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Agronomy 2017, 7(2), 27; doi:10.3390/agronomy7020027

Review
Bridging the Rice Yield Gaps under Drought: QTLs, Genes, and Their Use in Breeding Programs
International Rice Research Institute, DAPO BOX 7777, Metro Manila 1301, Philippines
*
Author to whom correspondence should be addressed.
Academic Editor: Silvio Salvi
Received: 5 January 2017 / Accepted: 27 March 2017 / Published: 9 April 2017

Abstract

:
Rice is the staple food for more than half of the world’s population. Although rice production has doubled in the last 30 years as a result of the development of high-yield, widely adaptable, resource-responsive, semi-dwarf varieties, the threat of a food crisis remains as severe as it was 60 years ago due to the ever-increasing population, water scarcity, labor scarcity, shifting climatic conditions, pest/diseases, loss of productive land to housing, industries, rising sea levels, increasing incidences of drought, flood, urbanization, soil erosion, reduction in soil nutrient status, and environmental issues associated with high-input agriculture. Among these, drought is predicted to be the most severe stress that reduces rice yield. Systematic research on drought over the last 10 years has been conducted across institutes on physiology, breeding, molecular genetics, biotechnology, and cellular and molecular biology. This has provided a better understanding of plant drought mechanisms and has helped scientists to devise better strategies to reduce rice yield losses under drought stress. These include the identification of quantitative trait loci (QTLs) for grain yield under drought as well as many agronomically important traits related to drought tolerance, marker-assisted pyramiding of genetic regions that increase yield under drought, development of efficient techniques for genetic transformation, complete sequencing and annotation of rice genomes, and synteny studies of rice and other cereal genomes. Conventional and marker-assisted breeding rice lines containing useful introgressed genes or loci have been field tested and released as varieties. Still, there is a long way to go towards developing drought-tolerant rice varieties by exploiting existing genetic diversity, identifying superior alleles for drought tolerance, understanding interactions among alleles for drought tolerance and their interaction with genetic backgrounds, and pyramiding the best combination of alleles.
Keywords:
drought; marker; pyramiding; QTLs; rice; genomics

1. Introduction

Rice feeds more than half of the global population. Global rice (paddy) production in 2015 trails 0.8 percent behind the 2014 outcome, 738.2 million tons (490.3 million tons, milled rice), obtained from an area of 160.6 million hectares, a decrease of 1.3% [1]. Asia, where 60% of the earth’s population lives, is the major producer and consumer of the world’s rice. Water, climate, season, rainfall, soil conditions, agriculture inputs, and genetic potential of germplasm are key determinants of crop productivity. Increasing population (Figure 1a), increasing demand for water (Figure 1b), water crisis (Figure 1c), drought (Figure 1d), failure to adapt to climate change, declining farm land, soil moisture, soil characteristics, deterioration in nutrient content, weed competitiveness, increasing intensity, and the frequency of biotic/abiotic stresses will amplify the challenges of achieving future food requirements. This will affect the economic growth and social stability of regions with food shortages. Farmers will earn a profit only if they successfully solve the algebraic puzzle of farming. Wheat, rice, maize, and other grains that are the staple food of the human population and the sources of feed for livestock account for more than 60% of the total crop evapotranspiration requirement, while soybeans and other oilseed crops account for 17%, and sugarcane 6% [2]. In such circumstances, the available water resources will not be sufficient to produce enough food for the increasing population. With changes in the climate and unpredictable rainfall, there is a possibility that nearly half of the world’s population may face water scarcity by 2030 [3]. Water scarcity will worsen in the world’s extremely dry regions and areas where water is already in short supply.
The contribution of plant breeding to improving commercially important crops, including major ones such as rice, maize, wheat, cotton, and pearl millet, at a global level is remarkable. Before the Green Revolution, traditional rice and wheat varieties were tall, photoperiod-sensitive, low-yielding and drought-tolerant, having a broad maturity duration and good grain quality. In the post-Green Revolution era, these traditional varieties were replaced by a few widely adapted varieties including inbreds and hybrids that are dwarf and photoperiod-insensitive, with early maturity, higher yield, poor grain quality, and low pest resistance. The dwarf rice varieties were bred by targeting irrigated ecosystems wherein ample water was thought to remain available for traditional practices of puddled transplanted system of rice cultivation. These varieties have high yield potential and good resistance to biotic stresses, but are highly susceptible to abiotic stresses such as drought. They are also prone to heavy yield losses even under mild drought stress [4]. In the course of post-Green Revolution breeding over the past 50 years, unknowingly, the drought tolerance contributing alleles of traditional cultivars have not been properly maintained in the modern cultivars. Recent understanding of molecular and physiological mechanisms for different abiotic stresses has opened up new opportunities to improve yield under adverse climatic conditions for many crops. There is still a need to bridge the large gap between yields in most favorable and stress conditions. Strategies involving bridging the yield gap and increasing yield stability and adaptability under variable environmental conditions are of importance in assuring food security and sustainability in the future. There is a need to move forward from the Green Revolution to a ‘gene revolution,’ which is more productive and more ‘green’ in terms of conserving natural resources and the environment [5].

2. Drought: The Key Concern in Food Security

Drought has been the main catalyst of many large famines of the past and has a major destructive effect on rice production in rainfed areas across Asia and sub-Saharan Africa. The most vulnerable, drought-prone areas are shown in Table 1. The most devastating drought events around the world were the Deccan Famine and those in the Horn of Africa, the United States, Vietnam, Australia, China, Brazil, the Sahel, Malawi, East Africa, Ethiopia, India, and Bangladesh. From 2003 to 2013, at least one medium- to large-scale natural disaster caused $70 billion in crop and livestock production losses; drought alone accounted for 44%. Asia is the most affected region, with total crop and livestock production losses amounting to $28 billion (40% of total losses), followed by Africa with $25 billion (Table 2) [6]. The 1987 drought in India, the 2004 drought in Thailand, and the 1978–2003 drought in China were estimated to have affected 60% [7], 2 million ha [8], and 14 million ha of cropped area, respectively. Drought events between 1980 and 2014 in sub-Saharan Africa affected 203, 86, 74, 61, and 48 million people in eastern Africa, southern Africa, western Africa, Ethiopia, and Kenya, respectively [6].
Drought induces critical losses in crop yield. Yield integrates many of the physiological and biochemical responses at cellular and molecular levels, influenced by a number of predictable and unpredictable factors that are genetically difficult to understand and manipulate. Therefore, long-term and systematic attention should be given to the complex issues surrounding drought in order to develop a better understanding and devise sustainable solutions.

3. Effect of Drought on Different Crops

Approximately 34% of rice is grown in rainfed lowland, 9% in rainfed upland, and 7% in flood-prone areas, while irrigated ecosystem covers 50% of total world rice area. Drought has been reported to produce devastating effects in rice at panicle initiation and flowering [4,9]; in maize at the tasseling and silking stages [10,11]; in sorghum and pearl millet at the booting and flowering stages [12]; in finger millet at the flowering stage; in sunflowers at head formation and the early grain-filling stage [13,14]; in groundnuts at the peg penetration and pod development stages; in soybean at the flowering and pod filling stages [15,16]; in black and green gram at the flowering and early pod development stages [17]; in cotton at the square formation and ball development stages [18,19]; and during the reproductive stage in rice [20,21]. Like in other crops, in rice drought has the most devastating effect at the reproductive stage. In rice, the damage to the crop is also significant at the seedling as well as vegetative stages. At the seedling stage, delay in monsoon rains, insufficient rain to puddle land, and preparation for transplanting force farmers to leave their land uncultivated. Severe drought at the vegetative stage reduces biomass production, causes the death of the plant and in severe cases, forces farmers to allow the grazing of the crops by cattle. Drought has a complex effect on plants [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42], and plants respond with many defensive adaptations (Figure 2). The major determinants of grain yield under drought are the variety [43], type of soil [44], length and timing of drought [45], severity of drought [46,47], season (early season, mid-season, or terminal stage, Table 3 [48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]), the age, period, and development stage of the plant [69], plant responses after stress elimination, and the interaction between the biotic/abiotic factors [70] and the region. Apart from this, drought stress also makes the rice crop more susceptible to biotic stresses (rice blast, brown spot, and bacterial blight), leading to a further decline in rice production. In many rice-growing areas in rainfed ecosystems, drought and submergence can occur in the same season at different growth stages of the plant or in different seasons, thus creating more complexity. Drought tolerance is a means for the rice plant to survive and produce a stable and satisfactory yield. There is urgent need for a strategy to get the highest yield out of every single drop of water on existing cropland to satisfy food needs in the future.
Water availability (drought and flood), soil problems (salinity, nutrient deficiencies, and toxicities), extreme temperatures (heat and cold) and biotic stresses (brown planthopper, gall midge, blast, tungro, bacterial blight) are the main constraints in South Asia, Southeast Asia, and Africa, where rice often suffers from extensive shock to sustain full yield potential. Surveys conducted by the Africa Rice Center in 12 sub-Saharan African countries reported a yield decline of 33% [71] when drought and flooding occurred together. Another study by the Africa Rice Center reported yield losses of 40% and 25% in Senegal and Uganda, respectively, due to salinity and iron toxicity [72]. Therefore, it is advantageous to select cultivars with multiple stress tolerance (drought, salinity, submergence, stagnant flooding, biotic stress, and high temperature) to allow the crop to survive if multiple stresses come at the same time.

4. Strategies to Manage Drought

Comprehensive information, early warning systems and cultivation of high-yielding, high-quality, drought- plus biotic stress-tolerant varieties in drought-prone areas could provide a solution to the problem of drought. Identification and introduction of suitable traits that narrow the gap between expected and actual yield; understanding realistic physio-morpho-molecular mechanisms of drought tolerance; and designing a standard screening method for a large population [73] could contribute to the development of drought-tolerant rice varieties. Adopting proper strategies such as larger scale standardized screening for grain yield under drought and understanding the components of yield based on morpho-physiological traits could contribute to breeders’ efforts to develop better drought-tolerant varieties. Conventional and marker-assisted breeding strategies based on the use of drought-tolerant donors, pre-breeding to use the lines derived from crosses involving donors, and the development of suitable mapping populations to identify QTLs/genes affecting yield could result in yield improvement and stability under drought stress. Breaking undesirable linkages between drought tolerance and tall plant height, drought tolerance and earliness, and drought tolerance and low yield potential [74] could help to develop semi-dwarf drought-tolerant varieties without any yield penalty. Molecular, cellular, physiological, biochemical, and developmental responses to abiotic stress involve several genes and gene functions controlling drought tolerance. Several efforts have been made to better understand the expression of drought-tolerance-related traits and the complex network of drought-related genes. Exogenous application of hormones and osmoprotectants to seed or growing plants, engineering for drought resistance, and high-throughput novel technologies could be useful tools in identifying genes to improve yield under drought (Figure 2).

4.1. Screening Strategies

Although it is difficult to understand how plants build up, combine, and exhibit the changing processes over the entire growth and development cycle, efforts have been made to standardize screening protocols, understand the mechanisms related to drought tolerance, and develop varieties that are tolerant of drought. The assessment of the type, intensity, degree of drought, and appropriate selection/screening for drought tolerance is a very crucial step. Each method has some advantage and limitations. Identification of drought-tolerant and -susceptible cultivars based on a few physiological measures (such as canopy temperature, water potential, and osmotic adjustment) [75] and specific environmental factors (such as weather and soil water availability) may not be adequate for breeders to use such donors in the breeding program. Screening of donor lines for grain yield under drought, performance of such lines under both stress and non-stress conditions [76,77,78,79], and use of robust statistical methods to clearly differentiate drought-tolerant and drought-susceptible lines [80,81,82,83] could be considered an appropriate methodology for drought screening [84]. Simultaneous screening for resistance to multiple biotic and abiotic stresses could be more beneficial to improve yield under multiple stress-prone environments.

4.1.1. Secondary Traits

Secondary traits are distinct components of prime plant traits such as grain yield. Secondary traits are important indicators of different physiological, molecular, and developmental changes involved in drought resistance, tolerance, and adaptation mechanisms. The effectiveness of selection for secondary traits such as root thickness, penetration ability and depth, greater hydraulic conductance, xylem thickness and osmotic adjustment, leaf area [85,86], leaf water potential [87], fresh and dry root weight, root volume, relative water content [26], root length [25], photosynthesis [88], early flowering, and harvest index [89] in rice to improve yield under drought is yet to be successfully demonstrated. This also goes for the anthesis-silking interval in maize [90], greenness in sorghum [91], and water-use efficiency in wheat [92]. Improvement in yield potential and yield stability across variable environments has also been reported by considering stay-green [93,94], an essential trait in several crops (maize, rice, sorghum) that gives plants resistance to drought, premature senescence [95], and lodging.
Selection for effective mobilization of the reserves from source to sink [96], osmoregulation [97], cuticular resistance, surface roughness [98], and membrane composition [99] suggested the importance of these traits in reducing drought-dependent yield loss. Stomatal conductance, maximal rates of photosynthesis [100], and developmental plasticity [101] were reported to be positively correlated, whereas leaf temperatures were negatively correlated with yield increase under stress in semi-dwarf spring wheat cultivars [100]. Another example of a successful breeding program for drought stress using carbon isotope discrimination as a substitute for water-use efficiency in increasing yield in wheat was reported by Rebetzke et al. [102] and Cattivelli et al. [103]. The limitations associated with these techniques involved the screening of only a limited number of plants because of high cost and screening under controlled conditions that may not reflect field conditions.
A number of putative secondary traits such as root density, root thickness, root distribution pattern [104,105], rooting depth [106,107], root branching, root-to-shoot ratio, root penetration [108,109,110,111,112], root length, root hydraulic conductance, transpiration demand [113], and water and nutrient uptake [111,114,115] have been suggested to confer drought tolerance [116]. Traits such as transpiration rate, biomass accumulation, stomatal conductance, leaf area [117,118,119], osmoregulation [93], relative water content, and leaf water potential [120] reported a positive association with grain yield under drought stress. Various reports suggested the role of genetic regions associated with secondary traits (Table 4, [121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136]) in enhancing grain yield under drought stress.

4.1.2. Grain Yield as a Selection Criterion under Drought

Even though screening for physiological traits is more accurate than the screening of complex quantitative agronomic traits, drought is still a complex process involving multiple steps starting from moisture-nutrient uptake by roots to grain formation by the panicle. Each physiological trait in turn fulfills one or two of the multiple sequential components needed to produce higher yield. Moreover, the appropriate combinations of these components to achieve increased yield under drought are not well understood. Grain yield, being a complex quantitative trait, was not considered earlier as a suitable selection criterion in breeding [93,105,137]. On the contrary, exploitation of genetic variation using direct selection for the trait for grain yield under drought and combining high yield potential with this trait has now been suggested as an appropriate alternative [138,139,140,141,142]. Several studies on comparative phenotypic screening of breeding material for grain yield under reproductive-stage drought stress and under a controlled environment [138,139,140,141,142,143] showed moderate heritability of grain yield under drought stress. Several experiments to standardize the procedure of phenotypic screening involving direct selection for grain yield as selection criteria (Figure 3) reported grain yield advantage under reproductive-stage drought stress with comparable yield under irrigated situations in uplands [53] and lowlands [50,144], and in multiple locations [145]. This type of cyclical stress will allow development, phenotyping, and selection for drought resistance in populations consisting of genotypes with broad growth duration.

4.1.3. High-Throughput Screening

The new tools of phenomics, such as carbon isotope discrimination (CID) [146], infrared thermography, canopy spectral reflectance [104,147], pulse amplitude-modulated fluorometry for chlorophyll fluorescence [148], normalized difference vegetation index (NDVI) [149] and photosynthetic reflective index (PRI) [150], positron emission tomography (PET), magnetic resonance imaging (MRI), and nuclear magnetic resonance [151,152] are now available to better understand the contribution of different morpho-physiological traits to grain yield. Planes, airborne instruments, and moving equipment with multispectral sensors can estimate the plant cover and nutrient needs of crops. The information collected from phenomics tools such as a high-density soil map to track porosity and mineral content, detectors to predict nutrient content and changes in response to inputs, contour mapping to observe water movements, and soil moisture detectors at multiple depths, when combined with GPS data, can give useful information about land productivity and will be useful for the following season’s planting pattern. Well-developed analytical tools/packages are essential for analyzing and interpreting the large amount of data produced by these modern techniques in the future.

4.2. Breeding Strategies

Research work is needed in breeding rice varieties with high grain yield potential, good yield under drought, yield stability, resistance to existing biotic stresses, good grain and cooking quality, and good relative performance in multiple locations and environmental (managed under drought-stress and non-stress environments) conditions.

4.2.1. Donor Identification

The preliminary and important step of any breeding program involves the identification of suitable donors. Selection of a specific donor from a large germplasm collection is a crucial step. The use of a specific donor with special characteristics for a specific environment may lead to the success of any varietal and trait development program. Most of the traditional donors have several undesirable traits and therefore are not suitable for direct use in any breeding program. These landraces have undesirable traits such as little ground cover, tall plant height, low yield potential, and poor grain and eating quality, but they have a desirable drought tolerance trait. On the other hand, modern rice varieties have desirable traits such as high yield, improved plant type (early vigor, medium height, and lodging resistance), tolerance of biotic stress, and good grain type (medium to long slender). However, they are drought-susceptible. Breeding for any desired trait to get new gene combinations requires exploitation of genetic variation (intra-specific, inter-specific, or inter-generic) that exist in traditional landraces carrying desirable characteristics and modern improved varieties with high yield potential [153]. The genotype at par performance in the target environment [154] and the trait with high heritability [155] can account for further high-throughput screening. The identified drought-tolerant donors such as PSBRc68, PSBRc80, PSBRc82, Aday Sel, Dagaddeshi, Kali Aus, Aus276, Kalia, N22, Apo, Dular, and IR77298-14-1-2 have been used in conventional breeding and QTL mapping studies at IRRI. Among these, improved donors such as PSBRc68, PSBRc80, PSBRc82, and IR77298-14-1-2 have been directly used in conventional breeding programs, whereas improved drought-tolerant lines free from undesirable linkages were derived from the mapping populations that involve traditional donors such as Aday Sel, Dagaddeshi, Kali Aus, Aus 276, Kalia, N22, Apo, and Dular and used in conventional breeding programs. In marker-assisted breeding programs, lines possessing the identified QTLs for grain yield under drought, which come from mapping populations that involve traditional donors, were used to improve mega-varieties.
A model drought-resistant rice variety for drought-prone environments can be considered as having better yields than any other presently available cultivar, not only under drought stress but also under irrigated conditions across different seasons and environments, being less sensitive to variable conditions [83,156,157,158], and possessing good grain quality and resistance to biotic stresses.

4.2.2. Conventional Breeding

Over the last 10 years, conventional breeding at distinguished worldwide research centers has made significant progress in developing biotic and abiotic stress-tolerant lines/cultivars of some important food crops such as chickpea [159], soybean [160], wheat [161,162,163], barley [164,165], rice [89], and common bean [166] using different protocols and designs. The drought breeding program at IRRI has led to the development of several high-yielding, drought-tolerant lines with a release of varieties across South and Southeast Asia and Africa since 2009 (Table 5). However, it is time-consuming, costly, and labor-intensive, and there is a high probability of transferring undesirable genes. A modified conventional breeding approach (Figure 4) involving an integrative sequential phenotyping, genotyping, and selection strategy to screen a large number of plants will improve the assessment of plant response to drought stress. This efficient, precise, cost-effective breeding approach may expedite the development of drought-tolerant rice varieties with a high frequency of favorable genes.

4.2.3. Marker-Assisted Breeding: Identification, Introgression, and Pyramiding of QTLs

Marker-assisted breeding adopted at IRRI involves: the development of mapping populations involving traditional drought-tolerant donors and modern high-yielding varieties; precise phenotyping in multi-environment, controlled, and drought-stress conditions; repeated years; identification of polymorphic markers; genotyping with polymorphic markers; linkage map construction; and QTL mapping using genotypic and phenotypic data.
Large-scale systematic study with several mapping populations for identification of major quantitative trait loci (QTLs) using yield as a selection criterion [89] led to the identification of several QTLs for grain yield under drought, followed by introgression of identified QTLs to develop drought-tolerant rice cultivars.
The success of screening strategies with careful assessment of size and structure of population has led to the development and release of several drought-tolerant lines with high yield under irrigated conditions [89]. Identification of genetic regions linked to drought tolerance using genotyping strategies such as selective genotyping (SG), whole-genome genotyping (WGG), bulk segregant analysis (BSA) [50,51,167,168], genome-wide association studies (GWAS, an improved version of marker-assisted selection) [169,170,171,172], and successful introgression in different genetic backgrounds using marker-assisted backcrossing [42,46,52,144,167,168,173,174], marker-assisted recurrent selection [175,176], and marker-assisted QTL pyramiding [89] has been reported. Mapping populations segregating for drought-tolerance-related traits led to the identification of 12 quantitative trait loci (QTLs) (Table 6) showing a large effect against high-yielding, drought-susceptible popular varieties: Swarna, IR64, MTU1010, TDK1, Sabitri, and Vandana [49,50,51,52,53,167,168,177,178,179,180] (Table 6). Gathering all data on the donors/recipients, factors, traits, genes, mechanisms, and technologies that sustain yield under drought and accumulating them into elite genotypes without negative effects on yield potential could be the best solution for rainfed environments.
The drought marker-assisted breeding program at IRRI has led to the development and release of high-yielding drought-tolerant lines (Table 7).
The major and consistent drought grain yield (GY) QTLs were reported to be collocated with QTLs for plant height and/or days to flowering [50,53,144,177]. The developed drought-tolerant lines possessed earliness, root plasticity traits, greater root length density, better water-use efficiency mechanism, better regulation of shoot growth [106,121,181], and a yield advantage of 0.8–1.0 t·ha−1 under severe drought. These short-duration varieties of 105–110 days without any yield decline possessed better adaptability to less water and variable environmental growing conditions. QTLs related to traits enhancing drought tolerance have been reported in cotton [136], pearl millet [182], maize [156], Sorghum [91], and barley [183]. Fine-mapping of QTLs to facilitate exact introgression devoid of undesirable linkages; identification of useful candidate genes; effectiveness in various genetic backgrounds and variable environment; and effective use, pyramiding, and interaction studies may now open new windows to the development of drought-tolerant rice cultivars. Fine-mapping of qDTY12.1 resulted in the partitioning of the qDTY12.1 into sub-QTLs and multiple intra-QTL genes (OsNAM12.1 transcription factor and co-localized target genes). This strengthened the view of more than a single gene underneath the functionality of one QTL and reiterate grain yield under drought, a complex trait [124]. Insertion mutants in the co-localized target genes in the qDTY12.1 region lead to an increase in the lateral roots compared to the wild type [124]. Fine-mapping of qDTY1.1 shows that qDTY1.1 harbors the green revolution gene ‘sd1’ [121].
Genetic linkages; complex gene network; QTL × QTL, QTL × background, QTL × environment interactions [175,184]; and pleiotropy are the most important aspects in breeding when studying the complexity of genetic regions related to drought biotic and abiotic stress traits. The linkage of qDTY1.1 and sd1 supports the fact that during the green revolution era the drought-tolerant alleles were not maintained properly during the development of dwarf varieties for the irrigated ecosystem. The debate continued on the pleiotropic effect of dominant allele of sd1 on drought vs. linkage of dominant allele of sd1 with drought tolerance. The possibility of a pleiotrophic effect indicated the separation of the drought-susceptible allele and dwarfness is impossible. Vikram et al. [121] have successfully demonstrated the linkage of qDTY1.1 with the sd1 gene, nullifying the debate on the linkages or pleiotropic effects of the sd1 gene. The development of new drought-tolerant dwarf lines is a successful example of breakage of linkages between qDTY1.1 and sd1 loci. Many studies reported the collocation of major and consistent drought grain yield (GY) QTLs such as qDTY1.1, qDTY2.3, qDTY3.1, qDTY3.2 and qDTY12.1, with QTLs for days to flowering and plant height [50,52,53,144]. The linkages of the drought QTLs were successfully broken and drought-tolerant lines in Swarna, IR64, and Vandana background were developed [74].
Pyramiding QTLs for a quantitative trait such as grain yield may be an effective approach to combine superior alleles and achieve the desirable phenotypic level of variation [185]. QTL pyramiding may be an appropriate approach to improve the efficiency of marker-assisted selection for desirable loci in rice breeding programs and to understand the interactions among genetic loci. Under severe reproductive-stage drought stress, grain yield advantage of 0.8–1.0 t·ha−1 was reported in QTL introgression programs involving popular high-yielding varieties IR64 and Swarna [144,178]. The QTL pyramiding program ongoing at IRRI in the background of popular rice varieties Swarna, IR64, Vandana, Sabitri, TDK1, Anjali, Samba Mahsuri, MRQ74, MR219, and some Korean lines (Jinmibyeo, Gayabyeo, Hanarumbyeo, and Sangnambatbyeo) uses the different marker-assisted breeding approaches shown in Table 8. It is evident from Table 8 that, even for the same QTL, researchers may have to find and use different sets of peak and flanking markers depending on the polymorphism of the donor and recipient and the identification of such polymorphic markers within the QTL region. Fine mapping, physiological and molecular characterization of the QTL interval to capture all the desirable genes with positive interactions contributing to drought tolerance is an important step before initiating a QTL introgression program.

4.3. Interactions between QTLs (Q × Q), QTLs and Genetic Background (Q × G), and QTLs and the Environment (Q × E)

Undesirable genetic linkages, QTL × genetic background (Q × G), and QTL × environmental interaction (Q × E) play an important role in restricting the use of QTLs in marker-assisted breeding [109,186,187]. The combined effect of alleles at more than one locus on a trait of interest, which departs from simply adding up the effects of the alleles at each locus, represents the case of genetic interaction. Many examples of such interactions are known [188], but the relative contribution of interactions to trait variation is questionable. The large sample size population, effective screening strategy, screening under variable conditions and environment, accurate genotyping, and analytical approach increase the power to detect the QTLs, Q × Q, and Q × E interactions. These interactions could be one of the possible reasons for the variable effect of QTLs in different genetic backgrounds and environments. Identification and pyramiding of positively interacting large-effect QTLs may provide a wider adaptability of QTLs across genetic backgrounds and environments. The effect of the QTLs varies with donors and recipients [50,51]. To achieve success in QTL pyramiding, there is a need to identify QTLs with large and consistent effect under variable environmental conditions; different intensities of stress; multiple genetic backgrounds; and positive interaction between QTLs different genetic backgrounds, QTLs × environment, and QTLs × genotype × environment for appropriate yield increase under drought [50,125]. The selection of donor and recipient varieties in a breeding program requires the consideration of factors such as flowering synchronization, cross compatibility, maturity duration, resistance/susceptibility to biotic and abiotic stresses, and adaptability to environment, and grain quality traits. Stability of grain yield QTLs under drought, different backgrounds, and environments have been reported by Bernier et al. [125] (qDTY12.1; 21 experiments conducted at IRRI and in eastern India), Mishra et al. [167] (qDTY12.1; at IRRI and Nepal) and Yadaw et al. [168] (qDTY3.2 at IRRI, Nepal). Seven DTY QTLs—qDTY1.1 [50,51,177], qDTY2.2 [52,178], qDTY3.1 [50,144], qDTY3.2 [51,168], qDTY4.1 [178], qDTY6.1 [50,177], and qDTY12.1 [54,167]—have shown consistent effect across two or more genetic backgrounds and ecosystems. Four of the identified qDTY QTLs—qDTY1.1, qDTY2.2, qDTY6.1 and qDTY12.1 [49,52,173] are also known to be associated with increased yield under dry direct-seeded/aerobic situation. Dixit et al. [189] reported positive interaction of qDTY2.3 and qDTY3.2 with qDTY12.1 and Shamsudin et al. [190] reported the positive interaction of qDTY2.2 and qDTY3.1 with qDTY12.1, significantly increasing the yield of qDTY12.1 positive lines. Identification of major QTLs for grain yield under drought with a larger and more consistent effect across genetic backgrounds and ecosystems has opened new opportunities of developing new rice varieties with better adaptations to predicted future scenarios.
Besides the contribution of a single genetic region, linkage, pleiotropy [191], and epistasis were reported to be key factors of quantitative traits [192] in wheat, soybean, and rice [109,193,194,195,196,197]. However, few studies have been conducted on the existing positive and negative interactions among different rice yield-related traits/QTLs under drought stress. Unfavorable linkages between desirable and undesirable traits such as high yield under drought, tall plant height, and very early flowering were successfully broken through breeding to develop high-yielding, medium-duration, drought-tolerant rice varieties [121,178]. qDTY3.2 was reported to interact with qDTY1.1 and qDTY12.1 for reduction in flowering duration [74]. Strong interactions between QTL-affecting quantitative traits have also been observed in maize, soybean, and other cereal crops [198,199,200,201].
A multi-disciplinary approach involving understanding physiological and molecular mechanisms associated with QTLs/genes across variable environments, identification and validation of genomic coordinates for correlated traits, differential expression of genes involved in metabolic processes, signal transductions, and response of identified genes can be used to explain drought tolerance in detail and to select/identify genotypes with stable and improved yield under multiple stresses.

4.4. Transgenic Approaches

Transgenic approaches involve the incorporation of specifically cloned genes by limiting the transfer of unwanted genes from the donor organism. Transgenic approach is being practiced throughout the world to improve resistance to biotic stresses and tolerance of abiotic stresses in a number of crops. Rapid progress in recombinant-DNA technology and development of accurate and efficient gene-transfer protocols have resulted in efficient engineering of genes encoding compatible organic solutes [202], and biosynthesis of glycine betaine in tobacco and maize [203,204]; trehalose-6-phosphate synthase or phosphatase (TPSP) in rice [205], and tobacco [206,207]; choline dehydrogenase in maize [204]; and pyrroline-5-carboxylate synthetase (P5CS) in wheat [208], tobacco [209], soybeans [210], and petunias [211].
Although the transgenic approach is expected to be faster and more precise, there are still constraints associated with it, including gene silencing, undesirable genetic alterations resulting from the transformation process, ethical issues, public acceptability, and the assurance time in biosafety regulations and release. Sometimes the transgenic lines that had shown remarkable performance under controlled laboratory or glasshouse conditions would not be able to survive under natural field conditions where they encounter a myriad of environmental factors. The growth and development stages of plants play a significant role in defining tolerance as the tolerance seen in transgenic lines at one particular stage may not be the same at other growth stages.

4.5. Novel Strategies

Besides conventional and marker-assisted selection, heterosis breeding, recurrent selection, bi-parental mating, disruptive mating, candidate gene identification, gene cloning, plant tissue culture, and foreign gene transfer, novel opportunities of exploiting the full potential of genomics-assisted breeding are on the way and will require an integrated knowledge of high-throughput phenotyping and molecular, physiological, and developmental processes that influence drought tolerance. Genomic selection allows breeders to consider the effect of a huge number of markers to calculate the Genomic Estimated Breeding Value (GEBV), and select a few desired individual plants for phenotypic selection in the field. On the other hand, traditional breeding involves many cycles of selections based on plant phenotypic evaluation or taking the result of a few trait-linked markers into account for quality, disease, and pest resistance. Breeders no longer need to select for individual traits; instead, they can select the combination of traits based on breeding value. This allows for easy selection; breeding cycles are shortened and several breeding programs can run at the same time by planting even a few good progenies within a limited budget.
The supplementation of old with modern breeding techniques and innovative technologies based on the science of genomics may greatly help in increasing crop productivity under drought. With the rapid progress in structural and functional genomics, proteomics will certainly be beneficial to polish existing approaches to achieve significant progress in future crop improvement. The development of genome-wide analytical tools may constitute a turning point towards the easier transfer of beneficial traits to locally adapted varieties. Genome-wide association studies (GWAS) have been widely used as a popular method to identify genetic regions related to drought tolerance traits in plants [169,170,171,172]. GWAS provides a better platform in screening a large number of accessions for genetic variation underlying diverse complex traits. Recent studies reported the combined approach of GWAS and candidate-gene sequencing as a more powerful approach than separate individual approaches [212].
The available rice genome sequence information will make it feasible to produce comprehensive datasets on all existing information on genes; gene function; biochemical and molecular pathways; protein profiles; metabolites and gene expression; comparison of the genome, genes, and intergenic regions between cereal species; and allele mining in the large collection of rice germplasm and wild species. A compilation of all this information will be a boon for the scientific community as it tries to develop new varieties with high yield and stabilize this trait along with resistance to pests and disease; tolerance to drought, salinity, flood, and cold; and improved nutritional quality. The involvement of similar transcription factors, various common stress-inducible genes, and similar physiological and molecular responses in both dicotyledonous and monocotyledonous plants under abiotic stress was reported in Arabidopsis, wheat, and rice [213,214,215,216]. The syntenic relationships between different cereal crops and grasses allow developmental biologists, biochemists, and physiologists to inspect the gene complements in related species to see which pathways are common and which are unique, and how these pathways may have been modified. The vast reservoir of available genetic resources (introgression lines, mapping populations, wild species, mutants, NILs (near-isogenic lines), RILs (recombinant inbred lines), improved breeding populations, and double haploids) and the huge amount of genomic, transcriptomic, proteomic, and metabolomic information in rice would be valuable materials in the structural and functional genomics of designing novel rice varieties for a particular ecosystem. High-throughput approaches such as DNA sequencing, SNP chips, microarray, serial analysis of gene expression (SAGE), site directed mutagenesis (T-DNA insertion, transposon tagging and homologous recombination), RNA-mediated interference, yeast two-hybrid screening, and metabolite quantification will help in identifying the conditions under which various genes are expressed and the phenotype that results when they are knocked out or when their expression is altered. This will assist with the identification of alleles conferring a superior phenotype. Bioinformatics will be useful to inter-link the phenotypic data gathered from different locations under different conditions for diverse germplasm with sequence information, which will ultimately provide information on candidate gene, gene function, and phenotypic and genotypic expression of specific genotypes, thereby helping with breeders’ development of elite cultivars [217]. Crop models involving the interaction of breeding, genomics, physiology, and system and functional biology will enable us to fill the gap between genotype and complex phenotype [218].

5. Conclusions

Agriculture has undergone dramatic shifts starting from the introduction of new semi-dwarf rice varieties in 1966. This shift has been less evident in rainfed areas due to the susceptibility of modern semi-dwarf varieties to most of the abiotic stresses prevalent in rainfed ecosystems. Under ongoing climate change, which is predicted to increase the frequency of moderate to severe drought, there is an immediate need to improve existing technologies and compile all the information we have for developing better rice varieties for drought-prone areas. This challenge can only be met with long-term systematic research on drought to generate a better understanding of rice plants that can survive with less water like other cereals.

Acknowledgments

The authors thank the Generation Challenge Program (GCP), Mexico; the Bill & Melinda Gates Foundation (BMGF); and BMZ, Germany, for financial support for QTL identification and introgression work. We thank the All India Coordinated Rice Improvement Program (AICRIP), NARES (National agricultural research and extension systems) partners and associated scientists for the evaluation of lines in different locations.

Author Contributions

N.S. was involved in conceptualizing and drafting the review article; A.K. was involved in the conceptualizing and critical revision of the review article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Food Agriculture Organization (FAO). Rice Marker Monitor. Available online: http://www.fao.org/economic/RMM (accessed on 28 April 2016).
  2. Lorey, D.E. Global Environmental Challenges of the Twenty-First Century: Resources, Consumption, and Sustainable Solutions (No. 3); Rowman and Littlefield Publishers: Lanham, MD, USA, 2002. [Google Scholar]
  3. United Nations Convention to Combat Desertification (UNCCD). Desertification: The Invisible Frontline. Available online: http://www.unccd.int/Lists/SiteDocumentLibrary/Publications/Desertification_The%20invisible_frontline.pdf (accessed on 20 January 2014).
  4. Kumar, A.; Bernier, J.; Verulkar, S.; Lafitte, H.R.; Atlin, G.N. Breeding for drought tolerance: Direct selection for yield, response to selection and use of drought-tolerant donors in upland and lowland-adapted populations. Field Crops Res. 2008, 107, 221–231. [Google Scholar] [CrossRef]
  5. Pingali, P.; Raney, T. From the Green Revolution to the Gene Revolution: How will the poor fare? Mansholt Publ. Ser. 2005, 4, 407. [Google Scholar]
  6. Food Agriculture Organization. The Impact of Natural Hazards and Disasters on Agriculture and Food and Nutrition Security: A Call for Action to Build Resilient Livelihoods; FAO: Rome, Italy, 2015. [Google Scholar]
  7. Sinha, A. Natural Disaster Management in India; A Country Report from Member Countries; Asian Disaster Reduction Center (ADRC): Kobe, Japan, 1999. [Google Scholar]
  8. Bank of Thailand. The Inflation Report; The Bank of Thailand: Bangkok, Thailand, 2005. [Google Scholar]
  9. Sabetfar, S.; Ashouri, M.; Amiri, E.; Babazadeh, S. Effect of drought stress at different growth stages on yield and yield component of rice plant. Persian Gulf Crop Prot. 2013, 2, 14–18. [Google Scholar]
  10. Boonjung, H.; Fukai, S. Effects of soil water deficit at different growth stages on rice growth and yield under upland conditions. 2. Phenology, biomass production and yield. Field Crops Res. 1996, 48, 47–55. [Google Scholar] [CrossRef]
  11. Song, Y.; Qu, C.; Birch, S.; Doherty, A.; Hanan, J. Analysis and modelling of the effects of water stress on maize growth and yield in dryland conditions. Plant Prod. Sci. 2010, 13, 199–208. [Google Scholar] [CrossRef]
  12. Mostafavi, K.; Shoahosseini, M.; Sadeghi, G.H. Multivariate analysis of variation among traits of corn hybrids traits under drought stress. Int. J. Agric. Sci. 2011, 1, 416–422. [Google Scholar]
  13. Nezami, A.; Hamid, R.K.; Rezazadeh, Z.B. Effects of Drought Stress and Defoliation on Sunflower. Desert 2008, 12, 99–104. [Google Scholar]
  14. Chimenti, C.A.; Pearson, J.; Hall, A.J. Osmotic adjustment and yield maintenance under drought in sunflower. Field Crops Res. 2002, 75, 235–246. [Google Scholar] [CrossRef]
  15. Nazariyana, G.; Mehrpooyanb, M.; Khiyavic, M. Study of effects of drought stress on yield and yield components of four sunflower cultivars in Zanjan, Iran. Plant Ecophysiol. 2009, 3, 135–139. [Google Scholar]
  16. Samarah, N.H.; Mullen, R.E.; Cianzio, S.R.; Scott, P. Dehydrin-like proteins in soybean seeds in response to drought stress during seed filling. Crop Sci. 2006, 46, 2141–2150. [Google Scholar] [CrossRef]
  17. Kobraei, S.; Etminan, A.; Mohammadi, R.; Kobraee, S. Effect of drought stress on yield and components of yield of soyabean. Ann. Biol. Res. 2011, 2, 504–509. [Google Scholar]
  18. Baroowa, B.; Gogoi, N. Morpho-physiological and yield responses of black gram (Vigna mungo L.) and green gram (Vigna radiate L.) genotypes under drought at different Growth stages. Res. J. Rec. Sci. 2016, 5, 43–50. [Google Scholar]
  19. Ma, F.Y.; Li, M.C.; Yang, J.R.; Ji, X.J.; Shentu, X.D.; Tao, H.J. A study of effect of water deficit of three periods during cotton anthesis on canopy apparent photosynthesis and WUE. Sci. Agric. Sin. 2002, 12, 1467–1472. [Google Scholar]
  20. Loka, D.A.; Oosterhuis, D.M. Water stress and reproductive development in cotton. In Flowering and Fruiting in Cotton; Oosterhuis, D.M., Cothren, J.T., Eds.; The Cotton Foundation: Candova, TN, USA, 2012; pp. 51–58. [Google Scholar]
  21. Hsiao, T.C. The soil plant atmosphere continuum in relation to drought and crop production. In Drought Resistance in Crops with Emphasis on Rice; IRRI: Los Banos, Philippines, 1982; pp. 39–52. [Google Scholar]
  22. O’Toole, J.C. Adaptation of rice to drought prone environments. In Drought Resistance in Crops with Emphasis on Rice; IRRI: Los Baños, Philippines, 1982; pp. 195–213. [Google Scholar]
  23. Harris, D.; Tripathi, R.S.; Joshi, A. On-farm seed priming to improve crop establishment and yield in dry direct-seeded rice. In Direct Seeding: Research Strategies and Opportunities; Pandey, S., Mortimer, M., Wade, L., Tuong, T.P., Lopes, K., Hardy, B., Eds.; International Research Institute: Manila, Philippines, 2002; pp. 231–240. [Google Scholar]
  24. Kaya, M.D.; Okçub, G.; Ataka, M.; Çıkılıc, Y.; Kolsarıcıa, Ö. Seed treatments to overcome salt and drought stress during germination in sunflower (Helianthus annuus L.). Eur. J. Agron. 2006, 24, 291–295. [Google Scholar] [CrossRef]
  25. Asch, F.; Dingkuhn, M.; Sow, A.; Audebert, A. Drought-induced changes in rooting patterns and assimilate partitioning between root and shoot in upland rice. Field Crops Res. 2005, 93, 223–236. [Google Scholar] [CrossRef]
  26. Sandhu, N.; Jain, S.; Battan, K.R.; Jain, R.K. Aerobic rice genotypes displayed greater adaptation to water-limited cultivation and tolerance to polyethyleneglycol-6000 induced stress. Physiol. Mol. Biol. Plants 2011, 18, 33–43. [Google Scholar] [CrossRef] [PubMed]
  27. Sinclair, T.R.; Ludlow, M.M. Who taught plants thermodynamics? The unfulfilled potential of plant water potential. Funct. Plant Biol. 1985, 12, 213–217. [Google Scholar]
  28. Anjum, F.; Yaseen, M.; Rasul, E.; Wahid, A.; Anjum, S. Water stress in barley (Hordeum vulgare L.). I. Effect on chemical composition and chlorophyll contents. Pak. J. Agric. Sci. 2003, 40, 45–49. [Google Scholar]
  29. Lei, D.; Ying, L.I.; Yong, L.I.; Qi rong, S.; Shiwei, G. Effects of Drought Stress on Photosynthesis and water status of rice leaves. Chin. J. Rice Sci. 2014, 28, 65–70. [Google Scholar]
  30. Hussain, M.; Malik, M.A.; Farooq, M.; Ashraf, M.Y.; Cheema, M.A. Improving Drought tolerance by exogenous application of glycine betaine and salicylic acid in sunflower. J. Agron. Crop Sci. 2008, 194, 193–199. [Google Scholar] [CrossRef]
  31. Wahid, A.; Rasul, E. Photosynthesis in leaf, stem, flower and fruit. In Handbook of Photosynthesis, 2nd ed.; Pessarakli, M., Ed.; CRC Press: Boca Raton, FL, USA, 2005; pp. 479–497. [Google Scholar]
  32. Farooq, M.; Wahid, A.; Kobayashi, D.N.; Fujita, S.; Basra, M.A. Plant drought stress: Effects, mechanisms and management. In Agronomy for Sustainable Development; Springer: Berlin, Germany, 2009; Volume 29, pp. 185–212. [Google Scholar]
  33. Turner, N.C.; Wright, G.C.; Siddique, K.H.M. Adaptation of grain legumes (Pulses) to water-limited environments. Adv. Agron. 2000, 71, 193–231. [Google Scholar]
  34. Grossman, A.; Takahashi, H. Macronutrient utilization by photosynthetic eukaryotes and the fabric of interactions. Annu. Rev. Plant Phys. 2001, 52, 163–210. [Google Scholar] [CrossRef] [PubMed]
  35. McWilliams, D. Drought Strategies for Cotton, Cooperative Extension Service Circular 582; College of Agriculture and Home Economics, New Mexico State University: Las Cruces, NM, USA, 2003. [Google Scholar]
  36. Ekanayake, I.J.; Steponkus, P.L.; De Datta, S.K. Spikelet sterility and flowering response of rice to water stress at anthesis. Ann. Bot. 1989, 63, 257–264. [Google Scholar] [CrossRef]
  37. Ekanayake, I.J.; Steponkus, P.L.; De Datta, S.K. Sensitivity of pollination to water deficits at anthesis in upland rice. Crop Sci. 1990, 30, 310–315. [Google Scholar] [CrossRef]
  38. Liu, J.X.; Liao, D.Q.; Oane, R.; Estenor, L.; Yang, X.E.; Li, Z.C.; Bennett, J. Genetic variation in the sensitivity of anther dehiscence to drought stress in rice. Field Crops Res. 2006, 97, 87–100. [Google Scholar] [CrossRef]
  39. Fu, J.; Huang, B. Involvement of antioxidants and lipid peroxidation in the adaptation of two cool-season grasses to localized drought stress. Environ. Exp. Bot. 2001, 45, 105–114. [Google Scholar] [CrossRef]
  40. Reddy, A.R.; Chaitanya, K.V.; Vivekanandan, M. Drought-induced responses of photosynthesis and antioxidant metabolism in higher plants. J. Plant Physiol. 2004, 161, 1189–1202. [Google Scholar] [CrossRef]
  41. Kasuga, M.; Liu, Q.; Miura, S.; Yamaguchi-Shinozaki, K.; Shinozaki, K. Improving plant drought, salt, and freezing tolerance by gene transfer of a single stress-inducible transcription factor. Nat. Biotechnol. 1999, 17, 287–291. [Google Scholar] [PubMed]
  42. Rontein, D.; Basset, G.; Hanson, A.D. Metabolic engineering of osmoprotectant accumulation in plants. Metab. Eng. 2002, 4, 49–56. [Google Scholar] [CrossRef] [PubMed]
  43. Rampino, P.; Pataleo, S.; Gerardi, C.; Mita, G.; Perrotta, C. Drought stress response in wheat: Physiological and molecular analysis of resistant and sensitive genotypes. Plant Cell Environ. 2006, 29, 2143–2152. [Google Scholar] [CrossRef] [PubMed]
  44. Cairns, J.E.; Impa, S.M.; O’Toole, J.C.; Jagadish, S.V.K.; Price, A.H. Influence of the soil physical environment on rice (Oryza sativa L.) response to drought stress and its implications for drought research. Field Crops Res. 2011, 121, 303–310. [Google Scholar] [CrossRef]
  45. Fischer, K.S.; Lafitte, R.; Fukai, S.; Atlin, G.; Hardy, B. Breeding Rice for Drought-Prone Environments; International Rice Research Institute: Los Baños, Philippines, 2003. [Google Scholar]
  46. Araus, J.L.; Slafer, G.A.; Reynolds, M.P.; Royo, C. Plant breeding and water relations in C3 cereals: What should we breed for? Ann. Bot. 2002, 89, 925–940. [Google Scholar] [CrossRef] [PubMed]
  47. Bartels, D.; Sour, E. Molecular responses of higher plants to dehydration. In Plant Responses to Abiotic Stress; Hirt, H., Shinozaki, K., Eds.; Springer: Berlin, Germany, 2004; pp. 13–37. [Google Scholar]
  48. Dixit, S.; Swamy, B.P.M.; Vikram, P.; Ahmed, H.U.; Sta Cruz, M.T.; Amante, M.; Atri, D.; Leung, H.; Kumar, A. Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities. Theor. Appl. Genet. 2012, 125, 155–169. [Google Scholar] [CrossRef] [PubMed]
  49. Dixit, S.; Singh, A.; Sta Cruz, M.T.; Maturan, P.T.; Amante, M.; Kumar, A. Multiple major QTL lead to stable yield performance of rice cultivars across varying drought intensities. BMC Genet. 2014, 15, 16. [Google Scholar] [CrossRef] [PubMed]
  50. Vikram, P.; Swamy, B.P.M.; Dixit, S.; Ahmed, H.U.; Sta Cruz, M.T.; Singh, A.K.; Kumar, A. qDTY1.1, a major QTL for rice grain yield under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet. 2011, 12, 89. [Google Scholar] [CrossRef] [PubMed]
  51. Ghimire, K.H.; Quiatchon, L.A.; Vikram, P.; Swamy, B.P.M.; Dixit, S.; Ahmed, H.; Hernandez, J.E.; Borromeo, T.H.; Kumar, A. Identification and mapping of a QTL (qDTY1.1) with a consistent effect on grain yield under drought. Field Crops Res. 2012, 131, 88–96. [Google Scholar] [CrossRef]
  52. Sandhu, N.; Singh, A.; Dixit, S.; Sta Cruz, M.T.; Maturan, P.C.; Jain, R.K.; Kumar, A. Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet. 2014, 15, 63. [Google Scholar] [CrossRef] [PubMed]
  53. Bernier, J.; Kumar, A.; Venuprasad, R.; Spaner, D.; Atlin, G.N. A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Sci. 2007, 47, 507–516. [Google Scholar] [CrossRef]
  54. Bauder, J. Irrigating with Limited Water Supplies; Montana State Univ. Comm. Ser. Montana Hall: Bozeman, MT, USA, 2011. [Google Scholar]
  55. Amiri, M.; Naseri, R.; Soleimani, R. Response of different growth stages of wheat to moisture tension in a semiarid land. World Appl. Sci. J. 2011, 12, 83–89. [Google Scholar]
  56. Wang, W.; Vinocur, B.; Altman, A. Plant responses to drought, salinity and extreme temperatures: Towards genetic engineering for stress tolerance. Planta 2003, 218, 1–14. [Google Scholar] [CrossRef] [PubMed]
  57. Praba, M.L.; Cairns, J.E.; Babu, R.C.; Lafitte, H.R. Identification of physiological traits underlying cultivar differences in drought tolerance in rice and wheat. J. Agron. Crop Sci. 2009, 195, 30–46. [Google Scholar] [CrossRef]
  58. Winkel, T.; Renno, J.F.; Payne, W.A. Effect of the timing of water deficit on growth, phenology and yield of pearl millet (Pennisetum glaucum (L.) R. Br.) grown in Sahelian conditions. J. Exp. Bot. 1997, 48, 1001–1009. [Google Scholar] [CrossRef]
  59. Yadav, R.S.; Hash, C.T.; Bidinger, F.R.; Cavan, G.P.; Howarth, C.J. Quantitative trait loci associated with traits determining grain and stover yield in pearl millet under terminal drought-stress conditions. Theor. Appl. Genet. 2002, 104, 67–83. [Google Scholar] [CrossRef] [PubMed]
  60. Adebayo, M.A.; Menkir, A. Assessment of hybrids of drought tolerant maize (Zea mays L.) inbred lines for grain yield and other traits under stress managed conditions. Nig. J. Genet. 2014, 28, 19–23. [Google Scholar] [CrossRef]
  61. Adee, E.; Roozeboom, K.; Balboa, G.R.; Schlegel, A.; Ciampitti, I.A. Drought-Tolerant Corn Hybrids Yield More in Drought-Stressed Environments with No Penalty in Non-stressed Environments. Front. Plant Sci. 2016, 7, 1534–1542. [Google Scholar] [CrossRef] [PubMed]
  62. Zarabi, M.; Alahdadi, I.; Akbari, G.A.; Akbari, G.A. A study on the effects of different biofertilizer combinations on yield, its components and growth indices of corn (Zea mays L.) under drought stress condition. Afr. J. Agric. Res. 2011, 6, 681–685. [Google Scholar]
  63. Daryanto, S.; Wang, L.; Jacinthe, P.A. Global synthesis of drought effects on maize and wheat production. PLoS ONE 2016, 11, e0156362. [Google Scholar] [CrossRef] [PubMed]
  64. Samarah, N.H.; Alquda, A.M.; Amayreh, J.A.; McAndrews, G.M. The Effect of late-terminal drought stress on yield components of four barley cultivars. J. Agron. Crop Sci. 2009, 195, 427–441. [Google Scholar] [CrossRef]
  65. Samarah, N.H.; Haddad, N.; Alqudah, A.M. Yield potential evaluation in Chickpea genotypes under late terminal drought in relation to the length of reproductive stage. Ital. J. Agron. Riv. Agron. 2009, 3, 111–117. [Google Scholar] [CrossRef]
  66. Nayyar, H.; Kaur, S.; Singh, S.; Upadhyaya, H.D. Differential sensitivity of Desi (small-seeded) and Kabuli (large seeded) chickpea genotypes to water stress during seed filling: Effects on accumulation of seed reserves and yield. J. Sci. Food Agric. 2006, 86, 2076–2082. [Google Scholar] [CrossRef]
  67. Nam, N.H.; Chauhany, S.; Johansen, C. Effect of timing of drought stress on growth and grain yield of extra-short-duration pigeon pea lines. J. Agric. Sci. 2001, 136, 179–189. [Google Scholar] [CrossRef]
  68. Tanveer ul, H.; Anser, A.; Sajid, M.N.; Muhammad, M.M.; Muhammad, I. Performance of canola cultivars under drought stress induced by withholding irrigation at different growth stages. Soil Environ. 2014, 33, 43–50. [Google Scholar]
  69. Zhu, X.; Gong, H.; Chen, G.; Wang, S.; Zhang, C. Different solute levels in two spring wheat cultivars induced by progressive field water stress at different developmental stages. J. Arid Environ. 2005, 62, 1–14. [Google Scholar] [CrossRef]
  70. Plaut, Z.; Butow, B.J.; Blumenthal, C.S.; Wrigley, C.W. Transport of dry matter into developing wheat kernels and its contribution to grain yield under post anthesis water deficit and elevated temperature. Field Crops Res. 2004, 86, 185–198. [Google Scholar] [CrossRef]
  71. Africa Rice. Boosting Africa’s Rice Sector: A Research for Development Strategy 2011–2020; Africa Rice Center: Cotonou, Benin, 2011. [Google Scholar]
  72. Africa Rice Center. Overcoming abiotic stresses to increase production. In Africa Rice Center Annual Report 2010: Building African Capacity on Policy Analysis and Impact Assessment; Africa Rice Center: Cotonou, Benin, 2011; pp. 10–14. [Google Scholar]
  73. Kamoshita, A.; Babu, R.C.; Boopathi, N.; Fukai, S. Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments. Field Crops Res. 2008, 109, 1–23. [Google Scholar] [CrossRef]
  74. Vikram, P.; Swamy, B.P.M.; Dixit, S.; Trinidad, J.; Sta Cruz, M.T.; Maturan, P.C.; Amante, M.; Kumar, A. Linkages and interactions analysis of major effect drought grain yield QTLs in rice. PLoS ONE 2016, 11, e0151532. [Google Scholar] [CrossRef] [PubMed]
  75. Turner, N.C.; Abbo, S.; Berger, J.D.; Chaturvedi, S.K.; French, R.J.; Ludwig, C.; Mannur, D.M.; Singh, S.J.; Yadava, H.S. Osmotic adjustment in chickpea (Cicer arietinum L.) results in no yield benefit under terminal drought. J. Exp. Bot. 2007, 58, 187–194. [Google Scholar] [CrossRef] [PubMed]
  76. Finlay, K.W.; Wilkinson, G.N. The analysis of adaptation in a plant breeding programme. Aust. J. Agric. Res. 1963, 14, 742–754. [Google Scholar] [CrossRef]
  77. Eberhart, S.A.; Russell, W.A. Stability parameters for comparing varieties. Crop Sci. 1966, 6, 36–40. [Google Scholar] [CrossRef]
  78. Fischer, R.A.; Maurer, R. Drought resistance in spring wheat cultivars. I. Grain yield response. Aust. J. Agric. Res. 1978, 29, 897–912. [Google Scholar] [CrossRef]
  79. Yadav, O.P.; Bhatnagar, S.K. Evaluation of indices for identification of pearl millet cultivars adapted to stress and non-stress conditions. Field Crops Res. 2001, 70, 201–208. [Google Scholar] [CrossRef]
  80. Karamanos, A.J.; Papatheohari, A.Y. Assessment of drought resistance of crop genotypes by means of the water potential Index. Crop Sci. 1999, 39, 1792–1797. [Google Scholar] [CrossRef]
  81. Idso, S.B.; Reginato, R.; Reicosky, D.; Hatfield, J. Determining soil induced plant water potential depression in alfalfa by means of infrared thermometer. Agron. J. 1981, 73, 826–830. [Google Scholar] [CrossRef]
  82. Motzo, R.; Giunta, F.; Deidda, M. Factors affecting the genotype x environment interaction in spring triticale grown in a Mediterranean environment. Euphytica 2001, 121, 317–324. [Google Scholar] [CrossRef]
  83. Rizza, F.; Badeck, F.W.; Cattivelli, L.; Li Destri, O.; Di Fonzo, N.; Stanca, A.M. Use of a water stress index to identify barley genotypes adapted to rainfed and irrigated conditions. Crop Sci. 2004, 44, 2127–2137. [Google Scholar] [CrossRef]
  84. Voltas, J.; Lopez-Corcoles, H.; Borras, G. Use of biplot analysis and factorial regression for the investigation of superior genotypes in multienvironment trials. Eur. J. Agron. 2005, 22, 309–324. [Google Scholar] [CrossRef]
  85. Babu, C.R.; Nguyen, B.D.; Chamarerk, V.; Shanmugasundaram, P.; Chezhian, P.; Juyaprakash, P.; Ganesh, S.K.; Palchamy, A.; Sadasivam, S.; Sarkarung, S.; et al. Genetic analysis of drought resistance in rice by molecular markers: Association between secondary traits and field performance. Crop Sci. 2003, 43, 1457–1469. [Google Scholar] [CrossRef]
  86. Lanceras, J.C.; Pantuwan, G.; Jongdee, B.; Toojinda, T. Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiol. 2004, 135, 384–399. [Google Scholar] [CrossRef] [PubMed]
  87. Jongdee, B.; Pantuwan, G.; Fukai, S.; Fischer, K. Improving drought tolerance in rainfed lowland rice: An example from Thailand. Agric. Water Manag. 2006, 80, 225–240. [Google Scholar] [CrossRef]
  88. Long, S.P.; Zhu, X.G.; Naidu, S.L.; Ort, D.R. Can improvement in photosynthesis increase crop yields? Plant Cell Environ. 2006, 29, 315–330. [Google Scholar] [CrossRef] [PubMed]
  89. Kumar, A.; Dixit, S.; Ram, T.; Yadaw, R.B.; Mishra, K.K.; Mandal, N.P. Breeding high-yielding drought-tolerant rice: Genetic variations and conventional and molecular approaches. J. Exp. Bot. 2014, 65, 6265–6278. [Google Scholar] [CrossRef] [PubMed]
  90. Liu, Y.; Li, S.; Chen, F.; Yang, S.; Chen, X. Soil water dynamics and water use efficiency in spring maize (Zea mays L.) fields subjected to different water management practices on the Loess Plateau, China. Agric. Water Manag. 2010, 97, 769–775. [Google Scholar] [CrossRef]
  91. Harris, K.; Subudhi, P.K.; Borrel, A.; Jordan, D.; Rosenow, D.; Nguyen, H.; Klein, P.; Klein, R.; Mullet, J. Sorghum stay-green QTL individually reduce post-flowering drought-induced leaf senescence. J. Exp. Bot. 2007, 58, 327–338. [Google Scholar] [CrossRef] [PubMed]
  92. Condon, A.G.; Richards, R.A.; Rebetzke, G.J.; Farquhar, G.D. Breeding for high water-use efficiency. J. Exp. Bot. 2004, 55, 2447–2460. [Google Scholar] [CrossRef] [PubMed]
  93. Campos, H.; Cooper, M.; Habben, J.E.; Edmeades, G.O.; Schussler, J.R. Improving drought tolerance in maize: A view from industry. Field Crops Res. 2004, 90, 19–34. [Google Scholar] [CrossRef]
  94. Tollenar, M.; Wu, J. Yield in temperate maize is attributable to greater stress tolerance. Crop Sci. 1999, 39, 1604–1897. [Google Scholar] [CrossRef]
  95. Rajcan, I.; Tollenaar, M. Source-sink ratio and leaf senescence in maize. I. Dry matter accumulation and partitioning during the grain-filling period. Field Crops Res. 1999, 90, 245–253. [Google Scholar] [CrossRef]
  96. Blum, A. Improving wheat grain filling under stress by stem reserve mobilisation. Euphytica 1988, 100, 77–83. [Google Scholar] [CrossRef]
  97. Moinuddin; Fischer, R.A.; Sayre, K.D.; Reynolds, M.P. Osmotic Adjustment in wheat in relation to grain yield under water deficit. Environ. Agron. J. 2005, 97, 1062–1071. [Google Scholar] [CrossRef]
  98. Kerstiens, G. Cuticular water permeability and its physiological significance. J. Exp. Bot. 1996, 47, 1813–1832. [Google Scholar] [CrossRef]
  99. Tyerman, S.D.; Niemietz, C.M.; Bramley, H. Plant aquaporins: Multifunctional water and solute channels with expanding roles. Plant Cell Environ. 2002, 25, 173–194. [Google Scholar] [CrossRef] [PubMed]
  100. Fischer, R.A.; Rees, D.; Sayre, K.D.; Lu, Z.M.; Condon, A.G.; Larque Saavedra, A. Wheat yield progress associated with higher stomatal conductance and photosynthetic rate and cooler canopies. Crop Sci. 1998, 38, 1467–1475. [Google Scholar] [CrossRef]
  101. Siddique, K.H.M.; Tennant, D.; Perry, M.W.; Belford, R.K. Water use and water use efficiency of old and modern wheat cultivars in a Mediterranean type environment. Aust. J. Agric. Res. 1990, 41, 431–447. [Google Scholar] [CrossRef]
  102. Rebetzke, G.J.; Condon, A.G.; Richards, R.A.; Farquhar, G.D. Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci. 2002, 42, 739–745. [Google Scholar] [CrossRef]
  103. Cattivelli, L.; Rizza, F.; Badeck, F.W.; Mazzucotelli, E.; Mastrangelo, A.M.; Francia, E.; Mare, C.; Tondelli, A.; Stanca, A.M. Drought tolerance improvement in crop plants: An integrated view from breeding to genomics. Field Crops Res. 2008, 105, 1–14. [Google Scholar] [CrossRef]
  104. Sirault, X.R.R.; James, R.A.; Furbank, R.T. A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Funct. Plant Biol. 2009, 36, 970–977. [Google Scholar] [CrossRef]
  105. Fukai, S.; Cooper, M. Development of drought-resistant cultivars using physiomorphological traits in rice. Field Crops Res. 1995, 40, 67–86. [Google Scholar] [CrossRef]
  106. Wade, L.; Bartolome, V.; Mauleon, R. Environmental response and genomic regions correlated with rice root growth and yield under drought in the OryzaSNP panel across multiple study systems. PLoS ONE 2015, 10, e0124127. [Google Scholar] [CrossRef] [PubMed]
  107. Pantuwan, G.; Ingram, K.T.; Sharma, P.K. Rice root system development under rainfed conditions. In Proceedings of the Thematic Conference on Stress Physiology, Rainfed Lowland Rice Research Consortium, Lucknow, India, 28 February–5 March 1994; International Rice Research Centre: Manila, Philippines, 1996; pp. 198–206. [Google Scholar]
  108. Pantuwan, G.; Fukai, S.; Cooper, M.; Rajatasereekul, S.; O’Toole, J.C. Yield response of rice (Oryza sativa L.) genotypes to different types of drought under rainfed lowlands. Part1. Grain yield and yield components. Field Crops Res. 2002, 73, 153–168. [Google Scholar] [CrossRef]
  109. Price, A.H.; Cairns, J.E.; Horton, P.; Jones, R.G.W.; Griffiths, H. Linking drought-resistance mechanisms to drought avoidance in upland rice during a QTL approach: Progress and new opportunities to integrate stomatal and mesophyll responses. J. Exp. Bot. 2002, 53, 989–1004. [Google Scholar] [CrossRef] [PubMed]
  110. Yadav, R.; Courtois, B.; Huang, N.; McLaren, G. Mapping genes controlling root morphology and root distribution in a double haploid population of rice. Theor. Appl. Genet. 1997, 619–632. [Google Scholar] [CrossRef]
  111. Lafitte, H.R.; Champoux, M.C.; McLaren, G.; O’Toole, J.C. Rice root morphological traits are related to isozyme group and adaptation. Field Crops Res. 2001, 71, 57–70. [Google Scholar] [CrossRef]
  112. Nhan, D.Q.; Thaw, S.; Matsuo, N.; Xuan, T.D.; Hong, N.H.; Mochizuki, T. Evaluation of root penetration ability in rice using the wax-layers and the soil cake methods. J. Fac. Agric. Kyushu Univ. 2006, 51, 251–256. [Google Scholar]
  113. Nobel, P.S. Physiochemical and Environmental Plant Physiology, 3rd ed.; Elsevier: Amsterdam, The Netherlands, 2005. [Google Scholar]
  114. Nguyen, H.T.; Babu, R.C.; Blum, A. Breeding for drought resistance in rice: Physiological and molecular genetics considerations. Crop Sci. 1997, 37, 1426–1434. [Google Scholar] [CrossRef]
  115. Kato, Y.; Abe, J.; Kamoshita, A.; Yamagishi, J. Genotypic variation in root growth angle in rice (Oryza sativa L.) and its association with deep root development in upland fields with different water regimes. Plant Soil 2006, 287, 117–129. [Google Scholar] [CrossRef]
  116. Deivanai, S.; Devi, S.S.; Sharrmila Rengeswari, P. Physiochemical traits as potential indicators for determining drought tolerance during active tillering stage in rice (Oryza sativa L.). Pert. J. Trop. Agric. Sci. 2010, 33, 61–70. [Google Scholar]
  117. Cabuslay, G.S.; Ito, O.; Alejar, A.A. Physiological evaluation of responses of rice (Oryza sativa L.) to water deficit. Plant Sci. 2002, 163, 815–827. [Google Scholar] [CrossRef]
  118. Richards, R.A. Selectable traits to increase crop photosynthesis and yield of grain crops. J. Exp Bot. 2000, 51, 447–458. [Google Scholar] [CrossRef] [PubMed]
  119. Tardieu, F.; Tuberosa, R. Dissection and modeling of abiotic tolerance plants. Curr. Opin. Plant Biol. 2010, 13, 206–212. [Google Scholar] [CrossRef] [PubMed]
  120. Kumar, R.; Malaiya, S.; Srivastava, M.N. Evaluation of morpho-physiological traits associated with drought tolerance in rice. Indian J. Plant Physiol. 2004, 9, 305–307. [Google Scholar]
  121. Vikram, P.; Swamy, M.; Dixit, S.; Singh, R.; Singh, B.P.; Miro, B.; Kohli, A.; Henry, A.; Singh, N.K.; Kumar, A. Drought susceptibility of modern rice varieties: An effect of linkage of drought tolerance with undesirable traits. Sci. Rep. 2015, 5, 14799. [Google Scholar] [CrossRef] [PubMed]
  122. Steele, K.A.; Price, A.H.; Shashidar, H.E.; Witcombe, J.R. Marker-assisted selection to introgress rice QTLs controlling root traits into an Indian upland rice variety. Theor. Appl. Genet. 2006, 112, 208–221. [Google Scholar] [CrossRef] [PubMed]
  123. Steele, K.A.; Virk, D.S.; Kumar, R.; Prasad, S.C.; Witcombe, J.R. Field evaluation of upland rice lines selected for QTLs controlling root traits. Field Crops Res. 2007, 101, 180–186. [Google Scholar] [CrossRef]
  124. Dixit, S.; Biswal, A.K.; Min, A.; Henry, A.; Oane, R.H.; Raorane, M.L.; Longkumer, T.; Pabuayon, I.M.; Mutte, S.K.; Vardarajan, A.R.; et al. Action of multiple intra-QTL genes concerted around a co-localized transcription factor underpins a large effect QTL. Sci. Rep. 2015, 5, 15183. [Google Scholar] [CrossRef] [PubMed]
  125. Bernier, J.; Kumar, A.; Venuprasad, R. Characterization of the effect of a QTL for drought resistance in rice, qtl12.1, over a range of environments in the Philippines and eastern India. Euphytica 2009, 166, 207–217. [Google Scholar] [CrossRef]
  126. Peleg, Z.; Fahima, T.; Krugman, T.; Abbo, S.; Yakir, D.; Korol, A.B.; Saranga, Y. Genomic dissection of drought resistance in durum wheat wild emmer wheat recombinant inbreed line population. Plant Cell Environ. 2009, 32, 758–779. [Google Scholar] [CrossRef] [PubMed]
  127. Morgan, J.M.; Tan, M.K. Chromosomal location of a wheat osmoregulation gene using RFLP analysis. Aust. J. Plant Physiol. 1996, 23, 803–806. [Google Scholar] [CrossRef]
  128. Teulat, B.; This, D.; Khairallah, M.; Borries, C.; Ragot, C.; Sourdille, P.; Leroy, P.; Monneveux, P.; Charrier, A. Several QTLs involved in osmotic adjustment trait variation in barley (Hordeum vulgare L.). Theor. Appl. Genet. 1998, 96, 688–698. [Google Scholar] [CrossRef]
  129. Teulat, B.; Borries, C.; This, D. New QTLs identified for plant water status, water-soluble carbohydrate and osmotic adjustment in a barley population grown in a growth-chamber under two water regimes. Theor. Appl. Genet. 2001, 103, 161–170. [Google Scholar] [CrossRef]
  130. Teulat, B.; Zoumarou-Wallis, N.; Rotter, B.; Ben Salem, M.; Bahri, H.; This, D. QTL for relative water content in field-grown barley and their stability across Mediterranean environments. Theor. Appl. Genet. 2003, 108, 181–188. [Google Scholar] [CrossRef] [PubMed]
  131. Teulat, B.; Merah, O.; Sirault, X.; Borries, C.; Waugh, R.; This, D. QTLs for grain carbon isotope discrimination in field-grown barley. Theor. Appl. Genet. 2002, 106, 118–126. [Google Scholar] [CrossRef] [PubMed]
  132. Guo, P.G.; Baum, M.; Varshney, R.K.; Graner, A.; Grando, S.; Ceccarelli, S. QTLs for chlorophyll and chlorophyll fluorescence parameters in barley under post-flowering drought. Euphytica 2008, 163, 203–214. [Google Scholar] [CrossRef]
  133. Chen, G.X.; Krugman, T.; Fahima, T.; Chen, K.G.; Hu, Y.G.; Roder, M.; Nevo, E.; Korol, A. Chromosomal regions controlling seedling drought resistance in Israeli wild barley, Hordeum spontaneum C. Koch. Genet. Resour. Crop Evol. 2010, 57, 85–99. [Google Scholar] [CrossRef]
  134. Diab, A.A.; Teulat-Merah, B.; This, D.; Ozturk, N.Z.; Benscher, D.; Sorrells, M.E. Identification of drought-inducible genes and differentially expressed sequence tags in barley. Theor. Appl. Genet. 2004, 109, 1417–1425. [Google Scholar] [CrossRef] [PubMed]
  135. Levi, A.; Paterson, A.H.; Barak, V.; Yakir, D.; Wang, B.; Chee, P.W. Field evaluation of cotton near-isogenic lines introgressed with QTLs for productivity and drought related traits. Mol. Breed. 2009, 23, 179–195. [Google Scholar] [CrossRef]
  136. Levi, A.; Ovnat, L.; Paterson, A.H.; Saranga, Y. Photosynthesis of cotton near-isogenic lines introgressed with QTLs for productivity and drought related traits. Plant Sci. 2009, 177, 88–96. [Google Scholar] [CrossRef]
  137. Fukai, S.; Basnayake, J.; Cooper, M. Modelling water availability, crop growth, and yield of rainfed lowland rice genotypes in northeast Thailand. In Proceedings of the International Workshop on Characterizing and Understanding Rainfed Environments, Bali, Indonesia, 5–9 December 1999; Tuong, T.P., Kam, S.P., Wade, L., Pandey, S., Bouman, B.A.M., Hardy, B., Eds.; International Rice Research Institute: Los Banos, Philippines, 2001; pp. 111–130. [Google Scholar]
  138. Venuprasad, R.; Lafitte, H.R.; Atlin, G.N. Response to direct selection for grain yield under drought stress in rice. Crop Sci. 2007, 47, 285–293. [Google Scholar] [CrossRef]
  139. Venuprasad, R.; Sta Cruz, M.T.; Amante, M.; Magbanua, R.; Kumar, A.; Atlin, G.N. Response to two cycles of divergent selection for grain yield under drought stress in four rice breeding populations. Field Crops Res. 2008, 107, 232–244. [Google Scholar] [CrossRef]
  140. Dixit, S.; Huang, B.E.; Cruz, M.T.S.; Maturan, P.T.; Ontoy, J.C.E.; Kumar, A. QTLs for tolerance of drought and breeding for tolerance of abiotic and biotic stress: An integrated approach. PloS ONE 2014, 9, e109574. [Google Scholar] [CrossRef] [PubMed]
  141. Kumar, A.; Verulkar, S.B.; Dixit, S.; Chauhan, B.; Bernier, J.; Venuprasad, R.; Zhao, D.; Shrivastava, M.N. Yield and yield-attributing traits of rice (Oryza sativa L.) under lowland drought and suitability of early vigor as a selection criterion. Field Crops Res. 2009, 114, 99–107. [Google Scholar] [CrossRef]
  142. Verulkar, S.B.; Mandal, N.P.; Dwivedi, J.L.; Singh, B.N.; Sinha, P.K.; Mahato, R.N.; Dongre, P.; Singh, O.N.; Bose, L.K.; Swain, P.; et al. Breeding resilient and productive genotypes adapted to drought-prone rainfed ecosystem of India. Field Crops Res. 2010, 117, 197–208. [Google Scholar] [CrossRef]
  143. Lafitte, H.R.; Price, A.H.; Courtois, B. Yield response to water deficit in an upland rice mapping population: Associations among traits and genetic markers. Theor Appl Genet. 2004, 109, 1237–1246. [Google Scholar] [CrossRef] [PubMed]
  144. Venuprasad, R.; Dalid, C.O.; Del Valle, M.; Zhao, D.; Espiritu, M.; Sta Cruz, M.T.; Amante, M.; Kumar, A.; Atlin, G.N. Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theor. App. Genet. 2009, 120, 177–190. [Google Scholar] [CrossRef] [PubMed]
  145. Ouk, M.; Basnayake, J.; Tsubo, M.; Fukai, S.; Fischer, K.S.; Cooper, M.; Nesbitt, H. Use of drought response index for identification of drought tolerant genotypes in rainfed lowland rice. Field Crops Res. 2006, 99, 48–58. [Google Scholar] [CrossRef]
  146. Richards, R.A.; Rebetzke, G.J.; Watt, M.; Condon, A.G.; Spielmeyer, W.; Dolferus, R. Breeding for improved water productivity in temperate cereals: Phenotyping, quantitative trait loci, markers and the selection environment. Funct. Plant Biol. 2010, 37, 85–97. [Google Scholar] [CrossRef]
  147. Gutierrez, M.; Reynolds, M.P.; Klatt, A.R. Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes. J. Exp. Bot. 2010, 61, 3291–3303. [Google Scholar] [CrossRef] [PubMed]
  148. Baker, N.R. Chlorophyll fluorescence: A probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 2008, 59, 89–113. [Google Scholar] [CrossRef] [PubMed]
  149. Jones, H.G.; Vaughan, R.A. Remote Sensing of Vegetation: Principles, Techniques and Applications; Oxford University Press: Oxford, UK, 2010. [Google Scholar]
  150. Gamon, J.A.; Penuelas, J.; Field, C.B. A narrow waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35–44. [Google Scholar] [CrossRef]
  151. Sardans, J.; Peñuelas, J.; Lope-Piedrafita, S. Changes in water content and distribution in Quercus ilex leaves during progressive drought assessed by in vivo 1H magnetic resonance imaging. BMC Plant Biol. 2010, 10, 188. [Google Scholar] [CrossRef] [PubMed]
  152. Melkus, G.; Rolletschek, H.; Fuchs, J.; Radchuk, V.; Grafahrend-Belau, E.; Sreenivasulu, N.; Rutten, T.; Weier, D.; Heinzel, N.; Schreiber, F.; et al. Dynamic 13C/1H NMR imaging uncovers sugar allocation in the living seed. Plant Biotechnol. J. 2011, 9, 1022–1037. [Google Scholar] [CrossRef] [PubMed]
  153. Ashraf, M.; Akram, N.A. Improving salinity tolerance of plants through conventional breeding and genetic engineering: An analytical comparison. Biotechnol. Adv. 2009, 27, 744–752. [Google Scholar] [CrossRef] [PubMed]
  154. Bänziger, M.; Setimela, P.S.; Hodson, D.; Vivek, B. Breeding for improved drought tolerance in maize adapted to southern Africa. In Proceedings of the 4th International Crop Science Congress, Brisbane, Australia, 26 September–1 October 2004. [Google Scholar]
  155. Falconer, D.S. Introduction to Quantitative Genetics; Longman: London, UK; New York, NY, USA, 1989. [Google Scholar]
  156. Dixit, S.; Singh, A.; Kumar, A. Rice breeding for high grain yield under drought: a strategic solution to a complex problem. Int. J. Agron. 2014, 15. [Google Scholar] [CrossRef]
  157. Ober, E.S.; Clark, C.J.A.; Le Bloa, M.; Royal, A.; Jaggard, K.W.; Pidgeon, J.D. Assessing the genetic resources to improve drought tolerance in sugar beet: Agronomic traits of diverse genotypes under drought and irrigated conditions. Field Crops Res. 2004, 90, 213–234. [Google Scholar] [CrossRef]
  158. Pidgeon, J.D.; Ober, E.S.; Qi, A.; Clark, C.J.A.; Royal, A.; Jaggard, K.W. Using multi-environment sugar beet variety trails to screen for drought tolerance. Field Crops Res. 2006, 95, 268–279. [Google Scholar] [CrossRef]
  159. Singh, K.B.; Omar, M.; Saxena, M.C.; Johansen, C. Registration of FLIP 87-59C, a drought tolerant chickpea germplasm line. Crop Sci. 1996, 36, 1–2. [Google Scholar] [CrossRef]
  160. Chen, M.; Wang, Q.Y.; Cheng, X.G.; Xu, Z.S.; Li, L.C.; Ye, X.G. GmDREB2, a soybean DRE binding transcription factor, conferred drought and high-salt tolerance in transgenic plants. Biochem. Biophys. Res. Commun. 2007, 353, 299–305. [Google Scholar] [CrossRef] [PubMed]
  161. Xinglai, P.; Sangang, X.; Qiannying, P.; Yinhong, S. Registration of ‘Jinmai 50’ wheat. Crop Sci. 2006, 46, 983–995. [Google Scholar] [CrossRef]
  162. Baenziger, P.S.; Beecher, B.; Graybosch, R.A.; Ibrahim, A.M.H.; Baltensperger, D.D.; Nelson, L.A. Registration of ‘NEO1643’ wheat. J. Plant Registr. 2008, 2, 36–42. [Google Scholar] [CrossRef]
  163. Haley, S.D.; Johnson, J.J.; Peairs, F.B.; Quick, J.S.; Stromberger, J.A.; Clayshulte, S.R. Registration of ‘Ripper’ wheat. J. Plant Registr. 2007, 1, 1–6. [Google Scholar] [CrossRef]
  164. Obert, D.E.; Evans, C.P.; Wesenberg, D.M.; Windes, J.M.; Erickson, C.A.; Jackson, E.W. Registration of ‘Lenetah’ spring barley. J. Plant Registr. 2008, 2, 85–97. [Google Scholar] [CrossRef]
  165. Noaman, M.M.; El Sayad, A.A.; Asaad, F.A.; El Sherbini, A.M.; El Bawab, A.O.; El Moselhi, M.A. Registration of ‘Giza 126’ barley. Crop Sci. 1995, 35, 1710. [Google Scholar] [CrossRef]
  166. Brick, M.A.; Ogg, J.B.; Singh, S.P.; Schwartz, H.F.; Johnson, J.J.; Pastor-Corrales, MA. Registration of drought-tolerant, rust-resistant, high-yielding pinto bean germplasm line CO46348. J. Plant Registr. 2008, 2, 120–134. [Google Scholar] [CrossRef]
  167. Mishra, K.K.; Vikram, P.; Yadaw, R.B.; Swamy, B.P.M.; Dixit, S.; Sta Cruz, M.T.; Maturan, P.; Marker, S.; Kumar, A. qDTY 12.1: A locus with a consistent effect on grain yield under drought in rice. BMC Genet. 2013, 14, 12. [Google Scholar]
  168. Yadaw, R.B.; Dixit, S.; Raman, A.; Mishra, K.K.; Vikram, P.; Swamy, B.P.M.; Sta Cruz, M.T.; Maturan, P.T.; Pandey, M.; Kumar, A. A QTL for high grain yield under lowland drought in the background of popular rice variety Sabitri from Nepal. Field Crops Res. 2013, 144, 281–287. [Google Scholar] [CrossRef]
  169. Brachi, B.; Aimé, C.; Glorieux, C.; Cuguen, J.; Roux, F. Adaptive value of phenological traits in stressful environments: Predictions based on seed production and laboratory natural selection. PLoS ONE 2012, 7, e32069. [Google Scholar] [CrossRef] [PubMed]
  170. Elshire, R.J.; Glaubitz, J.C.; Sun, Q.; Poland, J.A.; Kawamoto, K.; Buckler, E.S. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 2011, 6, e19379. [Google Scholar] [CrossRef] [PubMed]
  171. Huang, X.; Wei, X.; Sang, T.; Zhao, Q.; Feng, Q.; Zha, Y.; Li, C.; Zhu, C.; Lu, T.; Zhang, Z.; et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 2010, 42, 961–966. [Google Scholar] [CrossRef] [PubMed]
  172. Begum, H.; Spindel, J.E.; Lalusin, A.; Borromeo, T.; Gregorio, G.; Hernandez, J. Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa). PLoS ONE 2015, 10, e0119873. [Google Scholar] [CrossRef] [PubMed]
  173. Sandhu, N.; Jain, S.; Kumar, A.; Mehla, B.S.; Jain, R. Genetic variation, linkage mapping of QTL and correlation studies for yield, root, and agronomic traits for aerobic adaptation. BMC Genet. 2013, 14, 104–119. [Google Scholar] [CrossRef] [PubMed]
  174. Sandhu, N.; Torres, R.; Sta Cruz, M.T.; Maturan, P.C.; Jain, R.; Kumar, A.; Henry, A. Traits and QTLs for development of dry direct seeded rainfed rice varieties. J. Exp. Bot. 2015, 66, 225–244. [Google Scholar] [CrossRef] [PubMed]
  175. Xu, Y.; Crouch, J.H. Marker-assisted selection in plant breeding: From publications to practice. Crop Sci. 2008, 48, 391–407. [Google Scholar] [CrossRef]
  176. Ribaut, J.M.; Ragot, M. Marker-assisted selection to improve drought adaptation in maize: The backcross approach, perspectives, limitations, and alternatives. J. Exp. Bot. 2006, 58, 351–360. [Google Scholar] [CrossRef] [PubMed]
  177. Venuprasad, R.; Bool, M.E.; Quiatchon, L.; Sta Cruz, M.T.; Amante, M.; Atlin, G.N. A large effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol. Breed. 2012, 30, 535–547. [Google Scholar] [CrossRef]
  178. Swamy, B.P.M.; Ahmed, H.U.; Henry, A. Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought. PLoS ONE 2013, 8, e62795. [Google Scholar] [CrossRef] [PubMed]
  179. Kumar, R.; Venuprasad, R.; Atlin, G.N. Genetic analysis of rainfed lowland rice drought tolerance under naturally occurring stress in eastern India: Heritability and QTL effects. Field Crops Res. 2007, 103, 42–52. [Google Scholar] [CrossRef]
  180. Palanog, A.D.; Mallikarjuna Swamy, B.P.; Shamsudin, N.A.A.; Dixit, S.; Hernandez, J.E.; Boromeo, T.H.; Cruz, P.C.S.; Kumar, A. Grain yield QTLs with consistent-effect under reproductive-stage drought stress in rice. Field Crops Res. 2014, 161, 46–54. [Google Scholar] [CrossRef]
  181. Henry, A.; Gowda, V.R.P.; Torres, R.O.; McNally, K.L.; Serraj, R. Variation in root system architecture and drought response in rice (Oryza sativa): Phenotyping of the OryzaSNP panel in rainfed lowland fields. Field Crops Res. 2011, 120, 205–214. [Google Scholar] [CrossRef]
  182. Howarth, C.J.; Yadav, R.S. Successful marker assisted selection for drought tolerance and disease resistance in pearl millet. IGER Innov. 2002, 6, 18–21. [Google Scholar]
  183. Tuberosa, R.; Salvi, S. Genomics approaches to improve drought tolerance in crops. Trends Plant Sci. 2006, 11, 405–412. [Google Scholar] [CrossRef] [PubMed]
  184. Bovill, W.D.; Horne, M.; Herde, D.; Davis, M.; Wildermuth, G.B. and Sutherland, M.W.; Pyramiding QTL increases seedling resistance to crown rot (Fusarium pseudograminearum) of wheat (Triticum aestivum). Theor. Appl. Genet. 2010, 121, 127–136. [Google Scholar] [CrossRef] [PubMed]
  185. Nagai, K.; Kuroha, T.; Ayano, M.; Kurokawa, Y.; Angeles-Shim, R.B.; Shim, J.; Yasui, H.; Yoshimura, A.; Ashikari, M. Two novel QTLs regulate internode elongation in deepwater rice during the early vegetative stage. Breed. Sci. 2012, 62, 178–185. [Google Scholar] [CrossRef] [PubMed]
  186. Courtois, B.; Shen, L.; Petalcorin, W.; Carandang, S.; Mauleon, R.; Li, Z.K. Locating QTLs controlling constitutive root traits in the rice population IAC 165 × Co39. Euphytica 2003, 134, 335–345. [Google Scholar] [CrossRef]
  187. Bernier, J.; Atlin, G.N.; Serraj, R.; Kumar, A.; Spaner, D. Breeding upland rice for drought resistance. J. Sci. Food Agric. 2008, 88, 927–939. [Google Scholar] [CrossRef]
  188. Mäki-Tanila, A.; Hill, W.G. Influence of gene interaction on complex trait variation with multilocus models. Genetics 2014, 198, 355–367. [Google Scholar] [CrossRef] [PubMed]
  189. Dixit, S.; Swamy, B.P.M.; Vikram, P.; Bernier, J.; Sta Cruz, M.T.; Amante, M.; Atri, D.; Kumar, A. Increased drought tolerance and wider adaptability of qDTY12.1 conferred by its interaction with qDTY2.3 and qDTY3.2. Mol. Breed. 2012, 30, 1767–1779. [Google Scholar] [CrossRef]
  190. Shamsudin, N.A.A.; Swamy, B.P.M.; Ratnam, W.; Cruz, M.T.S.; Sandhu, N.; Raman, A.K.; Kumar, A. Pyramiding of drought yield QTLs into a high quality Malaysian rice cultivar MRQ74 improves yield under reproductive stage drought. Rice 2016, 9, 21. [Google Scholar] [CrossRef] [PubMed]
  191. Lebreton, C.; Lazic-Jancic, V.; Steel, A.; Pekic, S.; Quarrie, S.A. Identification of QTL for drought responses in maize and their use in testing causal relationships between traits. J. Exp. Bot. 1995, 46, 853–865. [Google Scholar] [CrossRef]
  192. Phillips, P.C. The language of gene interaction. Genetics 1998, 149, 1167–1171. [Google Scholar] [PubMed]
  193. Li, Z.K.; Yu, S.B.; Lafitte, H.R.; Huang, N.; Courtois, B.; Hittalmani, S.; Vijayakumar, C.H.M.; Liu, G.F.; Wang, G.C.; Shashidhar, H.E.; et al. QTL × environment interactions in rice. I. Heading date and plant height. Theor. Appl. Genet. 2003, 108, 141–153. [Google Scholar] [CrossRef] [PubMed]
  194. Veronica, C.; Roncallo, P.F.; Beaufort, V.; Cervigni, G.L.; Miranda, R.; Jensen, C.A.; Echenique, V.C. Mapping of main and epistatic effect QTLs associated to grain protein and gluten strength using a RIL population of durum wheat. J. App. Genet 2011, 52, 287–298. [Google Scholar]
  195. Zhao, Z.G.; Zhu, S.S.; Zhang, Y.H.; Bian, X.F.; Wang, Y. Molecular analysis of an additional case of hybrid sterility in rice (Oryza sativa L.). Planta 2010, 233, 485–494. [Google Scholar] [CrossRef] [PubMed]
  196. Han, Y.; Xie, D.; Teng, W.; Zhang, S.; Chang, W.; Li, W. Dynamic QTL analysis of linolenic acid content in different developmental stages of soybean seed. Theor. Appl. Genet. 2011, 122, 1481–1488. [Google Scholar] [CrossRef] [PubMed]
  197. Niu, Y.; Xu, Y.; Liu, X.F.; Yang, S.X.; Wei, S.P.; Xie, F.T.; Zhang, Y.M. Association mapping for seed size and shape traits in soybean cultivars. Mol. Breed. 2013, 31, 785–794. [Google Scholar] [CrossRef]
  198. Lukens, L.N.; Doebley, J. Epistatic and environmental interactions for quantitative trait loci involved in maize evolution. Genet. Res. 1999, 74, 291–302. [Google Scholar] [CrossRef]
  199. Lark, K.G.; Chase, K.; Adler, F.; Mansur, L.M.; Orf, J.H. Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proc. Natl. Acad. Sci. USA 1995, 92, 4656–4660. [Google Scholar] [CrossRef] [PubMed]
  200. Wang, B.; Wu, Y.; Guo, W.; Zhu, X.; Huang, N.; Zhang, T. QTL analysis and epistasis effects dissection of fiber qualities in an elite cotton hybrid grown in second generation. Crop Sci. 2007, 47, 1384–1392. [Google Scholar] [CrossRef]
  201. Zhang, K.; Tian, J.; Zhao, L.; Wang, S. Mapping QTLs with epistatic effects and QTL X environment interactions for plant height using a doubled haploid population in culti- vated wheat. J. Genet. Genom. 2008, 35, 119–127. [Google Scholar] [CrossRef]
  202. Ashraf, M.; Foolad, M.R. Roles of glycinebetaine and proline in improving plant abiotic stress resistance. Environ. Exp. Bot. 2007, 59, 206–216. [Google Scholar] [CrossRef]
  203. Zhang, G.H.; Su, Q.; An, L.J.; Wu, S. Characterization and expression of a vacuolar Na+/H+ antiporter gene from the monocot halophyte Aeluropus littoralis. Plant Physiol. Biochem. 2008, 46, 117–126. [Google Scholar] [CrossRef] [PubMed]
  204. Quan, R.; Shang, M.; Zhang, H.; Zhao, Y.; Zhang, J. Engineering of enhanced glycinebetaine synthesis improves drought tolerance in maize. Plant Biotechnol. J. 2004, 2, 477–486. [Google Scholar] [CrossRef] [PubMed]
  205. Wu, R.; Garg, A. Engineering Rice Plants with Trehalose-Producing Genes Improves Tolerance to Drought, Salt, and Low Temperature. ISB News Report 2003; pp. 3–7. Available online: http://www.isb.vt.edu/news/2003/mar03.pdf (accessed on 20 May 2016).
  206. Romero, C.; Belles, J.M.; Vaya, J.L.; Serrano, R.; Culianez-Macia, F.A. Expression of the yeast trehalose-6-phosphate synthase gene in transgenic tobacco plants: Pleiotropic phenotypes include drought tolerance. Planta 1997, 201, 293–297. [Google Scholar] [CrossRef] [PubMed]
  207. Karim, S.; Aronsson, H.; Ericson, H.; Pirhonen, M.; Leyman, B.; Welin, B. Improved drought tolerance without undesired side effects in transgenic plants producing trehalose. Plant Mol. Biol. 2007, 64, 371–386. [Google Scholar] [CrossRef] [PubMed]
  208. Vendruscolo, E.C.G.; Schuster, I.; Pileggi, M.; Scapim, C.A.; Molinari, H.B.C.; Marur, C.J. Stress induced synthesis of proline confers tolerance to water deficit in transgenic wheat. J. Plant Physiol. 2007, 164, 1367–1376. [Google Scholar] [CrossRef] [PubMed]
  209. Gubis, J.; Vaňková, R.; Červená, V.; Dragúňová, M.; Hudcovicová, M.; Lichtnerová, H. Transformed tobacco plants with increased tolerance to drought. S. Afr. J. Bot. 2007, 73, 505–511. [Google Scholar] [CrossRef]
  210. Ronde, J.A.D.; Cress, W.A.; Krugerd, G.H.J.; Strasserd, R.J.; Van Staden, J. Photosynthetic response of transgenic soybean plants, containing an Arabidopsis P5CR gene, during heat and drought stress. J. Plant Physiol. 2004, 161, 1211–1224. [Google Scholar] [CrossRef] [PubMed]
  211. Yamada, M.; Morishita, H.; Urano, K.; Shiozaki, N.; Yamaguchi-Shinozaki, K.; Shinozaki, K. Effects of free proline accumulation in petunias under drought stress. J. Exp. Bot. 2005, 56, 1975–1981. [Google Scholar] [CrossRef] [PubMed]
  212. Wilkening, S.; Chen, B.; Bermejo, J.L.; Canzian, F. Is there still a need for candidate gene approaches in the era of genome-wide association studies? Genomics 2009, 93, 415–419. [Google Scholar] [CrossRef] [PubMed]
  213. Xue, G.P.; Intyre, C.L.; Chapman, S.; Bower, N.I.; Way, H.; Reverter, A.; Clarke, B.; Shorter, R. Differential gene expression of wheat progeny with contrasting levels of transpiration efficiency. Plant Mol. Biol. 2006, 61, 863–881. [Google Scholar] [CrossRef] [PubMed]
  214. Mohammadi, M.; Kav, N.N.; Deyholos, M.K. Transcript expression profile of water-limited roots of hexaploid wheat (Triticum aestivum ‘Opata’). Genome 2008, 51, 357–367. [Google Scholar] [PubMed]
  215. Aprile, A.; Mastrangelo, A.M.; De-Leonardis, A.M.; Galiba, G.; Roncaglia, E.; Ferrari, F.; De-Bellis, L.; Turchi, L.; Giuliano, G.; Cattivelli, L. Transcriptional profiling in response to terminal drought stress reveals differential responses along the wheat genome. BMC Genom. 2009, 10, 279. [Google Scholar] [CrossRef] [PubMed]
  216. Ergen, N.Z.; Thimmapuram, J.; Bohnert, H.J.; Budak, H. Transcriptome pathways unique to dehydration tolerant relatives of modern wheat. Funct. Integr. Genom. 2009, 9, 377–396. [Google Scholar] [CrossRef] [PubMed]
  217. Delseny, M.; Sales, J.; Richard, C.; Sallaud, C.; Regad, F. Rice genomics: Present and future. Plant Physiol. Biochem. 2001, 39, 323–334. [Google Scholar] [CrossRef]
  218. Yin, X.; Struik, P.C.; Kropff, M.J. Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci. 2004, 9, 426–432. [Google Scholar] [CrossRef] [PubMed]
Figure 1. (a) Projected population curve (source: U.S. Census Bureau, International database 1950–2050, July 2015 update); (b) estimated global water demand (OECD: Organization for Economic Cooperation and Development; BRIC: Brazil, Russia, India and China; RoW: Rest of world; source: United Nation Food & Agriculture Organization); (c) severity pattern of water stress by country by 2040 (source: World Resource Institute); (d) estimated possibilities for future drought worldwide based on the Palmer Drought Severity Index (source: Aigup Dai, Wiley interdisciplinary Reviews: Climate Change, July 2012).
Figure 1. (a) Projected population curve (source: U.S. Census Bureau, International database 1950–2050, July 2015 update); (b) estimated global water demand (OECD: Organization for Economic Cooperation and Development; BRIC: Brazil, Russia, India and China; RoW: Rest of world; source: United Nation Food & Agriculture Organization); (c) severity pattern of water stress by country by 2040 (source: World Resource Institute); (d) estimated possibilities for future drought worldwide based on the Palmer Drought Severity Index (source: Aigup Dai, Wiley interdisciplinary Reviews: Climate Change, July 2012).
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Figure 2. Effect of drought and approaches in developing drought-tolerant rice varieties. RILs: Recombinant inbred lines, NILs: Near-isogenic lines, DH: Double haploid, NGO: Non-Governmental Organization; IYT: Intermediate Yield Trial, PYTs: Preliminary yield trial, ↑ (increase/enhance), ↓ (decrease/reduce).
Figure 2. Effect of drought and approaches in developing drought-tolerant rice varieties. RILs: Recombinant inbred lines, NILs: Near-isogenic lines, DH: Double haploid, NGO: Non-Governmental Organization; IYT: Intermediate Yield Trial, PYTs: Preliminary yield trial, ↑ (increase/enhance), ↓ (decrease/reduce).
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Figure 3. Standardized protocol for drought phenotyping screening at IRRI. DAS: days after seeding, DAT: days after transplanting.
Figure 3. Standardized protocol for drought phenotyping screening at IRRI. DAS: days after seeding, DAT: days after transplanting.
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Figure 4. Modified conventional breeding approach. OYT: Observational yield trials, AYT: advanced yield trials, MET: multi environmental trials.
Figure 4. Modified conventional breeding approach. OYT: Observational yield trials, AYT: advanced yield trials, MET: multi environmental trials.
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Table 1. Most vulnerable drought-prone areas across the world.
Table 1. Most vulnerable drought-prone areas across the world.
RegionAreas Most Vulnerable to DroughtDrought Events
Asia/PacificIndia, Nepal, Bangladesh, China, Laos, Cambodia, Pakistan, Afghanistan, Sri Lanka, Bhutan, Indonesia, Thailand, Myanmar, Vietnam, Malaysia1876, 1878, 1896, 1902, 1907, 1928, 1930, 1936, 1941, 1942, 1944, 1958, 1961, 1964, 1972, 1973, 1974, 1983, 1987, 1993, 1996, 2000, 2002, 2010
Middle EastYemen, the United Arab Emirates, Saudi Arabia, Iraq, Iran, Syria1940, 1998, 2000, 2007, 2010
EuropeFrance, Italy, Germany, northern Spain, Czech Republic1955, 1957, 1962, 1968, 1971, 1974, 2005, 2009, 2012
United StatesArizona, Kansas, Arkansas, Georgia, Florida, Mississippi, Alabama, South, North Carolina, Texas, Oklahoma, California1934, 1936, 1939, 1940, 1983, 2002, 2010, 2011
AfricaEthiopia, Kenya, Eritrea, Somalia, Uganda, Djibouti, Mauritania, Angola, Zambia, Zimbabwe, Mozambique, Malawi, Lesotho, Swaziland1888, 1972, 1973, 1983, 1985, 1991, 1992, 1999, 2002, 2002, 2003, 2010, 2011, 2012
Latin AmericaPeru, Chile, Argentina, Brazil, Mexico1630, 1640, 1650, 1782, 1884, 1992, 1999, 2011, 2015
AustraliaNew south wales, Queensland, Victoria, Tasmania, Sydney, Northam, York area of Western Australia1813, 1826, 1829, 1835, 1838, 1850, 1888, 1897, 1902, 1982, 1983, 2000
Source: Modified from Spring 2015 global attributes survey.
Table 2. Effect of drought on crops and livestock across the world.
Table 2. Effect of drought on crops and livestock across the world.
RegionCrop Losses (Billion USD)Livestock Losses (Billion USD)Total (Billion USD)
Africa21425
Asia27128
Latin America and Caribbean9211
Near East404
Central Asia104
% share of total Global losses42.435.878.2
Source: FAO based on data from FAOSTAT, 2003–2013.
Table 3. Yield losses in different crops as a result of drought.
Table 3. Yield losses in different crops as a result of drought.
CropStressYield ReductionReference
RiceLowland moderate reproductive stage45%–60%[48,49,50]
RiceLowland severe reproductive stage65%–91%[48,49,50,51]
RiceUpland mild reproductive stage18%–39%[48,52]
RiceUpland moderate reproductive stage70%–75%[48,52]
RiceUpland severe reproductive stage80%–97%[48,49,53]
WheatModerate reproductive stage10%–50%[54,55,56,57]
Pearl MilletPrior and beginning of flowering65%[58]
Pearl MilletEarly stress62%[59]
Pearl MilletLate stress28%[59]
MaizeMild-moderate-severe reproductive stage1%–76%[60,61,62,63]
BarleySevere reproductive stage73%–87%[64]
ChickpeaLate terminal drought49%–54%[65]
ChickpeaReproductive stage45%–69%[66]
Pigeon PeaReproductive stage40%–55%[67]
CanolaReproductive stage15%–35%[68]
Table 4. Genetic regions reported to be associated with secondary traits enhancing drought tolerance.
Table 4. Genetic regions reported to be associated with secondary traits enhancing drought tolerance.
CropChrTrait ImprovedReference
Rice1Root-shoot growth, deep root growth[109,121]
9Root length, root thickness, straw yield[122,123]
12Biomass, panicle number, lateral root, panicle branching[124,125]
Wheat2B, 4A, 5A, 7BCarbon isotope ratio, osmotic potential, chlorophyll content, flag leaf, rolling index[126]
2A, 2B, 3A, 3B, 5A, 5B, 6B, 7A,Osmotic adjustment[126,127]
Barley6HLRelative water content, leaf osmotic potential, osmotic adjustment, carbon isotope discrimination[128,129,130]
2H, 3H, 6H, 7HCarbon isotope discrimination[131]
2H, 4H, 6H, 7HChlorophyll, fluorescence[132]
1H, 2H, 3H, 5H, 6H,7HRelative water content[133,134]
2H, 3H, 4H, 5HOsmotic potential[134]
Sorghum1, 2, 3, 4Leaf area, delayed leaf senescence, stay green[91]
Cotton06, 02, 25Biomass production; panicle number, specific, leaf weight and chlorophyll, osmotic potential, stomatal density, stomatal conductance[135,136]
Table 5. High-yielding drought-tolerant varieties released from IRRI’s drought breeding program.
Table 5. High-yielding drought-tolerant varieties released from IRRI’s drought breeding program.
NameDesignationCountry Ecosystem aRelease YearDays to MaturityPlant Height (cm)
Katihan 1IR 79913-B-176-B-4PhilippinesUP201110590
Sahod Ulan 3IR 81412-B-B-82-1PhilippinesRL2011120107
Sahod Ulan 5IR 81023-B-116-1-2PhilippinesRL2011115130
Sahod Ulan 6IR 72667-16-1-B-B-3PhilippinesRL2011115100
Sahod Ulan 8IR 74963-262-5-1-3-3PhilippinesRL2011125100
Inpago LIPI Go 1IR 79971-B-191-B-BIndonesiaUP2011110115
Inpago LIPI Go 2IR 79971-B-227-B-BIndonesiaUP2011113114
CR dhan 40IR 55423-01IndiaUP2012110100
Sahod Ulan 12IR 81047-B-106-2-4PhilippinesRL2013105119
M’ZIVAR77080-B-B-34-3MozambiqueRL2013120130
CR dhan 201IR 83380-B-B-124-1IndiaAerobic2014118100
CR dhan 202IR 84899-B-154IndiaAerobic2014115100
CR dhan 204IR 83927-B-B-279IndiaAerobic2014110100
Sukha dhan 5IR 83388-B-B-108-3NepalRL2014125105
Sukha dhan 6IR 83383-B-B-129-4NepalRL2014125105
BRRI dhan 66IR 82635-B-B-75-2BangladeshRL2014113116
Katihan 3IR 86857-101-2-1-3PhilippinesUP201410787
DRR dhan 43IR 83876-B-RPIndiaRL2014115105
DRR dhan 44IR 93376-B-B-130IndiaRL2014115105
Katihan 2IR 82635-B-B-47-2PhilippinesUP201410784
BRRI dhan 71IR 82589-B-B-84-3BangladeshRL2015115112
Swarna ShreyaIR 84899-B-179-16-1-1-1-1IndiaRL2015112121
Sahod Ulan 15IR 83383-B-B-129-4PhilippinesRL2015115110
Sahod Ulan 20IR 86781-3-3-1-1PhilippinesRL2015115112
MPTSAIR 82077-B-B-71-1MalawaiRL2015120110
ATETEIR 80411-B-49-1MalawaiIR, RL2015118112
CAR 14IR80463-B-39-3CambodiaIR, RL2015115110
IdentifiedIR 84878-B-60-4-1PhilippinesRL201611397
a UP: upland, RL: rainfed lowland, IR—irrigated ecology. Source: Modified from Kumar et al. [89].
Table 6. QTLs identified for grain yield under drought in different backgrounds.
Table 6. QTLs identified for grain yield under drought in different backgrounds.
QTLsDonorsBackgroundsEcosystemsReference
qDTY1.1N22, Dhagaddeshi, Apo, CT9993-10-1-M, Kali Aus, Basmati 334Swarna, IR64, MTU1010Lowland, Upland[50,51,52,179]
qDTY2.1Apo, Aus 276Swarna, MTU1010Lowland[52,144]
qDTY2.2Aday sel, Kali AusMTU1010, IR64, Samba MahsuriLowland, Upland[178,180]
qDTY2.3Kali AusIR64Upland, Lowland[52,180]
qDTY3.1Apo, IR55419-04Swarna, TDK 1Lowland[49,144]
qDTY3.2N22, IR77298-5-6-18, Aday selSwarna, SabitriLowland, Upland[50,158]
qDTY4.1Aday SelIR64, Samba MahsuriLowland[178]
qDTY6.1Apo, Vandana, IR55419-04IR72, TDK 1Upland, Lowland[49,177]
qDTY6.2IR55419-04TDK 1Lowland[49]
qDTY9.1Aday selIR64Lowland[178]
qDTY10.1N22, Aday sel, Basmati 334IR64, MTU1010, SwarnaLowland[50,178]
qDTY12.1Way Rarem, IR74371-46-1-1Vandana, SabitriUpland, Lowland[53,167]
Table 7. High-yielding drought-tolerant varieties released from IRRI’s drought marker-assisted breeding program.
Table 7. High-yielding drought-tolerant varieties released from IRRI’s drought marker-assisted breeding program.
NameDesignationCountry EcosystemRelease YearDays to MaturityPlant Height (cm)
Sukha dhan 4IR 87707-446-B-B-BNepalRL2014125102
DRR 44IR 87707-445-B-B-BIndiaRL2014115110
Yaenelo 4IR 87707-446-B-B-BMyanmarRL2015115117
Yaenelo 5IR 87705-44-4-B-BMyanmarRL2016115117
Yaenelo 6IR 87707-182-B-B-BMyanmarRL2016115117
Yaenelo 7IR 87705-83-12-B-BMyanmarRL2016115117
Source: Modified from Kumar et al. [89].
Table 8. QTL pyramiding program ongoing at IRRI in the background of popular rice varieties through marker-assisted breeding.
Table 8. QTL pyramiding program ongoing at IRRI in the background of popular rice varieties through marker-assisted breeding.
Breeding Approach aQTLsMarkerTarget VarietyTarget Ecosystem
MASqDTY3.1, qDTY12.1qDTY3.1: RM416, RM16030, RM520AnjaliRainfed upland
qDTY12.1: RM28048, RM28130, RM28099, CG29430, indel8
qDTY12.1qDTY12.1: RM28048, RM28130, RM28099, CG29430, indel8KalingaRainfed upland
MABqDTY2.2, qDTY4.1qDTY2.2: RM236, RM279, RM555IR64Rainfed lowland
qDTY4.1: RM518, RM335, RM16368
qDTY1.1, qDTY1.2, qDTY2.2, DTY2.3, qDTY3.2, qDTY4.1, qDTY12.1qDTY1.1:RM11943, RM12023, RM12233IR64Rainfed lowland
qDTY1.2:RM212, RM3825, RM315
qDTY2.2: RM236, RM279, RM555
qDTY12.1: RM28048, RM28130, RM28099, CG29430, indel8
qDTY2.3: RM3212, RM573, RM1367
qDTY3.2:RM523, RM22, RM545
qDTY4.1: RM518, RM335, RM16368
qDTY1.1, qDTY2.1, qDTY3.1qDTY1.1:RM11943, RM12023, RM12091, RM12233SwarnaRainfed lowland
qDTY2.1: RM5791, RM521, RM3549, RM324, RM6374
qDTY3.1: RM416, RM16030, RM520
qDTY12.1qDTY12.1: RM28048, RM28130, RM28099, CG29430, indel8VandanaRainfed upland
qDTY2.2, qDTY4.1qDTY2.2: RM236, RM279, RM555Samba MahsuriRainfed lowland
qDTY4.1: RM518, RM335, RM16368
qDTY3.1, qDTY6.1, qDTY6.2qDTY3.1: RM55, RM168, RM186, RM293, RM468TDK1Rainfed lowland
qDTY6.1:RM204, RM217, RM508, RM586, RM587
qDTY6.2: RM3, RM541
qDTY3.2, qDTY12.1qDTY3.2: RM231, RM517SabitriRainfed lowland
qDTY12.1: RM28048, RM511, RM28199, RM28166
qDTY2.2, qDTY3.1, qDTY12.1qDTY2.2: RM236, RM279, RM12460MR219Rainfed lowland
qDTY3.1: RM416, RM16030, RM520
qDTY12.1: RM28048, RM511, RM28099, RM28166, CG29430, indel8, RM28130
qDTY2.2, qDTY3.1, qDTY12.1qDTY2.2: RM154, OSR17, RM12460MRQ74Rainfed lowland
qDTY3.1: RM416, RM15935, RM520
qDTY12.1: RM28048, RM511, RM28099, RM28166, CG29430, indel8, RM28130
qDTY1.1, qDTY2.2qDTY1.1:RM431, RM11943, RM12023, RM12091, RM12233JinmibyeoRainfed lowland
qDTY2.2: RM236, RM279
qDTY1.1, qDTY2.2qDTY1.1: RM12023, RM12146GayabyeoRainfed lowland
qDTY2.2: RM236, RM279
qDTY1.1, qDTY2.2qDTY1.1: RM11943, RM12233HanarumbyeoRainfed lowland
qDTY2.2: RM236, RM279
qDTY1.1, qDTY2.2qDTY1.1: RM11943, RM12233SangnambatbyeoRainfed lowland
qDTY2.2: RM109, RM279
MARSqDTY1.1, qDTY2.1, qDTY3.1, qDTY11.1qDTY1.1: RM212, RM486Samba MahsuriRainfed lowland
qDTY2.1: RM525, RM221
qDTY3.1: RM16, RM520
qDTY11.1: RM287
a MAS: marker-assisted selection, MAB: marker-assisted backcrossing, MARS: marker-assisted recurrent selection.
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