Water scarcity has critical impact on world’s food self-sufficiency and security. Almost one-fifth of the world's population, live in areas of physical scarcity of water and almost one quarter of the world's population, face economic water shortage (where countries lack the necessary infrastructure to take water from rivers and aquifers). Water use has been growing at more than twice the rate of population increase in the last century (FAO, 2007, http://www.fao.org/nr/water/docs/escarcity.pdf) . According to the International Water Management Institute (IWMI), agriculture, which accounts for about 70% of global water withdrawals, is constantly competing with domestic, industrial and environmental uses for a scarce water supply (Sentilinger, 2013, http://thewaterproject.org/ water-scarcity-and-agriculture) . Indian agriculture totally depends on monsoon and the agriculture sector in India uses 85% of the country’s available water. Less than 3% of the world’s water is fresh and even of this 3% over 2.5% frozen, locked up as glaciers and only 0.5% fresh water is available to man for various purposes. In such a scenario, if we continue to apply current water management practices, by 2050, the global agricultural sector will need to double the amount of water to feed the world. According to the UN Convention to Combat Desertification (UNCCD), with the existing climate change scenario, almost half the world's population will be living in areas of high water scarcity by 2030 (UN, 2014, http://www.un.org/waterforlifedecade/scarcity.shtml ).

Rice, world’s most important food crop that feeds over half of the global population, is the biggest consumer of fresh water diverted for irrigation. Globally rice is grown over an area of about 164.7 million ha with an annual production of 745.7 million tonnes (FAO, 2013, http://faostat3.fao.org/browse/Q/QC/E ). More than 90% of the world’s rice is grown and consumed in Asia where 60% of the earth’s population lives. China and India, which account for more than one-third of global population, produce over half of the world's rice. In India, area under rice cultivation is 43.5 million ha with an annual production of 159.2 million tonnes (FAO, 2013, http://faostat3.fao.org/browse/Q/QC/E). India is the largest user of groundwater in the world - if the trend of indiscriminate exploitation continues, over a quarter of the global total and about 60 per cent of aquifers in India will be in a critical condition in another 15~20 years. We need a shift from “land productivity” without concern for water use to “water productivity,” that is, getting the highest yield out of every drop of water used in agriculture. Some important questions arise here are: Should we continue with our present rice cultivation practices? Do we need to have a most productive and effective water saving cultivation method? Is it easy to breed for aerobic adapted rice varieties? In addition to the problem of water scarcity an equally important aspect of sustainable rice cultivation is the availability of labor to grow rice under traditional cultivation conditions as the rural population moves away from farming to work in urban areas in industry.

Improved root system is very important for maintaining crop yields, when plants are grown in insufficient supplies of water or nutrients. Plants having deeper root system colonize a large soil volume and improve the water uptake from the lower layers where water is expected to be available; this helps to maintain a good plant water potential which lead to zero or small yield decline under water limited conditions (Mumbani and Lal, 1983). Aerobic rice refers to a cultivation system in which rice is dry direct seeded in well-tilled leveled fields with uniform slope under unpuddled conditions. Aerobic rice have long and thick root system (Yadav et al., 1997; Ling et al., 2002), erect leaves, medium height, better water use efficiency and maintain high biomass and harvest index under upland conditions. In addition to reducing water use during land preparation and limiting seepage, percolation, and evaporation, aerobic rice had about 51% lower total water use and 32%~88% higher water productivity, expressed as gram of grain per kilogram of water, than flooded rice (Bouman et al., 2005), less labor use (Wang et al., 2002) and reduction of greenhouse gas emission from rice field (Mandal et al., 2010).

Even if several QTL for root traits (Zhang et al., 2001; Liu et al. 2008; Courtois et al. 2009) and grain yield under drought stress (Bernier et al., 2007; Venuprasad et al., 2009) have been identified, however, identification of consistent effect QTL for traits promoting adaptation to aerobic cultivation may be helpful in introducing varieties with high yield potential.

In our earlier study, 35 QTLs associated with 14 traits were mapped on chromosomes 1, 2, 5, 6, 8, 9, and 11 in MASARB25 × Pusa Basmati 1460 and 14 QTL associated with 9 traits were mapped on chromosomes 1, 2, 8, 9, 10, 11, and 12 in HKR47 × MAS26. Two QTL (qGY_8.1 and qGY_2.1) and one QTL (qGY_2.2) were identified for grain yield under aerobic conditions in the mapping populations MASARB25 × Pusa Basmati 1460 and HKR47 × MAS26, respectively (Sandhu et al., 2013).

In this paper, we report the net house evaluation of a F₂ population derived from the cross between “HKR47” (high yielding) and “MAS26” (aerobic) varieties of indica rice for yield, yield attribute traits and root traits. F₂ population has been used for linkage mapping of QTLs linked with traits promoting aerobic adaptation using already-mapped SSR markers.

1 Results
1.1 Phenotypic variation for agronomic traits
Aerobic rice cultivar, MAS26, out yielded the lowland variety, HKR47, under dry direct-seeded aerobic cultivation conditions. Root length, fresh & dry root weight and root thickness were higher in MAS26 compared to HKR47. A large variation for plant height (52.3~100.5 cm), effective number of tillers/plant (1~13), panicle length (17.7~27.9 cm), grain length/breadth ratio (2.41~3.31), grain weight (13.6~25.8 g) and yield per plant (1~26.2 g) were observed in HKR47 × MAS26 F₂ population. Root traits also showed wide range of variation; root length, fresh and dry root weight and root thickness ranged from 18.1~72.4 cm, 3.21~57.8 g, 1.17~17.1g and 1.77~5.5 cm, respectively (Table 1; Figure 1). Compared to the aerobic rice parental variety MAS26 (RL-51.7 cm, DRW -7.48 g), 88 plants had greater root length (51.8~72.4 cm) and 70 plants had greater dry root weight (7.53~17.1 g).

Table 1 Mean and range for various agronomic and root traits in HKR47 x MAS26 F2 population under net house conditions

Figure 1 Root traits showed wide range of variation

1.2 Genetic analysis
Out of 803 SSR markers widely distributed on 12 rice chromosomes, a total of 125 (about 16%) polymorphic SSR markers were used for the F₂ population genotyping. A DNA fingerprint database of selected 94 HKR47 × MAS26 F₂ plants was prepared using 125 SSR markers. On an average, 50.2 % alleles were from MAS26 and 49.8 % alleles were from HKR47 in all 94 F₂ plants. To demonstrate the genetic relationship among the F₂ plants and parental rice varieties, allelic diversity data was used for cluster tree analysis, NTSYS-pc. The parental rice varieties HKR47 and MAS26 had low similarity coefficient and bifurcate at coefficient value of 0.49. All the 94 F₂ plants did not cluster into major groups but showed more similarity to MAS26. Two dimensional PCA scaling exhibited that two parental genotypes were quiet distinct whereas 94 F₂ plants interspersed between the two parental lines with distribution of some plants towards MAS26 (Table 2; Figure 2; Figure 3).

Table 2 Phenotypic correlation coefficients among agronomic and root traits in F2 population

Figure 2 Dendrogram (NTSYS-PC) displaying diversity among 94 HKR47 × MAS26 F2 plants and parental genotypes using allelic diversity data at 125 SSR loci

Figure 3 Two dimensional PCA scaling displaying diversity among 94 F2 plants (HKR47 × MAS26) and parental genotypes using allelic diversity data at 125 SSR loci

A total of six QTL associated with four traits (Table 3; Figure 4; Figure 5) were detected. Three QTL for effective number of tillers per plant (qTN_8.1, qTN_8.2 and qTN_8.3 at map positions 36.0, 55.2, 60.9 cM, respectively), one for thousand grain weight (qTGW_8.1;59.1 cM), one for grain yield per plant (qYPP_8.1;60.1 cM) and one for root length (qRL_8.1, 43.7 cM) were identified. All the six QTL were mapped on chromosome 8 within a region of 24.9 cM (36~60.9 cM) (Figure 4; Figure 5). QTL qYPP_8.1 with RM72 being the peak marker with an R² value of 20.2% and qRL_8.1 with RM8243 being the peak marker with an R² value of 13.7% were identified for grain yield and root length under aerobic cultivated conditions, respectively. qYPP8.1 had maximum LOD score of 5.06. RM8020 and RM72 showed significant association with effective number of tillers/plant and RM8243 with root length. The QTL qTN_8.1, qTN_8.2, qTN_8.3, qTGW_8.1, qYPP_8.1 and qRL_8.1 accounted for 16.1, 21.0, 17.3, 27.4, 20.2 and 13.7 % phenotypic variation, respectively. Of the six QTL identified, one QTL (qRL_8.1) with an additive effect of 3.9 was from MAS26, the remaining five QTL were from HKR47.

Table 3 QTL associated with agronomic and aerobic root traits in HKR47 x MAS26 F2 population

Figure 4 QTL likelihood curves of LOD score of all the detected QTLs on chromosome 8 in HKR47 × MAS26 F2 population

Figure 5 Chromosomal locations of quantitative trait loci (QTL) identified via microsatellite marker analysis in HKR47 × MAS26 F2 population

2 Discussion
Groundwater is the primary source for irrigation, but overexploitation has resulted in a rapid decline of groundwater levels (Zhang et al., 2003). To tackle this situation, it has become essential to develop alternate ways of rice cultivation that require less water with no reduction in grain yield and quality. Several strategies are being pursued, including the development of water-efficient aerobic rice system, to reduce water requirement for rice cultivation including aerobic rice (Bouman, 2002).

Root system is considered as one of the important physiological parameter to struggle against the water scarce conditions. Under low-moisture stress, root characters are considered to be a vital component to fight against mechanism since they contribute to the regulation of plant growth and extraction of water and nutrients from deeper layers (Fukai and Cooper, 1995). Among the root morphological traits, maximum root length, dry root weight and root thickness were found to be associated with drought resistance in upland conditions. In the present study, root length, root biomass and root thickness were found to be higher in aerobic rice variety (MAS26) than in drought susceptible parent (HKR47). MAS26 had 47% higher root length, 37% higher dry root weight and 16% higher root thickness than HKR47. Eighty-eight and seventy plants had better root length and dry root weight than MAS26, respectively. It has been reported that a cultivar having more root number, high fresh and dry root can explore more soil volume for effective absorption of water (Amudha et al., 2009). Results also revealed that deep roots are associated with water stress tolerance in aerobic rice. Several research groups have reported identification of markers linked to the genes/QTL for root traits such as maximum root length, basal root thickness, dry root weight, total root weight and their potential role in water use efficiency or drought tolerance (Price et al., 1997; Yadav et al. 1997; Li et al. 2005; Toorchi et al., 2007; Liu et al. 2008; Qu et al., 2008).

Kato et al. (2010) studied root response to aerobic conditions in rice and observed that total root length under aerobic and near-saturated conditions was 10%~30% of that under flooding. Matsuo et al., (2009) reported that root number, fresh and dry root were higher in Sensho (upland variety) compared to Koshihikar (lowland variety) in hydroponic culture. Martin et al., (2007) reported that the rice varieties (ADT 39 and PMK 3) that were suitable for cultivation under aerobic conditions had longer and deeper root system compared to the other rice varieties. Increased root length allows roots to penetrate hard pans characteristic of some lowlands, thickness and density improves water uptake by producing more and larger root branches (Ingram et al., 1994). The improvement of upland rice through a deeper root system is thought by many to be a promising way to increase water uptake, and ultimately grain yield, under water stress conditions (Fukai and Cooper, 1995).
 
Although yield potential of aerobic rice genotypes are lower than the low-land indica rice varieties being cultivated under conventional flooded conditions but the cultivation of aerobic rice is quite promising where water is too scarce to grow lowland rice. In aerobic rice, the combined amount of rainfall and irrigation water from sowing to harvest varied from 470 to 650 mm, compared with 1200-1300 mm in lowland rice (Martin et al., 2007). In the present study, we reported that aerobic rice variety (MAS26) and several segregating HKR47 × MAS26 F2 plants performed well in terms of grain yield per plant as compared to HKR47 under aerobic cultivated conditions. Yield per plant of HKR47 × MAS26 F₂ population ranged between 1~26.22 g (HKR47 – 7.42 g, MAS26 – 8.34 g). Several studies supported our results, aerobic rice varieties yielded higher than lowland varieties under aerobic conditions (Bouman 2002; Xiaoguang et al., 2005; Kato et al., 2009; Murthy et al., 2011; Babu et al., 2011).

In order to evaluate the association between root and yield traits phenotypic correlation coefficient was calculated (Table 2). In the present study, yield per plant showed a significant positive correlation with plant height, effective number of tillers per plant, panicle length and thousand grain weight. Earlier studies also reported significant association between yield and plant height, panicle length and number of tillers (Girish et al., 2006; Nagaraju et al., 2013). It is necessary to study root morphological character’s influence on the grain yield and yield morphological traits. Under water limited conditions root system tries to develop adaptability that either may be beneficiary for the plants or may be tradeoff for yield. We identified a significant and positive correlation between yield per plant and root traits; root length (0.279, p=0.01), fresh root weight (0.232, p=0.01) and dry root weight (0.269, p=0.01). This shows that a well developed root system will help the plant in maintaining high plant water status which may leads to increase in yield potential under aerobic conditions. Plant height showed positive correlation with dry shoot weight (0.305, p=0.01). Positive association between plant height and shoot dry weight, maximum root length, root thickness and root dry weight has been observed by Ekanayake et al. (1985); and  Kanbar and Shashidhar (2004). The interrelationships between root morphological characters and yield-related traits clearly showed the role of root traits in rice plant adaptability under aerobic conditions. 

In the present study, we identified three QTL for effective number of tillers per plant, one for thousand grain weight, one for grain yield/plant and one for root length. A notable aspect of this study was that all the six QTL were mapped on chromosome 8 in a region of 24.9 cM segment (36-60.9 cM). QTL qRL_8.1 with a peak at RM8243 with an R² value of 13.7% associated with root length was identified. In this region three QTL for root length were reported by Sandhu et al., (2013) in the mapping populations MASARB25 × Pusa Basmati 1460 and HKR47 × MAS26. QTL qGY_8.1 for grain yield was reported to be located near to the qRL_8.1 (Sandhu et al., 2013). Sandhu et al. (2014) reported QTL for root hair length (qRHL_8.1 at 43.3 cM) in the Aus276/3*IR64 BC2F4 population. Qu et al. (2008) identified two QTL for root number, one for fresh root weight and three for root volume on chromosome 8 using a total of 120 RILs derived from cross between japonica upland rice IRAT109 and paddy rice Yuefu. Li et al. (2005) reported QTL for maximum root length, dry root weight and ratio of dry root weight to dry shoot weight using double haploid population from a cross between two japonica cultivars under three different conditions (upland, lowland and upland in pvc pipes). Courtois et al., (2003) identified a QTL for maximum root length on chromosome 8 using RILs derived from cross between IAC165 and Co39 rice varieties. Priyadarshini et al., (2013) evaluated the effectiveness of the root QTL on grain yield and water use efficiency in a set of 116 near isogenic lines (NILs) derived from cross IR64 and Azucena populations which were screened for grain yield and water use efficiency under severe reproductive-stage drought stress. These Collocating chromosomal loci governing different root and grain yield traits may provide breeders a better prospect to introgress such regions together as a unit in order to develop high yielding aerobic adapted cultivars.

QTL qYPP_8.1, for grain yield per plant at 60.1 cM with RM72 being the peak marker with an R² value of 20.2% was reported in this study.  Sandhu et al. (2013) reported QTL for grain yield at 72.2 cM in the mapping population MASARB25 × Pusa Basmati 1460. Sandhu et al., (2014) reported QTL for grain yield adjacent to qYPP_8.1 in the Aus276/3* IR64 BC₂F₄ population. Adjacent to qYPP8.1, Vikram et al. (2012) also reported QTL for grain yield under drought, qDTY_8.1, in Basmati334/ Swarna. Hanamaratti et al. (2007) reported QTL on chromosome 8 associated with relative yield and drought susceptibility index in IR64 × Binam-derived NILs under drought stress. Bernier et al., (2008) reported a minor QTL for grain yield at 28 cM position. Zhang et al., (2006) mapped a QTL related to drought tolerance on chromosome 8. Sandhu et al. (2013) reported QTL for effective number of tillers per plant, adjacent to qTN_8.2 and qTN_8.3, in the mapping population MASARB25 × Pusa Basmati 1460 and two QTL for effective number of tillers per plant with an R² value of 26.4% and 29.3%, respectively, in the mapping population HKR47 × MAS26. The QTLs identified earlier were not identified in the present study. It can be hypothesized that there was a strong G × E interaction among traits. It may be possible because of the small size of the population.  The co-location of QTLs inherited from different parents as shown by DPE (Table 3) suggest that it will be necessary to fine map the particular region, which assist the precise introgression of the QTLs in a marker assisted selection (MAS) program.

QTL analysis is an unbiased investigation of the genes affecting a particular trait which provides information on the location of important loci for the traits under study without any prior knowledge on the genes involved and reveals their possible genetic effects leading to phenotypes of interest. In the present study, six QTL (for four traits; yield per plant, effective number of tillers per plant, grain weight and root length) were identified within a region of 24.9 cM between RM8020 and RM72 markers on chromosome 8. A significant positive correlation has also been observed between yield per plant and effective number of tillers per plant, grain weight and root length. It might be possible that this 24.9 cM region is contributing towards root, yield and yield attributing traits and promoting adaptation to aerobic cultivated conditions. This region may facilitate marker assisted breeding to develop aerobic adapted high yielding rice varieties. It is quite interesting that this study led to the identification of a number of QTL on chromosome 8 which also possesses QTL for aroma and kernel elongation in Basmati rice. These two QTLs (aroma and cooked kernel elongation) are linked and present in the vicinity of QTL identified in the present study within 69~78.6 cM region on chromosome 8 (Jain et al., 2004; Jain et al., 2006). It indicates that it would be difficult to introgress these QTL promoting aerobic adaptation in Basmati rice.

3 Materials and Methods
3.1 Plant material
Seed harvested from the F₁ plants obtained from the cross between “HKR47” (used as female) and “MAS26” (used as male) varieties of indica rice, were used to raise F₂ population in pots during 2012 kharif season in the net house of Department of Molecular Biology, Biotechnology and Bioinformatics at CCS Haryana Agricultural University, Hisar. While “HKR47” is a low-land high yielding indica rice variety unadapted to cultivation in aerobic conditions whereas “MAS26” is an aerobic rice variety developed at University of Agricultural Sciences, Bangalore.

3.2 Net house evaluation and trait measurements
HKR47 × MAS26 F₂ population comprising of 184 plants were sown in the pots (one plant per pot) in a net house and raised to maturity under aerobic cultivated conditions. Seeds were sown in pots of 12ʺ height. Vermicompost manure was added in the soil at the time of pot filling. The pots were irrigated with one liter of water for the first fifteen days, then at an interval of three days up to panicle emergence and then at an interval of two days after panicle emergence. Yoshida nutrient solution was given to the plants growing in pots (500 mL per pot) after an interval of 21 and 70 days from the sowing date. The pots were kept weed-free by manual weeding. At physiological maturity, data was recorded on agronomic traits, plant height in cm (PH), effective number of tillers per plant (TN), panicle length in cm (PL), 1,000-grain weight in g (TGW), length/breadth ratio (L/B), and grain yield in g (YPP). Plant height was measured from the stem base to the tip of highest panicle (excluding awn). Fully developed tillers bearing panicles of each plant were counted at the time of maturity. Length of panicle was measured from the base of panicle until the tip of the last grain. The length and breadth of three seeds from each plant was recorded using digital Vernier Caliper. For grain weight determinations, 100-grain (dehusked) samples were taken from the harvested grains to compute 1000-grain weight. Grain yield per plot was recorded after harvesting, threshing, and drying to moisture content adjusted to 14%. The data on root length (RL, cm), fresh and dry root weight (FRW and DRW in g), root thickness (RT in cm), and dry shoot weight (DSW in g) was recorded and analyzed. For the measurement of root traits, plants were removed from the pots and gently washed. Root length was measured using a centimeter scale. The roots and shoots were then separated by cutting from the stem base. For fresh root weight, the roots were blotted gently with a blotting paper to remove any free surface moisture and then weighed immediately. For dry weight, the roots and shoots were dried in an oven set to low heat (50°C) overnight, and then cooled in a dry environment. Once cooled, weighed on a scale. The thickness of the root crown was measured using a vernier caliper.

3.3 Statistical analysis
The data was subsequently analyzed using OPSTAT (http://hau.ernet.in/opstat.html) to determine the variability and phenotypic (r) correlation coefficient analysis. Phenotypic correlation coefficients were tested against standardized tabulated significant value of r with (n–2) degree of freedom as per the procedure (Fisher and Yates, 1963).

3.4 Molecular characterization and QTL mapping
A total of 94 F2 plants selected on the basis of root traits, grain weight and grain yield (15% best and 15% worst) covering the entire range of these phenotypic traits were used for identification and mapping of QTLs associated with traits promoting aerobic adaptation. DNA was isolated from the leaf tissues of F2 plants using CTAB DNA isolation (Saghai-Maroof et al., 1984). Molecular marker analysis was carried out using 125 polymorphic SSR primers by ethidium bromide stained polyacrylamide gel electrophoresis (PAGE). PCR products from SSR analysis were scored visually for presence or absence of bands; data was scored as 1 (present) and 0 (absent) for each of the SSR locus. Genetic similarities between the genotypes were measured by the similarity coefficient based on the proportion of shared electromorphs using ‘Simqual’ sub-program of NTSYS-PC (Version 2.02 Exeter Software, Setauket, NY, USA) package (Rohlf, 1993). The resultant distance matrix data was used for two-dimensional scaling of rice genotypes using two-dimensional Principal Component Analysis (PCA). QTL analysis was done by using Win QTL cartographer version 2.5 (Shengchu Wang, Christopher J. Basten and Zhao-Bang Zeng, 2012, http://statgen. ncsu.edu/qtlcart/WQTLCart.htm) via composite interval mapping (CIM). The threshold log likelihood ratio (LOD) score was estimated empirically with 300 times permutations at a significant level of P=0.05.

Author’s contributions
AK was involved in the conception of the experiment, analysis, interpretation of the data, and drafting the article and final approval of the version to be published; NS was involved in revising manuscript content critically and final approval of the version to be published; SJ was involved with the analysis, interpretation of the data, in the critical revision of the manuscript and final approval of the version to be published; RKJ was involved in the design of the experiment, the critical revision of the manuscript and final approval of the version to be published.

Acknowledgements
The authors thank the Department of Science and Technology, New Delhi, India (DST No. SR/SO/PS/0041/2009), for providing financial support for this study.

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