEC was the lowest under high N treatment for almost all subsets defined using other quality traits in both seasons (Fig. 2C–J). DEC of GL<6.0 was significant higher than those of the other 4 subsets in DS (Suppl. Table 2). For the LWR subsets, LWR<3.0 and LWR>3.6 had the highest and lowest DEC in 5 of the 6 nitrogen and season combinations, respectively. Increasing in GW caused dramatic increase in DEC. DEC of GW<1.9 was only about half of that of GW>2.2 in DS. For example, DEC of GW<1.9 and GW>2.2 were 16.04% and 30.02% in DN0 , respectively. For the AC subsets, DEC of IAC was the highest under the same N rate, which was about twice of that of LAC in DS. GL significantly increased with increasing N rate in both seasons (Table 1). The effects of N on GW and LWR were in opposite direction in DS and WS. In DS, no significant differences were observed among N rates for GW and LWR, although GW was slightly increased while LWR was decreased by N application. In WS, N application significantly increased GW but had no significant effect on LWR. Under the same N rate, GL and LWR were bigger in WS than in DS.

L. Zhou et al. / Field Crops Research 172 (2015) 1–10

60

A

DEC (%)

50 30 20

a a b b a c b c c

aa b

10

a

b c

b

a a aa a

c

b c

b

B

a

a

a

40

5

c

a ab

a a b bb bb c

a

a b

bc

a b b

c

b

b

0

60

C

DEC (%)

50

a

40

a

b

30

a a

20

D a a a

b

c

a

c

b

c

b

c

b

b

10 0 60

DEC (%)

50

LAC

IAC

LAC

E

a b

40

HAC

a

c

a b

30 20

a b

c

a

bb

a b c

HAC

F

a

b

c

IAC a b

c

b

a c

b

c

c

10 0

<6.0

6.0-6.4

6.4-6.6

60

DEC (%)

30

a c

10

<1.9

DEC (%)

40

1.9-2.0

2.0-2.1

b

a

c

a b

b

c

2.1-2.2

>2.2

<1.9

1.9-2.0

a

b

20

a

b

c

c

10

<3.0

3.0-3.3

DS

b

c

b

c

2.0-2.1

2.1-2.2

>2.2

J a

a

a

>6.8

c

I

a

30

0

6.0-6.4 6.4-6.6 6.6-6.8 H

c

c

<6.0

c

60 50

b

b b

b

a

a

a

a

20 0

>6.8

G

50 40

6.6-6.8

3.3-3.6

b

b c

a

b

c

c

>3.6

<3.0

3.0-3.3

WS

3.3-3.6

>3.6

Fig. 2. Average DEC of each subset based on different trait classifications. Blank, gray and black sticks represent 0/0, 90/45 and 180/90 N treatments in DS/WS, respectively. Letter a, b and c above error bar indicate significantly different at P < 0.05 probability rate by a Tukey test. (A and B) Represent PGWC classifications. (C and D) Represent AC classifications. (E and F) Represent GL classifications. (G and H) Represent GW classifications. (I and J) Represent LWR classifications. DS and WS represent dry and wet season, respectively.

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L. Zhou et al. / Field Crops Research 172 (2015) 1–10

Table 1 Mean, standard deviation and range of 8 traits across 2 seasons for 3 N treatments. Traita

Env

DN0

DEC

Meanb Std Range

22.36b 14.69 0.2–60.28

PGWC

Mean Std Range

75.53a 31.87 4.25–100

GL

Mean Std Range

6.61c 0.33 5.15–7.11

GW

Mean Std Range

LWR

Mean Std Range

AC

Mean Std Range

23.11 3.71 11.7–29.7

23.05 3.65 12–30

HRY

Mean Std Range

54.54a 9.95 18.58–70.04

54.44a 10.29 9.74–69.88

MRY

Mean Std Range

67.04a 2.37 50.03–71.49

66.86a 2.52 37.32–71.02

DN90

DN180

16.27c 12.17 0–54.90 64.29b 33.28 1.51–100

WN0

10.72d 10.08 0–54.14

WN45

25.01a 15.05 0–57.36

WN90

21.38b 13.55 0.16–55.17

18.26c 12.50 0–52.60

46.91c 34.34 1.29–100

78.84a 30.92 1.17–100

74.14ab 31.07 3.34–100

68.55b 33.69 3.12–100

6.61c 0.33 5.01–7.2

6.69ab 0.32 5.27–7.36

6.65bc 0.31 5.32–7.17

6.70ab 0.33 4.76–7.18

6.71a 0.32 5.12–7.34

2.10a 0.14 1.74–2.73

2.08ab 0.15 1.74–2.7

2.09ab 0.15 1.74–2.78

2.05c 0.14 1.74–2.67

2.07bc 0.14 1.72–2.79

2.09ab 0.14 1.77–2.75

3.17c 0.29 1.89–3.98

3.19bc 0.3 1.87–3.98

3.22abc 0.3 1.91–3.98

3.26a 0.29 1.99–3.93

3.25a 0.3 1.78–4.03

3.23ab 0.29 1.87–4.13

22.6 3.32 10–29.4

22.88 3.96 11.6–31

22.36 4.02 10.5–29.2

22.62 3.7 12.3–29.6

55.56a 9.38 18.41–67.96

41.67c 12.34 4.19–70.68

45.23b 9.6 15.08–66.5

44.77b 9.83 11.38–68.09

67.25a 2.58 48.11–73.28

66.06b 4.66 51.64–75.68

62.57b 4.92 46–76.84

64.4c 4.29 48.77–77.04

a DEC = degree of endosperm chalkiness (%), PGWC = percentage of grain with chalkiness (%), GL = grain length (mm), GW = grain width (mm), LWR = ratio of grain length and width, AC = amylose content (%), HRY = head rice yield and MRY = milled rice yield. b Values within rows with the same letter are not significantly different at P < 0.05 probability level by a Tukey test.

8. Frequency of low chalkiness and high milling recovery lines Practically lines with DEC less than 5% and HRY higher than 60% and 50% in DS and WS, respectively, were defined as low chalkiness and high milling quality. Using this criterion, 30, 33, 78, 22, 18 and 23 of the tested 351 lines were classified as high quality in DN0 , DN90 , DN180 , WN0 , WN45 and WN90 , respectively (Table 3). Except for DN180 , the frequency of high quality lines was lower than 10%. The proportion of low chalkiness and high milling recovery lines varied among subsets (Table 3). 46.67% (7 of 15) lines in GL6.0–6.4 performed low chalkiness and high HRY in DN180 , which was about twice of that of whole set. The frequency of high quality lines in GW1.9–2.0 was about twice of that in the whole set across N rates in both seasons. LAC subset had the highest frequency of high quality lines. About or more than a quarter of lines in LAC subset were shown translucent and high milling recovery under DN0 , DN90 , DN180 and WN90 .

In DN180 , 159 lines had PGWC less than 30%. Among them, 74 lines performed low chalkiness (<5%) and high HRY (>60%) (Table 4). The frequency of high quality lines in PGWC<10 subset was 63.64% (28 of 44), which was much higher than that in the whole set. 12 lines in this subset showed high frequency (27.27%) in low chalkiness and high milling quality in WN90 also. In WN90 , only 76 lines had PGWC less than 30%, however, 21 of 23 observed high quality lines were belong to this subset. Particularly, the PGWC<10 subset had 22 lines under N 90 in WS. Interestingly, 14 and 12 of these 22 lines were very low in chalkiness and high in HRY under DN180 and WN90 , respectively, with 9 lines common in both seasons. PGWC>30 subset was 12% higher in DEC than the PGWC<30 subset. However, its HRY was 3% lower in DN180 (Suppl. Table 3). Similar trends were observed in WN90 . Compared to the whole set, the PGWC<30 subset had much higher frequency of high quality lines in both of DS and WS for almost all GL and AC subsets (Table 4).

Table 2 Correlation among observed grain quality traits in DS and WS. DEC DEC PGWC GL GW LWR AC HRY MRY

PGWC

GL **

0.8826 0.8998** −0.1219* 0.4384** −0.3588** 0.1903** −0.3815** 0.1767**

−0.1100* 0.3993** −0.3332** 0.1857** −0.3401** 0.1840**

−0.0879 −0.0514 −0.4809** 0.7761** 0.0583 −0.3238** −0.0799

GW

LWR **

0.3755 0.3402** −0.5037**

−0.9148** −0.0630 0.0840 0.2334**

AC

−0.3057 −0.2718** 0.7923** −0.9152** **

0.0701 −0.2183** −0.2243**

HRY **

0.1749 0.2356** 0.0381 0.0071 0.0069 −0.1366* 0.0776

Lower left and upper right triangular matrixes represent correlation within DS and WS, respectively. Trait abbreviations are as in Table 1. * Represents significant at P < 0.05. ** Represents significant at P < 0.01.

MRY

−0.3430 −0.3497** −0.3069** 0.1587** −0.2630** −0.0888 **

0.1448**

0.1698** 0.1445** −0.3042** 0.3722** −0.4129** 0.2177** 0.3196**

L. Zhou et al. / Field Crops Research 172 (2015) 1–10

7

Table 3 Number and frequency of low chalkiness and high HRY lines in each subset based on grain shapes and AC. Traits

Classification

Number

DN0

DN90

DN180

WN0

WN45

WN90

GL

<6.0 6.0–6.4 6.4–6.6 6.6–6.8 >6.8

15 15 59 162 100

0 2/13.33 8/13.56 11/6.79 9/9.0

0 3/20.0 8/13.56 13/8.02 9/9.0

2/13.33 7/46.67 16/27.12 37/22.84 16/16.0

0 2/13.33 5/8.47 11/6.79 4/4.0

0 1/6.67 6/10.17 8/4.94 3/3.0

1/6.67 1/6.67 5/8.47 12/7.41 4/4.0

GW

<1.9 1.9–2.0 2.0–2.1 2.1–2.2 >2.2

14 52 160 92 33

0 10/19.23 12/7.50 6/6.52 2/6.06

0 9/17.31 18/11.25 5/5.43 1/3.03

3/21.43 21/40.38 37/23.13 15/16.30 2/6.06

0 9/17.31 7/4.38 2/2.17 4/12.12

0 4/7.69 7/4.38 5/5.43 2/6.06

0 6/11.54 8/5.0 6/6.52 3/9.09

LWR

<3.0 3.0–3.3 3.3–3.6 >3.6

38 191 108 14

2/5.26 16/8.38 10/9.26 2/14.29

1/2.63 21/10.99 9/8.33 2/14.29

4/10.53 41/21.47 31/28.70 2/14.29

2/5.26 9/4.71 11/10.19 0

2/5.26 11/5.76 5/4.63 0

3/7.89 11/5.76 8/7.41 1/7.14

AC

LAC IAC HAC

54 219 78

20/37.04 5/2.28 5/6.41

15/27.78 10/4.57 8/10.26

19/35.19 39/17.81 20/25.64

7/12.96 11/5.02 4/5.13

5/9.26 7/3.20 6/7.69

13/24.07 5/2.28 5/6.41

351

30/8.55

33/9.40

78/22.22

22/6.27

18/5.13

23/6.55

Total

Data before and after “/” represent number and frequency of high quality lines in each subset. Trait abbreviations are as in Table 1.

Table 4 Number and frequency of high quality lines based on PGWC, GL & PGWC, and AC & PGWC classification. Environment

Subset

Number

DN180

WN90

Common

DN180

PGWC < 10 10 < PGWC < 20 20 < PGWC < 30 GL<6.0 &PGWC < 30 GL6.0–6.4 &PGWC < 30 GL6.4–6.6 &PGWC < 30 GL6.6–6.8 &PGWC < 30 GL>6.8 &PGWC < 30 LAC&PGWC < 30 IAC&PGWC < 30 HAC&PGWC < 30

44 69 46 2 8 28 73 48 40 85 34

28/63.64 32/46.38 14/30.43 2/100.0 6/75.0 16/57.14 36/49.32 14/29.17 21/52.50 38/44.71 14/41.18

12/27.27 3/4.35 3/6.52 0 1/12.50 5/17.86 8/10.96 4/8.33 11/27.5 5/5.88 2/5.88

10/22.73 1/1.45 1/2.17 0 1/12.50 4/14.29 5/6.85 2/4.17 8/20.00 1/1.18 3/8.82

159

74/46.54

18/11.32

12/7.55 9/40.91 2/7.41 1/3.70 0 1/25.0 4/22.22 5/13.51 2/12.50 8/25.81 1/4.55 3/13.08

Subtotal WN90

PGWC < 10 10 < PGWC < 20 20 < PGWC < 30 GL<6.0 &PGWC < 30 GL6.0–6.4 &PGWC < 30 GL6.4–6.6 &PGWC < 30 GL6.6–6.8 &PGWC < 30 GL>6.8 &PGWC < 30 LAC&PGWC < 30 IAC&PGWC < 30 HAC&PGWC < 30

22 27 27 1 4 18 37 16 31 22 23

14/63.64 12/44.44 8/29.63 0 4/100 11/61.11 15/40.54 4/25.0 14/45.16 9/40.91 11/47.83

12/54.55 5/18.52 4/14.81 1/100 1/25.0 5/27.78 10/27.03 4/25.0 12/38.71 3/13.64 6/26.09

Subtotal

76

34/44.74

21/27.63

12/15.79

Data before and after “/” represent number and frequency of high quality lines in each subset. Trait abbreviations are as in Table 1.

9. Discussion 9.1. Effect of N rates on DEC and HRY Rice chalkiness is a complex polygenic trait and easily influenced by environmental conditions and certain cultural practices, especially during grain filling (Liu et al., 2010; Siebenmorgen et al., 2013). In the present study, it was observed that DEC and PGWC decreased progressively and clearly with increasing N rates in both DS and WS (Table 1). This result was consistent with the report of Perez et al. (1990) that nitrogen fertilizer increases translucency of brown rice in both DS and WS. Additionally, Qiao et al. (2011) demonstrated that N fertilizer had a favorable effect in decreasing the ratio of chalky grains. It is well-known that the formation of chalkiness is associated with the shape, size and arrangement of starch granule in the endosperm, caused by an insufficient

substrate supply to the developing endosperm (Cheng et al., 2005; Fitzgerald and Resurreccion, 2009; Lisle et al., 2000). Thus, the observed reduction under high N rate might be due to more starch and protein accumulation in grains resulting in densely packed starch granule and space occupied with protein bodies. Nitrogen is an important factor affecting protein synthesis, carbon metabolism and nerve transmission (Lalonde et al., 2004; Scofield et al., 2009). Higher N availability enhances the translocation of assimilates from leaves to sink organ (Ariovich and Cresswell, 1983) and increases reserve protein content in grains (Champagne et al., 2009; FuertesMendizábal et al., 2010). Fan et al. (2005) reported that in wheat, nitrogen supply could regulate leaf photosynthesis and grain starch accumulation under drought or flood stress during grain filling. Therefore, these findings lead us to the possibility that N application could reduce the influence of adverse weather on grain chalkiness.

8

L. Zhou et al. / Field Crops Research 172 (2015) 1–10

80

A

HRY (%)

70

B a a a bb b

60 50

aa aa b cab b

40 30

80

C

D

HRY (%)

70 60

a a

b

a

c

50

a

b a

b

40 30

LAC 80

HAC

LAC

IAC

HAC

F

E

70 HRY (%)

IAC

60

b

50

aa

ba a

a ba

40 30

<6.0

80

6.0-6.4 6.4-6.6 6.6-6.8

>6.8

<6.0

G

6.0-6.4 6.4-6.6 6.6-6.8

>6.8

H

HRY (%)

70 60

c ba

50

cba

40 30

<1.9 80

>2.2

<1.9

I

70 HRY (%)

1.9-2.0 2.0-2.1 2.1-2.2

1.9-2.0 2.0-2.1 2.1-2.2

>2.2

J

60

b a a

c a b

3.0-3.3

3.3-3.6

50

b a

b

40 30

<3.0

3.0-3.3

DS

3.3-3.6

>3.6

<3.0

>3.6

WS

Fig. 3. Average HRY of each subset based on different trait classifications. Blank, gray and black sticks represent 0/0, 90/45 and 180/90 N treatments in DS/WS, respectively. Letter a, b and c above error bar indicate significantly different at P < 0.05 probability rate by a Tukey test. (A and B) Represent PGWC classifications. (C and D) Represent AC classifications. (E and F) Represent GL classifications. (G and H) Represent GW classifications. (I and J) Represent LWR classifications. DS and WS represent dry and wet season, respectively.

L. Zhou et al. / Field Crops Research 172 (2015) 1–10

HRY improved with N rate in WS, however, no obvious influence of N on HRY was observed in DS (Table 1). It is generally considered that N application had a positive effect on HRY (Leesawatwong et al., 2005; Liu et al., 2012; Perez et al., 1996), although the effect differed with cultivars (Leesawatwong et al., 2005). The insignificant effect of N treatment on HRY in DS might be due to the fact that in DS HRY was already high even without N application. It was also possible that the highest N rate applied was still not sufficient for further increasing HRY. Nangju and De Datta (1970) reported that an increase in N supply caused improved HRY on chalky cultivars, but not on non-chalky in DS (Nangju and De Datta, 1970). Thus, N indirectly influences HRY by its impact on chalkiness (Nangju and De Datta, 1970). HRY in DS was significantly higher than in WS (Table 1), suggesting that season had more influence on milling quality than N application. Although HRY could be improved by increasing N rate in WS, high N rate may cause lodging problem and is not a practical option for HRY improvement. Nevertheless, the negative effects of adverse climatic conditions on HRY could be reduced with good N fertilizer management. 10. Effect of AC on DEC and HRY We found that DEC and PGWC were very different among AC classes in both seasons. LAC subset was about 50% lower in DEC than the IAC subset under the highest N rate in DS and WS. Although there is no direct evidence that AC contributes to the occurrence of endosperm chalkiness, variation in AC was associated with the formation of grain chalkiness were reported in some studies (Cai et al., 2013; Cheng et al., 2005; Lisle et al., 2000). Mikami et al. (1999) found that some waxy (Wx) mutants in rice, which lacks amylose and contains almost exclusively amylopectin, had absolute opaque endosperm. Zhou et al. (2003) observed that AC decreased coupled with reduced grain opacity when Wx from Minghui 63 was introduced to Zhenshan 97 through marker assisted selection. Chalky kernels of the same genotypes had less AC compared with translucent rice kernels (Lisle et al., 2000; Patindol and Wang, 2003). Recently, it was reported that mutations in regulators for starch synthesis caused changes in AC and chalky endosperm, indicating that AC and chalkiness may be under co-regulation by certain factors (Fu and Xue, 2010; Wang et al., 2013). Therefore, the relationship between AC and chalkiness is complex and further studies are needed to fully understand why genotypes with intermediate AC have higher chalkiness. The low AC subset had highest HRY across all the 6 nitrogen-season combinations (Table 1, Fig. 3C and D). This might be explained by the observed strong and negative correlation between DEC and HRY (Table 2). 11. Effects of grain shapes on DEC and HRY Slender grain (GW<1.9 , GW1.9–2.0 or LWR>3.6 ) had obviously lower DEC and PGWC than other subsets in both seasons (Fig. 2G and J). Except for GL<6.0 , the variation of DEC was small, and GL had no significant effect on DEC in WS (Fig. 2A and B; Table 2). DEC was positively associated with GW and negatively with LWR. Thus, it could be inferred that grain shape had a major influence on chalk. Raju and Srinivas (1991) observed that the decrease in GW from 2.7 mm to 2.2 mm by mechanical constraint can eliminate white belly. Thus, rice chalkiness is reduced as GW decreases (Adu-Kwarteng et al., 2003; Tan et al., 2000). However, Borrell et al. (1999) observed that chalkiness was not related with grain breadth in some varieties tested. These findings suggests that GW, rather than GL, is an important factor responsible for chalkiness in rice. Grain size and shape partially influence HRY. Short and broad grains have higher HRY than long and slender grains, because medium grain rice is more resistant to forces during the milling

9

process (Jongkaewwattana et al., 1993). HRY was negatively correlated with GL and LWR (Table 1) and this was consistent with other report (Koutroubas et al., 2004). Interestingly, the variations of HRY among GW and LWR subsets in DN180 were very small, from 53.74% to 57.19% and 53.97% to 58.57%, respectively. However, GW<1.9 and LWR>3.6 subsets, had 5% lower in HRY than the other subsets with regular N input in WS (WN90 ). Although different GL subsets were similar in DEC, GL>6.8 subset had a much lower HRY (52.52%) than GL6.0–6.4 (60.12%). These results suggested that the low DEC in slender subset could partially reduce the negative effect of GW or LWR on HRY. Furthermore, it seemed that HRY was more associated with GL than GW or LWR since the differences in DEC among GL subsets were not high. 12. Breeding for high HRY Increasing HRY is a viable strategy to bridge the gap between rice production and consumption (Zhao and Fitzgerald, 2013). However, grains from a single plant are not enough for HRY analysis and thus early generation selection cannot be conducted without an efficient indirect selection criterion. Correlation analysis revealed that chalk (DEC and PGWC) and GL had the strongest correlation with HRY (Table 2). HRY decreased quickly with increasing PGWC and GL in both seasons (Fig. 3A and B, E and F). The correlation coefficient between DEC and PGWC was about 0.9 in both seasons, that is, DEC could be estimated by PGWC. It is possible to select for HRY indirectly through PGWC and GL. 63.64%, 46.38% and 30.43% of the lines in PGWC<10 , PGWC10–20 and PGWC20–30 under DN180 had HRY higher than 60% (Table 4). In WN90 , the frequency of high quality lines was low but still had 28.95% in the PGWC<30 subset. Especially, 9 of 22 lines observed with less than 10% in PGWC under N 90 in WS were commonly performed low DEC and high HRY in both dry and wet seasons, which indicating that it is more efficient to screen high quality lines in WS. Among the 351 tested lines, 78 and 23 lines were classified as high quality (low chalkiness and high HRY) under DN180 and WN90 , respectively (Table 3). 74 and 21 of these high quality lines under DN180 and WN90 had PGWC less than 30% (Table 4) and which could be quickly identified using PGWC. Furthermore, a high proportion lines that had good quality in DS were also of high quality in WS. As for GL, 46.67% lines in GL6.0–6.4 was of high quality under DN180 since chalkiness was lower compared with the other 4 subsets and GL was not too long (Table 3). After primary screening for PGWC, frequency of high quality lines was dramatically increased in each of the GL or AC subsets (Table 4), because PGWC<30 had lower DEC and higher HRY than PGWC>30 (Suppl. Table 3). The number of lines in the LAC, IAC and HAC subsets were 54, 219 and 78 lines, respectively, which were decreased to 31, 22 and 23 after PGWC screening in WS. Therefore, AC variation was still high after selection for low PGWC (Tables 3 and 4). This not only implies that combined selection is more powerful but also, it is possible to breed for low chalkiness and high HRY with different AC and grain shape characteristics. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.fcr.2014.12.004. References Adu-Kwarteng, E., Ellis, W.O., Oduro, I., Manful, J.T., 2003. Rice grain quality: a comparison of local varieties with new varieties under study in Ghana. Food Control 14, 507–514. Ariovich, D., Cresswell, C.F., 1983. The effect of nitrogen and phosphorus on starch accumulation and net photosynthesis in two variants of Panicum maximum Jacq. Plant Cell Environ. 6, 657–664.

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