Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars a
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Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars a

Course Number: LS 112, Spring 2010

College/University: Zhejiang University

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Field Crops Research 109 (2008) 1–23 Contents lists available at ScienceDirect Field Crops Research journal homepage: www.elsevier.com/locate/fcr Review Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments Akihiko Kamoshita a,*, R. Chandra Babu b, N. Manikanda Boopathi b, Shu Fukai c a Asian Natural Environmental Science Center,...

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Crops Field Research 109 (2008) 1–23 Contents lists available at ScienceDirect Field Crops Research journal homepage: www.elsevier.com/locate/fcr Review Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments Akihiko Kamoshita a,*, R. Chandra Babu b, N. Manikanda Boopathi b, Shu Fukai c a Asian Natural Environmental Science Center, University of Tokyo, 1-1-1 Midoricho, Nishitokyo 188-0002, Japan Department of Plant Molecular Biology and Biotechnology, Center for Plant Molecular Biology, Tamil Nadu Agricultural University, Coimbatore 641003, India c University of Queensland, School of Land, Crop and Food Sciences, Queensland, Australia 4072 b ARTICLE INFO ABSTRACT Article history: Received 15 December 2007 Received in revised form 11 June 2008 Accepted 14 June 2008 Keywords: Drought avoidance Drought screening Drought type QTL MAS Rainfed lowland rice Upland rice Many of the world’s rice-growing regions lack adequate irrigation facilities, and drought frequently reduces yield. This paper reviews drought-resistance traits in rice and their quantitative trait loci (QTLs), with emphasis on CT9993/IR62266, one of the most widely studied mapping populations, and suggests ways to develop cultivars that will perform well in drought-prone environments. Information about the type of drought faced in the target region – particularly the timing of the drought (late season terminal drought, early stage vegetative drought, and intermittent drought) and the intensity of the drought – are important in determining the specific plant traits required to improve drought resistance in rice. Most of these traits are related to drought avoidance strategy, so that the drought-resistant genotypes are able to maintain better internal water status, either by taking up more water through a better root system or by reducing the rate of plant water use. We identified and listed a number of QTLs for many droughtresistance traits, such as deep roots. We identified four key genomic regions on chromosomes 1, 4, 8, and 9 on which are co-located a number of QTLs for traits considered to be directly or indirectly responsible for grain yield under stress. These regions, once they have been more finely mapped, appear promising for eventual use in marker-assisted selection for development of drought-resistant rice varieties. In addition to selecting for specific traits or specific genomic regions, screening under managed drought conditions on the basis of yield itself or on spikelet fertility adjusted for flowering time appears useful, because of the relatively high degrees of heritability of these characters, for the development of drought-resistant rice cultivars, and it is currently practiced in some breeding programs. ß 2008 Elsevier B.V. All rights reserved. Contents 1. 2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Types of drought and drought-resistance traits . 2.1. Types of drought . . . . . . . . . . . . . . . . . . . 2.2. Drought-resistance traits . . . . . . . . . . . . . 2.2.1. Terminal drought. . . . . . . . . . . . 2.2.2. Vegetative stage drought. . . . . . 2.2.3. Intermittent drought . . . . . . . . . Selection of drought-resistant genotypes . . . . . . 3.1. Screening environments. . . . . . . . . . . . . . 3.2. Selection criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2 3 4 5 5 5 5 6 3. * Corresponding author. E-mail address: akamoshita@anesc.u-tokyo.ac.jp (A. Kamoshita). Abbreviations: ABA, abscisic acid; DH, doubled haploid; DRI, drought response index; EW, epicuticular wax; GA, gibberellic acid; G  E, genotype-by-environment interaction; LEA, late embryogenesis abundant; LWP, leaf water potential; MAS, marker-assisted selection; QTL, quantitative trait locus; RPR, root pulling resistance; RWC, relative water content; SSR, simple sequence repeat. 0378-4290/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2008.06.010 2 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 4. 5. 6. Molecular approaches for development of drought-resistant varieties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. QTL mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1. Co-location of QTLs for plant-type traits, and integrative, primary, and secondary drought-resistance traits . . . . . . . . . . . . 4.1.2. Fine mapping of QTLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Marker-assisted selection for drought resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Transgenics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Improvement of general adaptability and drought resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 10 12 17 18 18 18 19 19 19 1. Introduction Large areas of rice are grown under lowland and upland rainfed conditions. These areas respectively occupy 31% and 11% of the global rice-growing area (IRRI, 2001). Drought is a major limitation for rice production in rainfed ecosystems. Evenson et al. (1996) estimated global rice yield lost to drought to be 18 million tonnes annually or 4% of total rice production, which was valued conservatively at US$ 3.6 billion at that time. On average, the estimated yield lost to drought is 144 kg/ha annually in eastern India (Dey and Upadhyaya, 1996). Simulation modeling indicates that reduction in yield of ra are further divided into constitutive traits and induced traits. Traits in the lower part of the diagram (primary traits, plant-type traits, and phenology) are presumed to be controlled with fewer genes/ QTLs compared with traits in the upper part of the diagram (grain yield, integrative traits, and secondary traits). Traits on the left-hand side (phenology, plant-type traits, some secondary traits [e.g., leaf death score]) were more easily measured for selection compared with traits shown on the right-hand side (primary traits, some secondary traits [e.g., leaf water potential]). 4 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 yield components (i.e., integrative traits), and ultimately, yield (Kobata et al., 1994). Plant-type traits such as tiller number and plant height modify the expression of secondary and integrative traits by affecting transpirational demand. Genotypes with greater plant height are often larger in overall plant size, intercept more light and use water faster by transpiration, leading to lower plant water status (Kamoshita et al., 2004), higher leaf death scores, and more spikelet sterility (Pantuwan et al., 2002c; Kato et al., 2007b). Phenology, interacted with timing of drought, has a large effect on yield through integrative traits. Plant-type traits (e.g., plant height) and phenology (e.g., flowering time) usually are highly heritable (discussed in Section 3.1) and are extensively used in traditional plant breeding (Cooper et al., 1999a,b). Leaf rolling and canopy temperature (i.e., secondary traits) are also useful (Lafitte et al., 2004a; Hirayama et al., 2006) for quickly screening hundreds of lines. Although yields under stress generally have a higher phenotypic correlation with some of the yield components (e.g., grain number and percent fertile spikelets; integrative traits) than they do with primary traits, primary traits are likely to be controlled by fewer underlying genes or QTLs. Therefore, molecular characterization of primary traits (e.g., QTL analysis or candidate gene approaches) will presumably be a more promising avenue than the study of yield components. Recent transgenic studies have examined only primary traits of which the expression is induced by water deficit, as is discussed in Section 4.3. Trait type classifications are to some extent arbitrary. Thus, we classify flowering delay as an integrative trait. The plant traits that confer drought resistance depend on the type of drought. The following section describes the physiological and morphological traits likely to be useful against the three common types of drought. 2.2.1. Terminal drought Early flowering genotypes can escape from late season drought, and this is a simple, but often the most effective, way of increasing yield under terminal drought. Replacing late maturing cultivars with medium maturing cultivars that have good yield potential in rainfed lowlands, as has occurred in Cambodia, provides a better chance of escaping late season drought (Ouk et al., 2007). Earlier flowering time may be especially useful for upper positions in a toposequence, because standing water often disappears earlier there than in lower positions (Homma et al., 2003). Sets of upland experiments with diverse germplasm (i.e., indica, aus, and japonica subspecies) also show the advantages of earlier flowering over later flowering in terms of higher spikelet fertility, higher harvest index, and higher yield (Lafitte and Courtois, 2002). Among the genotypes with similar flowering times, maintenance of high leaf water potential (LWP) was often related with higher yields under terminal drought (Jongdee et al., 1997a, 2002; Pantuwan et al., 2002c; Jearakongman, 2005). Maintenance of higher LWP under drought is empirically related to better stem extension and panicle exsertion (Jearakongman, 2005), as well as to reduced delay in flowering (Pantuwan et al., 2002b). Jearakongman (2005) found a positive relationship between panicle exsertion rate and LWP under drought among 55 near-isogenic lines of the cultivar IR64 into which root trait QTLs from the cultivar Azucena had been introduced (Shen et al., 2001). Root signals such as ABA play important roles for stomatal control under mild drought and hydraulic force becomes important under severe drought (Tardieu and Davies, 1993; Ali et al., 1999; Comstock, 2002), but evidences are few in rice that genotypic differences in LWP are related with root signals such as ABA (Siopongco et al., 2008). It was suggested that some genotypes may have stronger stomatal sensitivity to root signals in response to soil water deficit to save water loss from stomata (Tardieu and Davies, 1993) and that others may continue higher assimilation of CO2 in spite of high proportion of closed stomata (Hu et al., 2006). Sibounheuang et al. (2001) showed consistent ranking of LWP as an important characteristic of drought resistance among six genotypes under different transpirational demands (by defoliation treatment) and under similar soil water availability (by mixed planting treatment) under upland drought conditions. This suggests an association between shoot water potential (e.g., in leaves and panicles) and internal plant water conductivity (Tsuno et al., 2004; Sibounheuang et al., 2006). Sibounheuang et al. (2006) found xylem diameter likely to be related to maintenance of xylem water conductivity; however, this needs to be tested in a wider range of germplasm together with vascular constrictions between roots and shoots. Umayal et al. (2001) observed that droughttolerant indica landraces in southern India had thicker roots with wider xylem vessels (similar dimensions to the diameters of roots, steles, and xylem vessels in traditional upland japonica cultivars) than susceptible cultivars. Wider xylem vessels may decrease chances of cavitation, which would reduce xylem water conductivity (Hsiao, 1973; Hacke et al., 2001; Stiller et al., 2003). Under terminal drought, slow use of water (conservative strategy) is beneficial if the effective root depth is limited. In rainfed lowland experiments in Thailand, where a severe drought developed suddenly, soil water from 0 to 45 cm soil depth tended to be extracted more slowly during 7 days of no water supply at booting stage by genotypes with smaller shoot systems that have reduced water demand (Pantuwan et al., 2002c). These genotypes produced higher yields under the severe drought. This strategy appears particularly useful when drought develops rapidly late in the growing season and the crop has high biomass and high water requirements. Genotypic variation in the amounts of epicuticular wax (EW) has been reported, production of EW increases under water stress, which can reduce non-stomatal water loss from leaves, but its effect on yield has not yet been demonstrated (Srinivasan, 2005). Deeper and thicker roots also may occur under upland conditions and some lowland conditions, resulting in the genotype extracting larger amounts of soil water and maintaining higher plant water status (e.g., Yoshida and Hasegawa, 1982). Kumar et al. (2004) reported an association between higher root pulling resistance (RPR) and maintenance of higher LWP under severe drought stress, as well as a positive correlation between grain yield and relative water content (r = 0.72, P = 0.01) and RPR (r = 0.59, P = 0.05). By using a system that restricted root growth to no more than 25 cm below the soil surface by means of a water-permeable sheet, Kato et al. (2007b) showed that genotypes with deeper root development (and with larger plant size) could maintain higher LWP when there was no root restriction, but suffered from a greater reduction in LWP and increased leaf death score with the presence of root restriction. This shows that a well-developed root system can help maintain plant water status, ultimately stabilizing yield under drought. The ability of rice to function well at reduced plant water potentials has not been demonstrated to be useful under field drought regimes. In pot experiments, Capell et al. (2004) showed positive effects of polyamine accumulation under drought conditions induced by replacing water with 20% polyethylene glycol for 6 days. They identified several genotypes with high spermine content, a polyamine involved in cellular metabolism, which may assist plant cells to maintain normal metabolisms under stress, and they showed that transgenic plants overproducing polyamines maintained growth under water stress. Osmotic adjustment allows maintenance of growth when water uptake from the root system is insufficient due to reduced availability of soil moisture, but A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 5 possibly, it also could increase extraction of water by the roots (Serraj and Sinclair, 2002). Genotypic variation in osmotic adjustment has been demonstrated in rice (Lilley et al., 1996; Babu et al., 2001). However, there has been no report demonstrating any positive phenotypic or genetic correlation between grain yield and osmotic adjustment in rice under stress. Genotypic variation in cell membrane stability is also reported in rice (Tripathy et al., 2000). Babu et al. (2004b) showed that transgenic rice with HVA1, a LEA gene from barley, had less membrane leakage than non-transgenic rice cultivars under water stress. Carbon isotope discrimination, often associated with transpiration efficiency, also varies with rice genotypes (Dingkuhn et al., 1991; Price et al., 2002c), but its association with grain yield under drought has not been demonstrated (Laza et al., 2006). Several studies have attempted to clarify physiological mechanisms underlying the genotypic variation in spikelet sterility under identical conditions of internal plant water deficit. Maintenance of open stomata, and hence continuous supply of carbohydrates to spikelets and higher enzyme activities (Sheoran and Saini, 1996), might assist fertilization and reduce spikelet sterility (Saini, 1997; Saini and Lalonde, 1998). The movement of carbohydrates to the anthers and peduncles is controlled not simply by supply from source tissues but also by sink strength and activity. The sink strength and activity is presumed to involve the GA versus ABA balance in inflorescences, ABA synthesis during stress and its degradation during recovery (Zhang et al., 2006), expression of glycoprotein in pollens, and cell-wall acid invertase activity in anthers and pollens (Bennett et al., 2005). The droughtresistant cultivar N22 had higher spikelet fertility than the drought-sensitive cultivar N118 as a result of increased antioxidant enzyme activities in the panicles under water stress (Selote and Khanna-Chopra, 2004). Liu et al. (2006) showed a smaller reduction in spikelet fertility in Moroberekan (16%) than in IR64 (80%) after 6 days of withholding water, starting 3 days before heading. This smaller reduction in spikelet fertility was attributed to better anther dehiscence and higher stigma pollen density in Moroberekan. 2.2.2. Vegetative stage drought Little research appears to have been conducted on genotypic variation in rice in terms of germination (e.g., the lowest limits of soil water content for germination and emergence) or transplanting under water limiting conditions in rainfed lowlands. Ikeda et al. (2007) found a cultivar-by-water-regime interaction for rooting ability after transplanting: a lowland cultivar produced new roots more quickly and allocated greater biomass to roots than an upland cultivar in the presence of standing water, but this was not the case without standing water. Seedling vigor may allow faster development of deep roots before or during the early stages of drought, thus accelerating water extraction and maintaining growth during drought (Kamoshita et al., 2000,2004). Landraces from southern India typically have higher seedling vigor, with rapid root and shoot growth and earlier ground coverage than improved cultivars. Price et al. (2002a,b) showed large interactive effects of initial soil moisture content on genotypic deep root development and partitioning of assimilates during the seedling stage when plants were grown in small containers. Because of the long time from the time of stress to harvest, drought-resistance traits during vegetative stage drought may not be related to grain yield (Lafitte et al., 2002a). Plant growth resumes after vegetative stage drought, and this recovery growth then affects the development of sink size as well as source supply to meet the demand of the grain. Field studies (Lilley and Fukai, 1994; Mitchell et al., 1998) and pot studies (Wade et al., 2000; Kamoshita et al., 2004) both show genotypic variation in short- term recovery growth (e.g., 1 week to a few weeks) after vegetative stage drought, and these authors have reported the relationships between this genotypic variation and the amount of leaf remaining at the end of drought and the ability to tiller after drought. Although the benefits of short-term drought recovery traits on yield are difficult to demonstrate, a number of studies have shown that later maturing and longer growth duration cultivars show less growth stagnation and drought damage and have a higher yield when they encounter mild water shortages during the vegetative to panicle initiation stages (e.g., Fukai and Cooper, 1995; Hayashi et al., 2006; Ikeda et al., 2008). 2.2.3. Intermittent drought A deep root system with higher root density is likely to be useful under intermittent drought if growing conditions permit root development at depth. Under upland conditions, the association between root length density and the amount of water extracted has been well demonstrated (Lilley and Fukai, 1994), including for genotypic variations (Nemoto et al., 1998; Kato et al., 2007a), and deep and thick root traits contribute to better growth and higher yield under drought stress (Lafitte and Courtois, 2002; Babu et al., 2003). In CT9993/IR62266 doubled haploid (DH) lines, three root traits measured in glasshouse experiments – root thickness, deep root weight, and root penetration index – were correlated positively with yield and yield components under severe preflowering drought conditions in an upland field experiment (Babu et al., 2003). On the other hand, deep rooting is often poorly expressed under rainfed lowland conditions (Pantuwan et al., 1997), and there is much less evidence of genotypic variation in root traits with regard to water extraction during drought (Kamoshita et al., 2000, 2004). The extent of deep root development measured by partitioning ratio (deep root ratio) is less under anaerobic flooded conditions (ca. 0.3–1.2%) than under aerobic drought conditions (ca. 3–17%) in pot experiments (Azhiri-Sigari et al., 2000; Kamoshita et al., 2004). Genotypic rankings for deep root traits expressed under anaerobic conditions were generally similar to those expressed under aerobic conditions, but in some cases genotype by environment interactions were detected (Champoux et al., 1995; Azhiri-Sigari et al., 2000). Genotypic differences in the ability to penetrate compacted soil layers have been reported (Babu et al., 2001; Samson et al., 2002); these differences are associated with the amount of water extracted from below the compacted layer (Hoque and Kobata, 2000; Kobata et al., 2000). 3. Selection of drought-resistant genotypes 3.1. Screening environments Phenotyping facilities for screening rice genotypes for drought resistance are increasingly available. Lafitte (2004) proposed the use of a managed drought environment (i.e., a drought condition controlled or intentionally manipulated to some degree by scientists) in order to provide the desired severity and timing of the drought. There are a number of advantages to such a system: (1) natural drought occurrence in rainfed lowland and upland rice in the wet season is unpredictable, thus limiting screening under the desired drought types; (2) managed drought uses resources (e.g., the budget of a drought project of only a few years) more efficiently than waiting for the occurrence of natural drought in the wet season; and (3) there is a high genetic correlation between yield under stress in a managed selection environment and that in the target environment (Atlin, 2004). However, there are also weaknesses in screening genotypes from managed drought environments (Kamoshita, personal communication). We will 6 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 briefly discuss the strengths and weaknesses of several screening environments. A controlled drought environment can be established more readily in the dry season (e.g., Lafitte et al., 2006), but the environment may not represent that of the farmer’s fields in the wet season and will confound the effects of photoperiod in sensitive genotypes. Pantuwan et al. (2004) reported that responses to dry conditions among genotypes in the dry season differed from responses under wet season drought. Nevertheless, the dry season may be useful for preliminary vegetative stage screening of large numbers of genotypes (Pantuwan et al., 2004). To reduce the chance of rainfall interfering with drought development in the wet season, trials may be planted later and water may then be drained from the field so that the crop has a better chance of being exposed to drought; this is particularly so for terminal drought trials (Pantuwan et al., 2002a; Jongdee, 2004). However, this method also causes bias against photoperiod sensitive cultivars. Late planting may not be readily achieved in some locations, as in southern India where pre-monsoon direct seeding is practiced. Drainage techniques can create severe stress under lowland conditions (Kumar et al., 2007), and the timing of drainage can be adjusted to induce different types of drought (Pantuwan et al., 2002a; Ouk et al., 2006). Another practice for establishing a managed drought environment is to use a high position in the toposequence (Kumar et al., 2007; Hayashi et al., 2007). Standing water disappears naturally often a few weeks earlier than it does in lower positions. A Thai breeding program currently utilizes different toposequence positions, and cultivars are selected for different toposequence positions after consideration of the large differences in the water and soil nutrient environments (Homma et al., 2003; Tsubo et al., 2006,2007). Rainout shelters can reduce damage from untimely rainfall and in China are used with drip irrigation and drainage systems (O’Toole, 2004; Li et al., 2005; Yue et al., 2005; Liu et al., 2008). However, the high cost of such facilities may limit their widespread use. A Japanese upland rice breeding program uses raised beds (ca. 30 cm above ground level) to screen drought-resistant genotypes (Hirayama and Suga, 1996; Kato et al., 2007b). These beds are prepared over a 5-cm-thick layer of gravel laid on the soil surface to prevent capillary rise of water into the raised bed. Kato et al. (2007b) showed that, compared with the natural rainfed upland conditions, rice crops grown in the raised bed system were exposed to less soil moisture in the surface layers during rain-free periods, increasing the differences in yield between shallow- and deeprooting cultivars. 3.2. Selection criteria For efficient screening, Blum (2002) suggested two points for assessing the utility of traits. The first is that important droughtresistance traits are normally constitutive and not stress adaptive. Constitutive traits, such as flowering time, the stay-green trait (delayed onset of leaf senescence), and root depth, can be routinely screened (without a drought challenge); generally, their role towards drought resistance may be considered greater than stressresponsive traits. Stress adaptive traits (responsive, induced) are those that are expressed only under drought. Such traits include active cellular accumulation of compatible solutes (osmoprotectants), antioxidant agents, heat shock proteins, and molecular chaperones, as well as osmotic adjustment and membrane stability. Blum’s second point is that plant water status, rather than plant function, controls crop performance under drought. Therefore, those genotypes that can maintain higher LWP, and RWC are drought resistant simply because of their superior internal water status. He argues that cases of sustained function under low water status – a possible alternative for drought resistance – are rare. His argument supports the classification of rice as a drought avoider, and maintenance of plant water status, rather than tolerance of drought with low plant water potential, is the key to drought resistance. For screening, traits, which are more likely to be constitutive but may also include induced traits, need to be highly heritable and easily measured. Calculation and interpretation of broad sense heritability (i.e., approximately 0.9 as high, 0.6 as moderate, and 0.2 as low) were explained by Lafitte et al. (2004a). Numerous studies have found moderate heritability of yield under drought conditions (Babu et al., 2003; Atlin et al., 2004; Yue et al., 2005; Kumar et al., 2007), supporting direct selection for yield, rather than for droughtresistance traits (Table 1). Kumar et al. (2007) reported higher broad-sense heritability of grain yield under severe terminal drought stress in 2 years (0.37–0.54) in the CT9993/IR62266 population than for secondary and integrative drought-resistance traits such as harvest index (0.22–0.36), spikelet sterility (0.12– 0.23), flowering delay (0.12–0.21), relative water content (0.07– 0.13), root pulling force (0.32–0.49), or root dry weight (0.27–0.43), all under field conditions (Table 1). Heritability of yield under stress is sometimes comparable with heritability under non-stress conditions (e.g., Kumar et al., 2007; Lafitte et al., 2004b) but also can be lower (e.g., 0.43 under terminal drought stress, 0.53–0.62 under intermittent stress, 0.81 under non-stress conditions in Jearakongman, 2005). In Brazil, direct selection for yield using a managed drought environment that matches the environment in the breeding domain has resulted in the identification of tolerant genotypes and development of higher yielding cultivars for upland rice (Pinheiro, 2004; Pinheiro et al., 2006). This approach has also been used for rainfed lowland rice in Thailand and Cambodia (Jongdee, 2004; Ouk et al., 2006,2007). On the other hand, because primary drought-resistance traits are associated with fewer genes and are under simpler genetic control than yield under stress, the heritability of primary traits should be higher than for secondary and integrative traits and yield, assuming that trait expression is accurately measured. In the CT9993/IR62266 population, primary traits such as deep and thick Table 1 Broad sense heritability measured either greenhouse (pot) or field experiments for drought-resistance trait groups (primary, secondary, integrative, plant-type, phenology, and grain yield) under different drought stress types (non-stress, vegetative, intermittent, terminal) Measured systems Drought-resistance traits drought stress types Primary traits Deep and thick rootsa Pot 0.60–0.84b, 0.40–0.81c, 0.44–0.61d, 0.55–0.84e, 0.61–0.90f, 0.26–0.83g – 0.62–0.92f (0.50–0.63)f, 0.50–0.92g (0.40–0.67)g 0.37–0.58h Field 0.80h, 0.32–0.49i 0.27–0.43 i – – Non-stress Terminal Intermittent Hard soils A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 Table 1 (Continued ) Measured systems Drought-resistance traits drought stress types Cell membrane stability Osmotic adjustment Secondary traits Relative water content Vegetative (severe) Vegetative (gradual lengthy) Pot 0.34j 0.62h Field – – 7 Intermittent Terminal Non-stress Intermittent Terminal Terminal Intermittent Terminal Non-stress Intermittent Terminal – – – – – – 0.79 – – – – f 0.39k 0.07–0.13i, 0.49–0.73l 0.41–0.53 m 0.43–0.46 m 0.50–0.63m 0.86l, 0.5–0.68n 0.33–0.63k, 0.78l, 0.69–0.83m, 0.58–0.79n 0.65–0.71l, 0.86–0.92m 0.25m 0.58–0.69 m 0.55m Leaf water potential Canopy temperature Leaf death and rolling Panicle exsertion Integrative traits Hearvest index Non-stress Terminal Non-stress Intermittent Terminal Non-stress Intermittent Terminal Non-stress Intermittent Terminal Non-stress Intermittent Terminal Intermittent Terminal Terminal – – 0.24l 0.22–0.36i, 0.60l 0.29–0.45i, 0.59k, 0.65–0.69l, 0.61–0.67m 0.38k, 0.65–0.76m 0.12–0.23i, 0.43l, 0.63 0.89k, 0.51m 0.67k, 0.62–0.70m 0.50l, 0.65m 0.84k 0.91k, 0.40m 0.37l, 0.28m 0.26–0.42i, 0.60–0.78 l 0.84l 0.40–0.58i, 0.37–0.60l 0.36–0.42 m 0.46m 0.12–0.21i, 0.17k, 0.44–0.69n Spikelet sterility – – Grain number per panicle – – – – – 1000-Grain weight Biomass – – – – – Drought response index Flowering delay Phenology Flowering Non-stress Intermittent Terminal – – – 0.36–0.53i, 0.94k, 0.75 0.93k, 0.96l 0.32–0.48i, 0.70l Plant-type traits Plant height Non-stress Intermittent Terminal – – – 0.78k, 0.94m 0.66k, 0.93–0.95m 0.90m Grain yield Non-stress Intermittent Terminal – (0.86)f – 0.45–0.62i, 0.45k, 0.61–0.84l, 0.81m, 0.72–0.92n 0.45k, 0.81l, 0.53–0.62m, 0.73–0.84n 0.37–0.54i, 0.59l, 0.43m Terminal and intermittent stress types were distinguished from each other by examining the reduction percentages of yield under stress relative to yield under non-stress conditions in each reference. Values in ( ) indicate traits induced in response to water stress; these are either relative values (stress/non-stress) or increment under stress (stress–non-stress). a Deep root weight below 30 cm depth, deep root ratio, deep root weight per tiller number, maximum root length, rooting depth, total root weight, root volume, root thickness at depth. b Yadav et al. (1997). c Kamoshita et al. (2002a). d Ekanayake et al. (1985). e Courtois et al. (2003). f Yue et al. (2006). g Zheng et al. (2003). h Zhang et al. (2001a). i Kumar et al. (2007). j Tripathy et al. (2000). k Lafitte et al. (2004a,b). l Babu et al. (2003). m Jearakongman (2005). n Yue et al. (2005). 8 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 Table 2a Literature review of studies to identify QTL clusters correlating with plant-type traits, phenology, primary traits, secondary traits, and yield and integrative traits for drought resistance across 15 mapping populations Populationa Phenotyping environmentb Measured or mapped traits c Plant-type Phenology Primary Secondary Yield and integrative GY, BY, GW, PSS – – SDW – GY, BY, HI, SN Zhang et al. (1999) Tripathy et al. (2000) Zhang et al. (2001a) Kamoshita et al. (2002a) Kanbar et al. (2002) Babu et al. (2003) Reference 1 CT/IR 100,154 CT/IR 104 CT/IR 154 CT/IR 154 CT/IR n.a. CT/IR 154 Upland (Si), lowland (W, Si) Uplandp (S) Uplandp (S, hardpan) Lowlandp (W, seedling stage) Uplandp (W) Upland (W, Si, St; 2 wet & 1 dry seasons) Lowland (W, Si, St; wet season) Upland (W, S) Lowland (W, St; 2 years) Uplandp (W), Upland (S; 3) Uplandp (W, hardpan) Uplandp (S) Uplandp (W) Upland (S; 3 sites/seasons) Uplandp (W, S (mild vegetative)) Uplandp (W, hardpan) Lowland (W), upland (Si), uplandp (W, S) Upland (W, S (flowering); 3 dry seasons) Lowlandh Uplandp Upland Uplandp Upland (S; 2 sites in 3 dry seasons) Uplandp (S; 2 seedling drought) Uplandp (W; 2 dry seasons) Upland (W, Si (flowering); 2 dry seasons) Upland Uplandp (W, S) Uplandp (W, hardpan) Lowlandp (W; seedling stage) Uplandp (W) Uplandp (S) Lowlandh (W), uplandpa (W) Lowlandp (W), uplandp (S; mild) Lowland (W), upland (Sv), uplandp (Sv) – – – – PH, TN, TPL – – – – – – – – CMS OA, PRL, PRT, PRW, RPI, RT, RPF, TRW DR%, DRW, DRW-T, RD, RT RN, RSLR, TRW – CT, DRS, DS, LD, LR – – – – LR, LD, RWC CT/IR 154 CT/IR n.a. CT/IR 105 PN PH, PL, PTN – – DH DH – EW RPF, TRW – CT, LD, LR, RWC GY, BY, SN, PSS, GW GY, BY, HI, SY GY, BY, DFT, HI, PSS, SDW – – – – RGR SDW – GY, SDW, SY Lanceras et al. (2004) Srinivasan (2005) Kumar et al. (2007) 2 Co/Mo 203 Co/Mo 202 Co/Mo 42 – – – – – PH, TN – PL, PTN, TN – – – – – – – DH, DM DRW-T, MRL, RSDR, RT, TRW, PRN, RN, RPI DT, OA DRSR, DRW, DRW-T, MRL, RT, TRW – MRL, RL, RN, RSDR, RT, TRW, TRW-T PRN, PRT, RN, RPI MRL, RN, RSDR, RT, RV, TRW – LR – RWC – LD, LR, RWC – – – Champoux et al. (1995) Ray et al. (1996) Lilley et al. (1996) Yadav et al. (1997) Courtois et al. (2000) Hemamalini et al. (2000) Zheng et al. (2000) Venuprasad et al. (2002) 3 IR/Az 105 IR/Az 85-105 IR/Az 56 IR/Az 109 IR/Az 90 IR/Az 85 PH, PN DH – GY, RGY, GP, GW, PSS, SN Lafitte et al. (2002b) 4 Ba/Az 178# Ba/Az 178# Ba/Az n.a. Ba/Az 104 Ba/Az 110–176 – – DH – – – MRL, RCL, RT – RL PRN, RN, RPI – – – – – MLRS, RSC, SR, TFSC – – LD, LR, RWC – – – – – Price and Tomos (1997) Price et al. (1997) Price and Courtois (1999) Price et al. (2000) Price et al. (2002d) Ba/Az 140 Ba/Az n.a. Ba/Az 96 – – PH, PN – – DH DRN, DRW, MRL, RSDR, RT, TRW d13C, SLA – – – LD, LR, RWC – – GY, BY, FSP, GP, GW, HI, SF GY, SY SDW – SDW Price et al. (2002b) Price et al. (2002c) Lafitte et al. (2004b) Ba/Az 177 Ba/Az 168 5 IR/IR 166 IR/IR 166 PH, PL PH – – DH – – – – MRL, RT, DRW PRL, PRN, RPI, PRT, RN DR%, DRW, DRW-T, RD, RT DRSR, DRW, DRW-T, MRL, RT, TRW OA SRL ARN, LRL, LRN, SRL MRL, RN, RSDR, RSFR, RT, TRFW, TRW LD, LR Gomez et al. (2005) MacMillan et al. (2006) Ali et al. (2000) Kamoshita et al. (2002b) – – 6 7 8 IA/Co 125 I/I 150 I/A 150 I/A 96 PH, TN – – – – – – – – – – – SDW – – – Courtois et al. (2003) Robin et al. (2003) Zhang et al. (2001b) Zheng et al. (2003) 9 I/Yu 116 – – – GY, IDR Li et al. (2005) A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 Table 2a (Continued ) Populationa Phenotyping environmentb Measured or mapped traitsc Plant-type Phenology Primary Secondary Yield and integrative DFT, DRI, RGY, RSF Yue et al. (2005) Reference 9 10 Z/I 180 Z/I 150 Lowland (W, Si (reproductive); 2 soils), uplandp (W, St) Uplandp (W, Si (flowering)) Upland (W, Si) Upland (W, Si (reproductive stage)) Lowland (W, St; dry season) Upland (W, S) Upland (W, S) – – DRV, MRD, RGV, RT CT, LD, LR – – Z/I 187 Z/I 187 PN PL, PN – – DIRD, DIDRV, DRV, MRD, RGD, RGV, RV – – DLR, LD RGY, RBY, RFP, RGW, RHI, RSF, RSN GY, GP, GW, SF GP, PBN, PND, SBN, SN Yue et al. (2006) – – Zou et al. (2005) Liu et al. (2008) 11 T/L 254 PH, PN DH – – GY, GW Xu et al. (2005) 12 I/N n.a. I/N n.a. PH, TN PH, PL, TN DH DH RT – CT, DRS, LD, LR, RWC CT, DRS, LD, LR, RWC – – – – BY GY, BY, HI, SY Boopathi et al. (2005) Beena (2005) 13 A/I 106 A/I 106 O/Y 98 V/W 436 Lowlandh (W, S) Lowlandh (W) Lowland (W, S) Upland (W, S in 2 dry seasons) – – PH, PN PH, PN – – – DH – BI, RAL, TRW MNRL, NRN, RA, RSDR, TRW – RGR, WUE – SDW, SPDW GY, DFT, DRI, HI, BY Kato et al. (2008) Horii et al. (2006) Ikeda et al. (2007) Bernier et al. (2007) 14 15 a The names of the populations are listed in Table 2b. The values under the population of each study signify the number of lines used to identify QTLs. Subscript suffixes ‘‘p’’, ‘‘h’’, and ‘‘pa’’ stand for ‘‘pot experiments’’, ‘‘hydroponics’’, and ‘‘paper culture’’, respectively. W, S, Si, St, and Sv signify ‘‘well-watered’’, ‘‘stress’’, ‘‘intermittent stress’’, ‘‘terminal stress’’, and ‘‘vegetative stress’’, respectively. c Measured and mapped traits are grouped into (1) plant-type traits, (2) phenology, (3) primary traits, (4) secondary traits, and (5) yield and integrative traits. (1) Plant-type traits contain plant height (PH), panicle length (PL), panicle number (PN), productive tiller number (PTN), tiller number (TN), and total plant length (TPL). (2) Phenology contains days to heading/flowering (DH), and days to maturity (DM). (3) Primary traits contain (3a) constitutive root traits and (3b) other induced traits. (3a) Constitutive root traits are total root weight (TRW), total root fresh weight (TRFW), root to shoot dry weight ratio (RSDR), root to shoot fresh weight ratio (RSFR), deep root weight (DRW), deep root to total root dry weight ratio (DR%), deep root weight per tiller (DRW-T), deep root number (DRN), deep root to shoot dry weight ratio (DRSR), rooting depth (RD), root length (RL), maximum root length (MRL), total root number (RN), root shoot length ratio (RSLR), adventitious root number (ARN), lateral root number (LRN), lateral root length (LRL), seminal root length (SRL), root thickness at different depth (RT), root cell length (RCL), maximum root depth (cm) (MRD), root volume (RV), deep root rate (%) in volume (DRV), root growth rate in depth (cm/day) (RGD), root growth rate in volume (ml/day) (RGV), root pulling force (RPF), root axis length (RAL), branching index (BI), new root number (NRN), maximum new root length (MNRL), rooting ability after transplanting (RA). (3b) Other primary induced traits are drought induced root growth in depth (cm) (DIRD), deep root rate in volume (%) induced by drought (DIDRV), penetrated root number (PRN), penetrated root thickness (PRT), root penetration index (RPI), penetrated root weight (PRW), penetrated root length (PRL), osmotic adjustment (OA), cell membrane stability (CMS), carbon isotope discrimination as water use efficiency (d13C), specific leaf area (SLA), dehydration tolerance (DT). (4) Secondary traits contain modified leaf rolling score (MLRS), leaf rolling (LR), number of days to leaf rolling (DLR), leaf drying (LD), drought score (DS), drought recovery score (DRS), relative water content (RWC), canopy temperature (CT), stomatal resistance (SR), rate of stomatal closure (RSC), time of fastest stomatal closure (TFSC). (5) Grain yield (GY) and integrative traits contain straw yield (SY), biomass yield (BY), delay in flowering time by drought (DFT), primary and secondary branch number in panicles (PBN, SBN), panicle neck diameter (PND), spikelet number (SN), percent spikelet sterility (PSS), fraction sterile panicle (FSP), grains per panicle (GP), spikelet fertility (SF), 1000-grain weight (GW), harvest index (HI), shoot dry weight (SDW), single panicle dry weight (SPDW), water use efficiency (WUE), relative grain yield (RGY), relative growth rate (RGR), relative spikelet fertility (RSF), relative biomass (RBY), relative rate of fertile panicle (RFP), relative harvest index (RHI), relative grain weight (RGW), relative number of spikelets per panicle (RSN), panicle harvest index (PHI), drought response index (DRI), index of drought resistance (IDR). b roots (0.40–0.81 in Kamoshita et al., 2002a) and osmotic adjustment (0.62 in Zhang et al., 2001a) measured in greenhouse experiments had heritabilities that were comparable to, or higher than, that of yield under severe stress (0.59 in Babu et al., 2003; 0.37–0.54 in Kumar et al., 2007) (Table 1). However, measurements of root traits under drought conditions in the field usually have large errors (Pantuwan et al., 2002c; Samson et al., 2002), and the broad-sense heritabilities of root traits measured in the field (e.g., Kumar et al., 2007) are in general lower than those measured under hydroponic systems (Ekanayake et al., 1985) and pot systems (Kamoshita et al., 2002a; Yue et al., 2006). Measurement of induced traits such as osmotic adjustment or cell membrane stability could involve greater errors than measurement of constitutive traits because of differences in the degree of water stress if experimental conditions are not precisely controlled. Even under managed drought conditions (under both glasshouse and field conditions), expression of osmotic adjustment increases as drought intensifies and LWP declines, resulting in the tendency toward a genotype-bydrought-intensity interaction for osmotic adjustment (Jongdee et al., 2002; Kamoshita et al., 2000, 2004). Heritabilities of secondary traits (such as plant water status, leaf death score, and leaf rolling) and integrated traits (such as spikelet sterility) are often comparable to or higher than the heritability of yield under stress. Jearakongman (2005) showed higher broad-sense heritability for pre-dawn LWP (0.63), leaf death and leaf rolling scores (0.86–0.92), panicle exsertion rate (0.55), plant height (0.90), total number of grains per panicle (0.66), and number of unfilled grains per panicle (0.75) than broadsense heritability of yield under terminal stress (0.43) among 55 IR64 near-isogenic lines (Table 1). In the CT9993/IR62266 10 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 population, Babu et al. (2003) measured secondary and integrated traits in drought-affected fields and reported high broad-sense heritabilities for relative water content (0.49–0.73), canopy temperature (0.86), leaf rolling (0.65–0.78), leaf drying (0.70– 0.71), harvest index (0.60), and spikelet fertility (0.43) compared with the broad-sense heritability of yield under stress (0.59–0.81). Integrative traits or secondary traits are likely to be more closely associated with grain yield under various drought conditions than are primary traits, and they may have wider applicability in breeding provided they add efficiency to selecting for yield per se. In the upland program in Brazil, integrative and secondary traits of low leaf rolling, good panicle exsertion, and low level of spikelet sterility are used as selection criteria (Pinheiro et al., 2006). Grain yield under drought is affected not only by droughtresistance traits but also by flowering time and by the potential yield. The variation in potential yield and phenology can be corrected for by calculating a drought response index (DRI) (Pantuwan et al., 2002b; Lafitte, 2004; Ouk et al., 2006), and drought-tolerant genotypes may be selected by using DRI. 4. Molecular approaches for development of drought-resistant varieties 4.1. QTL mapping QTLs linked to drought resistance have been mapped in at least 15 different populations (Tables 2a and 2b). Most of the mapping populations were derived from indica  japonica parents, and it is often the case that favorable alleles for drought-resistance traits are contributed by japonica lines. Considering that indica and japonica ecotypes are grown in different environments and that most breeding programs involve locally adapted rice accessions, the results for traits such as yield in indica  japonica populations have to be interpreted with care. Hence, it is desirable to look for genetic variation among indica ecotypes (e.g., IR58821/IR52561; Ali et al., 2000) as well as among japonica ecotypes (e.g., Akihikari/ IRAT109, Otomemochi/Yumenohatamochi; Horii et al., 2006; Ikeda et al., 2007) (Ingram et al., 1994) and to map QTLs using populations derived from lines adapted to target environments. Although there are a number of untapped donors for droughtresistance traits available from wild species and landraces, no population that uses a wild species as one of the parents has been developed for drought studies. The possibility of selectively introgressing useful genes from Oryza rufipogon to elite rice cultivars suggests a way of improving the performance of Oryza sativa while broadening the genetic base (Xiao et al., 1998). Other Table 2b The 15 populations reviewed in this study; types of population and their parents Population CT/IR Co/Mo IR/Az Ba/Az IR/IR IA/Co I/I I/A I/Yu Z/I T/L I/N A/I O/Y V/W CT9993/IR62266 (DH) Co39/Moroberekan (RI) IR64/Azucena (DH) Bala/Azucena (F2, RI) IR58821/IR52561 (RI) IAC165/Co39 (RI) IR62266/IR60080 (BC) IR1552/Azucena (RI) IRAT109/Yuefu (DH) Zhenshan97/IRAT109 (RI) Teqing/Lemont (BC) IR20/Nootripathu (RI) Akihikari/IRAT109 (BC) Otomemochi/Yumenohatamochi (RI) Vandana/Way Rarem (F3–F5) wild species of interest include Oryza australiensis, Oryza glaberrima, Oryza officinalis, and Oryza nivara. A number of field studies provide information on QTLs linked to grain yield and yield components under managed stress conditions, often during the dry season (Babu et al., 2003; Lanceras et al., 2004; Lafitte et al., 2004b; Xu et al., 2005; Yue et al., 2005; Jearakongman, 2005). It is important to summarize the current status of QTLs for drought resistance and to evaluate the degree to which they affect rice production under drought. By comparing the coincidence of QTLs for specific drought-resistance traits and QTLs for plant production under drought, it is possible to test whether a particular constitutive or adaptive trait is likely to be useful for improving drought tolerance in the field (Lebreton et al., 1995). Here, we compare QTL analyses (i.e., a semi-meta analysis) to identify the QTL segments that are expected to have an impact on rice yield under drought conditions. Several studies have been conducted under both well-watered and drought-stressed conditions (e.g., Zou et al., 2005; Kumar et al., 2007), and two studies had multiple stress regimes (Lanceras et al., 2004; Jearakongman, 2005), allowing preliminary assessment of the interaction between QTLs and drought stress types. We classified phenotyping environments into either well-watered control (C) or stress (S) conditions, and whenever possible, we further characterized drought stress environments as either intermittent (Si) or terminal (St). When the correlation coefficient between yield under stress and under well-watered conditions was relatively high (e.g., r = 0.56**), only a few cases of QTL-by-environment interaction were detected (Zou et al., 2005). In such an analysis, we acknowledge several limitations: (1) The sizes of the mapping populations are generally small (i.e., about 100–200 lines, except for the 436 lines in Bernier et al. (2007)). For example, the progeny of 100 lines could cause a large over-estimation of the phenotypic variance associated with detected QTLs (the Beavis effect) (Beavis, 1994; Xu, 2003). For a test with a significance level of 0.05, to achieve 90% power for detecting a QTL that accounts for 10% of the total trait variance in a DH population, simulations show that sample sizes of n = 158 and 281 are required for recombination frequencies of 0.1 and 0.2, respectively (Hackett, 2002). (2) Phenotyping protocols differ among studies. For example, the traits measured for ‘‘deep and thick root system’’ varied. ‘‘Thickness of roots’’ was measured at basal or deeper soil layers (e.g., Kamoshita et al., 2002b), and ‘‘deepness of roots’’ was measured as maximum rooting length (e.g., Hemamalini et al., 2000), as seminal or adventitious root length (Zheng et al., Parents Upland tropical japonica/lowland indica Irrigated lowland indica/upland japonica Irrigated lowland indica with sd-1/upland tropical japonica Upland indica with sd-1/upland tropical japonica Lowland indica/lowland indica Improved upland japonica/irrigated lowland indica Lowland indica/elite lowland tropical japonica Lowland indica/upland tropical japonica Tropical upland japonica/lowland indica Lowland indica/upland tropical japonica Lowland indica with sd-1/lowland japonica with sd-1 Irrigated lowland indica/upland indica Lowland japonica/upland tropical japonica Lowland japonica/upland temperate japonica Upland indica/upland tropical japonica DH: doubled haploid lines; RI: recombinant inbred lines; F2–F5: F2–F5 populations; BC: backcross population. A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 11 Fig. 3. Consistent QTLs mapped for drought resistance and plant production traits in rice chromosome 1 in different populations but using a common set of markers (underlined). Parts of rice chromosome 1 are shown as vertical bars with mapped markers indicated to the right and a cM ruler to the left; the population used in genetic map construction appears above the linkage group. Because of the differences in size and structure of the mapping populations, the different donors used for developing mapping populations, the different DNA marker platforms, and the limited number of common DNA markers for comparison, placement of the common drought resistance QTLs on the rice genetic linkage map in this review is tentative at best. The full names of the traits are indicated in Table 3. 12 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 2003), or as total or deep root dry weight (e.g., Yadav et al., 1997). (3) There are differences in DNA marker platforms, limited numbers of common DNA markers available for comparison, and inconsistent map distances across studies (e.g., Causse et al., 1994; McCouch et al., 2002). (4) Phenotyping errors are sometimes large. (5) Raw phenotypic and genotypic data were not available for all populations, as noted by Ahmadi et al. (2007), and we had to rely on published data for our semi-meta analysis. We first focused on previous studies using CT9993/IR62266, probably the most widely studied mapping population including field phenotyping, in which both of the parents are well adapted to rainfed rice-growing environments. We identified 34 genomic regions with multiple QTLs for putative drought-resistance traits (i.e., primary, secondary, integrative, phenology, and plant-type traits). Next, using maps developed by Causse et al. (1994), McCouch et al. (2002), and the International Rice Genome Sequencing Project (2005), we were able to compare with relative precision QTLs for drought-resistance traits that were mapped across populations. We considered ‘‘common QTLs’’ to be those that were identified in the same marker intervals in both populations when common markers were available in the area (i.e., the markers underlined in Figs. 3–6) (e.g., Kamoshita et al., 2002b), as well as those found in both populations in the adjacent chromosomal segments judged by the locations of simple sequence repeats (SSRs) relative to genetic marker maps (i.e., Supplementary Table 17 in International Rice Genome Sequencing Project, 2005) when no common markers were available in the area. Among the 34 regions, four key regions were identified where putative QTLs for yield or yield components under stress and drought-resistance component traits were identified across populations with their interval lengths of 35–64 cM (Figs. 3–6). We did not focus on common QTLs found in other populations if these were not also in CT9993/IR62266. There may be still other QTLs that are equally important but are not highlighted in this study, mainly because there is either limited or no information on their association with rice performance under drought conditions in the field. 4.1.1. Co-location of QTLs for plant-type traits, and integrative, primary, and secondary drought-resistance traits The marker interval R2417–RZ909 (64 cM) on chromosome 1 in rice (with reference to the genetic map derived from CT9993/ IR62266 DH lines; Zhang et al., 2001a) has frequently been associated with grain yield, various drought-resistance traits, and plant-type traits (Fig. 3, Table 3). Many QTLs for primary droughtresistance traits were identified under stress conditions in this region, including cell membrane stability (Tripathy et al., 2000), Fig. 4. Consistent QTLs mapped for drought resistance and plant production traits in rice chromosome 4 in different populations but using a common set of markers (underlined). For full legend see Fig. 3. A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 13 Fig. 5. Consistent QTLs mapped for drought resistance and plant production traits in rice chromosome 8 in different populations but using a common set of markers (underlined). For full legend see Fig. 3. osmotic adjustment (Lilley et al., 1996; Robin et al., 2003), and various root traits (e.g., maximum depth and penetrated root thickness; Hemamalini et al., 2000; Price et al., 2002a; Nguyen et al., 2004; Zheng et al., 2003; Yue et al., 2006). Under wellwatered control conditions, QTLs for various deep and thick root traits were identified across different genetic backgrounds in this region (Ray et al., 1996; Yadav et al., 1997; Ali et al., 2000; Kamoshita et al., 2002b; Courtois et al., 2003; Li et al., 2005). This region also contains QTLs for secondary traits such as plant water status under stress (e.g., LWP and RWC; Courtois et al., 2000; Price et al., 2002b; Babu et al., 2003; Srinivasan, 2005; Jearakongman, 2005) (Table 3), as well as leaf rolling (Courtois et al., 2000; Price et al., 2002b; Babu et al., 2003) and leaf drying (Courtois et al., 2000; Price et al., 2002b; Srinivasan, 2005; Boopathi et al., 2005; Yue et al., 2006). The positive effects of maintaining higher plant water status under stress came from the deeper rooting parents (e.g., Azucena, CT9993) in some cases (Courtois et al., 2000; Babu et al., 2003; Srinivasan, 2005), but not in others (Jearakongman, 2005). QTLs for integrated traits such as delay in flowering time (Yue et al., 2005), panicle exsertion rate, DRI (Jearakongman, 2005), and yield components (e.g., percentages of number of sterile panicles, spikelet fertility, weight per grain) (Lafitte et al., 2004b; Jearakongman, 2005; Yue et al., 2006), as well as for grain yield (Kumar et al., 2007), all under drought stress in the field, have been mapped in this region. This region is linked to plant-type QTLs with maximum of 46% variability in the data set, such as plant height, panicle length, and tiller and panicle number, under both control and stress conditions across several mapping populations (Yu et al., 1995; Huang et al., 1996; Lanceras et al., 2004; Boopathi et al., 2005; Jearakongman, 2005). This segment contains the position of sd-1, a major gene that controls semi-dwarfism that is widely used by IRRI because of its strong association with harvest index and responsiveness to fertilizer under well-watered conditions (Courtois et al., 1995). In populations in which a semi-dwarf cultivar (e.g., Bala or IR64) is used as one of the parents, alleles from the semi-dwarf parents consistently reduce plant height (Hemamalini et al., 2000; Lafitte et al., 2004b) and increase grain yield (Jearakongman, 2005) not only under control conditions but also under intermittent and terminal stress conditions. QTLs for grain yield under control or stress (Xu et al., 2005; Srinivasan, 2005), for relative yield (Yue et al., 2005; Jearakongman, 2005), and for differences in yield between control and stress conditions (Xu et al., 2005) were also identified in this region. Thus, the sd-1 locus may improve yield under both well-watered and drought conditions, and it will be meaningful to estimate the effects of this QTL cluster, R2417-RZ909, after adjusting for the effects of sd-1 in future studies. In silico analyses of the QTL region, RM212– RM319 on chromosome 1 (Xu-Sheng et al., 2005; Zeng et al., 2006) have identified 175 annotated genes, 16 of which may be involved in drought response (Xu-Sheng et al., 2005). 14 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 Fig. 6. Consistent QTLs mapped for drought resistance and plant production traits in rice chromosome 9 in different populations but using a common set of markers (bold and underlined). For full legend see Fig. 3. Another region consistently associated with drought tolerance in different populations is the interval RG939–RG620 (36 cM) on chromosome 4 (Fig. 4). A unique feature of this region is that both QTL co-location and positive phenotypic correlation have been obtained between root traits and yield under drought stress in CT9993/IR62266 (Babu et al., 2003), indicating the importance of drought avoidance through deep and thick roots. Many QTLs for root traits have been identified in this region in populations grown under well-watered control conditions (Champoux et al., 1995; Hemamalini et al., 2000; Zheng et al., 2000; Zhang et al., 2001b; Price et al., 2002a; Kamoshita et al., 2002b; Nguyen et al., 2004; Boopathi et al., 2005), as well as under simulated hardpan conditions (Ray et al., 1996; Zhang et al., 2001a; Nguyen et al., 2004) and drought (Yue et al., 2006) (Table 3). Secondary traits such as leaf rolling (Champoux et al., 1995; Courtois et al., 2000), leaf drying (Courtois et al., 2000; Boopathi et al., 2005), canopy temperature under stress, and stress recovery (Boopathi et al., 2005) have been mapped in this region, as well as QTLs for integrative traits such as flowering delay (Lafitte et al., 2004b), yield components (Babu et al., 2003; Lanceras et al., 2004; Srinivasan, 2005; Lafitte et al., 2004b), and grain yield (Babu et al., 2003). QTLs for plant-type traits such as tiller number or plant height are observed in some populations in this region (Ray et al., 1996; Babu et al., 2003; Lafitte et al., 2004b; Lanceras et al., 2004; Srinivasan, 2005; Gomez et al., 2005; Xu et al., 2005; Boopathi et al., 2005), but their contributions (R2), unlike those in the interval on chromosome 1, are not large compared with those of other QTLs identified in the same region. Therefore, this region may increase yield under stress through drought avoidance by increasing soil water uptake. Co-location of QTLs for root traits and yield under stress was observed on this region only in the CT9993/IR62266 population (not in the IR64/Azucena or Bala/ Azucena populations), in which both parent plants are of medium to tall height and are well adapted to rainfed rice ecosystems. Similarly, the marker interval RG978–RG598 (54 cM) on chromosome 8 has repeatedly shown associations with several primary drought-resistance traits in rice, as well as with plant water status and grain yield, across a variety of genetic backgrounds and environments (Fig. 5). QTLs for primary traits such as cell membrane stability (Tripathy et al., 2000), osmotic adjustment (Lilley et al., 1996; Zhang et al., 2001a; Robin et al., 2003), leaf EW, rate of non-stomatal water loss (Srinivasan, 2005), and various deep and thick root traits (Price et al., 2002b; Yue et al., 2006; Zhang et al., 2001a; Nguyen et al., 2004) under stress were also mapped in this region. A number of constitutive deep and thick root traits under non-stressed conditions were also found in different populations (Champoux et al., 1995; Ray et al., 1996; Yadav et al., 1997; Hemamalini et al., 2000; Kamoshita et al., A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 15 Table 3 Four chromosome regions with clusters of QTLs detected for plant-type, phenology, primary, secondary, and integrated traits of drought resistance in CT9993/IR62266 population in previous studies across various environments Trait Chromosome 1 Plant-type traits PH, plant height (S) PH, plant height (W) PN-H, panicle number per hill (W) PN-H, panicle number per hill (S) PN-H, panicle number per hill (S) PH, plant height (W) PH, plant height (S) PH, plant height (S) PH, plant height (S) PH, plant height (W) PH, plant height (S) PL, panicle length (S) PL, panicle length (W) Phenology traits D50F, days to 50% flowering (S) Primary traits CMS, cell membrane stability (S) PRT, penetrated root thickness (S) PRT, penetrated root thickness (S) TRDW, total root dry weight (S) DRW, deep root weight (W) DRR, deep root ratio (W) DR-T, deep root per tiller (W) RD, rooting depth (W) BRT, basal root thickness (W) RSLR, root-shoot length ratio (W) TRDW, total root dry weight (S) PRT, penetrated root thickness (S) Secondary traits RWC, relative water content (S) LR, leaf rolling (S) LD, leaf drying (S) LD, leaf drying (S) RWC, relative water content (S) RWC50W+, time to reach 50% relative water content (with wax) Predawn LWP (W, Si, St) Midday LWP (W) Midday LWP (Si) Midday LWP (St) RWC, relative water content (S) RWC, relative water content (S) RWC, relative water content (S) Integrated traits GP, grains per panicle (W) HI, harvest index (S) SDW, shoot dry weight at flowering (St) GY, grain yield (St) GY, grain yield (W) HI, harvest index (W) GY, grain yield (Si) GY, grain yield (St) GY, grain yield (W) GY, grain yield (d) GY, grain yield (W) GY, grain yield (W) GY, grain yield (W) Chromosome 4 Plant-type traits PH, plant height (S) PH, plant height (W) PN, panicle number (W) PN, panicle number (S) PN, panicle number (S) PH, plant height (S) PTN, productive tiller number (S) PL, panicle length (S) Marker interval R2 Allele for the positive effect Reference RG109–ME10_14 EM11_11–RG109 RG109–CDO345 ME4_18–CDO345 RG109–CDO345 RM102–RG909 RM102–RG909 RM102–CDO345 C813–CDO345 RG109–EM11_11 RG109–EM11_11 RG109–EM11_11 RG109–EM11_11 27.8 46.8, 46.5 21.7 20.7, 20.5, 20.8 18.6 46.1 41.1 36.2, 23.1 32.6 21.6 22.7 13.5 11.7 CT9993 CT9993 IR62266 IR62266 IR62266 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 Babu et al. (2003) Babu et al. (2003) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Srinivasan (2005) Srinivasan (2005) Srinivasan (2005) Srinivasan (2005) CDO345–ME10_14 10.0 IR62266 Srinivasan (2005) CDO345–ME10_14 RG345–RG957 RG345–RG957 RG109–EM11_11 R2417–RM212 C813–RG957 R2417–RM212 R2417–RM212 CDO345–ME10_14 CDO345–RZ909 RG727–RG109 RG957–RZ19 20.1 9.2 12.0 9.3 9.4 7.8, 8.1 9.8 7.1 8.8 7.8 9.3 12.0 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 Tripathy et al. (2000) Zhang et al. (2001a) Nguyen et al. (2004) Zhang et al. (2001a) Kamoshita et al. (2002a) Kamoshita et al. (2002a) Kamoshita et al. (2002a) Kamoshita et al. (2002a) Kamoshita et al. (2002a) Kanbar et al. (2002) Nguyen et al. (2004) Nguyen et al. (2004) RM212–C813 RG109–ME10_14 RG109–ME10_14 RZ909–CDO345 RM212–RZ417 RG109–EM11_11 RM315 RZ19 RM315 RG690 RZ730–RZ801 RG810–RG331 C949–RZ14 12.1 15.2 20.8 18.3 8.9 8.7 27.1, 47.6, – 13.6 – – 14.9, 12.0 6.1 13.9, 23.9 CT9993 CT9993 CT9993 CT9993 CT9993 IR62266 *IR64 *Azucena *IR64 *IR64 *Azucena *Azucena *Bala Babu et al. (2003) Babu et al. (2003) Babu et al. (2003) Srinivasan (2005) Srinivasan (2005) Srinivasan (2005) Jearakongman (2005) Jearakongman (2005) Jearakongman (2005) Jearakongman (2005) Courtois et al. (2000) Courtois et al. (2000) Price et al. (2002b) EM11_11–RG109 CDO345–RZ909 EM11_11–RG109 EM11_11–RG109 ME418–C813 ME418–C813 RG690–RG730 RZ810 OSR27–RM212 OSR27–RM212 RM212 RM302 RM3825 15.2 7.8 6.5 14.5 17.0 13.0 – 12.6 11.7, 5.0 9.0 5.1 3.7 6.7 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 *IR64 *IR64 *Teqing *Lemont *– *– *– Babu et al. (2003) Lanceras et al. (2004) Kumar et al. (2007) Kumar et al. (2007) Srinivasan (2005) Srinivasan (2005) Jearakongman (2005) Jearakongman (2005) Xu et al. (2005) Xu et al. (2005) Beena (2005) Beena (2005) Beena (2005) RG476–RG214 RG476–RG214 ME4_9–RG214 ME4_9–RG214 RG939–RG214 RG939–RG476 RG214–RM280 C335–EM18_18 14.4 16.0 21.3 16.3 18.9 10.9 16.5 12.2 CT9993 CT9993 IR62266 IR62266 IR62266 CT9993 CT9993 CT9993 Babu et al. (2003) Babu et al. (2003) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Srinivasan (2005) Srinivasan (2005) Srinivasan (2005) 16 Table 3 (Continued ) Trait Primary traits RPI, root penetration index (S) BRT, basal root thickness (S) PRT, penetrated root thickness (S) PRDW, penetrated root dry weight (S) RPF, root pulling force (S) DR/T, deep root per tiller (W) BRT, basal root thickness (W) RT25, root thickness at 25 cm (W) RPI, root penetration index (S) BRT, basal root thickness (S) RPF, root pulling force (W) PRDW, penetrated root dry weight (S) PRT, penetrated root thickness (S) Integrated traits GY, grain yield (S) BM, biomass (W) GP, grains per panicle (W) TSN, total spikelet number (W) TSN, total spikelet number (S) TSN, total spikelet number (S) PSS, percent spikelet sterility (W) SY, straw yield (S) GY, grain yield (W) GY, grain yield (W) Chromosome 8 Plant-type traits PN, panicle number (W) PN, panicle number (S) PN, panicle number (S) PN, panicle number (S) PH, plant height (W) PH, plant height (S) PH, plant height (S) Phenology traits DH, days to heading (W) D50F, days to flowering (St) Primary traits CMS, cell membrane stability (S) CMS, cell membrane stability (S) OA, osmotic adjustment (S) BRT, basal root thickness (S) BRT, basal root thickness (W) OA, osmotic adjustment (S) BRT, basal root thickness (S) EW, epicuticular wax (S) Secondary traits LR, leaf rolling (S) LD, leaf drying (S) RWC70W+, time to reach 70% relative water content (with wax) (W) RWC50W+, time to reach 50% relative water content (with wax) (S) RWC, relative water content (S) RWC, relative water content (S) Integrated traits HI, harvest index (S) BY, biological yield (S) PSS, percent spikelet sterility (S) PSS, percent spikelet sterility (S) GY, grain yield (W) GY, grain yield (S) GY, grain yield (S) GY, grain yield (S) Chromosome 9 Phenology traits DH, days to heading (S) DH, days to heading (W) Primary traits CMS, cell membrane stability (S) A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 Marker interval R2 Allele for the positive effect Reference RG939–RG476 RG939–RG476 RG939–RG476 RG939–RG476 RG214–RG620 RG476–RG214 RG476–RG214 RG476–RG214 RG214–R1017 RG939–RZ905 RG214–R2017 R0874–C1100 RG939–RZ905 11.0 37.6 31.3 11.5 19.9 4.7 22.6, 6.8, 29.9 15.9 18.3 37.3 24.7 12.8 30.6 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 Zhang et al. (2001a) Zhang et al. (2001a) Zhang et al. (2001a) Zhang et al. (2001a) Zhang et al. (2001a) Kamoshita et al. (2002a) Kamoshita et al. (2002a) Kamoshita et al. (2002a) Nguyen et al. (2004) Nguyen et al. (2004) Nguyen et al. (2004) Nguyen et al. (2004) Nguyen et al. (2004) RG476–RG939 RG620–C107 RG214–RG620 RG939–RG476 EMP3_10–RG214 RG939–RG476 RG214–RM127 RG214–RM280 RM451–RM317 C20_1000 15.8 16.4 13.6 10.6 13.4, 12.1 10.3 8.2 16.7 4.1 3.2 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 CT9993 *IRAT109 *– Babu et al. (2003) Babu et al. (2003) Babu et al. (2003) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Srinivasan (2005) Liu et al. (2008), Zou et al. (2005) Beena (2005) ME2_11–EM18_15 ME2_11–EM18_15 RM210–RG598 G187–RG598 ME6_13–EM18_5 ME6_13–EM18_5 ME2_11–RG598 23.1 18.3 17.2 15.3 20.6 21.2 20.2, 19.1, 18.6 IR62266 IR62266 IR62266 IR62266 CT9993 CT9993 CT9993 Lanceras Lanceras Lanceras Lanceras Lanceras Lanceras Lanceras et et et et et et et al. al. al. al. al. al. al. (2004) (2004) (2004) (2004) (2004) (2004) (2004) G2132–G1073 R1394A–G2132 18.1 4.0 IR62266 IR62266 Babu et al. (2003) Kumar et al. (2007) G2132–R1394A EM1815–RG598 G2132–R1394A EM14_1–RZ997 EM14_1–ME2_11 G2132–G1073 RZ997–RZ572 ME84–EM15_10 25.9 29.4 8.3 10.8 18.3 8.3 10.8 9.6 CT9993 CT9993 IR62266 CT9993 CT9993 IR62266 CT9993 CT9993 Tripathy et al. (2000) Tripathy et al. (2000) Zhang et al. (2001a) Zhang et al. (2001a) Kamoshita et al. (2002a) Nguyen et al. (2004) Nguyen et al. (2004) Srinivasan (2005) ME6_13–G187 G2132–G1073 G2132–R1394A ME8_4–EM15_10 RG1 G1073–G2132 10.1 12.7 11.1 8.4 35.0 9.2 IR62266 IR62266 CT9993 CT9993 *– *Bala Babu et al. (2003) Babu et al. (2003) Srinivasan (2005) Srinivasan (2005) Lilley et al. (1996) Price et al. (2002b) G187–ME2–11 ME2_11–G187 RM256–RM210 ME5_4–RM256 RM223–RM210 RM223–RM210 RM152 RM1235 14.7 9.4 11.2 7.8 – – 3.3 3.9 IR62266 IR62266 CT9993 CT9993 *Teqing *Lemont *– *– Babu et al. (2003) Lanceras et al. (2004) Lanceras et al. (2004) Lanceras et al. (2004) Xu et al. (2005) Xu et al. (2005) Beena (2005) Beena (2005) RG667–RM201 RM215–RG667 23.8 19.5 IR62266 IR62266 Babu et al. (2003) Babu et al. (2003) ME9_6–K985 15.7 IR62266 Tripathy et al. (2000) A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 Table 3 (Continued ) Trait PRT, penetrated root thickness (S) PRDW, penetrated root dry weight (S) PRDW, penetrated root dry weight (S) Secondary traits RWC, relative water content (S) Predawn LWP (St) Midday LWP (St) RWC, relative water content (S) Integrated traits BM, biomass (W) SDW, shoot dry weight at panicle initiation (W) BY, biological yield (S) BY, biological yield (S) GY, grain yield (W, Si) GY, grain yield (Si) GY, grain yield (Si) GY, grain yield (S) GY, grain yield (W) GY, grain yield (d) Marker interval ME9_6–K985 R41–ME2_10 R41–ME2_10 R2 18.5 16.8 16.8 Allele for the positive effect CT9993 CT9993 CT9993 Reference Zhang et al. (2001a) Zhang et al. (2001a) Nguyen et al. (2004) 17 RM215–RG667 RM107 RM107 RG451–RZ404 58.8 – – 7.9 CT9993 *Azucena *Azucena *IR64 Babu et al. (2003) Jearakongman (2005) Jearakongman (2005) Courtois et al. (2000) ME9_3–ME9_6 RM201–RG667 R41–ME9_3 RM201–RM215 OSR28 RM242 RM107 PO463D04–RM242 RM242–RM278 RM242–RM278 14.3 4.2 11.3 11.6 – – – 8.7 24.5 20.9 CT9993 IR62266 IR62266 IR62266 *Azucena *IR64 *Azucena *Azucena *Teqing *Lemont Babu et al. (2003) Kumar et al. (2007) Lanceras et al. (2004) Lanceras et al. (2004) Jearakongman (2005) Jearakongman (2005) Jearakongman (2005) Gomez et al. (2005) Xu et al. (2005) Xu et al. (2005) QTLs for plant water status and grain yield in the other populations are also added with asterisks (*). The letter in parenthesis after the trait name indicates the conditions under which the trait value was measured. (W) Measured under well-watered conditions; (S) measured under water stress; (Si) and (St) intermittent and terminal types of stress, respectively; (d) difference between stress and control. A dash (–) indicates that data are not available. 2002b; Li et al., 2005). Interestingly, under irrigated conditions, a QTL for stable carbon isotope ratio (d13C) in grain, an indicator of water use efficiency, has been mapped in this region (Laza et al., 2006). QTLs for RWC under stress have been repeatedly found across different genetic backgrounds (Lilley et al., 1996; Price et al., 2002b; Srinivasan, 2005). Other secondary traits such as leaf rolling (Champoux et al., 1995; Babu et al., 2003), leaf drying (Price et al., 2002b; Babu et al., 2003), and canopy temperature (Yue et al., 2005) under drought stress have been mapped in this region (Table 3). QTLs for integrative traits such as harvest index (Babu et al., 2003), biomass, percent spikelet sterility, panicle number (Lanceras et al., 2004), delay in flowering time, relative yield, relative spikelet fertility, DRI (Yue et al., 2005), and grain yield (Xu et al., 2005) under stress have been mapped in this region. QTLs for plant-type traits have also been found in several populations (Ray et al., 1996; Hemamalini et al., 2000; Lafitte et al., 2004b; Xu et al., 2005; Boopathi et al., 2005). The marker interval R41–RM215 (37 cM) on chromosome 9 is linked to drought resistance mostly through root traits, but also shows association with plant water status and grain yield (Fig. 6). QTLs for deep and thick root traits under control or stress conditions have been mapped in this region (Price et al., 2002b; Champoux et al., 1995; Yadav et al., 1997; Zhang et al., 2001b; Kamoshita et al., 2002b; Courtois et al., 2003; Zheng et al., 2003; Nguyen et al., 2004; Yue et al., 2006). A QTL for cell membrane stability has also been identified (Tripathy et al., 2000). QTLs for plant water status – i.e., RWC (Babu et al., 2003; Courtois et al., 2000) and LWP (Jearakongman, 2005) – under stress have been found, with their positive effects coming from the deeper rooting parents (Babu et al., 2003; Jearakongman, 2005) and from the short-statured parent (Courtois et al., 2000). QTLs for leaf rolling (Champoux et al., 1995; Courtois et al., 2000; Gomez et al., 2005) and leaf drying (Boopathi et al., 2005; Yue et al., 2006) under stress have also been mapped in this segment. This region harbors QTLs linked to integrative traits such as biomass (Lanceras et al., 2004; Jearakongman, 2005), number of grains per panicle (Lafitte et al., 2004b), relative spikelet fertility, and delay in flowering time (Yue et al., 2005) under stress conditions. QTLs for grain yield have also been identified in this region, under both control and stress conditions (Gomez et al., 2005; Xu et al., 2005; Jearakongman, 2005). These studies consistently identify regions on chromosomes 1, 4, 8, and 9 that influence a range of drought-resistance traits, yield, and yield components under stress. Therefore, to increase drought tolerance, it may be more effective to use molecular methods to select for specific regions rather than individual traits. Genomic regions mapped for several drought-resistance traits such as root traits, phenology, yield, and yield components under water stress at the same locus are expected to be important, since such regions not only govern drought tolerance but are responsible for grain yield under stress (O’Toole, 2004). These alleles with effects across different drought situations and genetic backgrounds are valuable and might be candidates for MAS. QTLs in these regions had R2 values ranging from 1.2% to 58.8% (Table 3), and although some are considered to be major (R2 value of greater than 10%; Yano and Sasaki, 1997), many have only small effects on drought resistance. The contribution to genetic gain as a result of selecting these small and large QTLs is likely to differ in each case, and quantitative assessment of QTLs by simulation models is required (Cooper et al., 1999c; Yin et al., 2003), as well as verification of the agronomic values of the QTLs by the development of near-isogenic lines. Interestingly, grain yield QTLs are contributed by both parents, as evidenced by transgressive segregation for grain yield under drought stress (Babu et al., 2003; Lanceras et al., 2004; Xu et al., 2005). This indicates that neither parent carries all of the positive or negative alleles for yield under stress. 4.1.2. Fine mapping of QTLs A QTL may cover a region containing several hundred to several thousand genes; this means that other factors are present in a given region in addition to the loci of primary interest. Thus, the QTL regions on chromosomes 1, 4, 8, and 9 highlighted in this review (with their interval lengths of 35–64 cM) need to be fine mapped to sequence candidate genes for drought-resistance. In silico identification of candidate genes in the targeted QTL regions associated with drought tolerance traits has been described recently (Zeng et al., 2006; Fu et al., 2007). Fine mapping of specific QTL regions can also be achieved via strategies such as adding more markers in the regions (provided the population is large enough to allow identification of recombinants around the QTLs), substitution mapping, and developing near-isogenic lines (Zhang et al., 1999). Nguyen et al. (2004) and Ganesh et al. (2003) 18 A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 have added more markers to the linkage map of CT9993/IR62266 DH lines and have fine-mapped certain already located QTLs to a distance of less than 1 cM. 4.2. Marker-assisted selection for drought resistance Once stable QTLs for drought resistance are identified, they should be introduced into a near-isogenic background to assess their agronomic value and contributions to yield in target environments (Price et al., 2002c). Shen et al. (2001) reported improvement of the rice root system by marker-assisted introgression of several root trait QTLs from Azucena into IR64. They also studied effects of these introgressed segments on other agronomic traits through pleiotropy or linkage drag. The potential impact of these QTLs on yield under drought was studied by evaluating the IR64 root introgression lines under rainfed conditions. The introgression lines had higher grain yield under stress than IR64 (Babu et al., 2004a). Similarly, five QTLs associated with root morphological traits from Azucena were incorporated into a popular Indian cultivar, Kalinga III, by using marker-assisted backcross breeding (Steel et al., 2006). The target segment on chromosome 9 (RM242–RM201) increased root length under irrigated and drought stress treatments, indicating that this QTL functions in a novel genetic background. Work is in progress in introgressing the above ‘potential QTLs’ on chromosomes 4 and 9 from CT9993 into IR20, IR64, and other locally adapted elite varieties in southern India (Babu, personal communication). Notwithstanding the potential suggested by QTL studies, MAS for drought resistance is not practiced widely. Reasons for this include: (1) Resolution in mapping the target QTLs is low. QTL positions are imprecise, with the associated confidence interval covering several megabases (Dupuis and Siegmund, 1999). This can cause crossovers and inefficient MAS (Zhang et al., 1999). A resolution of 15–20 cM in the QTL location may be acceptable as a first step before dissection of the introgressed fragments into smaller pieces to recover recombinants within these segments (Paterson, 1995). Ideally, the markers linked to the QTLs for a trait be within 1 cM of each other. (2) Markers flanking the QTLs often lack polymorphism in the elite lines into which the QTLs need to be introgressed, and largescale genotyping is very costly (Morris et al., 2003; Tuberosa et al., 2003). Development of simple PCR-based markers such as SSRs (McCouch et al., 2002) may help in addressing these problems. The cost of MAS will further decrease as new genotyping platforms, such as single nucleotide polymorphism (SNP) markers, become available (Collard et al., 2005). (3) Accurate phenotyping in the initial molecular mapping is the most important pre-requisite for success in selecting for drought resistance. However, phenotyping is often inadequate owing to poor measurement techniques, irrelevant manipulation of experimental drought conditions, lack of reliable plant and soil data, and lack of field experts to interpret the results of experiments on plant function under drought stress (Blum et al., 2005). (4) Most putative drought-resistance traits considered for rice have low heritability and are not consistently correlated with grain yield under drought conditions in the target environments because of the large G  E in rainfed rice as well as the large error variance under dry conditions (Atlin and Lafitte, 2002). (5) The effects of individual QTLs on phenotypes are relatively small. O’Toole (2004) acknowledged the difficulty of developing MAS when large numbers of small QTLs are often detected for putative drought-resistance traits. The task is to select three to five QTLs in a breeding program that account for a high proportion of the variance. 4.3. Transgenics Considerable progress has been made in developing transgenic rice lines tolerant to drought stress (Su et al., 1998; Datta, 2002; Toenniessen et al., 2003). Transgenic rice plants have been produced to over-express several candidate genes, such as  adc, encoding arginine decarboxylase, which modulates the plant polyamine content (Capell et al., 2004);  P5CS, encoding pyrroline-5-carboxylate synthetase involved in proline biosynthesis (Zhu et al., 1998);  HVA1, encoding late embryogenesis abundant (LEA) proteins (Xu et al., 1996; Cheng et al., 2002; Rohila et al., 2002);  TPS and TP, encoding trehalose-6-phosphate synthase and trehalose-6-phosphatase, involved in trehalose biosynthesis (Garg et al., 2002; Jang et al., 2003; Lee et al., 2003);  RWC3, encoding the water channel protein aquaporin (Lian et al., 2004);  OCPI1 (Oryza sativa chymotrypsin inhibitor-like 1), a stressresponsive proteinase inhibitor gene (Huang et al., 2007). Transgenic rice plants have also been produced to over-express transcription factors involved in the regulation of stress-inducible genes such as CBF/DREB1, DREB2, RD29B, RD22, and ICE1 (Shinozaki et al., 2003), CDPK encoding Ca2+-dependant protein kinase (Saijo et al., 2000), ABF3 and CBF3 (Oh et al., 2005), and SNAC1 encoding stress-responsive NAC 1 (Hu et al., 2006). Although considerable progress has been made in developing transgenic rice lines, their effectiveness in enhancing drought tolerance and yield under field conditions still needs to be assessed. The desiccation stress applied by most researchers evaluating transgenic plants for drought tolerance represents a ‘shock’ treatment. The plants are grown in small containers that provide unrealistically small soil volumes, and they are subjected to rapid stress cycles, mostly at the seedling stage, ranging from an hour to a few days (Rabbani et al., 2003; Babu et al., 2004b). When stress is imposed rapidly, there will be a greater number of injury-induced or survival-related responses than will be seen under the slower, progressive water deficit found under field conditions. It is important that experiments be conducted under conditions that more closely approximate stress development in the field in the target environments in terms of timing, duration, and intensity (e.g., Hu et al., 2006). In addition, because the current transgenic approach can deal only with induced responses to water deficit, the importance of induced traits relative to that of constitutive traits needs to be quantified, as mentioned in Section 3.1. Transgenic plants also can help us understand and manipulate the responses of plants to stress. The use of transgenics, coupled with recent developments in the areas of transcript mapping, functional genomics, and microarrays, may facilitate better understanding of drought-resistance mechanisms in rice (Salekdeh et al., 2002a,b; Bennett et al., 2005; Gorantla et al., 2005; Kathiresan et al., 2006). 5. Improvement of general adaptability and drought resistance When contrasting cultivars were compared over different locations and years under rainfed lowland conditions in southern India, Thailand, and Laos, genotypes with general adaptation and high yield potential performed better in most cases, particularly A. Kamoshita et al. / Field Crops Research 109 (2008) 1–23 19 Fig. 7. Grain yield of three groups of genotypes, high yielding (IR57514-PMI-5-B-12, open circles), intermediate yielding (KDML105 and RD6, solid squares), and low yielding (Chaing Seng, open triangles) plotted against environmental yield in Thailand and Laos and analyzed by using Finlay–Wilkinson tests. Two linear regression lines were drawn for the high and low yielding genotypes, respectively. Thirty-five genotypes were tested in multi-location trials. when mean yields were high (Fukai and Babu, personal communication, Fig. 7). Although we did not quantify the water deficits for all the data, this suggests the importance of improving the germplasm base for general adaptability (i.e., mean yield over multi-environment trails) and potential yield. Genotypic ranking of yield under mild drought conditions (i.e., with small yield reduction) is in general similar to irrigated conditions (Pantuwan et al., 2002a). Traditional sources of parents for crossing in rainfed lowland rice breeding programs may not be the most appropriate for improving potential yield level. The challenge is to combine the traits of high potential yield and drought tolerance for genotypes that flower at the most appropriate time for the target domain of the breeding program (Fukai et al., 1999). Recent IRRI genotype evaluation trials in the Philippines under five widely different environments, including irrigated lowlands and severely water stressed uplands, showed low but positive correlations between yield under non-stress and yield under mild-stress conditions, confirming that some cultivars produced high yields under either condition, and supporting the potential for combining high yield potential and moderate water stress tolerance (Atlin et al., 2006). For improving rice yield in low-input upland environments, Lafitte et al. (2002b) listed the importance of potential yield together with a number of traits for specific adaptation (e.g., disease tolerance, tolerance to low pH, low-radiation tolerance before flowering, and weed competitiveness). 6. Conclusions Managed drought environments in the field, such as dry season trials, delayed planting in the wet season, use of high toposequence locations, drainage, raised beds, and large-scale rainout shelters, have been developed to simulate the target environments for breeding. Selection for higher grain yield under managed stress, partly assisted by selection for secondary or integrative traits such as low leaf rolling score, low spikelet sterility, and high DRI, with their moderate to high degrees of heritability, shows promise. Understanding of genotypic responses to drought is increasing. Resistance traits differ under different types of drought (e.g., terminal drought, vegetative stage drought, and intermittent drought), but genotypic responses that contribute to drought avoidance (e.g., deep and thick roots and conservative water use by moderate plant size) and maintain higher plant water status are often found to be more important for higher yield under stress than are tolerance mechanisms. Transgenic rice, engineered for enhanced expression of primary induced traits for drought tolerance, has been studied under laboratory conditions, but the usefulness of these lines under field drought conditions remains to be tested. In our analysis of 15 mapping populations that have been evaluated in drought-resistance studies, with emphasis on the CT9993/IR62266 population, four QTL clusters on chromosomes 1, 4, 8, and 9 contained primary or secondary drought-resistance traits or integrated traits. QTLs for constitutive primary traits such as deep roots and plant-type traits such as plant height had higher R2 values than QTLs for induced traits, and were identified across different populations under both well-watered and stress conditions. The QTLs for root traits and plant-type traits, together with QTLs for plant water status, were more often co-located with integrated traits such as grain yield under stress. Although it is unlikely that a single primary or secondary trait will improve rice resistance to different types of drought, selection of some of the QTL clusters containing multiple drought-resistance traits is promising. In spite of the large amount of information on QTLs linked to various drought-resistance traits, routine use of these QTLs in MAS is not widely practiced. The accuracy of phenotyping in these QTL mapping studies is one concern. Further, use of molecular approaches may be limited because of the need to consider large number of QTLs with individually small effects. The effects that MAS for such QTLs will have on improvement of plant breeding can be estimated by the use of simulation models. Development of near-isogenic lines for these QTLs will allow testing of their true agronomic value. Several labs are currently working on markerassisted introgression of these QTLs into locally adapted elite rice lines. Acknowledgments We thank Dr Dave Mackill of IRRI and Professor Ken Fischer of the University of Queensland for their valuable comments on an early draft. This review paper was written through our involvement in rice drought-resistance projects supported by the Rockefeller Foundation and the Australian Centre for International Agricultural Research. We acknowledge their generous support. 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Zheng, H.G., Babu, R.C., Pathan, Md. M.S, Ali, L., Huang, N., Courtois, B., Nguyen, H.T., 2000. Quantitative trait loci for root-penetration ability and root thickness in rice: comparison of genetic backgrounds. Genome 43, 53–61. Zheng, B.S., Yang, L., Zhang, W.P., Mao, C.Z., Wu, Y.R., Yi, K.K., Liu, F.Y., Wu, P., 2003. Mapping QTLs and candidate genes for rice root traits under different watersupply conditions and comparative analysis across three populations. Theoretical Applied Genetics 107, 1505–1515. Zhu, B., Su, J., Chang, M.C., Verma, D.P.S., Fan, Y.L., Wu, R., 1998. Overexpression of a D1-pyrroline-5-carboxylate synthetase gene and analysis of tolerance to water and salt stress in transgenic rice. Plant Science 139, 41–48. Zou, G.H., Mei, H.W., Liu, H.Y., Liu, G.L., Hu, S.P., Yu, X.Q., Li, M.S., Wu, J.H., Luo, L.J., 2005. Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers. Theoretical Applied Genetics 112, 106–113.

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Plant Soil (2009) 315:195209 DOI 10.1007/s11104-008-9744-8REGULAR ARTICLEEffects of nitrogen mineralization on paddy rice yield under low nitrogen input conditions in irrigated rice-based multiple cropping with intensive cropping of vegetables in southw
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African Journal of Plant Science Vol. 3 (4), pp. 053-058, April 2009 Available online at http:/www.academicjournals.org/AJPS ISSN 1996-0824 2009 Academic JournalsFull Length Research PaperEvaluation of some hybrid rice varieties in under different sowin
Zhejiang University - LS - 112
Field Crops Research 102 (2007) 172177 www.elsevier.com/locate/fcrEvidence of varietal adaptation to organic farming systemsKevin M. Murphy a, Kimberly G. Campbell b, Steven R. Lyon a, Stephen S. Jones a,*aDepartment of Crop and Soil Sciences, Washing
Zhejiang University - LS - 112
Click HereGLOBAL BIOGEOCHEMICAL CYCLES, VOL. 23, GB0A04, doi:10.1029/2009GB003576, 2009Full ArticleforHuman alteration of the global nitrogen and phosphorus soil balances for the period 19702050A. F. Bouwman,1,2 A. H. W. Beusen,1 and G. Billen3Recei
Zhejiang University - LS - 112
Plant Soil (2008) 313:129139 DOI 10.1007/s11104-008-9685-2REGULAR ARTICLEInfluences of phosphorus starvation on OsACR2.1 expression and arsenic metabolism in rice seedlingsLi-Hong Wang & Gui-Lan Duan & Paul N. Williams & Yong-Guan ZhuReceived: 3 March
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ResearchInteractions of drought and shade effects on seedlings of four Quercus species: physiological and structural leaf responsesBlackwell Publishing LtdJos Luis Quero1,2, Rafael Villar2, Teodoro Maran3 and Regino Zamora11Grupo de Ecologa Terrestre
Zhejiang University - LS - 112
The Interrupted Gene. . . .CHAPTER O UTLINE Nonhomologous protein sequences can be produced from the same sequence of DNA when i t is read in different read ing frames by two (overlapping) genes. Homologous proteins that differ by the presence or
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Genes Are DNA. . . .CHAPTER OUTLINEIntroduction DNA Is the Genetic Material of Bacteria Bacterial transformation provided the first proof that DNA is the genetic material of bacteria. Genetic properties can be transferred from one bacterial strain to
Zhejiang University - LS - 112
Genes Code for Proteins . .I IBICHAPTER OUTLINEThe Genetic Code Is Triplet The genetic code is read in triplet nucleotides called codons. The triplets are nonoverlapping and are read from a fixed starting point. Mutations that insert or delete
UBC - MATH - 265
Homework problems for Math 265: Complex numbers 1. For which (real) values of are the solutions to r 2 + r + 1 = 0 real numbers? 2. For which (real) values of are the solutions to r 2 + ir 1 = 0 purely imaginary numbers? 3. Write the complex number (1 + i
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http:/en.wikipedia.org/wiki/ConvolutionSee also http:/www.jhu.edu/signals/convolve/
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Some examples of 2nd order linear ODEs with complex and repeated roots 11.1Oscillatory solutionsPure oscillations, no dampingy + 6y = 0, y (0) = 2, y (0) = 1Let us consider the equation The characteristic equation is r2 + 6 = 0 which has complex conj
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HOMEWORK 1: MATH 265, L KeshetDue in class on September 22, 2010Problem 1: In each case, solve for y (t): (a) y + 3y = 2et/2 with y (0) = 1. (b) y 4y = t with y (1) = 0. (c) ty + 2y = sin t with y (/2) = 0. (d) t2 y + 2ty = t3 + 1 with y (1) = 1. Soluti
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HOMEWORK 2: MATH 265, L Keshet(Final version) Due in class on September 29, 2010NOTE: Most problems on this assignment are straightforward. Problem 4 may take a bit more time and eort.Problem 1: In each case, solve the following second order ODEs for y
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HOMEWORK 4: MATH 265 Due in class on Oct 20 PARTIAL SOLUTIONSProblem 1: (a) Solve the nonhomogeneous ODE y + 16y = cos(t) with y (0) = 0, y (0) = 0 and express your solution in terms of the frequency of the forcing term > 0. Sketch the solution when = 3
UBC - MATH - 265
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HOMEWORK 5: MATH 265 Due in class on Oct 27Problem 1: Improper integrals. (a) Consider the integral I = 1 t1 dt. Show that this improper integral converges for p > 1 and nd its p value. Show that it diverges for p = 1. What happens when p < 1? (b) Consid
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Mathematics 265 Section:Full Name:(circle one) 101 103Student Number:Midterm TestOct 6, 2010Instructions: There are 6 pages in this test (including this cover page). 1. Caution: There may (or may not) be more than one version of this test paper. 2.
UBC - MATH - 265
Mathematics 265 Section:Full Name: (circle one) 101 103Student Number:Midterm TestOct 6, 2010Instructions: There are 6 pages in this test (including this cover page). 1. Caution: There may or may not be more than one version of this test paper. 2. E
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Mathematics 265 Section: 101Full Name:Midterm TestNov 10, 2010Student Number:Instructions: There are 6 pages in this test (including this cover page). 1. Caution: There may (or may not) be more than one version of this test paper. 2. Ensure that your
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Mathematics 265 Section: 103 Version AFull Name:Midterm TestNov 10, 2010Student Number:Instructions: There are 6 pages in this test (including this cover page). 1. Caution: There may (or may not) be more than one version of this test paper. 2. Ensure
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Mathematics 265 Section: 103 Version BFull Name:Midterm TestNov 10, 2010Student Number:Instructions: There are 6 pages in this test (including this cover page). 1. Caution: There may (or may not) be more than one version of this test paper. 2. Ensure
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