- Open Access
A Quantitative Trait Locus for Chlorophyll Content and its Association with Leaf Photosynthesis in Rice
© Springer Science + Business Media, LLC 2010
- Received: 21 May 2010
- Accepted: 9 July 2010
- Published: 25 July 2010
Leaf photosynthesis, an important determinant of yield potential in rice, can be estimated from measurements of chlorophyll content. We searched for quantitative trait loci (QTLs) for Soil and Plant Analyzer Development (SPAD) value, an index of leaf chlorophyll content, and assessed their association with leaf photosynthesis. QTL analysis derived from a cross between japonica cultivar Sasanishiki and high-yielding indica cultivar Habataki detected a QTL for SPAD value on chromosome 4. This QTL explained 31% of the total phenotypic variance, and the Habataki allele increased the SPAD value. Chromosomal segment substitution line (CSSL) with the corresponding segment from Habataki had a higher leaf photosynthetic rate and SPAD value than Sasanishiki, suggesting an association between SPAD value and leaf photosynthesis. The CSSL also had a lower specific leaf area (SLA) than Sasanishiki, reflecting its thicker leaves. Substitution mapping under Sasanishiki genetic background demonstrated that QTLs for SPAD value and SLA were co-localized in the 1,798-kb interval. The results suggest that the phenotypes for SPAD value and SLA are controlled by a single locus or two tightly linked loci, and may play an important role in increasing leaf photosynthesis by increasing chlorophyll content or leaf thickness, or both.
- Leaf area
- SPAD value
- Substitution mapping
Leaf photosynthesis is the component of canopy photosynthesis that accounts for most of the variation in biomass production and yield (Peng 2000; Yoshida and Horie 2009). While it is still controversial whether increasing leaf photosynthesis increases yield (Evans 1993; Sinclair et al. 2004), recent studies indicate that growth rate around heading stage is critically related with final yield in rice (Takai et al. 2006; Horie et al. 2006), and that new high-yielding rice cultivars, including both inbred and hybrid cultivars, have higher leaf photosynthetic rates than previously released ones, particularly at heading stage (Ohsumi et al. 2007; Peng et al. 2008). To examine this issue, it is necessary to identify genetic factors controlling leaf photosynthesis, and to compare yield potential between donor cultivars and near-isogenic lines (NILs) differing only in leaf photosynthetic ability (Zelitch 1982; Long et al. 2006; Hubbart et al. 2007).
The process of photosynthesis is difficult to measure directly, but a positive relationship between leaf photosynthesis and leaf chlorophyll content has been widely observed in rice (Makino et al. 1983; Kura-Hotta et al. 1987; Xu et al. 1997). Chlorophyll content is generally measured after extraction of chlorophyll from ground leaves with organic solvents (Porra et al. 1989). On the other hand, a digital chlorophyll meter (Soil and Plant Analyzer Development [SPAD] meter) provides a non-destructive method for estimating leaf chlorophyll content by measuring light absorption of specific spectral bands in living leaves (Watanabe et al. 1980; Chubachi et al. 1986). The methods for measurement of SPAD values are simple and quick, and close correlations between SPAD values and leaf photosynthesis values have been observed in rice (Huang and Peng 2004; Kato et al. 2004; Kumagai et al. 2009). Therefore, SPAD measurement may be a more appropriate method than destructive measurement of leaf chlorophyll content for use in genetic analysis of leaf photosynthesis.
Recent progress in the development of molecular markers has enabled the genetic mapping of quantitative trait loci (QTLs) for photosynthesis-related traits. Several putative QTLs have been detected for SPAD value or chlorophyll content in rice (Ishimaru et al. 2001; Teng et al. 2004; Abdelkhalik et al. 2005; Yue et al. 2006; Kanbe et al. 2008), and some of these have been confirmed by mapping in advanced-generation progeny (Kanbe et al. 2008). However, none of these QTLs has been precisely mapped as a Mendelian factor or characterized for its contribution to leaf photosynthesis.
In this study, we focused on the SPAD value of flag leaves at heading stage because higher leaf photosynthesis of flag leaves at heading stage may be critically related with high yield (Takai et al. 2006; Ohsumi et al. 2007). Then we identified a candidate QTL controlling SPAD value of flag leaves at heading stage by using backcross inbred lines (BILs) derived from a cross between japonica cultivar Sasanishiki and indica cultivar Habataki (Nagata et al. 2002). To confirm the putative QTL and to assess its association with leaf photosynthesis, we used chromosome segment substitution lines (CSSLs). In each of the CSSLs, a particular chromosome segment of Sasanishiki has been replaced by the corresponding segment from Habataki (Ando et al. 2008). Then, by using progeny derived from a cross between Sasanishiki and a CSSL harboring the target QTL, we conducted substitution mapping of the QTL. We also investigated the genetic relationship between SPAD value and specific leaf area (SLA), which is assumed to be correlated with leaf thickness. The QTL detected in this study appears to be associated with increased leaf photosynthetic rate and may also be associated with SLA.
QTL detection in BILs and CSSLs
Putative QTLs Controlling SPAD Value of Flag Leaves at Heading Stage and Days-to-Heading in BILs between Sasanishiki and Habataki
Leaf photosynthetic ability in SL414
Substitution mapping of the QTL for SPAD value
Putative QTLs Controlling SPAD Value and SLA Detected in an F2 Population Derived from SL414 × Sasanishiki
Grain yield in cereals is determined by the balance between sink size and source capacity. The genetics of sink size (e.g., grain size and grain number) has been well analyzed in rice plants, and several QTLs controlling grain number per panicle and grain size have been identified (Ashikari et al. 2005; Fan et al. 2006; Song et al. 2007; Shomura et al. 2008; Huang et al. 2009). On the other hand, genetic analyses of factors affecting source capacity, such as photosynthetic rate, have been limited, probably because of the need for time-consuming direct measurements, complex genetic control, and variability under various environmental conditions (Takai et al. 2009; Yamamoto et al. 2009). However, it is necessary to understand source ability in more detail to increase yield potential in rice. Therefore, we focused on chlorophyll content (SPAD value) as an index of leaf photosynthesis.
We detected a large-effect QTL (R2 = 31.3%) for SPAD value of flag leaves at heading stage on the long arm of chromosome 4 (Fig. 2). Since no other QTLs were detected in this population, the remaining 68.7% of phenotypic variance may be due to environmental factors, measuring error or false negative QTL with minor effect. Previous studies have also detected QTLs for chlorophyll content in this region (Yue et al. 2006; Kanbe et al. 2008). The positions of markers flanking these QTLs were similar among these studies, so the QTL detected here may be same as those identified previously. Besides, the map location of the QTL for SPAD value was different from those of QTLs for days-to-heading, indicating the QTL for SPAD value was not associated with a pleiotropic effect of a QTL for days-to-heading.
We confirmed the QTL identified in this study by substitution mapping in a set of CSSLs (Fig. 3). CSSLs are useful to characterize and detect QTLs because phenotypic differences can be evaluated within a uniform genetic background (Ebitani et al. 2005; Yamamoto et al. 2009). One of the CSSLs, SL414, contained a Habataki chromosome segment on the long arm of chromosome 4 and had significantly higher Pn and SPAD values than Sasanishiki (Fig. 4). The values of both traits in SL414 were intermediate between Sasanishiki and Habataki. These results indicate that the segment harboring the QTL for SPAD value was also highly associated with increased Pn.
In general, leaf photosynthesis by C3 crops is determined by both the CO2 supply obtained through stomata and the fixation of CO2 in the chloroplasts (Farquhar and Sharkey 1982). Our study did not detect any difference in gs between SL414 and Sasanishiki (Fig. 4), which indicates that the QTL detected here is involved in CO2 fixation rather than in CO2 supply. Rather, the difference in SPAD value between SL414 and Sasanishiki reflects a difference in chlorophyll content or leaf N content per unit leaf area. Higher chlorophyll content per unit leaf area may reflect the presence of a larger number of chloroplasts per mesophyll cell and/or higher chlorophyll content per chloroplast in cases where leaf thicknesses do not differ. However, previous studies have indicated that SPAD values may sometimes reflect variation in leaf thickness, because the readings are based on the leaf chlorophyll’s absorption of specific spectral bands of light, which may be influenced by leaf thickness (Peng et al. 1993; Jinwen et al. 2009). The SLA, which is assumed to be correlated with leaf thickness, was significantly lower in SL414 than in Sasanihiki, and similar between SL414 and Habataki (Fig. 4). These results suggest that the higher SPAD value of SL414 resulted from thicker leaves. Thicker leaves are considered to be important for increasing leaf photosynthesis because they can capture light energy efficiently by more chlorophyll per unit leaf area and they are better able to protect the area where the chloroplast surface faces intercellular spaces, allowing more efficient CO2 diffusion and transport (Terashima et al. 2006). Higher leaf N content per unit leaf area and higher gs (seen in Habataki compared with Sasanishiki) are believed to be important factors contributing to varietal differences in leaf photosynthesis (Asanuma et al. 2008; Takai et al. 2010). Since measurements taken on a unit leaf area basis are expected to be influenced by leaf thickness, the higher N content per unit leaf area in Habataki might be also caused by thicker leaves. To verify either leaf thickness is associated with the QTL for SPAD value, it is necessary to conduct further in-depth studies such as spectrophotometric chlorophyll measurement and comparison of the cross sections of leaf blade.
Using advanced-generation progeny derived from a cross between SL414 and Sasanishiki, we confirmed the QTL for SPAD value and delimited the candidate region to a 1798-kb interval between RM5503 and RM17525 (Fig. 6). Although a mutant gene associated with chlorophyll content, Gc, was recently mapped to chromosome 1 (Wang et al. 2008), no QTLs for SPAD value or chlorophyll content have previously been delimited; this is the first study in rice to identify a QTL involving a leaf photosynthesis-related trait.
It is of interest that the QTL for SLA was also delimited to the same region as the QTL for SPAD value. This result strongly suggests that the QTLs for SPAD value and SLA are associated with either the pleiotropic effects of a single QTL or the effects of two tightly linked loci. These results also indicate that the QTLs may play an important role in increasing leaf photosynthesis by increasing chlorophyll content or leaf thickening, or both. To determine whether pleiotropy or tight linkage is responsible for the apparent proximity of these QTLs, and to evaluate the specific contribution of the QTLs to leaf photosynthesis in rice plants, we are now working to clone the two QTLs. Moreover, cloning of the QTLs and development of NILs will help to elucidate whether an increase in leaf photosynthesis could contribute to yield improvement. Because leaf photosynthesis is one of the components of canopy photosynthesis associated with biomass production and yield (Peng 2000; Yoshida and Horie 2009), higher leaf photosynthesis would be expected to increase final yield, unless other components such as leaf area change. Our study suggests two possible means to increase leaf photosynthesis: morphological modification of leaves (e.g., thicker leaves) and physiological modification of leaves (e.g., higher chlorophyll content). A significant challenge to overcome is that morphological modifications such as thicker leaves might be accompanied by reductions in leaf area. Further studies are necessary to elucidate which modifications of leaf photosynthesis could improve yield.
Plant materials and cultivation
Two cultivars, Sasanishiki (japonica) and Habataki (indica), were used in this study. Habataki is a high-yielding cultivar from Japan (Kobayashi et al. 1990) with a greater photosynthetic rate in the flag leaves at heading stage than Sasanishiki (Takai et al. 2010).
We used 85 BILs (Nagata et al. 2002) and 39 CSSLs (Ando et al. 2008) derived from a cross between Sasanishiki and Habataki for the QTL analysis. Rice plants were grown in a paddy field at National Institute of Agrobiological Sciences (NIAS) in Tsukuba, Japan, in 2007. Thirty-day-old seedlings of each line were transplanted at one seedling per hill on 16 May. Each line was planted in a single row of 12 hills at a spacing of 15 cm between hills and 30 cm between rows. Basal fertilizer was applied: 56 kg N, 56 kg P, and 56 kg K ha−1. Additional N fertilizer was top-dressed at 30 kg N ha−1 2 weeks after transplanting. Three plants per line were selected for the measurement of SPAD value.
On the basis of initial results, we performed additional analyses using SL414, a Sasanishiki-derived CSSL in which part of the long arm of chromosome 4 is substituted with the corresponding segment from Habataki. SL414 was crossed with Sasanishiki, and 119 self-pollinated F2 progeny and the parents were raised in the NIAS paddy field for traits investigation in 2008. Thirty-day-old seedlings were transplanted into the field on 4 June. Plant density and fertilizer treatment were the same as in 2007. Each F2 plant was used for the measurement of SPAD value. For substitution mapping of the candidate QTL, additional 423 F2 seeds were sown in a growth chamber room, and we used DNA markers to identify 13 out of 542 (119 + 423) F2 plants with recombination near the QTL, and harvested F3 seeds. From each of the 13 F3 lines, we selected one F3 plant that was homozygous for the recombinant chromosome identified in the F2 parent in the growth chamber during the winter season in 2008. The F3 plants were self-pollinated, producing 13 F4 lines that were used for substitution mapping of the target QTL. F4 plants were grown in a randomized complete block design with three replications in a paddy field at the National Institute of Crop Science in Miraidaira, Japan, in 2009. Twenty-one-day-old seedlings were transplanted at one seedling per hill on 4 June. Each plot consisted of one row with 15 hills. The plant density was the same as in 2007. Basal fertilizer was applied: 60 kg N, 52 kg P, and 75 kg K ha−1. Fifteen plants per line (five in each plot) were selected for the measurement of SPAD value.
At heading stage, determined as the number of days from sowing to heading of the first panicle (days-to-heading) in five plants for each BIL and CSSL, the SPAD value of the fully extended flag leaf on the main stem was measured with a SPAD meter (SPAD-502, Konica-Minolta, Japan). Three out of five plants investigated for days-to-heading were used for the measurement of SPAD value for each BIL and CSSL. Six readings around the middle of each leaf blade were averaged. In 2008 and 2009, for Sasanishiki, Habataki, SL414, and SL414 progeny, SLA of flag leaves used for SPAD measurement was calculated as the ratio of leaf area to leaf dry weight; lower SLA values indicated thicker leaves. Digital images of the flag leaves were used for measurement of leaf area with computer software (LIA32, Nagoya University, Japan). Leaf dry weight was determined after oven-drying.
In 2008, we measured the photosynthetic rate of Sasanishiki, Habataki, and SL414 flag leaves at heading stage with a portable photosynthesis system (LI-6400, Li-Cor, Lincoln, NE, USA). Measurement was conducted on clear days between 0900 and 1300 h under a constant saturated light level of 2,000 μmol m−2 s−1 provided by red/blue light-emitting diodes. The leaf chamber temperature was maintained at 30°C, the reference CO2 concentration was 380 μmol mol−1, and the relative humidity was 75% ± 5%. Gas-exchange parameters were recorded once the topmost expanded leaf was enclosed in the chamber and the system software indicated that CO2, H2O, and flow in the chamber were stabilized. One flag leaf from each of ten different plants per cultivar or line was measured.
For QTL analysis of BILs and substitution mapping of CSSLs, 236 RFLP markers (Nagata et al. 2002) and 166 PCR-based markers (Ando et al. 2008), respectively, were used. An additional ten SSR markers developed by McCouch et al. (2002) and the International Rice Genome Sequencing Project (2005) were used for genotyping SL414 × Sasanishiki F2 plants. We used four SSR markers and one insertion-deletion (InDel) marker to determine genotypes of 13 F3-derived lines for substitution mapping of the target QTL. The InDel marker ID03_35 was constructed by using sequence information in the rice DNA polymorphism database (Shen et al. 2004). The sequences of the forward and reverse primers were 5′-GCTCCGGTGGCTCTTCGTG-3′ and 5′-AGGCTTAAGGCGAAAGGAAGT-3′, respectively. Total DNA of each plant was extracted from leaves by the CTAB method (Murray and Thomson 1980). Linkage maps were constructed in MAPMAKER/EXP 3.0 software (Lander et al. 1987). The chromosomal positions and effects of putative QTLs were determined by composite interval mapping in QTL Cartographer 2.0 software (Basten et al. 2002). The threshold of QTL detection was based on 1,000 permutation tests at the 5% level of significance (Churchill and Doerge 1994; Doerge and Churchill 1996). The additive and dominant effects and phenotypic variance explained by each QTL (R2) were estimated from the peak LOD score. For substitution mapping of CSSLs, the significance of the difference in SPAD value between Sasanishiki and each CSSL was determined by Dunnett’s test (JMP 6.0.3 software, SAS Institute, Cary, NC, USA).
We thank the staff of the technical support section of NIAS and NICS for field management. This work was supported by a grant from the Ministry of Agriculture, Forestry and Fisheries of Japan (Genomics for Agricultural Innovation, QTL1002).
- Abdelkhalik AF, Shishido R, Nomura K, Ikeshashi H. QTL-based analysis of leaf senescence in an indica/japonica hybrid in rice (Oryza sativa L.). Theor Appl Genet. 2005;110:1226–35.PubMedView ArticleGoogle Scholar
- Ando T, Yamamoto T, Shimizu T, Ma XF, Shomura A, Takeuchi Y, et al. Genetic dissection and pyramiding of quantitative traits for panicle architecture by using chromosomal segment substitution lines in rice. Theor Appl Genet. 2008;116:881–90.PubMedView ArticleGoogle Scholar
- Asanuma S, Tsuru Y, Nito N, Ookawa T, Hirasawa T. Factors affecting the differences in the maximum rate of photosynthesis measured by gas exchange method between rice cultivars Sasanishiki and Habataki. Jpn J Crop Sci. 2008;77(2):118–9.Google Scholar
- Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, et al. Cytokinin oxidase regulates rice grain production. Science. 2005;309:741–5.PubMedView ArticleGoogle Scholar
- Basten CJ, Weir BS, Zeng ZB. A reference manual and tutorial for QTL mapping, QTL cartographer. Version 1.16. Raleigh: North Carolina State University; 2002.Google Scholar
- Chubachi T, Asano I, Oikawa T. The diagnosis of nitrogen nutrition of rice plants (Sasanishiki) using chlorophyll meter. Jpn J Soil Sci Plant Nutr. 1986;57:190–3.Google Scholar
- Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics. 1994;138:963–71.PubMedPubMed CentralGoogle Scholar
- Doerge RW, Churchill GA. Permutation tests for multiple loci affecting a quantitative character. Genetics. 1996;142:285–94.PubMedPubMed CentralGoogle Scholar
- Ebitani T, Takeuchi Y, Nonoue Y, Yamamoto T, Takeuchi K, Yano M. Construction and evaluation of chromosome segment substitution lines carrying overlapping chromosome segments of indica rice cultivar ‘Kasalath’ in a genetic background of japonica elite cultivar ‘Koshihikari’. Breed Sci. 2005;55:65–73.View ArticleGoogle Scholar
- Evans LT. Crop evolution, adaptation and yield. New York: Cambridge University; 1993.Google Scholar
- Fan C, Xing Y, Mao H, Lu T, Han B, Xu C, et al. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet. 2006;112:1164–71.PubMedView ArticleGoogle Scholar
- Farquhar GD, Sharkey TD. Stomatal conductance and photosynthesis. Ann Rev Plant Physiol. 1982;33:317–45.View ArticleGoogle Scholar
- Horie T, Matsuura S, Takai T, Kuwasaki K, Ohsumi A, Shiraiwa T. Genotypic difference in canopy diffusive conductance measured by a new remote-sensing method and its association with the difference in rice yield potential. Plant Cell Environ. 2006;29:653–60.PubMedView ArticleGoogle Scholar
- Huang J, Peng S. Comparison and standardization among chlorophyll meters in their readings on rice leaves. Plant Prod Sci. 2004;7:97–100.View ArticleGoogle Scholar
- Huang X, Qian Q, Liu Z, Sun H, He S, Luo D, et al. Natural variation at the DEP1 locus enhances grain yield in rice. Nat Genet. 2009;41:494–7.PubMedView ArticleGoogle Scholar
- Hubbart S, Peng S, Horton H, Chen Y, Murchie EH. Trends in leaf photosynthesis in historical rice varieties developed in the Philippines since 1966. J Exp Bot. 2007;58:3429–38.PubMedView ArticleGoogle Scholar
- International Rice Genome Sequencing Project. The map-based sequence of the rice genome. Nature. 2005;436:793–800.View ArticleGoogle Scholar
- Ishimaru K, Yano M, Aoki N, Ono K, Hirose T, Lin SY, et al. Toward the mapping of physiological and agronomic characters on a rice function map: QTL analysis and comparison between QTLs and expressed sequence tags. Theor Appl Genet. 2001;102:793–800.View ArticleGoogle Scholar
- Jinwen L, Jingping Y, Pinpin F, Junlan S, Dongsheng L, Changshui G, et al. Responses of rice leaf thickness, SPAD readings and chlorophyll a/b ratios to different nitrogen supply rates in paddy field. Field Crops Res. 2009;114:426–32.View ArticleGoogle Scholar
- Kanbe T, Sasaki H, Aoki N, Yamagishi T, Ebitani T, Yano M, et al. Identification of QTLs for improvement of plant type in rice (Oryza sativa L.) Using Koshihikari/Kasalath chromosome segment substitution lines and backcross progeny F2 population. Plant Prod Sci. 2008;11:447–556.View ArticleGoogle Scholar
- Kato M, Kobayashi K, Ogiso E, Yokoo M. Photosynthesis and dry-matter production during ripening stage in a female-sterile line of rice. Plant Prod Sci. 2004;7:184–8.View ArticleGoogle Scholar
- Kobayashi A, Koga Y, Uchiyamada H, Horiuchi H, Miura K, Okuno K, et al. Breeding a new rice variety “Habataki”. Bull Hokuriku Natl Agric Exp Stn. 1990;32:65–84.Google Scholar
- Kumagai E, Araki T, Kubota F. Correlation of chlorophyll meter readings with gas exchange and chlorophyll fluorescence in flag leaves of rice (Oryza sativa L.) plants. Plant Prod Sci. 2009;12:50–3.View ArticleGoogle Scholar
- Kura-Hotta M, Satoh K, Katoh S. Relationship between photosynthesis and chlorophyll content during leaf senescence. Plant Cell Physiol. 1987;28:1321–9.Google Scholar
- Lander ES, Green P, Abrahamson J, Barlow A, Daley MJ, Lincoln SE, et al. MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics. 1987;1:174–81.PubMedView ArticleGoogle Scholar
- Makino A, Mae T, Ohira K. Photosynthesis and ribulose 1, 5-bisphosphate carboxylase in rice leaves: changes in photosynthesis and enzymes involved in carbon assimilation from leaf development through senescence. Plant Physiol. 1983;73:1002–7.PubMedPubMed CentralView ArticleGoogle Scholar
- McCouch SR, Teytelman L, Xu Y, Lobos KB, Clare K, Walton M, et al. Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA Res. 2002;9:199–207.PubMedView ArticleGoogle Scholar
- Murray MG, Thomson WF. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980;8:4321–6.PubMedPubMed CentralView ArticleGoogle Scholar
- Nagata K, Fukuta Y, Shimizu S, Yagi T, Terao T. Quantitative trait loci for sink size and ripening traits in rice (Oryza sativa L.). Breed Sci. 2002;52:259–73.View ArticleGoogle Scholar
- Ohsumi A, Hamasaki A, Nakagawa H, Yoshida H, Shiraiwa T, Horie T. A model explaining genotypic and ontogenetic variation of leaf photosynthetic rate in rice (Oryza sativa) based on leaf nitrogen content and stomatal conductance. Ann Bot London. 2007;99:265–73.View ArticleGoogle Scholar
- Peng S. Single-leaf and canopy photosynthesis of rice. In: Sheehy JE, Mitchell PL, Hardy B, editors. Redesigning rice photosynthesis to increase yield. Philippines: International Rice Research Institute; 2000. p. 213–28.View ArticleGoogle Scholar
- Peng S, Garcia FV, Laza RC, Cassman KG. Adjustment for specific leaf weight improves chlorophyll meter's estimate of rice leaf nitrogen concentration. Field Crops Res. 1993;85:987–90.Google Scholar
- Peng S, Khush GS, Virk P, Tang Q, Zou Y. Progress in ideotype breeding to increase yield potential. Field Crops Res. 2008;108:32–8.View ArticleGoogle Scholar
- Porra RJ, Thompson WA, Kriedemann PE. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochim Biophys Acta. 1989;975:384–94.View ArticleGoogle Scholar
- Shen YJ, Jiang H, Jin JP, Zhang ZB, Xi B, He YY, et al. Development of genome-wide DNA polymorphism database for map-based cloning of rice genes. Plant Physiol. 2004;135:1198–205.PubMedPubMed CentralView ArticleGoogle Scholar
- Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, et al. Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet. 2008;40:1023–8.PubMedView ArticleGoogle Scholar
- Sinclair TR, Purcell LC, Sneller CH. Crop transformation and the challenge to increase yield potential. Trends Plant Sci. 2004;9:70–5.PubMedView ArticleGoogle Scholar
- Song XJ, Huang W, Shi M, Zhu MZ, Lin HX. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet. 2007;39:623–30.PubMedView ArticleGoogle Scholar
- Takai T, Matsuura S, Nishio T, Ohsumi A, Shiraiwa T, Horie T. Rice yield potential is closely related to crop growth rate during late reproductive period. Field Crops Res. 2006;96:328–35.View ArticleGoogle Scholar
- Takai T, Ohsumi A, San-oh Y, Laza MRC, Kondo M, Yamamoto T, et al. Detection of a quantitative trait locus controlling carbon isotope discrimination and its contribution to stomatal conductance in japonica rice. Theor Appl Genet. 2009;118:1401–10.PubMedView ArticleGoogle Scholar
- Takai T, Yano M, Yamamoto T. Canopy temperature on clear and cloudy days can be used to estimate varietal differences in stomatal conductance in rice. Field Crops Res. 2010;115:165–70.View ArticleGoogle Scholar
- Teng S, Qian Q, Zeng D, Kunihiro Y, Fujimoto K, Huang D, et al. QTL analysis of leaf photosynthetic rate and related physiological traits in rice (Oryza sativa L.). Euphytica. 2004;135:1–7.View ArticleGoogle Scholar
- Terashima I, Hanba YT, Tazoe Y, Vyas P, Yano S. Irradiance and phenotype: comparative eco-development of sun and shade leaves in relation to photosynthetic CO2 diffusion. J Exp Bot. 2006;57:343–54.PubMedView ArticleGoogle Scholar
- van Ooijen JW. Accuracy of mapping quantitative trait loci in autogamous species. Theor Appl Genet. 1992;84:803–11.PubMedView ArticleGoogle Scholar
- Wang F, Wang G, Li X, Huang J, Zheng J. Heredity, physiology and mapping of a chlorophyll content gene of rice (Oryza sativa L.). J Plant Physiol. 2008;165:324–30.PubMedView ArticleGoogle Scholar
- Watanabe SY, Hatanaka Y, Inada K. Development of a digital chlorophyll meter: I. Structure and performance. Jpn J Crop Sci. 1980;49(special issue):89.Google Scholar
- Xu YF, Ookawa T, Ishihara K. Analysis of the photosynthetic characteristics of the high-yielding rice cultivar Takanari. Jpn J Crop Sci. 1997;66:616–23.View ArticleGoogle Scholar
- Yamamoto T, Yonemaru J, Yano M. Towards the understanding of complex traits: substantially or superficially? DNA Res. 2009;16:141–54.PubMedPubMed CentralView ArticleGoogle Scholar
- Yoshida H, Horie T. A process model for explaining genotypic and environmental variation in growth and yield of rice based on measured plant N accumulation. Field Crops Res. 2009;113:227–37.View ArticleGoogle Scholar
- Yue B, Xue WY, Luo LJ, Xing YZ. QTL analysis for flag leaf characteristics and their relationships with yield and yield traits in rice. Acta Genetica Sinica. 2006;33:824–32.PubMedView ArticleGoogle Scholar
- Zelitch I. The close relationship between net photosynthesis and crop yield. Bioscience. 1982;32:796–802.View ArticleGoogle Scholar
- Long SP, Zhu XG, Naidu SL, Ort DR. Can improvement in photosynthesis increase crop yields? Plant Cell Environ. 2006;29:315–30.PubMedView ArticleGoogle Scholar